Data Integrity vs. Data Quality in a Nutshell Data integrity refers to protecting data from anything that can harm or corrupt it, whereas data quality checks if the data is helpful for its intended purpose. Data quality is a subset of data integrity. One can have accurate, consistent, and error-free data, but it is only helpful once we have the supporting information for this data. Data integrity and quality are sometimes used interchangeably in data management, but they have different implications and distinct roles in enhancing data usability. The Importance of Data Quality and Data Integrity Data serves as the lifeblood of organizations, supporting every initiative from product development to marketing campaigns. The success of these decisions relies on the quality and trustworthiness of data, making data quality and integrity foundational to success. Data Quality: Empowering Informed Decision Making Data quality measures how well data meets requirements and fits the intended purpose. Experts usually assess it using various criteria, whose importance may vary based on the specific data, stakeholders, or intended use. Improved Decision Making Reliable analytics and insights depend on high-quality data. Data quality allows marketing campaigns to target audiences precisely. It also aligns product development with customer needs and supports data-backed operational improvements for maximum efficiency. Improved/Enhanced Customer Experience Organizations use complete and accurate customer data to personalize interactions through various platforms, like social media, websites, etc. High-quality data also helps anticipate the consumer’s needs and can identify issues swiftly to resolve them. This approach fosters customer loyalty and satisfaction, enhancing the brand’s perception. Optimized Operations High-quality data is a single source of truth, removing inconsistencies and discrepancies to prevent wasted effort. It streamlines workflows, reduces errors, and lessens the need for rework. As a result, productivity rises, costs drop, and overall efficiency improves. Data Integrity: Building Trust Data integrity concerns the inherent quality of data and aims to maintain this quality throughout the data lifecycle. This action includes all stages, from creation and storage to processing and analysis, ensuring the data remains accurate and consistent. Data security Security ensures that data remains protected from unauthorized access, modification, or deletion. Access controls, encryption, and intrusion detection systems prevent unauthorized individuals from altering or tampering with the data. Data security creates trust among partners and stakeholders and strengthens the organization’s reputation. Data lineage Data lineage tracks the origin and transformation of data. Lineage tracking upholds data integrity by keeping a clear audit trail of modifications and identifying the source and reason for each change. Auditability Auditing capabilities enable tracing changes to the data and identifying who made them. Logging all data modifications, including the time, responsible user, and nature of the change, reinforces data integrity. This process fosters transparency and accountability, which are crucial for building trust in the data. The Mutual Relationship Data quality and data integrity work together to enhance data usability. High-quality data becomes worthless if its integrity is not secure. Similarly, maintaining data integrity does not serve its purpose if the data is inaccurate or incomplete. For example, if we have a customer database filled with verified, complete information showing high data quality. However, if a system flaw allows unauthorized changes (indicating low data integrity), the entire database’s reliability is at risk. Data Integrity vs. Data Quality: Examples and Applications It’s evident that data quality and integrity are closely related, but understanding the subtle differences is essential to maximize the data’s value. Financial Reporting Consider a company preparing its quarterly financial report. Data integrity plays an important role in maintaining the accuracy and security of financial data. Data Security: Access controls restrict unauthorized users from modifying financial figures, safeguarding data integrity. Data Lineage: The company tracks the origin of revenue and cost data, ensuring it hasn’t been tampered with during its journey from sales figures to the final report. Auditability: Every modification made to the data, such as adjustments or corrections, is logged with timestamps and usernames. This audit trail allows for verification and ensures no fraudulent alteration of the data. Here, data integrity guarantees the financial report reflects the true state of the company’s finances, fostering trust with investors and stakeholders. Customer Segmentation Let’s consider a marketing team segmenting customers for a targeted email campaign. Here, data quality takes center stage: Accuracy: Customer email addresses must be accurate to ensure successful campaign delivery. Incorrect data (e.g., typos) would make the segmentation exercise futile. Completeness: Complete customer profiles, including purchase history and demographics, are crucial for effective segmentation. Missing data would limit the ability to create targeted customer groups. Consistency: Customer names and addresses should be formatted consistently across the database. Inconsistencies (e.g., variations in capitalization) can lead to duplicate entries and skewed results. How to Ensure Data Quality and Integrity Maintaining high data quality and data integrity requires a comprehensive data quality management strategy. Measures to Ensure Data Quality Data profiling: Data profiling helps pinpoint areas requiring improvement by identifying missing data, inconsistencies, outliers, and duplicate records. Regularly analyze data to determine such anomalies. Data cleansing: Implement processes to correct errors, remove duplicates, and ensure consistent formatting throughout the data set. Data cleansing involves using data cleaning tools and establishing clear data entry guidelines. Data Standardization: Data standardization transforms data into a format that computers can read and understand. By standardizing data, detecting errors and ensuring accuracy becomes much easier. This accuracy is essential for providing decision-makers with reliable and precise information. Data validation: Enforce data validation rules at the point of entry to prevent inaccurate or invalid data from reaching your destination systems. Validation includes defining acceptable value ranges, mandatory fields, and data type restrictions. Data quality metrics: Data quality metrics are tools to measure and improve data quality. Organizations can ensure they possess high-quality data by selecting and applying the appropriate metrics to evaluate the data. Data quality metrics include timeliness, completeness, accuracy, validity, duplication, and uniqueness. Data governance framework: Establish a data governance framework outlining data quality standards, ownership, and accountability. The framework should also define data quality metrics and procedures for monitoring and improving data health. Data lineage tracking: Implement data lineage tracking tools to understand the origin and transformations of data throughout its lifecycle. Data lineage tracking allows for tracing any potential issues back to their source. Measures to Ensure Data Integrity Remove duplicate data: Duplicate data creates ambiguity, leading to errors and breaches in data integrity. Large organizations employ dedicated teams to clean duplicate files. Whether to choose a team or utilize software to remove duplicates depends on the data volume or size of the organization. Access controls: Lacking effective access controls in an organization increases the risk to data integrity. Implementing the principle of least privilege is one of the most effective strategies. It only restricts access to essential users, maintaining strict control and preserving data integrity. Keep an audit trail: Audit trails give organizations clues to pinpoint the problem’s source for effective resolution as they record all system data, including database or file changes. They must be tamper-proof, preventing user manipulation. These trails should generate automatically, track every database and file event, link events to the users involved, and include timestamps for all occurrences. Regularly auditing these trails is a best practice to uncover weaknesses or areas for improvement and enhance data integrity. Data encryption: Data encryption protects data integrity within an organization by keeping it confidential. This security measure safeguards data during rest, i.e., when stored in a database and during transmission, for example, when moving to another database. Backup the data: To ensure data integrity, organizations should adopt a two-pronged approach. First, implement regular data backups to safeguard the information against potential losses from hardware malfunctions or cyberattacks. Secondly, establish a data recovery plan to enable the accurate restoration of data in accidental deletion or corruption cases. Data Integrity vs. Data Quality Feature Data Quality Data Integrity Focus Inherent characteristics of the data itself Maintaining the trustworthiness of data throughout its lifecycle Objective Ensures data is fit for its intended purpose Ensures data remains accurate, reliable, and unaltered Key Attributes Accuracy, Completeness, Consistency, Validity, Timeliness Security, Lineage, Auditability Impact Affects data analysis, decision-making, and operational efficiency Affects, compliance, and risk management Mechanism Data cleansing tools, data validation rules, data governance framework Encryption, access controls, audit trails, data backup and recovery Concluding Thoughts Data quality and data integrity are distinctive concepts but not mutually exclusive. A comprehensive data management strategy considers both enhancing data analyses and business decisions. Automated data management tools with built-in features to address data quality and integrity issues help organizations ensure their business decisions rely on healthy data. LIKE.TG provides a unified data management solution that helps you ensure data quality and integrity. With LIKE.TG, you can automate data cleansing, profiling, and validation tasks while leveraging built-in data governance features, such as data discovery, data quality rules, and data ownership—all within a single, no-code, user-friendly platform. Schedule a demo or download a free 14 day trial to experience LIKE.TG’s data management solution and improve your organizational data quality and integrity. Enhance Your Data Quality and Integrity with LIKE.TG Discover how LIKE.TG's solutions can elevate your data quality and integrity. Schedule a demo today to see how easy and efficient data management can be with our comprehensive, no-code platform. 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What is a Data Governance Framework? A data governance framework is a structured way of managing and controlling the use of data in an organization. It helps establish policies, assign roles and responsibilities, and maintain data quality and security in compliance with relevant regulatory standards. The framework, therefore, provides detailed documentation about the organization’s data architecture, which is necessary to govern its data assets. Moreover, the framework supports self-service tools, which help technical and non-technical users find and use the data they need for governance and analytics. With these tools, organizations can manage, change, and share data across all their systems, whether in the cloud or on-site. Why is a Data Governance Framework Needed? The data governance framework is important for applying consistent policies and rules to all organizational data, ensuring data reliability for various roles, such as business leaders, data stewards, and developers. Data Inaccuracy A data governance framework sets standards and processes to ensure data accuracy and consistency. Many organizations face challenges with inaccurate, inconsistent, or outdated data affecting insights and decision-making processes. The data governance framework enhances the quality and reliability of the organization’s data. Data Security It sets guidelines and controls to protect sensitive information, including encryption, access controls, and compliance measures to reduce security risks and comply with regulations. Data Silos A data governance framework simplifies data integration and standardization, breaks down silos by establishing control, and enables seamless data exchange and collaboration across the organization. Lack of Accountability and Ownership It emphasizes accountability by defining roles and responsibilities and assigning data stewards, owners, and custodians to oversee data management practices and enforce governance policies effectively. Inefficient Data Management This framework establishes structured processes and workflows for data management. It automates repetitive tasks, streamlines workflows, and improves operational efficiency. Benefits of a Data Governance Framework It provides a structured way to keep data across all business operations accurate and secure while ensuring it’s used responsibly. Sets Standards Accountability The initial phase of data governance is the establishment of clear standards, norms, responsibilities, ownership, and roles. This process ensures all members of the organization, from new hires to top executives, are aware of their duties in data management. The organization can avoid data mismanagement by setting defined accountabilities and uphold data quality and security. Establishes Key Indicators Metrics Indicators and metrics offer a detailed view of the data’s condition and safety, guiding strategic decisions. Key Quality Indicators (KQIs) gauge data quality, helping businesses ensure their data meets the required standards. Key Data Elements (KDEs) identify the most critical data within the organization, guiding data management priorities. Key Performance Indicators (KPIs) track data performance against business objectives, ensuring data aligns with company goals. Data Risk and Privacy Metrics measure the organization’s exposure to data breaches and compliance with privacy laws, which is essential for the regulatory environment. Builds a Unified Data Language A common business language and data quality rules help everyone in the organization understand data terms and standards similarly. This approach avoids confusion and errors in data management and use, making communication across the company more straightforward. Enhances Data Discovery Management The framework sets up methods for finding and understanding all the company’s data. This process involves figuring out how data is related and its history, handling different kinds of information and conducting data profiling and classification. These steps make it easier to manage data well, ensuring it’s correct and dependable for making decisions. A robust data governance framework helps in: Improving and managing data quality to ensure data is accurate and useful. Addressing data issues promptly to maintain data integrity. Identifying data owners to establish accountability for data assets. Building a data catalog to organize and classify data, making it easily accessible. Creating reference and master data to provide consistent data across the organization. Driving data literacy across the organization to enhance understanding and use of data. Provisioning and delivering data efficiently ensures it is available when and where needed. Pillars of a Data Governance Framework A comprehensive data governance framework relies upon fundamental pillars that ensure an organization’s effective and secure use of data assets. They combine the foundational elements with essential strategic approaches, creating a well-rounded framework for managing data across all enterprise levels. Data Quality Data quality is essential to a data governance framework as it implements the rules and processes for data validation, cleansing, and organization. It guarantees decision-makers access to accurate and consistent information, improving decision-making. Data Integration Data integration simplifies data governance by aggregating and harmonizing data from various sources across an organization into a unified view. This approach supports improved analysis, reporting, and decision-making by providing stakeholders with a complete business picture. Consequently, it ensures all organizational data adheres to set policies and compliance standards, reducing inconsistencies and errors. Data integration also improves data governance by allowing for centralized management, which makes it easier to control data quality, security, and access. It standardizes data handling practices throughout the organization, ensuring consistent implementation and monitoring of governance policies. Integrated data systems clean and correct data during consolidation, enhancing data quality and reliability for decision-making. This streamlined approach helps meet regulatory compliance and increases reporting efficiency, as all data is consistent and readily available. Data Privacy and Security This pillar is essential for keeping data assets confidential and available within the governance framework. It refers to managing and protecting data in a way that aligns with the organization’s data governance policies. Privacy means ensuring that data is secure from unauthorized access and readily available to authorized users, all under the oversight and rules established by the governance framework. Organizations can protect data by using strong encryption, setting access controls, and following compliance mechanisms. It builds trust among customers, partners, and stakeholders, protects the organization’s reputation, and reduces the risk of legal and financial fallout from data breaches. Data Architecture The role of data architecture is to provide a structured framework for designing, organizing, and managing data assets. Data architecture ensures data is stored, accessed, and used consistently by defining data models, schemas, and storage mechanisms. A clear data architecture aids data governance by setting straightforward guidelines for managing data, ensuring it complies with organizational policies and standards. Data Asset Governance It highlights the need to treat all data elements, including dashboards, code, and data science models, as essential assets within the governance framework throughout their lifecycle. It suggests adopting a comprehensive view of governance covering data and analytics to ensure thorough management and oversight of all data assets. Bottom-Up Approach As data use increases, companies should share the role of data governance across creators and users of data. This decentralized approach gives everyone responsibility for data governance, aligning them with more inclusive and collaborative data management models like data mesh. It means that governance is spread across different business areas, with each domain owner looking after their data while following the broader organizational guidelines. Routine Workflow Governance Data governance shouldn’t just be about meeting compliance or seen as an extra task. Instead, teams should integrate it into everyday work processes to make sure they base decisions on accurate data. It then becomes an essential element of strategic decision-making within the organization. A data governance framework is incomplete without its core elements: people, processes, and technology. Each plays a critical role in making the framework effective. People: The Core of Governance Successful data governance relies on collaboration and careful planning by people across the organization. Key considerations to implement and establish data governance include: Commitment: Assessing whether individuals are dedicated to the principles of data governance. Roles and Responsibilities: Clearly defining and formally documenting the roles and responsibilities of all involved in data governance. Skills and Data Literacy: Ensuring team members possess the necessary skills and understanding of data to implement governance strategies effectively. Change Management: Developing a plan with sponsors to support alignment and encourage organization-wide buy-in. Stakeholder Identification: Recognizing data governance stakeholders across the organization. Maintaining Expertise: Understanding where expertise resides within the organization to leverage it for governance initiatives. Processes Processes like data collection, storage, integration, and quality assurance are vital for verifying that data management practices are consistent across the enterprise. Important aspects to consider while handling these processes are: Realistic Definitions and Goals: Establishing practical and achievable data definitions, rules, and objectives. Modernization of Business Processes: Updating business processes to integrate data governance, enhancing the ability to deliver meaningful results. Integration of Governance: Reviewing and adjusting business rules to incorporate data governance seamlessly. Contributors include business and IT experts who provide the necessary context for data governance efforts. They are essential in: Technology: Enabling Effective Governance Technology provides the tools and platforms for implementing reliable data governance processes, such as data profiling, lineage, and metadata tools, and utilizing technology to automate, scale, and accelerate the data governance processes. How do Data Governance Frameworks Work? Data governance frameworks differ from one business to another, but they generally include several core components discussed above that guide how data is managed and used within an organization. Let us discuss further how these frameworks typically work. Understanding Ownership Ownership is identifying who sets up the rules, policies, definitions, and standards for managing data. For instance, a company can establish a Data Governance Office (DGO) to lead the data governance initiative. The initiative could include a dedicated team, a group of stakeholders, or even a single person, such as a data architect, depending on the size and needs of the organization. Data stewards are appointed across various departments to ensure everyone aligns with these standards and to offer recommendations. In larger companies, several councils can focus on different aspects of data management, such as data storage, quality, and protection. Setting Goals Defining clear goals and metrics is key to measuring the success of a data governance initiative. The Data Governance Institute suggests focusing on the impact of data governance initiatives on: How well they are aligned with the organizational objectives. The effectiveness in improving data quality and compliance in projects. The integration of governance principles into professional disciplines. The adoption of data governance practices among individuals within the organization. For example, a healthcare provider can set a goal to reduce patient data errors by 20% in the first year to improve patient care and reduce billing mistakes. Monitoring Performance Keeping track of how well the data governance framework is working involves setting accountabilities for tasks necessary for compliance, like assigning a data steward or appointing a data security officer. These tasks should be well-documented, repeatable, and cover various aspects of data governance, such as assigning decision rights, managing changes, resolving issues, and ensuring data quality. For instance, a financial institution can implement a system to regularly review and update access rights to sensitive financial data, ensuring only authorized personnel can view or modify this information. Approving Technology Choosing and approving technology is another important aspect, where stakeholders decide on the tools and systems used to process, store, and secure data. It also guarantees these technologies have the necessary controls to prevent data breaches. For example, relevant personnel in an e-commerce company may decide to approve using a cloud storage solution that offers encryption and multi-factor authentication to protect customer data. Establishing Collaboration Standards Deciding how data stakeholders create, use, or regulate data within the organization will collaborate, including determining who should be involved in decision-making processes and who needs to be informed about decisions. For instance, a manufacturing firm can include engineers, production managers, and quality control specialists in the discussions on collecting and using production line data to improve efficiency and product quality. Data Governance Framework Examples There are various tried and tested data governance frameworks in use. Each framework meets the needs and requirements of managing data effectively. The DAMA International Data Management Body of Knowledge (DMBOK) DAMA-DMBOK is a comprehensive framework that outlines the standard industry practices for data management. It covers many topics, including data governance, architecture, and modeling, providing a holistic approach to managing and governing data assets. The Data Governance Institute Framework (DGI) The Data Governance Institute (DGI) offers a framework focusing on governance practices. It emphasizes establishing roles, responsibilities, and processes to ensure data is managed as an asset. This framework is particularly beneficial for organizations looking to implement data governance with a clear emphasis on accountability and stewardship. The CMMI Data Management Maturity (DMM) Model The Data Management Maturity (DMM) model provides a comprehensive approach to enhancing an organization’s data governance practices. It outlines the essential components of data governance, including data quality, operations, and platforms, offering a path toward achieving higher levels of data management maturity. The EDM Council’s Data Management Capability Assessment Model (DCAM) The EDM Council’s DCAM is an industry-standard framework for evaluating and improving data management and governance practices. DCAM provides a structured approach with defined principles and capabilities across various aspects of data governance, including data architecture, quality management, and security. ISO/IEC 38500 IT Governance Framework Although ISO/IEC 38500 is primarily an IT governance framework, it has significant implications for data governance. It guides organizations using IT (including data assets) to achieve business goals, ensure regulatory compliance, and manage risks. How to Create a Data Governance Framework? Building and implementing a data governance framework involves a strategic process that prioritizes properly managing an organization’s data assets. This framework is essential for enhancing data governance across businesses. The following step-by-step guide simplifies the approach: 1. Understanding Data Governance a. Define the scope and objectives of the data governance initiative. b. Align stakeholders with data governance goals, such as improving decision-making, ensuring regulatory compliance, and enhancing operational efficiency. 2. Identifying Data Domains a. Categorize data into domains based on their relevance to the business, such as customer data, product data, financial data, etc. b. Understand the data landscape to manage data across the organization effectively. 3. Identifying Ownership Consumption a. Determine data owners responsible for the accuracy, privacy, and security of each data domain. b. Identify data consumers who need access to the data for their work. c. Establish accountability and clarity on roles and responsibilities. 4. Validating Documenting Data a. Document the data lifecycle, including its origin, internal flow, user interactions, and application purposes. b. Verify data accuracy and consistency. c. Record governance policies, standards, and procedures for reference and clarity in data management. 5. Assessing Data Security a. Conduct security and risk assessments on various data types to identify vulnerabilities and threats. b. Prioritize risks and implement protective measures for sensitive information. c. Ensure compliance with legal requirements and build trust with clients and partners by demonstrating a commitment to data security. Data Governance Framework Best Practices Businesses should embrace certain best practices to create a practical data governance framework that ensures long-term success. These practices aim to meet current needs while also adapting to future challenges. Engage Stakeholders Early in the Process Involve key stakeholders from various departments in defining the scope and objectives of data governance to align all activities with organizational goals and priorities. Utilize Data Catalogs Implement data catalogs to categorize and document data assets. This approach is about organizing data efficiently, making it easily accessible and understandable to users within the organization. It facilitates easy identification of data domains and ownership responsibilities by providing visibility into data assets and their associated metadata. Appoint Data Stewards Appoint dedicated data stewards for each data domain to oversee data quality and serve as contact points for all data-related queries. These stewards enforce data governance policies, maintaining the quality and security of the data within their respective domains. Automate Documentation Processes Leverage automation tools to streamline the documentation of data lifecycles, governance policies, and procedures. These tools enhance efficiency and accuracy in maintaining up-to-date records of data management activities. Conduct Regular Training Provide ongoing training sessions to educate stakeholders on data governance principles, best practices, and compliance requirements. This training ensures all participants understand and effectively implement the organization’s data governance policies. Implement Data Security Measures Use security mechanism such as encryption, access controls, and monitoring systems to protect sensitive data from unauthorized access, breaches, and compliance violations. Final Thoughts A strong data governance framework equips organizations to manage and derive maximum value from their data assets proactively. Companies can implement a framework by carefully engaging stakeholders, defining roles, and extensive planning. Implementing a data governance framework demands a commitment to continuous improvement and flexibility in response to new business and technology shifts. For successful implementation, using data governance tools or data management tools with governance features is essential. LIKE.TG’s platform simplifies data governance for businesses, offering tools for comprehensive data management. It allows for clearly assigning roles and responsibilities, ensuring data stewards and owners can easily oversee and control their data. This transparency aids in implementing governance policies and upholding data integrity organization-wide. LIKE.TG enables you to use your data more effectively and achieve better business results, all while minimizing data complexities. Start a free trial or book a demo today to explore LIKE.TG’s data management and governance solutions suite. Start Your Journey to Effective Data Governance with LIKE.TG Start managing your data more effectively with LIKE.TG. 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Data Governance vs. Data Quality Data governance and data quality are closely related, but different concepts. The major difference lies in their respective objectives within an organization’s data management framework. Data quality is primarily concerned with the data’s condition. It ensures the data is complete, accurate, reliable, and consistent. On the other hand, data governance refers to the overall management, maintaining compliance, and ensuring the security of data assets within an organization. Data governance sets the framework and guidelines under which data quality initiatives operate. Both data governance and quality are equally important for an organization to derive value from their data and make informed decisions. Understanding Data Quality Data quality measures how much data sets meet the specific requirements and expectations of their intended use, ensuring that decisions based on good quality data are rational and defensible. The significance of data quality stems from its role in decision-making. High-quality data allows organizations to understand their customers and identify new market trends. It ensures that: Marketing campaigns reach the right audience. Financial forecasts are reliable. Customer service is effective and personalized. Achieving high data quality is not a one-time activity but an ongoing, iterative process, called data quality management. It involves a series of steps, from data acquisition through storage and management to its final use. At each stage, specific measures must be taken to ensure data integrity. For example, data validation rules can be applied during collection to prevent errors, while regular audits can identify inconsistencies or outdated information that may affect data reliability. Understanding Data Governance Data governance is a thorough framework that ensures data is accurate, accessible, consistent, and protected, thereby supporting the organization’s objectives. Take data governance as a rulebook for effective data management, establishing protocols for the accurate collection, storage, and utilization of data. Data governance helps a company achieve its goals by providing a well-structured framework and setting clear and well-communicated policies that give all employees a common understanding of how data should be handled. This structure also defines the responsibilities of different roles within the organization, such as who is accountable for maintaining the accuracy of data and who has the authority to access sensitive information. The sensitivity of personal data and growing privacy concerns make data governance all the more important for organizations. In 2022, there were 1,774 instances of organizational data breaches, affecting over 392 million individuals globally. These incidents exposed legally protected personal information of people utilizing services ranging from financial and healthcare to social services. Exploring the Differences Between Data Governance and Data Quality Data Quality Data Governance Objective To ensure that the data is standardized and free of inconsistencies and errors. To ensure effective management, protection, compliance and utilization of organizational data. Techniques Data profiling, cleansing, standardization, enrichment, and validation. Data lineage, access controls, policy development, data stewardship, data cataloging and metadata management. Tools Data quality tools, specialized databases designed to support data quality tasks. Data governance platforms such as LIKE.TG and meta data management tools. Outcome Trustworthy data that can be used reliably for decision making. A controlled data environment, having clear data policies and improved data management practices. Where Data Governance and Data Quality Overlap The overlap between data governance and data quality lies in their mutual goal of ensuring that data is a reliable asset for the enterprise. Data governance determines who needs to do what with which data under specific conditions and identifies procedures and tools to enhance data quality. Data quality ensures that the data is accurate, complete, and valuable within this system. The connection between data quality and data governance comes from shared standards, metrics, and responsibilities of data roles. It helps create a symbiotic relationship that enhances the overall value of an organization’s data: Framework and Execution: Data governance provides the framework and policies for data management, while data quality focuses on executing these policies. For instance, data governance might establish a policy that all data should be consistent and accurate. Data quality measures like data cleaning and validation, would then be used to ensure that this policy is effectively implemented. Enhancing Trust: Data governance and data quality work together to enhance trust in data. Data governance ensures clear policies and procedures for data management, which can increase confidence in the data. Meanwhile, data quality measures ensure that the data is accurate, reliable, and relevant, further enhancing trust. Risk Management: Data governance sets the rules for compliance with regulations and standards, while data quality ensures that the data adheres to these rules. This complementary relationship is crucial for risk management, as it helps to prevent breaches of regulations and standards, which can result in penalties and damage to the organization’s reputation. Decision-Making: High-quality data is essential for informed decision-making. Data governance ensures that the right data is available to the right people at the right time, while data quality ensures that this data is accurate and reliable. Together, they enable organizations to make decisions based on trustworthy data. Data cataloging is another area where data quality and governance overlap. From the governance perspective, a data catalog provides a holistic picture of what data exists and who’s responsible for it—allowing for setting data standards and access controls. On the data quality side, the catalog helps identify potential issues through data profiling and lineage tracking, which enables data stewards to implement processes for improving data accuracy and consistency. 6 Steps to Integrate Data Quality within a Data Governance Framework Integrating data quality within a data governance framework is a strategic initiative that significantly enhances the value of an organization’s data. Such integration ensures that data is well-managed but also accurate, reliable, and useful for decision-making. Here are some steps to effectively integrate data quality within a data governance framework: Define Data Quality Goals Begin by outlining what high-quality data means for your organization. This step generally includes accuracy, completeness, consistency, timeliness, and relevance. It is important to align these goals with the organization’s overall objectives and communicate them clearly to all stakeholders. Establish Data Quality Metrics After defining the goals for data quality, establish metrics to measure data health. Data quality metrics could include error rates, completeness percentages, or consistency scores. These quantitative measures will assess data quality and track improvements over time. Incorporate Data Quality into Policies Procedures All data governance policies and procedures, including those for data collection, storage, processing, and usage, should prioritize data quality as a critical consideration. Incorporating data quality into these procedures ensures its maintenance throughout the data lifecycle. Assign Data Quality Responsibilities Integrating data governance and data quality calls for assigning responsibilities for maintaining data health. For example, designating a data steward to define and enforce data quality standards for their assigned data, or establishing data quality roles within the IT department to focus on data profiling, data cleansing, and implementing data quality checks. Implement Data Quality Tools Techniques The use of data quality tools and techniques is crucial in improving the quality of data. Modern data quality tools combine data standardization, validation, profiling, and cleansing, along with several other features, in a single package. Automating data quality tasks with these tools simplifies data quality management. Monitor Improve Data Quality The final step is to monitor data quality using the established metrics continuously. Regular monitoring identifies any issues or trends and enables corrective action as needed. Incorporating data quality within a data governance framework allows organizations to ensure their data is well-managed, accurate, reliable, and valuable. Conclusion Data governance and data quality are closely linked, influencing the reliability and usability of data. Despite their differences in scope and application, data quality and data governance create a unified approach to data management, which allows companies to maximize the benefits of their data assets. Building on this, LIKE.TG offers an effective solution that simplifies sharing and managing data across a company through a single platform. LIKE.TG’s data management platform ensures data quality and trustworthiness by providing the percentage of data health based on defined data quality rules. It enhances organizational decision-making by automatically correcting errors, detecting inaccuracies, and standardizing data. Data governance contributes by organizing data assets, managing data access, and making data trustable and understandable. If you’re looking to enhance your decision-making process with easier access to data, consider book a demo now! Ready to boost your decision-making with high-quality, trustworthy data? Try LIKE.TG’s data governance platform to see how you can improve data health, correct errors, and standardize your data effortlessly. Start you 14-days-trial now!

Data filtering plays an instrumental role in reducing computational time and enhancing the accuracy of AI models. Given the increasing need for organizations to manage large volumes of data, leveraging data filtering has become indispensable. What Is Data Filtering? Data filtering is the process of narrowing down the most relevant information from a large dataset using specific conditions or criteria. It makes the analysis more focused and efficient. Data filtering lets you quickly analyze relevant data without sifting through the entire dataset. You can filter data regardless of type, including numbers, categories, text, and complex time-series data. Data Filtering vs. Data Sorting vs Data Sampling While data filtering helps process large volumes of data, it is not the only method. Data sampling and sorting can also help draw insights from a large dataset. Here’s a brief overview and comparison: Data Filtering: Selects a subset of data based on specific criteria. Data Sorting: Arrange data in a specified order, either ascending or descending. Data Sampling: Chooses a representative subset from a larger dataset for analysis. Parameter Data Filtering Data Sorting Data Sampling Purpose To narrow down data to meet specific conditions. To organize data in a meaningful order. To analyze a smaller, manageable subset of data that represents the whole. Process Uses criteria to include or exclude data. Rearrange data based on chosen attributes. Randomly or systematically selects data points from the entire dataset. Outcome A reduced dataset focused on relevant data points. An ordered dataset based on specific attributes. A smaller dataset that reflects the characteristics of the more extensive set. Each method can be used by itself or in combination to extract insights from large volumes of data. What is Data Filtering Used For? Evaluating a Dataset: Filtering aids in exploratory data analysis by helping identify patterns, trends, or anomalies within a dataset. Processing Records: Data filtering streamlines workflows by processing records based on predefined criteria. Remove Irrelevant Data: Filtered data can help remove irrelevant data before restructuring via pivoting, grouping/aggregating, or other means. Benefits of Using Data Filtering Organizations prioritizing data filtering are better positioned to derive valuable insights from their data. Here is how data filtering can help you gain a competitive advantage. Enhances Focus: Data filtering allows you to ignore irrelevant data, enabling a sharper focus on information that aligns with their goals, which can improve the quality of insights. Increases Accuracy: Filtering out outliers and erroneous records contributes to a more reliable data analysis process and improves the accuracy of the results. Optimizes Resource Use: Working with smaller, filtered datasets can reduce the resources needed for analysis, leading to potential cost savings. Supports Custom Analysis: Data filtering accommodates unique analytical needs across various projects or departments by creating datasets tailored to specific criteria. Types of Data Filtering Techniques Data filtering techniques can help you quickly access the data you need. Basic Filtering Methods Basic filtering involves simple techniques like range or set membership. For example, in a database of temperatures recorded throughout a year, a range filter could be used to select all records where the temperature was between 20°C and 30°C. Similarly, a set membership filter could select records for specific months, like June, July, and August. Filtering by Criteria Filtering by criteria involves more advanced filtering based on multiple criteria or conditions. For instance, an e-commerce company might filter customer data to target a marketing campaign. They could use multiple criteria, such as customers who have purchased over $100 in the last month, are in the 25-35 age range, and have previously bought electronic products. Filtering by Time Range Temporal filters work by selecting data within a specific time frame. A financial analyst might use a time range filter to analyze stock market trends by filtering transaction data to include only those that occurred in the last quarter. This helps focus on recent market behaviors and predict future trends. Text Filtering Text filtering includes techniques for filtering textual data, such as pattern matching. For example, a social media platform might filter posts containing specific keywords or phrases to monitor content related to a specific event or topic. Using pattern matching, they can filter all posts with the hashtag #EarthDay. Numeric Filtering Numeric filtering involves methods for filtering numerical data based on value thresholds. A healthcare database might be filtered to identify patients with high blood pressure by setting a numeric filter to include all records where the systolic pressure is above 140 mmHg and the diastolic pressure is above 90 mmHg. Custom Filtering Custom filtering refers to user-defined filters for specialized needs. A biologist studying a species’ population growth might create a custom filter to include data points that match a complex set of conditions, such as specific genetic markers, habitat types, and observed behaviors, to study the factors influencing population changes. These techniques can be applied to extract meaningful information from large datasets, aiding in analysis and decision-making processes. Data Filtering Tools and Software Data filtering can be performed via manual scripting or no-code solutions. Here’s an overview of these methods: Filtering Data Manually Manual data filtering often involves writing custom scripts in programming languages such as R or Python. These languages provide powerful libraries and functions for data manipulation. Example: In Python, the pandas library is commonly used for data analysis tasks. A data scientist might write a script using pandas to filter a dataset of customer feedback, selecting only entries that contain certain keywords related to a product feature of interest. The script could look something like this: Python import pandas as pd # Load the dataset df = pd.read_csv(‘customer_feedback.csv’) # Define the keywords of interest keywords = [‘battery life’, ‘screen’, ‘camera’] # Filter the dataset for feedback containing the keywords filtered_df = df[df[‘feedback’].str.contains(‘|’.join(keywords))] Using No-Code Data Filtering Software No-code data filtering software allows you to filter data through a graphical user interface (GUI) without writing code. These tools are designed to be user-friendly and accessible to people with little programming experience. With Regular Expressions capabilities, you have the flexibility to write custom filter expressions. Example: A bank’s marketing department wants to analyze customer transaction data to identify potential clients for a new investment product. The data includes various transaction types, amounts, and descriptions. The team is particularly interested in clients who have made large transactions in the past year that may indicate an interest in investment opportunities. Using a no-code data filtering tool, the marketing team can filter records that contain terms like ‘stock purchase,’ ‘bond investment,’ or ‘mutual fund’ in their transaction description field. They also set a numeric filter to include transactions above a certain amount. The tool’s GUI allows them to easily input these parameters without writing complex code. The result is a filtered list of clients who meet the criteria, which the bank can then use to target their marketing campaign for the new investment product. Feature Manual Filtering (Python/R) No-Code Data Filtering with Regular Expressions Ease of Use Requires programming knowledge User-friendly with intuitive GUI Pattern Matching Complex filter expressions need coding Simplified filter implementation Learning Curve Steep requires learning syntax Minimal, often with helpful tutorials Speed of Setup Time-consuming script development Quick setup with immediate results Accessibility Limited to those with coding skills Accessible to non-technical users Maintenance Requires ongoing script updates Often includes automatic updates Scalability Can be less efficient for large datasets Designed to handle big data efficiently Cost Efficiency Potential for higher long-term costs Cost-effective with subscription models Collaboration Less collaborative, more individual-focused Encourages collaboration with shared access Best Practices for Effective Data Filtering It’s essential to follow the best practices below to ensure that data filtering is as effective and efficient as possible: Define Clear Objectives Having clear goals for what you want to achieve with data filtering. Before you begin, ask yourself: What specific insights am I trying to obtain? Which data is relevant to my analysis? How will the filtered data be used? Clear objectives guide the filtering process, ensuring the results align with your analytical or operational goals. Understand Data Structure and Format A thorough understanding of the data’s structure and format is essential. Consider the following: Is the data structured, semi-structured, or unstructured? What are the data types of the columns I’m interested in? Are there any relationships between the data points that need to be preserved? Understanding these aspects helps apply the most appropriate filters and prevents potential issues such as data loss or misinterpretation. Utilize Multiple Filters for Complex Analysis For complex analysis, a single filter might not be sufficient. Instead, use a combination of filters to drill down into the data: Apply a range filter followed by a categorical filter to narrow your dataset. Use text filters with numeric filters to further segment the data. Multiple filters can provide a more nuanced view of the data, revealing deeper insights. Validate Results and Adjust Filters as Needed Regular validation of filtering results is essential to ensure accuracy. After applying filters, check if: The results meet your initial objectives. The filtered data makes sense in the context of your goals. Any anomalies or unexpected results need investigation. If the results aren’t satisfactory, adjust the filters and re-validate. This iterative process helps refine the filtering strategy to produce the best possible outcomes. Adhering to these best practices helps maximize the effectiveness of data filtering, leading to more reliable and actionable insights. Data filtering significantly enhances the computational efficiency of training AI models, improving their accuracy. The advent of no-code data filtering tools has further streamlined this process, enabling you to develop AI systems that are not only more precise but also more efficient. How LIKE.TG’s No-Code Data Filtering Saves 80% of Your Time LIKE.TG Dataprep is a no-code data filtering tool that eliminates the need for complex coding, streamlines repetitive tasks, ensures consistency across projects, and offers immediate insights into data health, collectively saving up to 80% of the time typically spent on data preparation. It offers: Drag-and-Drop Interface uses Point-and-Click fields to filter data, simplifying data preparation. Dataprep Recipes standardize data preparation across multiple datasets, significantly reducing time and effort. Data Health Visuals provide immediate visual feedback on the quality of your data, allowing you to quickly identify and address issues such as inconsistencies or missing values. Real-Time Grid provides a dynamic dataframe that updates in real-time as data is transformed within the platform, giving you an interactive view of the data and illustrating the immediate effects of data manipulation. Automated Dataflows: reduce the need for manual intervention. Intuitive Filter Expressions perform complex pattern matching through the user-friendly interface, saving time on writing and debugging code. Prebuilt Connectors enable quick integration with various data sources. Advanced Data Validation and Profiling ensure data accuracy and consistency, allowing you to validate data against predefined rules and profile data for quality analysis. Ready to transform data management and save valuable time? Try LIKE.TG Dataprep, the all-in-one data preparation tool that simplifies data filtering, integration, and transformation. Start your journey with LIKE.TG Dataprep today and revolutionize how you work with data!

What is Data Provenance? Data provenance is a method of creating a documented trail that accounts for data’s origin, creation, movement, and dissemination. It involves storing the ownership and process history of data objects to answer questions like, “When was data created?”, “Who created the data?” and “Why was it created? Data provenance is vital in establishing data lineage, which is essential for validating, debugging, auditing, and evaluating data quality and determining data reliability. Data Lineage vs. Data Provenance Data provenance and data lineage are the distinct and complementary perspectives of data management. Data lineage mainly provides a high-level trail of data’s journey (from origin to consumption), including any changes made. Data provenance is what adds depth to this trail. It tells us about data’s origins and the changes made to it and provides insights into who created the data, made those changes, data quality, etc. Learn more about the differences between data provenance vs. data lineage. Data Lineage Data Provenance Scope Tracks the entire data transformation journey (from source to destination). Focuses on the origin and historical record of the data. Focus “How” and “Where” the data is transformed. “Who” and “When” the data was created. Key Information Source, transformations, integrations. Source system, creation time, responsible entity. Data Representation Typically visualized as a directed acyclic graph (DAG). Often presented as metadata associated with the data element. Use Cases Identifying data errors, tracing changes, and ensuring consistency. Verifying data credibility, detecting bias, and regulatory compliance. The Importance of Data Provenance Data provenance is a fundamental requirement for ensuring trust, reliability, and efficacy of data-based decision making. Opaque Data Pipelines The growing complexity of modern data systems and pipelines makes it difficult to track data lineage. Businesses lack complete visibility into how data is transformed and moved across various systems, creating a blind spot for potential issues. Data provenance empowers businesses to understand how data flows through their systems by revealing data’s origin, transformations, and movements. This transparency allows for proactive identification of potential issues before they escalate. Data Quality Concerns Without clear data lineage, identifying inconsistencies and anomalies becomes a time-consuming task. Errors or biases can go undetected, potentially leading to flawed decisions with significant consequences. Data provenance facilitates the detection of anomalies and inconsistencies within data. By tracing the lineage, businesses can pinpoint the source of errors and correct them proactively to ensure data integrity. For instance, when a sales dashboard shows a sudden revenue spike, data provenance identifies where the anomaly started, facilitating quick resolution and preventing faulty data from affecting decisions. Navigating Regulations The rapid increase of data privacy regulations like GDPR and HIPAA mandates that organizations demonstrate responsible data governance. Data provenance enables organizations to prove their compliance with these regulations. In an audit, data provenance offers a clear trail that details the origin and handling of sensitive data, reducing potential legal issues and building trust with stakeholders. Empowers AI and Analytics The effectiveness of artificial intelligence (AI) and data analytics relies on the quality and trustworthiness of data. Data provenance enables scientists to identify biases or inconsistencies that could distort model outputs by tracing data lineage. Also, it supports explainability in AI decision-making, tracing back to how the AI reached its conclusions. The Role of Data Provenance in Ensuring Data Integrity and Authenticity Verifying the Origin and Lineage Data provenance creates a verifiable data custody chain. It records data sources and all transformations, helping organizations spot and address potential impurities in data. For instance, an organization collects customer data from various sources and merges it into a single database. Data provenance verifies the origin of each data stream, ensuring the data remains untampered and free from inaccurate information during collection. This careful tracking builds trust in the data’s authenticity by allowing easy source verification. Detecting Unauthorized Modification It also helps organizations detect and prevent unauthorized changes by tracking changes to data points, making anomalies and inconsistencies visible. For instance, if a financial record changes unexpectedly, data provenance flags it for investigation. This process helps catch and fix unauthorized access or manipulations early, protecting data integrity. It also supports access control by tracking who interacts with data and pinpointing those responsible for changes. Facilitating Reproducibility and Auditability Reproducibility is the ability to replicate research and analytical results. Data provenance records the data used, its changes, and the analysis processes, allowing researchers to recreate a study’s original conditions accurately. The accurate replication prevents the spread of incorrect or misleading findings by allowing the independent verification of results. Data provenance also supports auditability by providing a clear audit trail for regulatory reviews or compliance audits. Identifying Errors in Data Pipelines Data lineage tracking enables organizations to spot and fix errors and biases in data pipelines. For example, if a loan approval algorithm shows bias against certain demographics, data provenance lets data scientists examine the training data. They can find and correct biases introduced during data collection or transformation. This process improves data pipelines, making them more accurate and fairer. How Does Data Provenance Work Data provenance works as a system that utilizes various technologies to enhance the integrity and trustworthiness of the data. Following mechanisms define its effectiveness: Metadata Management: The foundation of Data Provenance Data provenance relies on capturing metadata, which includes the data’s origin, creation time and date, transformations, and responsible individuals. Capturing a comprehensive metadata record creates a clear audit trail for each data point. Ensuring Data Integrity with Cryptographic Techniques Cryptographic techniques are mathematical tools to crawl and unscramble data for tamper-proof verification. Data provenance systems use cryptographic techniques for securing metadata and ensuring its immutability, which means that any change to data cannot go unnoticed. One key method is to assign cryptographic hashes, a unique fixed-size character string from a piece of data. Changes in data modify the hash value, making tampering evident when the stored hash doesn’t match the reassigned hash of the altered data. Utilizing Distributed Ledger Technology Businesses can use data provenance with distributed ledger technology (DLT), including blockchain, to secure and share data across a decentralized network. This approach removes the need for a central authority, as any changes to the data record require majority network validation. The majority network approval enhances transparency by blocking unauthorized changes. DLT also supports controlled access, allowing authorized stakeholders to view the audit trail and confirm a data point’s history, promoting trust and collaboration in data ecosystems. Verification with Automated Mechanisms Organizations can leverage data provenance tools to enable easy verification and lineage tracking, using metadata and cryptographic hashes to check data authenticity and integrity. Also, these tools provide user-friendly interfaces, allowing end-users to access and verify information directly. The ease of using such tools empowers people to assess the trustworthiness of their data confidently. Data Provenance Use Cases Supply Chain Transparency Data provenance allows supply chains to track the documented movement of goods from raw materials to finished products by creating a digital record of origin, processing steps, and certifications for each product. This transparency empowers them to verify the authenticity and quality of their products against predefined standards, ensuring compliance with laws and ethical sourcing practices. Cybersecurity Defenses Data provenance establishes a clear audit trail for data access and manipulation, through which organizations can pinpoint unauthorized activities and security breaches. It is specifically helpful in industries where faster response time to security incidents is essential, like the financial sector. Simplifying Claims Management Data provenance allows verification of claims origin, eliminating fraudulent submissions. It transforms dispute resolution with a clear audit trail that pinpoints the source of any discrepancies. Also, the ability to examine past data manipulation in claims enhances risk assessment, allowing for proactive identification of potential issues. This process leads to quicker fraud detection and reduced risks of non-compliance and fraud. Concluding Thoughts As our dependency on data grows, utilizing data provenance methods and techniques to ensure trust in data is imperative. Data provenance equips organizations with tools to make informed decisions based on reliable information. LIKE.TG is a comprehensive data management platform that equips organizations with tools to track and analyze data movement. Data lineage feature allows the tracing and transformation of data assets, from source to destination. Also, impact analysis helps to identify how data is modified and utilized through various pipelines, enhancing the understanding of dependencies and potential risks associated with data changes. Download a 14-day free trial to experience how LIKE.TG helps with data lineage and provenance. Enhance Your Data Transparency with LIKE.TG Experience LIKE.TG's comprehensive data management platform to ensure the integrity and trustworthiness of your data. 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OLTP is a transaction-centric data processing that follows a three-tier architecture. Every day, businesses worldwide perform millions of financial transactions. This fact brings to mind client-facing personnel such as bank tellers and supermarket cashiers tapping away on keyboards and at cash registers, and with good reason. According to ACI Worldwide, a payment systems company, there was a 42.2% growth in global real-time transaction volumes in 2023, amounting to 266.2 billion transactions. However, businesses outside the financial and retail sectors also generate plenty of transactions daily. And each of these transactions requires safe storage for future review, usage, and other applications. Online Transaction Processing (OLTP) is designed to store these large volumes of transactional data in a data warehouse, and this blog discusses it in detail. Let’s begin. OLTP is distinguished by the following two factors: Its dedicated design, which supports the execution and recording of hundreds (or more) of concurrent transactions. The ‘online’ component in its name, which refers to how it leverages the internet to handle transactions in real-time. Examples of OLTP Systems ATMs and online banking processes Ticketing, booking, and reservation platforms Credit card payment processing systems Order entry Record-keeping OLTP vs. OLAP OLTP and Online Analytical Processing (OLAP) are two different types of data processing systems that businesses use in conjunction with each other. While OLTP is primarily designed for recording transactions, OLAP databases are designed to help data teams query and analyze them. Here’s a comparison between the two: OLTP OLAP Built for quickly storing vast volumes of online transactions. Built to collect data from varying sources to enable data analysis. Designed for recording business transactions and operations in real-time. Designed to analyze all kinds of historical business data. OLTP queries are simple. OLAP queries are complex. Standard size ranges from 100 Mb to 10 GB. Larger than OLTP systems. Their typical size varies from 1 Tb to 100 Pb. Often modifies data and has a combination of read and write tasks. Doesn’t modify data and has read-only workloads. Needs frequent backups and a comprehensive recovery mechanism in place. Can be backed up infrequently without problems. Characteristics of OLTP Systems Quick Responses: OLTP systems boast rapid responses to facilitate real-time operations. Response time is typically measured in milliseconds. Indexed Datasets: These systems feature indexed datasets to help users search for, query, and retrieve data quickly. High Availability: Given an OLTP system’s importance in real-time applications, ensuring high availability minimizes downtime. Simpler Operations: Common operations include insertions, deletions, and data updates. These are all simpler operations since these systems don’t cater to complex analytical requirements. Which Databases are Used for OLTP Workloads? OLTP workloads need relational database management systems (RDBMS) for data storage and modification, as relational databases are purpose-built for transaction applications. Here are a few examples: 1. PostgreSQL/Postgres PostgreSQL’s customizability and flexibility make it ideal for OLTP. In particular, Postgres’ Multi-Version Concurrency Control (MVCC) enables simultaneous data access for multiple transactions while preventing one transaction from blocking another. This improves performance and efficiency. 2. Oracle Database Oracle Database is a comprehensive RDBMS that supports different use cases. Besides OLTP, Oracle Database is used for data warehousing and mixed (OLTP and data warehousing) applications. 3. Microsoft SQL Server Much like Oracle Database, Microsoft SQL Server also supports data warehousing and business intelligence-related tasks in addition to OLTP workloads. 4. MySQL MySQL is widely used as both an open-source and commercial RDBMS. Its scalability, ease of use, availability, and transactional capabilities make it a great option for OLTP workloads. 5. MongoDB MongoDB is flexible, scalable, and suitable for enterprises looking for a NoSQL option to process their OLTP workloads. Payment systems are among MongoDB’s common use cases, and it has a flexible document-centric model that works well for OLTP tasks. The Benefits and Challenges of Working with OLTP Workloads OLTP systems have both their benefits and their challenges, highlighted below: Benefits: The two biggest benefits of an OLTP system are concurrency and ACID (atomicity, consistency, isolation, and durability) compliance. Concurrency allows an OLTP system to effectively and simultaneously cater to multiple users. Two or more users can freely make transactions in a system because its concurrency will keep these transactions from conflicting or overlapping with each other. ACID compliance comprises the following components: Atomicity: breaks database transactions down into smaller components to ensure the integrity of the transaction as a whole. Consistency: database transactions are subject to certain data validation rules and only the data that matches these rules is written into the database. Isolation: database transactions are concurrently processed without any conflicts. Durability: ensures that after a transaction is complete, the resulting data changes are written into the database. This maintains data changes despite technical faults. ACID compliance enables OLTP databases to manage transactions from start to finish. It ensures the functionality of a transaction by breaking down each transaction into smaller components, applying data validity rules, processing them concurrently, and then saving the data. Besides concurrency and ACID compliance, other advantages of an OLTP system include: Normalized Data: OLTP systems generally use normalized data structures. Doing so minimizes redundancy, enables optimized storage, and prevents data anomalies. Transaction Rollback: An OLTP system’s transaction rollback capability lets users reverse transactions or other changes in case of a hardware issue or another type of error. Data Integrity: Maintaining data integrity and consistency is easy as ACID compliance and normalization increase the transactions’ reliability and keep the database accurate. Challenges Common challenges associated with using OLTP databases include: Querying Historical Data: They are built for handling current transactions and real-time data. Consequently, querying historical data using an OLTP system is difficult, especially compared to OLAP. ACID Overheads: ACID compliance, while beneficial, can lead to some overhead. Better data integrity can affect the system’s performance, as ACID transactions require additional processing power overhead. Frequent Maintenance: Maintaining complex OLTP systems can be somewhat resource-intensive in a dynamic and expansive environment. As noted above, these systems need frequent backups, maintenance, and optimization, which require dedicated, ongoing efforts to maintain high availability. How to Select the Right Database for OLTP Workloads Keeping the following factors in mind can help data teams select the right database: Understanding Performance Requirements Knowing the estimated transaction volume, concurrency requirements, and ideal response times is essential. The selected database should effectively handle an organization’s transactional load. Support for ACID Compliance ACID compliance is vital for ensuring data integrity in an OLTP system, so a database with comprehensive ACID guarantees is the right fit for an OLTP workload. Concurrency Control Simultaneous transactions are another key feature; managing them effectively requires a database to have sufficient concurrency control mechanisms, such as: Locking Optimistic Concurrency Control (OCC) Timestamp Ordering Snapshot Isolation Multi-Version Concurrency Control (MVCC) Serializable Snapshot Isolation (SSI) Scalability Scalability can have varying importance depending on the business model and application. A database that can scale horizontally (by integrating more servers) as well as vertically (by increasing server resources) can keep up with a range of workloads. Data Model Generally, relational databases work well with structured data, and NoSQL databases are more suitable for semi-structured and unstructured data. Database selection for OLTP workloads should match the application’s schema requirements. Durability and Fault Tolerance Mechanisms such as replication, automatic failover, and backups assist with data durability and fault tolerance, making them necessary for preventing data loss. Consistency Models A database can feature one of the following consistency models: Strong Consistency: This model provides immediate and consistent visibility of updates across all nodes. This is the highest consistency level but may increase latency and decrease availability. Eventual Consistency: In this model, synchronous updates eventually bring all replicas into the same state. Since it prioritizes low latency and high availability, it’s a better choice for distributed systems. Causal Consistency: Causally related operations are consistently ordered across nodes in this model. It preserves the causal relationship between events and balances consistency and performance. Since each model has a trade-off between consistency, availability, and partition tolerance, data personnel can select one based on the application requirements. Security Security capabilities may vary between databases, but features such as authorization, encrypting, authentication, and auditing can boost compliance with organizational security policies and regulations. Community and Support Infrastructure A database’s support services and the size of its community are also important factors to consider. A large, active community can be a useful resource for learning best practices and acquiring insights, and a support ecosystem can help troubleshoot issues quickly. Cost A database’s total cost of ownership (TCO) — which includes licensing, infrastructure costs, maintenance, and support expenditure — can impact its viability for a business. Ideally, a database should provide good value for money and meet organizational requirements. Conclusion Having been around since the 1970s, online transaction processing systems support transactional applications across a wide range of sectors. There has been progress towards combining OLTP and OLAP systems on a unified platform (such as MySQL Heatwave by Oracle). However, OLTP systems largely maintain their distinct functionality, which organizations can fully leverage by using the right database. LIKE.TG features built-in connectors for different databases — such as MySQL, PostgreSQL, MongoDB, and Oracle — for running OLTP workloads. Its hassle-free, no-code platform lets users design and deploy OLTP data models easily. Reach out to our team to learn more.

According to Gartner, 80% of companies worldwide are expected to have efficient data management systems in place by 2025. This projection highlights the growing recognition of data governance tools as essential enablers for maintaining and enhancing the quality and security of organizational data within these data management systems. In this blog, we will talk about some of the best data governance tools and software to consider in 2024. The goal is to help analyze different tools select the right solution for your company. Understanding Data Governance At its core, data governance is about taking control of the data management process. It guarantees that your data is accurate, accessible, and secure, making it a reliable foundation for decision-making. As such, data governance leads to data that is healthy and used responsibly and effectively, with a clear understanding of its source, who has access to it, and how it can be used. Today, data breaches are all too common, and regulatory demands like GDPR and CCPA require strict data handling procedures. By using modern data governance software, you rest assured that your company-wide data is handled safely and ethically, ultimately helping you to build trust with your clients and partners. What are Data Governance Tools? Data governance tools are software and solutions that are the backbone for managing a company’s data ecosystem. These software applications are engineered to facilitate the organization, quality control, and protection of data assets. Note that the terms “data governance tools,” “data governance software,” “data governance solutions,” and “data governance platforms” all convey similar meanings and are used interchangeably in the article. Key Functions of Data Governance Tools Data governance tools are essential in managing your organization’s data and keeping it well-organized, so your teams can easily retrieve and access it. These tools automatically scan and index databases and files to identify and eliminate errors and inaccuracies in the data sets, providing reliable data for decision-making. Data governance tools are also significant in protecting sensitive information from unauthorized access, which enhances security in the face of rising cybersecurity threats. They support compliance with strict regulations, such as GDPR, by tracking the financial data lifecycle and documenting data storage, usage, and sharing practices. These tools assist in making personalized recommendations by providing a framework to analyze customer data. This personalization, in turn, enhances customer experience and supports the development of business strategies backed by accurate data. How Data Governance Tools Work In practice, data governance tools have become a part of the overall data management strategy. Given the complexity of today’s data stacks, data teams frequently use these tools with their existing data systems and processes, often leveraging automation and AI to scale their data governance efforts further. Therefore, these tools are crucial for modern data management across industries, including finance, healthcare, retail, and technology, as they help these industries manage vast amounts of data safely and smartly, maintaining accuracy and compliance with laws. Automated Data Discovery Classification With data governance software, you can automatically scan your organizational data landscape to identify and classify data stored on-premises or in the cloud. For example, modern data governance tools can recognize sensitive information, such as personally identifiable data or financial records, enabling you to categorize and better protect it. This way, users in your organization will be able to understand what data they have, how it’s categorized, and what data they need to work with, making data governance scalable and more manageable. Quality Control Mechanisms Data governance tools allow organizations to create specific guidelines on how data should be collected, stored, and used, ensuring everyone follows the same standards. They automate the oversight of data from its creation to its eventual archiving or deletion, ensuring that data is up-to-date and relevant. An example of this is the implementation of data retention policies that automatically archive data that has not been accessed for over a year, keeping the system efficient and reducing storage costs. Data Lineage Tracking Data lineage tracking is critical for regulatory compliance and for troubleshooting data issues. Therefore, organizations must understand the journey of their data from its origin to its current form to ensure transparency and auditability. Data governance tools offer advanced lineage features that visualize the flow of data through systems, helping organizations like banks trace data back to its source. Adapting Business Operations and Maintaining Data Transparency Data governance tools equip businesses with the flexibility to adjust their operational frameworks to stay aligned with the company’s evolving needs. For instance, as a company grows, these tools can help reconfigure data access permissions and workflows to match new organizational structures or compliance requirements. Furthermore, these tools can track data movement across various systems and processes. This traceability ensures that businesses can see where their data comes from, how it’s used, and where it goes, which is important for auditing and compliance. An example is when a customer makes a purchase; data governance tools can trace the customer’s data from the point-of-sale system, through the inventory management system, and into the financial reporting system. Best Data Governance Tools to Consider in 2024 1. LIKE.TG LIKE.TG Data Stack is meticulously designed to reinforce data governance across organizations, providing a foundation for managing, securing, and leveraging data assets. Each feature of LIKE.TG directly contributes to enhancing data governance practices: Discover Data Easily: Use LIKE.TG’s advanced search and filtering to quickly locate the exact data you need, enhancing your ability to access and utilize information efficiently. Enrich Your Data: Attach relevant metadata to your data assets, building a shared understanding of each asset’s purpose and significantly improving data management practices. Own Your Data: Take control of governance for your data assets. As an owner, you directly handle access requests, confirming data is used appropriately and securely. Unify Data Language: Establish a comprehensive business glossary. This glossary creates a shared set of terms and definitions across your organization, minimizing misunderstandings and standardizing data use. Set Data Quality Standards: Implement data quality rules to measure and ensure your data’s accuracy, completeness, consistency, and reliability, keeping your data trustworthy and up to date. Profile Your Data: Through data profiling, actively examine and summarize your datasets to understand their structure and quality. This process helps identify potential vulnerabilities, verifying your data is secure and effectively managed. 2. Ataccama One Ataccama One is a data governance and management solution that operates across on-premises, cloud, and hybrid environments. It is an AI-driven tool that supports data governance teams, stewards, scientists, and engineers by streamlining data governance processes through automation and intelligence. Pros It offers governance features, including data lineage and access controls, establishing data security. Provides detailed profiling and management of metadata, allowing for improved data understanding and governance across the data lifecycle. Implements role-based access controls and stewardship capabilities, ensuring that authorized personnel access and manage data, aligning with governance policies. Cons Integrating Ataccama with your current systems can be complicated and needs technical skills. It is resource-intensive, especially when it comes to large datasets. 3. Collibra Collibra Data Governance is a component of the Collibra Data Intelligence Cloud platform, designed to improve data governance and management within organizations. Pros Offers a Policy Manager application for centralized control of data policies and standards. Supports metadata documentation through its data dictionary, enabling users to easily find and understand data. Provides visualization of data lineage, showing the journey of data across systems and how it is processed and used. Cons Requires intensive training to use its advanced features and automation, which can be tough at first. Getting all its features to work well with the systems might be challenging if new to it. 4. Erwin Data Intelligence Erwin Data Intelligence by Quest streamlines enterprise data governance by offering tools that include data cataloging, quality assurance, and data literacy. Through its integrated approach, the software promotes informed decision-making and compliance with internal policies. Pros Collects and catalogs metadata, simplifying generating data lineage and performing data quality assessments. Helps create and maintain a centralized repository of business terms and definitions. Enables tracing of data origin and transformations throughout its lifecycle, supporting transparency, compliance, and auditability in data governance efforts. Cons The tool’s performance slows down with larger models, making report generation challenging. Generating reports for large models can be time-consuming and may lead to application crashes, resulting in lost changes. 5. Informatica Informatica offers a data governance solution that helps organizations manage and govern their data across multiple cloud platforms. Pros Automatically scans and indexes metadata, enhancing data understanding and governance. Integrates master data management and AI-based integration patterns to ensure accurate and consistent data. Offers visualization of data lineage and history, supporting automated tracing of data movement for impact analysis and compliance. Cons The tool’s high complexity demands extensive training and lacks customization options for relationships, limiting visual and integrative capabilities. Navigation is complicated and offers limited scheduling options, struggling with performance when multiple jobs are scheduled. 6. OvalEdge OvalEdge is a data management tool that automates data categorization and indexing. It works on-premises or in the cloud and offers data discovery, governance, and policy management features. Pros Utilizes NLP and ML to organize data with tags, usage statistics, and custom tags, simplifying data discovery and organization. Crawls data sources, including data lakes and analytics software, to index metadata, enhancing data understanding and governance. Generates reports on data usage and errors, offering valuable insights into data health and activity. Cons The data quality feature is underdeveloped and contains many minor bugs. The visual interface is prone to errors. 7. Talend Talend Data Fabric is a platform that combines data integration, quality, and governance functionalities. It aims to streamline data management across its lifecycle, from integration to governance. Pros Automatically crawls, organizes, and enriches metadata, while identifying data silos and assessing data trustworthiness. Facilitates a team-based workflow for setting priorities and tracking data governance projects, enhancing collaboration and efficiency in data management. Offers data lineage tracing and compliance tracking features, supporting data privacy efforts and regulatory compliance across the organization. Cons Managing complex data flows can become extremely complicated, making it difficult to debug and identify issues when unexpected results occur. The tool provides poor explanations for errors, often leaving you unsure why an error occurred. 8. IBM Cloud PAK IBM Cloud Pak for Data is a cloud-based platform that offers effective data governance, quality, and privacy management. It includes AI-powered data discovery, metadata enrichment, and quality management capabilities. Pros Utilizes machine learning to automate the discovery, profiling, and cataloging of data, streamlining the organization of metadata and data assets. Provides self-service portals for various roles, enabling easy access to data and facilitating collaborative data governance and analysis. Includes tools for creating visual representations of data relationships, profiles, and lineage, aiding in the understanding and using data. Cons The software struggles with user authentication complexities and limited control over secret management in IAM-enabled environments. It lacks a comprehensive training plan and the capability to enforce quotas, hindering effective data governance. 9. Oracle Enterprise Metadata Management OEMM is a tool for harvesting, cataloging, and governing metadata from various data sources. Its features enable organizations to gain a comprehensive understanding of their metadata landscape. Pros Automatically gathers metadata from various sources, enabling a unified view across relational databases, data warehouses, and BI platforms. Offers detailed data lineage tracing and impact analysis functions, aiding in understanding the data flow and the implications of data changes. Seamlessly integrates with Oracle Enterprise Data Quality, supporting a comprehensive data governance strategy. Cons The system demands extensive training for proper management and a complex interface that requires in-depth study before professional use. The user interface is prone to glitches and can be slow to respond, impacting efficiency and usability. 10. Alation Alation is designed to simplify managing data assets and governance policies in hybrid cloud and multi-cloud environments. The Data Governance App aims to streamline secure access to reliable data across IT systems by incorporating AI and machine learning. Pros Allows users to create and map governance policies to specific data assets, facilitating clear and effective policy management. Provides AI-driven automated data curation functions and identifies potential data stewards, optimizing the stewardship process. Supports creating and configuring data governance workflows without requiring coding, making process setup more accessible. Cons The absence of term tables in the Alation Analytics area complicates managing business glossary creation. The BI functionality falls short compared to RDBMS support, with confusing data mapping and dashboard organization Benefits of Using Data Governance Tools Incorporating data governance tools into data management strategy gives organizations numerous advantages, revolutionizing how they handle, utilize, and exploit their data. Additionally, these tools enable organizations to leverage data in ways that could potentially lead to significant improvements in their operations and decision-making processes. Ensures Data Accuracy and Consistency Data governance software provides several quality features that ensure your data stays healthy. For example, by automatically checking and correcting data across systems, these tools help businesses avoid costly mistakes arising from inaccurate data, such as making poor strategic decisions or misreporting financials. Improves Compliance and Security Data governance tools help you manage data access, monitor usage, and protect sensitive information. Clarity on who has access to and is responsible for what data will help your data teams operate more efficiently and ensure compliance with data governance policies. Boosts Operational Efficiency Data governance tools facilitate streamlined business operations by automating several data management tasks. For instance, these tools help you organize data, making it easier and quicker for your data teams to locate the required information. Similarly, modern data governance platforms can automate access controls and lineage tracking, which means you can monitor data movement across your organization and demonstrate compliance with regulatory requirements. Facilitates Better Decision-Making Strategically using data governance tools empowers you to make critical business decisions based on the most accurate and up-to-date information. These tools tackle two significant challenges to data-driven decision-making: data quality and accessibility. As such, these tools are precious in dynamic markets where strategic agility can provide your organization with a competitive edge. Enhances Data Security Maintaining trust and safeguarding intellectual property is a core component of a data governance framework, especially when your customers entrust you with vast amounts of sensitive information. A data governance platform with robust security features can help you secure organizational data sets. For example, you can implement encryption and access controls to protect data against unauthorized access. Reduces Costs Data governance tools can help your business avoid expenses related to manual data cleanup, regulatory fines, and data breaches. These platforms also offer a viable solution to ever-increasing storage costs by identifying and eliminating redundant, obsolete, or trivial (ROT) data. Furthermore, they enable more accurate forecasting and resource allocation, which leads to better overall financial management. Selecting the Right Data Governance Tool As evident by the list of data governance tools above, the market offers several platforms and solutions for data governance. While this competition will undoubtedly benefit the consumers, it can easily make selecting the right governance tool complex and time-consuming. However, you can make an informed decision if you consider certain important factors when selecting your organization’s ideal data governance tool. Here’s a detailed guide on what to keep in mind while browsing through different data governance solutions: Understanding of Data Landscape Before choosing a tool, it is essential to clearly understand data architecture, including where data resides, how it flows within the organization, and who uses it. This understanding will act as a guide in selecting a tool that can handle specific data ecosystems. It means choosing software that can integrate with the databases the organization utilizes, support their business processes, and accommodate their data formats. The goal is to find a tool that can interact with all aspects of an organization’s data landscape, from storage and processing to analysis and reporting, to facilitate effective data management across the entire entity. Define Data Governance Goals Identify what goal to achieve with data governance. Whether it’s improving data quality, certifying regulatory compliance, enhancing security, or fostering a data-driven culture, these goals will influence the right tool. When setting goals for data governance, an organization might aim to enhance data quality, ensure they meet regulatory standards, boost their data security, or develop a culture that values data-driven decisions. These objectives will guide them in choosing the most suitable data governance tool. For instance, if a company’s primary goal is to improve data quality, they would look for a tool that excels in identifying and correcting inaccuracies in their data. This process could mean selecting software that offers advanced data profiling and cleansing capabilities, enabling the organization to detect errors automatically and ensure their data is accurate and reliable. Ease of Use Ease of use is essential when selecting the right data governance tool. It directly impacts how quickly your team can adopt and effectively utilize it. A user-friendly interface and intuitive features mean less time spent on training and more on achieving your data governance goals. For example, suppose a tool offers a straightforward dashboard where users can easily navigate to find data quality metrics, manage data access, or set governance policies. In that case, your team is more likely to use it efficiently and accurately. This ease of use ensures you can leverage the data to drive decision-making and value creation rather than struggling with complex software. Assess the Flexibility for Integration The tool should easily fit in with your existing data systems and workflows to avoid data silos and redundancy. This flexibility minimizes disruptions as your data teams leverage the current IT infrastructure and technology. For example, if your company uses a specific CRM and analytics platform, the right data governance tool should easily connect to both, allowing for seamless data flow and management. This connection means your team can continue working with familiar systems while benefiting from data governance capabilities, such as improved data quality and access control, without drastic changes to their daily processes. Scalability And Flexibility To meet the fifth criterion, data governance tools must scale and flex with your data’s growth and diversity. They should manage your data’s increasing size, different types, and the speed at which it’s generated. Moreover, as the demands and roles of those who use your data evolve, these tools must adapt, offering ways to tailor and tweak settings to match your unique governance needs. For example, a tool that integrates seamlessly with your current databases and analytics platforms while allowing you to define who can access what data ensures that your governance system grows with your company. Cost And Value Assessing the cost and the value that data governance tools bring is essential. These tools must justify their expense by significantly lowering data-related risks, boosting the reliability of your data, and increasing its overall worth. They should align with your financial constraints and resource availability, offering flexible payment options like monthly subscriptions, one-time licenses, or usage-based fees. Furthermore, they must include support and upkeep services to ensure smooth operation. For instance, investing in a tool that offers detailed analytics on data usage and quality improvements can show a tangible return on investment by enhancing decision-making processes and compliance, making it a cost-effective choice for your data governance strategy. Evaluate the Support and Community Consider the vendor’s level of support, including training, customer service, and community forums. A strong support network can ease the implementation process and maximize the tool’s value for your organization. For instance, imagine a tool with comprehensive training modules, a responsive customer service team, and an active user community. This environment means you can quickly learn how to use the tool effectively, get help when encountering problems, and exchange tips with other users. Such support smooths out the initial learning curve and ensures ongoing assistance and improvement, making it easier for your organization to adopt and benefit from the tool. Must-Have Features in a Good Data Governance Tool Metadata Management Beyond cataloging, the tool should offer deep insights into the data’s metadata, allowing us to understand its lineage, quality, and lifecycle. This feature is crucial for tracing data back to its source and understanding its transformation over time. Workflow Automation for Data Stewardship Automating governance workflows can significantly improve efficiency and ensure consistent application of governance policies. Look for tools that offer customizable workflows to match the governance processes. Collaborative Features for Cross-Team Engagement Data governance is a cross-functional effort. The tool should promote collaboration across different departments, enabling users to contribute to data quality, compliance, and management. Advanced Security Features Beyond basic access controls, seek tools offering sophisticated security features like anomaly detection, encryption, and detailed audit trails. This security is essential for protecting sensitive data and complying with stringent regulations. Comprehensive Reporting and Analytics The ability to generate detailed reports is important for monitoring the effectiveness of your data governance initiatives. These insights can help fnd areas for improvement and demonstrate compliance with regulatory bodies. Concluding Thoughts Data governance tools are essential for businesses to keep their data accurate, safe, and in line with rules and regulations. These tools help businesses use their data well, ensuring every information is valuable and secure. LIKE.TG Data Stack is an all-in-one solution for efficiently managing and governing data. Its easy-to-use interface allows data handling without the need to code. Whether combining data sources, cleaning data, or ensuring it meets quality standards, LIKE.TG simplifies the process. If you’re looking to improve how you manage your data, give LIKE.TG Data Stack a try. Visit our website and sign up for a 14-day free trial. Experience how LIKE.TG can make data governance straightforward and effective for businesses. Take Control of Your Data with LIKE.TG Ready to streamline your data management and governance processes? Try LIKE.TG Data Stack today and experience how easy it can be to ensure data accuracy, security, and compliance. Sign up for a 14-day free trial on our website and see the difference. Start a Free Trial

If you use a CRM system and want to integrate it with your applications and systems, you can use LIKE.TG’s Custom API (CAPI) Connectors for seamless connectivity within minutes. Connecting to a CRM system is ideal because it enables you to get your data in a centralized location, eliminating the need to download files every time you want to access your CRM data. In this blog, we’ll show you how to connect your CRM system using LIKE.TG’s CAPI Connectors. LIKE.TG CAPI Connectors LIKE.TG CAPI Connectors enable seamless connectivity to various CRMs, BI tools, drives, databases, social media, and other platforms through API integration. Our GitHub repository offers a diverse collection of API-based connectors, ensuring effortless access to data from a wide range of sources and making it part of your data pipelines. The key benefits of LIKE.TG CAPI Connectors include: User-Friendly Interface: CAPI Connectors simplify the connectivity process by offering a drag-and-drop functionality. This intuitive interface empowers users to connect with any platform that supports API effortlessly. Streamline Workflows: With a vast collection of API-based connectors available in the GitHub repository, you have the flexibility to connect with various systems and platforms. Customization Options: If you don’t find the desired connector in the existing list, LIKE.TG CAPI Connectors offer the advantage of creating custom connectors within a few hours. Time and Cost Savings: By providing an extensive range of pre-built connectors and the ability to create custom connectors quickly, LIKE.TG CAPI Connectors saves both time and costs associated with developing connectivity solutions from scratch. Using CAPI Connectors, you can create and maintain custom API collections even if the API provider does not offer existing documentation for its APIs. Connect your CRM system Using LIKE.TG’s CAPI Connector Step 1: Click the Import API button in the API Browser. The API Browser shows a consolidated view to import, maintain and access all your CAPI connectors. Step 2: Set the API Import Source as Custom Connectors. Let’s say you want to connect to AgileCRM. In the Connectors field, select Agile CRM and import it to the project. Step 3: Configure the API Server URL and add authentication details. Step 4: To the left, you have all API operations for Agile CRM data resources available in the API Browser. In this example, we have simply drag-and-drop List Contacts and add it to the flow designer. Step 5: You can simply transform and write the data of your choice to your desired destination, which can be an application or a database. In this example, we’ve exported the List Contact data to a Database table destination. Step 6: Using the data preview feature, you can get an instant preview of the output data in the same window. This allows you to review and verify your datasets. That’s it! In just a few simple steps, you can connect your CRM to your databases, applications, or systems. Learn More Pre-built Custom Connectors Create or customize API Collection API Browser Connect to Slate CRM Using LIKE.TG Slate CRM is a Customer Relationship Management (CRM) software designed for higher education institutions. It offers a comprehensive suite of tools for recruitment, admissions, enrollment management, communication, event management, reporting, and analytics. Its features include application processing, communication automation, data segmentation, personalized messaging, event planning, and tracking of engagement metrics. Using LIKE.TG, you can connect to Slate CRM in minutes. Using the steps above, you can create a custom API to connect to Slate CRM in minutes. Since Slate CRM has APIs available for rapid integration, you can readily import the API to be used as a CAPI connector. This means that you can get access to real-time data within minutes. To learn more, contact our sales team today!

What is metadata management? Before shedding light on metadata management, it is crucial to understand what metadata is. Metadata refers to the information about your data. This data includes elements representing its context, content, and characteristics. It helps you discover, access, use, store, and retrieve your data, having a wide spread of variations. Metadata of an image. Image by LIKE.TG. Let’s look at some of the metadata types below: Operational metadata: details how and when data occurs and transforms. This metadata type helps to manage, monitor, and optimize system architecture performance. Examples include time stamps, execution logs, data lineage, and dependency mapping. Legal metadata: involves the legal aspects of data use. This variation aims to protect data creators and regulate how one uses data. Examples include copyrights, licensing, and usage rights. Descriptive metadata: describes content (what, when, where, and who). It helps to identify, discover, find, and use data resources. Examples include title, author, keywords, and summary. Technical metadata: includes technical details of the data file. This metadata variation ensures proper data interpretation by software programs. Examples include file size, format (pdf, jpeg), encoding (UTF-8), and compression type. Process metadata: tracks data handling steps. It ensures data quality and reproducibility by documenting how the data was derived and transformed, including its origin. Examples include actions (such as data cleaning steps), tools used, tests performed, and lineage (data source). Preservation metadata: preserves data for long-term access, ensuring it remains usable over time by providing information for future care. Examples include backup location, migration history (format changes), and digital signatures. Types of metadata. Image by LIKE.TG. To help you simplify your journey to metadata management, we will delve into the notion, explaining its significance, various types, how to structure it for optimal results, the value you will gain, what tools are involved, and how you can choose a fitting one, along with best practices ensuring value maximization. Let’s start with the basics. Metadata management is the “practice” of managing data about data. Metadata management helps create a common language for your data. In other words, it standardizes your data by collecting, organizing, and maintaining it using a set of activities, policies, and technologies. As a result, your data becomes quick to discover, easier to understand, and more accessible by humans and machines. Let’s take an example of a library catalog. A library wouldn’t just store books on random shelves; it would categorize them, label them, and have entries in a catalog system. Metadata management does the same thing for your data. It makes finding, interpreting, and using the metadata easier, helping you find what you need and ensuring it is error-free. As a result, everyone working with the core of data, including tech experts, salespersons, managers, and search engines, can better maximize its benefits, driving informed decisions. Example of metadata management Imagine a vast library of movies and shows on streaming platforms like Netflix or Prime Video as your data collection. It contains an extensive variety of content. This content is your data. However, finding that perfect movie becomes daunting without a proper cataloging system. This situation is where metadata management simplifies things. Each piece of content includes details such as title, genre, synopsis, actors, language, release date, and more—known as metadata. It functions like a detailed catalog entry, offering a quick guide for viewers to skim through the content and find what they like. As stated above, streaming services leverage metadata management to effectively categorize content (metadata) by multiple variables. Metadata enables them to deliver personalized recommendations, custom playlists, and dynamic grouping of content—all driven by metadata analytics. Subsequently, this strategy personalizes the streaming experience to match users’ preferences, making it easier for them to explore new favorites and revisit cherished classics. Master data management vs. Metadata management Before proceeding, it’s essential to clarify that while both master data management (MDM) and metadata management are crucial components of data management and governance, they are two unique concepts and, therefore, not interchangeable. Master data describes the core information necessary for the operations or transactions within a business. This data includes but is not limited to, the unique identifiers or attributes of business entities—for example, product IDs, customer names, and company branches. Master data management is a business function that aims to create a unified, accurate, and consistent view of this data. It involves collecting, cleaning, managing, sharing, and protecting this data to ensure everything runs smoothly. Examples include information like file formats such as mp3 for audio files and pdf for documents, among others. Metadata management is the strategy that centers on organizing, managing, and governing metadata. Why is metadata management important? Data is only valuable if it is reliable. It isn’t easy to know whether it is accurate, up-to-date, or relevant without proper context. Metadata management is elemental in providing this context to data and is the cornerstone for effective data governance and intelligent data management, ensuring your data is reliable and authentic. Using precise policies and standards, this practice helps you manage data about your data (metadata) and monitors its quality and relevance, ensuring compliance with regulations. Additionally, it uncovers insights that simplify data discovery, risks associated with it, its value, how to use it, and the relationship between data elements, enabling seamless data flow mapping. Moreover, when powered by AI and machine learning, metadata management solution terms as “active,”—meaning it attains potency to integrate with other applications, including data analytics programs and simultaneously gets enhanced by utilizing human knowledge, ultimately generating new and accurate insights. Interrelation between metadata and other applications. Image by LIKE.TG. Here are some of the questions different personas or teams within the same organization may ask themselves (thanks to metadata management) and assess their performance while ensuring a single source of truth for everyone: Ways metadata management empowers data-driven decisions across teams. Image by LIKE.TG. These value additions are only the tip of the iceberg. With over 5.44 billion global internet users as of April 2024, the amount of data created, stored, copied, and consumed is expected to explode to 181 zettabytes by 2025. That said, aptly managing the metadata of such breadth is more crucial than ever. Benefits of metadata management Having received various names, from “the business glossary for data” and “data cataloging” to being known as “a single source of truth,” metadata management plays a pivotal role comparable to that of the Rosetta Stone in the realm of data. It deciphers the meaning and context of data for everyone, just as the Rosetta Stone unlocked ancient Egypt’s history and culture, making it understandable. For a better understanding, let’s take a look at some of its benefits, making it indispensable for data-driven businesses: Data discoverability and usability Data consistency and unification End-to-end data lineage and governance Time and cost savings Data integration and collaboration Data discoverability and usability: Metadata management is a comprehensive data catalog that allows you to discover relevant datasets. This discoverability increases accessibility, enabling better data utilization and informed decision-making. Data consistency and unification: It establishes a standardized approach to defining data within an organization. Thus, each department has access to the metadata it needs to understand and manage its systems, data, and operations effectively. This practice fosters consistency, streamlines data governance, unifies the company’s approach to data, and leads to fewer data retrieval issues. End-to-end data lineage and governance: Metadata management records a dataset from its origin to each transformation it undergoes. These detailed logs help track data for regulations and perform data quality assessments and root cause analysis, ensuring data quality and risk mitigation. Time and cost savings: Automating and standardizing metadata management allows companies to reallocate their resources to high-priority tasks. This practice saves the resource time previously spent on manual tasks. Additionally, it leads to savings in areas like data design, conversion, and mapping. Data integration and collaboration: Metadata management allows seamless integration into various sources, systems, and applications by documenting data structures, formats, content, and relationships. Additionally, it helps grasp the connections and dependencies between different data assets, ensuring integration and collaboration across departments. Benefits of metadata management. Image by LIKE.TG. Due to such meaningful contributions, the metadata management market growth is surging globally, showing an impressive compound annual growth rate (CAGR) of 22.18% from 2023 to 2028. Enterprise metadata management market – Growth rate by region. Source: Mordor Intelligence. How does metadata management work? Effective data utilization hinges on a solid data foundation. Metadata management provides this concrete shared foundation by organizing and comprehending data metadata. Here’s a breakdown of how it works: Step 1: Metadata collection It begins by collecting metadata from various data sources, including on-premises and cloud systems. These data sources include data lakes, warehouses, databases, file systems, applications, and data integration tools. This process aims to accurately understand the data’s structure, format, and content, ensuring its reliability and suitability for analytics and data science initiatives. Step 2: Metadata curation After collection, the raw metadata is curated with relevant business context for shared understanding. It is categorized and organized by purpose and usage, enriched with attributes and relationships. This data is then cataloged in a central repository, ensuring it is searchable and accessible. Furthermore, user feedback, including ratings, reviews, and certifications, further enhances its value by providing insights into its usefulness to others. Step 3: Intelligent metadata management The final step, intelligent metadata management, uses AI and machine learning to analyze these datasets deeply. These technologies examine metadata to discover data lineage, reveal hidden connections, and identify similar datasets, ranking them as per user needs. This process facilitates users with the discovery of the most relevant information for any given task. Ultimately, this approach transforms raw data into a strategic asset, empowering confident, data-driven decisions. What is a metadata management framework (MMF)? Metadata management framework refers to an organization’s approach to creating, processing, governing, and utilizing its metadata. This framework encompasses the following components: Goal and objective: Identifying a focused area you’re trying to solve or improve. Stakeholders: Engaging relevant data owners from the beginning ensures the framework is shaped correctly from the start. Strategy: Defining the types of metadata that you will work on and setting priority goals and objectives for metadata management. Architecture: Outlining standards, models, and guidelines to ensure a unified approach across the organization. Processes: Creating processes and workflows for collecting, documenting, and maintaining metadata with defined roles and responsibilities. Tools and Technologies: Selecting and implementing tools and technologies that are easy to use, allow dataset standardization, and align with your organization’s requirements and goals for data cataloging, modeling, repositories, profiling, and lineage. Integration: Ensuring your metadata integrates with the existing tech stack. Governance: Establishing metadata governance processes to ensure metadata integrity, security, and compliance. Additionally, setting up a data governance body to oversee your metadata management framework and resolve conflicts. Training and education: Offering training and education to enhance awareness and competencies in metadata management. This practice includes a comprehensive program that informs stakeholders about metadata management’s importance, their roles, and the use of established tools and processes. Continuous improvement: Regularly assessing and enhancing the framework based on user feedback and observed usage trends. LIKE.TG Metadata Management Framework Metadata management use cases Effective metadata management unlocks a wide range of functionalities for organizations. Let’s explore some of these applications: Improved efficiency and troubleshooting Enhanced data analytics Data utilization and cost-optimization Automated data security and compliance Effective data governance Streamlined onboarding and integration Improved efficiency and troubleshooting: Automated lineage tracking, a vital metadata management component, visualizes data flow from its origin to any transformations it undergoes. This approach empowers data teams to analyze root cause with lightning speed by pinpointing the exact source of problems within queries or reports. As a result, it saves a significant chunk of time for data professionals, improving problem-solving capabilities across the team. Enhanced data analytics: Well-managed metadata is fundamental to delivering quality and consistency – the prerequisites of effective data analytics. It facilitates self-service analytics and business intelligence initiatives by making data assets more readily discoverable and accessible to business users, fostering a data-driven culture. Data utilization and cost-optimization: By analyzing usage patterns, such as identifying frequently or infrequently accessed tables or inefficient queries, metadata management enables organizations to make informed decisions on optimizing their data storage and processing resources. These insights allow cost-saving costs and enhanced data warehouse efficiency. Automated data security and compliance: Metadata management is critical in automating data classification in line with regulatory requirements (e.g., PII under EU GDPR or internal team data). This mechanism enforces security protocols, helping organizations maintain compliance and data security while reducing risks associated with data breaches. Effective data governance: Data governance requires a comprehensive view of data to realize its potential fully. Metadata management enables this, managing the entire data lifecycle while ensuring its quality, accessibility, availability, and security. Streamlined onboarding and integration: An enterprise metadata management software provides end users direct access to relevant metadata within their tools, simplifying onboarding processes and organizational integration for new employees. Metadata management tools A metadata management tool is software designed to help organizations collect, organize, store, and manage metadata efficiently. These tools enable organizations to fully understand and leverage their data, providing a centralized system for cataloging, monitoring, and analyzing data lineage, relationships, and usage patterns. Capabilities of a metadata management solution Metadata management tools encompass many features like data catalog, business glossary, data lineage, data profiling, interoperability, and impact analysis. Their market is expected to go through a compound annual growth rate of 18.4% by 2029. Although these tools offer numerous benefits, it is essential to carefully assess the program before adopting one. Here are some of the capabilities to look for in a metadata management solution: Data inventory: What data do you have, and where is it stored? Data inventory, also known as data mapping, acts like a directory, listing all your organization’s data assets and where they’re stored. This granular view facilitates data governance by ensuring response data usage. For instance, you can track how the data you retrieved (i.e., response data) from the metadata research (e.g., customer names) is used later. This capability lets you see who is accessing the data, how the respective person uses it, and its impact on the data (data lineage). Data catalog: Where is our data, and how can we access it? Unlike data inventories, data catalogs are like advanced search engines for your data. These automated tools actively scan and catalog data across various storage locations – databases, data lakes, and data warehouses – making it much easier for business users to discover the specific datasets they need. Data lineage: Where has our data been, and what changes did it go through? Data lineage acts like a behind-the-scenes map for your data. It tracks the journey of your data from its origin (i.e., source) through any transformation it undergoes to where it is ultimately used (i.e., destination). This information is beneficial for impact analysis, where you must predict the consequences of changes to data sources or processing methods. Tagging How can we enrich and adapt our data for future needs? Tagging involves assigning relevant labels, called metadata tags, to data inventory to categorize and describe them with keywords, making them highly searchable and adaptable to changing business needs. Some tools can even automate tagging based on patterns, saving time and reducing errors. Data matching How can we ensure our metadata accurately reflects data asset relationships? Data matching helps identify connections between different pieces of metadata. Take an example of two datasets that might have similar information but slightly differ in names or formats. Metadata management tools with built-in data matching capability automatically compare these datasets and identify potential matches, making your data searches more accurate. Collaboration What does our data mean (offer context) to our teams? Effective metadata management requires clear communication between teams about data definitions and usage. So, collaboration features like commenting, annotations, and version control create a central platform for dialogue on these aspects. For which documenting every interaction among teams is vital and must be offered by the metadata management solution. This transparency fosters knowledge sharing, helps address inconsistencies in data understanding, and ultimately leads to better, data-driven decisions. Six questions to ask when choosing a metadata management tool. Image by LIKE.TG. Metadata management best practices Here are some of the ways you can utilize to maximize your metadata management value and its sustainability: Establish a unified metadata foundation: Create a central repository for metadata to provide insights across all data management practices. Harness the power of all six metadata categories: Drive metadata-driven intelligence by capturing the six metadata categories – operational, legal, descriptive, technical, process, and preservation – to maximize the value of your data. Integrate AI/ML to augment your metadata management: Leverage AI/ML on top of your unified metadata foundation to make cloud data processes intelligent and adaptable. Empower users with an AI-powered data catalog: Capture and enrich enterprise metadata with business context using an AI-powered data catalog, making relevant data discoverable, accessible, understandable, and trustworthy. Ensure scalable and comprehensive metadata management: Achieve unparalleled management of metadata, offering extensive connectivity across multi-cloud, on-premises environments, applications, and ETL/BI tools; deep metadata extraction and data lineage comprehension capabilities (e.g., through database code parsing) and scalability to support millions of metadata assets, fulfilling the demands of the modern data landscape. Govern AI/ML models effectively: Establish governance for AI/ML models by managing their metadata, enabling performance measurement against business objectives, and mitigating algorithmic biases. Establish a metadata leadership program: Ensure metadata leaders are part of the governance team to operationalize metadata management policies, fostering success through leading by example. Invest in an appropriate metadata management tool: Once you have finalized your metadata strategy, scope, roles, and standards, you’ll be equipped to identify the essential features your business requires in a metadata management system. This analysis may lead you to acquire a new, dedicated tool or leverage existing metadata repositories within your current data management or business intelligence tools. Maintain consistency across the organization: Once initiated, consistently create metadata following policies and standards and communicate its significance to all data users and stakeholders, ensuring their commitment and engagement. Final Words Like a map empowers exploration, well-managed metadata helps maximize your data value. Metadata management establishes a standardized approach for describing and organizing your data assets. It enhances data discoverability and understanding, enhancing informed decision-making at all levels. LIKE.TG’s metadata-driven data warehousing tool helps you prioritize metadata management by automating your metadata’s discovery, categorization, and management. It provides enterprise-wide shared data understanding, making your metadata easily searchable and readily interpretable. At LIKE.TG, we help organizations unlock the true potential of their data and establish data-driven success. Request a live product demonstration to see it in action, or get in touch with our experts to explore how LIKE.TG DW Builder can easily enrich your metadata infrastructure! Automate Metadata Management with LIKE.TG Unified Metadata-Driven Solution By automating metadata discovery, categorization, and management, LIKE.TG creates a standardized approach that improves data discoverability and understanding — fostering informed decision-making across all organizational levels. Explore how our metadata-driven toolworks. Download Free 14-Day Trial

The success of data warehouse solutions depends on how well organizations implement test cases to guarantee data integrity. As organizations evolve, data warehouse testing becomes crucial to adhere to industry best practices. What Is Data Warehouse Testing? Data warehouse testing is the process of verifying the integrity, accuracy, and consistency of data stored within a data warehouse. This testing is essential because it ensures that the data collected from various sources retains its quality and preciseness when integrated into the warehouse. The necessity of testing data warehouses cannot be overstated. It involves thoroughly validating the data integration process, which is pivotal for maintaining data quality and accuracy. Data warehouse testing checks whether the data transferred from different sources to the warehouse is correct, complete, and usable. Accurate data is the foundation of trustworthy analytics, which businesses and organizations rely on to make strategic decisions. Data warehouse testing enables reliable analytics and informed decision-making by maintaining data quality throughout the analytics process. Data Warehouse Testing vs. ETL Testing Data warehouse testing and ETL testing are intertwined but serve different purposes within the data lifecycle. ETL testing is a subset of data warehouse testing, specifically focusing on the Extract, Transform, Load (ETL) stages of data movement. ETL testing ensures that the data extraction from source systems, transformation to fit the business needs, and loading into the target data warehouse occurs without errors and aligns with the requirements. On the other hand, data warehouse testing encompasses a broader scope. It includes ETL testing and extends to validating the data storage, retrieval mechanisms, and overall performance and functionality of the data warehouse. This type of testing verifies that the data warehouse operates as expected and supports the business processes it was designed to facilitate. Therefore, ETL testing is concerned with the accuracy and integrity of data as it travels from source to destination, and data warehouse testing is concerned with the end-to-end aspects of the data warehouse environment, ensuring its readiness for analytics and decision support. Importance of Data Warehouse Testing The data warehouse is more than just a data repository; it is a strategic enterprise resource providing valuable insights for data-driven decision-making. It consolidates data from various sources into a comprehensive platform, enabling businesses to gain a holistic view of their operations and make informed decisions. However, the strategic value of the data warehouse is contingent on the quality of the data it contains. A study from Information System Frontiers points out that poor data quality often leads to unsatisfactory decisions. That’s why testing the data warehouse is crucial. Data warehouse testing rigorously validates data extraction, transformation, and loading processes, data integrity, and the performance of the data warehouse. This testing finds and fixes errors early, ensuring the data is trustworthy and consistent. Research shows that testing boosts confidence in the data warehouse, especially regarding data quality. Ultimately, data warehouse testing enables businesses to leverage the full potential of data warehouses, confidently make data-driven decisions, and stay ahead in the market. Types of ETL Tests in a Data Warehouse Implementing robust ETL testing methodologies is essential for upholding data integrity and maximizing the value derived from the data warehouse. This table provides a high-level overview of each test type. In practice, the specifics of each test depend on the requirements of the ETL process and the characteristics of the data being handled. Test Description Example Metadata Testing Metadata testing confirms that the table definitions conform to the data model and application design specifications. This test should include a data type check, data length check, and index/constraint check. Verifying that the data type of a column in the source matches the corresponding column in the target, ensuring consistency in data types. Data Completeness Testing Data Completeness testing ensures the successful transfer of all anticipated data from the source to the target system. Tests include, comparing and validating data between the destination and source, as well as counts, aggregates (avg, sum, min, max). Check if all source table records have been successfully transferred to the target table without any omissions or duplicates. Data Quality Testing Data Quality tests validate the accuracy of the data. Data profiling is used to identify data quality issues, and the ETL is designed to fix or handle these issues. Automating the data quality checks between the source and target system can mitigate problems post-implementation. Identifying and correcting misspellings in customer names during the ETL process to ensure consistency and accuracy in the target database. Data Transformation Testing Data Transformation comes in two flavors: white box testing and black box testing. White box data transformation testing examines the program structure and develops test data from the program logic/code. Testers create test cases using ETL code and mapping design documents. These documents also help them review the transformation logic. In black-box testing, users must examine application functionality without looking at internal structures for transformation testing. White box testing involves reviewing the ETL code to ensure data transformation rules are correctly implemented according to the mapping design document. In contrast, black box testing focuses on verifying the functionality of the transformation process without considering the internal logic. ETL Regression Testing Validating whether the ETL process produces the same output for a given input before and after each change. Run regression tests after modifying the ETL code to ensure the data output remains consistent with previous versions. Incremental ETL Testing Incremental ETL testing confirms the accurate loading of source updates into the target system. Checking if new records added to the source database are correctly captured and loaded into the target data warehouse during the incremental ETL process. ETL Integration Testing ETL integration testing is end-to-end testing of the data in the ETL process and the target application. Testing the entire ETL workflow, including data extraction, transformation, and loading, to ensure seamless integration with the target application. ETL Performance Testing ETL performance testing involves comprehensive end-to-end verification of the system’s ability to handle large and/or unexpected volumes of data. Evaluating the performance of the ETL process by simulating large data volumes and measuring the time taken for data extraction, transformation, and loading operations. How to Test a Data Warehouse: The Process Testing a data warehouse is critical to its development lifecycle, ensuring data integrity, performance, and reliability. These steps can help organizations establish a thorough and effective data warehouse testing process, leading to a reliable and efficient data-driven decision-making platform. Here is an example of a retail company recently implementing a data warehouse to manage its vast transactional data, customer information, and inventory details. Identifying Entry Points The organization can start the data warehousing testing by pinpointing the data entry points. These entry points include data sources, ETL processes, and end-user access points. Understanding these sources helps in creating targeted test cases. The bank can identify multiple data entry points: Data Sources: Customer relationship management (CRM) systems, loan processing applications, and investment tracking platforms. ETL Processes: Real-time data streaming and batch processing jobs that handle data extraction, transformation, and loading. End-User Access Points: Online banking portals, mobile apps, and internal analytics dashboards. Preparing Collaterals The next step is gathering all necessary test collaterals, such as data models, ETL specifications, and business requirements. These documents serve as a blueprint for the testing process. The bank will need to gather the following collaterals: Data Models: Complex models representing customer demographics, financial products, and transactional relationships. ETL Specifications: Detailed rules and mappings govern how data is processed and integrated into the warehouse. Business Requirements: Critical reports and analytics that the business stakeholders need to drive decision-making. Designing a Testing Framework Next, organizations must develop a robust testing framework that aligns with the data warehouse architecture. This framework should cover unit testing, system testing, integration testing, and user acceptance testing (UAT). The bank should create a testing framework designed to include: Unit Testing: Individual tests for each component within the ETL pipeline. System Testing: Holistic testing of the data warehouse’s ability to handle the entire data lifecycle. Integration Testing: Ensuring the data warehouse integrates seamlessly with other business systems. User Acceptance Testing (UAT): Validation by business users that the warehouse meets their reporting needs. Adopting a Comprehensive Testing Approach Implement a comprehensive testing strategy that includes: Data Validation: Ensure the data loaded into the warehouse is accurate, complete, and consistent. For instance, organizations can check for data accuracy and completeness against source systems. Transformation Logic Verification: Test cases are created to verify each business rule applied during the ETL process. The bank can ensure that all business logic, such as interest calculations and risk assessments, is applied correctly. Performance Testing: Load testing is conducted to assess the system’s response under heavy data loads. The bank can evaluate the system’s performance under peak load conditions and optimize query response times. Security Testing: Role-based access controls are tested to ensure users have appropriate permissions. The bank must verify that data security and user access controls function as intended. It should also confirm that sensitive financial data is securely stored and accessed. Ongoing Testing Once data warehouse testing is complete, ongoing testing throughout the lifecycle is crucial. The bank can commit to ongoing testing throughout the data warehouse lifecycle to: Catch Issues Early: Regular testing in the development phase to identify and fix issues promptly. Adapt to Changes: Continuous testing to accommodate changes in financial regulations and market conditions. For instance, automated regression testing can help ensure that new data sources or business rules don’t introduce errors. Maintain Quality and Performance: Scheduled testing to ensure the data warehouse’s integrity and efficiency remain high. The bank can perform periodic audits to maintain data quality and performance. Challenges in Data Warehouse Testing Testing a data warehouse is a complex task involving navigating through many challenges. Addressing common obstacles such as data heterogeneity, high volumes, scalability, and data mapping is essential for several reasons: Data Heterogeneity: With data coming from various sources in different formats, ensuring consistency and accuracy is crucial. Inconsistent data can lead to flawed analytics and business intelligence outcomes. High Volumes: The sheer volume of data in a warehouse can be overwhelming, making it challenging to perform comprehensive testing within reasonable timeframes. Scalability: As businesses grow, so does their data. A data warehouse must be scalable to handle increasing loads, which adds complexity to the testing process. Data Mapping: Accurate mapping of data from source to destination is vital. Errors in data mapping can result in significant discrepancies, affecting decision-making processes. Moreover, end-to-end data flow testing becomes increasingly complex, as users must verify the entire process from data extraction at the source to its final form in the data warehouse. End-to-end data flow testing includes testing the ETL processes, data transformations, and loading mechanisms. The complexity arises due to the need to validate the integrity and accuracy of data at each stage, often requiring sophisticated testing strategies and tools. The Role of Automated Data Integration Tools Automated data integration tools can significantly alleviate these challenges. These tools handle data heterogeneity by transforming disparate data into a unified format. They can manage high volumes efficiently, often in real-time, ensuring that the data warehouse is always up-to-date. Scalability is built into these tools, allowing them to adjust to varying data loads with minimal manual intervention. Moreover, automated tools provide reliable data mapping capabilities, reducing the risk of human error and ensuring that data is accurately transferred from source to destination. Leveraging advanced features such as data profiling, quality checks, and automated data validation helps these tools streamline the testing process. They offer a more efficient and accurate approach to data warehouse testing, enabling organizations to maintain high-quality data repositories essential for informed decision-making. According to a study by the International Journal of Recent Technology and Engineering, automated data warehouse testing can save up to 75% to 89% of the time spent on testing. How LIKE.TG Streamlines End-to-End Data Warehouse Testing Overcoming the challenges in data warehouse testing is not just about ensuring the system works; it’s about guaranteeing the reliability of data-driven insights that businesses rely on. Automated data integration tools like LIKE.TG play a pivotal role in achieving this goal, providing a robust solution to the complexities of data warehouse testing. LIKE.TG is an end-to-end data management platform helps organizations implement the end-to-end testing process, making it more efficient and effective. Here are some key features that LIKE.TG offers: Unified Metadata-Driven Solution: Provides a no-code solution that allows for the design, development, and deployment of high-volume data warehouses with ease. Dimensional Modeling and Data Vault 2.0 Support: Supports advanced data warehousing concepts, enabling businesses to build scalable and flexible data storage solutions. Automated Data Quality Checks: Profile, cleanse, and validate data to ensure it is ready for the data warehouse using built-in data quality modules. No-Code Development Environment: The Point-and-click interface allows users to create and edit entity relationships without writing a single line of code. Data Model Deployment: Easily deploy or publish it to the server for data consumption. Job Scheduling and Monitoring: Robust job scheduling and monitoring features automate the data warehousing process, ensuring that your data is always up-to-date and accurate. Leveraging these features, LIKE.TG significantly reduces the time and effort required to build and maintain a data warehouse. It’s an ideal solution for businesses integrating disparate data sources into a single source of truth and maintaining an auditable, time-variant data repository. Ready to transform your data warehousing projects? Start the 14-day free trial with LIKE.TG today and experience the power of automated, no-code data warehousing.

Did you know data scientists spend around 60% of their time preprocessing data? Data preprocessing plays a critical role in enhancing the reliability and accuracy of analytics. This blog will discuss why data preprocessing is essential for making data suitable for comprehensive analysis. What is Data Preprocessing? Data preprocessingis the critical first step in analyzing data. It lets you transform raw data into an understandable and usable format for analysis. It’s a comprehensive process that ensures the data is primed and ready for the subsequent exploration, modeling, and interpretation stages. While data preprocessing must be performed before building machine learning models, it’s not the only step that precedes analytics. Here’s how these steps differ: Data Cleaning vs Data Preprocessing While often used interchangeably,data cleaninganddata preprocessingare not the same. Data cleaning is a subset of preprocessing, primarily concerned with identifying and correcting errors and inconsistencies within the dataset. On the other hand, data preprocessing is an umbrella term that includes data cleaning and other processes such as normalization, transformation, and feature extraction, which are essential to prepare data for analysis. Data Exploration vs Data Preprocessing Data explorationis like detective work, where you look for patterns, anomalies, and insights within the data. It involves asking questions and getting answers through visual and quantitative methods.Data preprocessing, however, is the groundwork that makes such exploration possible. It involves cleaning, transforming, and organizing data to be effectively explored and analyzed for meaningful insights. Data Preparation vs Data Preprocessing Data preparationanddata preprocessing are also used synonymously, but they can have different connotations. Data preparation can be a broader category, including preprocessing, data collection, and integration. It encompasses the entire process of getting data ready for analysis, from when it’s gathered to when it’s fed into analytical tools. Data preprocessing, while part of the preparation, is specifically focused on transforming and conditioning data before analysis. Why is Data Preprocessing Important? The integrity of data analysis is highly dependent on the quality of data preprocessing. Data preprocessing determines the usability and interpretability of data, laying the groundwork for accurate machine learning and AI models. Eliminating Errors Cleaningis a pivotal data preprocessing technique. It allows you to eliminate errors, impute missing values, and rectify inconsistencies. For example, a customer dataset with redundant entries due to technical mistakes would undergo cleaning to ensure each customer record is unique and accurately represented. Making Data Uniform Normalizationis comparable to establishing a level playing field, where disparate measures are adjusted to a uniform scale, enabling equitable comparisons. For instance, normalization can help you analyze the performance of stocks from different countries despite stock prices being available in various currencies and scales. With normalization techniques such as min-max, you can convert all stock prices into a common currency, for example, USD, and then apply a min-max scaling to compare the relative performance of stocks on a uniform scale. Finding Hidden Patterns Diligent preprocessing can reveal concealed patterns and insights. A marketing team analyzing social media data can identify peak engagement times aligned with spam activity. However, excluding anomalies through data cleaning will allow you to pinpoint genuine peak engagement periods and optimize strategy. Big Data Preprocessing As datasets grow in size and complexity, preprocessing becomes even more critical. Big data has a large volume, is heterogeneous, and needs to be processed rapidly. Preprocessing transforms raw big data into a cleaner, more structured format, removing noise and making it easier to process. Similarly, advanced techniques such as parallel processing, distributed computing, and automated preprocessing pipelines are indispensable for processing big data effectively. How to Preprocess Data Data preprocessing involves several key stages that transform raw data into a format ready for analysis. Data Profiling Understanding your data is the first step in preprocessing. Data profiling involves examining the data using summary statistics and distributions to understand its structure, content, and quality. This step can reveal patterns, anomalies, and correlations crucial for informed preprocessing. Example: A retail manager analyzes a dataset of customer purchases to find average spending, most common items, and times of purchase to devise a data-driven marketing strategy. Data Cleansing Data cleansing detects and corrects corrupt or inaccurate data records such as errors, outliers, duplicates, and missing values. Methods like imputation for missing data or pruning for outliers help ensure the accuracy of your dataset. Example: Sales managers correct misspelled product categories or remove duplicate records in sales data. Data Reduction Data reduction aims to decrease the data volume while producing the same or similar analytical results. Techniques like dimensionality reduction, binning, histograms, clustering, and principal component analysis can simplify the data without losing informative patterns and trends. Example: A researcher only uses the most relevant features from a customer survey to predict buying habits rather than the entire dataset. Data Transformation Data transformation helps modify data for specific needs. It encompasses a variety of steps such as aggregation, normalization, and sorting, among others, each playing a vital role in understanding data. For example, data aggregation amalgamates individual data points to furnish a consolidated overview, like summarizing monthly sales figures. Similarly, feature creation devises new variables from the existing dataset, which aids in more effectively discerning the intrinsic trends within the data. Data transformation can also be used to create new attributes within the dataset. You can use mathematical expressions to extract ZIP codes from an address and store them separately or create new attributes from existing features. Example: A healthcare data analyst leverages mathematical expressions to create new features like Body Mass Index (BMI) through existing features like height and weight. Data Enrichment Enhancing data with additional sources or derived attributes can provide more depth and context. It involves incorporating demographic information into customer data or adding weather data to sales figures to account for seasonal effects. Example: A data analyst adds weather data to a retailer’s sales data to see if weather patterns affect buying trends. Data Validation Before moving on to analysis, it’s crucial to ensure the integrity of your data. Data validation checks that the data meets specific criteria, such as constraints, relations, and ranges. It helps to confirm that the data is accurate, complete, and reliable. Example: A finance executive checks whether all entries in a transaction dataset fall within expected date ranges and transaction amounts. How is Data Preprocessing Used? Ensuring High-Quality Data Data preprocessing influences the accuracy of analysis directly. Preprocessed data, devoid of irrelevant noise and inconsistencies, allows models to discern and learn from important features, enhancing prediction accuracy and decision-making prowess. Preprocessing includes several activities, such as cleaning data, handling missing values, normalizing or scaling features, encoding categorical variables, and reducing dimensionality. Each step helps refine the dataset so that the machine learning algorithms can interpret the data correctly and efficiently. For instance, feature scaling ensures that all the input features have equal weightage, preventing any single feature from disproportionately influencing the model’s output. Similarly, encoding categorical variables into a numerical format is essential for some algorithms that only take numerical data as input. Refining Model Accuracy and Performance Preprocessing data in machine learning allows us to remove many obstacles that can hinder model performance. Doing so helps us make more accurate, reliable, and robust predictions. Preprocessing guards againstoverfitting, where a model might otherwise internalize the noise as part of the signal, compromising its ability to generalize to new data. Techniques like normalization and feature scaling foster a model’s adaptability. Feature engineering, an essential facet of model development, is greatly facilitated by preprocessing. It enables innovative features from existing data, refining model performance. For instance, there’s a medical survey dataset with hundreds of features. Through data preprocessing, particularly feature selection, you can pinpoint the most relevant features—such as age, symptoms, and medical history—that are key to predicting a disease. Doing so discards less important details, like a patient’s favorite color, enhancing the predictive model’s accuracy without modifying the original data. Accelerate the Learning Process and Model Reliability The efficiency of the training process also benefits immensely from preprocessing. Algorithms can more swiftly identify patterns in clean data, thus reducing the time, effort, and energy spent training the algorithm. All these are vital considerations in big data environments. Furthermore, the reliability of insights gleaned from AI and machine learning hinges on the precision of preprocessing. It ensures the data input into models is trustworthy, enabling dependable and actionable predictions. Data Preprocessing Techniques Data preprocessing techniques help you fine-tune data for machine learning models or statistical analysis. Here’s how these techniques help preprocess data: Data Imputation Missing data can skew analysis and lead to inaccurate models. Strategies for handling missing values include imputation (filling in missing values with statistical measures like mean or median) or using algorithms that can handle missing data, such as random forests. Reduce Noisy Data Noisy data can obscure meaningful patterns. Techniques like smoothing (using rolling averages) and filtering (applying algorithms to remove noise) help clarify the signal in data. For instance, a moving average can smooth out short-term fluctuations and highlight longer-term trends. Identify and Remove Duplicates Duplicate data can distort analysis, leading to biased results. Detection can be as simple as searching for identical records or as complex as identifying near-duplicates using fuzzy matching. Removal ensures each data point is unique, maintaining the integrity of your dataset. Feature Engineering Creating new features from existing data can unlock profound insights. This process might involve combining two variables to create a new one, such as calculating the Body Mass Index from weight and height or extracting parts of data (like the day of the week) for time series analysis. Feature Scaling or Normalization Scaling features to a uniform range ensures that no single feature dominates the model due to scale. Methods include min-max scaling, which rescales the feature to a fixed range, usually 0 to 1, or standardization, which centers the feature on zero with unit variance. Dimensionality Reduction Dimensionality reduction techniques, like Principal Component Analysis, lower the variables under consideration, simplifying the model without losing significant information. This method can improve model performance and reduce computational complexity. Discretization Converting continuous features into discrete bins can make the data more manageable and improve model performance. For example, age can be binned into categories like ’18-25′, ’26-35′, etc., to simplify analysis and reveal generational trends. Feature Encoding Categorical data encoding methods, such as one-hot or label encoding, convert categorical variables into numerical form for model training. Encoding is essential for algorithms that require numerical input. Data Preprocessing Tools Data preprocessing tools simplify how you interact with extensive data, making it easier to shape and polish complex data. Some data preprocessing tools that make this transformation possible are: Pandas: This Python library offers a wide array of functions for handling data, making it ideal for cleaning, filtering, and aggregating large datasets. Scikit-learn: Scikit-learn is equipped to handle everything from feature scaling to encoding categorical variables, ensuring your data is in the best shape for modeling. OpenRefine: Designed for the challenges of messy data, OpenRefine is a standalone tool that cleans and transforms data. It’s beneficial for standardizing data formats and enriching datasets with information from external sources. Automated data preprocessing tools empower you to focus on deriving insights rather than getting entangled in preparing data. Data preprocessing ensures that raw data is ready for analysis and empowers you to extract meaningful insights. However, it requires technical skills, domain expertise, and strategic decision-making to lay the foundation for accurate and reliable analytics. How LIKE.TG Streamlines Data Preprocessing Using No-Code LIKE.TG’s no-code solution revolutionizes data preprocessing by eliminating the traditional barriers of technical expertise and extensive coding. The tool has an intuitive interface with drag-and-drop features that simplifies complex data integration tasks. This no-code approach simplifies the integration and curation of data, speeding up the process and enhancing data quality by consistently identifying anomalies and patterns. The benefits of LIKE.TG’s no-code platform are manifold: Speed: Accelerate the data preparation process, delivering faster insights. Accuracy: Minimize human error with automated data extraction and transformation. Cost-Efficiency: Reduce the need for specialized personnel and training. Agility: Quickly adapt to changing data requirements with flexible tools. Scalability: Effortlessly handle growing data volumes and complexity. LIKE.TG’s user-friendly platform democratizes the data preparation process, enabling you to automate data collection, cleaning, transformation, and organization regardless of technical expertise. LIKE.TG offers significant time and effort savings, making it a standout choice in data preprocessing tools. Ready to transform your data preprocessing workflow?Embrace the power of no-code data management and unlock the potential within your data. Experience the power of LIKE.TG’s no-code platform firsthand by signing up for a 14-day free trial and take the first step towards streamlined data preprocessing.

Data aggregation is the process of combining and summarizing data from disparate sources into a cohesive dataset. It prepares data for analysis, making it easier to obtain insights into patterns and insights that aren’t observable in isolated data points. Once aggregated, data is generally stored in a data warehouse. Then, you can leverage it to gain a holistic perspective on your operations and market trends, design effective risk management practices, and make more informed decisions overall. This blog offers an in-depth look at data aggregation to help you understand what it is, how it works, and how it benefitsyour business when done right. Understanding Data Aggregation What is Data Aggregation? Data aggregation is performed using specialized tools called data aggregators. Depending on the nature of the data and your objectives, the aggregation process can include grouping, averaging, summing, and counting. Individual records and atomic data rows are consolidated into a summarized dataset following these operations — resulting in aggregated data. Examples of Aggregated Data Number of students in a given school district: The total number of enrolled students in each school district can offer insights into the state of education in different city areas. Number of customers by city: Instead of assessing each customer individually, a business can group its customers in a particular town to determine their shared characteristics, interests, and preferences. Financial reporting: Financial data is regularly aggregated for reporting purposes. For example, balance sheets, income, and cash flow statements are consolidated to create comprehensive financial reports. These reports help stakeholders understand an enterprise’s financial health. Comparing Aggregated and Disaggregated Data These are some of the factors that differentiate aggregated and disaggregated data from each other: Aggregated Data Disaggregated Data Replaces individual data elements with summaries that represent various groups as a whole. Comprises individual data elements or groups that aren’t summarized or collated. Accelerates querying and analysis, allowing you to quickly evaluate vast data. Features the same granularity as the original data, allowing you to assess each data point individually if needed. Offers a concise representation of the whole dataset, enabling you to identify patterns and trends. Provides a detailed view that is useful for accessing and analyzing individual data records. What are Data Aggregators? Data aggregators are crucial to data aggregation and management. These tools collect data from various sources and feature built-in capabilities for aggregate measurements. They utilize different techniques to collect, clean, and process data. They can also help you track data lineage to trace your aggregated data. The Only Data Aggregation Tool You Need Zero coding but plenty of aggregate functions and an interface that's ideal for business and technical users alike. Your free trial awaits. Start Using LIKE.TG Today! Importance and Benefits of Data Aggregation for Businesses 1. Decision-Making Data aggregation provides comprehensive insights that can assist you in making better strategic decisions for your organization. Consolidating, summarized data from wide-ranging sources ensures you aren’t considering just one perspective in your analysis. The result is a more detailed picture of trends, opportunities, and potential room for growth. 2. Performance Monitoring Data aggregation facilitates you in monitoring key performance indicators (KPIs) more effectively. When you keep up with sales, revenue, operational efficiency, and customer satisfaction, you can assess your business’ performance capably and course-correctly to achieve your goals. 3. Risk Management Aggregating your data helps identify potential risks and take the necessary steps to mitigate them. Including various sources in your data aggregation process lets you determine potential anomalies and threats. You can then take decisive, proactive measures to protect your business. 4. Increased Efficiency Data centralization is one of the crucial components of data aggregation. Storing all relevant information in a single accessible location reduces the time and resources required to access and analyze data, which enhances efficiency. 5. Better Insights Aggregated data unlocks deeper insights into your business. It spotlights vital factors such as your market dynamics, operations, and current and potential customer base. Analyzing aggregated data allows you to unravel correlations, trends, and patterns to drive growth. 6. Competitive Edge Data aggregation gives you a competitive advantage over other businesses in your niche. You can make data-driven decisions in less time and with better accuracy. Over time, this edge allows you to adapt and respond to changing market patterns more effectively than others. Data Aggregation Types and Techniques There are various types of data aggregation. Your requirements and how you plan to use the data will determine which approach suits your use case. Temporal As the name suggests, temporal aggregation summarizes data over specified time intervals. These intervals can be as short as hours or days and as long as months or years. This aggregation type is preferable to conduct trend or pattern analysis over time. Temporal aggregation is extensively utilized in time-series modeling, financial analysis, and economic forecasting. You can use it to identify seasonality or cyclical patterns in your data. Spatial Spatial aggregation is helpful to summarize your data based on geographical locations or other spatial units — such as postal codes, towns, cities, states, or countries. You can use spatial aggregation to analyze spatial patterns, identify regional differences in data, or perform geographic analyses. It’s commonly used in urban planning and geographic information systems (GIS). Hierarchical Hierarchical aggregation arranges data into hierarchical structures like groups, subgroups, categories, and subcategories. This aggregation is useful for cases requiring drill-down analyses or hierarchical reporting. These are some uses of hierarchical aggregation in a few industries: Finance: Evaluating financial data by transaction, account type, and branch. Manufacturing: Overseeing the production cycle by SKU, production line, and factory. Government: Using regional and administrative level demographic data to guide decision-making. Healthcare: Reviewing patient data by medical condition/diagnosis, department, and hospital. Supply Chain: Optimizing distribution and inventory levels by studying customer, route, and warehouse/storage facility details. Techniques of Data Aggregation Data aggregation techniques are critical in data summarization and analysis. These techniques range from simple processes to more complex operations, enabling you to analyze vast data volumes efficiently. Here are ten common data aggregation techniques: Grouping: Arrange data in groups based on specified criteria, such as grouping purchase orders by department or product type. Summation adds numerical values for calculating totals, for example, determining the total purchase expense in a cycle. Counting tallies the occurrences or records, such as the number of transactions every day in a purchases dataset. Averaging calculates the average value or mean of a set of numbers. This technique is useful for determining the average purchase amount or the average number of items in a purchase order. Minimization identifies the lowest value of an attribute across a group of records, such as the smallest purchase amount each day. Maximization determines an attribute’s highest or largest value across a group of records, such as the most significant purchase amount each day. Median calculation finds the middle value in a sorted list of numerals and provides a measure of central tendency. Mode calculation finds the value that occurs most frequently in a group of records. In a purchases dataset, we can summarize each city’s most common product category. Weighted averaging averages values and assigns different weights to each of them. This technique is useful for calculating weighted average prices or scores. Hierarchical aggregation aggregates the data at multiple granularity levels in a hierarchical structure. For example, aggregating sales data by city, country, or region. How to Aggregate Data: Key Steps Involved 1. Define Your Objectives You need to define your objectives for data aggregation before starting the aggregation process. Identify your uses for the aggregated data and determine how it can aid you in making decisions and achieving your goals. 2. Collect Your Data For effective data aggregation, you must collect data from relevant sources. Besides being relevant, your data must be complete, up-to-date, and accurate. Automated tools can help you streamline data collection and eliminate the errors associated with manual processes. 3. Enhance Data Quality Next, enhance your data’s quality to improve its reliability. At this stage, you can implement data validation checks, enforce data governance policies and procedures, perform data profiling, and standardize data formats and values to keep your datasets consistent. 4. Aggregate Data Once you’ve collected your data and undergone the necessary procedures to improve its quality, you can start aggregating it. As discussed above, your aggregation process may involve grouping or summarizing your data — depending on your requirements. 5. Validate The Results Lastly, you’ll need to validate the results of the data aggregation process. Compare the aggregated data with your source data, identify any discrepancies that need attention, and fix them as required. There's a Faster Way of Aggregating Data Numerous businesses are already benefiting from LIKE.TG's enterprise-grade data platform. Yours can be next. Find out how LIKE.TG can cater to your unique use case. Speak to Our Team Challenges in Data Aggregation Missing or Incomplete Data: Incomplete data can disrupt aggregation and generate inaccurate or skewed results. Implement effective strategies such as imputation techniques or data sampling to counter its impact on the aggregated results. Handling Large Volumes of Data: Aggregating vast amounts of data is a resource-intensive process that can affect performance. Consider turning to scalable data aggregation solutions to easily process and analyze massive datasets. Addressing Privacy and Security Concerns: You may encounter privacy and security concerns when aggregating confidential data. Protect sensitive data throughout the aggregation process by implementing data privacy and security measures such as access controls, encryption, and anonymization methods. Managing Data Sources and Integration: Since data aggregation involves working with numerous sources, data integration can often be challenging owing to each source’s different format, semantics, and structure. A comprehensive data integration process can help you ensure seamless data compatibility regardless of your sources. Effective Data Aggregation: Considerations and Best Practices Focusing on these key considerations and best practices helps ensure effective and reliable data aggregation that results in quality data: Ensuring Data Quality and Consistency Data quality and consistency are of the utmost importance when aggregating data. Remember to monitor and validate your data to ensure it remains accurate, complete, and relevant. Choosing The Right Data Aggregation Methods You should also choose the correct aggregation methods, which will vary depending on the nature of your data and your objectives for your analysis. Data complexity, granularity, and volume are crucial when selecting a data aggregation technique. Documenting All Processes and Underlying Assumptions When aggregating data, document all processes and assumptions you use to obtain the aggregated results. Maintaining comprehensive documentation ensures transparency and repeatability, besides being valuable for troubleshooting and auditing. Performing a Sensitivity Analysis Conducting a sensitivity analysis determines how variations or uncertainties in the data affect the aggregated results. A typical sensitivity analysis involves the following: Identifying the key parameters or variables that can impact the aggregated data. Modifying those parameters within reason to simulate varying conditions. Analyzing how changes in these variables affect the aggregated results. Evaluating the aggregated results’ reliability under various scenarios. Documenting the sensitivity analysis process to gain insights into the aggregated data’s reliability. Data Governance and Compliance Inadequate data governance and compliance procedures can risk your data security, quality, and integrity. Prioritizing adherence to data governance standards keeps your business compliant with data regulations, improves auditability and transparency, and maintains data integrity. Scalability and Performance Your organization’s data aggregation should have scalability and performance at its core. These factors account for further growth and expansion. Additionally, they maintain aggregation efficiency even with growing datasets, improve productivity and mitigate bottlenecks, ensure optimal resource utilization, and future-proof your data aggregation process. Data Quality Assurance Data quality is central to every data management process. In data aggregation, focusing on maintaining data quality has multiple benefits. It ensures access to accurate insights, mitigates potentially expensive errors, improves decision-making, and elevates your organization’s reputation by proving your commitment to excellence. Engaging with Stakeholders Like many other organizational activities, data aggregation is most effective when you incorporate suggestions and input from stakeholders. Engaging with them while developing your data aggregation process helps you better understand varying departmental requirements, cultivate trust and buy-in, facilitate adoption and ownership, and foster collaboration to ensure alignment. Tools for Data Aggregation There are different kinds of data aggregation tools and technologies available to help you with data integration. Here’s a closer look at each of them: Spreadsheet Software Spreadsheet software provides elementary data aggregation functionality. You can organize, manipulate, and analyze data in tabular format within them. While they’re great for basic aggregation and ad-hoc analysis, a lack of advanced features prevents them from supporting large-scale data processing and automation requirements. For instance, a small business owner can use Microsoft Excel or Google Sheets to aggregate sales data from different store locations to calculate total revenue for the quarter. Database Management Systems Database management systems (DBMS) allow you to store, query, and aggregate large volumes of structured data. SQL databases support relational data aggregation, and NoSQL databases efficiently handle unstructured and semi-structured data. For example, if a financial institution needs to aggregate and analyze transactions from different branches to find possible fraudulent activity, it can use a DBMS. Business Intelligence Platforms Business Intelligence (BI) platforms offer advanced data aggregation and visualization features so you can analyze and present business data. Some of these features include reporting tools, dashboards, and data modeling. These platforms support complex data aggregation and analysis tasks across finance, operations, and marketing. For example, a retail company’s marketing department can use a BI platform for aggregating customer data from various channels — email, website, and social media. This aggregated data can help create customer segmentations and profiles, leading to more targeted marketing campaigns. Programming Languages and Libraries Programming languages can be powerful — albeit more difficult— tools for aggregating data. These languages feature extensive libraries for manipulating and aggregating data and performing statistical analyses. Consider a data scientist using Python and working with genomic data for a research project. They’ll use the panda’s library to analyze this data to identify genetic markers linked to a disease or medical condition. Data Integration Platforms Data integration platforms like LIKE.TG provide data aggregation solutions for collecting, cleansing, and integrating data from disparate sources. With features such as data connectors, transformation tools, and built-in workflow automation functionality, these platforms streamline data aggregation while maintaining data quality. For instance, a retail business can use LIKE.TG to summarize purchases across different cities Calibri (Body) in a purchase orders dataset to find information such as average, max, min, purchase amounts, and number of purchases across different cities. This aggregated data can help the business craft a more targeted marketing strategy. Uses of Data Aggregation Data aggregation is essential across different industries, seven of which are: 1. Financial Services By consolidating financial data from various sources, data aggregation enables thorough financial analysis and leads to informed investment decisions. Specific applications of data aggregation in finance include: Risk Management: Evaluating credit risk, operational risk, and market risk. Portfolio Management: Assessing asset allocation, portfolio performance, and investment strategy. Regulatory Compliance: Ensuring compliance with Markets in Financial Instruments Directive 2014 (MiFID 2), the Dodd-Frank Act, Basel III, and other regulations. 2. Retail and E-Commerce Analyzing aggregated data from different channels helps businesses in the retail and e-commerce domain with the following applications: Inventory Management: Maintaining optimum stock levels, minimizing stockouts, and lowering carrying costs. Sales Analysis: Evaluating product performance, sales trends, and customer behavior. Customer Segmentation: Developing targeted marketing campaigns, offering more personalized recommendations, and boosting customer retention rates. 3. Healthcare Aggregating and evaluating medical data helps healthcare facilities with the following: Improving Patient Care: Creating customized treatment plans to provide more targeted care and lead to better patient outcomes. Population Healthcare Management: Identifying patterns in population health, disparities in healthcare accessibility, and disease outbreaks. Studying aggregated data can also aid in developing community healthcare initiatives. Streamlining Clinical Research: Aggregated clinical trial data enables researchers to analyze patient demographics, treatment efficacy, and safety profiles for drug development and regulatory approval. 4. Manufacturing Different types of aggregated data in the manufacturing sector serve different uses: Production Planning: Assessing aggregated production data to optimize resource utilization, decrease downtime, and improve manufacturing processes. Supply Chain Management: Aggregated supply chain data enables businesses to monitor supplier performance, check inventory levels, and identify and correct supply chain bottlenecks. Quality Control: Aggregated quality control data aids in identifying flaws, determining their root causes, and applying corrective measures. 5. Marketing and Advertising Studying the insights gained from various kinds of aggregated data helps marketing teams with the following: Campaign Performance: Aggregated marketing campaign data is useful for measuring ROI, assessing customer engagement, and optimizing advertising spend. Market Research: Aggregated consumer data (such as survey responses, social media posts, and online activity) reveals market trends and consumer preferences. Customer Analytics: Aggregated customer data allows marketing personnel to create more accurate customer profiles and personas for targeted messaging. 6. Transportation and Logistics A variety of data is available for aggregation in the transportation and logistics sector. A few examples and uses include: Fleet Management: Aggregated vehicle and driver data helps optimize routes, lower fuel consumption, and increase fleet efficiency. Supply Chain Visibility: Aggregated logistics data aids in tracking shipments, monitoring delivery times, and inventory management. Demand Forecasting: Aggregated demand data can help predict future transportation needs, alleviate supply chain disruptions, and optimize resource allocation. 7. Government/Public Sector Data is available abundantly in the government/public sector — and in the context of data aggregation, here are a few ways they can leverage it: Policy Development: Aggregated economic, social, and demographic data can guide policy development, impact resource allocation, and help identify and resolve common public welfare challenges. Emergency Response: During emergencies, aggregated real-time data enables prompt response coordination and facilitates timely assistance. Performance Measurement: Aggregated government program data is crucial for evaluating program effectiveness, tracking outcomes, and maintaining accountability. Conclusion Aggregation offers you a uniquely comprehensive view of your data. It reveals insights that individual data points can’t deliver on their own. To derive maximum value from your data, you should design a well-planned aggregation process that aligns with your organizational requirements — and choose the right data aggregation tool. With its award-winning, highly acclaimed no-code interface and robust data management capabilities, LIKE.TG is the intuitive, user-friendly solution to your data aggregation problems. You can ingest data from various sources, use the Aggregate transformation object, and apply aggregate functions, such as Sum, Count, First, Last, Min, Max, Average, Var, or Standard Deviation, and load data to your preferred destination. LIKE.TG helps you make the most of your data by aggregating it for high-level insights. Start your FREE trial today, or contact our team for more information.

A solid understanding of internal technical and business terms is essential to manage and use data. Business glossaries are pivotal in this aspect, facilitating improved understanding and collaboration among teams. A business glossary breaks down complex terms into easy-to-understand definitions, ensuring that everyone in the organization, from the newest recruit to the CEO, is on the same page regarding business language. This clear and uniform understanding fosters seamless communication — an essential aspect of organizational success. A business glossary helps: Simplify the onboarding of new employees by familiarizing them with company terminology. Provide quick access to clear definitions for effective communication in daily operations. Ensure consistent understanding of terminology among cross-functional teams, enhancing collaboration. Understanding the Business Glossary A business glossary is a repository of data-related terms and definitions specific to a company’s industry, processes, and products. It includes industry-specific jargon, acronyms, and business-specific terminologies, all systematically organized for easy reference. Its primary purpose is to promote consistency, clarity, and a shared understanding of these terms across the organization. A centralized repository of definitions guarantees that employees, stakeholders, and partners use the same language to discuss concepts and processes. It acts as a learning resource for employees, especially newcomers and those transitioning between departments, helping them familiarize themselves with company-specific jargon. With clear definitions readily available, employees can make more informed decisions, contribute effectively to discussions, and collaborate more productively, ultimately driving organizational success. For instance, in a retail organization, a business glossary can serve as a comprehensive reference tool containing definitions of terms relevant to the industry’s operations. Within this glossary, terms like “Inventory Turnover Ratio,” “Average Transaction Value,” and “Customer Segmentation” are defined. Each definition is tailored to the specific context of the retail sector, ensuring clarity and consistency in communication among employees across departments. Key Components of a Comprehensive Business Glossary A business glossary consists of four key components: Terms and Definitions: Unique business terms and their associated meanings within an organization. Reference Data: Standard information that serves as a benchmark for comparison or categorization. Metadata: Descriptive information that offers insights about the data, including its structure, location, and accessibility. Classifications: These are the categories of data sorted based on type, sensitivity, and relevance to business operations. Business Glossary vs Data Dictionary vs Data Catalog A Business Glossary is often mistaken for a Data Dictionary or a Data Catalog, but it’s important to note that they are not the same. All three have a unique purpose in organizing, defining, and accessing data assets within an organization. Business Glossary A business glossary ensures everyone in a company speaks the same language by defining key terms. For instance, in a healthcare institution, “Patient Admission” might be “the process of formally registering a patient for treatment or care within the facility.” Data Dictionary A data dictionary provides detailed descriptions of data elements, such as their format and meaning. For example, the “Patient ID” field in the healthcare dataset could be “a unique identifier assigned to each patient for record-keeping and identification purposes.” Data Catalog A data catalog serves as a searchable inventory of available data assets, making it easy for users to find and understand data. For instance, the data catalog of a healthcare organization might list datasets like “Patient Records” and “Medical Procedures,” helping teams access the data they need for decision-making quickly and efficiently. Aspect Business Glossary Data Dictionary Data Catalog Purpose Defines business terms and concepts. Defines data elements and their meanings. Provides centralized access to data assets. Focus Standardizing terminology across the organization. Providing detailed descriptions for data use. Finding, understanding, and using data assets. Components Definitions, examples, usage clarification. Data element descriptions, format, meaning. Metadata, data source details, lineage. Scope Business concepts and metrics Individual data elements Datasets, sources, relationships Primary Users Business stakeholders, analysts, decision-makers Data analysts, developers, database administrators Data analysts, business users, data scientists In summary, while a business glossary focuses on business terminology, a data dictionary provides technical details about the data, and a data catalog facilitates data discovery and search. Benefits of a Business Glossary A well-maintained business glossary offers several benefits, including: Standardized Internal Communication: Maintaining consistent communication becomes increasingly vital as an organization grows or spans multiple locations. A business glossary aligns the understanding of core terms among all members within the organization. Consistently defining terms reduces the risk of misinterpretation or confusion during discussions, meetings, or project collaborations. This clarity in communication streamlines workflows prevents errors stemming from misunderstandings, and fosters a more cohesive work environment. Improved Data Quality and Governance: Access to high-quality data is crucial for making informed business decisions. A business glossary is critical in ensuring data integrity by clearly defining data collection, storage, and analysis terms. When everyone adheres to standardized terminology, it minimizes data interpretation and usage discrepancies. Moreover, a well-defined glossary supports effective data governance practices by establishing guidelines for data management, access controls, and compliance with regulatory requirements. Increased Productivity and Trust Clarity in communication and understanding promotes efficiency and productivity within an organization. A business glossary provides a common understanding of terms, streamlining communication and saving time. Employees who spend less time clarifying terms or resolving misunderstandings can focus more on their core tasks and responsibilities. Additionally, a shared understanding of terminology fosters trust among team members and between different departments, as everyone knows they speak the same language and work towards the same objectives. Streamlined Cross-Functional Collaboration: Many organizations struggle with siloed communication and disparate interpretations of terms across different departments or teams. A business glossary promotes cross-functional alignment by combining different perspectives and creating a joint understanding of essential concepts. When stakeholders from various areas of the organization use a common vocabulary, it facilitates smoother collaboration, reduces conflicts arising from miscommunication, and accelerates project delivery. This alignment is particularly valuable in interdisciplinary initiatives or transformational projects where coordination and synergy are critical for success. How to Create a Business Glossary Organizations must follow a well-planned and systematic approach to ensure the glossary is comprehensive and effective. The key steps that they should follow include: Research and Compilation Organizations should research to identify a comprehensive range of terms commonly used in the industry or business domain. This initial phase involves compiling an exhaustive list encompassing frequently encountered terms and those specific to the organization’s niche. Investing time in comprehensive research establishes a solid foundation for a robust glossary that accurately reflects the terminology relevant to the business landscape. Categorization Once the terms are compiled, organizations will categorize them into logical groupings or themes based on their functional relevance within the business operations. This categorization streamlines navigation and facilitates efficient retrieval of desired information. By organizing terms into cohesive categories, the usability and accessibility of the glossary are enhanced, enabling users to locate information quickly and effectively. Glossary Definition With the list categorized, the next step is to develop clear, concise definitions for each term featured in the glossary. Businesses need to craft definitions with utmost clarity, using simple language understandable to a broad audience. They should also avoid overly technical or jargon-laden explanations, opting for a language accessible to all organizational stakeholders. By articulating definitions clearly and concisely, the meaning and context of each term become simple, fostering greater understanding and comprehension. Review and Validation The final step is to engage subject matter experts and relevant stakeholders in a comprehensive review and validation process. This collaborative effort ensures the accuracy and completeness of the glossary, leveraging diverse perspectives and expertise within the organization. Feedback and input are solicited from stakeholders to validate definitions and identify any potential inaccuracies or omissions, thereby enhancing the overall quality and reliability of the glossary. Incorporating feedback from stakeholders validates the relevance of terms and ensures that the glossary effectively meets users’ needs. Stakeholders Involved in Creating a Business Glossary Creating a comprehensive and effective business glossary requires collaboration and input from various stakeholders across the organization. Here’s a list of individuals or teams responsible for creating a business glossary: Business Analysts: Collaborate with stakeholders to identify business needs, document processes, and define key terms in the glossary. Subject Matter Experts (SMEs): Offer specialized knowledge to ensure the accuracy and relevance of definitions, particularly in industry-specific contexts. Data Stewards: Oversee data governance aspects, ensuring that glossary definitions align with data management policies, quality standards, and regulatory requirements. IT Professionals: Implement and maintain technical solutions to support the glossary, including metadata management systems and integration with other enterprise systems. Project Managers: Lead the planning, execution, and monitoring of the glossary initiative, ensuring timelines are met. They also monitor resource allocation and ensure that risks are managed effectively. Business Users: Act as end-users of the glossary, providing feedback on usability, relevance, and accuracy of definitions to ensure alignment with their operational needs. Senior Leadership: Provide high-level support, advocating for the importance of the glossary, securing resources, and promoting its adoption across the organization. Implementing a Business Glossary in Data Governance Strategy Assessing the benefits of a business glossary in a data governance strategy highlights its crucial role in maintaining data consistency and compliance across the organization. Organizations need to invest in suitable solutions to implement a business glossary successfully. Using specialized resources, such as data governance platforms and metadata management systems, can significantly aid in managing and governing business glossaries. Data Governance Platforms: These platforms serve as centralized hubs for managing business terms and definitions, ensuring data consistency across different departments. They offer features for cataloging data assets, managing policies, ensuring data quality, and overseeing metadata. Integrating the business glossary with broader governance processes enhances its effectiveness, aligning terms with data quality standards and compliance requirements. Metadata Management Tools: These tools automate the discovery and harvesting of metadata from various sources, ensuring that your business glossary remains comprehensive and up-to-date. They standardize metadata to define data elements and relationships, ensuring a uniform understanding of data across the organization. Additionally, they track data lineage and support impact analysis, providing insights into data usage. When integrated with data governance, they enforce compliance, enhancing overall data governance effectiveness. A Final Word A well-curated business glossary is the foundation of effective data governance, ensuring data accuracy, compliance, and alignment with the organization’s objectives. It serves as a common language, bridging communication gaps among various departments within the company. With advanced tools and technologies, managing and maintaining a business glossary has become significantly more streamlined and efficient. These innovations have simplified the process and enhanced the overall effectiveness of data governance, paving the way for organizational success. LIKE.TG’s data governance solution offers a comprehensive suite of features designed to streamline creating and maintaining a business glossary. With LIKE.TG’s enterprise platform, organizations can leverage AI to name their data assets and create descriptions. With an intuitive and code-free UI, LIKE.TG enables self-service data governance across the organization, empowering business users to take control of their data. 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Digitalization has led to more data collection, integral to many industries from healthcare diagnoses to financial transactions. For instance, hospitals use data governance practices to break siloed data and decrease the risk of misdiagnosis or treatment delays. Therefore, managing data to protect its integrity and security leads to high-quality, reliable data that empowers organizations to make informed decisions. Also, protecting the rights and interests of both the organizations processing the data and the individuals whose data is being processed is equally important. As a result, processes and standards have been developed to manage or govern this data effectively. Data governance is a process of managing data within an organization, as it defines how data is stored, archived, backed up, protected, and accessed by authorized personnel. Effective data governance relies on established principles and frameworks that guide the organization’s data management strategy and act as a foundation to ensure consistency, accessibility, and reliability of data. Data Governance Principles Data governance principles act as a blueprint for managing the data, and facilitate decision-making regarding data storage, access, and use. This article will focus on five basic data governance principles essential in implementing a practical framework. 1. Accountability Accountability refers to the clear assignment of responsibility for data-centric activities and decisions within a company. It ensures that individuals and teams comprehend their responsibilities in managing and protecting data assets. It enables organizations to pinpoint areas of improvement and make necessary adjustments to enhance data governance practices by holding individuals accountable for their responsibilities. The most critical step in establishing accountability is incorporating strategies and mechanisms, like data validation rules, data cleansing processes, and regular data audits. These mechanisms verify and maintain data accuracy throughout its lifecycle, from collection and entry to storage and use. 2. Standardized Rules and Regulations It refers to consistent guidelines and procedures for data management throughout an organization. Standardized rules and regulations create a consistent data handling approach, like utilizing same methods for data entry, access, and storage, across different departments, removing the variations in practices. Standardization starts with a common language by creating a centralized data glossary that serves as a single source of truth for data definitions. Also, establishing data classification schemes to categorize data based on sensitivity, security requirements, and access controls, directly translates into actionable data validation rules. 3. Data Quality Standards Data quality standards are guidelines within a data governance framework to ensure that the data used for business processes is high quality, reliable, and fit for its intended purpose. When data quality standards are in place, employees can confidently use data to analyze trends, identify opportunities, and mitigate risks. High-quality data enables informed decision-making by providing reliable information to individuals within the organization. Implementing data quality requires key dimensions like accuracy, completeness, and timeliness. To achieve these dimensions, establish data cleansing procedures, both manual and automated, to address existing inaccuracies and schedule regular cleansing activities. 4. Data Compliance In data governance, organizations must comply with all relevant data laws, regulations, and industry standards. An effective data governance program must include comprehensive policies and procedures that guarantee ongoing compliance. These policies should address data collection, processing, storage, transmission, disposal, access, and privacy. Moreover, these compliance strategies should adapt and respond to the evolving needs. To implement data compliance, conduct regular audits, risk assessments, and compliance checks to ensure they follow all applicable laws and regulations. Additionally, they should regularly train employees on data compliance to integrate compliance into the organization’s culture. 5. Transparency Data transparency in the context of data governance principles refers to making all data governance processes and actions within an organization clear and accessible to stakeholders. Transparent governance processes ensure stakeholders understand how data is managed, who is responsible for it, and who has access to it. This understanding promotes accountability and ensures that everyone within the organization knows their roles and responsibilities regarding data governance. Achieving transparency in a data governance framework is a three-step process. First, develop clear and accessible data policies that outline data collection practices, usage guidelines, and user permissions. Next, establish communication channels to keep stakeholders informed about data governance initiatives, including data inventory updates and changes to data access protocols. Finally, implement data access request processes that are clear, documented, and readily available to all authorized personnel. Conclusion An effective data governance program facilitates informed decision making by making sure that data is accurate and reliable. Every organization needs a basic set of principles or guidelines to build their data governance strategy. Implementing these principles promotes a culture of data responsibility, guarantees data quality and consistency. Organizations that adopt and regularly assess the effectiveness of these principles lay a solid groundwork for data management. Although the basic data governance principles are essential, a successful framework requires customization to the organization’s unique needs. This tailoring and customization can be done utilizing the right tool. LIKE.TG offers a complete data management suite to simplify and customize data governance for businesses. It allows clear assignment of roles, ensures that data stewards and owners can easily monitor the data, and aids in transparency of data related actions. LIKE.TG enables a strong data governance framework to manage and utilize your data assets proactively. Start a 14-day free trial to explore LIKE.TG’s data management and governance solutions. Empower Your Data Governance with LIKE.TG Take control of your data governance with LIKE.TG's unified platform. Sign up for a 14-day free trial today and discover how LIKE.TG can help you enhance accountability, ensure compliance, and elevate transparency within your organization's data management practices. Start a Free Trial

What is Online Analytical Processing (OLAP)? Online Analytical Processing (OLAP) is a computing technology through which users can extract and query data to analyze it from different points of view. These typically high-speed and complex queries are performed on massive data volumes stored in a data lake, data warehouse, or other large-scale repositories. Typical OLAP applications include business intelligence (BI) and forecasting and reporting. A Data Warehouse Automation Platform That's Optimized for OLAP Systems Start Using LIKE.TG Data Warehouse Builder Today! Your Free Trial Awaits OLAP Architecture Online analytical processing (OLAP) systems contain multidimensional data. Unlike two-dimensional data — arranged simply in rows and columns — multidimensional data has numerous attributes or features. As a result, it’s represented in more than two dimensions. For example, a multidimensional purchases data set may contain the following attributes: Transaction ID Timestamp Supplier ID Product Code Product Category Product Name Quantity To effectively store such information and keep it ready for querying, you’ll need to build a multidimensional OLAP system comprising the components below: Data Warehouse A data warehouse is a central repository of organizational data from different sources. Files, applications, and databases are some of the most prominent sources. However, depending on your business model, it can also collect data from ERP, CRM, and IoT devices Extract, Transform, Load (ETL) Tools Data warehouses use the extract, transform, and load (ETL) process to transform and standardize data collected from different sources before OLAP tools can use it. Modern ETL tools speed up the process by automatically obtaining, preparing, processing, and loading the data for analytics. Try Our Enterprise-Ready ETL Solution for Yourself High performance, zero coding — accelerate your ETL processes today! Start Your FREE Trial OLAP Server An online analytical processing (OLAP) server is a specialized system that leverages rapid execution to facilitate complex queries. It’s the powerhouse that drives the system and is generally the middle analytical tier in a data warehousing solution. It transforms and arranges data into cubes to ensure rapid responses to queries and enable advanced analytics. OLAP Database You can connect a separate database to the data warehouse to prevent the analysis from straining the data warehouse’s resources. These databases are called OLAP databases. OLAP Cube An OLAP or data cube assists in analytical processing by representing data multi-dimensionally, allowing you to perform analysis from varying perspectives. These cubes are often described as “rigid” because they have a predefined schema or structure that dictates how data is organized within the cube. This rigidity is a trade-off: The fixed schema means faster query performance and simplified analysis, but, at the same time, it also means limited flexibility and difficulty in handling new data types that weren’t part of the original schema design. Adding a new dimension almost always requires remodeling the cube. OLAP Analytic Tools OLAP tools facilitate interaction with the OLAP cube, enabling you to perform advanced analytical operations such as: Drill-Down:This operation aids in increasing the level of detail in a data set, either by moving from a higher to a lower position in a dimension’s concept hierarchy or by simply adding another dimension. For example, you can start with yearly purchase data and drill down to quarterly, monthly, and daily figures. Roll-Up: Roll-up is the reverse of the drill-down operation and allows users to move from higher to lower granularity levels. For example, you can start with daily purchases and roll up to monthly, quarterly, or yearly totals to examine broader trends. Slice: Slicing selects a subset of data using one or more dimensions, helping you focus on particular aspects. You can slice data to evaluate any dimension you’re interested in, such as region, time, or product category. Dice: Dicing creates a smaller data cube by simultaneously providing values for multiple dimensions. It helps you analyze data across various dimensional combinations. Pivot: Also called rotation, pivot changes a data cube’s orientation to enable viewing it from different perspectives. When pivoting a cube, you can rearrange measures and dimensions to facilitate comparison and analysis. How Do OLAP Systems Work? Online analytical processing (OLAP) systems work in a three-step process: When to Use OLAP Solutions Generally, online analytical processing solutions are ideal whenever large data volumes undergo complex analyses to obtain insights that can drive decision-making. Here are a few instances where using OLAP systems is the right call: Complex Analytical Requirements These systems are useful when you need to perform multidimensional analysis with numerous dimensions and measures. Large Data Volumes When there are significant data volumes, analyzing them through traditional relational database management systems (RDBMS) isn’t feasible because of performance constraints. Since data is pre-aggregated and summarized before users query the system, OLAP cubes ensure fast response times, even for big data. Ad-Hoc and Interactive Analysis When exploring data interactively, examine details and conduct ad-hoc analysis to identify patterns, trends, and anomalies. OLAP cubes offer a user-friendly interface for business analysts and managers to explore large data sets without writing complex queries. Aggregated Reports When you require aggregated reports and summaries with varying granularities — such as weekly, monthly, or quarterly summaries. Time-Series Analysis When analyzing data over time periods, you can easily drill down and roll up through different periods to determine trends and historical patterns. Hierarchical Data Analysis When you’re analyzing hierarchical data structures — such as product, geographical, and organizational hierarchies—because OLAP excels at handling data with hierarchical structures. Predictive Analytics and Forecasting When you need predictive analytics, forecasting, and what-if analysis to guide strategy development and decision-making. Business Intelligence and Performance Management When you need a comprehensive BI solution for assessing key performance indicators and gaining insights into business operations. Types of OLAP Systems (MOLAP vs. ROLAP vs. HOLAP) There are three types of OLAP systems: Multidimensional OLAP (MOLAP) Relational OLAP (ROLAP) Hybrid OLAP (HOLAP) Multidimensional online analytical processing system works directly with a multidimensional OLAP cube. Relational online analytical processing conducts dynamic, multidimensional data analysis on data contained in relational tables (without first arranging it in a cube). Hybrid online analytical processing works with both relational and multidimensional databases within the same OLAP architecture. Generally, MOLAP is the fastest and most widely used kind of multidimensional data analysis. ROLAP is ideal when the ability to work with large data volumes is preferable over high performance. A HOLAP system is suitable when working with mixed data types. In such scenarios, some data is better stored relationally and some multidimensionally. OLAP Compared with Other Data Analytics Techniques Here’s a look at how OLAP compares to other data analytics techniques. 1. OLAP vs. OLTP OLAP OLTP OLAP is built for analytical processing, focusing on complex queries, multidimensional queries, and aggregations that enhance decision support. In contrast, online transaction processing (OLTP) is designed to handle real-time transactional data, so it’s optimized for rapid data entry and retrieval. OLAP systems generally store aggregated and summarized data in multidimensional OLAP cubes or hypercubes to accelerate analysis and querying. OLTP systems store detailed transactional information in relational databases. Read more about OLTP vs. OLAP. 2. OLAP vs. Data Mining OLAP Data Mining OLAP complements data mining by offering a structured environment to analyze and visualize pre-aggregated data interactively. Data mining uses machine learning and statistical algorithms to glean patterns, insights, and trends from large raw data sets. 3. OLAP vs. Data Warehousing OLAP Data Warehousing OLAP offers multidimensional structures that help users organize and analyze data for BI and decision-making when implemented on top of a data warehouse. Data warehousing includes integrating, cleaning, and storing data from various sources in a centralized repository. OLAP Advantages These are the most significant advantages of using an OLAP system: Multidimensional Analysis: Itgives you a more holistic view of organizational performance by enabling multidimensional analysis, providing deeper insights into the data’s patterns, trends, and relationships than unidimensional analysis can. Accelerated Query Response: These systems accelerate query responses even when working with massive data sets by pre-aggregating data and storing it multi-dimensionally. You can explore data and conduct complex analyses using these systems without facing delays. Ad–Hoc Analysis: Thanks to its support for ad-hoc analysis, you can ask and get answers to complex analytical questions without predefined queries, improving analytical flexibility and agility. Data Visualization: When you pair these systems with data visualization tools and platforms, you can conveniently create reports, dashboards, and charts for visualizing data and effectively communicating insights. Visual representations of data make it easier to understand for non-technical stakeholders and assist in decision-making. Decision Support: OLAP can assist in decision-making as it simplifies analysis, allowing you to evaluate alternatives and ultimately make informed choices based on meaningful insights. This capability helps you optimize resource allocation, strategic planning, and performance management. How to Implement an OLAP Solution? Implementing an online analytical processing (OLAP) solution is a multi-part process. Here’s an overview: Defining the Requirements Before designing an OLAP system, you should determine the business objectives and analytical requirements that it’ll fulfill. List the data sources, measures, dimensions, and KPIs you’ll include in the system. Data Modeling Start by designing the dimensional model for your OLAP system, including dimension tables, fact tables, and hierarchies. Remember to select the proper data modeling techniques depending on your database schema requirements. You can choose from star schema, snowflake schema, or other dimensional modeling approaches. Data Integration Obtain data from different source systems such as transactional databases, ERP and CRM systems, and external sources. Transform and cleanse the data to ensure quality, accuracy, and consistency. Once the data is transformed, you can load it into the target system. OLAP Cube Design Identify the measures, hierarchies, and dimensions each cube will feature based on the analysis requirements established earlier. Pre-calculate and store aggregated data at varying granularity levels within the OLAP cubes to optimize querying. OLAP Server Implementation Choose and implement an OLAP server or platform that meets the project’s requirements. Set up the server to connect to the data warehouse and access the OLAP cubes. Configure the security settings, access controls, and necessary authorizations to maintain data integrity and confidentiality. Client Tools and Interfaces Select and deploy client tools or interfaces so users can interact with the system. Provide user-friendly interfaces that simplify querying, analysis, reporting, and data visualization. Testing and Optimization Conduct testing to validate the OLAP system’s functionality, accuracy, and performance. Optimize the system for usability, query performance, and scalability based on feedback and usage patterns. Deployment and Maintenance Deploy the OLAP system into production and assess its performance. Create regular data refresh, cube processing, and system updates maintenance protocols. Regularly evaluate and improve the system to cater to evolving organizational requirements. These evolving requirements can include refining your data model, adjusting the OLAP cube structure, or updating your ETL processes. They are generally required due to adding new data sources, modifying existing sources’ schema, and analytical requirements. Alternatively, you might need to optimize the database’s performance. Summing It Up With their unique, analytics-centric functionality, OLAP systems play a critical role in business intelligence. They provide data analysts, business owners, and stakeholders with the insights they need to make the right decisions at the right time. LIKE.TG’s no-code data integration platform supports OLAP systems. Additionally, LIKE.TG Data Warehouse Builder automates data warehousing and is optimized for use with OLAP. The platform also features a data model designer you can use for creating an OLAP architecture from an existing OLTP system. Experience the LIKE.TG difference and discover how it can transform your data warehousing processes.Start your free trial today!

Data Provenance vs. Data Lineage Two related concepts often come up when data teams work on data governance: data provenance and data lineage. While they may seem similar at first glance, there are fundamental differences between the two concepts. Data provenance covers the origin and history of data, including its creation and modifications. On the other hand, data lineage tracks the data’s journey through various systems and processes, highlighting its flow and transformation across different data pipeline stages. What is Data Provenance? Data provenance is the origin and history of a piece of data. It involves recording data creation and details of applied changes – for example, when the data was modified and by whom. Data provenance aims to provide a complete picture of how the data came to be, including the processes it went through and any changes made along the way. Data provenance answers questions like: What is the source of this data? Who created this data? What changes, if any, were made to this dataset? This information helps ensure data quality, transparency, and accountability. This knowledge is particularly valuable in highly regulated industries, such as healthcare or banking, where data trust is essential for compliance. Why is Data Provenance Important? Data provenance allows analysts to identify corrupted data on time. For example, in the case of a data breach, having a robust data provenance system in place can help determine the point of entry and the extent of the breach, enabling swift and targeted responses to mitigate the impact. This proactive approach enhances the overall trust in the data and streamlines data validation processes. Plus, according to the 1:10:100 rule of data quality, it costs only $1 to prevent low-quality data from damaging decision-making – and $10 for correcting errors and $100 for doing nothing. Organizations can assess the data’s reliability and trustworthiness by understanding the origin and history of data. This information is crucial when dealing with sensitive data, as it helps maintain data privacy. Data provenance also supports data collaboration and sharing. When multiple teams or organizations work with the same data, clearly understanding its provenance fosters trust, facilitates collaboration, and reduces redundancy. Examples and Use Cases of Data Provenance Some examples and use cases to better understand how data provenance can be applied in various industries: Healthcare: Data provenance enables healthcare providers to maintain an accurate record of patient’s medical history, which is vital to deliver the expected level of patient care. It also helps improve and maintain the integrity of healthcare research by ensuring researchers understand how data is collected and analyzed. For instance, tracking the provenance of medical records can help identify any changes made to the data, ensuring data integrity and accuracy. Financial institutions: Banks must ensure regulatory compliance and effective risk management in the financial sector. Visibility into the source and movement of financial data, such as transaction records, enables them to ensure that it has not been tampered with. This transparency reduces fraud and ensures the accuracy and reliability of financial reports. Education: Data provenance also plays a significant role in educational institutes. With the increasing use of technology in classrooms, educators rely on data to assess student performance and tailor instruction accordingly. By tracking the provenance of educational data, such as test scores and learning analytics, educators can gain insights into students’ progress over time, identify areas of improvement, and personalize their teaching strategies. This empowers educators to provide targeted support and enhance student learning outcomes. What is Data Lineage? Data lineage focuses on the flow and transformation of data from its source to its destination. It is concerned with understanding how data moves and changes as it is processed, refined, and integrated into various systems and applications. In short, data lineage provides a detailed record of the data’s journey through these systems. In a sense, data lineage expands upon data provenance. Data lineage shows the impact of data transformations on downstream processes and decisions. It helps answer questions like “Which datasets were used to generate this report?” and “What extract, transform, and load (ETL) processes were applied to the data?”. By mapping the lineage of data, organizations can uphold the accuracy and reliability of analytics, reporting, and decision-making processes. Why is Data Lineage Important? Data lineage is crucial for data governance, data management, and regulatory compliance. It ensures transparency and accountability by providing visibility into the entire data flow and transformations. Moreover, data lineage plays a vital role in enhancing data security. Organizations can better monitor and protect sensitive information by tracking the data flow from source to destination. Another aspect of data lineage is its role in enhancing data quality—it helps pinpoint where data health issues arise. Without lineage, data teams will easily spend hours sifting through transformations to find the root cause. By visualizing the flow of data from its origin to its consumption, data lineage tools enable data stewards and analysts to identify potential bottlenecks, inconsistencies, or errors in the data pipeline. This visibility allows for proactive data management strategies, such as data cleansing, standardization, and enrichment, to be implemented, ultimately improving the quality and reliability of data-driven insights and business decisions. Examples and Use Cases of Data Lineage Business intelligence (BI) and reporting: Data lineage ensures accurate and trustworthy reports and dashboards. It allows organizations to understand how the data used in these reports was sourced, transformed, and aggregated. Regulatory compliance: Finance, healthcare, and insurance industries have strict compliance requirements. Data lineage provides an audit trail of the data used in regulatory reports or calculations, which helps demonstrate regulatory compliance. Data governance and data stewardship: Data lineage is a valuable tool for data governance and stewardship initiatives. It helps organizations understand the effect of changes to their data infrastructure or data policies on downstream processes and applications. By mapping the data lineage, organizations can identify any risks, dependencies, or data quality issues that must be addressed. Data Provenance vs. Data Lineage: Key Differences Now that we have explored data lineage and data provenance, it is important to understand their key differences. The main distinction between data provenance and lineage lies in their focus and scope. Data provenance emphasizes the origin and history of a piece of data, tracking its sources and transformations. It focuses on the “breadcrumb trail” of a specific data element, providing valuable insights into its quality, reliability, and accountability. On the other hand, data lineage focuses on the flow and transformation of data as it moves through different systems and processes. It provides a holistic view of how data is transformed, aggregated, or joined, enabling organizations to understand the impact of these transformations on downstream processes and decisions. Should you Prioritize Data Provenance or Data Lineage? Prioritizing one over the other depends on your organization’s specific needs and goals. Data provenance should be a priority if your primary concern is ensuring data quality, reliability, and compliance. Tracking the origin and transformations of data helps identify potential issues, ensure accountability, and meet regulatory requirements. On the other hand, if your focus is on understanding the impact of data transformations on downstream processes and decision-making, data lineage is key. By mapping the flow and transformations of data, you can assess the reliability of analytics, reports, and data-driven decisions. However, it’s important to note that these concepts are closely connected in many cases, with data provenance forming the foundation for data lineage. Both data provenance and data lineage are essential features of a comprehensive data management and governance strategy. So, it’s not about choosing one over the other. It’s about finding a balanced approach that considers both provenance and lineage in data management and analysis. This way, you can address your data’s origins and history (provenance) and its flow and relationships (lineage). Implementing both data provenance and data lineage can provide a comprehensive understanding of your data assets and enable efficient data governance and decision-making. Best Practices for Implementing Data Provenance and Lineage Implementing data provenance and data lineage requires careful planning and consideration. Here are some best practices to help you get started: Clearly articulate your organization’s data provenance and lineage requirements. Understand the specific questions you need to answer and the outcomes you want to achieve. Focus your efforts on tracking the provenance and lineage of mission-critical data elements. Document and capture detailed information about the processes, systems, and transformations throughout the data lifecycle to understand the lineage and provenance of your data. Leverage automation tools and technologies for tracking data provenance and lineage to minimize human error risk and ensure consistent and accurate documentation. Identify and consult key stakeholders early on in the process to account for their needs and requirements and ensure that the implementation aligns with the organization’s wider goals. Your data provenance and lineage framework should be able to handle the additional complexity and scale seamlessly as your organization grows and the volume of data increases. Consider using advanced technologies such as distributed ledger technology or cloud-based solutions that can accommodate the growing demands of your data ecosystem. Conduct regular audits and data quality checks to validate the accuracy and reliability of your data. Use the information captured through data provenance and lineage to identify discrepancies, outliers, or anomalies. Data provenance and lineage are not one-time initiatives. Continuously refine and enhance your data management processes based on the insights from tracking the provenance and lineage of your data. Conclusion Data provenance and data lineage are critical concepts in data governance and management that provide valuable insights into the origin, history, and flow of data. While they share a common goal of enhancing data quality, accountability, and decision-making, their focus and scope differ. The optimal strategy for data teams is to find the right balance. They can look into incorporating modern data solutions that offer various governance features. LIKE.TG is a no-code enterprise data management platform that offers lineage and impact analysis for data assets. It enables business users to easily track and govern their data with robust features and a simple, user-friendly UI that’s easy to use even for business users. LIKE.TG also provides an AI-powered business glossary, data enrichment, profiling, and a Data Marketplace for effective data governance. Are you looking to implement a comprehensive data governance strategy in your company? Let us help you by contacting us.

Electronic Data Interchange (EDI) is a popular communication method that enterprises use to exchange information accurately and quickly with trading partners. EDI transmits data almost instantaneously — serving as a fast and efficient mode for exchanging business documents. ANSI X12 vs. EDIFACT are the two most common EDI standards used for EDI, but they have different structures, styles, and usage. This blog will discuss the differences between X12 and EDIFACT and how a no-code EDI solution can help streamline your EDI processes. What Is EDIFACT? EDIFACT is a global EDI standard developed by the United Nations. It’s widely used internationally — particularly in Europe — for international trade across different sectors. EDIFACT has a structured format for business messages with six-character alphanumeric codes (e.g., ORDERS for purchase orders). What is ANSI X12? ANSI X12 is an EDI standard developed by the American National Standards Institute (ANSI). It’s primarily used in North America for various industries, such as retail, healthcare, and logistics. ANSI X12 has a structured format for business documents with three-digit numeric codes (e.g., 850 for purchase orders). ANSI X12 vs. EDIFACT: Key Differences The primary difference between EDIFACT and X12 lies in how their transaction sets are structured, the document style they use, the geographic region where they are used, and the terminators (or separators) they use. Here’s a more detailed comparison: Structure ANSI X12 uses a hierarchical structure of segments and elements, each identified by a unique identifier. Transaction sets consist of segments grouped in a predefined order. EDIFACT also uses a hierarchical structure but is more flexible. Messages are composed of segments, which are divided into data elements, allowing more comprehensive descriptions. Document Style ANSI X12 transaction sets are identified by three-digit numeric codes (e.g., 850 for purchase orders and 810 for invoices). On the other hand, EDIFACT messages are identified by six-character alphanumeric codes (e.g., ORDERS for purchase orders, INVOIC for invoices). Geography ANSI X12 is predominantly used in North America, especially in the United States and Canada. In contrast, EDIFACT is widely used internationally, especially in Europe and other regions involved in global trade. Security ANSI X12 supports various security measures, including encryption and secure transmission protocols, but often relies on the security measures of the transmission medium. It supports ASC X12.58 – Security Structures. EDIFACT, similar to ANSI X12, supports encryption and secure protocols. It also includes provisions for secure authentication and non-repudiation (a user cannot deny having performed a transaction) in some implementations. It supports the following: ISO 9735-5 – Security rules for batch EDI ISO 9735-6 – Secure authentication and acknowledgment message ISO 9735-7 – Security rules for batch EDI ISO 9735-9 – Security key and certificate management message Terminators/Separators ANSI X12 uses specific characters to separate data elements (typically ‘*’), segments (typically ‘~’), and sub-elements (typically ‘:’). EDIFACT uses different characters, typically “+” for segment terminators, “:” for element separators, and”’” for message terminators. This flexibility allows for customization to avoid conflicts with data content. Use Cases ANSI X12 is commonly used in retail, healthcare, and logistics sectors in North America. Examples include sending purchase orders, invoices, and healthcare claims. EDIFACT is suited for international trade, used by industries such as automotive, shipping, and global supply chains. Examples include customs declarations, shipping notices, and international invoices. The table below compares ANSI X12 to EDIFACT: ANSI X12 EDIFACT Structure Hierarchical segments and elements Flexible hierarchical segments and elements Document Style Three-digit numeric codes (e.g., 850) Six-character alphanumeric codes (e.g., ORDERS) Geography Predominantly North America International, especially Europe Security Supports encryption, relies on transmission medium security Supports encryption, secure authentication, non-repudiation Terminators/Separators * for data elements, ~ for segments, : for sub-elements + for segments, : for elements, ’ for messages Use Cases Retail, healthcare, and logistics in North America International trade, automotive, shipping EDI Transaction Document Codes Used in ANSI X12 and EDIFACT ANSI X12 and EDIFACT provide a comprehensive set of codes for efficient and accurate electronic data interchange. These codes are essential for automating business processes, reducing errors, and enhancing operational efficiency. ANSI X12 Document Codes ANSI X12 uses a structured set of three-digit numeric codes to identify various transaction sets. These codes standardize the format for different types of business documents, ensuring consistent communication across systems. Here are some commonly used ANSI X12 transaction codes: 850: Purchase Order – Used to place an order for goods or services. 810: Invoice – Used to bill the buyer for goods or services provided. 856: Ship Notice – Used to communicate the details of a shipment. 820: Payment Order/Remittance Advice – Used for payment information and instructions. 837: Health Care Claim – Used by healthcare providers to submit claims to payers. 997: Functional Acknowledgment – Used to acknowledge receipt and processing of an EDI transaction. EDIFACT Document Codes Unlike ANSI X12, EDIFACT uses six-character alphanumeric codes to identify various message types. These codes facilitate a wide range of business processes and are used internationally. Here are some commonly used EDIFACT message codes: ORDERS: Purchase Order – Used to request the delivery of goods or services. INVOIC: Invoice – Used to request payment for goods or services provided. DESADV: Despatch Advice – Used to inform about the dispatch of goods. PAYORD: Payment Order – Used to convey payment instructions. IFTMIN: Instruction for Despatch – Used to instruct the transport of goods. RECADV: Receiving Advice – Used to acknowledge receipt of goods. Benefits of Using ANSI X12 EDIFACT Using ANSI X12 and EDIFACT offers significant advantages for businesses in managing their EDI processes. Here are some of the key benefits of adopting these standards: Increased Accuracy: Both standards reduce the need for manual data entry, significantly decreasing the risk of human errors and ensuring that data is consistently accurate across all transactions. Cost Savings: ANSI X12 and EDIFACT minimize the need for paper-based processes and reduce manual labor, which helps businesses save on operational costs. Enhanced Security: Both standards support secure data transmission methods, ensuring that sensitive business information remains protected during exchange. Scalability: As businesses grow, ANSI X12 and EDIFACT can handle increasing volumes of transactions without compromising performance, making them suitable for small businesses and large enterprises. Now, let’s see the specific benefits of using each standard to learn about their unique strengths and applications: ANSI X12 Standardization: ANSI X12 provides a standardized format for exchanging business documents, which reduces the need for custom data translation and ensures consistency across transactions. Efficiency: ANSI X12 automates data exchange, which minimizes manual data entry, reducing errors and increasing processing speed. Interoperability: Widely adopted in North America, ANSI X12 ensures seamless communication between various regional businesses and systems. Regulatory Compliance: ANSI X12 helps businesses comply with regulatory requirements in highly regulated sectors, such as healthcare (HIPAA), ensuring that data exchange adheres to industry standards. EDIFACT Global Standard: EDIFACT is the international standard for EDI, supporting global trade by providing a common language for business transactions across different countries and industries. Flexibility: The EDIFACT structure supports a wide range of business processes and can handle complex, detailed data requirements, making it suitable for diverse international transactions. Comprehensive Messaging: EDIFACT supports various message types, from purchase orders to shipping notices, enabling comprehensive business communication. International Compliance: EDIFACT aligns with international regulations and standards, ensuring that businesses can meet compliance requirements for cross-border trade. How a No-Code EDI Solution Can Help No-code can enhance the benefits of using ANSI X12 and EDIFACT by simplifying their management and processing. These platforms enable businesses to set up and manage EDI processes without needing programming skills. With intuitive drag-and-drop interfaces and pre-built templates, no-code solutions, such as LIKE.TG, make configuring and deploying EDI systems easy. This accessibility accelerates time-to-market. Additionally, no-code platforms often include built-in automatic updates, ensuring businesses can focus on their core operations while maintaining robust, secure EDI transactions. Conclusion ANSI X12 and EDIFACT support efficient and accurate EDI across different regions and industries. ANSI X12, with its numeric transaction codes, is predominantly used in North America, providing standardization and interoperability for sectors like retail, healthcare, and logistics. Conversely, EDIFACT supports global trade with its alphanumeric codes, meeting the diverse needs of international business transactions. LIKE.TG EDIConnect is the ideal solution for managing both ANSI X12 and EDIFACT standards, offering an intuitive, no-code platform that simplifies EDI implementation and integration. Designed to handle the complexities of EDI, it supports a wide range of EDI transaction sets and messages, ensuring seamless communication between domestic and international business partners. Streamline your EDI processing with LIKE.TG EDIConnect. Request a demo session and see how you can benefit from accurate, faster, and secure ANSI X12 and EDIFACT data exchange.

What is Data Governance Data governance covers processes, roles, policies, standards, and metrics that help an organization achieve its goals by ensuring the effective and efficient use of information. It sets up the processes and responsibilities necessary to maintain the data’s quality and security across the business. Data governance manages the formal data assets of an organization. The key elements of data governance involve the following: Data categorization Clearly defined roles and responsibilities Data ethics Data Governance Framework A data governance framework specifies who can perform which actions on the data, under what circumstances, and by what methods. It ensures consistent data policies and rules are applied, creating data reliability. Building a solid data governance framework involves several key pillars. These pillars include; establishing data quality standards, integrating data from various sources, prioritizing data privacy and security, and defining a clear data architecture. Data Governance Best Practices The success of a data governance framework relies on a bottom-up approach, where data creators and users share responsibility for governance. Organizations can create an effective strategy that empowers them to manage their data assets effectively by implementing these data governance best practices: Set up Metrics to Measure Program Success Track key metrics such as the number of users trained and data sources governed. This tracking demonstrates the program’s impact on improving data awareness, accountability, and overall quality. Align Clear Roles and Responsibilities with Organizational Objectives Effective data governance relies on a well-defined structure that assigns ownership and accountability across the organization. Here’s a breakdown of key roles important for a successful data governance program: Data Governance Council: This high-level body provides strategic direction for the program. The board ensures that data governance processes are implemented within everyday operations, promoting consistent departmental data management. Data Owners: Data ownership assigns clear accountability for specific data assets. Data owners are responsible for defining how their data asset is used, creating a sense of stewardship, and promoting responsible data practices. Data Stewards: Data stewards implement data governance processes and work closely with data users to identify and address data quality issues, ultimately improving the reliability of data-driven insights. Data Managers: Data managers oversee the technical infrastructure that stores and manages data assets. They design and implement database systems that meet the organization’s specific data needs and ensure data security protocols are followed. Data Users: The individuals who leverage data for analysis and reporting fall under this category. Data users are empowered to access relevant information within defined parameters. By understanding data governance policies, they can utilize data responsibly and contribute to data-driven decision-making within an organization. Enforce Data Security Policies as Close to the Data Source It minimizes unauthorized access and simplifies data governance throughout its lifecycle by limiting access points. For instance, implement access controls on customer data within the transactional system where it’s created. Security policies minimize the risk of unauthorized access and simplify data governance throughout its lifecycle. Be Transparent About Your Data Privacy Practices Being transparent prioritizes data privacy and empowers customers to control their information. This step can involve obtaining user consent for specific data uses and implementing technical safeguards to prevent unauthorized access. Building trust through robust data privacy practices creates customer loyalty and decreases potential legal risks. Power Your Data Governance Strategy with Automation Here’s how automation empowers organizations to manage their data governance strategy effectively: Automated Access Controls: Automated access controls leverage software to enforce granular permissions based on user roles and groups. This streamlines access management and reduces the risk of unauthorized data exposure. Automated Data Discovery: Automated data discovery tools can crawl through data repositories, enabling users to pinpoint the information they need easily. The automation eliminates time wasted searching for data across the organization. Automatic Data Lineage Tracking: Automation tools can eliminate the need for manual data lineage mapping by automatically capturing data movement throughout various systems. It improves data transparency and simplifies impact assessments when data changes are required. Data Enrichment with Metadata: Automation can streamline the process of attaching rich metadata to data assets. These tools ensure a common understanding of data meaning across teams by automatically capturing details about data origin, usage, and definitions, promoting consistent data management, and minimizing misinterpretations that can lead to errors in analysis. Automatic Business Glossary Creation: Automation can facilitate creating and maintaining a centralized business glossary. These tools ensure a standardized data language by automatically capturing and integrating definitions of key data terms throughout the organization. The clarity minimizes confusion and fosters more reliable data analysis across all departments. Automated Policy Propagation: Data governance policies must be consistently applied across all data assets. Automation can streamline this process by propagating data protection policies to all dependent data elements. For instance, automatically masking sensitive data in derivative reports ensures consistent privacy compliance. Automatic Audit Logging: Record-keeping is essential for data governance. Automated tools can capture every interaction with data, providing a detailed audit log. These tools facilitate security investigations and help organizations understand how data is being used. Organizations can streamline compliance efforts by automating audit logs and gaining valuable insights into data usage patterns. Publicize the Program’s Goals, Processes, and Success Metrics Integrate data governance awareness into employee onboarding programs and technical training sessions, foster a culture of data accountability, and empowerthem. Schedule Regular Reviews of Your Data Governance Policies Regular review, at least annually, ensures the policies remain relevant despite evolving regulations, technologies, and business needs. A consistent review process allows for continuous improvement and adaptation of your data governance framework. Challenges with Data Governance Data governance, while crucial for managing an organization’s data assets, faces several hurdles that can hinder its effectiveness: Inconsistent Data Management: A single instance of poorly managed data can lead to security breaches or compliance issues. Organizations must strive for comprehensive data governance, incorporating all data sources and departments. Fragmented Data Definitions: Inconsistent terminology across various data systems can cause misinterpretations and hinder collaboration. Organizations must prioritize creating and maintaining a unified data dictionary to ensure everyone understands data definitions consistently. Undefined Data Stewardship: The lack of a designated data steward or the presence of multiple stewards can lead to a leadership vacuum or conflicting interpretations of data governance policies. Insecure Data Access Across Systems: Even with strong access controls in individual systems, data security risks can arise if all systems don’t adhere to consistent data governance practices. Organizations must ensure that all systems accessing governed data implement security measures. Operational Constraints: Operational limitations can impede data governance efforts. For instance, difficulty reconciling data across disparate systems can lead to workarounds compromising data integrity. Organizations need to streamline operations and eliminate data silos to ensure consistent data management practices across departments. Mergers and Acquisitions: Frequent acquisitions or mergers can create data management challenges. Integrating data from different companies can be complex, leading to inconsistencies and data quality issues. Organizations must have a plan for integrating acquired data while adhering to data governance principles. Concluding Thoughts Data governance supports strategic decision-making through high-quality and well-managed data. Therefore, organizations must incorporate effective data governance best practices when implementing a framework to maximize data security, value, and compliance with standards. Using the right tool is one of the most effective ways to improve data governance. LIKE.TG offers a comprehensive data management suite that simplifies the data governance process. With features like automated data profiling, the creation of a business glossary, and role-based access control, LIKE.TG empowers organizations to implement effective data governance practices. Simplify your data management with LIKE.TG. Download a 14-day free trial or contact us to get started. Empower Your Data Governance Strategy with LIKE.TG Streamline your data governance efforts and maximize data value. Download a free 14-day trial of LIKE.TG's data management suite and experience the power of automated data discovery, lineage tracking, and policy enforcement. Take control of your data today! Start a Free Trial

What is a Database Schema? A database schema, or DB schema, is an abstract design representing how your data is stored in a database. Database schemas can be visually represented using schema diagrams, such as the one below: A database schema diagram visually describes the following: The structure and organization of your data Different elements within the database — such as tables, functions, field names, and data types — and their relationships Database schemas are at the heart of every scalable, high-performance database. They’re the blueprint that defines how a database stores and organizes data, its components’ relationships, and its response to queries. Database schemas are vital for the data modeling process. They provide a framework for how a database’s elements relate to each other and work together. Well-designed database schemas help you maintain data integrity and improve your database’s effectiveness. Let’s explore how they work, their types and benefits, and how you can build them correctly. Database Schema vs. Database Instance A few key differences distinguish a database schema and a database instance from each other, which are as follows: A database schema is the blueprint that describes the organization, structure, and element relationships within the database. In contrast, a database instance is a sample taken from a database at a particular point in time. Secondly, a database schema is a framework and doesn’t contain any data itself. In contrast, a database instance is a snapshot of the entire database and its contents. Lastly, a database schema doesn’t change frequently over time, but a database instance can vary depending on the moment a data team obtains it. Types of Database Schemas There are different types of schemas within the context of databases: 1. Conceptual Schema A conceptual schema provides a high-level view of the essential entities, attributes, and relationships within a database system. Instead of deep diving into specific details about entities such as tables, columns, and views, conceptual schemas abstract specific implementation details and focus on the business meaning of the data. A conceptual schema helps you understand your data’s underlying structure. It lets you spot discrepancies or issues before they affect data applications, analytics, or insights. 2. Logical Schema A logical schema provides basic details about the data. Unlike a conceptual schema, a logical schema also describes specific objects like tables and columns and isn’t limited to abstract concepts. Logical schemas help you ensure that your data is organized and stored effectively. Since these schemas detail the relationships between entities, they’re even more helpful than conceptual schemas for identifying issues. 3. Physical Schema A physical schema is the most elaborate of all three schemas, providing the most detailed description of data and its objects — such as tables, columns, views, and indexes. Unlike a logical schema, a physical one offers technical and contextual information. It describes the storage media each table in a database uses and any associated constraints, enabling developers to choose the appropriate storage media for each table. What are Database Schemas Used for? Database schemas serve multiple purposes, some of which include: Application Development Database schemas are the data models that applications interact with. Applications can query and manipulate data in a structured way using schemas. For developers, schemas serve as documentation describing the database’s structure. The availability of detailed information equips developers to understand how they should interact with the database, write queries, and adhere to best practices. Backups and Recovery Database schemas help maintain the consistency and reliability of data backups. Their clear, well-defined structure simplifies data restoration while enhancing the accuracy of backups. Similarly, database schemas also assist with disaster recovery. When dealing with corrupted or lost data, a well-designed schema can help restore a database to its original condition and maintain data integrity. Data Analysis and Reporting Data analysis and report generation become easier thanks to the structured format that database schemas provide. In data warehousing, schemas help define the structure of data marts and warehouses and aid in complex querying and aggregations that are needed for business intelligence tasks. Multiple Applications, One Reliable Solution Forward engineer a database schema and have it ready to use within minutes, no matter the application. Find Out How What is Database Schema Design? Database schema design is creating an outline or a plan that defines how data will be stored, accessed, and managed in a database. It involves specifying the tables, their fields, data types, relationships, constraints, and other characteristics that determine how data will be stored, accessed, and used within the database. A database schema diagram can be either visual or logical, and these are the most frequently used database schema designs: Relational Model A relational model also arranges data in rows and columns in tables like a flat model. However, you can show relationships between entities in a relational model. As an example, consider a table containing student records. Each row can represent an individual student, and each column can include an attribute, as seen below: Hierarchical Model A database schema following a hierarchical model will have tree-like structures, with child nodes attached to parent nodes. Such a model helps store nested data, i.e., a data structure with one or more structures of identical or varying types. For example, a dataset listing families in a neighborhood can have parents’ names in the parent node and children’s names in the child nodes. An entry in such a dataset could look like this: Flat Model A flat model arranges data more simply, typically in a single or bi-dimensional array. This model works best for straightforward data that lacks complex relationships and can be arranged tabularly. A spreadsheet is an excellent example of a flat model as it arranges data simply into rows and columns. As another example, simple data (numbers 1–100) arranged tabularly will look like this: Star Schema A star database schema sorts data into ‘dimensions’ and ‘facts.’ A dimension table will contain descriptive data, whereas a fact table will have a numerical value, as shown below: Here, The Fact Table “Sales” contains numerical measures, such as sales amounts and the foreign keys to related dimension tables. The Dimension Tables contain descriptive attributes related to the measures. “Date Dimension” contains information on dates. “Product Dimension” contains information on products “Customer Dimension” includes information on customers The Fact and Dimension Tables are joined via foreign key relationships. Snowflake Schema In a snowflake schema, each table typically represents a single-dimension attribute. A snowflake database schema provides a logical representation of the data. Dimension tables are normalized/divided into multiple related tables, each containing a subset of the attributes. Relationships between dimension tables form a hierarchy, with a separate table representing each level of the hierarchy. Note that the snowflake schema has a more normalized approach than the star schema. This approach can save storage space and improve data integrity but results in comparatively more complex queries. In this example: The Fact Table “Sales” is hierarchically connected to multiple dimension tables — Date Dimension, Product Dimension, and Customer Dimension. Each dimension table can be normalized further, which will create a snowflake-like structure with branches connecting to additional tables, as seen below: In this structure, The Fact Table “Sales” is at the center of the schema. Each Dimension Table connects to the Fact using its respective foreign keys. This is a relatively straightforward example of a snowflake schema. As more tables branch out from the dimensions, the schema’s complexity will increase accordingly. Network Model The network database schema organizes data into sets and relationships, which facilitates complex interconnectedness between data and different data points. This structure is ideal if you want to model many-to-many relationships. This schema aligns closely with real-world use cases, where you can find multiple relationships between multiple entities, as seen in the example below: Among the entities in this scenario: The Employees set contains employee records, each with a unique EmployeeID and EmployeeName. The Departments set contains department records, each with a unique DepartmentID and DepartmentName. The Projects set contains project records, with a unique ProjectID and ProjectName assigned to each. Similarly, The Works_In table depicts many-to-many relationships between the employees and departments and shows which departments the employees work in. The Manages table features many-to-many relationships between projects and departments and shows which projects are managed by which department. Benefits of Database Schemas A database schema offers several benefits: 1. Ensuring Data Integrity While designing a database schema, you can configure primary and foreign keys and other constraints to define relationships between tables and clearly ensure referential integrity. For instance, in a relational database, one table’s primary key will refer to another table’s foreign key, keeping related data consistent across tables. Primary keys also guarantee that each record in a table is uniquely identifiable, minimizing data duplication. Similarly, check constraints keep data values compliant with specified conditions, preventing incorrect data entry. When applied to required fields, constraints can help you ensure data completeness by mandating the provision of essential data. With these keys and constraints, a database schema specifies the data type allowed in each column (e.g., whether it’s an integer, a string, or some other data type) and its expected format (e.g., date format, number of decimal places, etc.). These rules minimize the chances of errors as data is entered into the relevant tables, thereby maintaining data integrity and giving the database a consistent structure. Faster Data Retrieval Well-designed schemas define relationships between database tables, optimizing queries and improving performance in a few different ways: Configuring foreign keys helps a database determine how one table is related to another. The database then uses foreign keys to look up and match critical values rather than scanning whole tables. The database query optimizer can use the relationships defined in the schema to create more efficient execution plans. The optimizer also uses well-defined relationships to determine the right data retrieval methods, such as the order of table joins and which indexes it should use. Relationships are used to configure views that can precompute and store the results of elaborate joins. This process creates ready-to-use datasets, minimizes the need for repetitive computation, and accelerates read operations. Enhancing Security While designing a database schema, you can incorporate permissions and access levels for various roles or users. This idea limits access to sensitive data to authorized users only. It also mitigates the risk of breaches and misuse. Offering Scalability Scalability is one of the hallmarks of a correctly designed schema. Database schemas are built with scalability in mind to accommodate expanding data requirements and increases in data volume. You can introduce new relationships, tables, and columns into the schema without disrupting functionality. Simplifying Updates A clear database schema design simplifies data updates and other database management processes such as schema modifications, backups, performance optimization, and data integrity checks. You can quickly determine which columns or tables to update or modify while keeping the process compliant with your data integrity rules. Enhanced Interoperability A standardized schema facilitates interoperability between various applications and systems across an organization. Smooth, hassle-free data integration between disparate systems is crucial, especially for data-driven enterprises. Enhanced interoperability ensures everyone works with the most up-to-date version of their required data. Best Practices Following these best practices ensures that your database schema performs as intended: 1. Understand Your Requirements Start by outlining your requirements and goals. These can vary depending on your business model, the application(s) of the database, and the data it’ll store and manage. Clarifying objectives and applications early lets you build a schema that aligns with your requirements. It also enables engineers, analysts, and other technical stakeholders to work with data confidently. 2. Follow Proper Naming Conventions Your naming conventions can impact database queries’ performance and quality, and here are some tips to keep in mind: Keep column and table names concise since longer names consume more disk space. Simpler, to-the-point names work best — for example, ‘StockItems’ instead of ‘ItemsinStock.’ Avoid including quotes, spaces, hyphens, or other special characters. These can complicate the querying process or lead to errors and invalid communication. When naming columns, specify data types and ensure that the data type you choose supports the full range of values for the corresponding data set. Spellcheck and proofread when specifying field names. If not corrected, typos and errors in field names can cause errors down the line. 3. Implement Access Control and Authentication Measures You can reduce data breaches and abuse by implementing data security and safety measures. Access control is a reliable technique, but also consider incorporating the following into your database schema: Different types of authentications. Mandating registration for new users (i.e., no ‘guest users’). Encryption or hashing techniques as added protection for columns containing sensitive information. 4. Maintain Documentation Database developers, programmers, and analysts are your database’s primary users. However, you may want to share data with other business stakeholders, including non-technical personnel. Maintaining proper database schema design documentation lets you do this easily. 5. Balance Normalization with Query Optimization Day-to-day database operations — particularly those related to transactions — involve updates, insertions, and deletions. Over time, this can result in inconsistent or redundant data, creating inconsistencies and discrepancies. For example, if your database has the same record in two locations, only one record may be updated, but not the other. Normalization prevents this by defining entities so there aren’t duplicate records in your database. However, you need to strike a balance between normalizing and optimizing query performance. Excessive normalization involves a lot of tables and joins, resulting in complex schemas and affecting query performance. 6. Set Up Unique Primary Keys Set up a unique primary key for every table in your database to serve as an identifier for rows. Analysts use this primary key to assess your data model and tables’ relationships with each other. The primary key also minimizes instances of duplicated rows, increasing data integrity and mitigating potential application errors. 7. Ensure Flexibility Your database schema design should be flexible enough to accommodate future changes. Prioritizing flexibility ensures that as your data requirements evolve, your database schema design can keep up with them. Database Schema Design Using SQL Database design through SQL involves manually structuring the relationships and tables in a database using certain principles and steps. Let’s look at an online grocery delivery service as an example to design a simple database schema: 1. Identifying the Requirements Before designing the schema, you need to establish the requirements. In this scenario, the online grocery delivery service will primarily need to store information on products, categories, customers, and orders. Secondarily, it will need to track customer orders. 2. Entity-Relationship Diagram A simple Entity-Relationship Diagram (ERD) for this business will look as follows: 3. Normalization The ERD here is already relatively normalized. In instances when it’s not, the process of normalization breaks downlarger tables into smaller ones to ensure that each table represents a single logical entity and efficiently stores data without redundancy. 4. Defining The Tables Next, you’ll need to define the tables using SQL: 5. Defining Columns and Data Types If the columns aren’t already defined and the appropriate data types for each table aren’t specified, you’ll first need to understand the nature of the data you’ll store in each column. Secondly, you’ll choose the most suitable data type to represent that data while ensuring data integrity. 6. Defining Primary and Foreign Keys Each table should have well-defined primary and foreign keys to help you uniquely identify records and establish relationships between the tables, respectively. 7. Adding Constraints You can add additional constraints at this stage if needed, such as default values or unique constraints. These constraints ensure data quality, integrity, and consistency and enforce business rules and requirements. 8. Creating Indexes You can create indexes on the columns you frequently use in search operations depending on your query patterns. Note that the above steps create a basic hypothetical business schema. The schema’s complexity and the difficulty of manually designing it will increase as your business expands and your requirements become more varied. A Shorter Path to Your Database Schema Find out how LIKE.TG can simplify and accelerate database schema design — with no coding required. Start Your FREE Trial Why You Should Opt for an Automated Alternative Manually creating a database schema can be a long, complicated task — but LIKE.TG simplifies it considerably.You can fully automate DDL/Diff script creation and execution or have LIKE.TG create it for you so you can execute it manually. Using LIKE.TG, you can save time and get your database schema ready quickly without dealing with extensive coding. Conclusion A well-designed database schema is just a few clicks away. Enjoy faster and more reliable database schema design in a no-code environment. Reach out to our team today for more information.

What is Online Analytical Processing (OLAP)? Online Analytical Processing (OLAP) is a computing technology through which users can extract and query data to analyze it from different points of view. These typically high-speed and complex queries are performed on massive data volumes stored in a data lake, data warehouse, or other large-scale repositories. Typical OLAP applications include business intelligence (BI) and forecasting and reporting. A Data Warehouse Automation Platform That's Optimized for OLAP Systems Start Using LIKE.TG Data Warehouse Builder Today! Your Free Trial Awaits OLAP Architecture Online analytical processing (OLAP) systems contain multidimensional data. Unlike two-dimensional data — arranged simply in rows and columns — multidimensional data has numerous attributes or features. As a result, it’s represented in more than two dimensions. For example, a multidimensional purchases data set may contain the following attributes: Transaction ID Timestamp Supplier ID Product Code Product Category Product Name Quantity To effectively store such information and keep it ready for querying, you’ll need to build a multidimensional OLAP system comprising the components below: Data Warehouse A data warehouse is a central repository of organizational data from different sources. Files, applications, and databases are some of the most prominent sources. However, depending on your business model, it can also collect data from ERP, CRM, and IoT devices Extract, Transform, Load (ETL) Tools Data warehouses use the extract, transform, and load (ETL) process to transform and standardize data collected from different sources before OLAP tools can use it. Modern ETL tools speed up the process by automatically obtaining, preparing, processing, and loading the data for analytics. Try Our Enterprise-Ready ETL Solution for Yourself High performance, zero coding — accelerate your ETL processes today! Start Your FREE Trial OLAP Server An online analytical processing (OLAP) server is a specialized system that leverages rapid execution to facilitate complex queries. It’s the powerhouse that drives the system and is generally the middle analytical tier in a data warehousing solution. It transforms and arranges data into cubes to ensure rapid responses to queries and enable advanced analytics. OLAP Database You can connect a separate database to the data warehouse to prevent the analysis from straining the data warehouse’s resources. These databases are called OLAP databases. OLAP Cube An OLAP or data cube assists in analytical processing by representing data multi-dimensionally, allowing you to perform analysis from varying perspectives. These cubes are often described as “rigid” because they have a predefined schema or structure that dictates how data is organized within the cube. This rigidity is a trade-off: The fixed schema means faster query performance and simplified analysis, but, at the same time, it also means limited flexibility and difficulty in handling new data types that weren’t part of the original schema design. Adding a new dimension almost always requires remodeling the cube. OLAP Analytic Tools OLAP tools facilitate interaction with the OLAP cube, enabling you to perform advanced analytical operations such as: Drill-Down:This operation aids in increasing the level of detail in a data set, either by moving from a higher to a lower position in a dimension’s concept hierarchy or by simply adding another dimension. For example, you can start with yearly purchase data and drill down to quarterly, monthly, and daily figures. Roll-Up: Roll-up is the reverse of the drill-down operation and allows users to move from higher to lower granularity levels. For example, you can start with daily purchases and roll up to monthly, quarterly, or yearly totals to examine broader trends. Slice: Slicing selects a subset of data using one or more dimensions, helping you focus on particular aspects. You can slice data to evaluate any dimension you’re interested in, such as region, time, or product category. Dice: Dicing creates a smaller data cube by simultaneously providing values for multiple dimensions. It helps you analyze data across various dimensional combinations. Pivot: Also called rotation, pivot changes a data cube’s orientation to enable viewing it from different perspectives. When pivoting a cube, you can rearrange measures and dimensions to facilitate comparison and analysis. How Do OLAP Systems Work? Online analytical processing (OLAP) systems work in a three-step process: When to Use OLAP Solutions Generally, online analytical processing solutions are ideal whenever large data volumes undergo complex analyses to obtain insights that can drive decision-making. Here are a few instances where using OLAP systems is the right call: Complex Analytical Requirements These systems are useful when you need to perform multidimensional analysis with numerous dimensions and measures. Large Data Volumes When there are significant data volumes, analyzing them through traditional relational database management systems (RDBMS) isn’t feasible because of performance constraints. Since data is pre-aggregated and summarized before users query the system, OLAP cubes ensure fast response times, even for big data. Ad-Hoc and Interactive Analysis When exploring data interactively, examine details and conduct ad-hoc analysis to identify patterns, trends, and anomalies. OLAP cubes offer a user-friendly interface for business analysts and managers to explore large data sets without writing complex queries. Aggregated Reports When you require aggregated reports and summaries with varying granularities — such as weekly, monthly, or quarterly summaries. Time-Series Analysis When analyzing data over time periods, you can easily drill down and roll up through different periods to determine trends and historical patterns. Hierarchical Data Analysis When you’re analyzing hierarchical data structures — such as product, geographical, and organizational hierarchies—because OLAP excels at handling data with hierarchical structures. Predictive Analytics and Forecasting When you need predictive analytics, forecasting, and what-if analysis to guide strategy development and decision-making. Business Intelligence and Performance Management When you need a comprehensive BI solution for assessing key performance indicators and gaining insights into business operations. Types of OLAP Systems (MOLAP vs. ROLAP vs. HOLAP) There are three types of OLAP systems: Multidimensional OLAP (MOLAP) Relational OLAP (ROLAP) Hybrid OLAP (HOLAP) Multidimensional online analytical processing system works directly with a multidimensional OLAP cube. Relational online analytical processing conducts dynamic, multidimensional data analysis on data contained in relational tables (without first arranging it in a cube). Hybrid online analytical processing works with both relational and multidimensional databases within the same OLAP architecture. Generally, MOLAP is the fastest and most widely used kind of multidimensional data analysis. ROLAP is ideal when the ability to work with large data volumes is preferable over high performance. A HOLAP system is suitable when working with mixed data types. In such scenarios, some data is better stored relationally and some multidimensionally. OLAP Compared with Other Data Analytics Techniques Here’s a look at how OLAP compares to other data analytics techniques. 1. OLAP vs. OLTP OLAP OLTP OLAP is built for analytical processing, focusing on complex queries, multidimensional queries, and aggregations that enhance decision support. In contrast, online transaction processing (OLTP) is designed to handle real-time transactional data, so it’s optimized for rapid data entry and retrieval. OLAP systems generally store aggregated and summarized data in multidimensional OLAP cubes or hypercubes to accelerate analysis and querying. OLTP systems store detailed transactional information in relational databases. Read more about OLTP vs. OLAP. 2. OLAP vs. Data Mining OLAP Data Mining OLAP complements data mining by offering a structured environment to analyze and visualize pre-aggregated data interactively. Data mining uses machine learning and statistical algorithms to glean patterns, insights, and trends from large raw data sets. 3. OLAP vs. Data Warehousing OLAP Data Warehousing OLAP offers multidimensional structures that help users organize and analyze data for BI and decision-making when implemented on top of a data warehouse. Data warehousing includes integrating, cleaning, and storing data from various sources in a centralized repository. OLAP Advantages These are the most significant advantages of using an OLAP system: Multidimensional Analysis: Itgives you a more holistic view of organizational performance by enabling multidimensional analysis, providing deeper insights into the data’s patterns, trends, and relationships than unidimensional analysis can. Accelerated Query Response: These systems accelerate query responses even when working with massive data sets by pre-aggregating data and storing it multi-dimensionally. You can explore data and conduct complex analyses using these systems without facing delays. Ad–Hoc Analysis: Thanks to its support for ad-hoc analysis, you can ask and get answers to complex analytical questions without predefined queries, improving analytical flexibility and agility. Data Visualization: When you pair these systems with data visualization tools and platforms, you can conveniently create reports, dashboards, and charts for visualizing data and effectively communicating insights. Visual representations of data make it easier to understand for non-technical stakeholders and assist in decision-making. Decision Support: OLAP can assist in decision-making as it simplifies analysis, allowing you to evaluate alternatives and ultimately make informed choices based on meaningful insights. This capability helps you optimize resource allocation, strategic planning, and performance management. How to Implement an OLAP Solution? Implementing an online analytical processing (OLAP) solution is a multi-part process. Here’s an overview: Defining the Requirements Before designing an OLAP system, you should determine the business objectives and analytical requirements that it’ll fulfill. List the data sources, measures, dimensions, and KPIs you’ll include in the system. Data Modeling Start by designing the dimensional model for your OLAP system, including dimension tables, fact tables, and hierarchies. Remember to select the proper data modeling techniques depending on your database schema requirements. You can choose from star schema, snowflake schema, or other dimensional modeling approaches. Data Integration Obtain data from different source systems such as transactional databases, ERP and CRM systems, and external sources. Transform and cleanse the data to ensure quality, accuracy, and consistency. Once the data is transformed, you can load it into the target system. OLAP Cube Design Identify the measures, hierarchies, and dimensions each cube will feature based on the analysis requirements established earlier. Pre-calculate and store aggregated data at varying granularity levels within the OLAP cubes to optimize querying. OLAP Server Implementation Choose and implement an OLAP server or platform that meets the project’s requirements. Set up the server to connect to the data warehouse and access the OLAP cubes. Configure the security settings, access controls, and necessary authorizations to maintain data integrity and confidentiality. Client Tools and Interfaces Select and deploy client tools or interfaces so users can interact with the system. Provide user-friendly interfaces that simplify querying, analysis, reporting, and data visualization. Testing and Optimization Conduct testing to validate the OLAP system’s functionality, accuracy, and performance. Optimize the system for usability, query performance, and scalability based on feedback and usage patterns. Deployment and Maintenance Deploy the OLAP system into production and assess its performance. Create regular data refresh, cube processing, and system updates maintenance protocols. Regularly evaluate and improve the system to cater to evolving organizational requirements. These evolving requirements can include refining your data model, adjusting the OLAP cube structure, or updating your ETL processes. They are generally required due to adding new data sources, modifying existing sources’ schema, and analytical requirements. Alternatively, you might need to optimize the database’s performance. Summing It Up With their unique, analytics-centric functionality, OLAP systems play a critical role in business intelligence. They provide data analysts, business owners, and stakeholders with the insights they need to make the right decisions at the right time. LIKE.TG’s no-code data integration platform supports OLAP systems. Additionally, LIKE.TG Data Warehouse Builder automates data warehousing and is optimized for use with OLAP. The platform also features a data model designer you can use for creating an OLAP architecture from an existing OLTP system. Experience the LIKE.TG difference and discover how it can transform your data warehousing processes.Start your free trial today!

What is metadata? Metadata refers to the information about data that gives it more context and relevance. It records essential aspects of the data (e.g., date, size, ownership, data type, or other data sources) to help users discover, identify, understand, organize, retrieve, and use it—transforming information into business-critical assets. Think of it as labels on a box that describe what’s inside. Metadata makes it easier to find and utilize the data that you need. Typical metadata elements include: Content details: Title, description, tags, and categories Ownership/access information: Creator, creation date, modifier, and access controls What are some examples of metadata? Let’s take a look at an example for a better understanding: Metadata of a spreadsheet file: Metadata of a spreadsheet file. Image by LIKE.TG. Spreadsheets contain various types of metadata, including column headers and the number of rows and columns. In the above example, the table row, column headers, and the comment by Saba are examples of metadata, as they provide context to the data in the following rows. This type of metadata facilitates working with the present data. Other metadata elements in a spreadsheet include source descriptions, relationships between tables, data validation rules, data types, column statistics (missing values, minimum or maximum values, distribution), data ownership information, creation date, comments, and update history. Other examples: In addition to a spreadsheet, metadata facilitates the organization and categorization of a wide range of digital assets, including images, webpages, computer files, documents, geographic information system (GIS) files, relational databases, graph databases, emails, application code, multimedia content (videos), and audio recordings. Why is metadata important more than ever today? Metadata management goes beyond describing the content. It helps make crucial information searchable and discoverable — widely adopted today for organizational and efficient data management purposes. To name a few, it: Improves data organization Metadata handles massive volumes of data through categorization. The descriptive, technical, and structural details about your data serve as the foundation to label, categorize, locate, and retrieve the data, saving time and effort—thereby enhancing productivity. Enhances data management It provides insights into data lineage (its date of origin and the transformations it underwent), who can access it, and how it interrelates with other data. This helps evade data inconsistencies or errors, improving data quality and subsequently enhancing business processes. Augments decision-making capabilities The increasing data complexity makes analyzing vast volumes of data challenging and unrealistic. Metadata contextualizes and adds more structure to this data, enabling better understanding. Additionally, when powered with AI and machine learning algorithms, the process of anomaly detection and correction, tagging, and categorizing automates, improving data discoverability and data accuracy, enabling informed decisions. Assists in compliance and security When it comes to data storage, usage, and retention, especially PII, every business must adhere to the outlined compliance regulations (e.g. HIPAA) irrespective of the industry. Metadata facilitates this by providing information on data owner or creator(s) and lineage, aiding in keeping track of the document lifecycle. Additionally, metadata such as access levels, encryption, and audit trails ensure data integrity and confidentiality. In addition, metadata supports efficient data integration and smooth data migration, along with enabling collaboration and fostering affiliations among enterprise systems like ERP and CRM. Moreover, it is a powerful tool for modern content discovery and elevated customer experience. It allows potential buyers to find relevant titles through quick search and browsing. Clear metadata helps customers understand product details and make informed purchasing decisions. The role of metadata in data management Metadata provides a wealth of information about your data assets to unlock their true value. Image by LIKE.TG. Here’s how it effectively manages your data across dimensions: Supplementary context: Metadata adds a layer of explanation to your data stored in databases or systems. It details the source of the data’s origin (where it came from), its format (how it’s structured, e.g., numbers or text), and any transformations it’s been through (such as calculations or adjustments). This additional context allows users to interpret the data accurately and choose the most appropriate techniques to extract reliable insights. Search and filter: Metadata facilitates internal text-based search functionalities within databases. Descriptive attributes within the metadata, such as keywords, titles, and summaries, provide a context within search results and enable users to quickly locate relevant datasets through internal search tools. Additionally, metadata allows filtering and sorting search results based on specific criteria, saving time and effort otherwise spent sifting through irrelevant data. Logical structure: Metadata streamlines how your data is organized. It defines the connections between data elements, dependencies on each other, and their hierarchy within the larger dataset. This detailed breakdown allows for a logical and well-organized data structure, which makes storing, retrieving, and integrating different datasets more efficient. Illustration of how metadata establishes key data relationships. Image by LIKE.TG. Lineage tracking: Metadata allows tracking the origin and transformations a dataset within a database undergoes throughout its lifecycle. A documented history with lineage allows users to assess the validity and reliability of data, ensuring they’re working with accurate information for informed decision-making. Data quality checks: Embedded within metadata can be information on data quality checks and validation processes performed on the data stored in the databases. By offering more visibility into the data lifecycle, metadata enables users to evaluate the accuracy and completeness of a data set before utilizing it for analysis. Access control: Metadata facilitates the management of access permissions for database data by providing information about user roles, data sensitivity levels, and other attributes relevant to access control. For instance, a database administrator might set a rule that only users with the “manager” role can access data marked “confidential” in the metadata. By specifying user roles and authorization levels, metadata helps ensure that only authorized personnel can access sensitive or confidential data, enforcing accountability and data security. Clear access instructions: Once a relevant data set is located, metadata details the access methods, including authentication and authorization requirements. With clear access instructions, users can obtain the data they need for analysis without any confusion, streamlining the data retrieval process. Standardized sharing: It allows the creation of consistent data formats across various datasets in your database system. By providing a standardized format, metadata facilitates seamless data sharing between teams, departments, and organizations, fostering collaboration, data discovery, research, and knowledge-sharing practices. Reduced reliance on data teams: With readily available and well-documented data (thanks to metadata), users can independently locate and understand the data they need from the system. Consequently, reduced workload empowers data teams to focus on more complex tasks. Data interoperability: Metadata facilitates interoperability among various systems within your tech stack by establishing a common language. It defines data formats, models, and exchange protocols to ensure harmonization and seamless exchange between databases. Audit trails and accountability: As discussed earlier, metadata tracks who accessed or modified specific datasets within databases. This creates a detailed activity record, enabling organizations to identify responsible parties, prevent unauthorized changes, and minimize data breaches. In version control, it facilitates rollback procedures in case of errors, ensuring data integrity. Streamlined compliance: Metadata serves as the translator, enabling consistent data-handling practices across various systems. It allows adherence to corporate policies and regulatory requirements, regardless of where the data resides, and minimizes errors and inconsistencies that could lead to compliance issues. Role of metadata in data management. Image by LIKE.TG. Types of metadata Based on the functionalities it serves when it comes to information management, metadata is categorized into various types, some of which are: Descriptive metadata Descriptive metadata encompasses essential details about the data resource (what, when, where, and who) encompassing its content, context, and characteristics. It includes title, author name, keyword, summary, publication date, category, and physical characteristics (for physical media). Function: It helps users discover relevant data quickly and facilitates version control within document management systems. Structural metadata Structure metadata is the internal organization of a data source. It specifies hierarchical relationships between components, enabling efficient data exploration and analysis. Function: It allows users to efficiently navigate through complex data structures and improve data usability and user understanding through enhanced data presentation (e.g., website navigation menu, slide numbers in a presentation, and parent-child relationships in databases). Administrative metadata Administrative metadata provides crucial information to help manage or control a data resource—i.e., insights into who owns a data resource, how users can access it, and how users manage it. This type of metadata is often automatically created upon content entry in a content management system (CMS) and plays a pivotal role in data management, navigation, delivery, and retrieval within CMS. It includes details like data’s creation date, author, ID number, encode or media type, access permissions, retention period, and copyright or usage rights. This type of metadata is instrumental in maintaining data integrity and compliance across sectors. Function: It helps manage data governance and security, ensuring data is accessed, stored, and used appropriately, following regulations for data retention. These three metadata types are often connected and work together. Here’s an example of how they interrelate with each other. Interrelation between Descriptive, Structural, and Administrative metadata. Image by LIKE.TG. Technical metadata This metadata type provides information about the technical characteristics of digital resources. It describes the rules and structure for storing data, including data models and backup protocols. It includes elements like file size, format (e.g., PDF, JPEG, MP4), character encoding (e.g., UTF-8, ASCII), compression methods, data lineage, security settings, software used to create it, and hardware or device details. Examples: Data transformation rules, schema, field names, indexing details, row or column count, data source information, data types, data relationships, and access control. Table schema – Example of technical metadata. Image by LIKE.TG. Function: Technical metadata maximizes data interoperability, identifies data inconsistencies, provides visibility into data lineage (origin and transformations), streamlines data integration by capturing data source information, enhances searchability and retrieval of digital assets, aids in data preservation and proper archiving, and enables seamless navigation and data analysis. Active metadata vs. passive metadata Active metadata is dynamic. It is an always-on, API-driven process that integrates with your data stack tools, collects metadata from them, creates intelligence, and drives action without human intervention. In short, it describes the data, adds context to it, and can alter based on interactions. For instance, active metadata identifies the most frequently used datasets from historical usage records and recommends an optimized schedule for data pipeline execution. However, it doesn’t stop here. Beyond just suggestions, it autonomously adjusts the data pipeline through direct integration, thus advancing a fully DataOps centric approach. Passive metadata, on the other hand, is a traditional, more static process. It describes the data, is manually created by humans, and doesn’t alter its behavior based on events. Examples include tags, data descriptions, data types, and data sources. Here’s a visual depiction of passive and active metadata and how the former paves the way for the latter. Active metadata vs. passive metadata. Source: Gartner Best to use metadata effectively Here are some of the best practices to guide you in effectively utilizing your metadata: Know your whys: Define the purpose of your metadata. Identify the business value of your captured metadata and how it supports your systems, processes, and organization. Design a custom schema: Create a structured framework (schema) to categorize and organize your assets based on your specific needs. Make it discoverable: Plan for easy content discovery and access. Explain who are the intended users and what tools and methods they will use to find and utilize the metadata. Focus on value, not volume: Select and describe every piece of metadata you collect. Only include properties that directly connect with your goals and purpose. Simplify for better usage: Keep your metadata simple and straightforward to encourage users to contribute essential information. Avoid using excessive fields and prioritize essential properties. For instance, when adding metadata for an image, fields like title, description, and tags are crucial for categorization. Additionally, remove irrelevant pre-populated default properties to create a cleaner interface. Ensure it aligns with business goals: Develop a governance plan. This includes establishing clear roles and responsibilities, data quality management, and policies for managing metadata, ensuring consistency and compliance in metadata elements. In addition, identify sources and characteristics of your metadata to effectively adapt your governance plans in response to evolving needs. Learn more about metadata governance best practices. Final Words Effectively managing data within your organization is crucial to drive data-driven decisions. While data holds immense value, fully utilizing it requires context, which is why metadata is crucial. By providing a deeper understanding of your data’s characteristics, lineage, and location within the databases, metadata empowers efficient data discovery, ensures quality and compliance, and simplifies long-term preservation. LIKE.TG takes data management a step further by leveraging a metadata-driven data architecture and AI-powered ETL processes. Through automated metadata management, data visualization, and data lineage insights, LIKE.TG ensures your data remains consistently compliant and relevant — enabling informed data-driven decisions. Get in touch with our experts to see how our solution aligns with your data needs orschedule a personalized demo and see it in action! Empower Your Data Strategy with LIKE.TG's Metadata-Driven Solution With automated metadata management, data visualization, and lineage insights, LIKE.TG's metadata-driven architecture ensures data relevance, compliance, and integrity—all without requiring a single line of code! Request a personalized demo

What is Data Observability? Data observability is a process that actively monitors an organization’s data for accuracy, health, and usefulness. It is the ability of an organization to have comprehensive visibility over its entire data landscape, including data pipelines, infrastructure, and applications. Data observability allows the organization to quickly identify, control, prevent, remediate, and fix data outages, all within agreed service level agreements (SLAs). With data observability, organizations gain a deeper understanding of their data’s health by enabling them to monitor different metrics. For example, data volume to keep a check on any sudden surges or drops in data, error rates to ascertain data quality issues, and latency to ensure data remains accessible. Based on these signals or metrics, organizations gain insights that improve data management and governance, which means better business decisions. The Origins of Data Observability In the mid-1800s, Scottish physicist James Clerk Maxwell encountered the challenge of monitoring complex systems. He recognized the impossibility of directly observing every piece of information within such systems. Maxwell’s solution involved focusing on critical outputs and drawing inferences about the system’s overall health based on these observations. This pioneering work laid the groundwork for the concept of observability. The need for a deep understanding of data health and integrity throughout its lifecycle sparked the rise of data observability as an important field in data management. Data Observability vs. Data Quality Data Observability Data Quality Objective Provide real-time visibility into data pipelines and their health. Ensure data adheres to predefined standards for accuracy, completeness, consistency, and validity. Focus System behavior and data flow. Intrinsic characteristics of the data itself. Impact Proactive identification and resolution of data issues, promoting trust in data-driven decisions. Improved data reliability and fitness for intended use. Scope Encompasses the entire data lifecycle, from data ingestion to transformation and delivery. Primarily focuses on the data itself at a specific point in time. Usage Monitors data pipelines for anomalies, latency, schema changes, and data lineage. Validates and cleanses data to meet specific business rules and data governance requirements. Application Alerts data teams to potential issues before they impact downstream processes. Ensures data is accurate and reliable for reporting, analytics, and machine learning models. The Importance of Data Observability The exponential growth of data in modern organizations highlights the importance of maintaining data quality and reliability for informed decision-making. Data observability is a solution that empowers organizations to effectively manage their complex data infrastructures and ensure the integrity of their insights. Traditional data quality checks offer a limited view as they focus on data at a specific point in time. Data observability, however, provides a deep understanding of the data lifecycle. It involves continuously monitoring key metrics throughout data pipelines, giving organizations real-time or near real-time insights into their data systems’ health and behavior. These insights are important to prevent the effects of poor data quality, like distorted reports and skewed analytics. Being a proactive measure, data observability lets data teams actively spot and fix anomalies, latency, schema changes, or lineage problems before they impact insights. In machine learning, ignoring data observability can lead to a progressive decline in the model’s performance. For example, ML models are prone to data drift without proper data observability and can become ineffective over time. Data observability helps detect these drifts, ensuring models remain reliable. Moreover, the complexity and volume of modern data pipelines present challenges in maintaining data quality and clear data lineage. Data observability equips teams with the necessary tools to solve these challenges by providing real-time insights into the data architecture. It ensures a smooth data flow, reducing disruptions and speeding up the delivery of valuable insights. 5 Pillars of Data Observability Data observability’s effectiveness relies on several key pillars. These pillars are a framework that provides monitoring and analysis of information used throughout the organization. Freshness Freshness refers to how up-to-date a data set is. The primary focus of this pillar is the timeliness of data within the pipelines. Organizations need to define acceptable latency thresholds for data updates. Monitoring metrics like data age, update frequency, and timestamps help identify delays or disruptions in data ingestion or transformation processes. Fresh data ensures insights are based on the latest information, leading to more informed decision-making. Distribution Data observability helps monitor key statistical properties like mean, median, and standard deviation. It also helps identify underlying issues with the source data itself. Timely identification of deviations from expected data distribution patterns allows data teams to investigate and resolve these issues before they distort analytical outcomes. Volume Unexpected increases or decreases in data volume can indicate potential issues like data source problems, pipeline bottlenecks, or data quality deterioration. With data observability, teams can monitor fluctuations in data volumes, allowing them to get to the root cause and streamline data flow. Schema Data schema shows the structure and organization of data within pipelines. Organizations can continuously monitor the schema changes, including additions, deletions, or modifications to data fields with data observability. Schema monitoring enables data teams to identify potential schema drifts that could disrupt downstream processes or introduce inconsistencies in data analysis. Lineage Data lineage tracks the origin, transformation steps, and destination of data within pipelines. If a data quality issue is identified, data lineage allows teams to trace it back to its source and pinpoint the affected downstream processes. Data observability focuses on mapping data lineage that facilitates the root cause analysis and efficiently addresses the data quality issues. Key Components and Features of Data Observability Data Monitoring Data pipelines are complex networks that transport information and must be monitored. Data observability equips organizations with metrics that offer a comprehensive view of data flow throughout the pipeline. Alerting Systems Data observability leverages alerting systems to notify data teams of any anomalies or issues detected within pipelines. These alerts are triggered by deviations from pre-defined thresholds for various metrics, allowing teams to address potential problems before they escalate. Metadata Management Effective data observability relies on organized and readily accessible metadata, that defines details like data schema, lineage, and ownership, providing context. Metadata management ensures the information remains up-to-date and readily available, empowering data teams to understand the meaning and origin of data flowing through pipelines facilitating efficient troubleshooting and impact analysis. Data Lineage Tracking Data lineage visually represents the transformations and movements that data undergoes within pipelines. It empowers teams to trace data back to its source, identify potential issues, and ensure data quality remains consistent throughout its lifecycle. Root Cause Analysis Root cause analysis empowers data teams to address issues efficiently and prevent them from recurring in the future. Data observability enables data teams to leverage insights from data monitoring, alerts, and lineage tracking to pinpoint the source of data quality problems. How to Implement Data Observability Step 1: Inventory Data Use Cases Successful data observability initiative involves understanding how data is used across the organization. The preliminary step includes identifying the departments or teams that rely on data, the specific types of data they utilize, and the intended purpose of each data set. By understanding these use cases, organizations can prioritize data observability efforts based on the potential impact on business functions. Step 2: Align the Organization Implementing data observability requires collaboration across various teams within an organization. Communicate the importance of data observability to all stakeholders, emphasizing how it benefits various departments and ultimately strengthens data-driven decision-making. Communication creates a culture of data ownership and invests in the success of the implementation. Step 3: Implement Data Quality Monitoring This stage involves implementing tools to monitor various data quality metrics. These metrics include data freshness, completeness, accuracy, and consistency. Monitoring these metrics can give organizations an understanding of overall data health and identify potential areas for improvement. Step 4: Optimize Data Quality To optimize data quality, teams should have clearly defined steps for what to do when a problem arises. Also, assign ownership for different incidents and implement tools to streamline troubleshooting and root cause analysis. Organizations can reduce the potential impact on downstream processes and decision-making by optimizing incident resolution processes. Step 5: Risk Prevention This final step focuses on implementing strategies to prevent data quality incidents from occurring in the first place. This step involves data validation rules at data ingestion points, data lineage tracking to identify potential issues early in the data pipeline, and automation of data quality checks throughout the data lifecycle. Organizations can minimize data quality issues and ensure the trustworthiness of their data by prioritizing preventative measures. Benefits of Implementing Data Observability A well-designed data observability strategy delivers a range of benefits. Boosts Data Quality With cleaner data, organizations can make better data-driven decisions, leading to improved operations, happier customers, and stronger overall business performance. Data observability empowers teams to identify issues as soon as they occur, like missing values, duplicate records, or inconsistent formats, before they disrupt dependent workflows. Fast-Tracks Troubleshooting Data observability tools help teams quickly identify errors or data deviations. Features like real-time monitoring, anomaly detection, and alerts enable faster troubleshooting and issue resolution, minimizing downtime costs and severity. Helps Break Down Silos Data observability platforms offer shared dashboards that give various stakeholders visibility into critical data sets and foster better team collaboration and communication. Optimizes Efficiency Data observability tools pinpoint bottlenecks and performance issues, allowing engineers to optimize systems for better resource usage and faster processing times. Additionally, automation reduces manual effort in maintaining data health, freeing up data engineers to focus on extracting value from the data. Strengthens Compliance Data observability helps ensure data meets accuracy, consistency, and security standards in regulated industries like finance, healthcare, and telecommunications. This reduces the risk of non-compliance and related penalties. Enhances Customer Experience High-quality data is important to understand customer needs, preferences, and behaviors. Data observability helps maintain accurate and up-to-date customer data, improving customer satisfaction and loyalty through personalized experiences. Drives Revenue Growth Data observability offers new insights, identifies trends, and uncovers potential revenue opportunities by keeping data health in check. Organizations can use their data more effectively, leading to increased revenue and growth. Challenges of Data Observability Complex Infrastructure The heterogeneity of technologies, distributed systems, and decentralized management make achieving consistent data collection and holistic visibility difficult. Standardizing metrics and leveraging a central observability platform can help. Data Volume The complexity of systems often causes data volume to increase. As systems and applications produce more data, observability tools can get overwhelmed, limiting their ability to analyze and offer insights. This can lead to increased latency, which can impair effective observability. Therefore, scaling these tools is important to ensure they don’t miss relevant signals amidst the data flood. Data Silos The inability to correlate data from different sources makes identifying root causes and trends difficult. It also limits the effectiveness of data observability tools. Breaking down data silos is essential for organizations to gain meaningful insights and enhance data observability. Cloud Challenges When moving to the cloud, it’s important to consider how changes in data collection levels might affect data observability, especially since some cloud vendors offer limited options for instrumentation. Data Observability Best Practices Defining Data Quality Metrics Data quality metrics answer essential questions like “How much data is missing?” “How closely does the data reflect reality?” And “are there any discrepancies within similar datasets?” Establishing metrics that answer these questions allows businesses to identify data quality issues, reducing the risk of basing decisions on incorrect or incomplete information. Simplify Infrastructure Monitoring Focus on data that provides actionable insights into system health and performance. This practice reduces the burden on observability tools and allows for a more focused analysis. Centralized Logging Use data observability tools that help to centralize log data from every component in the data infrastructure. The unified view simplifies troubleshooting and root cause analysis of issues. Also, consider tools that provide data lineage tracking, as they help map the data flow through pipelines, making it easier to identify potential issue sources. Visualization of Raw Data Transform the raw data into visuals like graphs, charts, and dashboards, as visualization allows for real-time monitoring of key data points or metrics. Clear and concise visualizations make data observability accessible to a broader audience, promoting team collaboration. Stakeholders across departments can easily understand data trends and health, leading to better communication and shared ownership of data quality. Regularly Audit Data Pipelines Regular audits allow quicker resolution by identifying bottlenecks early on. These audits ensure smooth data flow throughout the pipeline and guarantee unhindered access to critical data points. Organizations can identify deviations in data quality before they impact the business by focusing on maintaining pipeline health. How to Choose the Right Data Observability Tool Data observability tools are essential for gaining deep insights into the organization’s infrastructure. The question of “choosing the right data observability tool” does not have a straightforward answer because not all tools are created equal. Here’s what to consider when selecting the right tool: Comprehensive Data Collection: A good observability tool should gather data from various sources across the network, infrastructure, servers, databases, cloud applications, and storage. It should also have features to review, sample, and process this data to provide a holistic view. Extensive Visualization: A core function of a good observability tool is to provide a comprehensive data visualization capability. Visualization allows teams to easily understand complex data sets and identify trends or patterns that indicate data quality problems. Integration with Existing Architecture: Look for a tool that seamlessly integrates with the existing data architecture and sources. Ideally, it should monitor data at rest (without extraction) and in motion throughout its lifecycle. Advanced Features: Look for tools incorporating artificial intelligence for Operations (AIOps) and advanced analytics. These features automate tasks and provide deeper insights, allowing the tool to better support business goals alongside IT needs. Ease of Use: The ideal tool should integrate smoothly with the existing workflows and require minimal upfront work. Prioritize tools that don’t require extensive data standardization, mapping, or data pipeline alterations for a smoother implementation experience. Concluding Thoughts The true value of data lies in its quality and accessibility. Data observability offers deep insights into an organization’s data infrastructure, ensuring the health and reliability of data pipelines. This focus on data quality, in turn, improves data-driven decision-making while leveraging a company’s data assets. By implementing a data observability strategy and following best practices, organizations can overcome challenges that arise due to a complex infrastructure, data silos, and ever-growing data volumes. Not every business needs an advanced data observability platform because data quality management is a strong foundation for healthy data and, if done right, can tackle most data challenges effectively. While data observability offers deep insights, especially in scenarios with complex data pipelines and extremely high volumes of data, teams should prioritize enhancing data quality as it delivers the most direct and significant benefits. Once the foundation is set, businesses can consider data observability as an extra layer of visibility and proactive management. LIKE.TG’s data management solutions easily blend with your existing infrastructure, enabling adequate data ingestion, transformation, and merging from multiple sources without intricate configurations. LIKE.TG prioritizes data quality and ensures the reliability and integrity of data pipelines. Visit our website or schedule a demo and see how LIKE.TG can improve your data management practices and enhance the overall visibility of your entire data landscape. Gain Clarity in Your Data Infrastructure with LIKE.TG's Data Management Solutions Streamline Your Data Processes Today with LIKE.TG - Ensure Data Quality, Enhance Visibility, and Drive Informed Decision-Making. Schedule a Demo Now! Request a Demo

What Is Data Analytics? Data analytics involves analyzing raw,unorganized data to make conclusions about it. The process involves examining extensive data sets to uncover hidden patterns, correlations, and other insights. With today’s technology, data analytics can go beyond traditional analysis, incorporating artificial intelligence (AI) and machine learning (ML) algorithms that help process information faster than manual methods. Data analytics has several components: Data Aggregation: Collecting data from various sources. Data Mining: Sifting through data to find relevant information. Statistical Analysis: Using statistics to interpret data and identify trends. Predictive Analytics: Employing models to forecast future trends based on historical data. Data Visualization: Presenting data visually to make the analysis understandable to stakeholders. These steps help organizations make informed decisions based on data-driven evidence, optimize operations, and predict future trends. Data analytics is different from data science in that data analytics focuses on analyzing past data to inform present decisions, while data science often explores data to predict future outcomes. Read more about data science vs. data analytics in detail. Why is Data Analytics Important for Businesses? Data analytics drives decision-making, improves operational efficiency, saves costs, and gives organizations a competitive edge. Here’s why leveraging data analytics is crucial for any business: Enhancing Decision Making Data analytics significantly enhances decision-making and provides a competitive advantage. It empowers organizations to scrutinize internal processes, market trends, and customer behavior. Consequently, businesses can make better decisions based on empirical data rather than guesswork. This data-driven approach reduces risks and maximizes the potential for success in strategic choices. According to a study, organizations that embrace data-driven decision-making and analytics can boost productivity by 9-10%. Furthermore, Forbes reports that 42% of sales leaders believe analytics has significantly improved their ROI. In terms of competitive advantage, data analytics offers insights that empower businesses to defeat competitors. By understanding customer preferences and market conditions, companies can customize their products and services for customers, often before competitors have identified these trends. This proactive approach has helped 90% of Fortune 500 Companies gain a competitive edge. Operational Efficiency Data analytics helps enhance operational efficiency and cost savings. It enables organizations to manage resources, reduce waste, and improve processes effectively, thus optimizing operations. For instance, predictive analytics can anticipate demand surges, allowing businesses to adjust their supply chains dynamically, improving service delivery. According to PwC, data analytics can enhance manufacturing uptime by 9%, decrease costs by 12%, mitigate safety, health, environmental quality risks by 14%, and prolong the lifespan of aging assets by 20%. Moreover, pinpointing inefficiencies and allocating resources can help organizations significantly reduce costs. For instance, optimizing marketing strategies through data-driven analytics can help organizations achieve a better ROI. As per McKinsey, analytics aided one manufacturer in reducing total costs by 7.8%. Improved Customer Relationships Data analytics enables businesses to understand and predict customer behavior, leading to better customer relationships. Personalized marketing, targeted promotions, and timely customer service responses, all informed by data insights, enhance customer satisfaction and loyalty. One MIT Sloan Review research revealed extensive data analytics helps organizations provide individualized recommendations, fostering loyal customer relationships. What Is Big Data Analytics? Big data analytics is a specialized branch of analytics that deals with large data sets that traditional data processing software cannot manage effectively. This form of analytics helps uncover hidden patterns, unknown correlations, and other helpful business insights from diverse, complex, and large-scale data sources, often in real time. Here are the nine Vs that characterize big data: Volume: The amount of generated data is massive and continues to grow exponentially. Velocity: The speed at which this data is generated and processed to meet demands is exceptionally high. Variety: Data comes in all formats – from structured, numeric data in traditional databases to emails, unstructured text documents, videos, audio, financial transactions, and stock ticker data. Veracity: The uncertainty and reliability of data. Ensuring data quality and accuracy becomes challenging with the high volume and variety of data. Veracity addresses the trustworthiness and integrity of the data. Value: The worth that can be extracted from big data. Despite the other Vs, the primary goal is to derive meaningful insights and value to drive decision-making and strategic initiatives. Variability: Data inconsistency over time can affect the accuracy of data models and analyses. These changes include changes in data meaning, usage patterns, and context. Visualization: The ability to represent data visually, making it easier to understand, interpret, and derive insights. Effective visualization techniques are crucial for presenting complex data in an accessible format. Validity: The correctness and accuracy of data for its intended use. Valid data ensures that the insights and decisions based on it are sound and reliable. Volatility: The lifespan of data and how long it remains relevant and useful. Some data loses its value quickly, necessitating timely processing and analysis. Traditional data analyticsis well adaptedfor smaller data sets with structured data. In contrast, big data analytics involves complex, voluminous data that requires businesses to incorporate advanced technologies and tools to process it. These frequently include ML algorithms, predictive analytics, and data mining techniques. What are the 4 Types of Data Analytics? Understanding the different data analytics types can help businesses tailor their data strategies effectively. Each analytics approach serves a unique purpose and offers specific insights that can guide various aspects of business operations. Descriptive Analytics: What Happened? Descriptive analytics helps summarize raw data and convert it into interpretable form. It involves metrics like mean, median, mode, and standard deviations or more complex data visualizations such as graphs and charts. Data scientists and analysts use this type of analytics to answer the fundamental question of “what happened?” by processing historical data to identify patterns and trends. Example: A company uses descriptive analytics to determine the most sold products in the last quarter or to analyze website traffic trends over the past year. Diagnostic Analytics: Why Did It Happen? While descriptive analytics shows a trend or a change in operations, diagnostic analytics digs deeper to find the cause. This approach often involves more complex processes like drill-down, data discovery, mining, and correlations. Example: A decrease in sales in the previous quarter can be analyzed through diagnostic analytics to find specific reasons, such as market conditions or internal challenges. Predictive Analytics: What Will Happen? Predictive analytics leverages forecast techniques and statistical models to understand the future. It relies on historical data and machine learning techniques to identify the chances of future results. Example: Using predictive analytics, a retailer could predict future sales trends based on seasonal buying patterns and current market dynamics. Prescriptive Analytics: How to Make It Happen? Prescriptive analytics suggests actions to achieve predicted outcomes. This type of analytics uses advanced technologies, including machine learning, business rules, and statistical algorithms, which allow businesses to make informed decisions. Example: To ensure the fastest delivery times, a logistics company uses prescriptive analytics to optimize delivery routes and schedules based on variables like traffic, weather conditions, and customer location data. Together, these four types of analytics represent an organization’s maturity curve. As businesses evolve, they often move from descriptive to more advanced prescriptive analytics, leveraging deeper insights and proactive decision-making capabilities. Critical Steps in the Data Analytics Process The data analytics process is a sequence of steps organizations follow to harness data and extract meaningful insights. This systematic approach ensures accuracy and maximizes data value across various business operations. 1. Data Collection The first step in the data analytics process is gathering data. This data can come internally from CRM systems, ERP systems, and databases and external sources like social media, public databases, and market research. Businesses use various techniques, such as extract, transform, and load (ETL), to gather data from these sources. The quality and volume of collected data directly impact the insights it can generate. 2. Data Storage Data needs a secure and reliable home. This could be a cloud-based storage solution, on-premises servers, or a combination of both. The chosen storage method hinges on the data’s characteristics and intended use. For structured, historical data optimized for querying and reporting, a data warehouse is generally ideal. Alternatively, a data lake can be a good fit for storing vast amounts of raw, unstructured data from various sources. This data lake can then feed into the data warehouse or be directly analyzed by data scientists for exploratory tasks or advanced analytics like machine learning. 3. Data Cleaning and Preparation Once data is collected, it must be cleaned and prepared for analysis by removing errors or inaccuracies, handling missing values, and standardizing data formats. Preparing and cleaning the data is a critical step in the process as it ensures the reliability of the analysis. 4. Data Analysis With clean data, the next step is to analyze the data, which can vary depending on the business objectives and the type of analytics being conducted. Techniques can range from simple statistical analysis and data visualization to complex predictive or prescriptive modeling. 5. Data Interpretation This stage involves making sense of the data analysis results. Data analysts must interpret the data to draw practical conclusions and insights that inform business decisions. This stage often requires a deep understanding of the business context and the technical aspects of data processes. 6. Data Visualization and Reporting Data is often visualized as charts, graphs, and tables to make the data analysis understandable and accessible to stakeholders. Effective visualization and reporting are crucial for communicating findings clearly and effectively, enabling decision-makers to quickly grasp complicated concepts and results. 7. Decision Making Finally, the insights gathered from data analysis are used to make informed business decisions. The insights guide strategic planning, operational changes, or tactical initiatives. The ultimate goal of data analytics is to drive actions that will improve business outcomes. What Are the Different Data Analytics Techniques? Businesses can apply various techniques to make sense of their data and extract useful information. These techniques vary in complexity and application, and choosing the right one depends on the organization’s specific needs and goals. Some of the most common data analytics methodologies are: Statistical Analysis Businesses apply statistical techniques to analyze and interpret data to identify correlations, patterns, and trends. It is one of the foundational methods in data analytics and can range from simple descriptive statistics to complex inferential statistics. Time Series Analysis Time series analysis looks at data over time; it establishes the relation between the value of data and the time of its occurrence. It is usually applied to isolate trends of recurrences or financial forecasting. Example: A farmer has yield data for their crops over several years. They use time series analysis to identify patterns and predict future yields. Regression Analysis Regression analysis involves studying the correlation between one or more independent variables and a singular dependent variable. It helps determine how the dependent variable changes when the independent variables are changed. Example: A car manufacturer wants to know how fuel efficiency (dependent variable) changes with engine size (independent variable). They use regression analysis to find whether the relationship is directly or inversely proportional. Factor Analysis Factor analysis simplifies a complex data set by reducing the number of variables. Analysts use it to discover latent patterns they cannot easily perceive from a large set. Example: A marketing research company collects data on consumer spending in categories like groceries, dining out, entertainment, travel, health, and education. The goal is to understand consumer behavior to shape marketing strategies. Factor Analysis comes into play to simplify this complex data. It groups these spending categories into a few factors, each representing a spending pattern. For example: ‘Essential Spending’ might combine groceries and health expenses. ‘Luxury Spending’ might group dining out, entertainment, and travel. ‘Health and Education Spending’ might merge health and education costs. These factors, inferred from the variables, are not directly observable but simplify the data, making it easier for the company to understand and target consumer behavior. They also help reveal hidden relationships among the spending categories. This way, Factor Analysis reduces the dimensionality of the original data, aiding in its interpretation. Cohort Analysis This technique separates a data set into subgroups with similar characteristics, most often demographically based. Creating cohorts makes it easy for analysts to examine the data for any given subset in depth. Example: An app developer groups users based on the month they first used it. This grouping allows them to see usage patterns for each group, helping them better understand user behavior. Machine Learning Machine learning is an advanced analytics technique that uses algorithms to process data, train from it, and determine or predict something. Unlike static, rule-based analytics, machine learning can update predictions as new data becomes available. Example: E-commerce companies use machine learning to personalize product recommendations to users based on past browsing and purchase history. Data Mining Data mining discovers patterns in high-volume data sets by leveraging machine learning, statistics, and database systems. It is designed to extract hidden predictive insights from large databases and turn them into actionable knowledge. Example: Credit card companies detect unusual patterns in spending behavior by leveraging data mining. Text Analytics Text analytics, or text mining, derives high-quality information from text. This technique analyzes customer feedback, social media conversations, or news to get information on public sentiment, customer preferences, and market trends. Example: Companies monitor social media mentions using text analytics to understand public sentiment about their brand and competitors. Optimization and Simulation These techniques simulate different scenarios and determine optimal outcomes. Businesses can use these methods to foresee the effects of potential changes in their operations and choose the best options under various circumstances. Example: Monte Carlo Simulations Monte Carlo Simulations are a risk analysis technique that models different possible outcomes and their likelihood. They are generally applied to reduce risks or avert potential losses. These simulations factor in several values and variables, being far better predictors than most other means of data analytics. How is Data Analytics Used in Business? Data analytics has become a pivotal tool in the business landscape, influencing various industries by providing deeper insights into operations, customer behavior, market trends, and financial performance. Here’s how businesses apply data analytics in practical scenarios: Marketing Optimization Data analytics enables businesses to refine marketing strategies by understanding customer preferences, buying habits, and engagement patterns. Analyzing this data helps organizations increase conversion rates and customer retention. Example: Retail companies analyze customer data to identify purchasing patterns and tailor marketing efforts to different segments, resulting in more personalized and effective promotions. Supply Chain Management Applying data analytics in the supply chain helps businesses predict demand more accurately, reduce costs associated with overstocking or understocking, and optimize inventory levels. Example: A manufacturing firm uses predictive analytics to forecast product demand seasonally, adjusting production schedules and inventory management accordingly to minimize waste and ensure supply meets demand. Financial Analysis Data analytics helps firms make financial decisions by predicting future trends, analyzing investment risks, and detecting fraudulent activities. Example: Financial institutions employ analytics to assess credit risk by analyzing applicants’ historical data and predicting their loan repayment ability. 4. Customer Experience Enhancement Analyzing customer interactions and feedback across various channels allows businesses to improve services, products, and customer journeys. Example: Telecommunication companies use analytics to track customer service calls, identify common issues, and train customer service representatives more effectively to enhance the customer experience. 5. Human Resources Management Data analytics aids in optimizing recruitment processes, monitoring employee performance, and enhancing workforce planning. Example: HR departments use analytics to analyze job application data and employee performance, helping to predict employee success and identify areas for development within teams. 6. Operational Efficiency Analytics can uncover inefficiencies in operations, helping businesses to improve service delivery, streamline processes, and reduce costs. Example: Airlines use analytics to optimize flight paths and crew assignments, reducing fuel costs and improving on-time performance. What are Data Analytics Tools, and How Do They Help? Data analytics tools are software applications that analyze data and extract actionable insights. These tools vary widely in complexity, from simple statistical tools to advanced machine learning platforms. Here’s an overview of some key types of data analytics tools and how they assist businesses in making informed decisions. Business Intelligence Platforms Business Intelligence (BI) platforms integrate data from across the enterprise and provide dashboards, reports, and data visualization capabilities. These tools are essential for businesses that monitor key performance indicators (KPIs) and make quick, informed decisions. Example: Tableau and Power BI are popular BI tools that help users create dashboards that are interactive and dynamc. These diagrams visualize data trends and patterns. Statistical Analysis Software These tools are used for complex data analysis, including predictive modeling and hypothesis testing. They are crucial for businesses that require detailed statistical insights to guide their strategies. Example: SPSS and SAS are widely used for their advanced statistical capabilities, helping researchers and businesses perform intricate data analysis. Data Mining Tools Data mining tools are designed to discover patterns and relationships in large data sets. These tools are valuable for businesses looking to explore data for unexpected correlations and new opportunities. Example: Apache Mahout and Monkeylearn provide potent data mining capabilities that help businesses uncover hidden insights and predict future trends. Machine Learning Platforms Machine learning platforms are advanced analytics tools that automatically learn from experience. They are ideal for businesses that require sophisticated modeling that adapts to new data. Example: TensorFlow and Scikit-Learn are machine learning platforms that enable businesses to develop complex models that improve over time, driving innovation and efficiency. How LIKE.TG Enables Data Analytics Data analytics is an indispensable tool that offers profound insights into every facet of business operations. It can revolutionize data-driven growth, enhancing decision-making and optimizing marketing efforts to improve operational efficiency and customer relationships. LIKE.TG is a robust data integration solution that simplifies the complexities of data analytics. Designed to cater to technical and non-technical users, LIKE.TG facilitates the seamless extraction, transformation, and loading of data, ensuring businesses can focus on deriving insights rather than managing data. Union League Club of Chicago (ULCC) The Union League Club of Chicago (ULCC) needed to enhance data management across diverse systems for improved analytics and reporting. However, synchronizing data across its three main systems—Northstar, ClubEssentials, and ResortSuite—was challenging. ULCC leveraged LIKE.TG’s powerful, enterprise-grade ETL engine to overcome this, replacing their outdated ETL solution. This upgrade provided: A scalable data integration platform, enabling seamless data synchronization. Transformation. Extraction across their systems. As a result, ULCC has achieved a 70% reduction in the time required to create synchronized data tables, improved analytics and reporting capabilities, and data-driven decision-making. GaP Solutions GaP Solutions aimed to automate data processing for analytics and reporting. They received up to 10,000 unstructured invoices every month. Manually entering this data posed significant challenges taking more than 24 hours to prepare invoice data for analytics. LIKE.TG automated the invoice processing workflow, offering template-based extraction, built-in transformations, custom validation checks, and automation workflows. The result? A drastic reduction in processing time from 24 hours to under three minutes. Gap Solutions now enjoys automated invoice processing, improved data availability, and enhanced analytics and reporting capabilities, which help them significantly boost decision-making and operational efficiency. Cherry Health Cherry Health sought to streamline its electronic recordkeeping process, reduce costs, and ensure data availability for analytics. They had to process diverse data sources, including electronic scanned forms, text, reports, log files, PDFs, Excel, and several databases. LIKE.TG provided a unified platform for seamless data syncing, transformation, and extraction from these varied sources. The solution’s automated workflows and user-friendly interface facilitated the transformation of unstructured patient data into a structured format ready for analytics. These features led to comprehensive data integration from diverse sources, improved data availability, and increased operational efficiency, enhancing Cherry Health’s analytical capabilities. Businesses that leverage enhanced data analytics better align strategic objectives with actionable data insights, propelling them toward greater innovation and efficiency. LIKE.TG’s platform is designed to facilitate this alignment, making it easier for companies to leverage data for analytics. LIKE.TG lays the groundwork for analytical capabilities by ensuring data is accessible and primed for analysis, allowing businesses to react swiftly to market dynamics and internal feedback loops. Here’s how: Key Features: User-Friendly UI: Expedite data warehouse tasks with a user-friendly, no-code environment. Comprehensive Data Integration: Consolidate data from various sources, including databases, cloud platforms, web applications, and more, utilizing an extensive range of native connectors and REST APIs. Efficient Data Modeling: Effortlessly construct a logical schema for data warehouses by importing or reverse engineering a database schema into widely-used data modeling patterns such as 3NF, dimensional modeling, and data vault. Reliable Model Verification: Robust built-in data model verification features conduct hundreds of validation checks to guarantee the integrity of model deployment. Simplified Automated Pipelines: Minimize the intricacy of constructing load pipelines to a few simple drag-and-drop actions with our automation feature. Versatile Data Transformations: Modify data using a library of over 600 transformations, which can be automatically converted into the native code of the target platform when operating in ELT mode. Dynamic Reporting and Analysis: Use OData queries to retrieve information from data marts and warehouses and consume it directly in leading BI tools such as Power BI, Tableau, etc. Experience the power of LIKE.TG’s data integration solution firsthand. Sign up for a free 14-day trial and discover how LIKE.TG transforms data into strategic assets.

A database is a storage system that stores data in an organized manner for easy access and management. In just the last two years, 90% of the world’s data has been created, and the volume of global data doubles every two years. All this data is stored in databases. So, whether you’re checking the weather on your phone, making an online purchase, or even reading this blog, you’re accessing data stored in a database, highlighting their importance in modern data management. So, let’s dive into what databases are, their types, and see how they improve business performance. Database definition A database is a structured collection of data designed to store, manage, and retrieve information. In a database, also denoted as DB, data is normally arranged within tables, defined by rows and columns, like a spreadsheet layout that facilitates data organization. The structure makes it easy to query data, which is mostly done using Structured Query Language (SQL)—the language used to interact with relational databases. You need a database management system (DBMS), or database software, to interact with a database and manage (access, modify, update, or delete) the data it contains. These systems ensure you can interact with the database through a unified interface. Some examples of DBMS include: MongoDB Oracle Database PostgreSQL MySQL SQL Server The functionality of databases extends to various operations, such as inserting new data, updating existing data, deleting old data, and querying data based on your specific criteria. For example, in a database containing customer information, you might want to retrieve data for all customers who live in a specific city. The criterion in this case would be “City equals New York.” This would result in a dataset of customers whose city field in the database matches “New York.” This type of data retrieval criteria is known as a “selection” operation and is a core function facilitated by SQL. Want to know how LIKE.TG Data Stack simplifies database management? Hint: it's a 100% no-code platform! Learn More Types of databases There are different types of databases. The right database for your organization will be the one that caters to its specific requirements, such as unstructured data management, accommodating large data volumes, fast data retrieval or better data relationship mapping. Here are some types of databases: Relational databases (SQL databases) Relational databases are also referred to as SQL databases. As the name suggests, these databases are based on a relational model where the data is organized into tables (entities) with rows (tuples) and columns (attributes). Relational database management systems (RDBMS) use SQL to query and manage data. Some examples of relational databases include MySQL, PostgreSQL, and SQL Server. Relational databases are excellent for applications that require strong data integrity, complex queries, and transactions, such as financial systems, customer relationship management systems (CRM), and enterprise resource planning (ERP) systems. Non-relational databases (NoSQL databases) Unlike relational databases, NoSQL databases, also called non-relational databases, don’t require a fixed schema. These databases are suitable for managing semi-structured or unstructured data. Types of NoSQL databases include document stores such as MongoDB, key-value stores such as Redis, and column-family stores such as Cassandra. These databases are ideal for big data applications, real-time web applications, and distributed systems. Some common use cases include social network management and content management. Hierarchical databases The hierarchical database model organizes data in a tree-like structure with parent-child relationships. Each record type, except the root, has one parent. It’s used for one-to-many relationships but is less stable due to data redundancy, limited independence, querying challenges, and scalability issues. This model is primarily used in applications where data is naturally organized in a hierarchical manner, like XML data management. Hierarchical databases find their use in scenarios requiring fast, predictable navigation and querying of structured, tree-like data relationships. For example, computer file systems, where folders and subfolders form a natural tree structure, organizational charts within companies, and bill of materials (BOM) used in manufacturing can be efficiently represented and queried using hierarchical databases due to their inherent hierarchical nature. Network databases It is a type of database model where data is organized using a network-like structure. In a network database, records can have multiple parent and child records, enabling many-to-many relationships. Network databases provide greater flexibility when compared to their hierarchical counterparts. Network databases are well-suited for complex applications, such as telecommunications, transportation networks, and advanced inventory systems. Document-oriented databases A document-oriented database stores data in flexible, schema-less documents in formats like JSON or BSON. The focus is on storing and querying documents. The documents can vary in structure within the same collection, allowing for easy unstructured or semi-structured data storage. These databases are ideal for management systems, such as e-commerce applications, and scenarios that require the storage of complex, nested data structures for easy and fast updates. Object-oriented databases Object-based databases are designed to work with object-oriented programming languages and store objects directly. In this database model, the focus is on storing and managing objects in reference to their methods and functions. These databases typically support features like inheritance, polymorphism, and encapsulation and are best for applications like computer-aided design (CAD), multimedia projects and applications, software development, digital media, and gaming. Distributed databases Distributed databases have become the go-to solution for organizations managing massive datasets or requiring high availability and scalability. Unlike traditional databases confined to a single server, these databases fragment data across multiple interconnected computers. The fragmentation allows them to distribute the workload of processing and storing information, offering several key advantages—enhanced scalability, availability, resilience and fault tolerance. Data warehouses Data warehouses are a specialized type of database designed for a specific purpose: large-scale data analysis. While traditional databases excel at storing and managing operational data for day-to-day transactions, data warehouses focus on historical and aggregated data from various sources within an organization. A data warehouse leverages the core strengths of databases—data storage, organization, and retrieval—and tailor them specifically to support data analysis and business intelligence (BI) efforts. These are some of the most common databases. Today, cloud computing, artificial intelligence (AI), and machine learning (ML) are pushing the boundaries of databases. Modern databases include NewSQL databases, cloud databases, autonomous databases (also called self-driving databases), blockchain databases, and multi-model databases. Learn more about different types of databases. Instantly connect to and move data between your databases Download 14-day free trial What are the benefits of using a database? Databases support the digital services used by millions of end-users daily and bring many benefits that enhance how these services operate. Organizations leverage databases for several use cases, such as business intelligence (BI), customer relationship management (CRM), and inventory management. Here are different ways databases benefit organizations: Databases control data redundancy A database serves as a central repository of data, which helps save storage space and reduce redundancy through data integration. Techniques like normalization, primary and foreign keys, and indexes prevent data from duplication. Instead of storing the same information repeatedly, each piece is placed efficiently within the database structure. This allows data to be retrieved from various tables when needed, based on the established relationships. For example, if you manage a library database, you only store member details once instead of repeating them for every book borrowed. Each transaction links to the member’s unique ID, so you can access their information without redundant storage. Databases provide improved data insights Integrating data into a single repository enables you to gain more insights from the same data set. The centralized storage method replaces the older, file-based systems where data was scattered across multiple files, making retrieval slower and more complex. For example, because file-based systems require you to keep inventory and sales data separate, the inventory department might not know your best sellers. However, with a database, both departments can access everything from a single location, improving collaboration between teams and productivity, which in turn leads to improved decision-making and business strategies. A database secures sensitive information through access controls Using a modern database management system (DBMS) enhances data security by restricting access to unauthorized users through various access controls. The system determines a user’s role within the organization and their rights for various operations like data retrieval, insertion, updating, and deletion. For instance, database administrators have the privilege to access all data across all the databases in an organization. However, a branch manager’s access to information is limited to their specific branch. The use of access controls ensures that sensitive data remains protected and only available to authorized personnel, thus minimizing risks of data breach and improving overall data security. Databases provide scalability in handling ever-increasing data Databases provide a way to manage the increasing volumes of data and ensure that applications stay responsive and available as they scale. Databases adapt to the growing volumes of data through two common approaches. Vertical scaling (scaling up) This approach involves adding more hardware resources to an existing database server to handle increased load. While there’s a limit to how much you can scale a single server, modern hardware can support significant growth. For example, enterprise-grade servers can be equipped with terabytes of RAM and multiple CPUs, offering substantial processing power for database operations. Horizontal scaling (scaling out) With horizontal scaling, the data is distributed across multiple machines by adding nodes (a cluster) to handle more data and read/write operations that would not be possible in a single server. Horizontal scaling is virtually limitless, depending on the database architecture. For instance, some large-scale operations in an enterprise may manage petabytes of data across thousands of servers. Beyond these two approaches, the sharding technique is also used in both relational and NoSQL databases to scale data. Sharding involves dividing data into smaller parts (shards) that are distributed across several servers to improve processing speed for large data sets. For example, Google’s Bigtable and Amazon’s DynamoDB use sharding techniques to manage their massive databases. Instantly connect to and move data between your databases Download 14-day free trial Major components of a database Databases are the core of modern information management, but what are the elements that make a database a database? Here are the major components that define its structure: Data Data is the most important component of any database. It is the stored information that the systems within an organization interact with and manage. Data in a database is organized in a way that allows for easy access, manipulation, and analysis, supporting various business operations and decision-making processes. Database engine A database engine is the underlying software that enables the database to operate effectively and carry out its intended functions. Some examples of database engines include: MongoDB (for NoSQL databases) Amazon Aurora Google Cloud Spanner Organizations also use embedded database engines and in-memory database engines to improve application performance. Database schema A database schema defines the structure and constraints of your database. In terms of relational databases, it specifies the tables, the fields within each table, and the relationships between them. It’s a model of how your data will look. The schema doesn’t contain any data itself; it just facilitates database design per your organization’s needs. Database access language It refers to the language you can use to write queries and commands to interact with databases. SQL is the most prevalent language, but variants like MySQL and Presto also exist. Query processor As the name suggests, a query processor processes your database queries in executable form. Metadata Metadata serves as an instruction manual for your database, providing essential details about its contents and structure. Stored in data dictionaries, this “data about data” helps the database software understand how to interact with the information stored within. Hardware components Hardware components are the physical components of a system on which a database runs. These are required for storing data, executing software, and ensuring smooth operation of the database. The hardware components of a database include: Storage devices: these are devices that hold all the data a database manages; these can be hard drives (HDDs) or solid-state drives (SSDs). The choice between HDDs and SSDs can affect the speed and efficiency of data retrieval. Memory (RAM): It temporarily holds data that is actively being used or processed, making data retrieval operations quicker. Processor (CPU): The central processing unit, CPU, performs the data processing operations, such as command execution, query processing, and transactions. Network components: These are the communicators of the database. Network components include network cards and routers that connect the database server to the wider network, allowing it to send and receive data from other systems and users. Database challenges While databases offer numerous benefits, they have their own set of challenges. Here are a few database challenges to be aware of: Working with databases can be expensive Working with databases, whether cloud-based or on-premises, can be expensive. Cloud-based solutions, while initially cheaper for storing and managing data, often employ consumption-based pricing models, leading to unpredictable costs associated with its usage, storage, and processing. On-premises databases demand heavy investment in hardware and software, alongside additional maintenance expenses. In both cases there’s the need for specialized database experts and IT professionals to manage and maintain these systems, also adding to the cost. Additionally, unexpected downtime from hardware or software failures can further escalate the costs. Migrating data between databases is a complex undertaking When you’re moving data between databases, you’re reading from one and writing to another at the same time. During this transition, determining which database holds the true, up-to-date data can be a challenge because of asynchronous updates and latency issues. If both the source and the destination databases are in active use during the migration, new data added to the source system may not be immediately reflected in the destination system. This discrepancy can lead to inconsistencies, which, in turn, make it difficult to determine which system contains the most current data. Managing live database updates Performing live database updates poses several risks—from data integrity issues to performance degradation and potential downtime. Concurrency problems and incomplete transactions lead to data corruption. Structural modifications consume extensive resources, which impacts system performance and causes blocking. In these scenarios, ensuring high availability becomes challenging, as some updates necessitate taking the database offline. For example, removing or repairing nodes can temporarily impact system performance in a database like Cassandra. Similarly, in an older version of MySQL before 5.6, adding a column to a table would lock the entire table. Risk of data loss due to database failure Databases are complex systems and, despite the best efforts, are prone to various types of failures that can’t always be prevented. Issues like software bugs can interact with the database and corrupt or accidentally delete the data. Similarly, physical issues like hardware failures, network disruptions, and systemic overloads from sudden high demand, also known as the thundering herd effect, can add more to these risks. Instantly connect to and move data between your databases Download 14-day free trial How to choose the right database The right database can significantly improve the ability of an organization to manage its data effectively. However, there are different types of databases, and choosing the right one can be tricky. When choosing a database, start by understanding your data needs. Identify whether your data needs will be best met with a SQL or NoSQL database. Data needs Data structure: Analyze the type of data the organization needs to store—structured, semi-structured, or unstructured. Relational databases excel with structured data, while NoSQL options cater to more flexible data models. Data volume and growth: Consider the current data size and anticipated growth. Choose a database that can handle increasing data volumes without compromising performance. Performance requirements Read vs. write workload: How will the organization use its data? For example, frequently pulling customer information for reports and analysis is a read-heavy scenario. On the other hand, if the business involves a lot of real-time updates, like processing online transactions or managing inventory in real-time. Those scenarios favor databases optimized for frequent writes. Query complexity: Think about the complexity of queries the end-users will be running. Relational databases are ideal for complex queries with joins across multiple tables, while NoSQL databases might be better suited for simpler queries. Technical expertise In-house skills: Organizations must evaluate their team’s expertise in managing different type of databases. Some databases require specialized skills for administration and maintenance. Integration needs: Consider how the database will integrate with existing systems and applications. Choose a database that offers compatible tools and APIs for seamless integration. Cost considerations Explore licensing models for different database solutions. Consider open-source options alongside commercially licensed products. Security features Evaluate the database’s security features, including access control, data encryption, and disaster recovery capabilities. These are crucial for protecting sensitive business data. Future considerations Scalability: Think about future growth. Choose a database that can scale horizontally (adding more servers) or vertically (adding processing power) as the organization’s data needs evolve. Flexibility: Consider the potential for future changes in data structures or query patterns. Choose a database that offers flexibility to adapt to evolving requirements. It’s also important to evaluate other important factors such as latency and throughput. Tips and best practices to maintain databases Here are some key tips and best practices to maintain databases: Regular backups Regular backups provide a safety net in case of accidental data deletion, hardware failure, or cyberattacks. It is always a good idea to establish a backup schedule based on your data update frequency and choose a reliable backup method (full backups or incremental backups) to ensure a complete recovery point. Automate manual database management tasks where possible As businesses grow, their databases become more complex, which can slow down updates. A survey revealed that 41% of database managers need over a week to approve changes due to many simultaneous requests. Using automated tools to manage manual database tasks can help you track, manage, and automate database schema changes across all systems. Using these automated tools, you can also identify potential bottlenecks in data management, speeding up decision-making and implementation. Defragment your database Fragmented data means that related data points are spread across the storage, slowing down read and write speeds. By defragmenting the data, you consolidate these fragments, enabling faster access and processing and quicker response times for data queries. Defragmenting a database also leads to more efficient resource use and overall enhanced application performance, directly impacting your business operations and user satisfaction. Maintain documentation Proper documentation helps keep a database healthy. Document the database schema, user access controls, backup procedures, and any troubleshooting steps implemented. This facilitates knowledge transfer within data teams and ensures smooth operations, even with personnel changes. Instantly connect to and move data between your databases Download 14-day free trial Conclusion Databases are complex, and choosing the right database for your enterprise involves careful consideration of factors such as the type of data you are handling (structured or unstructured), scalability needs, and the specific functionalities that are required to meet your business goals. Beyond just choosing between SQL or NoSQL databases, your organization also needs a modern data management solution to effectively manage data as per business needs. LIKE.TG is one such platform; it’s an AI-powered data management platform with built-in data governance features. With LIKE.TG you can: Integrate data from multiple databases via automated data pipelines Consolidate data into your target database, whether on-premises or in the cloud Migrate data between databases using native connectors Ensure data quality before, during, and after data migration If a truly unified data management experience is what you need, then opt for LIKE.TG Data Stack. It enables you to: Extract and manage unstructured data from a wide range of data sources Build automated data pipelines to integrate data Build a data warehouse in a matter of days, not months Streamline B2B data exchange with LIKE.TG EDI management Build, publish, and monitor APIs with LIKE.TG API management And much more—all without writing a single line of code. Ready to try LIKE.TG? Download a 14-day free trial or sign up for a demo.

Data lineage is an important concept in data governance. It outlines the path data takes from its source to its destination. Understanding data lineage helps increase transparency and decision-making for organizations reliant on data. This complete guide examines data lineage and its significance for teams. It also covers the difference between data lineage and other important data governance terms and common data lineage techniques. What is Data Lineage? Data lineage refers to the journey of data from origin through various transformations and movements across different systems, processes, and environments within an organization. It provides a clear understanding of how data is created, used, and modified and insights into the relationships between different data elements. Data lineage typically includes metadata such as data sources, transformations, calculations, and dependencies, enabling organizations to trace the flow of data and ensure its quality, accuracy, and compliance with regulatory requirements. Data Lineage vs. Data Provenance vs. Data Governance Data lineage, data provenance, and data governance are all crucial concepts in data management, but they address different aspects of handling data. Aspect Data Lineage Data Provenance Data Governance Definition Data Lineage tracks data flow from origin to destination, documenting its movement and transformations. Data Provenance captures metadata describing the origin and history of data, including inputs, entities, systems, and processes involved. Data Governance establishes framework, policies, and processes for managing data assets within an organization. Focus Flow of data Origin and history of data Management and control of data assets Purpose Ensure data quality, traceability, and compliance. Enhance data trustworthiness, transparency, and reproducibility. Manage data consistently, securely, and in compliance with regulations and organizational objectives. Key Questions Where does the data come from? How is it transformed? Where is it used? How was the data created? What entities and processes were involved? Who has access to data? How should data be classified and protected? What are the procedures for data quality monitoring and remediation? Example Tracking the flow of data from databases to reports in a company. Recording the instruments used, parameters set, and changes made during scientific research. Implementing policies specifying data access, classification, protection, and quality monitoring in an organization. Why is Data Lineage Important? Data lineage is crucial for several reasons: Trust and Confidence: Data lineage ensures transparency in data origin and transformations, building trust in its accuracy and reliability throughout its lifecycle. Regulatory Compliance: It helps organizations adhere to regulations by tracking data handling, storage, and usage, facilitating audits, and demonstrating compliance with regulatory requirements. Data Quality Management: Identifies and corrects data quality issues by tracing data to its source, enabling organizations to maintain high data integrity and reliability standards. Root Cause Analysis: Pinpoints errors’ origins, enabling implementation of preventive measures and ensuring data-related issues are effectively addressed at their source. Data Governance: Forms the foundation for establishing data management policies and procedures. Governance ensures that data is handled responsibly, securely, and by organizational objectives and standards. Business Intelligence: Ensures insights from BI tools are based on accurate and relevant data, empowering decision-makers with reliable information for strategic planning and performance evaluation. Data Lineage and Data Classification Data classification involves organizing data into categories based on origin, sensitivity, access permissions, content, and more. Meanwhile, data lineage focuses on understanding how this data moves, migrates, and transforms. When automated, data lineage and classification assist businesses in risk management, safeguarding sensitive data, and swiftly locating specific information. Both data lineage and classification facilitate: Data location/search: Classification simplifies the search for relevant data. Lifecycle investigation: Provide insights into the entire data lifecycle, enabling better management decisions and resource allocation. Risk Mitigation: Proactively identifies and mitigates data breaches or unauthorized access risks. How Data Lineage Works Here’s how data lineage typically works: Data Capture: The process begins with capturing raw data from its source. This could be data generated internally by systems such as databases, applications, and sensors or externally from sources like APIs, third-party vendors, or manual inputs. Metadata Collection: Alongside the data, metadata is also collected. Metadata consists of information about the data. This information includes its source, format, structure, and any applied transformations. This metadata is vital for comprehending the context and lineage of the data. Transformation and Processing: Once teams capture the data, it often goes through various transformations and processing steps. This process could involve data cleaning, filtering, aggregating, joining with other datasets, or applying business logic to derive meaningful insights. Each transformation somehow alters the data, and metadata is updated to reflect these changes. Lineage Tracking: As data moves through different systems and processes, its lineage is tracked and recorded at each stage. This step includes capturing information about where the data came from, what transformations were applied, and where it is being sent next. Lineage information typically includes timestamps, data owners, dependencies, and relationships between different datasets. Visualization and Analysis: Data lineage information is often visualized through diagrams or lineage graphs, which provide a clear, graphical representation of how data flows through the organization’s infrastructure. These visualizations help stakeholders understand the end-to-end data journey and identify dependencies, bottlenecks, and potential points of failure. Data Governance and Compliance: Data lineage ensures data governance and regulatory compliance. Organizations can demonstrate accountability, traceability, and data quality assurance to regulatory bodies and internal stakeholders by providing a complete audit trail of data movement and transformations. Impact Analysis and Risk Management: Data lineage also enables organizations to perform impact analysis and assess the potential risks associated with changes to data sources, processes, or systems. Organizations can make insightful decisions and reduce risks proactively by understanding how changes in one part of the data ecosystem may affect downstream systems or analytics. Data Lineage Techniques There are different approaches to performing data lineage. Here is an overview of these techniques: Lineage by Data Tagging This technique tags data elements with metadata describing their characteristics, sources, transformations, and destinations. These tags provide a clear understanding of how data is used and transformed as it moves through different processing stages. Example: A retail company tags each sales transaction with metadata detailing the store location, timestamp, and product information. As the data moves through various stages of analysis, such as aggregation by region or product category, each transformation step is recorded with corresponding lineage metadata. This act ensures traceability from the raw transaction data to the final analytical reports. Self-contained Lineage This technique involves embedding lineage information directly within the data itself. This embedding could be headers, footers, or embedded metadata within the data file. Self-contained lineage ensures that the lineage information travels with the data, making it easier to track and understand its history. Example: A marketing department maintains a spreadsheet containing campaign performance metrics. The spreadsheet includes a dedicated “Lineage” tab where each column is annotated with information about its source (e.g., CRM system, advertising platform), data transformations (e.g., calculations, filtering), and destination (e.g., dashboard, report). This self-contained lineage information allows analysts to understand the data’s history without external documentation. Lineage by Parsing Lineage by parsing involves analyzing data processing pipelines or scripts to infer the data lineage. This technique parses through the code or configuration files of data transformations to identify data sources, transformations applied, and final outputs. By understanding the processing logic, lineage can be reconstructed. Example: A financial services firm parses Python scripts used for data transformations in its risk management system. The organization infers lineage information such as source tables, join conditions, and target tables by analyzing the scripts’ logic and SQL queries. This parsed lineage data is then used to generate a graphical representation of data flow from raw market data to risk models. Pattern-based Lineage Data lineage is inferred based on predefined patterns or rules in pattern-based lineage. These patterns could be regular expressions, data schemas, or other structural indicators that define how data is transformed and propagated. Pattern-based lineage can automate lineage tracking by identifying common patterns in data transformations. Example: A software company employs pattern-based lineage techniques to track data flow in its CRM system. By identifying common patterns in data import/export processes and database queries, such as “Load Customer Data” or “Export Sales Reports,” the organization automatically infers lineage relationships. This approach simplifies lineage tracking in large-scale CRM deployments with numerous data integration points. Data Lineage Use Cases Modern businesses increasingly seek real-time insights, yet their acquisition hinges on a thorough understanding of data and its journey through the data pipeline. Teams can enhance workflows using end-to-end data lineage tools in various ways: Data modeling: Enterprises must define underlying data structures to visualize different data elements and their corresponding linkages. Data lineage aids in modeling these relationships, illustrating dependencies across the data ecosystem. As data evolves, with new sources and integrations emerging, businesses must adapt their data models accordingly. Data lineage accurately reflects these changes through data model diagrams, highlighting new or outdated connections. This process aids analysts and data scientists conduct valuable and timely analyses by better understanding data sets. Data migration: When transitioning to new storage or software, organizations use data migration to move data from one location to another. Data lineage offers insights into the movement and progress of data through the organization, from one location to another, aiding in planning system migrations or upgrades. It also enables teams to streamline data systems by archiving or deleting obsolete data, improving overall performance by reducing data volume. Compliance: Data noncompliance can be time-consuming and costly. Data lineage is a compliance mechanism for auditing, risk management, and ensuring adherence to data governance policies and regulations. For instance, GDPR legislation, enacted in 2016, protects personal data in the EU and EEA, granting individuals greater data control. Similarly, the California Consumer Privacy Act (CCPA) mandates businesses to inform consumers about data collection. Data lineage tools are crucial for ensuring compliance as they provide visibility into the flow of data. Impact Analysis: Data lineage tools provide visibility into the impact of business changes, particularly on downstream reporting. For example, changes in data element names can affect dashboards and user access. Data lineage also assesses the impact of data errors and their exposure across the organization. By tracing errors to their source, data lineage facilitates communication with relevant teams, ensuring trust in business intelligence reports and data sources. Data Lineage Tools Data lineage tools enable organizations to understand and manage dataflows within an organization. Here are some key features commonly found in data lineage tools: Automated Lineage Discovery: The tool should automatically discover and map data lineage across various sources, systems, and transformations, reducing manual effort. End-to-End Lineage Visualization: Providing a clear, visual representation of data lineage from source to destination, including databases, applications, and processes. Versioning and Change Tracking: Tracking changes to data lineage over time, enabling users to understand how data flows have evolved and who made the changes. Metadata Management: Capture and manage metadata associated with data sources, transformations, and lineage relationships, ensuring data governance and compliance. Data Quality Monitoring: Monitoring data quality throughout the lineage, identifying issues such as data inconsistencies, anomalies, or quality degradation. Dependency Mapping: Identifying dependencies between different data elements, systems, and processes, helping users understand the relationships between data entities. Business Glossary Integration: Integration with a business glossary or data dictionary to provide context and meaning to data elements and lineage information. Search and Discovery: Advanced search capabilities to quickly find specific data elements, sources, or lineage paths within large datasets. Security and Access Control: Role-based access control (RBAC) and encryption mechanisms ensure only authorized users can view and modify data lineage information. Conclusion Data lineage is a vital part of effective data governance. From improving data quality and ensuring compliance to facilitating strategic decision-making, understanding data lineage gives organizations valuable insights into their data. Using this knowledge, data teams can optimize processes, mitigate risks, and maximize their data’s potential. LIKE.TG is an end-to-end data management tool with comprehensive data governance features. It empowers business users to manage and control data with a simple, no-code interface and extensive customer support. Try LIKE.TG now with a free 14-day trialor get in touch to discuss a specific use case.