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Navigating Workplace Accident Claims with LIKE.TG
The U.S. Bureau of Labor Statistics reports that the incidence rate of nonfatal workplace accidents has decreased over the years, which can be attributed to the implementation of preventive measures in private industry. Despite this positive trend, companies deal with large volumes of unstructured data that demand effective management. Addressing these complexities is easier with LIKE.TG’s unstructured data extraction solution. It allows companies to process and analyze information faster than ever, streamlining workplace accident claims processing. Introducing LIKE.TG’s Unstructured Data Extraction Solution LIKE.TG ReportMiner simplifies data extraction from various sources, especially unstructured data related to workplace accidents. Its user-friendly interface empowers users of all skill levels to effortlessly extract data without extensive IT knowledge.The solution’s main feature is its template-based extraction model. Users can create templates that instruct the tool on identifying and extracting essential data from various sources, like PDFs and scanned documents related to workplace accidents. Essential Features of Report Miner for Streamlining Workplace Accident Claims Some essential features of ReportMiner include; Automated Data Extraction LIKE.TG ReportMiner extracts essential information from complex workplace accident claims documents with great precision. The tool is also capable of handling a range of document formats, including scanned documents that can be transformed into searchable text within the tool. This feature is particularly useful before building an extraction template. It also incorporates different elements within the documents, including tables, headers, footers, key-value pairs, structured data, and any other unique sections within the document according to patterns defined by the user. This allows insurance companies to extract complex data layouts present in workplace accident claims documents, facilitating analysis. Automated Workflows Handling the various stages between initial workplace incident reporting and final resolution can be a challenging task, particularly in the context of workplace accidents. However, with LIKE.TG ReportMiner’s automated workflow functionality, organizations can automate these processes according to their requirements and thus enhance efficiency, compliance, and accuracy throughout the claims management cycle, particularly in addressing workplace incidents. The tool standardizes the data extraction process from unstructured sources and automates it to ensure consistency and accuracy in data handling, thus saving time. ReportMiner’s strength lies in its ability to automate data extraction with validation, ensuring the accuracy of data used for further processing. Data Accuracy and Validation In workplace claims, making sure that the information is correct is crucial. Therefore, LIKE.TG uses advanced techniques to extract accurate information from various sources like scanned images, text files, and PDF documents. These techniques ensure that the data extracted is free of errors and discrepancies that would otherwise arise from manual data entry processes. LIKE.TG is also capable of cross-referencing extracted information against predefined criteria. This is further beneficial in removing any inconsistencies in the data, ensuring the data extracted is highly reliable to be processed further. Claims Standardization Standardization of claims in workplace claims is essential as it ensures that all relevant information such as witness statements, injury details, and incident location follow a consistent pattern. Having standardized extraction templates ensures all the necessary information is captured in a structured and consistent manner, making it easier to analyze and process workplace claims. LIKE.TG ReportMiner standardizes claims processing to improve efficiency and clarity while enhancing communication and collaboration among different departments involved in the claims management lifecycle. Having uniform data fields across all claims ensures the key details are recorded consistently and are easily accessible for reporting and further analysis. The Strategic Benefits of Report Miner for Workplace Accident Claims Extracting valuable insights from vast amounts of data generated in workplace accident claims is now easier with LIKE.TG’s unstructured data extraction solution. It helps insurance companies minimize risks and streamline their operations.Other benefits include: Enhanced Compliance LIKE.TG ReportMiner presents a framework that complies thoroughly with the regulatory requirements and internal policies regarding workplace accident claims. LIKE.TG achieves this by implementing validation processes that involve validating the extracted data against predefined rules, regulations, and policies. It guarantees the accuracy and integrity of extracted data by using advanced algorithms and validation mechanisms. That said, all the necessary data and information concerning accident claims are captured precisely and stored in compliance with industry standards. ReportMiner provides the flexibility to adhere to regulations related to such claims, minimizing the possibility of non-compliance and legal liabilities. Greater Employee and Customer Satisfaction By automating data extraction, employees can focus on more value-added tasks. This results in an increase in employee productivity while minimizing frustration associated with repetitive tasks. Such streamlined claims processing leads to improved communication with claimants that results in higher levels of satisfaction among customers. In essence, ReportMiner contributes to a positive brand image and helps develop long-term relationships with the customers. Reduction of Manual Labor and Paperwork Manual data entry and verification processes are prone to typos, inconsistencies, and human errors, this would compromise the integrity of reports. Automation eliminates the need for manual data entry and verification. This enhances accuracy, streamlines the entire process, and reduces errors in handling workplace accident claims. ReportMiner efficiently extracts incident details, descriptions, and witness statements, saving time and minimizing the chances of errors. Step-by-Step Guide to Processing Workplace Accident Claims with ReportMiner Here, we will investigate how LIKE.TG ReportMiner streamlines the intricate workflow from initial data extraction after report submission to claim resolution, taking the batch of workplace accident claims as the use case. STEP 1: Start by importing the workplace claim PDF form into the data pipeline designer. For this, you can drag the PDF Form Source object from the toolbox to extract data from a fillable PDF file without having to create an extraction template. Step 2: To configure your file, right click on the PDF Form Source and choose Properties from the drop-down menu. A configuration window will appear; now, provide the file path (where your workplace claim PDF is stored) and click next. STEP 3: Select next and a layout builder screen will appear. Here you can modify both the labels of fields and their corresponding data types. Step 4: Then click OK. On the dataflow designer, the PDF Form Source object will display fields according to the layout specified within the PDF form. The claims information or the extracted data lacks structure initially, but it can be further utilized and analyzed by organizing it into an Excel spreadsheet. Data Cleanse transformation can be employed after extracting data to perform data cleansing, such as removing whitespaces. The final version of the extracted data is then written to the Data Destination by specifying a connection to the company’s database. The data extracted here is from a single PDF source only. It can be managed for a whole batch as well. This ensures data from each PDF file is extracted, processed and written to the destination correctly according to the predefined rules. This file presents extracted data from PDF files and arranges them accordingly. It highlights unstructured data extraction and provides a preview of the output data. Wrapping Up! LIKE.TG ReportMiner offers a valuable solution for managing workplace accident claims efficiently. With features like automated data extraction businesses can effortlessly extract and analyze unstructured data related to workplace incidents. By using ReportMiner, organizations can streamline their accident claims process, enhance data accuracy, and ultimately improve workplace safety practices. LIKE.TG ReportMiner stands as a reliable tool for navigating workplace accident claims with ease and confidence. Handle Workplace Claims Effectively with LIKE.TG! Unlock the power of unstructured data with LIKE.TG's guide using Report Miner for workplace accident claims. Streamline your process and gain valuable insights effortlessly. Start your 14-days-trial now!
Workplace Claims: A Close Look at the Importance of Quick Settlements
Workplace claims are legal actions or complaints that employees set forth against their employers due to violations of employment laws or contractual agreements. In recent times, employees feel encouraged to speak up for their rights with no workplace harassment, discrimination or unjust treatment. This increased awareness has raised legal standards and regulatory frameworks and thus, employees feel more empowered to report instances of harassment and discrimination. The resulting increased number of claims necessitates advanced automation tools to handle them properly. Such a situation demands automation of the extraction process, categorizing, and data analysis from diverse sources like surveys, employee reports, and incident logs. This helps the insurance companies in gaining actionable insights swiftly and accurately. Such efficiency and responsiveness can address all workplace grievances and promote a healthy work environment. Significance of Unstructured Data in Workplace Claims In workplace claims unstructured data plays a significant role. Here is how: Provides Contextual Insights Unstructured data is a valuable resource in workplace claims because it provides context and depth to the issues at hand. Different data sources, such as chatlogs and emails, can serve as evidence for any workplace claim. For example, if an employee alleges mistreatment, the company can examine communication records to verify the claims. Similarly, in cases of unfair termination, reviewing documents may reveal discrepancies in treatment. This approach ensures that decisions regarding the claim are fair, impartial, and based on information. Presenting concrete evidence enhances the credibility and transparency of the resolution process. Highlights Systematic Issues Unstructured data serves as a source for highlighting trends within a company, especially in the context of workplace claims. For example, if a significant number of claims relate to unfair treatment or discrimination, analyzing communication patterns and document trails can help identify underlying cultural or organizational issues. Such insight can be better achieved with automated data extraction as it enables companies to efficiently sift through large volumes of data. This enables the stakeholders to address root causes effectively, implement targeted interventions, and nurture a more inclusive and respectful workplace culture. Manages Risks Proactively Proactive risk management means companies can take measures to prevent a situation long before it becomes a serious issue. In the context of workplace claims, unstructured data such as chatlogs and emails can help organizations see potential problems in the early stages. Using automated data extraction solutions, they can quickly gather and analyze the information to identify trends and warning signs. For instance, if many employees complain about the same issue, such as unfair treatment, the company can assess the extracted information and address it before it becomes a problem. This approach is less costly in the long run. The Importance of Streamlined Claims Processing through Automation Claims processing facilitated by automation is of utmost importance in handling workplace claims effectively. Here’s why: Ensures Data Integrity Automation promotes data integrity, particularly with unstructured data and preserving data integrity is essential to ensure the reliability of information used in decision-making processes. When claims are processed manually, they are at a higher risk of human error and accidental data loss during handling. However, automation reduces such risks as these procedures implement standardized processes and protocols data management and preservation. Automated systems are almost always secured with strong security measures to protect data from tampering and unauthorized access. Furthermore, automation protects sensitive information and maintains its integrity throughout its lifecycle. Greater Cash Flow Traditional claims processing methods involve lengthy administrative procedures, which incur significant costs and consume a lot of time too. However, if the same process is automated, it would streamline the entire claims process all the way from initial submission to resolution. This results in better and more efficient outcomes. Such swift resolutions facilitated by automation also alleviate the financial strain caused by prolonged settlement negotiations. With a reduced turnaround time, companies can minimize the resources expended on legal fees and can maintain a healthy financial position. That said, a company with enhanced cash flow would be better able to reinvest resources into core business activities, research, development, and talent acquisition. Promotes Compliance with Legal Obligation The best practice to protect the company’s reputation during workplace claims is compliance with legal obligations, laws, industry standards, and regulations. Automation plays an important role in ensuring companies adhere to regulatory timelines. They help organizations meet the deadlines set by regulatory bodies and legal frameworks. Such automated systems also allow for better tracking and interpreting of legal requirements. Automation also facilitates the implementation of standardized processes that reduces the risks of errors, which could otherwise lead to regulatory non-compliance. Helps Save Time and Resources Resolving workplace claims is time-consuming. It demands both time and money. However, with automation, companies can ensure faster claims processing and quick settlements. This would avoid lengthy disputes and reach mutually acceptable resolutions in almost no time. By automating the key aspects of the claims resolution process, like data collection, analysis, and communication, companies can significantly reduce the time and resources required to address workplace claims. With quick settlements, the resources can be allocated efficiently, and the disruptions of business operations can also be avoided. Adaptability and Agility Using automated systems, companies can enhance the speed and accuracy of claim resolutions. It also facilitates the implementation of measures to prevent the recurrence of workplace issues. Analyzing data trends and patterns enables organizations to identify potential areas of concerns. Companies that embrace automated systems show adaptability and agility in responding to challenges in the workplace and other changing market dynamics. Ensuring that workplace claims get addressed swiftly, organizations can focus on the real deal. How Technology is Facilitating Quick Settlements Traditionally, claims management has remained a time-consuming activity and complex process for enterprises. Modern technology has optimized this process, but it can now be done better through automated data extraction solutions. This allows for quick settlements, which is advantageous. AI-Powered Data Extraction: Technology-enabled claim settlements are now eased through automation. AI has made unstructured data processing easier. Using AI, companies can automatically extract bulk data from different sources such as documents, forms, and emails. In workplace claims processing, AI powered data extraction can be employed to handle tasks like data entry and validation. This speeds up claim processing and reduces manual work. Optical Character Recognition (OCR): OCR digitizes and extracts text from documents, including scanned images and PDFs. This technology proves beneficial in the rapid conversion of physical documents into editable and searchable digital files. Pattern Recognition: Efficient settlement procedures rely on the effective use of pattern recognition, particularly through Machine Learning (ML) algorithms. These algorithms are essential for speeding up the resolution of workplace claims by continuously improving their ability to understand and organize data patterns in documents. By analyzing processed documents ML algorithms become better at recognizing patterns, making it faster to identify valuable information. Wrapping Up! While unstructured data can be challenging for workplace claim processing, automated data extraction solutions, such as LIKE.TG ReportMiner, have significantly optimized the system. By effortlessly handling various data sources and supporting multiple formats, ReportMiner reduces the manual effort required, thereby streamlining the entire data extraction process. Moreover, by leveraging advanced AI technology, ReportMiner saves time and enhances accuracy. This streamlined approach benefits employee satisfaction and workplace harmony and expedites workplace claims settlements, ultimately benefiting both employees and employers alike. Ready to experience the efficiency of LIKE.TG ReportMiner? Sign up for our 14-day trial and request a personalized demo today to witness how it can simplify your workflow! Automate Your Claims with LIKE.TG Discover the power of efficient data extraction with LIKE.TG's Report Miner! Download your free trial today. Download Now
BigQuery vs. Redshift: Which One Should You Choose?
Considering BigQuery vs. Redshift for your data warehousing needs? This guide is for you. Both BigQuery and Redshift stand as leading cloud data warehouse solutions each offering a multitude of features catering to multiple use cases. Google’s BigQuery offers seamless scalability and performance within its cloud platform, while Amazon’s Redshift provides great parallel processing and tuning options. Let’s simplify the decision-making process by breaking down the differences between Redshift vs. BigQuery, helping you find the perfect fit for your business. What is Google BigQuery? Google BigQuery is a part of Google’s overall cloud architecture, the Google Cloud Platform (GCP). It operates as a serverless and fully managed service, eliminating the need for managing infrastructure and allowing businesses to prioritize data analysis and insight generation. Google BigQuery is built on top of Dremel technology, which is a query service that enables users to run SQL-like queries. At its core, Dremel uses a distributed execution model that distributes the workload across multiple nodes within Google’s infrastructure. BigQuery is among the initial major data warehouses, featuring exceptional query performance. Using Google’s infrastructure and technologies, such as Capacitor, Juniper, Borg, and Colossus, BigQuery can execute complex analytical queries against massive datasets within seconds. What is Amazon Redshift? Amazon Redshift is the first-ever cloud data warehouse that offers a fully managed, petabyte-scale service. Redshift is designed to manage large datasets and complex analytical queries with high performance. Amazon acquired the primary source code for Redshift from ParAccel, which is a company that was developing the ParAccel Analytic Database (a PostgreSQL-based database). Redshift is built on the PostgreSQL fork but has a lot of unique capabilities. For instance, Redshift has a unique column structure and makes use of distribution styles and keys for data organization. Since Redshift is designed to handle large amounts of data, companies can scale the data warehouse up or down based on their requirements to easily accommodate data volumes. Moreover, there are no charges incurred when the warehouse is idle, which means you pay only for what you use. Build your data warehouse seamlessly, without a single line of code. Discover how LIKE.TG's DW Builder transforms data integration with native connectivity to BigQuery and Redshift. Check Demo Now! BigQuery vs. Redshift: Architecture When comparing Google BigQuery to Amazon Redshift in terms of architecture, there are some key differences to consider. Firstly, BigQuery operates on a serverless architecture, while Redshift offers greater overall control.In BigQuery, Google manages all the aspects of the warehouse, including provisioning, scaling, and maintenance. But it abstracts the users from the BigQuery infrastructure. With this approach, users can focus on processing massive datasets without having to worry about infrastructure management. The resources are allocated automatically depending on the number of queries you execute. On the other hand, Amazon Redshift follows a more traditional architecture that is based on a cluster of nodes. This architecture includes a leader node that takes care of client connection and query execution, while multiple compute nodes store and process data. Redshift uses a massive parallel processing (MPP) architecture to parallelize and distribute the queries across compute nodes. Redshift generally allows you to have better control over your resources so you can manage tasks, including scaling, patching, and backup. BigQuery vs. Redshift: Scalability Scalability is mainly limited by three major factors that is lack of dedicated resources, continuous ingestion, and tightly coupled storage and compute resources. BigQuery has a serverless architecture and it automates resource provision and scaling. Therefore, scaling is well-planned and well-structured in the case of BigQuery. It generally works on either on-demand pricing or flat-rate pricing. In the on-demand pricing model, assigning slots (compute resources) is fully controlled by BigQuery, whereas the flat-rate pricing model reserves slots in advance. The auto-scaling capability generally suits companies with fluctuating data volumes or unpredictable workloads. In contrast, Amazon Redshift cannot distribute the load across clusters even with RA3. This limits its scalability. To support the query concurrency, it can scale up to 10 clusters easily; however, Redshift can handle only 50 queued queries across all clusters. Though Redshift is scalable, its manual cluster management approach requires monitoring and configuration adjustments that would potentially introduce complexity. In a test run by an independent researcher, it was found that BigQuery was significantly faster than Redshift when dealing with a large dataset, which may suggest better scalability for BigQuery. However, note that several of these tests have been performed, and deciding on a clear winner is easier said than done. BigQuery vs. Redshift: Performance Comparing the performance of Redshift and BigQuery involves considering factors like concurrency, optimization techniques, query speed, and data processing capabilities. Considering that both BigQuery and Redshift are run by tech giants the differences in their performance are negligible. The columnar storage format and distributed execution model of BigQuery enable parallel processing of queries across multiple servers that results in rapid data retrieval and analysis. Moreover, its automatic query optimization features, including execution plans and dynamic query reordering, enhance query performance and efficiency. This minimizes latency and maximizes throughput. That said, BigQuery is a great solution for real-time analytics and interactive querying cases where speed and responsiveness are of considerable importance. BigQuery also has a built-in caching mechanism that automatically caches the results of every query for 24 hours, which can significantly speed up repeated queries. However, for small, ad-hoc queries, BigQuery may be slower than Redshift due to its reliance on distributed computing. On the other hand, the Amazon Redshift is manufactured on a massively parallel processing (MPP) architecture that allows it to perform well for data warehousing and analytical workloads. Redshift has more tuning options than many others, but you cannot expect it to deliver much faster compute performance than other cloud data warehouses. Redshift also offers workload management features, including query queues and concurrency scaling, to prioritize and manage query execution based on user-defined criteria. However, its manual cluster management approach may introduce overhead in terms of cluster configuration and maintenance, impacting its overall performance. Redshift vs. BigQuery: Which One to Choose? When choosing between the two, companies should assess their preferences and requirements before picking any of these data warehouses. Here are a few use cases to help you decide. When to Use Google BigQuery Large-Scale Data Analytics: BigQuery’s serverless architecture and ability to handle petabytes of data make it an ideal choice for large-scale data analytics. Data Exploration: BigQuery is designed for ad-hoc analysis and data exploration. It allows users to perform SQL-like queries on big datasets. Real-Time Analytics: BigQuery supports real-time analytics through its streaming API, making it perfect for analyzing live data. Integration with Google Ecosystem: If your organization already uses Google Cloud Platform services, using BigQuery can provide seamless integration. When to Use Amazon Redshift: Complex Query Execution: Redshift maintains a strong performance when executing complex, compute-heavy queries. Its column-based storage and MPP architecture are designed for this purpose. Data Warehousing Operations: Redshift is ideal for traditional data warehouse operations, where the primary requirement is storing structured and semi-structured data. Predictable Pricing: If predictable pricing is a priority, Redshift may be a better choice as its pricing is per node, which can often be more predictable and affordable. Integration with AWS Ecosystem: If your organization is already invested in the AWS ecosystem, using Redshift can simplify data warehousing operations. The Path Forward: Future-Proof Data Warehousing For future-proof data warehousing, it’s significant to select a solution that can adapt to evolving data demands and analysis technologies. Here is what you can expect from Redshift and BigQuery in the future. BigQuery’s Petabyte Scale: BigQuery has the capability to manage large datasets without any hassle. No matter if it requires dealing with customer transaction data or years of dealing with billions of sensor readings from IoT devices, BigQuery can handle all that efficiently by accommodating your data needs. This scalability is advantageous for enterprises that expect sustained growth in their data volumes over time. Redshift’s Real-time Options: Despite its emphasis on batch processing, Redshift offers real-time analytics functionalities through its integration with Amazon Kinesis Firehouse. With this, near real-time data ingestion into Redshift becomes possible. This turns out beneficial in cases that require immediate insights, like stock price monitoring and fraud detection.While this feature addresses some real-time needs, those looking for instantaneous analytics at scale will find BigQuery a more suitable option due to its inherent design for better performance for low-latency queries and real-time processing. Choosing the Right Future-Proof Platform Choosing the ideal data warehousing solution for future-proofing your infrastructure depends upon the specific needs and priorities of your organization. Here’s a guide to help you pick the right one: Looking for AI/ML Integration? Choose BigQuery as it stands out for seamless integration with Google’s AI and machine learning tools like Vertex AI and TensorFlow. This native integration allows for easy analysis and enables the development of ML models directly within the data warehouse environment. Want to Focus More on Real-time Analytics? BigQuery emerges as a superior choice. It features serverless architecture and automatic scaling, achieving real-time insights with minimal latency. That’s difficult with Redshift as it may require additional configuration and management overhead to handle real-time data effectively. Have Significant Investments in AWS? Consider Redshift as it offers tight integration with other AWS services. By using Redshift, you can ensure seamless interoperability and maximize the benefits of existing AWS infrastructure. Looking for a Completely Serverless Architecture? BigQuery is the optimal choice. It runs on a fully serverless architecture that eliminates the need for any kind of server management. That said, scalability and resource allocation become easier. Considering the Integration of Unstructured Data? Go ahead with Redshift with Spectrum, as it has better capabilities for analyzing some forms of unstructured data. However, if the data primarily consists of unstructured formats like text and images, BigQuery will be a better option as it provides better built-in features for handling such unstructured data. Working with Massive Datasets? BigQuery will be your perfect companion as it excels at handling massive datasets. It can manage large data volumes and ensures optimal performance and scalability even with your growing data needs. Evaluating these considerations and aligning them with your business’s objectives and requirements will help you choose a future-proof data warehousing platform that positions you to continue to leverage the power of data for years to come. LIKE.TG Provides Native Connectivity to Redshift and BigQuery This wraps up our “BigQuery vs. Redshift” comparison. Both platforms offer high-performance and scalable cloud data warehousing, each with its own set of features, pricing models, and usability. Being supported by tech giants like Amazon and Google, either option is a solid choice. However, selecting the one that fits your data warehousing needs is essential. LIKE.TG provides native support for both BigQuery and Redshift. Whether you’re migrating an existing data warehouse or creating a new one, our no-code platform, LIKE.TG DW Builder, enables you to design, develop, and deploy enterprise-grade data warehouses quickly and efficiently. Start your 14 days trial now! Start Your DW Migration Now with LIKE.TG! Migrate to any of your favorite data warehouses through LIKE.TG DW Builder. Get in touch with us today to get your 14-day free trial. Start your 14-days-trial now!
ETL Testing: Processes, Types, and Best Practices
ETL testing is a set of procedures used to evaluate and validate the data integration process in a data warehouse environment. In other words, it’s a way to verify that the data from your source systems is extracted, transformed, and loaded into the target storage as required by your business rules. ETL (Extract, Transform, Load) is how data integration tools and BI platforms primarily turn data into actionable insights. During an ETL process, ETL tools extract data from a designated source, transform it into a structure and format supported by the destination system, and load it into a centralized storage location, typically a cloud data warehouse or data lake. ETL testing comprises different tests conducted at different stages of the ETL process. These tests validate and verify the data to ensure accuracy and minimize data loss. This blog offers an in-depth discussion on ETL testing and its types, its necessity, the steps it entails, and how to do it right. What is ETL Testing? Data now heavily impacts businesses at all levels, from everyday operations to strategic decisions. This growing role has driven the global business intelligence (BI) and analytics tools market to an estimated value of nearly $17 billion. The data contained in a data warehouse is often your enterprise’s only source for generating insights and creating actionable strategies. Therefore, it must only contain accurate, reliable, and up-to-date data if it’s to serve as an effective single source of truth for your business. Automation significantly minimizes the likelihood of errors during ETL, which are otherwise prevalent in a manual ETL pipeline. However, automation must be supplemented with additional validation measures — and this is where ETL testing is useful for the following reasons: It mitigates errors left undetected by automation. It ensures the centralized data’s health and quality. It acts as an independent layer of verification, offering further assurance that the automated ETL process is functioning correctly and producing the expected results. Database Testing vs ETL Testing Database testing is a data validation procedure similar to ETL testing, but the similarities end there. Here are some differences between the two: ETL Testing Database Testing Typically performed on data in a data warehouse. Generally performed on transactional systems. Primary purpose is to ensure that the data being tested is moving as it’s supposed to. Aims to ensure that all data follows the data model’s predefined rules. Verifies that source counts match the counts in the destination. Verifies the absence of orphan records. Checks for duplications in the loaded data. Checks for redundant tables, missing data in columns, and database normalization. Why We Need ETL Testing You can consider skipping ETL testing if you have a simple data flow with minimal transformations, and your source and target systems are stable and reliable. However, assessing your data processes’ complexity should be a standard practice before forgoing testing — this can help you avoid data inconsistencies and errors downstream. There are many instances when ETL testing proves valuable, some of these include: After data integration or migration projects are completed. When loading data into a newly configured data warehouse for the first time. When adding a new data source to an existing data warehouse. During data movement and transformation. When there are suspected errors in ETL processes preventing them from running normally. When there’s a lack of confidence in data quality in either a source or the destination. The ETL Testing Process Testing protocols are subjective and customizable to an enterprise’s requirements and processes. As such, there isn’t a one-size-fits-all model for ETL testing. However, ETL testing typically comprises the following steps: 1. Understanding Business Requirements Designing an effective ETL testing process requires understanding your organization’s business requirements. This involves examining its data models, business workflows, reports, sources and destinations, and data pipelines. This understanding enables ETL testers to know what they’re testing and why. 2. Data Source Identification and Validation In the next part of the ETL testing process, identify your source data and perform schema checks, table validation, and other initial checks. This is to ensure that the ETL testing process aligns with the requirements identified by studying your business model and workflows. 3. Creating and Executing Test Cases Source-to-target mapping and test case design are the next steps, and often include the following: Transformations based on business test cases SQL scripts for conducting source-to-target comparisons Execution flows Following the preparation and validation of these elements, test cases are executed in a staging environment. Typically, testers can’t use sensitive production data, which necessitates the availability ofreal-time synthetic data. You can create this data manually or through test data generation tools. 4. Data Extraction and Reporting In the next step, perform the ETL tests according to business requirements and use cases. During test case execution, identify the different kinds of errors or defects, try to reproduce them, and log them with adequate details and screenshots. In the reporting stage, you can record defects onto specialized Defect Management Systems and assign them to designated personnel for fixing. For successful resolution of errors, analysts need to provide the following: Sufficient documentation for reproducing the test case Relevant screenshots A comparison between the actual and expected results for each test case 5. Applying Transformations Next, you must ensure that data is adequately transformed to match the destination data warehouse’s schema. Besides validating the data flow, you’ll also check the data threshold and alignment. The goal here is to verify that the data type for each table and column matches the mapping document. 6. Loading Data into The Data Warehouse You’ll do a record count check both before and after moving the data from the staging environment to the data warehouse. Verify rejection of the invalid data and acceptance of the default values. 7. Re-Testing the Bug (Regression Testing) After fixing the bug, retest it in the same staging environment to verify that it doesn’t have any traces left. Regression testing also helps ensure that no new defects occur while correcting the previous one. 8. Summary Report and Test Closure In the final step, close reports detailing the defects and test caseswith comments and all related documentation. Before closing the summary report, test its options, filters, layout, and export functionality. The summary report details the testing process and its results, and lets stakeholders know if and why a step wasn’t completed. Types of ETL Testing Each step of the ETL testing process involves different types of tests, some of which are: 1. Production Validation and Reconciliation This test validates the order and logic of the data while it’s loaded into the production systems. It compares the production system data with the source data to prevent non-compliance, data errors, or faults in the schema. 2. Source-to-Target Validation This test checks that the source system data count matches the data loaded in the destination system/warehouse. 3. Metadata Testing This test matches the data types, indexes, lengths, constraints, schemas, and values between the source and target systems. 4. Completeness Testing This verifies that all source data is loaded into the destination system without duplication, repetition, or loss. 5. Transformation Testing When multiple transformations are applied to one type of data, this test helps confirm that all data has transformed consistently based on the applicable rules. 6. Accuracy Testing Following the completion of all transformations, the data’s accuracy is verified. There can be changes in the data’s formats and schema, but the information and its quality should not change during the transformations. 7. Data Quality Testing This testing type focuses on data quality to identify invalid characters, precisions, nulls, and patterns. It reports any invalid data. 8. Report Testing This type of testing checks the data in the summary report, determines if the layout and functionality are appropriate, and performs calculations for additional analytical requirements. 9. Application Migration Testing Application migration testing verifies whether the ETL application is functioning properly following migration to a new platform or box. 10. Data and Constraint Checks This testing technique checks the datatype, length, index, and constraints. Common Challenges in ETL Testing There are several factors that can disrupt or adversely affect the ETL testing process. Below are the challenges that ETL testers encounter the most: Complex Data Transformations and Processes: Applying multiple transformations to expansive datasets can be a complicated and time-intensive procedure. The same happens when too many complex data integrations and business processes exist. Poor Data Quality: ETL testing requires accurate, clean, and high-quality data for the best results. Poor input data quality can affect the reliability of the process. Resource-Intensive: ETL testing can become resource-intensive when large, complicated source systems are involved. Decreased Performance: Big data volumes can hinder processing or end-to-end performance, ultimately affecting data accuracy and completeness. Changes in Data Sources: Any changes in the data sources can potentially impact data accuracy, completeness, and quality. Personnel Requirements: Enterprises need people with experience in ETL and data expertise to ensure the design and implementation of robust ETL testing processes. ETL Testing Best Practices The following best practices can help you optimize your ETL testing processes: 1. Working with Business Test Cases It’s not enough to develop a functional ETL testing process. It also needs to cater to varying business requirements that are different for every organization. An ETL testing process should complement your existing workflow, not disrupt it. 2. Using Clean Source Data Clean source data is a fundamental requirement for an effective ETL testing process. Rather than leaving it for the end, you should start ETL testing with clean source data to save time and obtain better results. 3. Testing for Efficiency Working with BI tools necessitates consistent access to updated data, so you should ensure both data completion and quick data delivery. Optimize the ETL testing process for accelerated testing and speedy results. 4. Automation While full automation is ideal, even partial automation is better than zero automation. Automated tools like LIKE.TG allow you to streamline the ETL testing process while resolving the common challenges associated with manual work. Experience Faster and More Reliable ETL Automation LIKE.TG's all-in-one ETL solution is what your enterprise needs for streamlined ETL testing. Ensure top-notch data quality at all times while enjoying no-code convenience. Get started today! Start My FREE Trial Choosing The Right ETL Testing Tool Here are a few factors you should keep in mind when choosing an ETL testing tool for your business: Intuitive Interface An intuitive interface makes it easy for you to design and implement ETL process. Look for a graphical interface with drag-and-drop functionality for enhance ease-of-use and accessibility. Automated Code Generation Automated coding can eliminate the effort and time it takes for manually coding and developing processes. It can also significantly reduce errors. Built-in Data Connectors Your ETL testing tool of choice should be equipped with built-in data connectors. These connectors can improve data access and make it easier to work with your go-to file formats, databases, legacy systems, or packaged applications. Content Management Features Content management capabilities enable rapid context switching between ETL development, testing, and production environments. Debugging Features An ETL testing tool with powerful debugging features can help you keep up with data flows in real time and quickly create reports on row-by-row behavior. Conclusion Accurate data analysis is crucial for any organization wanting to leverage its data to get ahead in its industry. When done right, ETL testing can boost an organization’s confidence in its data by improving its integrity and reliability. The resulting business intelligence is sound and helps mitigate business risk. Additionally, automated ETL testing tools boost a data warehouse’s stability and performance faster than manual coding, make the ETL process more organized and manageable, and enable the rapid identification and correction of anomalies in the ETL process.Businesses today are moving away from manual ETL testing and integrating automated ETL testing solutions into their workflow to reap these benefits. Yours can too. LIKE.TG is an ETL automation solution with built-in features for ETL testing, integration, validation, transformation, and more. Its ETL automation and advanced profiling capabilities allow users to reconcile data at every step of the ETL process easily. Verifying data quality and identifying errors is easier with LIKE.TG’s simple, no-code, drag-and-drop interface. Find out how LIKE.TG can transform your ETL procedures and help you maintain data quality for accurate analyses. Reach out to our team or start your FREE trial today.
Automated Claims Processing: A Comprehensive Guide
Claims processing is a multi-faceted operation integral to the insurance, healthcare, and finance industries. It’s a comprehensive procedure that involves carefully examining a claim. Claim processing is not a single-step process; instead, it involves multiple stages, each serving as a critical control point to ensure the accuracy and fairness of the claim resolution. Industries are increasingly adopting automated claims processing to boost efficiency and accuracy in this critical function. According to a Statista study, 25% of insurance companies are exploring the possibility of transitioning to automation for claims processing in the foreseeable future. This technology-driven approach uses advanced tools to automate the traditionally manual stages of claims processing. It offers a more streamlined, accurate, and time-efficient method of handling claims. While it significantly reduces the potential for human error, it also retains the need for human oversight in complex decision-making scenarios. This automation marks a significant step forward in claims management, offering the potential for improved efficiency and customer satisfaction. How Does Claims Processing Work? Claim Submission: The claims process is initiated when the policyholder files a First Notice of Loss (FNOL) to the insurance company. This step is the policyholder’s first communication with the insurer regarding a loss, providing initial details about the incident. Digital technology simplifies this step, allowing policyholders to submit their FNOL through digital platforms such as mobile apps or online portals, enhancing accessibility and efficiency. Claim Review: Upon receipt of the FNOL, the claim handler meticulously reviews the policy to determine the coverage. This stage thoroughly examines the policy terms and conditions and the claim’s specifics. The review process is significantly streamlined by automation, which detects crucial policy terms and cross-references the claimant’s details with external databases, ensuring a comprehensive and accurate review. Claim Verification: The insurer then proceeds to authenticate the claim by collecting additional data. This step may include damage assessments, incident photographs, witness statements, or relevant health documentation. The verification process is expedited by automation, which gathers data from various sources, enhancing the speed and precision of the process. Claim Decision: The next step involves the insurer evaluating whether to approve or deny the claim based on the policy terms and the verified claim details. Automation aids this stage by applying pre-set guidelines to establish the payout and coverage. However, a provision for human intervention is maintained for final checks to ensure fairness and accuracy, combining automation’s efficiency with humans’ nuanced decision-making ability. Payment Disbursement: Once the claim is approved, the insurer initiates the payment to the claimant. This step involves calculating the final settlement amount and arranging for its disbursement. The payment process is enhanced by automation, which uses digital payment methods, ensuring swift transactions and clear records, thereby enhancing transparency and traceability. Claim Data Analysis: After the completion of the claims process, the insurer can conduct an in-depth analysis of the claims data. Automation tools facilitate this analysis by providing structured data for easy examination and interpretation. The analysis offers valuable insights, enabling insurers to spot trends, detect potential fraudulent activities, and identify areas for process enhancement. Manual vs Automated Claims Processing Imagine a scenario where a policyholder is involved in a car accident. In a traditional manual claims processing system, the policyholder first must report the accident to the insurance company, often by phone or email. They would then need to fill out a detailed claim form and gather supporting documents such as photographs of the damage, a copy of the police report, and any medical reports if there were injuries. Once these documents are submitted, a claims handler at the insurance company takes over. The handler manually reviews each document, extracting necessary information such as the date, time, and location of the accident, the extent of the damage, and the cost of any medical treatments. This extraction process is time-consuming and requires meticulous attention to detail to ensure no information is missed. The claims adjuster then verifies the extracted information. They cross-reference the accident details with the police report, check the medical bills against the treatment details provided by the hospital, and compare the photos of the damage with the auto repair shop’s estimate. This manual verification process is tedious and prone to human error. The Shift to Automation Insurance companies increasingly seek ways to automate claims processing to mitigate these challenges. Automation speeds up the process and enhances accuracy and efficiency, reducing costs, minimizing errors, and improving customer satisfaction. According to McKinsey, automation can potentially reduce the expenses associated with a claims journey by up to 30%. A key technology driving this transformation is Intelligent Document Processing (IDP). IDP combines the power of artificial intelligence and Optical Character Recognition (OCR) to extract data from unstructured documents, such as claims forms. In claims processing, IDP can significantly speed up the process by automating tasks such as data extraction from claim forms and claim verification. For instance, OCR technology can scan and extract data from claim forms, regardless of their formats or sources. The artificial intelligence component of IDP ensures the accuracy of this process, enabling the system to learn from experience, identify patterns, make connections, and even understand the context of the extracted data. By harnessing the power of IDP, insurance companies can deliver quicker and more accurate claim decisions, enhancing their service and improving customer satisfaction. Here’s a comparison table to illustrate the differences: Manual Claims Processing Automated Claims Processing Speed Slower due to human intervention at each step Faster due to automation at each step Accuracy Prone to human error Higher accuracy due to automated checks Efficiency Less efficient due to manual handling More efficient due to streamlined processes Cost Higher operational costs Lower operational costs Data Analysis Limited and time-consuming Structured data for easy and quick analysis Transparency and Traceability Limited Enhanced due to digital records Who Benefits from Claims Processing Automation? Automated extraction of unstructured data has reshaped the insurance industry, bringing about significant improvements in four key areas: Claims Processing: Automation allows for swiftly extracting pertinent information from various data sources, making manual data entry redundant. Reducing errors and accelerating the claims process leads to substantial cost savings for insurance companies and expedites claim settlements for policyholders. Additionally, real-time tracking of claims, an automated feature, provides clients with unprecedented transparency. Claims Investigation: Identifying patterns and anomalies in vast amounts of unstructured data has proven invaluable in claims investigation. Such proactive measures minimize losses due to fraudulent claims and ensure that policyholders’ premiums are not unjustly inflated to cover the cost of fraud. The integrity of the insurance system is maintained, protecting the interests of honest policyholders. Claims Management: Automation revolutionizes the claims management process by easily compiling relevant information from various data sources. This efficient system improves resource allocation and decision-making for insurance companies. It keeps policyholders informed about the status of their claims, resulting in a more organized and efficient claims management process. Customer Service: Extracting relevant information from unstructured data sources, such as emails and social media posts, enables insurance companies to respond to customer inquiries quickly and accurately. This process leads to higher customer satisfaction and enhances the overall customer experience. Furthermore, personalized communication with customers, another feature facilitated by automation, adds another layer to the customer service experience. Benefits of Automated Claims Processing Automated claims processing brings forth numerous benefits, enhancing efficiency, accuracy, and customer satisfaction in claims processing. Some notable benefits include: Accelerated Claims Lifecycle: Automated claims processing significantly reduces the time it takes from the initial submission of a claim to its final settlement. It swiftly extracts and processes relevant information from unstructured data, such as medical reports, accident scene photographs, or witness testimonies. This acceleration of the claims lifecycle improves operational efficiency and enhances the claimant’s experience by providing quicker resolutions. Intelligent Claims Triage: Automated systems can categorize claims based on their complexity or potential risk by analyzing unstructured data. Simple claims can be fast-tracked for immediate settlement, while complex or high-risk claims can be routed to experienced adjusters for detailed investigation. This intelligent triage optimizes resource allocation and ensures that each claim receives the appropriate level of attention. Claims Surge Management: In situations where there is a surge in claims volume, such as after a natural disaster or a large-scale accident, automated claims processing is invaluable. The system can handle large claims simultaneously, extracting data from various sources and processing claims swiftly. Therefore, insurance companies can efficiently assist customers and maintain consistent service levels. Enhanced Fraud Detection: Given that an estimated 5-10% of insurance claims are fraudulent, claims processing automation becomes critical. It helps claim adjusters detect patterns and anomalies in claims data that may signal fraudulent activity. They can also use this technology to cross-reference the claim data with historical data, policy terms, and industry benchmarks. Any discrepancies or unusual patterns are flagged for further investigation, enabling insurance companies to mitigate risks, reduce losses, and maintain the integrity of their operations. Use Cases of Automated Claims Processing While automated claims processing benefits insurers and policyholders, its impact extends beyond these advantages. This technology has been instrumental in streamlining operations across various industries, primarily: Healthcare Insurance The healthcare sector often grapples with the complexity and volume of insurance claims. In this context, automated claims processing is crucial. The automated system is designed to extract and process data from medical reports and hospital bills. It then cross-references each claim against the specific health insurance policy terms, verifying covered treatments, policy limits, and deductibles, ensuring that each aspect of the claim aligns with the policy provisions. Automation ensures accurate, fair, and timely claim settlements, significantly enhancing the efficiency of the healthcare insurance process. Auto Insurance The auto insurance industry, with its diverse range of claims from minor damages to total loss, requires a robust and efficient system for claims processing. Automated claims processing can efficiently manage data from accident reports and repair bills, determining whether the claim is valid and within the policy’s coverage. This step is crucial in maintaining the integrity of the insurance process and preventing fraudulent claims. Subsequently, the system calculates the settlement amount, reducing processing time, minimizing human errors, and improving customer trust in the auto insurance process. Employment Insurance Workers’ compensation claims, a significant component of employment insurance, can be complex due to the intricate nature of the data involved. Automated claims processing simplifies this process by extracting relevant data from injury reports, medical documents, and employment records. It verifies each claim against the workers’ compensation policy and calculates the compensation amount, expediting the claims process, ensuring accurate compensation, and improving employee satisfaction. Best Practices for Automating Claims Processing Transitioning to an automated claims processing system can significantly enhance an organization’s operational efficiency. However, to maximize the benefits of automation, it’s essential to adhere to certain best practices. These guidelines can help ensure a successful transition and optimal utilization of the automation system: Set Clear Objectives: Successful automation begins with clear and defined objectives. An organization should pinpoint the specific outcomes it aims to achieve with automation, such as reducing claim resolution time, enhancing accuracy, or elevating customer service levels. Choose the Right Technology: The selection of automation technology should be tailored to the organization’s specific needs, with the capacity to integrate with existing systems and handle the complexity of the claims process. Prioritize Data Security: Given the sensitive nature of data involved in claims processing, it’s essential for the chosen solution to adhere to data security standards and regulations, safeguarding customer information. Train Staff: Despite automation, the human element remains vital. Therefore, staff training on the new system is necessary to ensure effective usage and prompt issue resolution. Regularly Review and Update: As technology and business needs evolve, it’s essential for an organization to regularly review and update the automation system, ensuring it continues to meet changing needs and stays abreast of technological advancements. The 3 Must-Haves of an Automated Claims Processing Solution While implementing automation in claims processing, selecting a solution that meets specific needs and boosts operational efficiency is crucial. The ideal solution can significantly streamline the claims process. Here are three key features to consider during selection: System Interconnectivity: A solution should seamlessly integrate with important systems such as CRM, policy administration, and billing. This integration facilitates smooth data flow, eliminates data duplication, and boosts efficiency, leading to quicker and more accurate claim resolutions. Diverse Data Management: An automation system should be adept at handling a vast amount of unstructured and disparate data. A system with this capability enables faster and more precise data processing, crucial for extracting necessary information for claim resolution and accelerating the claims process. Unified Data Access: An automation system should centralize data from various sources into a single repository. A system with this feature promotes better collaboration, quicker decision-making, and constant system availability, all of which are key in streamlining the claims process and enhancing its efficiency. Conclusion Integrating automated claims processing with a focus on managing unstructured data marks a pivotal advancement in the insurance industry. This sophisticated approach streamlines cumbersome processes and enables insurers to extract valuable insights from previously untapped sources. Organizations can enhance efficiency, accuracy, and customer satisfaction by leveraging cutting-edge technologies like artificial intelligence and natural language processing. However, successful implementation requires adopting a robust solution. This is where LIKE.TG steps in. It offers a comprehensive suite of features to meet the unique demands of insurers. From seamless integration with existing systems to powerful data extraction capabilities, LIKE.TG equips insurance organizations with the necessary tools to thrive in today’s data-driven environment. To learn more about LIKE.TG’s solution, download our 14-day free trial today! Ready To Transform Your Claims Process? Learn how LIKE.TG ReportMiner, an advanced unstructured data extraction solution with AI-powered data extraction capabilities, can streamline claims processing. Download Free Trial
Top 7 AWS ETL Tools in 2024
Amazon Web Services (AWS) ETL refers to a cloud-based set of tools and services that help extract data from different sources, make it usable, and store it in a way that makes it easy to analyze and make decisions based on it. AWS ETL tools offer a unique advantage for businesses seeking to streamline their data processes. These tools are efficient, scalable, and adaptable, making them ideal for a wide range of industries, from healthcare and finance to retail and beyond. According to a recent report by Gartner, more than half of businesses plan to move towards industry cloud platforms by 2028. This shift reflects a new approach to utilizing cloud technologies as a strategic asset, and a utility for innovation and competitive differentiation.AWS is helping businesses prepare for the future by leveraging cloud technology. It is at the forefront of this transformation, particularly in optimizing data workflows through AWS ETL tools. In this article, we’ll discuss all about AWS ETL tools, exploring the best options available in 2024 to help you make an informed decision. What are AWS ETL Tools? AWS offers a range of ETL tools to help businesses integrate and manage their data. These tools make it easy for businesses to collect, process, and analyze data, turning it into useful insights. There are four key AWS ETL tools with unique benefits that can help businesses improve their data management. 1. AWS Data Pipeline AWS Data Pipeline is a web service that helps make it easier to move and transform data across different AWS storage, compute services, and on-premises sources. It has a simple interface that lets the user drag and drop data sources and destinations onto a visual canvas. With AWS Data Pipeline, a user can create ETL data pipelines by defining specific connection attributes. AWS Data Pipeline offers an automation feature, which lets the user schedule the data workflows to run automatically. This means that the data is always up-to-date and ready for analysis. This level of automation streamlines operations and makes data processing more efficient. The tool is fault-tolerant, meaning it’s built to handle failures and it automatically retries tasks that encounter errors. AWS Data Pipeline also provides customizable notifications to keep the user informed of their data workflows’ status. Finally, AWS Data Pipeline can manage resources automatically and provide pre-built templates for complex data handling scenarios, freeing businesses from the technical complexities of data processing. 2. AWS Glue This serverless ETL platform is designed to automate the data preparation work, making it a hassle-free process. By pointing AWS Glue to the data stored within AWS, it automatically identifies data, infers its structure, and catalogs the metadata in the AWS Glue Data Catalog. The automatic discovery and organization of data make it instantly ready for ETL processes, and accessible for querying, making sure that analytics pipelines are insight driven. AWS Glue has a user-friendly GUI that allows users to create data transformation pipelines with minimal disruption, even if they don’t have deep coding knowledge. As users visually assemble the ETL job, AWS Glue generates the necessary PySpark code behind the scenes, making it easy to use. The Glue Data Catalog and automated schema discovery streamline the ETL process by organizing data metadata centrally, making it easy to find, access, and transform. 3. AWS Lambda AWS Lambda is a serverless computing service offered by Amazon Web Services (AWS) that executes code in response to various events, automatically handling the underlying computing resources. It enables users to extend AWS services with custom logic or develop backend services without server management. Lambda supports multiple programming languages, including Python, Node.js, Java, Ruby, Go, and Power Shell, providing flexibility based on users’ preferences. As an ETL tool, AWS Lambda can process and transform data seamlessly. By leveraging Lambda functions, users can design ETL pipelines to extract data from different sources, transform it according to specific requirements, and load it into target destinations. Lambda responds to various events, such as HTTP requests via Amazon API Gateway, object modifications in Amazon S3 buckets, and table updates in Amazon DynamoDB, triggering the execution of user-defined code to perform desired actions. 4. AWS Glue DataBrew AWS Glue DataBrew is a tool that simplifies and speeds up the data preparation process for analysis and machine learning. It is like having a skilled assistant who takes care of all the data preparation tasks. With DataBrew, users can easily pull in data from different sources and clean it up, fix errors, and transform it into a more useful format using an intuitive interface. This tool saves time and makes data preparation accessible to everyone, regardless of coding skills. After refining the data, DataBrew can automate these steps. This means that when similar data is received next time, DataBrew will remember the process and repeat it automatically, without any user intervention. It is a tool for businesses that want to use their data for making decisions or building machine learning models but don’t want to get slowed down in the technicalities of getting the data ready. AWS Data Pipeline Vs AWS Glue AWS Glue is a modern, serverless service that focuses on automation and integration within the AWS ecosystem. It offers an easy and efficient way to process and transform data. On the other hand, AWS Data Pipeline is a more traditional workflow service that automates the movement and transformation of data. Feature AWS Data Pipeline AWS Glue User Interface Drag-and-drop; Web-based management console; CLI Visual and code-based GUI Data Processing Batch Batch and streaming Scaling Manually scalable to handle large datasets Automatically scales on demand Execution Environment Can be used on AWS cloud or on-premises Serverless; fully managed by AWS Integration with AWS Services Integrates with AWS storage and database services Extensive integration with AWS services, including direct support for data stores and analytics services Orchestration Supports scheduling, dependency tracking, and error handling Advanced job management, including triggers, and workflow orchestration with AWS Glue DataBrew Schema Management No native schema discovery or management Automated schema discovery, cataloging, and management with AWS Glue Data Catalog Transformation Capabilities Custom scripts for data transformation Supports both built-in transformations and custom scripts. Pricing Charged based on the frequency of activities and where they run Hourly rate for ETL jobs and crawlers, with a minimum charge; additional charges for Data Catalog and DataBrew usage Top 8 Tools to ETL Data in AWS Choosing an ETL Tool for AWS that meets your business requirements can be a difficult task, especially if we consider the range of AWS ETL tools available in the market. To simplify this process, we have compiled a list of the best AWS ETL Tools. 1. LIKE.TG LIKE.TG is a comprehensive data integration platform that simplifies and accelerates your data processes with a focus on automation and user-friendliness. It empowers both business and technical users to manage complex data workflows without extensive IT dependency. Seamlessly integrate with AWS, enhancing your data quality and ETL processes using LIKE.TG’s cloud capabilities. Ensure the integrity and cleanliness of your data before storing it in AWS services with advanced data quality management features. Efficiently transform data using pre-built transformations and functions, eliminating the need for extensive coding expertise. Create and manage ETL pipelines on AWS effortlessly with an intuitive drag-and-drop interface. Extract valuable information from unstructured data sources directly into AWS with AI-powered data extraction capabilities. Easily ingest data from various sources and formats into AWS using built-in connectors. Optimize your data processing tasks on AWS by automating and orchestrating data workflows, ensuring timely data availability. Enhance data mapping from source to destination with an AI-powered auto mapper, crucial for maintaining data quality and consistency in AWS environments. 2. AWS Glue AWS Glue is a fully managed ETL service that automates the preparation of your data for analytics, offering a seamless way to organize, cleanse, and ultimately transform your data. Pros AWS Glue’s serverless architecture eliminates the need to manage infrastructure, allowing user to focus on analyzing the data. It offers cost-effectiveness, billing based on the resources utilized. AWS Glue simplifies ETL job creation through a user-friendly interface and automatic code generation. Cons The service offers limited control over compute resources, which might not align with all project requirements or specific compute needs. AWS Glue supports only Python and Scala for scripting, potentially complicating the integration of existing ETL scripts written in other languages. 3. AWS Data Pipeline AWS Data Pipeline is a web service for data processing and transfer among AWS, on-premises, and storage sources. Pros The AWS Data Pipeline offers support for a range of AWS databases and provides an intuitive control interface It creates resources and clusters only when necessary. Data is protected both at rest and in motion through AWS’s access control mechanisms. Cons Managing multiple installations and configurations on compute resources within data pipelines can be challenging Beginners may find the representation of preconditions and branching logic complex. For users requiring data from external services, AWS Data Pipeline may not be the most suitable choice. 4. Stitch (acquired by Talend) Stitch simplifies data replication from various sources to your data warehouse, enabling efficient and secure data analytics. Pros The platform’s setup and user-friendly interface makes your data pipeline creation quick. With support for over multiple data sources, you can integrate a diverse range of data into your analysis workflows. Stitch’s adherence to SOC 2, HIPAA, and GDPR ensures your data processes meet security and privacy standards. Cons The platform’s limited customization may restrict the ability to tailor data pipelines to complex or specific business needs. Its transformation capabilities are limited, potentially necessitating additional tools for complex data manipulation. Stitch provides limited monitoring options, posing challenges for businesses that need real-time monitoring and alerts. 5. Integrate.io Integrate.io is a cloud-based platform that simplifies data integration from multiple sources. It streamlines the process of aggregating and transforming data for your analytics needs. Pros It enhances data transformation processes, facilitating quick and efficient workflows for handling data. The tool simplifies integrations, particularly with Salesforce environments, improving the data consolidation tasks. Its security features, including encryption and SOC 2 compliance, safeguards the data throughout the ETL process. Cons The platform’s basic error logging can make troubleshooting complex issues more difficult. The interface might present challenges as you navigate more sophisticated data pipeline configurations. A few aspects of platform features are not fully integrated. 6. Talend (acquired by Qlik) Talend offers a solution for your ETL needs, facilitating data extraction, transformation, and loading with its open-source platform. Pros It offers cost savings and customization possibilities with its open-source nature, making it an economical choice for the data integration tasks. The connectors allow easy linking to various data sources and applications, improving integration capabilities. It offers drag-and-drop functionality that simplifies ETL process creation and management. Cons Talend presents a steep learning curve for newcomers to ETL or data integration. While Talend boasts a large user community, its support options are more limited than traditional ETL tools. Talend can face performance challenges with very large data sets or complex transformations. 7. Informatica Informatica streamlines data integration and governance across various source systems, enhancing data warehousing and business intelligence efforts. Pros Its graphical interface facilitates the ETL process design, session monitoring, and overall task management with ease. The platform can manage multiple data types, including queued messages and unstructured data, ensuring complete data integration. With Informatica’s centralized web-based platform, organizations can achieve data governance and reductions in administrative costs. Cons The Workflow Monitor lacks a sorting feature, making it difficult to distinguish between essential and non-essential folders. The Repository Manager does not support moving items between folders or importing XML export files. Developing mappings and workflows is not possible with the current toolset. How to Select the Right AWS ETL Tool When it comes to AWS ETL tools, there are many options out there, all claiming to be the best for the data processing needs. It can be overwhelming to choose the right one. To help make an informed decision, let’s explore the most important factors to consider: Data Sources It Supports The tool should offer broad support for various data sources. This includes traditional databases like MySQL or PostgreSQL, cloud services, and even streaming data platforms. For example, a company using a mix of on-premises databases and cloud-based services will benefit from an ETL tool that can seamlessly integrate data from all these sources. Data Replication Features The ability to replicate data efficiently is essential. The tool should provide real-time data replication capabilities, ensuring that data is continuously synchronized between source and destination. This is important for businesses that rely on up-to-date data for real-time analytics and decision-making. For instance, an e-commerce platform may need to constantly replicate data to analyze customer behavior and inventory levels in real-time. Zero Coding Additionally, for businesses aiming to minimize coding effort or eliminate it entirely, no-code AWS ETL tools are a valuable option. These tools provide a user-friendly interface where users can create, manage, and monitor ETL jobs without writing a single line of code. Drag-and-drop interfaces and pre-built connectors for common data sources and destinations make these tools accessible to users regardless of their technical expertise. An example would be a marketing team without programming skills needing to integrate customer data from various platforms for analysis; a no-code ETL tool would enable them to set up and manage these integrations effortlessly. Customer Support Service Good customer support is vital. The ETL tool should come with comprehensive support options, including 24/7 live support, extensive documentation, and community forums where users can exchange tips and solutions. This is particularly important for businesses that may encounter complex integration scenarios or need guidance on optimizing their ETL processes. A company might prefer a tool with a reputation for responsive and helpful support to quickly resolve any issues that arise. Pricing The cost of the ETL tool is a significant consideration. Pricing structures vary widely, from subscription-based models to pay-as-you-go plans based on the volume of data processed. Businesses should look for transparent pricing that aligns with their data usage patterns and budget constraints. For small startups or companies with fluctuating data processing needs, a tool offering scalable pricing or a free tier for lower volumes of data could be most beneficial. Concluding Thoughts Picking the right AWS ETL tool is key for companies looking to use their data efficiently. These tools not only streamline data processes but also empower organizations to make informed decisions based on accurate insights. As you explore the best options for your data management needs, consider how features like automation, scalability, and data quality align with your goals. LIKE.TG stands out as the leading AWS ETL tool, offering unparalleled ease of use, automation capabilities, and comprehensive data quality features. Experience LIKE.TG’s transformative power for yourself with a free 14-day trial or schedule a demo to see how it can streamline your data processes and drive your business forward. Accelerate Your Data Processes Simplify the complex process of Data Processing. Try LIKE.TG for free for 14 days and optimize your ETL. Experience how LIKE.TG streamlines data processes, and optimize your workflows. Request a Demo
Automate Tax Form Data Extraction in 5 Easy Steps
A Smartsheet report found that over 40% of workers spend at least a quarter of their workweek manually extracting data. Tax specialists in many organizations spend hours or even days sorting through piles of paper or PDF documents, looking for relevant information, and entering it into spreadsheets or databases. That’s a lot of time and money wasted on a tedious and error-prone process. Fortunately, there is a better way to handle tax form data extraction. With LIKE.TG ReportMiner, you can automate the entire process and save yourself time, money, and hassle. Why Is Tax Form Data Extraction Important? Tax form data extraction, especially from documents like W-2, 1099, and 1098, is crucial for individuals, businesses, tax professionals, lenders, financial institutions, researchers, and analysts for various reasons: Accurate Tax Filing:Automating data extraction ensures accurate and timely tax return filing for individuals and businesses, reducing the risk of penalties, interest, and audits from the IRS. Enhanced Services:Tax professionals and accountants can provide better services to clients with accurately extracted data, like tax planning, compliance, optimization, and resolution. Risk Reduction:Lenders and financial institutions can accurately verify borrower income and creditworthiness, reducing the risk of default and fraud. Insightful Analysis:Researchers and analysts can study economic and social trends based on tax data like income distribution, tax burden, incentives, and evasion. Automating tax form data extraction is essential as it: Saves Time Resources:Reducing manual effort and errors in data entry while increasing processing speed. Improves Accuracy Consistency:Standardizing data extraction models minimizes variations in data quality. Enhances Compliance Security:Ensuring extracted data meets tax regulations and is securely stored. How LIKE.TG Transforms Tax Form Data Extraction LIKE.TG ReportMiner is a powerful unstructured data extraction solution that automates and streamlines tax form data extraction. LIKE.TG ReportMiner captures and converts unstructured data into a structured format. It leverages advanced OCR and AI capabilities to extract data from any document, including scanned PDFs, digital PDFs, and other unstructured sources. See LIKE.TG's Tax Form Data Extraction in Action Simplify and streamline your tax form data extraction process with LIKE.TG's powerful automation tools. Schedule a demo to see how LIKE.TG can transform your processes. Schedule a Demo With LIKE.TG ReportMiner, you can: Create and reuse extraction templates: You can easily create and reuse extraction templates for different tax forms using the intuitive drag-and-drop interface. You can create extraction templates for common tax formats, such as W-2, 1099, 1040, and more. They can build these templates using ReportMiner, customize them based on their specific needs and reuse on several docs with the same format. You can also define the rules and logic for data extraction, such as data types, formats, validations, and transformations. Extract data from multiple sources: You can extract data from multiple sources, such as scanned PDFs, digital PDFs, images, and other unstructured documents. With high accuracy and speed, you can use the built-in OCR engine to recognize and extract data from scanned or image-based documents. You can create extraction templates with a few clicks, so they no longer have to provide patterns and create data regions and fields manually. This reduces the manual work in creating an extraction template, especially helpful when documents have complex or extensive layouts. Integrate and export data to multiple destinations: You can integrate and export the extracted data to various destinations, such as databases, files, BI tools, or other applications. You can use the built-in connectors to connect to popular data sources and destinations, such as SQL Server, Oracle, Excel, CSV, XML, JSON, and Power BI and Tableau APIs. You can also use the REST API connector to integrate with other custom or third-party applications. Solution Demonstration: How LIKE.TG Automates End-to-End Tax Form Data Extraction Step 1: Creating the Project The first step is to create an LIKE.TG project. For this use case, you don’t need to add source file to the project either. With LIKE.TG, you can process any unstructured file, such as scanned PDFs, digital PDFs, forms, .TXT, .CSV and other unstructured documents. For this example, you will use a digital PDF file that contains W-2 forms. Figure 1 Step 2: Email Handling Automation To access W2 tax form data, you must monitor incoming emails for W2 tax form attachments, move them to a designated folder, and mark them as read. You can automate this by building and scheduling a data pipeline. This will ensure timely processing of W2 forms upon receipt, streamline data extraction workflow, facilitate centralized attachment management, and enhance email management. To do this, you will need to: Configure an email source in the dataflow artifact of LIKE.TG ReportMiner, where you configure the email connection. Dataflow to download attachments in a local folder from Unread emails with “Forms” in the Subject (this dataflow is scheduled to run continuously): Figure 2 Set the filter criteria to select only the emails with ‘Forms’ in the subject line. Figure 3 In the Email Source object, enable the option to download attachments and specify the folder path where you want to store the downloaded attachments. Email attachments automatically appear in the chosen folder: Figure 4 The Excel destination in the dataflow shown in Figure 2 stores paths for all downloaded attachments: Figure 5 Step 3: Building Report Model for the Tax Form The next step is to build a report model for the W2 tax form so that you can define the data fields, data types, extraction rules. This will enable precise extraction of W2 from data fields, enhancing the quality and reliability of extracted information. To do this, you will need to: Add a new report model to the project. You can add a sample W2 tax form file as the report layout and adjust the page settings and margins as needed. Typically, users must specify the regions they want to extract data from and the data fields within the regions. However, LIKE.TG’s advanced AI-powered extraction engine allows you to create W2 extraction template with a single click: Figure 6 When you click Build using AI on the previous screen, the following appears: Figure 7 After the above processing ends, we have the AI built report model ready: Figure 8 Step 4: Data Pipeline for Processing a Single Tax Form The next step is to create a data pipeline for processing a single W2 tax form to extract data from W2 tax form PDFs, standardize numeric fields, validate tax information, generate validity fields, and transfer processed data to a destination database. This will ensure consistent and accurate extraction of critical information from W2 forms, enhance data consistency and readability, ensure data accuracy and compliance, facilitate W2 tax form status monitoring and error identification, and enable centralized storage of extracted W2 data. To do this, you will need to: Create a new dataflow in LIKE.TG and add the report model you created in the previous step as the source object. Add a transformation object, such as DataCleanse to clean the data, such as removing characters like $, unwanted whitespaces, replace null numerics with zeros etc. Figure 9 Add a validation object and apply business rules to validate tax information, such as checking for missing values, matching with reference data, validating tax rates, etc. Figure 10 Add a new field to the tax record indicating the validity status of each W2 tax form, such as valid, invalid, or pending, based on the validation results. Figure 11 Add a destination object, such as a database table or a flat file, and use it to transfer the processed data to a destination database for secure storage and retrieval. The following is a preview of the data loaded into the db when a single W2 tax form is processed: Figure 12 Step 5: Workflow Automation The final step is to automate the entire data extraction workflow so that you can trigger processing of W2 tax form files each whenever a new file appears in the email attachment folder you have prepared. This will automate attachment retrieval, ensure a continuous data flow for processing, facilitate easy access to processed attachments, streamline processing by automating file handling, ensure efficient file management, and enable real-time database updates. To do this, you will need to: Create a new workflow in LIKE.TG and add ContextInfo. ContextInfo takes context information of the scheduled job runtime. Here it will get the filepath that will trigger this flow. Add a dataflow task and use it to execute the dataflow you created in the third step to process a single W2 tax form. Add a file system task and use it to delete the processed W2 tax form file from the attachments folder after the dataflow execution. Figure 13 The workflow triggers the processing of W2 tax forms as your team receives them in the email attachments folder. Upon completion, the workflow will automatically store records from each email attachment in the destination database: Figure 14 Each time your workflow receives a new W2 form attachment via email, it goes into processing (seen by the change in name). Figure 15 After a W2 tax form is processed, it will be deleted from the folder, making sure only fresh files appearing in the folder are processed: Figure 16 Schedule the workflow to run on file drop trigger in the attachment folder. Automation’s Impact on Tax Form Data Extraction Reduce tax form data extraction time: Automated tax form data extraction allows you to reduce the data extraction time from hours to minutes by automating and streamlining the process. You can process over 100,000 tax forms in a fraction of the time and meet the deadlines and regulations for tax filing and reporting. With LIKE.TG, your team can reduce time spent on data extraction by up to 80%. Improve data quality and accuracy: Automated tax data form extraction allows you to improve the data quality and accuracy by 90%, using built-in data cleansing and validation features. You can ensure the completeness and correctness of the extracted data and avoid any errors or penalties. Save costs and resources: Automated tax data form extraction allows you to save costs by eliminating the need for manual data entry or complex and expensive software. As a result, your team can reduce labor and operational costs and increase the productivity and efficiency of the staff. Unlock the Power of Automated Tax Form Data Extraction Discover how automation can revolutionize your tax form data extraction process. With LIKE.TG, gain unparalleled efficiency and accuracy. Sign up now to explore a free 14-day trial and experience the difference. Sign Up for a Free Trial With automated tax form extraction, you can extract data from tax forms quickly, easily, and accurately, leveraging data for reporting, analysis, or compliance. Your company can acheive improved customer satisfaction and retention, giving a competitive edge in the market. LIKE.TG ReportMiner is a powerful data extraction solution that handles any tax form and unstructured data source. Whether you need to extract data from W-2, 1099, 1040, or any other tax form, LIKE.TG ReportMiner can do it in minutes. No coding or scripting is required. Just point and click. Imagine the hours you could save and the errors you could avoid with this automation. If you are interested in trying LIKE.TG ReportMiner for yourself, you can start a free 14-day trial today or schedule a custom demo with our experts and get a personalized walkthrough of LIKE.TG ReportMiner’s features and capabilities. Don’t miss this opportunity to take your data extraction to the next level with LIKE.TG ReportMiner.
How to Automate Data Extraction from Patient Registration Forms in Healthcare
Automating data extraction from patient registration forms in healthcare is crucial to enhancing patient care efficiency, accuracy, and overall quality. Over 71% of surveyed clinicians in the USA agreed that the volume of patient data available to them is overwhelming. This abundance of data highlights the importance of streamlining the extraction process. Manual extraction is time-consuming and prone to errors, hindering patient safety. This blog discusses the importance of data extraction in healthcare, the benefits of using AI-powered tools, and how LIKE.TG can help automate patient information extraction from registration forms to make it faster and more efficient. The Role of Data in Patient Care Patient data includes a wide range of information, such as names, contact details, demographics, medical history, medications, allergies, and test results. Access to this data enables healthcare providers to get a holistic view of the patient’s health, make informed decisions, and deliver personalized care. By extracting data from medical records, healthcare providers can identify patterns and trends in patient outcomes. This information can then be used to develop evidence-based treatment protocols and guidelines. Furthermore, data extraction allows for the integration of patient data from various sources, such as electronic health records, laboratory systems, and wearable devices. This integration enables healthcare professionals to comprehensively understand the patient’s health status, facilitating more accurate diagnoses and tailored treatment plans. The Importance of Automating Data Extraction in Healthcare Manual data extraction is a labor-intensive process that often leads to inconsistencies and inaccuracies. Human errors, such as transcription mistakes and data misinterpretations, pose significant risks to patient safety. Also, the high volume of patient registration forms amplifies the inefficiency of manual data extraction processes. Additionally, manual data extraction limits the scalability of healthcare systems. As the demand for healthcare services grows, efficient, scalable data management solutions become increasingly important. With automation, healthcare providers can streamline data extraction processes, allowing faster and more reliable access to patient information. Using an Automated AI-Powered Data Extraction Tool In the healthcare industry, automation has optimized various aspects of healthcare operations. It involves leveraging cutting-edge technologies like artificial intelligence (AI) to streamline the data extraction process. Sophisticated algorithms and software solutions can automatically scan and extract data from documents and forms, eliminating the need for manual intervention. These technologies can recognize and interpret various forms of data, such as patient demographics, medical history, and test results. Using an automated AI-powered data extraction tool in healthcare has numerous benefits. Some of these benefits include: Faster Data Extraction: The adoption of automation in healthcare expedites the retrieval of critical patient information, proving particularly invaluable in time-sensitive situations. This acceleration in data access plays a pivotal role in streamlining care delivery, making it more efficient and effective. Higher Accuracy: Implementing automated data extraction processes in healthcare significantly enhances precision by eliminating the potential for manual errors. This not only improves patient safety but also contributes to more favorable treatment outcomes. Cost Savings: The integration of automated processes in healthcare not only boosts efficiency but also leads to substantial cost savings. By minimizing reliance on manual labor, healthcare organizations can allocate resources more judiciously, resulting in improved financial outcomes. Improved Data Management and Analysis: Automated data extraction serves as a catalyst for centralizing patient information and fostering better organization. This centralized data not only enhances the overall management of healthcare data but also facilitates insightful analysis, aiding informed decision-making on population health trends, treatment effectiveness, and resource allocation. Enhanced Decision-Making: Automation empowers healthcare providers with data-driven insights, ushering in a transformative era in decision-making. This innovative approach revolutionizes choices in patient care, resource allocation, and overall strategy, ultimately leading to improved outcomes and enhanced patient experiences in the healthcare industry. LIKE.TG: The Perfect Tool for Automating Data Capture from Patient Registration Forms LIKE.TG is an advanced AI-powered solution designed to streamline and automate the process of data extraction from patient registration forms. With its advanced features and user-friendly interface, LIKE.TG is the perfect tool for healthcare organizations seeking efficiency and accuracy in managing patient information. Key features of LIKE.TG: Intelligent Data Extraction: LIKE.TG’s AI-driven engine efficiently retrieves information from diverse document layouts by identifying the required fields. It adeptly handles variations across different layouts, ensuring swift and precise extraction. Batch Processing: LIKE.TG’s batch processing feature simultaneously supports data extraction from multiple documents and forms. This feature streamlines the overall extraction process for enhanced efficiency and productivity. Real-Time Processing: LIKE.TG’s Scheduler incorporates the File Drop feature, enabling the solution to process and extract information from a file as soon as it appears in a folder, facilitating real-time processing. Advanced Data Preparation: LIKE.TG has built-in data cleansing and validation capabilities, ensuring the extracted information undergoes thorough scrutiny for accuracy and integrity. Error Handling and Logging: LIKE.TG offers logging capabilities to capture and track any errors or issues encountered. Integration with External Systems: LIKE.TG ReportMiner seamlessly integrates with external systems or databases, enabling the direct loading of extracted data into preferred destinations. Steps to Automate Data Extraction from Patient Registration Forms Let’s take a use case in which patient data is contained within a PDF form, as shown below. This patient registration form is fillable and has radio buttons, checkboxes, drop-down menu, and text boxes. LIKE.TG’s power can be leveraged to perform the data capture process from multiple such forms. One such form is shown below. Step 1: Configure the PDF Form Source Within LIKE.TG’s main designer, drag and drop a PDF Form Source object on the designer and provide the file path for one of the forms we want to process. With this, we have defined our PDF form as the source. Step 2: Perform Data Transformations and Apply Data Mapping in the Dataflow The form collects the first name and last name in different fields. Use an Expression transformation to merge the two fields to get the full name. Use the same transformation to extract a full address field from the multiple address fields in the form. The PDF form extracted day, month, and year separately. To get a full date, use the built-in function ToDate available in the toolbox. Once all the data transformations are applied, map the extracted data to an Excel destination (check the append to file option to have data from all the forms written to the same file as we run this data pipeline for each form we want to process). Step 3: Preview Data Instantly Preview the output to see if data has been extracted correctly. Step 4: Configure File System Item Source Object to Extract Data from All Files in a Folder Every healthcare provider must extract data from multiple patient registration pdf forms. To automate this process of extracting data from all the pdf forms in a folder, use the file system item source object and provide the folder path. Map this file system object to a workflow object that defines the processing sequence for each PDF form. The inner workflow defines the execution of the data pipeline we created to extract data followed by the file action to move the processed file to a dedicated folder once the data extraction process is complete. Step 5: Schedule to Run This Process Automatically Use the Job Scheduler to execute the entire process weekly. This way, all new patient registration forms collected over a week will be processed automatically without manual intervention. The screenshot below shows the preview of the Excel destination file, which has all the extracted data after the execution of the workflow. Conclusion The adoption of LIKE.TG ReportMiner, with its intelligent data extraction capabilities and user-friendly interface, empowers healthcare providers to overcome the challenges associated with manual methods. By following the outlined steps, healthcare providers can seamlessly integrate LIKE.TG ReportMiner into their workflows, ensuring the automated extraction of vital patient information and paving the way for a more efficient and data-driven healthcare system. Experience the power of LIKE.TG with a 14-day free trial or a personalized demo with our experts—unlock the potential of AI-driven form processing today!
How to Load Data from AWS S3 to Snowflake
According to a study by Statista, the cloud storage market was valued at $90.17 billion in 2022 and will reach a value of $472.47 billion by 2030. These figures indicate a growing shift toward cloud computing and data storage solutions. A typical scenario in modern data management involves data transfer from cloud storage to cloud-based computing platforms. Amazon’s Simple Storage Service (S3) is among the go-to options for the former, and businesses trust Snowflake for the latter. As a result, S3 to Snowflake data transfers are common for businesses looking to benefit from the synergy of S3’s scalable storage and Snowflake’s top-notch data warehousing features. This blog discusses the S3 to Snowflake data loading process and explains two ways of setting it up so you can pick the right one. Let’s get started. Amazon S3 Overview Amazon S3 is an object storage service by Amazon Web Services (AWS). It’s backed by the scalable storage infrastructure supporting Amazon’s massive global e-commerce network. As a high-speed, web-based cloud storage service, Amazon S3 creates online backups of data and applications on AWS. It’s well-suited to meet data storage requirements of any scope from different industries. Integration with Other AWS Services: You can link S3 to other AWS security and monitoring services such as Macie, CloudTrail, and CloudWatch. Directly linking vendor services to S3 is also an option. Easy and Secure Data Transfer: You can transfer data to S3 over the public internet through S3 APIs. Amazon S3 Transfer Acceleration can help you transfer data more quickly, and AWS Direct Connect creates a private connection between S3 and your data center. Use Cases: Amazon S3 is a good storage option for organizations of varying sizes thanks to its scalability, security, availability, and performance capabilities. Typical S3 use cases include the following: Data storage Data archiving Document storage Data backup Log file storage Software delivery Website hosting Data lakes and data warehouses Snowflake Overview Snowflake is a fully managed Software-as-a-Service (SaaS) started in 2012 and launched in 2014. It offers a single platform for tackling users’ data warehousing, data lakes, data science, data application and development, and data engineering requirements. It also supports the secure sharing and consumption of real-time or shared data. This cloud-based data warehousing solution simplifies the storage, processing, and analysis of structured and semi-structured data. Snowflake’s separation of computing and storage distinguishes it from conventional data warehouses. This allows you to allocate resources to different functions independently based on your requirements. Here are some key features of Snowflake: No Hardware and Limited Software: As a true SaaS offering, Snowflake isn’t burdened by virtual or physical hardware you would have to install or configure. There’s also virtually no software that you’ll need to set up. Hassle-Free Maintenance: Snowflake undertakes all maintenance, updates, tuning, and management tasks — easing the administrative burden on users. Cloud Provider Agnosticism: Snowflake is known for being cloud provider agnostic. It’s available on AWS, Azure, and GCP and retains its functionality and end-user experience on each of them. How to Load Data from S3 to Snowflake? There are two ways you can load data from S3 to Snowflake: manual or automated. Below, we’ll discuss manual data transfer using the COPY INTO command and automated data transfer using a third-party integration tool such as LIKE.TG. Manual Data Transfer from S3 to Snowflake The following steps are involved in manually setting up a data transfer between S3 and Snowflake: 1. Setting Up the Amazon S3 Bucket If you don’t already have an Amazon S3 bucket, create one where your data is stored. Verify that Snowflake has the necessary permissions to access this bucket. You may need to set up an AWS Identity and Access Management (IAM) role, grant it the required permissions, and attach it to the Snowflake account. 2. Configuring Snowflake External Stage Log in to your Snowflake account. Create an external stage using the Snowflake web interface or SQL commands. Mention the Amazon S3 bucket path as the location for the external stage. 3. Create Snowflake Table Define a Snowflake table schema that matches the data structure of your Amazon S3 files. Make a table in Snowflake for the data transferred from S3. 4. Set Up Snowflake Integration Create a Snowflake integration object to establish a connection between Snowflake and Amazon S3. Specify necessary details, such as the AWS access key, AWS secret key, and the external stage associated with the S3 bucket. 5. Grant The Required Permissions Ensure that the Snowflake user or role has the necessary permissions to access the external stage and load data into the Snowflake table. 6. Load Data into Snowflake Use the COPY INTO command in Snowflake to load data from the external stage (Amazon S3) into the Snowflake table. Specify the source file format, the destination table, and other relevant options. 7. Monitor and Troubleshoot Monitor the data transfer process to ensure smooth, error-free operations. Check Snowflake and Amazon S3 logs for error messages and troubleshoot in case of any errors. If you wish to automate the data transfer from S3 to Snowflake, you can set up a schedule using Snowflake’s task scheduler or a third-party scheduler tool. Define a schedule for executing the COPY INTO command to load new data from Amazon S3 into Snowflake at specified intervals. AWS S3 to Snowflake Data Transfers. Simplified. Enjoy complete control over your AWS S3 to Snowflake pipelines without writing a single line of code. Try LIKE.TG for two weeks and experience the difference for yourself. Start My FREE Trial The Limitations of Manual Data Transfer from AWS S3 to Snowflake Manually loading data from AWS S3 to Snowflake may appear simple but it still has certain limitations. Below are some of the major ones you should know about: Data Transfer Costs: Transferring vast data volumes from S3 to Snowflake can quickly increase data transfer costs. These costs can add up over time and become prohibitively expensive. Network Latency: Network latency can impact data transfer speeds in an S3 – Snowflake transfer. This depends mainly on the geographical distance between your Snowflake instance and the AWS region with your S3 bucket, and your network connectivity. Data Security: Both AWS and Snowflake offer mechanisms to ensure secure data transfer, such as IAM roles and HTTPS encryption. However, misconfigured protocols or gaps in security methods can potentially expose confidential information during transit. Data Format Compatibility: Snowflake supports formats such as JSON, CSV, Avro, and Parquet. However, you can face problems if your S3 data is in a format that Snowflake doesn’t support directly — this will necessitate preprocessing or transforming the data before loading. Scalability and Concurrency: Snowflake is equipped with powerful concurrency and scalability features. However, it’s wiser to optimize your data-loading processes and resource allocation to mitigate performance bottlenecks, slower data loading, and disrupted services. Error Handling and Monitoring: Data-loading processes are prone to errors due to network issues, data format incompatibility, or lack of required permissions. You’ll have to implement comprehensive error-handling mechanisms to identify and mitigate such problems promptly. Regulatory Compliance: Based on the nature of your data and applicable regulatory requirements, ensuring compliance with data governance standards such as the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), and Payment Card Industry Data Security Standard (PCI DSS) can require substantial effort on your part. Automated Data Transfer from AWS S3 to Snowflake Using LIKE.TG 1. Browsing Cloud Files First, note that you can browse cloud files in an Excel source or any other file source in LIKE.TG. 2. Setting Up Cloud Connections Selecting the ‘Browse Cloud Files’ option will take you to a browser where you can set up cloud connections. Here, you can set up Amazon S3 by selecting ‘Add New Connection’ and inputting credentials, as seen below. 3. Choosing The Right File Next, you’ll choose the file you want to access in AWS S3. The selected file will appear in the source file path as follows: 4. Check the File Layout When you check the layout of the file in LIKE.TG, this is how it’ll show up: Once configured, the source object will look like this: 5. Setting up Snowflake Next, you’ll set up Snowflake by configuring a Database Destination object with Snowflake credentials: Along with the Snowflake connection, you can optionally configure a Staging Area. In the image below, the Amazon S3 bucket serves as the staging area. You have now configured Snowflake as the Destination Objectand the Dataflow will appear as shown below: 6. Mapping, Previewing Output, and Running The Dataflow Next, you’ll create mappings from the Source Object (S3) to the Destination Object (Snowflake): ‘Preview Output’ for the Destination Object will look like this: Lastly, when you run the Dataflow using the ‘Start Dataflow’ button, LIKE.TG will write the data to the destination, completing the S3 to Snowflake data transfer. Manual S3 to Snowflake Data Transfers vs Automated Transfers in LIKE.TG As seen above, setting up manual S3 to Snowflake data transfers is a valid option. However, the process can be complicated, especially for non-technical users. To recap, these are the steps involved: S3 bucket setup IAM role creation External stage creation in Snowflake Setting up table schema and making a table in Snowflake Creation of a Snowflake integration object Loading data from S3 into the Snowflake table using the COPY INTO command Monitoring and troubleshooting the data transfer process as needed In contrast, LIKE.TG simplifies S3 to Snowflake data transfers with its intuitive interface, drag-and-drop functionality, and ease of use. It lets you conveniently create and automate data transfers in a few clicks, regardless of your technical proficiency. Conclusion LIKE.TG streamlines end-to-end data management for a range of industries. Its powerful, enterprise-ready solution allows you to design, deploy, and automate S3 to Snowflake pipelines — with zero coding required. Find out how you can use LIKE.TG to transform your data transfer processes. Reach out to our team or start your FREE trial today!
7 Data Quality Metrics to Assess Your Data Health
No matter their size, all organizations rely heavily on the data they collect and manage. This data ranges from customer information to sales records, employee performance, and more. However, if this data is inaccurate, outdated, or incomplete, it becomes more of a liability than an asset, making it more important to measure its health. To do so, they need data quality metrics relevant to their specific needs. Organizations use data quality metrics, also called data quality measurement metrics, to assess the different aspects, or dimensions, of data quality within a data system and measure the data quality against predefined standards and requirements. What is Data Quality? Data quality measures the data’s ability to meet the criteria for completeness, accuracy, validity, uniqueness, timeliness, and fitness for purpose. Data that meets the requirements set by the organization is considered high-quality—it serves its intended purpose and helps in informed decision-making. For instance, high-quality data in a healthcare system consists of precise and up-to-date patient records comprising patient demographics, medical history, diagnoses, treatments, and outcomes. Such a detailed dataset is maintained by trained data quality analysts, which is important for better decision-making and patient care. These professionals conduct data quality assessments by individually assessing each data quality metric and estimating overall data health. The aggregate provides the organizations with a certain percentage to define the accuracy of data. What are Data Quality Metrics? Data quality metrics are specific indicators used to evaluate how good or bad a data set is. In other words, whether the data set is fit for purpose. As part of data quality management, these metrics help quantify the state of data using specific criteria that are objectively defined and applied. For example, you can set up particular data quality metrics to measure the percentage of incomplete records, count the number of incorrect entries, or determine the proportion of duplicate data. Why is there a need for data quality measurement metrics? Data quality metrics are not just a technical concern; they directly impact a business’s bottom line. Gartner reports that organizations lose an average of $12.9 million annually due to low-quality data. Furthermore: 41% of data warehouse projects are unsuccessful, primarily because of insufficient data quality. 67% of marketing managers believe poor data quality negatively impacts customer satisfaction. Due to low data quality, companies can lose 8% to 12% of their revenues. Make Decisions Based on Data You Can Trust With LIKE.TG Ensure accuracy, reliability, and completeness of your data using LIKE.TG's advanced profiling tools. Start you 14-days-trial now! Now, to mitigate the consequences of poor-quality data, there needs to be something that quantifies the current state of data, and to do that, you need data quality metrics. These metrics evaluate data in four key dimensions: Intrinsic: Focuses on the data’s credibility, objectivity, and reputation. Contextual: Emphasizes the relevance, timeliness, and completeness of data. Representational: Focuses on the formatting and presentation of the data. Accessibility: Deals with the ease of access to the data. These data quality dimensions are essential to a data quality framework and help ensure data is well-rounded and reliable. Using data quality metrics, you can set targeted objectives to guide your teams in addressing commonly occurring data quality issues. 7 Data Quality Metrics to Track Data quality metrics can vary depending on the sector and the data’s intended use. However, certain metrics are commonly adopted across many industries for their fundamental importance in assessing data health. Here are some frequently used data quality metrics examples: Completeness Ratio It refers to the extent to which a data set contains all the required or expected data elements. The completeness ratio measures the proportion of complete data entries compared to the total number of expected entries within the data set. This ratio helps us understand whether the data is complete and contains all the necessary information to draw correct conclusions. For instance, a customer database requires customer information such as name, address, email, and phone number for each customer. If the database contains one or more missing fields, we would have a lower completeness ratio, indicative of lower data quality. Similarly, a high completeness ratio indicates complete data records useful for analysis. Costs of Data Storage Sometimes, data storage costs keep rising while the amount of usable data remains the same. It happens due to redundancy, duplications, and inconsistencies within datasets and is a sign of poor-quality data. Unhealthy data also complicates the backup and recovery processes, as finding and restoring accurate data becomes challenging in the event of data loss.Conversely, if your data operations remain constant but you observe a fall in data storage costs, it’s likely your data is of high quality. Ratio of Data to Errors The error ratio is a measure to determine the percentage of incorrect records in a dataset compared to the total number of records. The error ratio helps you identify problem areas by providing a percentage of flawed data. To calculate the error ratio, you divide the number of records with errors by the total number of records in your data set. Suppose you have a list of 1000 addresses, and 100 of them contain errors such as wrong zip codes or misspelled city names. The error ratio would be 100/1000, which equals 0.10 or 10%. This result means that 10% of your address data is incorrect. Timeliness Index This data quality metric assesses how quickly data is collected, processed, and available for use. To do so, it looks at the time elapsed between an event’s occurrence and its data’s availability. For instance, if you need certain data ready within every 30 minutes, and it does, that data will be considered timely. A higher timeliness index indicates that data is readily accessible and up to date. Similarly, a lower timeliness index suggests inefficiencies or delays in data delivery or availability. Amounts of Dark Data Dark data refers to the data that an organization collects, processes, and stores but does not use for any purpose. Not all large amounts of data that organizations collect qualify as dark data. It becomes “dark” primarily because it is not actively used or managed. Dark data can become a data quality problem because; It can contain outdated or inaccurate information, impacting the overall accuracy and reliability of your company’s data sets. It often includes unprotected sensitive information, exposing risk to data breaches. Dark data does not necessarily imply poor data quality but can indicate areas where data quality could be compromised. Consistency Score Another data quality metric to keep track of is the consistency of data, which refers to its uniformity and coherence across various sources, systems, and time periods. The consistency score can be measured by setting a threshold that indicates the amount of difference that can exist between two datasets. If the information matches, it is said to be consistent. Typically, robust data integration strategies are employed to remove any inconsistencies in multiple data systems. Duplication Rate It measures the proportion of duplicate entries or records within a dataset. It confirms whether the given information in a dataset is unique and appears once only. Duplication can be present in datasets containing customer data but can be removed. Data deduplication tools and algorithms identify and remove duplicate records from the dataset. The tools compare entries based on predefined criteria, such as similarity thresholds. They then merge or remove the duplicates accordingly. How to Effectively Use Data Quality Metrics? There isn’t a one-size-fits-all approach to data quality measurement metrics; they depend on your business’s aims, where your data comes from, and the rules you follow. Understanding these factors is the key to using data quality metrics effectively. Here is how you can use these metrics to their best. Understand your Content Requirements Data Model To effectively implement data quality metrics, you need a clear understanding of what your data should look like and how it should behave — these are your “content requirements.” Alongside your content requirements you need a “data model,” essentially a blueprint of how your data is structured and relates within your database or data system. This model helps ensure that your data metrics are tailored to how your data is organized. Define Your Data Quality Dimensions Define data quality dimensions strategicallyso that you can use the most relevant data quality metrics to monitor data health. It allows you to employ a targeted approach that enhances the reliability and usefulness of your data. For example, when analyzing financial transactions, prioritizing data quality dimensions like accuracy and consistency ensures that the data is uniform and correct. Alternatively, if you are managing a marketing campaign, prioritizing the completeness and relevance of customer data enables you to tweak your messaging effectively. As you refine these key dimensions, you will see clear improvements in your metrics, such as higher data accuracy and greater completeness, depending on your focus areas. Set Clear Goals for Your Data Quality Metrics Setting realistic data quality goals can improve your metrics’ overall performance. For example, suppose you want to ensure your customer information is almost always complete. Setting a target range based on your goals and industry standards, such as having no more than 3% of your data incomplete, establishes clear expectations and ties your data quality metrics to specific outcomes, such as improving a user’s shopping experience. Moreover, documenting particular use cases can help your teams realize the importance of aligning data quality with business goals and demonstrate how these metrics fit into your broader business strategy. Regularly Monitor Your Data Quality Metrics Keep a close eye on your data quality metrics and update them as needed. Continuing with the example of setting a target range or number, if, after monitoring, you discover that your customer data shows more than 3% missing values—higher than your set target—you should evaluate further to identify the underlying problems. While the initial reaction might be to reevaluate your entire data management strategies, examining more specific and immediately relevant factors is recommended. Issues such as data entry errors or flaws in data collection methods are often the culprits and should be addressed before considering broader strategic changes. Conclusion While managing data quality can be challenging as it costs companies a lot of time and money, it can be improved using key data quality metrics. These metrics provide a clear, quantifiable way to assess and enhance data accuracy, consistency, and reliability. Integrating a comprehensive tool like LIKE.TG can be particularly effective to enhance these efforts further. LIKE.TG enhances data management by offering features such as automated data cleansing transformations, customizable data quality rules, and thorough data profiling and validation, ensuring that data meets quality standards and is managed efficiently at scale. Start with a 14-day free trial and experience how LIKE.TG can transform your data quality management today.
The 7 Best Python ETL Tools in 2024
The global big data analytics market is projected to reach a $655.53 billion valuation by 2029, compared to $241 billion in 2021. This massive growth shows how enterprises are increasingly turning to data analytics to guide various business operations. As part of this shift, enterprises rely heavily on Extract, Transform, Load (ETL)processes for turning raw data into actionable insights. These processes are a key component in data warehousing and facilitate data movement between different organizational systems, increasing data accessibility. Various ETL tools are available today, written in different languages such as Java, Apache Hadoop, and JavaScript. However, ETL with Python — a general-purpose and high-level programming language — is among the leading choices. This blog discusses what makes Python a great option for building an ETL pipeline, how you can set it up, and the best Python ETL tools and a better, no-code data integration alternative. Python ETL Tools: An Overview The phrase ‘Python ETL tools’ doesn’t refer to a single, monolithic software program. Rather, it’s a catch-all term for numerous libraries and frameworks built using Python for targeting different stages of the ETL process. These stages and their corresponding Python ETL tools are listed below: Workflow Management Tools In Python ETL, workflow management helps you schedule engineering and maintenance processes. Workflow applications such as Apache Airflow and Luigi, while not designed explicitly for ETL processes, can help you execute them through the following features: Dependency Management: By defining task dependencies, you can ensure that tasks are executed in the correct sequence. This is crucial for data integration processes where extraction must precede transformation, which in turn must precede loading. Scheduling: Scheduling ETL tasks lets you run them at defined intervals or specific times, which automates the process and ensures the availability of timely updates. Parallel Execution: Workflow management tools let you run multiple tasks concurrently so that you can process data faster. Monitoring and Logging: These tools’ monitoring dashboards and logging capabilities let you track ETL tasks’ progress and identify and fix any issues. Retry Mechanisms: These tools can automatically retry failed tasks in case of disruptions or failures to ensure data integrity. Alerting: You can set up alerts or notifications in case of task failure or other instances to proactively manage your ETL processes. Dynamic Workflows: You can configure dynamic workflows in these tools that adapt to changes in schemas, data sources, or business requirements — increasing your ETL processes’ flexibility and adaptability. Code Reusability: Thanks to modularization and code reuse, you can efficiently build ETL pipelines and maintain them over time. Integration with Python Libraries: These tools integrate seamlessly with Python libraries and packages that are designed for data processing and analytics tasks, such as pandas, NumPy, and SciPy. Extensibility: Workflow management tools’ extensibility lets you integrate with different data sources, external systems, and databases through custom operators and plugins. Tools for Moving and Processing Data Python tools that handle data movement and processing can also help you design Python ETL workflows. Here’s how: Data Extraction: BeautifulSoup, requests, and similar libraries help with web scraping and API access for obtaining data from disparate sources. Data Transformation: pandas and NumPy offer remarkable data manipulation capabilities, and NLTK and spaCy can help with text processing. Data Loading: Python has database connectors (such as SQLAlchemy) that help you load transformed data into databases. Automation and Workflow: You can automate ETL processes using Python scripts or use a workflow management tool (like Apache Airflow) as discussed above Error Handling and Logging: Python has try-except blocks to handle errors, and the logging module ensures visibility into ETL task execution. Parallel Processing: Multiprocessing and threading enable parallel task execution, improving performance for large datasets. External System Integration: Python libraries offer easy integration with cloud services (such as boto3 for AWS), ensuring hassle-free interaction with external systems in a distributed environment. Self-Contained Python ETL Toolkits Python ETL toolkits are comprehensive libraries or frameworks offering end-to-end ETL capabilities within a single package. These toolkits’ integrated functionalities help you develop and deploy Python ETL pipelines easily — here’s how: Unified Environment: These toolkits provide a cohesive environment where you can perform all stages of ETL within the same framework without having to integrate multiple libraries or tools. Simplified Development: You get high-level abstractions and intuitive APIs for common ETL tasks within these toolkits, so you don’t have to write code for them. Pre-Built Components: Self-contained Python ETL toolkits are typically equipped with pre-built modules or components for handling frequently used data formats, databases, and transformations. This saves you time and effort and eliminates the need to code from scratch. Data Connectivity: These toolkits have built-in connectors and adapters for different data sources and destinations — such as databases, cloud storage, APIs, and file formats — to ensure seamless data movement across systems. Transformation Features: There’s a wide range of transformation methods, operators, or functions within these toolkits for data manipulation, enrichment, cleansing, and aggregation. This simplifies complex data transformations. Scalability and Performance: Self-contained Python ETL toolkits are often designed to scale with increasing data volume and have optimization features to enhance performance, such as parallel processing, caching mechanisms, and distributed computing. Monitoring and Management: These toolkits may have built-in monitoring, logging, and management functionalities to effectively track ETL jobs’ progress, resolve errors, and manage inter-task dependencies. Flexible Deployment: These toolkits offer flexibility in deployment, offering support for standalone applications, containerized environments like Docker, or integration with workflow management systems such as Apache Airflow. Why Use Python for Building ETL Pipelines? Here are some of the reasons behind using Python for ETL pipeline design: Open-Source Accessibility Python has been developed on an open-source, community-based model. The Python Software Foundation is dedicated to propagating Python open-source technology. As an open-source language, Python has few restrictions when it comes to platform and run environments. It works well with different platforms and runs on Windows and Linux with minimal modifications. While there are other open-source programming languages such as Java and R, Python offers greater ease of use and a far more extensive ecosystem of libraries and frameworks. Big Data Suitability Python requires less coding than other programming languages, making it simpler to write and maintain ETL scripts. It’s also equipped with well-tested libraries for increased functionality. These libraries cater to some of the most common big data tasks, simplifying computing and analytics with packages for numerical computing, statistical analysis, visualization, machine learning, and data analysis. Other languages such as Java and Scala have frameworks like Apache Hadoop and Apache Flink to support big data processing. However, Python’s simplicity makes it more preferable for ETL pipeline development and iteration. Data Processing Speed Python is known for its high data processing speed. Its code features a simpler syntax and is easier to manage than other programming languages, resulting in faster execution of tasks. Optimizations and parallelization techniques further improve Python’s data processing performance. Lower-level languages such as C and C++ are often considered faster than Python for computational tasks. However, these languages typically require more complicated and verbose code, which ultimately slows development and affects its efficiency. Support for Unstructured Data Python also features built-in support for processing unstructured and unconventional data. Since most modern enterprise data is unstructured, Python is an organization’s key asset in this domain. Compared to other programming languages, this is where Python’s readability wins out and makes it particularly suitable for unstructured data processing. The language features resources that can effectively tackle unstructured data processing challenges. Here are a few examples: Natural Language Toolkit (NLTK): Has a suite of programs and libraries for natural language processing. spaCy: A Python library for enterprise-grade Natural Language Processing (NLP), with pre-trained models for various NLP tasks. scikit-learn: A Machine Learning (ML) library with multiple data analysis and preprocessing tools. All of The Python ETL Functionality, None of The Code With LIKE.TG, you can rapidly build, deploy, and automate ETL pipelines that are tailored to your business requirements — no coding, just a few clicks. Get started today. Start Your FREE Trial How to Build an ETL Pipeline in Python Below is an example of setting up an ETL pipeline using Python, specifically the Pandas library. The use case here involves extracting data from a CSV file, transforming it to add a new column indicating the length of text in a specific column, and then loading the transformed data into a new CSV file. Step 1: Extract Data Here, The function ‘extract_data’ uses the path to the input CSV file (‘input_file’) as its parameter. Inside the function, the ‘pd.read_csv()’ from the pandas library reads the data from the CSV file into a pandas DataFrame. The DataFrame containing the extracted data is returned. Step 2: Transform Data Here, The function ‘transform_data’ takes the extracted DataFrame (‘data’) as its parameter. Inside the function, the new column ‘text_length’ is added to the DataFrame using ‘data[‘text_column’].apply(lambda x: len(str(x)))’. This lambda function will calculate the length of the text in each row of the ‘text_column’. The transformed DataFrame is returned. Step 3: Load Data Here, The function ‘load_data’ takes the transformed DataFrame (‘data_transformed’) and the output CSV file’s path (‘output_file’) as its parameters. Inside the function, ‘data_transformed.to_csv()’ writes the transformed data to a new CSV file specified by ‘output_file’. Lastly, ‘index=False’ helps avoid writing row indices to the CSV file. Input and Output File Paths Here, two paths are specified. One to the input CSV file containing the data to be processed (‘input_file’) and the other to the output CSV file where the transformed data will be saved (‘output_file’). Executing The ETL Process Here, The Python ETL process is executed by calling the ‘extract_data’, ‘transform_data’, and ‘load_data’ functions sequentially with the appropriate arguments. The extracted data is stored in the ‘data’ DataFrame. The ‘transform_data’ function is called with the extracted (‘data’) as input, and the transformed data is stored in the ‘data_transformed’ DataFrame. Lastly, the transformed data is loaded into the output CSV file specified by ‘output_file’. Note that this code creates a very basic Python ETL pipeline. The more data an organization generates and consumes, the more complex the pipelines it will need to build and maintain. When it comes to building ETL pipelines in Python, increasing complexity can outweigh the benefits — which makes Python ETL tools a more feasible alternative. The Best Python ETL Tools in 2024 Some of the best Python ETL tools are discussed below: 1. Apache Airflow Apache Airflow is an open-source Python framework that uses Directed Acyclic Graphs (DAGs) to let users organize and manage their ETL pipelines. It supports the authoring, scheduling, and monitoring of workflows. Pros Cons DAGs combine multiple Python scripts into a dependency graph and enable parallel processing of tasks. Airflow lacks versioning of data pipelines. Users can’t redeploy a deleted Task or DAG. Airflow’s interactive user interface makes it easy to monitor ETL pipelines by visualizing and restarting workflow dependencies, successes, and failures as needed. Doesn’t preserve metadata for deleted jobs, making debugging and data management difficult. Operators can extend Airflow’s functionality to cover various use cases or enable its use as a data integration platform instead of a data tool. Users need some DevOps proficiency to get it running. 2. Luigi Luigi was originally developed by Spotify and is a Python framework that enables users to stitch multiple tasks together. Pros Cons Intuitive architecture, with jobs written in Python. There is no distribution of execution, so worker nodes are prone to overloading. Ideal for backend developers looking to automate straightforward ETL pipelines. Limited documentation and error-handling capabilities can complicate understanding and correcting task failures. Relatively easy to use with command-line interface integration. Not practical for real-time workflows due to its use of batch computing. 3. Pandas The Pandas library features the DataFrame object, a data structure that allows easy, user-friendly data manipulation. How Pandas simplifies data transformation is evident in the example of setting up ETL in Python, discussed earlier in this blog. Pros Cons Facilitates quick and easy data transformations. Offers limited extraction and loading functions. Great for small- to medium-sized datasets. Not ideal for large-scale data processing and in-memory operations. Convenient for writing simple scripts. Scalability is possible but not easy. 4. Petl Petl is ideal for meeting basic ETL requirements without advanced analytics. Pros Cons Petl uses minimal system memory and isn’t resource-intensive. Only offers basic transformation capabilities. Its extendable functionality uses simple code to add new data sources and destinations. Doesn’t handle large datasets well. Easy to use. Not suitable for use cases requiring high-speed operations or time-sensitive outcomes. 5. Bonobo Bonobo is a simple, lightweight Python ETL framework that allows users to build data pipelines via scripting. Pros Cons Bonobo doesn’t require learning new APIs, only an elementary understanding of Python. Good for simple and semi-complex data schemas but not for larger datasets. Allows rapid, parallel deployment of pipelines. Simplistic data handling that organizes data as a series of rows in nodes, making it challenging to perform analytics on the dataset as a whole. Open-source, scalable, and supports a wide range of data sources such as JSON, CSV, XLS, XML, and SQL. Still at the alpha stage with an uncertain development future. Version 0.6 was released over six years ago. 6. Pyspark As a Python API, PySpark allows users to access and use Apache Spark (the Scala programming language) directly through Python. Pros Cons PySpark is built to handle huge datasets, with features such as lazy loading, parallel processing, and Resilient Distributed Datasets (RDDs). Despite impressive transformation capabilities, PySpark has somewhat limited extract and load functionality. Supports most Apache Spark features such as DataFrame, Spark SQL, Machine Learning, Spark Core, and Streaming. Complex pipelines can require users to write their own extractors. Allows users to write ETL tasks in either a Python-like or an SQL-like form. Errors in PySpark show references to the Python code and Java stack trace errors, making debugging difficult. 7. Bubbles What differentiates Bubbles from the other Python ETL tools discussed here is its metadata-based pipeline descriptions. This framework is written in Python but isn’t limited to it and features easy usability with other languages. Pros Cons Bubbles is technologically agnostic, ensuring users can focus on ETL processes without worrying about the technology behind them or data access. Requires Python 3.3, and there are no plans of backporting it to older Python versions. Allows users to set up ETL quickly. Sometimes, composition isn’t possible when adding custom operations if the objects are from different databases As an abstract and versatile framework, Bubbles is focused on the ETL processes instead of a specific programming language. Some conversion operations are meant to be used in Python only and can be resource-intensive when used on large datasets. When Should You Use a Python ETL Tool? Python ETL tools are ideal in one or more of the following scenarios: When an organization wants to code its own ETL tool and has developers or programmers who are proficient in Python. When organizational ETL requirements are straightforward and easily executable. When ETL requirements are highly specific and only Python can cater to them effectively. LIKE.TG as a Better Alternative to Python ETL Despite their considerable benefits, most Python ETL tools also share some drawbacks, such as: These tools require substantial knowledge and proficiency in Python for building and automating custom ETL pipelines. Many tools are more suitable for small- to medium-scale processes. Some tools’ scalability and speed can be limiting factors for rapidly growing organizations. Enterprises want an intuitive interface, high processing speeds, reliability, and scalability from their ETL solutions. Additionally, automating ETL testing using Python requires skilled ETL testers with proficiency in both ETL testing methodologies and the Python programming language. Therefore, many organizations look for an alternative to standard Python ETL tools that eliminates the need for hiring and retaining professionals and the associated costs. Enter LIKE.TG. LIKE.TG offers a tried-and-tested no-code environment, a unified interface, and hassle-free integration with different data sources and destinations. Its sophisticated data cleansing and transformation capabilities allow users to quickly turn raw data into refined insights. It’s the leading Python ETL alternative for enterprises wanting to build, deploy, and maintain ETL pipelines without writing a single line of code. Ready to automate your ETL processes? Book a demo or start a FREE trial today.
Data Ingestion vs. ETL: Understanding the Difference
Working with large volumes of data requires effective data management practices and tools, and two of the frequently used processes are data ingestion and ETL . Given the similarities between these two processes, non-technical people seek to understand what makes them different, often using search queries like “data ingestion vs ETL”. Data ingestion focuses on the initial collection and import of data, preparing it for storage or future use. ETL, on the other hand, takes this process further by not only ingesting data but also transforming it. The transformation involves cleaning the data to remove inaccuracies or irrelevant information, structuring it into a predefined schema for easy querying, and adding valuable context before loading it into its destination, such as a data warehouse. Let’s have a closer look at data ingestion and ETL to understand how they are different and how they are often used together. This will help us understand their roles in a modern data pipeline architecture. What is Data Ingestion? Data ingestion is the process of collecting, importing, and transferring data from different sources such as files, databases, and streaming platforms into a storage or processing system It’s an initial step in the data processing pipeline, where raw data from sources such as files, streams, APIs, or databases is acquired and prepared for further analysis and storage. For instance, data ingestion for a retail company would involve collecting sales data from sources like mobile apps, e-commerce platforms, and customer feedback forms that would include product information, customer profiles, transaction records, and inventory updates. The data ingestion process would then involve extracting data from each source and loading it into a centralized data repository. There are two types of data ingestion techniques: Batch data ingestion: It involves collecting and moving data at regular intervals. Streaming data ingestion: This includes collecting data and loading it into the target repository in real time. is What is ETL? Extract, transform, and load (ETL) is a type of data integration process that was standardized in the 1970s. It involves extracting data from multiple sources, transforming it into a consistent format, and finally, loading it into the target system, which is typically a database, a data warehouse, or a data lake.Extraction involves retrieving data from different source systems such as APIs, files, databases, or streams. This step requires querying databases or reading files to gather the essential information. Transformation involves converting the extracted data into a format suitable for analysis and reporting. Some common transformation operations include normalization, data cleansing, aggregation, and enrichment. Finally, loading involves moving the transformed data into the target system for reporting or BI analysis. More recently, cloud computing has made it possible to flip the final two stages of ETL so that it works in the sequence Extract, Load, and Transform (ELT). However, the primary objective remains the same: integrate data from different sources, organize it into a standardized format or structure, and finally prepare it for analysis and decision-making. Data Ingestion vs. ETL: Differences Both these processes help improve the data knowledge of an organization as they change the data to the right format. Moreover, ETL and data ingestion both involve considerations for data quality. ETL emphasizes deduplication, data cleansing, and validation to ensure consistency and accuracy. Similarly, data ingestion focuses on acquiring raw data reliably. Despite all the similarities, data ingestion and ETL have some clear differences. For starters, the goal with ETL is to extract, transform, and load data into the target repository in a consistent format to maintain data quality and integrity. On the other hand, data ingestion aims to gather and import raw data from different sources into a centralized repository for further analysis and processing. Here are some more differences between data ingestion and ETL: Data ingestion precedes ETL in the data processing pipeline and serves as the initial step in aggregating raw data. ETL comes later and aims to prepare data for analysis and reporting. ETL involves data transformation, cleansing, and integration activities, while data ingestion involves data movement. The aim of data ingestion is to collect raw data, which might still have many quality issues. However, ETL always cleans the information and changes it into the right format before loading it into the target system. Data ingestion processes almost always trigger processes in other systems, while ETL pipelines end right after loading data into the target system. Data ingestion supports both batch and real-time processing, while ETL usually moves data in batches on a regular schedule. Key Considerations for Choosing Between Data Ingestion and ETL Real-time data requirements: Data ingestion is ideal in this case as it facilitates real-time or near-real time processing better. It allows us to ingest and analyze data streams as they arrive. This proves beneficial in decision making. Batch processing cases: ETL is more suited to batch processing cases where data is collected and processed in batches. This easily helps manage large volumes of data efficiently as it applies transformations and loading data into the target systems at scheduled intervals. Structured data requirements: ETL can easily extract both structured and unstructured data from multiple sources. Hence, it can be used when there’s a need to extract, transform, and load data from structured sources like relational databases. Predictable data processing: The characteristics like scalability and cost-effectiveness make ETL an ideal choice for predictable data processing tasks. Organizations can schedule ETL jobs during off-peak hours when the system loads are low. This reduces operational costs and optimizes resource utilization. Compatible source and target system: When source and target systems are compatible and require little to no transformation, data ingestion is the way to go. Data ingestion allows companies to ingest the data directly into the target system without manipulation of any kind. Data ingestion or ETL? Both! With the ongoing debate on data ingestion vs ETL, it is essential to understand that it’s not about choosing one over the other. Instead, both play a key role in the data lifecycle and complement each other for seamless data flow and processing. Here’s why choosing both data ingestion and ETL is a wise approach: Data ingestion ensures that every piece of data, no matter its source, is captured. This inclusiveness is needed because organizations rely on a wide variety of data types and sources to inform their decisions. While data ingestion gathers the data, ETL transforms this raw data into a format that’s ready for analysis. Without ETL, the data might remain in a state that’s difficult or impossible to analyze effectively. It ensures data accuracy and reliability by standardizing formats and cleaning up any inconsistencies. To sum up, data ingestion helps kickstart the data integration and management process by capturing raw information. ETL further transforms this data into valuable information. Together, they enable organizations to carry out strategic planning and make informed decisions. Benefits of Data Ingestion and ETL Data ingestion and ETL provide several benefits for businesses, enabling them to effectively handle and utilize their data. Some of the key benefits include; Real-time Analytics: Data ingestion and ETL (streaming) support real-time processing. This means businesses can process and analyze data as it arrives, thus facilitating timely responses to events. Ingesting and processing data continuously helps organizations to respond promptly to changing business conditions. Scalability and flexibility: Data ingestion and ETL make it easier for organizations to scale data processing and storage by enabling them to handle massive amounts of data from diverse sources efficiently. Using parallel processing and optimization techniques, companies can accelerate data processing and ingestion workflows. Maintains data integrity and data quality assurance: Apart from collecting data, ETL and data ingestion processes also include mechanisms that ensure data quality and integrity. This can include data cleansing, validation, deduplication, and error handling. Considering these, it’s easier to prevent issues and improve the overall reliability of the data analytics and reporting. Cost Efficiency: Operational costs associated with data management can be reduced using specialized ETL tools and data ingestion tools. These tools automate the data ingestion and ETL processes, which eliminates the need for manual intervention. Consequently, companies can achieve cost efficiency while maintaining high data quality standards. Support for Advanced Analytics: ETL and data ingestion allow the integration of advanced technologies like predictive modeling, machine learning, and data mining as they prepare and organize the data, providing the necessary groundwork. Organizations can retrieve information about valuable patterns and correlations and drive actionable insights. Data Ingestion Use Cases: Data ingestion is important in acquiring and moving data into a system for initial processing or storage. Here are some use cases where data ingestion is specifically applicable. IoT Data Management: Data ingestion is the foundational step in managing data from Internet of Things (IoT) devices. It collects, processes, and stores the large amount of data generated by these devices. Data ingestion allows the organizations to capture data from different sources in real-time or near real-time. Moreover, data digestion makes it possible to integrate the IoT data into existing data processing pipelines, cloud-based platforms, and data lakes. Customer Data Onboarding: Data ingestion integrates external customer data sources into the data infrastructure of an organization. The customer information is incorporated from different channels, including third-party vendors, marketing databases, etc. This efficient data collection allows organizations to ingest large amounts of customer data in real-time or batch processes. Ingesting data in a centralized repository helps an organization in targeted advertising campaigns and marketing initiatives. Log File Analysis: Log-based ingestion is common in performance analysis and security monitoring. Data is ingested from log files generated by systems, applications, or devices that include valuable information about user interactions and system performance. Ingesting log data enables organizations to proactive detection and response to threats. Financial Market Data Processing: Information like currency exchange rates, market indices, stock prices, and trading volumes prove essential in market analysis and risk management. Therefore, its essential to get such information from different sources into a centralized data repository. Ingesting financial market data helps an organization perform various analytical and quantitative tasks including algorithmic trading, modeling, and risk assessment. ETL Use Cases ETL processes are used in various industries for integrating data from multiple sources. Here are some common use cases of ETL: Automating Manual Workflows ETL, when implemented through ETL tools can be used to automate manual workflows. By using ETL tools organizations can design automation logic, monitor operations for continuous optimization and schedule data processing. This helps organizations to enhance efficiency, streamline operations, and reduce manual intervention in data-related tasks. Data Warehousing ETL is preferred for data warehousing as it has comprehensive data processing capabilities. It ensures quality and usability within the warehouse through quality assurance, integration of multiple sources, and management of historical data. Providing these functionalities, ETL makes sure the data in the warehouse is reliable and optimized Streamlining Data Migration Data migration can be streamlined using ETL, particularly when transferring from an on-premises system to the cloud. It begins by pulling both structured and unstructured data from a source system, then cleans and filters it, and finally loads the cleaned data into the target database. ETL helps companies move large amounts of data while ensuring proper organization and ease of use in the target system. Conclusion While different processes, organizations use data ingestion and ETL together to take full advantage of their data assets. Data ingestion facilitates the collection and storage of raw data from diverse sources, while ETL processes are needed to transform and load this data into structured repositories for analysis, reporting, and decision-making. Whether you need to ingest data from multiple sources or ETL it into your data warehouse for analysis and decision-making, you need a modern, no-code data integration solution to simplify and automate the process. This is where LIKE.TG comes into the picture with its Data Pipeline Builder. LIKE.TG Data Pipeline Builder enables you to build fully automated data pipelines to ingest data and execute ETL workflows without writing a single line of code. Streamline company-wide data integration with many native connectors, embedded data quality and governance features, built-in transformations, parallel processing ETL engine, and an intuitive UI. Execute Workflows Without a Single Line of Data Transform how you handle data. 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What is a Data Catalog? Features, Best Practices, and Benefits
What is a Data Catalog? A data catalog is a central inventory of organizational data. It provides a comprehensive view of all data assets in an organization, including databases, datasets, files, and data pipelines. They enable organizations to efficiently manage data by facilitating discovery, lineage tracking, and governance enforcement. Establishing a data catalog is part of a broader data governance framework and includes: creating a business glossary, increasing data literacy across the company and data classification. They are usually created after a company has defined its data, labeled it, identified the relevant stakeholders responsible for data security, and assigned them access roles. Data Catalog vs. Data Dictionary A common confusion arises when data dictionaries come into the discussion. Both data catalog and data dictionary serve essential roles in data management. The former emphasizes discovery and exploration, while the latter focuses on defining and describing data elements in detail. While a data catalog serves as a centralized inventory of metadata, a data dictionary focuses on defining data elements and attributes, describing their meaning, format, and usage. The former offers a comprehensive view of an organization’s data assets. It enables data discovery and exploration by enabling users to easily search and explore available data assets. Additionally, data catalogs include features such as data lineage tracking and governance capabilities that help with data quality management and compliance. On the other hand, a data dictionary typically provides technical metadata and is commonly used as a reference for data modeling and database design. Benefits of a Data Catalog How does a data catalog benefit an organization? Are the benefits just limited to data analysts? Not really! Once implemented correctly, the benefits of a data catalog extend throughout the organization, including: Improved Data Accessibility: With a catalog of data, users can easily find and access relevant data assets for analysis and decision-making. The greater accessibility not only streamlines access to data but also and also enhances productivity. Enhanced Data Trust: Trusted data is important for effective decision-making. Acting as a repository for metadata, data catalogs facilitate metadata management and data lineage tracking. This increased transparency allows users to easily understand the meaning, origin, and quality of the data they’re working with. So, the transparency helps identify potential errors and inconsistencies, leading to more trustworthy and reliable data. Data Governance Enforcement: They also act as a platform for enforcing data governance policies, monitoring data usage, and ensuring regulatory compliance, all while promoting data discoverability and fostering a data-driven culture. Increased Collaboration and Knowledge Sharing: They improve collaboration and communication among data stakeholders by providing a platform for sharing knowledge and documenting data assets. Reduced Data Redundancy and Duplication: After collecting data, catalogs help organizations identify and eliminate redundant or duplicate data assets and reduce storage costs. What Does a Data Catalog Include? Naturally, the next questions usually are how a data catalog works and what it’s made of? First, let’s look at what information it contains. A data catalog consists of various types of metadata, including: Key Components Operational metadata: This is also known as process metadata. It includes information about the execution of processes and workflows within the system. Operational metadata often includes timestamps, execution logs, transaction IDs, resource usage metrics, and system configuration settings. It provides a lineage of data to allow analysts to track a data asset’s recency and relevancy. Technical metadata: Also called structural metadata, this data type outlines how data is organized in a system. It includes details like the format, structure, and characteristics of data elements, like data types, field lengths, formats, keys, indexes, relationships, and storage locations. This metadata is essential for understanding whether data manipulation is necessary before analysis. Business metadata: Business metadata provides business context and meaning to data assets, such as their suitability for a task and overall value. It typically includes data definitions, business rules, data ownership, usage policies, and business glossary terms. How Does a Data Catalog Work? A data catalog works by collecting, organizing, and providing access to metadata about an organization’s data assets. Here’s how it typically operates: Data Ingestion: Metadata from various sources, such as databases, data warehouses, data lakes, and files, is ingested into it. Metadata Organization: The extracted metadata is organized and stored within the catalog. This functionality includes data definitions, schema details, data lineage, and usage statistics. Data Discovery: Users can search and explore the repository to discover relevant data assets based on their metadata attributes, keywords, or tags. This feature allows users to find the data they need quickly and efficiently. Collaboration: Many catalogs offer features to support and foster collaboration and knowledge sharing. These features could be anything from annotations and comments to ratings and reviews. Data Lineage Tracking: It tracks the lineage of data, showing how it moves and transforms across different systems and processes. This capability provides insights into data provenance and helps users understand the origin and impact of the data. Data Governance: It provides mechanisms for defining and implementing governance rules that administrators can use to enforce data governance policies and controls, ensuring that data is managed and used in compliance with organizational policies and regulatory requirements. How to Build a Data Catalog? Creating a catalog involves multiple important steps. Requirement Gathering: The first step is all about identifying the organization’s data management needs and objectives. Involving stakeholders from the outset simplifies the process of defining the requirements and understanding why the organization needs a centralized repository for data assets. Data Discovery: The next step is identifying and inventorying all data sources within the organization, including databases, data lakes, files, and applications, to understand the scope of the data catalog. Metadata Collection: Next, users harvest metadata from the identified data sources, including definitions, schema information, and data lineage. Metadata can also be collected via manual curation in cases where data is sensitive or complex. Metadata Standardization: Standardization and normalization of the collected metadata come next. It involves defining metadata standards, mappings, and taxonomies to harmonize metadata attributes. This step ensures consistency and compatibility across different data sources. Metadata Enrichment: This part involves enhancing metadata with additional contextual information, such as business glossary terms, data classifications, and data usage policies. Metadata Storage: Establishing a centralized repository where users can store, organize, and access the collected metadata. Data Catalog Population: Next, users populate the data catalog with standardized and enriched metadata. User Access and Governance: Administrators then implement data governance policies and define access controls and permissions to manage access rights and modify the data catalog. User Training and Adoption: Team leads also provide training and support to users to help them understand how to navigate and utilize the data catalog effectively for their data management and analytics tasks. Encourage user adoption through communication and outreach efforts. Continuous Maintenance and Updates: Data stewards regularly update and maintain the catalog. Ongoing metadata management, data profiling, and data quality assessment activities ensure the accuracy and relevance of the cataloged information. Having an effective data governance strategy helps greatly in implementing these steps. Since a data catalog is part of a larger governance strategy, stakeholder buy-in, effective data collection, and literacy are needed to make these instruments work. Challenges in Implementing a Data Catalog and How to Avoid Them Common challenges in adopting a data catalog include: Data Silos: Data may be scattered across various systems and departments, leading to data silos that hinder the effectiveness of the data catalog. Data Quality Issues: Inaccurate, incomplete, or inconsistent metadata can undermine the usability and trustworthiness of the data catalog. User Adoption: Lack of awareness, training, or perceived value may result in low user adoption rates, limiting the impact of the data catalog on organizational data management practices. Best Practices To overcome these challenges, data leaders within companies can: Promote Effective Data Collection: Implement processes and technologies to effectively integrate data from disparate sources, breaking down data silos and providing a unified view of the organization’s data assets. Establish High Data Quality Standards: Define and enforce quality standards to ensure that metadata in the data catalog is accurate, complete, and consistent. Implement data profiling and cleansing techniques to proactively identify and address data quality issues. Provide User Training and Create a Data Culture: Offer comprehensive training programs, documentation, and create a data-driven culture to educate users about the benefits and capabilities of data governance measures. Highlight real use cases and success stories to demonstrate the value of the data catalog in improving data management and decision-making processes. Additionally, involve users in the design and development process to ensure that it meets their needs and preferences. Data Catalog Use Cases Let’s look at some use cases across multiple industries: Understanding an asset’s history and improved compliance Compliance officers can rely on the organization’s data repository to ensure data integrity. Utilizing the data lineage feature, they gain invaluable insights into the origin, movement, and transformations of data across disparate systems and processes. Officers can verify the accuracy and reliability of reports and ensure compliance with regulatory requirements. They can also identify potential vulnerabilities, assess the impact of data breaches or system failures, and implement risk mitigation strategies effectively. Empowering self-service analytics In certain fast-paced sectors like healthcare, medical researchers, clinicians, and administrators require timely access to data for diagnosis and recommendations. Through a data catalog, they can quickly discover and access curated healthcare datasets, patient records, and clinical trial data. With intuitive search capabilities and detailed metadata provided by the data catalog, researchers can quickly find relevant data assets based on their research needs and objectives. Improved collaboration Usually, various teams, including marketing, sales, and finance, must collaborate on a campaign. Using data stored in a central location, members from different departments can access and share relevant customer data, transaction records, and demographic information in a single place. As the project progresses, data scientists step in to analyze the shared datasets. They apply advanced analytics techniques to identify patterns and trends in customer behavior. They then work with the marketing and sales teams to develop targeted marketing campaigns and personalized promotions based on the insights derived from the data analysis. Data monetization A telecommunications company can use its catalog to identify customer usage data, including call records, data usage patterns, and location information. Once identified, the team can categorize the customer data into valuable datasets for monetization. By packaging and selling anonymized and aggregated customer data, the company provides valuable insights into consumer behavior, demographics, and mobility patterns. Data Catalog Tools Data catalog tools are essential in supporting and implementing a data governance strategy. Modern tools have extensive features while being very easy to setup and use. However, not all tools are equal, and deciding on one can be difficult. So, a comprehensive software should offer the following key features: Metadata Collection and Management: This includes capabilities for harvesting metadata from various cloud and on-premises data sources, including databases, data lakes, files, and applications. Data Discovery: Intuitive search and browse functionalities to discover and explore data assets based on metadata attributes, keywords, business glossary, or tags. Data Lineage Tracking: It should be able to track the lineage of data, showing how data moves and transforms across different systems and processes. Data Governance Enforcement: The solution should have enforcement for data governance policies and controls, like data classification, access controls, and compliance regulations. AI- powered Automation: A robust tool can automate tasks ranging from data ingestion and metadata storage to business glossary creation using the power of AI. Analysts can also use AI to provide in-tool recommendations and auto-naming capabilities. Collaboration and Sharing: It should allow users to collaborate and share insights via a business glossary, documentation, ratings, and annotations about data assets. Robust Integration Capabilities: Integrating with other data management and analytics tools, such as data integration, visualization, and machine learning platforms. It should also support REST APIs for external connectivity. Security and Compliance: Built-in security features and compliance controls, including encryption, data masking, and audit logging. LIKE.TG – Data Catalog Tool with end-to-end Capabilities If ‘you’re looking to invest in data governance, off-the-shelf tools like LIKE.TG help streamline the entire process. LIKE.TG Data Governance provides a central platform for managing your company data assets without writing any code. Powered by AI, it has multiple features for streamlining the entire governance process. The AI-boosted features include: Data discoverability, Data enrichment, Data Profiling, and a Business glossary. LIKE.TG Data Governance is part of LIKE.TG Data Stack, our end-to-end data management platform. It empowers business users to manage the entire data management project in a single tool, from ETL/ELT to data warehousing and analytics—all code-free. Combined with award-winning customer care, LIKE.TG is the preferred choice for companies investing in data governance. Want to see how LIKE.TG can help you create a robust data governance strategy? Schedule a personalized demo. Learn more about our data management and governance platform.
Enterprise Data Management (EDM): Strategy, Benefits, Best Practices
This article covers everything about enterprise data management, including its definition, components, comparison with master data management, benefits, and best practices. What Is Enterprise Data Management (EDM)? Enterprise data management (EDM) is a holistic approach to inventorying, handling, and governing your organization’s data across its entire lifecycle to drive decision-making and achieve business goals. It provides a strategic framework to manage enterprise data with the highest standards of data quality, security, and accessibility. This means that it includes all the tools, processes, and even people that make it easy for your organization to work with data. It’s not a single person’s duty to manage enterprise data—in fact, its sheer volume requires a dedicated team of cross-functional personnel. Consequently, managing enterprise data is generally the responsibility of database administrators (DBAs), data scientists, IT administrators, and data stewards. This is not an exhaustive list, and the specific roles and responsibilities can vary depending on the size and structure of your organization. Why is Enterprise Data Management Important? The increasing digitization of business operations has led to the generation of massive amounts of data from various sources, such as customer interactions, transactions, social media, sensors, and more. This data, often referred to as big data, holds valuable insights that you can leverage to gain a competitive edge. For example, with a data warehouse and solid foundation for business intelligence (BI) and analytics, you can respond quickly to changing market conditions, emerging trends, and evolving customer preferences. Data breaches and regulatory compliance are also growing concerns. Navigating this complex landscape without establishing robust security measures and a data governance framework exposes your organization to financial and legal repercussions. As such, organizations need a formalized approach to data management, especially at the enterprise level. They need a dependable enterprise data management system—a combination of frameworks, programs, platforms, software, and tools—to use data to their advantage. Craft an Effective Enterprise Data Management Strategy A robust enterprise data management strategy is a prerequisite to ensuring the seamless and secure handling of information across the organization. Download this whitepaper and create an end-to-end data management strategy for your business. Download Whitepaper Master Data Management vs. Enterprise Data Management Together, enterprise data management and master data management (MDM) offer effective ways to overcome data challenges, enabling you to harness your data’s full potential. Both aim to enhance data governance, improve data quality, and ensure that data is accurate and consistent across the organization. However, it’s also important to note that they serve different purposes and have distinct functionalities. First, enterprise data management takes on a holistic approach—it serves as the overarching strategy, eliminating the chaos of siloed information and conflicting versions of the truth. It focuses on establishing policies and processes throughout the entire data lifecycle, from acquisition and storage to analysis and utilization. On the other hand, master data management complements it by focusing on the master data. And second, enterprise data management accounts for all data types, including structured, semi-structured, and unstructured data. In contrast, master data management includes core entities like customers, products, employees, and other key elements that are shared and used across the organization. The table below summarizes master data management vs. enterprise data management: Master Data Management (MDM) Enterprise Data Management (EDM) Scope Creating and managing a single version of specific critical business data (master data). Management of all enterprise data, including master data. Focus Area Ensuring the accuracy and consistency of a limited set of critical data. Takes a holistic approach to data quality, security, and governance. Data Types Primarily deals with core entities like customers, products, employees, etc. Addresses various types of data, including structured, unstructured, and semi-structured data. Applicability Well-suited for organizations with a need for precise management of specific data entities. Suitable for organizations seeking a comprehensive strategy for managing diverse data types across all business areas. Components of Enterprise Data Management Generally speaking, the components of enterprise data management build upon the core principles of data management, which means there’s a bit of an overlap between the two. These components provide specific functionalities that contribute to a comprehensive strategy for enterprise data management. The main components of enterprise data management include: Data Governance Data governance sets the ground rules for data ownership, access, and usage across your organization. Building a data catalog and making clear data management policies enables you to handle data responsibly and ensure compliance with regulations. Data Integration It’s commonplace for data to be scattered across departments and business units. Data integration bridges these gaps as it eliminates silos, combining information from various sources into a unified, holistic view of your organization. Data Quality Management Not all data is created equal. Inaccurate or inconsistent information leads to flawed decisions. Data quality management enables you to implement processes for data cleansing, validation, and ongoing monitoring, providing your teams with reliable data that’s fit for analysis. Master Data Management To maintain the integrity and coherence of critical business information, such as clients, products, and workforce details, you need master data management. Your teams are in a better position to make more informed decisions with accurate master data. MDM also helps organizations achieve a single source of truth (SSOT) that streamlines company-wide collaboration. Metadata Management Data is valuable, but understanding what that data means is invaluable for organizations. Functioning as a data dictionary, metadata management defines the structure and meaning of your data assets. It also facilitates effective data discovery and knowledge sharing within the organization. Data Security and Privacy Enterprise data management goes beyond organizing and storing data; it also ensures data security and privacy. The ever-evolving regulatory environment means that your organization must always be capable of navigating a changing set of rules and standards that govern data. Enterprise Data Management Strategy An enterprise data management strategy is a comprehensive plan outlining how your organization will handle data throughout its lifecycle. It’s a roadmap that covers the acquisition, storage, integration, processing, protection, access, and utilization of data to ensure it becomes a valuable asset for your business. Enterprise data management goes beyond the technical aspects of handling data—your EDM strategy must also take into account the human factors involved in managing enterprise data. In other words, it should foster a data-driven culture that prioritizes communication, collaboration, and ongoing training with the goal of empowering your teams so they can leverage data for organizational success. Formulating an Enterprise Data Management Strategy Because enterprise data management demands a tailored approach, there’s no one-size-fits-all strategy. Here’s a high-level blueprint that serves as a guide to creating an enterprise data management strategy: Business Goals and Data Needs: Start by aligning your EDM strategy with your organization’s overall business goals. Identify the specific data needs that will support achieving these goals. What questions do you need your data to answer? Data Inventory and Governance: Get a clear picture of your current data landscape. This includes identifying and mapping all your data sources, understanding their formats, assessing their quality, defining ownership, and establishing security and access controls. Change Management and Training: Shift your company towards a data-driven culture and equip your employees with data literacy skills by providing ongoing training programs. Technology and Tools: Equip your team with the right software and infrastructure for managing enterprise data at scale. This includes data management platforms, data integration tools, and other solutions. Metrics and Performance Monitoring: Define key performance indicators to track the effectiveness of your enterprise data management strategy and identify areas for improvement. Craft an Effective Enterprise Data Management Strategy A robust enterprise data management strategy is a prerequisite to ensuring the seamless and secure handling of information across the organization. Download this whitepaper and create an end-to-end data management strategy for your business. Download Whitepaper The Benefits of Enterprise Data Management A successful enterprise data management strategy offers several benefits. These include: Improved Decision-Making Imagine marketing campaigns targeted at the wrong customer segments due to inaccurate data. However, with a centralized, clean pool of data, your teams are in the ideal position to generate insights that inform decision-making. Business decisions directly affect the bottom line—with an effective enterprise data management system, the decision-makers in your organization have the power to not only boost innovation but also mitigate risks associated with data breaches and non-compliance. Enhanced Operational Efficiency Data silos across the enterprise mean isolated departments working with incomplete or potentially inconsistent information. Breaking down these silos and achieving a SSOT requires a centralized repository that acts as a single point of entry and access for all departments. Strategically storing and managing enterprise data eliminates these silos and inconsistencies, enabling seamless data sharing and collaboration across departments. This translates to streamlined workflows, reduced errors, faster completion times, and reduced costs. Strengthened Compliance With complete control over your data, it’s much easier to make way for data governance practices, access controls, and data retention policies. And when implemented properly, these practices help your organization comply with various data privacy regulations, like GDPR or CCPA, and minimize legal risks. Additionally, a well-managed data infrastructure facilitates efficient internal and external audits as it simplifies the complexities in data collection and verification during compliance checks. Being transparent about data security and privacy not only demonstrates compliance with regulatory requirements but also strengthens your enterprise’s reputation. Enterprise Data Management Challenges When implemented right, an enterprise data management strategy has the potential to empower your organization to leverage trusted data for its data-driven initiatives. However, implementing it comes with its own set of challenges, chief among which are the following: Eliminating data silos across the enterprise Enterprise data often gets compartmentalized within departments and systems, creating isolated silos. These silos make it difficult to get a unified view of your organization’s data, hindering accurate data analysis and decision-making. Integrating company-wide data Imagine managing enterprise data from dozens of departments, each with its own systems and formats. Customer information might be scattered across marketing databases, sales CRM, and loyalty programs. Financial data could reside in separate accounting and ERP systems. The challenge lies not only in combining data from multiple sources but doing so in a way without compromising its integrity. Improving data quality Enterprise data can be a messy affair, especially when it comes to ensuring data quality. Incomplete records, inconsistencies, and outdated information mean businesses risk security vulnerabilities, wasted resources, and ultimately, customer dissatisfaction. The complexity of data ecosystems in many enterprises further amplifies the challenge. Complying with regulations One of the biggest challenges, especially in heavily regulated industries, is steering through the regulatory landscape. Implementing and maintaining processes and systems that align with these diverse and changing requirements can be resource-intensive. Additionally, different regulations sometimes have conflicting or overlapping demands, posing a challenge in creating a comprehensive compliance strategy. Modernizing legacy systems EDM requires that there’s a clear understanding of data origin and transformations. However, legacy systems store data in outdated formats or proprietary databases and lack proper documentation on how data flows through the system, where it originates, and how it’s transformed. The lack of data lineage makes it challenging to understand the meaning of data and establish its trustworthiness, and migrating this data to a modern system requires careful planning and execution to retain its accuracy. Finding and retaining skilled data professionals Managing enterprise data requires qualified professionals who understand data architecture, best practices, and the specific tools used by the organization. Similarly, developing and executing a successful data strategy also needs experienced personnel. However, the demand for skilled data professionals far exceeds the available talent pool, making it challenging for organizations to attract and retain top talent. LIKE.TG's No-Code Data Management Platform Whether it's extracting and integrating unstructured data, API management, building a custom data warehouse, or EDI management, LIKE.TG's 100% no-code platform allows you to manage and govern your data with ease. Learn More What is an Enterprise Data Management Platform? Enterprise data management platforms are software systems that help businesses in managing the lifecycle of their data. Different platforms offer different levels of convenience and features; some are fully automated, while others still require you to write scripts to integrate and manage data. For example, LIKE.TG is an automated data management platform that enables you to manage enterprise data from end-to-end—without writing a single line of code. How Do Enterprise Data Management Solutions Help Businesses? Enterprise data management software helps businesses simplify and automate several tasks. For example: Automatically extracting unstructured data from various sources using AI. Building ETL pipelines in an intuitive, drag-and-drop UI. Moving data seamlessly from on-premises systems to the cloud environment. Improving data quality management and governing enterprise data. Integrating with legacy systems to retrieve data and modernizing the enterprise data architecture. Enterprise Data Management Best Practices The success of an enterprise data management program doesn’t rely solely on managing data but also on the people who are tasked to oversee it. So, it’s important to incorporate some best practices into your EDM program. Secure a senior leader as an enterprise data management champion to drive adoption across the organization. Define clear goals, understand what your organization wants to achieve with its data, and communicate it to all stakeholders. Educate and provide the requisite training to everyone, regardless of technical background, to understand, interpret, and leverage data effectively. Implement a data catalog to understand what data you have, where it resides, and how it’s used. Prioritize data security, privacy, and governance. Implement procedures that enable access to accurate and timely data. Manage Enterprise Data from End-to-End with LIKE.TG Enterprise data management includes several complex tasks, such as extracting unstructured data from disparate sources, building data pipelines for data integration and movement, managing data warehouses and other destination systems, designing and managing APIs, and the list goes on. However, what simplifies and automates these tasks is enterprise data management software—and one such solution that does it the best is LIKE.TG. LIKE.TG is an end-to-end data management platform that allows you to manage enterprise data in a single, unified UI. With LIKE.TG, you can: Extract and manage unstructured data with ease Integrate data between your systems, whether they’re on-premises or in the cloud, via ETL, ELT, CDC, APIs Improve data quality and prepare it for analytics, reporting, and BI Design, build, and deploy a custom enterprise data warehouse in a matter of days Build, publish, and monitor APIs from a centralized wizard Catalog, enrich, structure, and store your data while enabling secure access to it and sharing it within your organization Simplify building, parsing, and processing EDI documents with your trading partners And much more—all without writing a single line of code. Simplify enterprise data management with LIKE.TG. Download a 14-day free trial or signup for a free demo to get started.
All You Need to Know About Data Completeness
Data completeness plays a pivotal role in the accuracy and reliability of insights derived from data, that ultimately guide strategic decision-making. This term encompasses having all the data, ensuring access to the right data in its entirety, to avoid biased or misinformed choices. Even a single missing or inaccurate data point can skew results, leading to misguided conclusions, potentially leading to losses or missed opportunities. This blog takes a deep dive into the concept of data completeness, exploring its importance, common challenges, and effective strategies to ensure that datasets are comprehensive and reliable. What is Data Completeness? Data completeness refers to the extent to which all necessary information is present in a dataset. It indicates whether there are any missing values or gaps in the data. When all relevant data points are included, a dataset is considered complete. In contrast, incomplete data contains missing or empty fields, which can hinder analysis and decision-making. Examples of Incomplete Data Survey Data with Missing Responses Customer Database with Inconsistent Entries Financial Records with Incomplete Transactions The Importance of Complete Data When it comes to drawing conclusions and making informed decisions, data completeness matters more than businesses often realize. Data Completeness leads to: Improved Accuracy: Complete data ensures that analyses, models, and decisions are based on the most accurate representation of the situation. Incomplete data may lead to skewed results or erroneous conclusions. Increased Reliability: With complete data, findings and predictions gain higher reliability, minimizing the likelihood of errors stemming from data gaps and enhancing the trustworthiness of results. Optimized Decision-making: Complete data empowers decision-makers with the necessary information to make informed and timely decisions. It reduces uncertainty and enables stakeholders to assess risks and opportunities more accurately. Long-term Planning: Complete datasets support long-term planning efforts by providing reliable historical data, enabling organizations to identify trends and make informed projections for the future. Higher Customer Satisfaction: Complete data supports better understanding of customer needs and preferences, enabling organizations to tailor products, services, and experiences effectively. The Role of Data Completeness in Data Quality Completeness is one of the six primary dimensions of data quality assessment. Data quality is a broader term that encompasses various aspects of data, including completeness, accuracy, consistency, timeliness, and relevance, among others. It represents the overall condition of data and its fitness for use in a specific context or application. Data completeness, on the other hand, refers to the extent to which all required data elements or attributes are present and available in a dataset. Data completeness is a measure that directly affects the accuracy and reliability of data. When important attributes or fields are missing, it can lead to erroneous analyses and incorrect conclusions. Incomplete data may also skew statistical measures, such as averages or correlations, potentially leading to flawed insights. Rather than engaging in the data quality vs. data completeness debate, it is crucial to recognize that prioritizing data completeness is fundamental for ensuring high data quality. Data Completeness vs Data Accuracy vs Data Consistency Understanding the differences between data completeness, data accuracy, and data consistency is crucial for ensuring the quality and reliability of data in any organization. Here’s a comparison table highlighting the differences between data completeness, data accuracy, and data consistency: Aspect Data Completeness Data Accuracy Data Consistency Definition Presence of all required data elements or attributes in a dataset. Correctness, precision, and reliability of data values. Uniformity and conformity of data across different databases, systems, or applications. Focus Ensures all expected data points are present without any missing values. Ensures data values reflect real-world entities accurately and reliably. Ensures data remains synchronized and coherent across various sources or systems. Concerns Missing data points, gaps in datasets. Errors, discrepancies, inconsistencies in data values. Conflicts, contradictions, discrepancies between datasets or systems. Importance Essential for comprehensive analysis and decision-making. Critical for making informed decisions and accurate reporting. Vital for reliable analysis, preventing errors, and ensuring trust in data. Example Ensuring all sales transactions are recorded in a sales database. Verifying that customer contact information is correctly entered in a CRM system. Ensuring product prices are consistent across different sales channels. Mitigation Implementing data validation checks, data collection protocols. Data cleansing, verification against reliable sources. Implementing data integration strategies, synchronization mechanisms. How To Determine and Measure Data Completeness There are several approaches to assess data completeness, including attribute-level and record-level approaches, as well as techniques like data sampling and data profiling. Here’s an overview of each approach: Attribute-level Approach In the attribute-level approach, each individual data attribute or field within a dataset is examined to determine its completeness. To measure completeness at this level, users can calculate the percentage of non-null or non-missing values for each attribute. For categorical attributes, users may also look for the presence of all expected categories or values. Example: A dataset contains customer information, including attributes like name, age, email, and phone number. To measure completeness at the attribute level, one would examine each attribute to see how many records have missing values. For instance, if 90% of the records have a value for the “age” attribute, but only 70% have an email address, the email attribute would be considered less complete. Record-level Approach In the record-level approach, entire records or rows of data are evaluated for completeness. This involves assessing whether each record contains all the necessary attributes or fields, and if those fields are populated with meaningful data. Completeness can be measured by calculating the percentage of fully populated records in the dataset. Example: Continuing with the customer information dataset example, with the record-level approach, each record is assessed as a whole. If a record is missing any essential attribute (e.g., name or email), it would be considered incomplete. For instance, if 70% of records have non-null name and email, the dataset will be 70% complete. Data Sampling Data sampling involves selecting a subset of data from the larger dataset for analysis. Sampling can be random or stratified, depending on the characteristics of the dataset and the objectives of the analysis. By analyzing a sample of the data, you can infer the completeness of the entire dataset, assuming the sample is representative. Example: Let’s say there’s a massive dataset with millions of records. Instead of analyzing the entire dataset, one might randomly sample 1,000 records and assess completeness within this sample. If the sample is representative of the overall dataset, findings can be extrapolated to estimate completeness across the entire dataset. Data Profiling Data profiling is a systematic analysis of the structure, content, and quality of a dataset. It involves examining various statistical properties of the data, such as distributions, frequencies, and summary statistics. Profiling can help identify frequency of missing values, outliers, duplicates, and other data quality issues that may affect completeness. Tools like histograms, summary statistics, frequency tables, and outlier detection algorithms can be used for data profiling. Example: Using data profiling tools or techniques, one can generate summary statistics and visualizations to identify frequency of missing values across different attributes. For instance, a histogram could be generated showing the distribution of missing values for each attribute or calculating the percentage of missing values for each attribute. 5 Common Challenges in Ensuring Data Completeness Data Entry Errors: Human errors during data entry, such as typos, missing values, or incorrect formatting. Incomplete datasets may contain missing values due to various reasons, including equipment malfunctions, respondent non-response, or data collection errors. Data Integration Issues: Combining data from multiple sources can cause incompatibilities in data structures or identifiers, which can lead to incomplete or inconsistent datasets. Data Quality Control: Inadequate quality control processes can lead to incomplete data, as errors may go undetected during data collection or processing. Lack of Data Governance: Absence of clear data governance policies and procedures can result in inconsistent data definitions, ownership issues, and poor data management practices, ultimately leading to incomplete datasets. Obsolete Data Systems and Architectures: Inadequate infrastructure or outdated technologies may hinder data collection, processing, and storage. Incomplete data sets can also be due to data privacy regulations and compliance requirements which may limit access to certain data. Strategies to Ensure Data Completeness Establish Clear Data Entry Protocols: Organizations should develop clear guidelines and protocols for data entry to ensure consistency and accuracy. This includes defining data fields, formats, and validation rules to minimize errors during data entry. Implement Data Validation Checks: Automated data validation checks should be implemented to identify incomplete or inaccurate data entries in real-time. This can include range checks, format checks, and cross-field validations to ensure data accuracy and completeness. Regular Data Audits: Conducting regular audits of the data can help identify incomplete or missing data points. These audits should involve comparing the dataset against predefined standards or benchmarks to ensure completeness and accuracy. Use Data Profiling Tools: Data profiling tools can access the contents of a dataset, providing statistics such as minimum and maximum values, unique value count, missing value count etc. By leveraging these tools, organizations can proactively address data completeness issues and take corrective actions. Implement Data Quality Monitoring: Establishing a robust data quality monitoring process allows organizations to continuously monitor the completeness of their data. Alerts and notifications can be set up to flag any deviations from expected data completeness levels. Incorporate Data Governance Policies: Implementing data governance policies ensures that data completeness requirements are clearly defined and enforced across the organization. This includes assigning responsibilities for data stewardship and establishing processes for data quality management. Data Enrichment Strategies: In cases where data completeness is compromised, organizations can employ data enrichment techniques to fill in missing data points. This may involve integrating external data sources or using algorithms to extrapolate missing values based on existing data. Using Automated Tools for Complete Data Automated tools play a crucial role in ensuring the completeness and reliability of data across various domains. These tools facilitate the collection, processing, and analysis of large datasets efficiently, enabling organizations to derive valuable insights and make informed decisions. By automating tasks such as data cleaning, integration, and analysis, these tools streamline workflows and minimize errors, resulting in more accurate and actionable information. Additionally, automated data visualization enables stakeholders to understand complex patterns and trends quickly, facilitating communication and decision-making processes. Moreover, automated tools help organizations maintain data security and compliance with regulations, mitigating risks associated with data handling. LIKE.TG: Ensuring Data Completeness with Advanced No-Code Data Management LIKE.TG offers an end-to-end no-code data management platform equipped with advanced and automated capabilities for data integration, extraction, and preparation. With a wide range of features, LIKE.TG empowers users to create and maintain automated data pipelines that deliver accurate and timely data. With LIKE.TG, users can seamlessly extract and cleanse data from unstructured sources, leveraging AI-powered document processing capabilities. Users can effortlessly integrate data from diverse file sources and database providers, supported by a data pipeline builder that accommodates various formats, systems, and transfer protocols. This reduces the challenge of incompatibilities in data structures or identifiers, which often lead to incomplete or inconsistent datasets. Through the LIKE.TG Dataprep feature, users can cleanse, transform, and validate extracted data with point-and-click navigation, supported by a rich set of transformations including join, union, lookup, and aggregation. With attributes like active profiling, data quality rules, and preview-centric grids, LIKE.TG ensures data cleanliness, uniqueness, and completeness, providing users with attribute-level profile and vivid graphical representations to easily identify patterns of completeness or lack thereof. LIKE.TG also offers ease of integration, allowing users to effortlessly utilize cleaned and transformed data in analytics platforms, thus enabling informed decision-making based on comprehensive and reliable data. Achieve data completeness effortlessly with LIKE.TG today – Book a personalized demo now!
Star Schema Vs. Snowflake Schema: 4 Key Differences
Organizations rely on high-performance data warehouses for storing and analyzing large amounts of data. An important decision in setting up a data warehouse is the choice between Star Schema vs. Snowflake Schema. The star schema simplifies the structure of a database by directly connecting dimension tables to a central fact table. The star shaped design streamlines data retrieval and analysis by consolidating related data points, thereby enhancing the efficiency and clarity of database queries.Conversely, the snowflake schema takes a more detailed approach, breaking down dimension tablesinto additional tables, resulting in more complex relationships where each branch represents a different aspect of the data. Since a chosen schema sets forth the blueprint for organizing and structuring data within the data warehouse, it’s important to understand the key differences between snowflake schema vs. star schema to make the right choice.So, in this blog, we will discuss all about star and snowflake schema, including their important characteristics, example queries, and when to use which. Let’s delve in and see what each of these schema types offer and how they differ. What is a Star Schema? Star schema is a type of data warehouse schema that consists of one or more fact tables referencing multiple dimension tables. This schema revolves around a central table called the “fact table.” It’s surrounded by several directly connected tables called the “dimension tables.” Additionally, there are foreign keys that link data from one table to another, establishing a relationship between the two by using the primary key of another table. This process serves as a means of cross-referencing, ensuring connectivity and coherence within the database structure. The fact table contains quantitative data, often called measures or metrics. The measures are typically numeric, like speed, cost, quantity, and weight, and they can be aggregated. The fact table contains foreign key references to the dimension tables, which contain non-numerical elements. These are descriptive attributes like product details (name, category, brand), customer information (name, address, segment), time indicators (date, month, year), etc. Each dimension table represents a specific aspect or dimension of the data. A dimension usually has a primary key column and is referenced by the fact table through foreign key relationships. In a star schema: The fact table, which holds the primary metrics, is located at the center. Each dimension table is directly linked to the fact table but not to the other dimension tables, hence having a star-like structure. The simplicity of Star schema facilitates aggregated reporting and analysis and streamlines data retrieval operations. This is because the queries typically involve fewer joins compared to more normalized schemas. The reduced complexity and simple structure optimize data access and processing, which is well-suited for cloud-based data warehousing solutions. Moreover, it’s clear delineation between dimensions and facts enables users to analyze information across various dimensions easily. This makes star schema a foundational model in business intelligence applications, too. Characteristics of Star Schema Some main characteristics of a star schema are as follows: Central fact table: There’s a primary fact table containing metrics in the center. It represents activities, events, and business transactions. Dimension tables: They surround the fact table and represent the specific aspect of the business context. Dimension tables show descriptive attributes. Primary-foreign key relationships: The link between the fact and dimension table is established through primary-foreign key relationships, allowing for the aggregation of data across different dimensions. Connection with dimension tables: There are no connections made between the dimension tables. All the dimension tables are connected to the central fact table only. Denormalized structure: The dimension tables are often denormalized, which is beneficial in reducing the need for joins during queries as the necessary attributes are included within a single dimension instead of splitting them across multiple tables. Optimized query performance: Features like direct relationships between fact and dimension tables and the denormalized structure contribute to optimized query performance. This enables star schemas to handle complex analytical tasks and is thus a good fit for data analysis and reporting. Star schemas are ideal for applications involving multidimensional analysis of data, such as OLAP (Online Analytical Processing). OLAP tools support the star schema structure efficiently to perform roll-up, drill-down, aggregation, and other analytical operations across different dimensions. What is a Snowflake Schema? A snowflake schema is an extension of the star schema model, where dimension tables are normalized into multiple related tables resembling the shape of a snowflake. In the snowflake schema, there’s a central fact table that holds quantitative measures. This fact table is directly linked to dimension tables. These dimension tables are normalized into sub-dimensions that hold specific attributes within a dimension. Compared to a star schema, snowflake schema reduces data redundancy and improves data integrity, but it introduces additional complexity to the queries due to the need for more joins. This complexity often affects the performance and understandability of the dimension model. Characteristics of Snowflake Schema The following are the main characteristics of a snowflake schema: Normalization: In a snowflake schema, dimension tables are normalized, unlike in a star schema, where the tables are denormalized. This means that attributes within dimension tables are broken down into multiple related tables. Hierarchical structure: The normalization of dimension tables creates a hierarchical structure that gives a snowflake-like appearance. Relationship between tables: Normalization leads to additional join relationships between the normalized tables that increase the complexity of queries. Performance: Joining multiple normalized tables in a snowflake schema necessitates more computing power due to increased query complexity, potentially impacting performance. Data integrity: Snowflake schemas reduce redundancy and eliminate update anomalies. This ensures data is stored in a consistent and normalized manner. Flexibility: Snowflake schemas provide flexibility in organizing and managing complex data relationships that give a more structured approach to data analysis. Key differences between Star and Snowflake Schema 1. Architecture The dimension tables are denormalized in the star schema. This means they are represented as single tables having all attributes contained within them. The structure of this schema resembles a star, showcasing a fact table in the center and dimension tables radiating out from it. A snowflake schema, on the other hand, has normalized dimension tables. This means they are broken down into multiple related tables. Such a normalization creates a hierarchical structure that resembles a snowflake, having additional levels of tables branching off from the main dimension tables. 2. Normalization Star schemas are denormalized, where all attributes are within a single table for each dimension. This denormalization is done intentionally to speed up performance. However, its downside is that there might be data redundancy, i.e., the same data appearing in multiple dimension tables, requiring more storage. A snowflake schema represents a normalized dimension table, with attributes broken down into multiple related tables. Snowflake schema design avoids data redundancy, improves data quality, and uses less storage space than a star schema. 3. Query Performance Considering there are fewer join operations and a simpler table structure in a star schema, the query performance is usually better compared to the snowflake schema. On the other hand, snowflake schema has complex join operations, which require access to data across multiple normalized tables. As a result, snowflake schema generally results in slower query performance. 4. Maintenance Depending on several factors, such as data complexity and updates and storage space, maintaining both star and snowflake schemas can be challenging. However, star schemas are generally easier to maintain compared to snowflake schemas due to their fewer join operations that simplify query optimization. However, the denormalized structure contributes to some level of redundancy, which requires careful management to improve the accuracy of data analysis and insights. The normalization process in snowflake schemas increases complexity and makes it difficult to maintain. The joins require additional attention to maintain acceptable performance levels. Moreover, managing updates and inserts in the snowflake schema is more complex as there’s a need to propagate changes across multiple related tables. This can be compared to a star schema, where data is more concentrated in fewer tables. Updates typically affect only one or a few tables, making them simpler to manage. Example Query Let’s take the example of “Employee Performance Evaluation by Department and Training Courses Attended” to see how snowflake and star schemas are formed. Star Schema: This query involves querying the fact table containing performance evaluation data and joining it with the dimension tables that represent departments, employees, and training courses. This fact table typically has a foreign key relationship with dimension tables. For instance, the dimension tables can include department dimensions (department ID, manager, name, etc.), employee dimensions (employee ID, job, title, etc.), and training course dimensions (course ID, name, duration). Star schema is commonly used for simpler analytical needs where denormalization is preferred for performance reasons. Snowflake Schema: In a snowflake schema, the dimension tables are further normalized into sub-dimensions, such as department hierarchy, training course categories, and employee details. The additional joins needed to access the normalized data slow down the execution times. Star Schema Vs. Snowflake Schema: Choosing the Right Schema for Your Organization Both the star schema and snowflake schema offer unique advantages, but choosing the right one for your enterprise data warehouse requires careful consideration. Here are some important factors to keep in mind when deciding between a star and snowflake schema: Analytical needs: Evaluate the types of analyses and queries that your business requires, considering whether they lean towards more complex hierarchies or simple, straightforward analyses. If your business requires straightforward analyses with minimal complexity, a star schema might be preferable due to its simpler structure and fewer join operations. On the other hand, if your analyses involve complex hierarchies and relationships between dimensions, a snowflake schema may be more suitable for its ability to represent these intricate relationships in a more organized manner. Scalability: Consider future growth and scalability requirements to ensure the chosen schema can accommodate the changing data needs and scale effectively. For smaller data sets and simpler queries star schema’s denormalized structure performs better. In contrast, snowflake schema’s normalized approach may provide greater scalability and flexibility to handle larger datasets and more complex queries as your data needs grow over time. Data Volume: Assess the effect of massive datasets on storage costs and capabilities before making your schema selection. Star schema’s denormalized design may lead to higher storage costs due to redundancy, whereas a snowflake schema’s normalized structure can help optimize storage efficiency by reducing redundancy. If storage costs are a significant concern, a snowflake schema may be a more cost-effective option for managing large volumes of data. Understand your data: Analyze the structure and complexity of your data, including the relationships between different analyses. This will help in picking the right schema for your business. If your data exhibits a high degree of normalization and requires detailed analysis with many-to-many relationships between dimensions, a snowflake schema may be more appropriate. However, if your data is relatively simple and can be represented with fewer dimensions and straightforward relationships, a star schema may suffice. Goals: Determine whether you require quick insights or comprehensive details. Opt for star schema for quick reporting and snowflake schema for detailed analysis. A star schema is well-suited to faster reporting and simple analysis, making it ideal for organizations that prioritize speed and agility in decision-making. In contrast, a snowflake schema offers more granularity and detail, making it suitable for organizations that require in-depth analysis and a deeper understanding of their data. Conclusion Star schema and snowflake schema have their merits and demerits. One of the reasons the star schema is more common is its simplified structure. While star schema is less complex and has a quicker query execution, it can result in data redundancy and scalability limitations, which the snowflake schema addresses through its normalization of dimension tables. Whether you choose star schema or snowflake schema, you can significantly simplify your data warehouse development journey with automation. LIKE.TG Data Warehouse Builder automates every aspect of building a data warehouse. From schema design to data integration and beyond, LIKE.TG DW Builder enables you to build and deploy a fully functional data warehouse for your organization—all without writing a single line of code. Experience hassle-free data warehouse development with LIKE.TG. Start your free trial today and see how it smooths out the process. Simplify complex data integration with a user-friendly, no-code approach. Explore how LIKE.TG Data Warehouse Builder transforms data integration, enabling businesses to leverage the power of their data without needing technical expertise. Check Demo Now!
Information Governance vs. Data Governance: A Comparative Analysis
Every digital interaction generates data. This data can provide invaluable insights and drive effective decision-making when managed effectively. However, according to a survey, up to 68% of datawithin an enterprise remains unused, representing an untapped resource for driving business growth. One way of unlocking this potential lies in two critical concepts: data governance and information governance. Data governance focuses on the technical and operational aspects of managing data, while information governance looks at the wider policies, procedures, and strategies guiding data usage. They are different, yet they complement each other, providing a holistic approach to managing data. Understanding Data Governance Data governance is a set of processes, policies, standards, and metrics that control and manage the use of data within organizations. Its primary purpose is to ensure organizational data assets’ quality, integrity, security, and effective use. The key objectives of data governance include: Enhancing Clear Ownership: Assigning roles to ensure accountability and effective management of data assets. Ensuring Data Quality: Implementing measures to improve data accuracy, consistency, and reliability. Implementing Lifecycle Management: Establishing efficient data creation, storage, and disposal processes. Protecting Access Controls: Implementing robust mechanisms to safeguard sensitive data from unauthorized access or modification. Understanding Information Governance Information governance is a broader term that includes data governance within its framework. It refers to the strategies, policies, and procedures that manage and utilize all information within an organization. It’s a strategic approach that positions information as a crucial asset, enhancing decision-making and organizational growth. Information governance ensures that all available information is used effectively, aiding in compliance efforts and achieving strategic goals. The main objectives of information governance include: Ensuring Regulatory Compliance: Aligning practices with laws and regulations to mitigate risks and maintain trust. Fostering Knowledge Sharing: Promoting collaboration to leverage collective expertise for innovation and improvement. Implementing Security Measures: Enforcing encryption and monitoring to protect sensitive information. Streamlining Record Keeping: Improving processes for efficient organization, retrieval, and retention of records. Information Governance vs. Data Governance: Key Differences While data and information governance play integral roles in managing an organization’s data and information assets, they differ in scope and focus. Aspect Data Governance Information Governance Focus Management of all kinds of data assets Management of all forms of information assets Objectives Ensuring the accuracy, consistency, security, and compliance of data assets Understanding the business value and compliance needs of the information assets Activities Data quality management, metadata management, data privacy and security Document management, records management, compliance Scope Technical aspects of data management within systems Broader perspective, including information related to documents, knowledge, and intellectual property Stakeholders Data stewards, IT departments Executives, Legal teams, IT departments, Information security teams, etc. To summarize, while data governance focuses on managing data assets, information governance widens the lens to ensure that all information assets are effectively and compliantly utilized, aligning with the overall organizational objectives. Real-World Scenarios of Data Governance and Information Governance in Action Learning how data governance and information governance approaches are applied across industries is important to understand their practical relevance. Here are some real-world scenarios where each approach is effectively implemented: Data Governance E-commerce Quality Assurance: In e-commerce, data governance ensures product quality consistency. Platforms can standardize product information and monitor data quality, which enhances customer trust, minimizes returns, and drives competitiveness. Healthcare Data Security: Data governance is vital to protect patient information. By implementing strict access controls and encryption measures, healthcare organizations safeguard sensitive data, ensuring patient privacy and confidentiality. Information Governance Financial Compliance Management: Financial institutions maintain regulatory compliance through information governance. They can mitigate compliance risks and maintain stakeholder trust by identifying and organizing data subject to regulations, such as Dodd-Frank or Basel III, and enforcing the corresponding policies. Education Knowledge Management: Educational institutions optimize knowledge sharing with information governance. By organizing academic resources and implementing access controls, institutions support teaching and learning goals, fostering innovation and collaboration. How do Data Governance and Information Governance Work Together? Data governance and information governance are complementary frameworks that effectively manage organizational data and information assets. Integrating data governance and information governance practices helps address the technical and business aspects of data and information management. Data governance ensures data integrity, accuracy, and security within organizational systems. In contrast, information governance ensures that all information assets, including documents, records, and intellectual property, are managed effectively throughout their lifecycle. Benefits of Implementing Data and Information Governance Together Combining data governance and information governance practices enables organizations to adopt a comprehensive approach to governance, covering all aspects of managing data and information. This holistic approach enables organizations to: Maximize Data and Information Value: Organizations can derive maximum value from their data and information resources by effectively managing their data and information assets. Data governance ensures accurate, consistent, and secure data, while information governance ensures that information assets are utilized to maximize their value to the organization. Mitigate Risks and Ensure Compliance: Integrating data governance and information governance practices helps organizations mitigate risks associated with data breaches, privacy violations, and non-compliance with regulations. Data governance ensures compliance with data-related regulations and standards. However, Information governance ensures compliance with broader information management requirements, such as GDPR and HIPAA, enhancing regulatory compliance and risk management efforts. Enhance Decision-Making and Innovation: Integrated governance practices provide a comprehensive framework for managing data and information assets, which can support informed decision-making and drive innovation. Data governance ensures decision-makers access accurate, reliable, and secure data. Information governance ensures that relevant information assets are accessible, well-managed, and compliant, facilitating collaboration and innovation. Best Practices for Implementing Data and Information Governance Together Given the benefits of combining these two approaches, How can users effectively implement them? Integrating data and information governance practices requires a strategic approach and adherence to best practices. Here are seven best practices to achieve this: Establish Clear Policies and Frameworks: Define clear data governance and information governance policies and frameworks that align with organizational objectives and regulatory requirements. These policies should outline roles, responsibilities, and processes for managing data and information assets across the organization. Engage Stakeholders Across Departments: Collaboration and buy-in from stakeholders across departments are crucial for successfully implementing governance practices. Engage key stakeholders, including business units, IT teams, legal, compliance, and risk management departments, to ensure that governance policies address the needs and priorities of all stakeholders. Define Data and Information Assets: Identify and classify data and information assets based on their sensitivity, criticality, and value to the organization. This step involves creating a data catalogas a centralized inventory system for easy data discovery. The catalog categorizes assets by type, ownership, and usage, offering a comprehensive view of available data. Implement Data and Information Lifecycle Management: Develop and implement processes for managing the lifecycle of data and information assets, from creation and acquisition to retention and disposal. This step includes establishing data retention policies, archiving procedures, and disposal mechanisms to ensure compliance with regulatory requirements and mitigate data and information exposure risks. Ensure Data Quality and Integrity: Implement data quality management practices to maintain data assets’ accuracy, completeness, and consistency. These practices involve establishing data quality standards, conducting data profiling and cleansing activities, and monitoring data quality metrics regularly to identify and address issues proactively. Provide Ongoing Training and Awareness: Educate employees about the importance of data governance and information governance practices through training programs, workshops, and communication campaigns. Foster a culture of data stewardship and information security awareness to encourage adherence to governance policies and promote responsible data and information management practices. Continuous Monitoring and Improvement: Regularly monitor and assess the effectiveness of governance practices through audits, reviews, and performance metrics. This process involves identifying areas for improvement based on stakeholder feedback and changing organizational needs and regulations. Additionally, prioritize adaptability by continually refining governance frameworks and methods to address the evolving demands of data and information management. Investing in technology solutions, such as data management solutions, data governance platforms, document management systems, and metadata management tools, is important to support governance practices effectively. These tools can automate data classification, enforce governance policies, and provide visibility into data and information assets across the organization. By implementing these best practices and leveraging solutions like LIKE.TG, businesses can unlock the full potential of their data assets while mitigating risks and maximizing opportunities for innovation and growth. Conclusion Data governance and information governance are pivotal for teams looking to achieve efficient data and information management within their organization. They form the basis for seamless company-wide data access and collaborative, cross-functional decision-making. They foster trust in data by establishing clear data integrity, security, and compliance protocols. As a result, teams can confidently use insights to make informed decisions. LIKE.TG’s data governance solution offers a centralized platform for overseeing company data assets effortlessly, eliminating the need for coding. Integrated within LIKE.TG Data Stack, our comprehensive data management solution, LIKE.TG Data Governance empowers business users to handle the entire data pipeline seamlessly. Moreover, it ensures the timely availability of the right data assets to the right teams – all within a single, unified tool. Are you looking for guidance to implement data and information governance? Contact us now.
LIKE.TG’s Guide to Insurance Data Quality and Governance
Data forms the foundation of the modern insurance industry, where every operation relies on digitized systems, including risk assessment, policy underwriting, customer service, and regulatory compliance. Given this reliance, insurance companies must process and manage data effectively to gain valuable insight, mitigate risks, and streamline operations. A strategic approach to data management is needed to meet these demands — particularly a greater focus on high data quality and robust governance to guarantee accuracy, security, and compliance. The Dual Imperative: Upholding Data Quality and Governance Data quality and governance are essential data management components, particularly in the insurance industry. These elements are crucial for its smooth and effective functioning. Data Quality: Ensuring Accurate Decision-Making Reliable and accurate data is critical for assessing and pricing risks associated with policyholders. It enables insurers to set appropriate premiums and coverage limits, minimizing underwriting losses. Moreover, accurate data is vital for meeting regulatory requirements, as regulators mandate the provision of verifiable data for financial reporting and compliance purposes. Additionally, high-quality data enhances decision-making within insurance companies, allowing them to effectively identify market trends, anticipate shifts in customer preferences, and tailor products and services. Key aspects of data quality include: Conducting accurate risk assessment Developing reliable pricing strategies Ensuring data integrity and verifiability for regulatory compliance Data Governance: Safeguarding Sensitive Information Data quality complements governance, which includes the policies, procedures, and controls put in place to regulate data collection, storage, access, and usage within an organization. Effective governance practices safeguard sensitive information, mainly Personally Identifiable Information (PII), from unauthorized access, breaches, and misuse. Adhering to robust governance frameworks allows insurers to ensure compliance with data privacy regulations. As a result, it helps them mitigate legal risks and maintain stakeholder trust. Important aspects of governance include: Establishing data handling policies Implementing access controls Ensuring compliance with data privacy regulations (e.g., GDPR, HIPAA) The synergy between data quality and governance is crucial for the insurance industry’s integrity, regulatory compliance, and stakeholder trust. Prioritizing both aspects allows insurers to improve risk management practices, increase operational efficiency, and uphold the security and reliability of sensitive data throughout their operations. Strategies for Effective Data Governance and Quality Management Implementing effective data governance and quality management practices is essential for the insurance industry. Here are some best practices to consider: Establish Clear Roles and Responsibilities: Introduce a system for requesting and approving access to data assets. This system, guided by clear policies and procedures, includes cataloging assets, defining ownership, and assigning roles and responsibilities. It operates under a well-structured governance framework, ensuring accountability and consistency in data management. Implement Robust Data Governance Frameworks: Adopt a structured approach to data governance by implementing frameworks such as COBIT, DAMA, or ISO 8000. These frameworks provide guidelines and best practices for managing data assets, ensuring data quality, and mitigating risks associated with data management. Invest in Data Quality Tools and Technologies: Use data quality tools to assess, cleanse, and monitor data quality across the organization. These tools can help identify and rectify data errors, inconsistencies, and duplications, ensuring the reliability and accuracy of data used for decision-making and reporting. Provide Ongoing Training and Education: Offer training programs and educational resources to employees at all levels to enhance their understanding of data governance principles and best practices. Ensure employees have the resources to manage data and adhere to data governance policies effectively. Implement Data Security Measures: Implement strong security protocols to protect sensitive data from unauthorized access. This includes encrypting data, implementing access controls, conducting regular security audits, and staying up-to-date with data privacy regulations. Establish Data Quality Metrics and KPIs: Define metrics to measure the effectiveness of data governance and quality management initiatives. Monitor and track these metrics regularly to identify areas for improvement. This monitoring helps ensure continuous enhancement of data quality and governance practices. How LIKE.TG Drives Data Quality and Governance in Insurance Operations As discussed above, investing in the right data management tools is crucial for ensuring data quality and governance. LIKE.TG is a leading provider of enterprise-level data management solutions. Its solutions are designed to handle complex data structures, making it an ideal choice for the insurance industry. Delivering high-performance, scalable, and intuitive data management capabilities, LIKE.TG empowers insurers to make informed decisions based on accurate, timely, and reliable data. Data Quality Management with LIKE.TG LIKE.TG’s data integration solution is a robust platform designed to ensure the highest data quality standards for the insurance industry. It implements a series of checks and balances throughout the data integration process, validating data at every stage, from extraction and transformation to loading. This rigorous validation process is crucial in the insurance industry, where data accuracy, such as policy details, claims data, and customer information, directly affects the level of service provided to policyholders. LIKE.TG’s solution identifies and manages data inconsistencies, duplicates, and errors, ensuring the data used in decision-making processes is accurate and reliable. Moreover, it standardizes data formats, ensuring uniformity and consistency across different data sources. Access to reliable data is particularly important in the insurance industry, where data is often sourced from various channels, including agents, brokers, and digital platforms. By standardizing data formats, LIKE.TG’s solution eliminates discrepancies and enhances the accuracy of data-driven decisions. LIKE.TG’s Data Governance Solution LIKE.TG’s data governance solution provides a robust framework for managing access to sensitive insurance data. This solution seamlessly integrates with LIKE.TG’s data integration solution, enhancing the overall data management process. A key feature of LIKE.TG’s data governance solution is its ability to centralize access to all data assets, enabling seamless sharing across departments within the insurance company. While ensuring easy access to data, the solution implements controls to restrict access appropriately. This balance between data accessibility and control is crucial in the insurance industry, where different teams need specific datasets to carry out functions like claim processing, risk assessment, policy management, and customer service. Additionally, LIKE.TG’s solution ensures that only authorized personnel can access sensitive insurance information, which helps protect PII. The solution achieves this through request-based access controls. Under this system, a user is required to request access to a specific asset formally, and the asset owner has the authority to grant or deny this access. This feature is particularly crucial in the insurance industry, where sensitive data such as policyholder details, claim histories, and financial information must be strictly controlled and protected. Use Case Scenario Let’s consider ABC Insurance, a large-scale insurance company, to understand how LIKE.TG’s solutions can be applied in a real-world scenario. It relies on various data sources for risk assessment and policy management. However, disparate data sources and inconsistent data management practices have resulted in poor data quality. This has led to significant challenges: Inaccurate Risk Assessments: The company struggles to accurately assess risks associated with policyholders, impacting their ability to set appropriate premiums and coverage limits. Data Breach Concerns: Inadequate data governance measures have left ABC Insurance vulnerable to data breaches, potentially exposing sensitive customer information to unauthorized access and compromising data security. Implementing LIKE.TG’s Solutions: ABC Insurance implements LIKE.TG’s comprehensive data management and governance solutions to address these challenges, leveraging specific features to tackle their issues head-on. Enhancing Data Quality: LIKE.TG’s solution offers advanced data profiling, cleansing, and standardization features. For example, ABC Insurance can apply validation checks such as ensuring that all policyholder addresses are in a valid format (e.g., street address, city, state, zip code). These data quality rules ensure data accuracy and consistency across the organization. Ensuring Robust Data Governance: LIKE.TG’s data governance solution enables ABC Insurance to establish granular access controls, ensuring that only authorized personnel can access sensitive customer data. For instance, at ABC Insurance, access to policyholder medical records is granted to members of the Underwriting Department only upon their formal request and subsequent approval by the authorized asset owner. Furthermore, access to specific types of information within these records, such as sensitive personal information can be restricted to only authorized personnel within the department. Results and Benefits: Following the implementation of LIKE.TG’s solutions, ABC Insurance experiences significant improvements across various aspects of their operations: Accurate Risk Assessments: LIKE.TG’s data profiling, cleansing, and standardization features allow ABC Insurance to achieve greater accuracy in risk assessments, enabling it to set appropriate premiums and coverage limits, ultimately reducing underwriting losses. Enhanced Data Security: LIKE.TG’s data governance solution allows ABC Insurance to establish robust access controls, reducing unauthorized access to sensitive customer information. Improved Decision-Making: Access to reliable data enables ABC Insurance to make informed decisions, identify market trends, and develop tailored products and services, which improves business outcomes and drives success. Conclusion A growing demand for personalized customer service and stricter data protection rules will increase the insurance industry’s reliance on data. Hence, it becomes essential for insurance companies to invest in advanced data management solutions like LIKE.TG. LIKE.TG offers a unified, user-friendly solution combining data quality management and governance. Its automation capabilities streamline complex data processes, allowing insurance companies to focus more on strategic decision-making and less on manual data tasks. LIKE.TG’s solutions empower insurance companies to improve data accuracy, enhance security, and ensure efficient data management processes. Plus, the flexibility and scalability of LIKE.TG’s platform makes it a future-proof investment to deal with the surge in data volume. Take the next step in your data management journey. Download a free trial of LIKE.TG’s solution today and see how it can empower your insurance company!
What is Metadata Governance?
What is Metadata Governance? Metadata governance is the systematic approach to managing metadata, or data about data, to ensure it is accurate, consistently formatted, and securely stored. The main goal of metadata governance is to improve data quality, ensure data privacy, and streamline metadata management. As metadata provides the necessary details about data, such as when it was created, by whom, and its content or purpose, it becomes easier for employees to find, understand, and trust the data they use. It gives the needed context and unique characteristics to data. For example, in a corporate setting, metadata about a sales report could include information about its creation date, the department that created it, and the type of data it contains. Metadata as a Foundation of Data Governance Metadata serves as the foundation of data governance by offering the contextual framework for understanding, managing, and using data within an organization. It guides the data governance structure with essential data attributes and usage information, enabling informed decisions about data assets. In data governance, metadata ensures that data definitions and relationships are consistently applied across the organization, eliminating ambiguities and fostering a unified approach to data interpretation. It also enhances data discoverability, allowing users to quickly find relevant data for analysis, reporting, and decision-making. Importance of Metadata Governance Governing metadata is an important component of the data governance framework. Without proper metadata governance, organizations risk data misinterpretation, compliance breaches, and inefficiencies in data use. For instance, without clear metadata governance, two departments can use the same data differently, leading to inconsistencies and potential errors in reporting. Metadata governance ensures consistency by defining and enforcing rules for how data is described, accessed, and used across an organization. It also encompasses procedures that manage data’s lifecycle, enabling uniform understanding and use of data, thus preventing discrepancies in how different departments interpret and report data. Main Components of Metadata Governance Effective metadata governance includes the following six components: Metadata Standards and Guidelines These are rules outlining how metadata should be structured and formatted. They determine what metadata to collect, how to classify and categorize it, and conventions for naming and describing it. Roles and Responsibilities Clearly defined roles ensure each stakeholder understands their part in metadata governance. This step includes defining who creates, maintains, accesses, and makes decisions about metadata. Metadata Repository A centralized database stores and manages metadata. It should be easily accessible to stakeholders and equipped with search capabilities for finding and retrieving specific metadata. Processes and Procedures It details how metadata is created, updated, and managed. They also define how metadata quality is unaffected through validation, discrepancy resolution, and change tracking. Tools and Technology Systems and software used to create, manage, and access metadata. These tools should support organizational standards and processes while being user-friendly. Metrics and Performance These assess the effectiveness of metadata governance. Data quality metrics allow organizations to measure the health of metadata and maintain its accuracy. The measures generally include metadata accuracy, completeness, consistency, and stakeholder engagement level. Advantages of Effective Metadata Governance Implementing effective data governance brings numerous benefits to the organization by streamlining the operations and optimizing the data as a strategic asset. Improved Data Quality By providing clear context and meaning for data, metadata governance ensures accurate, consistent, and complete information. This reliable foundation leads to more trustworthy analysis and insights. Faster Decisions Finding and understanding the data an organization needs becomes easy with metadata governance, reducing search time and allowing businesses to make quicker, data-driven decisions. Stronger Collaboration Through metadata governance, everyone in the organization uses and interprets data the same way. This shared knowledge base allows for more effective teamwork and consistent use of data across various projects and initiatives. Easy Compliance In industries like power and energy, data privacy regulations are important. Metadata governance helps manage and control data effectively, ensuring compliance with legal requirements. Usable Data High-quality, well-documented data becomes more valuable with strong metadata governance, opening doors to monetization opportunities with customers, partners, and other stakeholders. Enriched Data Management Strong metadata governance strengthens the entire data management approach. Solid Foundation: By meticulously managing metadata, organizations build a strong base for their data. A strong base improves the organization, quality, and usability of information. Clear Guidelines: Metadata governance sets clear rules for handling data, making it easier and faster to manage the data assets. Improved Workflows: Metadata governance optimizes the data lifecycle from creating and storing data to accessing and disposing of it. An optimized system leads to smoother data workflows and a more efficient system. Five Steps to Implement Metadata Governance Setting up a successful metadata governance program is a strategic process that requires thoughtful planning, precise action, and continuous oversight. Assess the Current Metadata Landscape The first step is to take stock of the existing metadata landscape. This involves identifying what metadata is currently being captured, how it’s being stored, and the systems in use. For example, a retail company examines its product information management system to understand how product attributes, such as size, color, and price, are described and categorized. This initial assessment helps pinpoint gaps and opportunities for improvement. Define Clear Goals and Objectives Next, organizations must outline specific goals and objectives for their metadata governance. An example objective could be enhancing data quality for better analytics or improving data sharing across departments. Establish specific goals like enhancing product metadata accuracy to boost online shopping experiences, providing a focused direction, and enabling progress tracking. Establish a Governance Structure Creating a governance structure means defining who is responsible for managing and overseeing metadata within the organization. This step involves assigning roles like a metadata manager or governance committee and detailing their responsibilities. For instance, the metadata manager might ensure that product metadata across all online platforms remains consistent and up to date while the governance committee oversees the overall strategy and resolves any disputes. Develop and Enforce Policies and Standards With roles and responsibilities in place, the next step is to develop clear policies like metadata access, quality, and distribution. These guidelines dictate how metadata should be formatted, the taxonomy to be used, and procedures for updating metadata. A financial services firm, for instance, would establish policies around tagging financial transactions with metadata that includes the transaction type, amount, date, and involved parties, ensuring consistency and facilitating easier reporting and analysis. Regularly Review and Refine Practices Metadata governance isn’t a set-it-and-forget-it process. Regular reviews and updates are required to ensure the practices remain relevant and effective. This step could involve periodic audits of metadata quality, surveys of users to understand new needs, or assessments of the governance structure to identify areas for improvement. For example, a media company would review its content metadata practices annually to ensure that new content types are adequately captured and described. Opportunities in Metadata Governance Companies can optimize their data management resources and gain a competitive edge by strategically leveraging opportunities in metadata governance. Encouraging Team Collaboration One of the key opportunities lies in collaboration across different departments and teams. When diverse teams come together to share their knowledge and best practices about metadata, the organization benefits from a more comprehensive understanding of its data assets. This collaborative approach can develop standardized metadata practices keeping them consistent across the organization, enhancing overall data quality management. For instance, creating cross-functional working groups dedicated to metadata governance can facilitate discussions on common challenges and opportunities. These groups can work on developing shared metadata standards, guidelines, and vocabularies that all departments can adopt, creating consistency and facilitating smoother data integration efforts. Advanced Metadata Solutions Modern data management tools and platforms offer features and capabilities that help organizations govern and manage data, including metadata. In contrast, dedicated metadata management tools offer functionalities such as automation of metadata collection and processing, enhanced data cataloging, and more efficient metadata asset integration. These features significantly reduce the manual effort involved in metadata governance and management, allowing teams to focus on more strategic data governance initiatives. Implementing a metadata management solution that integrates seamlessly with existing data systems can simplify cataloging data assets, maintaining up-to-date metadata records, and providing users easy access to the information they need. For example, automated metadata tagging and classification systems can help organizations quickly categorize their data assets based on predefined rules, making data easier to find, understand, and use. Strategies for Effective Metadata Governance To fully capitalize on the opportunities, organizations should consider the following strategies: Promote Metadata Literacy: Educate all employees on the importance of metadata and its role in effective data governance. Organizations can provide training on how to create, use, and manage metadata correctly. Establish Clear Metadata Governance Policies: Develop and implement clear policies that outline metadata management roles, responsibilities, and procedures. Such policies enable everyone to understand their part in maintaining high-quality metadata. Measure Success: Define clear metrics to measure the effectiveness of metadata governance initiatives. Businesses can track improvements in data quality, reductions in data discovery times, or increases in user satisfaction with data systems. Final Words Integrating metadata governance into a company’s data governance strategy transforms the value of its data. It enhances data reliability and usability, making it a strategic asset that can drive decision-making and innovation. The journey towards effective metadata governance requires a thoughtful approach, which involves understanding the current landscape, refining practices, and leveraging new opportunities for collaboration and technological enhancement. LIKE.TG simplifies metadata governance efforts by automating metadata discovery, categorization, and management – empowering users to complete these tasks more accurately and in less time. With LIKE.TG, businesses can establish a metadata governance framework that enhances data quality and facilitates faster decision-making. Implement Metadata Governance to Optimize Data Management Looking to enhance metadata governance? Try LIKE.TG for free for 14 days and optimize your data management. Experience how LIKE.TG streamlines data governance processes. Learn More
How to Build a Data Governance Strategy for Your Organization
An effective data governance strategy is crucial to manage and oversee data effectively, especially as data becomes more critical and technologies evolve. This strategic approach to data governance aligns with findings from a McKinsey survey, suggesting that companies with solid data governance strategies are twice as likely to prioritize important data — leading to better decision-making and organizational success. However, creating a solid strategy requires careful planning and execution, involving several key steps and responsibilities. What is a Data Governance Strategy? A data governance strategy is a comprehensive framework that outlines how data is named, stored, and processed. A vital aspect of this strategy includes sharing data seamlessly. This objective is to ensure data integrity, accessibility, and compliance with regulatory frameworks. Building upon this foundation, a good data strategy is a blueprint for organizations to achieve their overarching data aims. It does so by adding specific details to the broader goals. For instance, if an organization aims to launch a new product, a good strategy will ensure that up-to-date data backs up the launch and related decisions. It would focus on what the customer wants, how the market is behaving, and what other competitors are doing, all through the lens of fresh, accurate data. In short, a data governance strategy includes the following: Establishing principles, policies, and procedures for data management. Developing a strategic plan with resource allocation and timeline. Implementing governance bodies to oversee compliance. Aligning the overarching data strategy. Ensuring ongoing monitoring and adaptation. Three important components of data governance strategy ensure an organization’s practical management of data assets. These components offer a comprehensive plan for maximizing the value of data assets. Data Governance Framework A data governance framework is the backbone and foundational structure of the data governance strategy. It encompasses a set of rules, principles, policies, and processes that govern data collection, storage, and use. Under the data governance framework, organizations consider questions like: What standards and policies are in place to manage data? Who is responsible for various data governance roles? What are data privacy and security protocols? How will they monitor data access and sharing? Data Governance Roadmap A data governance roadmap essentially outlines the strategic plan and timeline for implementing the data governance framework. The goal is to identify key milestones, initiatives, and activities for successfully executing a data governance strategy. Think of it as a project plan that guides an organization to where it is now and where it wants to be regarding data governance. In a governance strategy, the roadmap identifies areas needing improvement, areas performing well, and those that have not met expectations, enhancing data utilization and strategy performance. Under the data governance roadmap, organizations consider questions like: What resources, such as tools and budget, are required? What are the key milestones and what is the timeline being considered for each stage? What projects are planned to enhance data governance? How will the KPIs be measured and reported? How will the KPIs be measured and reported? Data Governance Program A data governance program executes policies defined in a data governance framework guided by the strategic direction set in the roadmap. In simple terms, it deals with the operationalization aspect of any data governance strategy, which is needed to ensure that the principles and plans turn into concrete actions and outcomes. Under a data governance program, organizations consider questions like: How are data governance principles applied in daily operations? How is the impact of data governance programs on quality and business outcomes measured? How is cross-functional collaboration facilitated? How are the data quality issues identified and resolved within the strategy? Why is a Data Governance Strategy Needed? IDC predicts that by 2025, the worldwide volume of data is expected to expand by 163 zettabytes, covering information across physical systems, devices, and clouds. Processing and managing such a large amount of data requires an effective data governance strategy. It is needed to navigate the complexities of data systems. A data governance strategy helps with the following concerns: Need for Compliance It helps establish clear rules and procedures to ensure adherence to relevant regulations like GDPR. This strategy helps prevent data mishaps and builds a culture where data is handled wisely, protecting the company’s image. Data Security A good data governance strategy also includes measures to protect the data from breaches. By effectively implementing access controls, authentication mechanisms, data masking, and other security measures, teams can protect sensitive information effectively. Cost Reduction It helps reduce operational costs by reducing errors and redundancies in the data. Data governance strategies promote seamless data sharing; hence, fewer resources are spent on data maintenance, contributing to overall cost savings. Employee Productivity It clearly defines roles and responsibilities for data management. This clarity leads to employees spending less time searching for information and dealing with complex data systems, allowing them to focus on higher-value tasks and projects. Better Decision-making A good data governance strategy equals data integrity, and it ensures decision-makers have access to timely and reliable data. This results in better strategic outcomes and, ultimately, better business performance. Building a Data Governance Strategy for Your Organization Here are five actionable steps to build an effective data governance strategy for your organization: 1. Assess Current Data Management State Evaluating the current data state is the foundation for an effective data governance strategy. To effectively govern data, start by understanding what the data entails and whether it aligns with the objectives of data of the data governance strategy. Here is how to approach it in detail: Catalog Your Data Identify and map out data by clearly outlining each data set within the organization. This process includes structured and unstructured data on digital servers, online platforms, manual records, and cloud repositories. One tip is automating your data cataloging. Automated data solutions allow organizations to have a comprehensive data overview, thus making it possible to more effectively understand and interpret data as indexed in a data catalog. Review and Identify Gaps After cataloging, evaluate how the data is stored, managed, and used. Consider any procedures related to data quality and security that are currently in place. Closely look for mismatches between existing data practices and the ideal state or regulatory standards and collaborate with different departments to gather insights on their data usage. 2. Define Goals and Objectives For a strategy to be successful, it needs a clear direction. This step begins with defining the data governance goals. The significance of establishing well-defined goals and objectives is deeply rooted in the strategy’s capacity to facilitate quick and seamless data sharing. When creating a plan, ensure that goals are clear and cover everything from big to small targets. For instance, a data governance strategy can have the following goals: Achieving 100% compliance with data privacy regulations. Reducing data-related errors by 50% within the next year. Enhancing data quality through routine audits. Streamlining data sharing between departments. Here is how to approach defining goals in a data governance strategy: Define Key Performance Indicators (KPIs) For each goal, define clear KPIs that will help measure progress. The KPIs can include metrics like the reduction in data errors, improvement in data accessibility, or the number of staff trained in data governance practices. Set Priority for Each Goal Not all goals can be handled at once. Prioritize data governance strategy goals based on their urgency and impact on the organization. Start with the most impactful and urgent goals, such as ensuring full compliance with GDPR/CCPA regulations within 6-8 months to avoid legal penalties. Try the SMART Approach When working on data governance objectives, ensure that the goals are Specific, Measurable, Achievable, Relevant, and Time-bound. This clarity will facilitate easier monitoring of progress and evaluation of the strategy’s effectiveness. 3. Identify Resources for Data Governance Selecting the right tools and personnel is crucial for data governance success. An IBM report shows companies with robust data governance and the right resources see a significant increase in revenue, highlighting the importance of having skilled individuals. To identify the necessary resources for a data governance strategy, a three-tiered data governance lead (DGL) is efficient. DGL ensures proper data use and aligns data rules with business goals across all departments. In a DGL, the: Steering Committee (SC) leads, Data Governance Office (DGO) coordinates, Representatives from each business area, including IT, come together as the Data Governance Working Group (DGWG). Here is how to identify the right resources for data governance strategy: Evaluate Skills Emphasize the recruitment of experienced personnel while highlighting the skills necessary for success, including effective communication, the ability to build consensus, and a deep understanding of governance industry practices. Set Performance Metrics Use necessary metrics to check the effectiveness of the chosen tools and the team’s performance. Evaluation helps identify areas of improvement and measure the success of your data governance strategy. 4. Implement Metadata Management Metadata management is a core step in building an effective data governance strategy. Metadata provides data with specific context, such as its format, source, creation date, and version. In simple terms, it adds meaning to the data. By implementing metadata management into the data governance strategy, you create a structured environment where data is not only secure and compliant but also readily accessible and usable for analytics and decision-making, Here’s how to approach this: Define Metadata Standards Create clear standards for metadata, including formats, naming conventions, and taxonomy, to ensure consistency across data assets. Audit Existing Metadata Conduct a comprehensive audit of existing metadata across the organization. This examination includes identifying all data sources, their associated metadata, and any gaps in metadata documentation. Implement A Repository Create a centralized metadata repository or a catalog that serves as the single source of truth for metadata across the company. The repository should be accessible to all stakeholders, supporting searchability and metadata management. 4. Establish Governance Framework A data governance framework serves as a blueprint for your data governance strategy, containing all the policies and procedures. For instance, policies may specify data retention periods and access controls, while procedures detail the monitoring and maintenance of data quality. Approach the governance framework in your data governance strategy by: Setting Goals Define what you want to achieve with your governance strategy. Also set a scope by identifying which data assets will be governed and the departments or business units involved. Creating Policies and Standards Develop clear data governance policies outlining roles, responsibilities, standards, and procedures. Policies should cover data quality, privacy, security, and usage. Establish data standards and metrics for measuring data quality, compliance, and the effectiveness of governance activities. Assigning Roles Define a governance structure that includes roles such as data stewards, data owners, and a data governance council or board. Assign specific responsibilities for data management and governance activities to individuals or teams. Implementing a Data Governance Strategy As mentioned before, putting your data governance strategy into practice involves following the roadmap, using the framework, and managing the governance programs. Start by following the roadmap, which tells you what steps to take and when. This approach helps keep the implementation focused and aligned with your primary business goals. Alongside, ensure the rules and responsibilities set out in your framework are followed daily. This adherence means having teams check that everything is running smoothly and making any necessary adjustments. By keeping on top of these tasks, your strategy will fit well with current operations and strengthen the overall management and use of data in your organization. Wrapping Up! To sum up, a good data governance strategy is the main step in creating a successful data governance program, and each governance strategy is unique, with its own set of goals and objectives. By keeping points like being clear when defining goals and opting for the right resources and tools, organizations can lay the groundwork for a reliable data governance strategy, enhancing efficiency and data-driven insights. LIKE.TG’s data governance solution enhances this process by offering a no-code, unified platform that simplifies data management, ensuring data integrity and enabling seamless sharing. Try out a free 14-days trial today for a comprehensive solution to all your data governance needs. Enhance Your Data Governance Strategy with LIKE.TG Don't wait to transform your data governance framework into a more manageable and effective system. Discover how LIKE.TG's no-code solution can empower your organization. Start you 14-days-trial now!
Best Data Mining Tools in 2024
What Is Data Mining? Data mining, also known as Knowledge Discovery in Data (KDD), is a powerful technique that analyzes and unlocks hidden insights from vast amounts of information and datasets. Data mining goes beyond simple analysis—leveraging extensive data processing and complex mathematical algorithms to detect underlying trends or calculate the probability of future events. What Are Data Mining Tools? Data mining tools are software that assist users in discovering patterns, trends, and relationships within vast amounts of data. They come in various forms, from simple to complex, catering to different needs. Type of Data Mining Tool Pros Cons Best for Simple Tools (e.g., Excel, Tableau) – User-friendly interface for beginners. – Data visualization and simple pattern recognition. – Quick and easy to learn. – Limited capabilities; suitable only for basic tasks. – Lack advanced algorithms and features. Simplifying data visualization and basic analysis. Advanced Tools (e.g., Python libraries, R) – Sophisticated algorithms for complex tasks. – Comprehensive machine learning capabilities. – Customizable and extensible. – Steeper learning curve; requires coding skills. – May overwhelm beginners. Enabling advanced analytics and custom model development. Domain-Specific Tools – Tailored for specific industries or domains. – Specialized features relevant to the industry. – Efficient for specific use cases. – Limited flexibility outside the targeted domain. – May not cover all data mining needs. Streamlining industry-specific data processing. Big Data Tools (e.g., Apache Spark, Hadoop) – Scalable for large datasets. – Distributed processing capabilities. – Handles parallel processing efficiently. – Complex setup and configuration. – Requires expertise in distributed computing. Simplifying distributed data processing. Text Mining Tools (e.g., NLTK, spaCy) – Extract insights from unstructured text. – Useful for sentiment analysis, topic modeling, etc. – Supports natural language processing. – Limited to text-based data. – May struggle with noisy or poorly formatted text. Simplifying text preprocessing and sentiment analysis. Web Mining Tools (e.g., Scrapy, Beautiful Soup) – Scrapes web content and analyzes weblogs. – Useful for competitive intelligence and content extraction. – Automates data collection from websites. – Requires knowledge of web scraping techniques. – May encounter legal and ethical issues. Simplifying web data extraction and content analysis. Why Are Data Mining Tools Important? Data mining allows businesses to analyze historical data, helping them predict future outcomes, identify risks, and optimize processes. Data mining tools help organizations solve problems, predict trends, mitigate risks, reduce costs, and discover new opportunities. Whether it’s choosing the right marketing strategy, pricing a product, or managing supply chains, data mining impacts businesses in various ways: Finance: Banks use predictive models to assess credit risk, detect fraudulent transactions, and optimize investment portfolios. These tools enhance financial stability and customer satisfaction. Healthcare: Medical researchers analyze patient data to discover disease patterns, predict outbreaks, and personalize treatment plans. Data mining tools aid early diagnosis, drug discovery, and patient management. Marketing: Marketers rely on customer segmentation, recommendation engines, and sentiment analysis. These tools enhance targeted advertising, customer retention, and campaign effectiveness. Customer Insights: Data mining tools enable users to analyze customer interactions, preferences, and feedback. This helps them understand customer behavior and pinpoint buying patterns, allowing them to tailor offerings, improve customer experiences, and build brand loyalty. Process Optimization: Data mining tools help identify bottlenecks, inefficiencies, and gaps in business processes. Whether it’s supply chain logistics, manufacturing, or service delivery, these tools optimize operations, reduce costs, and enhance productivity. Competitive Advantage: Data mining tools help businesses harness data effectively, revealing market trends, competitor strategies, and emerging opportunities. Top 8 Data Mining Tools 1. Apache Mahout Apache Mahout is a linear algebra framework that supports scalable machine learning and data mining. It offers several algorithms and tools tailored for developing machine learning models capable of processing large datasets. With its distributed architecture, Apache Mahout allows scalability over machine clusters. It also allows mathematicians and data scientists to create and execute custom algorithms for various machine-learning models. Key Features: Mathematically expressive Scala DSL Support for multiple distributed backends (including Apache Spark) Integration with Hadoop and Spark Scalability Algorithm support Pros: Can handle large datasets. Offers fast model training and prediction times. Supports a wide range of machine-learning algorithms. Integrates with platforms like Hadoop. Cons: There’s a high learning curve for using Apache Mahout. Best For: Implementing custom machine learning algorithms. 2. MonkeyLearn: MonkeyLearn is a machine-learning-based text analysis platform. It utilizes artificial intelligence to analyze and understand textual data. Therefore, it can help businesses extract insights from text-based sources such as social media posts, customer reviews, articles, and more. Key Features: Text Mining Specialization Custom Machine Learning Models No Code Integration Capabilities Pros: Easy to use and integrate with other platforms. Can handle large volumes of data. Offers fast model training and prediction times. Cons: Sometimes the segregation is generic based on the email content and needs more examples to learn. Financial category is not easily segregated/tagged. Challenging to have Monkey Learn bucket support tickets into distinct user-readable buckets based on ticket text. Best For: Businesses that need to process large volumes of data quickly and easily integrate their data mining models with other platforms. 3. Oracle Data Mining: Oracle Data Miner is an extension to Oracle SQL Developer for data scientists and analysts. It enables users to leverage Oracle databases for building, evaluating, and comparing machine learning models directly within the database environment. Oracle Data Miner provides access to advanced algorithms for data mining and machine learning. Users can integrate these algorithms into their SQL queries, allowing efficient model-building and evaluation processes within the familiar Oracle SQL Developer interface. Key Features: Interactive Workflow Tool Explore and Graph nodes for visualizing data Automated Model Building features Integration with RWorks with Big Data SQL Pros: Seamless integration with the Oracle Database Enterprise Edition. Can handle large volumes of data. Offers a graphical user interface for easy data mining. Multiple data mining algorithms and techniques are available. Cons: Requires more technical knowledge to use effectively. Microsoft Excel is required to decrypt data. Integration failures can occur due to complexity in the system across other platforms. Dependence on Oracle Database. Best For: Businesses that require a wide range of data mining algorithms and techniques and are working directly with data inside Oracle databases. 4. Sisense Sisense is a data analytics platform emphasizing flexibility in handling diverse data architectures. It offers the ability to connect with various data sources, which benefits businesses with complex data structures. The data mining platform offers features such as data preparation, exploration, and the creation of machine learning models, all aimed at optimizing performance and quality. Key Features: Ad-hoc Analysis Centralized Data Hub Data Connectors Scalable Data Handling Interactive Dashboards Pros: Easy to use and integrate with other platforms. Can handle large volumes of data. Offers fast model training and prediction times. Cons: Limited to certain types of models (e.g., classification, regression, and clustering). May not be suitable for businesses with complex data mining needs. Best For: Businesses that require a user-friendly interface for creating and deploying predictive models. 5. SAS Enterprise Miner SAS Enterprise Miner is a data mining tool offering offers various predictive modeling, data mining, and analytics capabilities. The data mining tool provides users access to various statistical, data mining, and machine learning algorithms. Key Features: Interactive GUI and batch processing Data preparation and exploration Model building and evaluation Multithreaded high-performance procedures Self-sufficiency for business users Pros: Easy to use and integrate with other platforms. Can handle large volumes of data. Offers fast model training and prediction times. Cons: Users expressed their dissatisfaction with the software’s interface. Several users have found the software difficult to learn. Best For: Businesses that need to process large volumes of data quickly and easily integrate their data mining models with other platforms. 6. Knime KNIME is an open-source analytics platform. It’s notable for its adaptable and modular design. It equips users with the capability to conduct extensive data transformations, explorations, and analyses, all facilitated by a user-friendly graphical interface. Knime’s modular structure allows for the straightforward assembly and personalization of data workflows. It also connects to an array of pre-designed nodes and components. Key Features: Drag-and-drop workflow creation Integration with R Open-source nature Customizable workflows Community support Pros: Easy to use and integrate with other platforms. Can handle large volumes of data. Offers fast model training and prediction times. Accessible and customizable due to its open-source nature. Cons: Some users have reported issues integrating Knime with specific platforms, such as Jupyter notebooks. Best For: Businesses that require robust data analytics capabilities without the complexity of more intricate data mining systems. 7. Orange Orange is an open-source tool for data mining, visualization, and analysis, crafted to support exploratory tasks and interactive visualizations. The tool comes equipped with an extensive array of visualization instruments and widgets, enabling the examination and analysis of various datasets. Key Features: Visual programming Machine learning widgets Customizable machine learning models Pre-trained classifiers and extractors No coding required Versatility Pros: Can handle large volumes of data. Offers fast model training and prediction times. Offers various machine learning algorithms. Integrates with platforms like Python. Cons: Manual Troubleshooting. Advance analysis is not so easy. Support isn’t always reliable. A high learning curve. Best For: Businesses that need to visually program custom machine learning models. 8. RapidMiner RapidMiner is an open-source platform widely recognized in the field of data science. It offers several tools that help in various stages of the data analysis process, including data mining, text mining, and predictive analytics. The data mining tool is designed to assist users in extracting insights from data. Key Features: Distributed Algebraic optimizer R-Like DSL Scala API linear algebra operations Text analysis and sentiment detection Pros: Easy to use and integrate with other platforms. No coding skills needed Easy to set up Dashboard is clean Offers fast model training and prediction times. Cons: Performance issues with large datasets Software stability Data output limitations Best For: Businesses that need to process large volumes of data quickly and easily integrate their data mining models with other platforms. How to Choose the Right Data Mining Tool Selecting the appropriate data mining tool can significantly influence the outcomes of data analysis efforts. To assist users in navigating this choice, the following guide outlines the essential considerations for choosing a data mining tool that aligns with their specific needs: 1. Understanding Data Requirements Before diving into the selection process, users must have a clear understanding of their data: Data Types: It’s imperative to ensure that the chosen tool is adept at handling the particular types of data users work with, be it structured or unstructured. Data Volume: The tool’s capacity to efficiently process the amount of data users plan to analyze should not be overlooked. 2. Define Your Requirements Clarifying requirements upfront can streamline the selection process: Analytical Needs: Users should pinpoint the types of analysis they aim to conduct, such as predictive modeling, clustering, or regression. User Expertise: The tool should correspond to the proficiency level of its users, catering to environments ranging from code-intensive for data scientists to graphical user interfaces for business analysts. 3. Evaluate Tool Capabilities A thorough evaluation of the tool’s capabilities is crucial: Functionality: Seek out tools that boast a comprehensive feature set in line with the analytical tasks users intend to perform. Performance: The tool’s capability to manage complex computations and sizable datasets is a key performance indicator. Scalability: The chosen tool should accomodate the growth of user data needs and remain relevant as their organization develops. 4. Integration and Compatibility The tool’s ability to integrate and coexist with existing systems is vital: Data Sources: Confirm that the tool offers support for the data sources that users employ. Software Ecosystem: The degree to which the tool integrates with other software in the user’s tech stack, such as databases, BI platforms, or cloud services, should be considered. 5. Support and Documentation The level of support and resources available can greatly affect user experience: Vendor Support: Opt for tools that are supported by dependable vendor assistance or a strong user community. Documentation and Training: Adequate learning materials and troubleshooting guides are essential for mastering the tool and resolving potential issues. 6. Trial and Testing Hands-on experience with the tool can provide valuable insights: Free Trials: Users are encouraged to utilize free trials or community editions to gauge the data mining tool’s capabilities firsthand. Weighing these factors can help users choose a data mining tool that satisfies their immediate requirements. It’s important to remember that the most suitable tool is the one that best harmonizes with the users’ data, objectives, and available resources. The Prerequisite to Data Mining: LIKE.TG Data mining requires meticulous data preparation and processing. This is whereLIKE.TG, a leading end-to-end data management platform, comes into play. LIKE.TG offers a comprehensive suite of features that swiftly prepares data for analysis. It empowers users to construct end-to-end data pipelines, leveraging sophisticated ETL features and a robust enterprise-grade integration engine. A key aspect of data preparation is the extraction of large datasets from a variety of data sources. LIKE.TG excels in this area, offering automated and bulk extraction from disparate sources, including unstructured sources, databases, data warehouses, cloud data providers, file systems, transfer protocols, web services, and various file formats. Transformation and conversion capabilities are another crucial component of data preparation. LIKE.TG provides users with advanced tools for reformatting data to meet specific analysis requirements or converting data from one format to another, ensuring both flexibility and efficiency. Data quality is a priority for LIKE.TG. It incorporates built-in features for data cleansing and scrubbing, and its rule-based data quality verification ensures the accuracy and integrity of data. Finally, LIKE.TG’s user-centric design simplifies complex tasks. Its intuitive drag-and-drop or single-click operations eliminate the need for extensive coding, significantly boosting productivity and efficiency in data mapping, validation, and cleansing tasks. In essence, LIKE.TG provides a comprehensive solution for making data analytics-ready, thereby facilitating efficient data mining. Key Features: AI-Driven Data Management: Streamlines unstructured data extraction, preparation, and data processing through AI and automated workflows. Enterprise-Grade Integration Engine: Offers comprehensive tools for integrating diverse data sources and native connectors for easy mapping. Interactive, Automated Data Preparation: Ensures data quality using data health monitors, interactive grids, and robust quality checks. Advanced Data Transformation: Offers a vast library of transformations for preparing analysis-ready data. Dynamic Process Orchestration: Automates data processing tasks, allowing for execution based on time-based schedules or event triggers. User-Centric Design: With its no-code, drag-and-drop interface, LIKE.TG makes data management accessible to users of all technical backgrounds. Seamless Integration: Integrating with a wide array of data sources, both on-premises and cloud-based, ensures a smooth data management experience. Comprehensive Data Handling: Offers a unified platform for all data-related tasks, from extraction to insights, backed by a vast library of data operations. How LIKE.TG Enables Robust Data Mining Workflows Data mining helps organizations extract valuable insights from their data. However, without automated data pipelines, it’s difficult for organizations to ensure the integrity and usefulness of data throughout the analysis process. LIKE.TG empowers organizations to create data pipelines with minimal effort, leveraging automation to streamline the data mining process. Data pipelines play a pivotal role in processing data from disparate sources. They seamlessly integrate data from various origins and transform it into a format that is ready for analysis. This transformation process, which includes data cleaning, normalization, aggregation, and conversion, ensures a consistent and unified view of data. Furthermore, data pipelines offer the advantage of real-time processing, providing up-to-date information that is crucial for prompt decision-making. Automated data pipelines also save time and resources by reducing manual errors in the extraction, transformation, and loading (ETL) process. As organizations grow, their data grows correspondingly. Data pipelines, designed to scale, accommodate this growth, ensuring the data infrastructure keeps pace with organizational needs. Lastly, data pipelines prioritize maintaining high data quality. They ensure data consistency, identify and correct errors, and remove duplicates through built-in features for data cleansing, validation, and verification. Here’s how LIKE.TG achieves this: AI-Powered Document Extraction: LIKE.TG’s advanced AI technology enables users to capture data fields from unstructured files. Data Transformation and Conversion: Users can easily transform and prepare datasets for analysis using built-in transformations. Automated Rule-Based Data Quality: Users can ensure data extracted is accurately and reliably through rule-based verification and correction. No-Code Data Integration: Allows business users to manage complex data processes with minimal IT intervention, thanks to its no-code platform. Automation: With LIKE.TG, much of the data pipeline process is automated.Users can extract, transform, validate, and load data seamlessly, which significantly reduces manual effort and the potential for errors. Scalability: LIKE.TG’s solution is capable of handling growing data volumes and complexity without a drop in performance. Ready to transform your data mining processes with unparalleled efficiency and ease? Download your free 14-day trial and experience the power of seamless data integration or schedule a personalized demo to see LIKE.TG in action.
The Best Data Ingestion Tools in 2024
Data ingestion is important in collecting and transferring data from various sources to storage or processing systems. In this blog, we compare the best data ingestion tools available in the market in 2024. We will cover their features and pros and cons to help you select the best software for your use-case. What is Data Ingestion? Data ingestion is collecting and importing data from various sources into a database for further analysis, storage, or processing. This process can handle data in two primary modes: real-time processing, where data is ingested and processed immediately as it is generated. The other is batch processing, where data is collected at specific intervals and processed simultaneously. What are Data Ingestion Tools? Data ingestion tools are software and solutions designed to automate data transfer from various sources, like cloud storage, file systems, etc., to designated storage or analysis systems. These data ingestion tools streamline data gathering, reduce the need for manual intervention, and enable organizations to focus more on data analysis and insight generation. There are different types of data ingestion tools, each catering to the specific aspect of data handling. Standalone Data Ingestion Tools: These focus on efficiently capturing and delivering data to target systems like data lakes and data warehouses. They offer features like data capture, batch and real-time processing, and basic data transformation capabilities. While standalone data ingestion tools can fit data ingestion-specific use cases, organizations prefer solutions that are more flexible. ETL (Extract, Transform, Load) Tools: While ETL tools can handle the overall data integration process, they are also often used for data ingestion. Data Integration Platforms: Data integration platforms offer multiple data handling capabilities, including ingestion, integration, transformation, and management. Real-Time Data Streaming Tools: These tools ingest data continuously as it appears, making it available for immediate analysis. They are ideal for scenarios where timely data is critical, like financial trading or online services monitoring. Benefits of Data Ingestion Tools Data ingestion tools offer several benefits, including: Faster Data Delivery: Automating data ingestion with the help of tools speeds up processing, enabling more efficient scheduling. Improved Scalability: Automated data ingestion tools facilitate adding new data sources as the company grows and allow for real-time adjustments to data collection processes. Data Uniformity: Using data ingestion tools enables the extraction of information and converting it into a unified dataset. Organizations can use this information for business intelligence, reports, and analytics. Easier Skill Development: Data ingestion tools are designed with non-technical users in mind and often feature simplified interfaces that make learning and using them easier. How Do Data Ingestion Tools Work? Data ingestion tools help move data from various sources to where it can be stored and looked at. These tools utilize multiple protocols and APIs like HTTP/HTTPS, ODBC, JDBC, FTP/SFTP, AMQP, and WebSockets to efficiently connect with and transfer data from sources such as databases, cloud storage, files, and streaming platforms. First, these tools collect data from the sources using set commands or custom ones to find the data. Sometimes, data from different sources might be in different formats or structures. So, the data ingestion tools transform data to ensure consistency in format and structure. Then, these tools put this data into databases or data warehouses for analysis. Data ingestion tools also offer data movement directly into the destination system for situations where loading the data as quickly as possible is the priority. Why are Data Ingestion Tools Important? Data comes in many forms and from many places. A business might have data in cloud storage like Amazon S3, databases like MySQL, and coming in live from web apps. Transferring this data to necessary locations would be slow and difficult without data ingestion tools. Data ingestion solutions simplify and accelerate this process. They automatically capture incoming data, allowing businesses to quickly analyze their data and make timely decisions based on current events rather than outdated information. These tools are also flexible, as they can efficiently manage dynamic data sources, seamlessly incorporating data from new sources without requiring a complete system. This flexibility allows businesses to update and expand their data management strategies without disruption continuously. For example, if a company starts getting data from a new source, the tool can add that without starting from scratch. Top 8 Data Ingestion Tools in 2024 Various data ingestion tools in the market offer plenty of features and cater to specific business requirements. Below is a list of some of the best data ingestion solutions and their key features. LIKE.TG LIKE.TG is an enterprise-grade data platform that simplifies and streamlines data management. From data ingestion and validation to transformation and preparation to loading into a data warehouse, it accelerates time-to-insight by automating data movement. LIKE.TG’s suite of solutions caters to unstructured data extraction, data preparation, data integration, EDI and API management, data warehouse building, and data governance. With LIKE.TG’s no-code platform, ingest data from various sources into your data ecosystem without writing a single line of code. LIKE.TG offers native connectors to databases, file formats, data warehouses, data lakes, and other sources. You can easily access and ingest data from any source, regardless of format or location. LIKE.TG’s built-in transformations help to clean, enrich, and transform your data. From simple data cleansing to complex data transformations, LIKE.TG prepares your data for analysis and decision-making without technical expertise. LIKE.TG’s intuitive UI and unified design simplify data ingestion. Easily navigate the platform, design data pipelines, and quickly execute workflows. LIKE.TG’s parallel processing ETL engine enables you to handle large volumes of data efficiently. It provides optimal performance and scalability, allowing you to meet the growing data demands of your enterprise. LIKE.TG provides award-winning customer support and extensive training and documentation to help you maximize your data ingestion efforts. The platform offers the support and resources you need, from onboarding to troubleshooting. Keboola Keboola is an ETL platform designed for performing complex tasks. It provides custom options for data ingestion. Keboola facilitates a clear view and understanding of ETL setups. The platform accommodates various stores, such as Snowflake, Redshift, etc., and allows for SQL, Python, and R transformations. Pros Offers pre-built connectors to streamline data ingestion across multiple data sources and destinations. Users can write transformations in various languages and load or directly store the data within Keboola. Offers customized data sourcing for authentic analysis. Cons Modifying the schema or manipulating data can be complex with internal file-based storage. The cross-branch change review sometimes fails to detect the changes. Users must manually set up the webhooks or API triggers to import event data. Airbyte Airbyte is an open-source data integration platform. It allows businesses to build ELT data pipelines. It enables data engineers to establish log-based incremental replication. Pros The Connector Development Kit (CDK) allows for creating or modifying connectors in almost any programming language. Replicates a decent volume of data using change data capture (CDC) and SSH tunnels. Users can use straight SQL or DBT to transform the data. Cons Scheduler sometimes interrupts jobs unexpectedly. Regular updates require users to install new versions often. Predicting usage and controlling costs become difficult as data volumes grow. Matillion Matillion ETL is a data ingestion tool allowing users to create pipelines using a no-code/low-code, drag-and-drop web interface. Pros Its primary focus is on batch data processing, which is optimized for the transformation and loading phase of the ETL process within the cloud data warehouses. It replicates SQL tables using change data capture (CDC) by design. Matillion’s cloud-native transform engine scales to manage large datasets. Cons It sometimes struggles to scale hardware infrastructure, particularly EC2 instances, for more resource-intensive transformations. Users often complain about outdated documentation with new version releases. Matillion struggles with collaboration. Teams larger than five face challenges working together on the same data ingestion workflows. Talend Talend is a low-code platform that collects data from different sources and transforms it for insights. The tool integrates data ingestion, transformation, and mapping with automated quality checks. Pros It offers pre-built components for data ingestion from different sources. Users can design or reuse data pipelines in the cloud. It offers low-code and automated data replication. Cons Talend’s software is complex, requiring learning time before using it confidently, even for simple data ingestion pipelines. Documentation for features is often incomplete. Version upgrades, capacity changes, and other common configuration tasks are not automated. Hevo Data Hevo Data is a no-code, cloud-based ETL platform designed for business users without coding skills, simplifying the data ingestion. Pros The API allows easy integration of Hevo into the data workflow and enables performing pipeline actions without accessing the dashboard. It offers end-to-encryption and security options. The no-code data ingestion pipelines use a graphical UI to simplify creating ingestion workflows. Cons It limits data integration into BI tools or exporting data to files through integration workflows. It does not offer customization of components or logic. Also, the user cannot write code on his own. It offers very limited data extraction sources. Apache Kafka Apache Kafka is an open-source distributed platform suitable for real-time data ingestion. Pros It supports low latency for real-time data streaming. It can adjust storage and processing to handle petabytes of data. The platform ensures data persistence across distributed and durable clusters. Cons It is a complex software that requires a steep learning curve to understand its architecture. Users face challenges while working on small data sources. Kafka’s replication and storage mechanisms require significant hardware resources. Amazon Kinesis Amazon Kinesis is a cloud-hosted data service that extracts, processes, and analyzes your data streams in real-time. This solution captures, stores, and processes data streams and videos. Pros It offers low latency, meaning analytics applications can access streaming data within 70 milliseconds after collection. The Kinesis app integrates with many other AWS services, allowing users to build complete applications. It automatically provisions and scales resources in on-demand mode. Cons It is not a suitable tool for on-premise data ingestion or multi-cloud, as it is integrated within the AWS ecosystem. Users must utilize separate services to analyze or store data, as it only focuses on data migration. It does not offer clear documentation, which is often confusing to the consumers. How to Choose the Right Data Ingestion Platform? Opting for the right data ingestion tool directly impacts the data management strategy of an organization. Various factors should be considered while choosing the data ingestion platform. Data Sources and Formats Businesses should consider if the tool supports connectivity with all relevant data sources, including databases, cloud services, APIs, and streaming platforms. Also, they need to verify if the tool can handle various data formats, such as structured, semi-structured, and unstructured data, to meet their specific data ingestion requirements. Scalability and Performance The scalability of the data ingestion tool is key for handling increasing data volumes without sacrificing performance. Businesses should look for features like parallel processing and distributed architectures. These can handle large datasets effectively, ensuring data is processed smoothly and quickly as the company expands. Data Transformation Capabilities It is important to evaluate the tool’s data transformation features, including data cleaning, enrichment, aggregation, and normalization capabilities. Businesses should consider the tools to perform these transformations before ingesting the data into their storage or processing systems to maintain data quality and consistency. Ease of Use and Deployment Businesses should opt for a tool that offers a user-friendly interface and intuitive workflows to minimize the learning curve for their team members. Additionally, they need to choose a tool with flexible deployment options, such as cloud-based, on-premises, or hybrid deployments, to suit their business requirements and preferences. Integration and Interoperability The right data ingestion tool seamlessly integrates with existing data infrastructure and tools. Businesses should look for pre-built connectors and APIs that facilitate integration with databases, data warehouses, BI tools, and other systems in their data ecosystem. This practice enables smooth data flows and leverages existing investments effectively. Cost and ROI Businesses should evaluate the data ingestion tool’s total cost of ownership (TCO), including licensing fees, implementation costs, and ongoing maintenance expenses. They need to consider the tool’s pricing model and calculate the potential return on investment (ROI) based on improved efficiency, faster time to insights, and better decision-making enabled by the tool. Concluding Thoughts Data ingestion tools play an essential role in data integration tasks by streamlining the transfer of large datasets. They help you set up a strong ingestion pipeline for managing data, saving time and effort. Utilizing a top data ingestion tool is a fundamental step in the data analytics process. These tools also enable you to monitor and improve data quality, maintaining compliance with privacy and security standards. If you are seeking a comprehensive data ingestion tool, LIKE.TG is the right choice. LIKE.TG’s no-code, modern data integration solution can simplify and automate the process of ingesting data from multiple sources. Schedule a demo or download a free trial of LIKE.TG to experience effortless data ingestion. Don’t wait; quickly simplify your data management to drive better business outcomes. Start Streamlining Your Data Management Today Schedule a demo with LIKE.TG today and see for yourself how straightforward and efficient data ingestion can be. If you're ready to experience the benefits first-hand, try LIKE.TG for free and start transforming your data workflow without any delays. Request a Demo
Improving Healthcare Data Governance and Integration with LIKE.TG
One of the key processes in healthcare data management is integrating data from many patient information sources into a centralized repository. This data comes from various sources, ranging from electronic health records (EHRs) and diagnostic reports to patient feedback and insurance details. Also, the volume of healthcare data is rising with every passing day, making healthcare data governance increasingly complex and, at the same time, essential for ensuring patient privacy, data accuracy, and regulatory compliance. According to RBC, the digital universe of healthcare data is expected to increase at a compound annual growth rate of 36% by 2025. In response to this growing complexity and volume, LIKE.TG offers a comprehensive solution to seamlessly integrate diverse healthcare data sources and govern data assets, ensuring that all patient information is unified and accessible. Healthcare Data Governance Data governance in healthcare is a multifaceted approach that encompasses the establishment of policies and standards to ensure the effective management, security, and integrity of patient data. The delineation of roles and responsibilities for data stewardship, where individuals are entrusted with overseeing the lifecycle of data from collection to disposal, is an important component of healthcare data governance. In parallel, data governance initiatives entail the deployment of advanced technologies to enhance patient data protection and access control mechanisms. Encryption, access controls, and robust authentication protocols are among the tools utilized to safeguard sensitive patient information from unauthorized access and cyber threats. With a data governance framework, healthcare organizations can harness the full potential of their data as a strategic asset, improving not just the bottom line but also patient care and clinical outcomes. Integrating Disparate Healthcare Data Data management is crucial for improving patient care and efficiency within the healthcare system. Healthcare providers often deal with data from various sources, such as EHRs, lab reports, imaging systems, and wearable health devices. However, a solution-focused approach can simplify the integration process and offer significant benefits. Breaking down data silos is necessary to obtain a unified view of patient data. By integrating information from different sources, healthcare professionals can gain a comprehensive understanding of a patient’s health and improve the quality of care they provide. For instance, integrating real-time data from wearable devices with EHRs enables healthcare professionals to make timely interventions and tailor care plans according to individual needs. Once the data is integrated, governance can further facilitate healthcare providers. By enhancing data discovery, hospitals can easily search and retrieve patients’ information. It ensures that data across all platforms is consistent, accurate, and easily accessible. Additionally, data governance enables healthcare providers to make more informed decisions, which can lead to better patient outcomes. According to the McKinsey Global Institute, effective healthcare data integration and use of big data could generate up to $100 billion annually across the US healthcare system, highlighting the potential benefits of enhancing data integration and governance. Healthcare Data Governance with LIKE.TG Integrating diverse healthcare data into a unified system is only the first step towards transforming healthcare operations. The real utilitylies in using the combined data effectively, made easier by LIKE.TG‘s advanced data governance features for organizing and finding data. Data Cataloging for Data Retrieval LIKE.TG‘s data governance solution comes with a data cataloging function that acts like a search engine for healthcare data, offering advanced search and filtering capabilities. This functionality allows healthcare professionals to quickly locate specific data sets or patient information. For example, a doctor looking for a patient’s history with conditions can quickly retrieve all relevant records without sifting through multiple systems or databases. Data Management through Data Tagging Additionally, LIKE.TG’s data tagging feature enhances how healthcare professionals manage and access data. By allowing users to tag data assets with specific labels, it simplifies organizing and finding data based on criteria such as disease type, patient demographics, or treatment outcomes. For example, if a healthcare provider tags patient records with “diabetes” and “2024 check-ups,” any team member searching for recent diabetes cases can quickly retrieve all relevant patient files without navigating through extensive databases. Streamlined Workflows The synergy between data integration and data governance through LIKE.TG streamlines workflows and enhances decision-making capabilities. Healthcare professionals can access a comprehensive view of patient data, enabling more accurate diagnoses, personalized treatment plans, and predictive health insights. This integrated approach to data management and discovery boosts overall healthcare operational efficiency. With more accessible and systematically organized data, healthcare organizations can eliminate unnecessary administrative tasks and improve resource allocation. Practical Application of LIKE.TG for Healthcare Transformation Let’s say, Bright Future Clinic recently adopts LIKE.TG’s data management solution to tackle the challenge of efficiently managing chronic disease patients, focusing specifically on heart disease management. LIKE.TG facilitates data governance but also seamlessly integrates with existing systems and workflows. By integrating diverse healthcare data into a unified system, the clinic aims to streamline operations and improve patient care. The clinic uses the data cataloging function as a search engine, enabling healthcare professionals to instantly find specific datasets or patient details. For instance, when a patient with a history of heart disease visits, a doctor quickly accesses their complete medical records, including previous EHRs, lab results, and medication history, without the need to sift through multiple systems. Additionally, by implementing data tagging, the clinic organizes patient records and relevant health data with tags such as “heart disease,” “2024 consultations,” or “medication adherence.” This approach simplifies the organization and retrieval of data based on specific criteria, enhancing the ability to provide personalized and timely care. The integration of LIKE.TG’s solution streamlines the clinic’s workflows, significantly improves decision-making, allowing healthcare providers to focus more on patient care and less on administrative tasks. Advantages of LIKE.TG’s Integrated Approach LIKE.TG’s integrated approach to healthcare data governance and management streamlines the entire data lifecycle, from acquisition to analysis, ensuring data remains an important asset for enhancing patient care and operational efficiency. Improved Data Consistency LIKE.TG’s data integration and governance solutions standardize the handling of patient data, ensuring every department works with the same, accurate information. Enhanced Data Security With LIKE.TG, healthcare providers can enforce strict data security measures. Role-based access ensures that only authorized personnel can view sensitive patient information, reducing the risk of data breaches. Easier Data Discovery LIKE.TG’s data cataloging feature simplifies the search for patient information into a straightforward task. Healthcare professionals can quickly find specific patient records, treatment histories, or medication details using advanced search and filtering, thereby speeding up the care process. Reduced Operational Costs By automating data integration and improving data discovery, LIKE.TG helps healthcare providers operate more efficiently, cutting down on unnecessary tests and administrative overhead. Improve Healthcare Data Governance and Integration with LIKE.TG Through seamless integration and data cataloging, healthcare professionals can access a comprehensive view of patient information, driving more informed decisions and personalized care. Whether it’s improving data consistency, enhancing security, facilitating easy discovery, or reducing operational costs, LIKE.TG provides the tools needed to achieve excellence in healthcare data governance. LIKE.TG’s data integration and governance solutions offer a transformative approach to handling healthcare data. By unifying data from diverse sources and ensuring its easy discovery and use, healthcare providers can significantly improve patient care, enhance operational efficiency, and ensure data security. Optimize healthcare data governance and management with LIKE.TG. Experience the difference with a 14-day free trial and discover how LIKE.TG can transform your data into one of your most valuable assets for patient care and operational success. Manage Your Healthcare Data with LIKE.TG Looking to simplify the complex process of data integration and governance in healthcare? Try LIKE.TG for free for 14 days and optimize your workflow. Start a Free Trial
The Top 7 Data Aggregation Tools in 2024
As data variety and volumes grow, extracting insights from data has become increasingly formidable. To put it into perspective, a bibliometrics study reveals that an astounding 2.5 million new papers are published annually. Processing this information is beyond traditional data processing tools. Automated data aggregation tools offer a spectrum of capabilities that can overcome these challenges. They gather, process, and analyze data from diverse sources. From handling modest data processing tasks to managing large and complex datasets, these tools bolster an organization’s data infrastructure. This blog reviews the top 7 data aggregation tools, exploring how each solution ensures that every byte of an organization’s data is harnessed for strategic insights. What are Data Aggregation Tools? Data aggregation tools are solutions designed to collect data from multiple sources, process it, and present it in a summarized format. These tools are essential for consolidating diverse data sets into a coherent whole, enabling organizations to analyze and derive insights from combined data. They typically include functionalities for extracting data from various databases, transforming it to align with business needs, and loading it into a central repository for easy access and analysis. Why Are Data Aggregation Tools Important? Data aggregation tools allow businesses to harness the power of their collective data, often siloed across different systems and formats. By aggregating data, these tools provide a unified view crucial for informed decision-making, trend analysis, and strategic planning. They also save time and resources by automating the data collection and transformation processes, reducing the likelihood of errors and ensuring data integrity. Who Uses Data Aggregation Tools? Business analysts, data scientists, IT professionals, and decision-makers across various industries rely on data aggregation tools to gather and analyze data. They are particularly valuable in finance, healthcare, marketing, and retail sectors, where data is a critical asset for understanding market trends, customer behavior, and operational efficiency. Essentially, any organization aiming to leverage data for competitive advantage will benefit from data aggregation tools. Example Scenario: Data Aggregation Tools in Action This example demonstrates how data aggregation tools facilitate consolidating financial data from multiple sources into actionable financial insights. Scenario:A multinational corporation has branches worldwide, each operating with its financial systems and maintaining separate ledgers, expense records, and revenue reports. The corporation’s CFO needs to prepare a consolidated financial statement for the annual report. However, the financial data is fragmented across different systems, currencies, and regulatory standards, making it challenging to compile a comprehensive statement. Application of Data Aggregation Tool: Extraction:The tool extracts financial data from each branch’s ledgers, expense records, and revenue reports. Transformation:It converts various currencies into a single reporting currency and aligns disparate financial practices to the company’s standardized accounting principles. Loading:The transformed data is loaded into a central financial system. Aggregation:The tool aggregates the data to summarize total expenses, revenues, and financial performance metrics across all branches. Analysis:With the aggregated data, the CFO can analyze financial health, identify cost-saving opportunities, and assess the performance of each branch. Outcome:The CFO successfully creates a unified financial statement that accurately reflects the corporation’s global financial status, enabling stakeholders to make informed investment decisions and strategic plans. The 7 Best Data Aggregation Tools for Business LIKE.TG LIKE.TG is a data aggregation tool that combines a user-friendly interface with robust technical features. It caters to business and technical users without requiring extensive IT skills. At its core, LIKE.TG boasts a potent ETL engine that automates data integration. Additionally, the platform’s customizable automation enhances efficiency by scheduling tasks and providing real-time monitoring to address integration errors quickly. These features streamline data integration, ensuring users enjoy uninterrupted data flow. Likewise, LIKE.TG’s adaptability shines in handling diverse data sources. The platform leverages a high-performing ETL engine for efficient data movement and transformation, including mapping, cleansing, and enrichment. Key Features: AI-Driven Data Management: Streamlines data extraction, preparation, and data processing through AI and automated workflows. Enterprise-Grade Integration Engine: Offers comprehensive tools for integrating diverse data sources and native connectors for easy mapping. Interactive, Automated Data Preparation: Ensures data quality using data health monitors, interactive grids, and robust quality checks. Advanced Data Transformation: Offers a vast library of transformations for preparing analysis-ready data. Dynamic Process Orchestration: Automates data aggregation tasks, allowing for execution based on time-based schedules or event triggers. User-Centric Design: With its no-code, drag-and-drop interface, LIKE.TG democratizes data management, making it accessible to users of all technical backgrounds. Seamless Integration: Integrating with a wide array of data sources, both on-premises and cloud-based, ensures a smooth data management experience. Comprehensive Data Handling: From extraction to insights, LIKE.TG offers a unified platform for all data-related tasks backed by a vast library of data operations. Best for: Businesses looking for an end-to-end data management solution from extraction to data integration, data warehousing, and even API management. Alteryx Alteryxis a data analytics platform offering a suite of data aggregation tools. Its open-data engineering environment streamlines data preparation tasks, enabling users to aggregate data and share results without coding expertise. Alteryx’s drag-and-drop interface and deployment options allow collaboration and sharing among team members. Alteryx’s data preparation, blending, and cleansing features provide a solution for processing large data volumes. Key Features: Data collection Data processing and presentation Integration with various sources User-friendly interface Multi-server support, backup and recovery, and maintainability. Pros: User-friendly interface for data preparation and analysis Wide range of data sources and connectors Flexible and customizable reporting and visualization options Scalable for large datasets Offers a variety of pre-built templates and tools for data analysis Cons: Some users have reported that Alteryx’s customer support is lacking. The documentation and assistance videos can be unclear and overly technical. Integrating this platform for white-label/embedded use cases can be challenging. Best for: Data analysts and businesses needing a robust data aggregation tool. IBM Cloud Pak for Data IBM Cloud Pak for Datais an integrated data and AI platform that aids in removing data silos and improving data security and accessibility. It offers a modular set of software components for data management. The tool has features such as data fabric and AI lifecycle management, governance, security, integration, observability, and master data management. Key Features: Cloud-Native Solution Unified Interface Data Fabric Architecture Built on Red Hat® OpenShift® Container Platform Pros: Modular software components for streamlined data analysis and management. Seamless integration with Red Hat OpenShift container platform Data virtualization layer for consolidating distributed data sources Supports a wide range of IBM and third-party services spanning the entire data lifecycle Cons: Some users have reported that it can be difficult to scale due to the need to purchase additional licenses or upgrade to a higher-tier plan. While IBM Cloud Pak for Data is touted as a low-code platform, considerable coding is still required for expression languages, advanced application programming interfaces, and webhooks. Complex setup and configuration Steep learning curve for some features Best for: Businesses seeking to enhance productivity and simplify data management complexities. Stata Statais a statistical software that offers data management and statistical analysis capabilities. It has a collapsecommand feature. This feature enables users to group data and compute summary statistics like mean, median, mode, and standard deviation. The command also allows the creation of new variables derived from existing ones. Stata’s interface and extensive documentation further enhance its accessibility to novices in statistical software. Key Features: Broad Suite of Statistical Features Publication-Quality Graphics Automated Reporting Python Integration (PyStata) Community-Contributed Features Pros: Wide range of statistical analysis and modeling capabilities User-friendly interface for data manipulation and analysis Supports a wide range of data formats and sources Offers a variety of pre-built templates and tools for data analysis Cons: Some users report that the Stata interface is not intuitive. Limited visualization capabilities compared to other data aggregation tools Limited collaboration features Steep learning curve for some advanced features Best for: Researchers and data analysts across diverse sectors. Google Looker Google Lookeris a cloud-based business intelligence platform designed to support businesses in collecting, analyzing, and visualizing data from various sources. Its data modeling layer helps users integrate data from disparate databases, CRMs, and systems into a single view. Looker’s calculated fields and aggregate awareness features optimize query performance and facilitate the extraction of insights from large datasets. Key Features: Aggregate awareness Integrated data exploration GUI with visual profiling and insight tools. Pros: User-friendly interface for data exploration and analysis Seamless integration with Google Cloud Platform Offers a wide range of data visualization options Supports a wide range of data sources and connectors Cons: Large dashboards have a slow loading time. No SQL CLI. Not enough high-resolution dashboards. Needs better user management.IAM, SSO, etc., are for the embedded use case. Limited scalability for large datasets Steep learning curve for beginners Best for: Businesses analyzing extensive data volumes seeking query efficiency. Zoho Analytics Zoho Analyticsoffers a platform for data analytics and aggregation. The platform is complete with dashboards and reports that support decision-making. Zoho’s aggregate formulas enable insight generation from summary statistics, while its data blending capabilities ensure a single view from multiple sources. Zoho Analytics supports various data import formats and provides pivot tables for summarizing data across multiple dimensions. Its APIs help with data integration and customization, enabling streamlined reporting. Key Features: Data preparation with ZOHO DataPrep Quick visual creation Filter and user filter Simple drag-and-drop dashboard Pros: Extensive APIs for seamless data integration and dynamic report embedding. User-friendly interface for data preparation and analysis Wide range of data sources and connectors Flexible and customizable reporting and visualization options Supports real-time data integration and analysis Offers a variety of pre-built templates and tools for data analysis Cons: Limited customization options for more advanced users, such as creating custom formulas or customizing the look and feel of reports and dashboards. Does not currently support connecting to all data sources, such as NoSQL databases or unstructured data sources. Difficult to scale due to the need to purchase additional licenses or upgrade to a higher-tier plan. Limited data source integration compared to other data aggregation tools Limited customization options for dashboards and reports Best for: Companies seeking a flexible and intuitive data analytics tool. Integrate.io Integrate.iois a cloud-based data integration platform streamlining, connecting, and aggregating data from diverse sources. The platform has several pre-built connectors for cloud services and on-premises applications. It is also complemented by a no-code interface that helps users to create custom connectors. Key Features: Easy In-Pipeline Data Transformations Simple Workflow Creation to Define Dependencies Between Tasks Salesforce to Salesforce Integrations Supports connectivity to over 200 e-commerce-related data stores Data Transformation Tools Pros: User-friendly interface for data integration and transformation Supports a wide range of data sources and connectors Offers a variety of pre-built templates and tools for data integration Highly customizable and extensible Cons: The drag-and-drop interface becomes extremely tough to use for complicated pipelines. Deployment of pipelines can be quite confusing. Error logs are not always detailed. Transforming JSON or other nested data is not easy. Best for: Scenarios that involve Salesforce Integration, File Data Preparation, REST API Ingestion, and Database Replication. How to Choose a Data Aggregation Tool Choosing the right data integration tool is crucial for effective data management and analysis. A data aggregation tool must meet an organization’s needs and be flexible enough to adapt to future changes. It should be able to handle the growing volume and variety of data and provide the functionality users need to transform and analyze data effectively. Identify Data Sources List all potential data sourcessuch as databases, cloud storage, and third-party APIs. Verify compatibilitywith the data aggregation tool for each source. Test connectionsto ensure reliable data retrieval. Assess Connectivity Evaluate the tool’s abilityto connect with listed data sources. Check for pre-built connectorsor APIs that facilitate easy integration. Ensure support for real-time data accessif needed for operations. Evaluate Scalability Understand the tool’s architectureand how it handles large data sets. Conduct stress teststo see how the tool performs under heavy data loads. Review the provider’s roadmapfor future scalability improvements. Consider Speed and Efficiency Benchmark processing timesfor typical data aggregation tasks. Compare performanceagainst other tools. Look for featuresthat optimize performance, like in-memory processing. Check for Data Transformation Features: Identify the types of data transformations required, such as filtering, sorting, or merging. Test the tool’s transformation capabilitieswith data samples. Ensure the tool can handle complex transformationswithout significant performance impact. Ensure Data Governance Check for compliancewith relevant data protection regulations. Assess the tool’s data quality managementfeatures. Verify that the tool provides audit trailsand version control. Look for Customization and Flexibility Determine if the tool allows for customizationto fit specific needs. Test the tool’s flexibilityby simulating changes in data processes. Ensure the tool can adaptto future changes in data strategy. User-Friendly Interface Evaluate the UI/UXof the tool through a hands-on trial. Gather feedbackfrom potential users within the organization. Check for the availability of training resourcesor certifications. Support and Documentation Research the level of supportoffered, including availability and response times. Review the documentationfor comprehensiveness and clarity. Look for an active user communityor forums for additional support. Essential Features for Choosing a Data Aggregation Tool Not all data aggregation tools are created equal. Here’s a detailed breakdown of the essential features and capabilities that any robust data aggregation tool should offer: Flexible Grouping Options The ability to aggregate data by grouping it based on specified column(s) is fundamental. Look for data aggregation tools that allow for flexible grouping options, including single or multiple columns, date ranges, or custom criteria. This flexibility ensures you can tailor the aggregation process to suit your data requirements. Comprehensive Summarization Methods A reliable data aggregation tool should support various summarization methods to derive meaningful insights. These methods may include: Count: Determine the total number of occurrences or records within each group. Sum: Calculate the total sum of numerical values within each group. Average: Compute the mean or average value of numeric data within each group. Minimum and Maximum: Identify the smallest and largest values within each group. Variance and Standard Deviation: Measure the dispersion or spread of data around the mean. Ensure the data aggregation tool offers these standard summarization methods and the flexibility to create custom aggregations as needed. Data Cleansing and Transformation Look for built-in capabilities to cleanse and transform raw data before aggregation. This includes handling missing values, removing duplicates, standardizing formats, and converting data types. Robust data cleansing ensures the accuracy and reliability of aggregated results. Scalability and Performance Evaluate the data aggregation tool’s scalability and performance capabilities, especially when dealing with large datasets. It should efficiently handle increasing volumes of data without compromising speed or accuracy. Consider factors such as parallel processing, optimized algorithms, and support for distributed computing frameworks. Data Source Compatibility Ensure compatibility with various data sources, including databases, cloud storage platforms, APIs, and file formats. The data aggregation tool should seamlessly integrate with your existing data infrastructure and support real-time or batch data ingestion. Customization and Extensibility Seek data aggregation tools that offer customization options and extensibility features to adapt to evolving business needs. This may include custom functions, scripting capabilities, and integration with external libraries or frameworks for advanced analytics and visualization. Security and Compliance Prioritize data security and compliance requirements when selecting a data aggregation tool. Look for features such as role-based access control, encryption, audit trails, and compliance certifications (e.g., GDPR, HIPAA) to ensure the protection and privacy of sensitive data. User-Friendly Interface and Accessibility A user-friendly interface with intuitive navigation and visualization capabilities enhances user adoption and productivity. Additionally, consider accessibility features such as cloud-based deployment, cross-platform compatibility, and mobile support for seamless access to aggregated data from anywhere, anytime. Take the Next Step with LIKE.TG Data aggregation tools offer various features tailored to meet the evolving needs of businesses of all sizes. From simplifying complex data landscapes to empowering decision-makers with actionable insights, these tools are indispensable in harnessing the true potential of data. LIKE.TG’s combination of user-friendliness, powerful AI capabilities, extensive connectivity, focus on data quality, and customization options make it a standout choice for organizations looking to streamline data aggregation. Explore the power of LIKE.TG first-hand. Startyour free trialtoday to see how LIKE.TG can transform your data aggregation workflow.
Modernizing Higher Education Curriculum Planning with LIKE.TG’s Data Solutions and Governance
A 2021 Educase report shows that 75% of surveyed institutions have started or are planning extensive digital transformation strategies to improve their data utilization. The importance of a strategic approach to data utilization in this context cannot be overstated. Effective data management frameworks ensure that data across the institution is accurate, consistent, and used in alignment with internal policies and external regulations. These frameworks support a data-informed culture where decisions are based on reliable evidence rather than intuition or assumption. Unified Data for Strategic Curriculum Development Integrating data in higher education institutions is essential for making strategic decisions that impact curriculum planning. By processing information from diverse data sources, universities can gain a holistic view that informs targeted, effective strategies. This approach also allows educational institutions to pinpoint areas where curriculum may be lacking or where there’s an opportunity to introduce new, relevant courses that align with emerging industry trends. For example, suppose data reveals a growing interest among students in sustainable technologies, coupled with a rise in job market demand in this sector. In that case, a university can decide to develop new courses or even degree programs focused on sustainability. Curriculum planning benefits from a unified data perspective by giving decision-makers a complete picture of the academic scenario. This complete view empowers them to make informed choices that enhance student learning experiences, which may increase graduation rates and improve employability upon graduation. Integrating data from scattered sources into a coherent view needs data management tools and the expertise to interpret complex datasets. University Data Management and Governance with LIKE.TG The right data management tool is essential for unified data. LIKE.TG’s data transformation capabilities and comprehensive business glossary help achieve a unified view of university data. It streamlines data processing and increases efficiency across departments. LIKE.TG’s no-code UI transforms university data management and governance by allowing higher education institutions to manage and analyze vast amounts of data without coding easily. This user-friendly interface streamlines complex data processes such as extraction, integration, and migration. Segmentation Simplification of Data LIKE.TG’s no-code approach allows universities to segment and analyze student data visually. Using drag-and-drop features, administrators can easily categorize students by academic performance or enrollment status, making it easy to tailor curricula to meet diverse student needs through the interface, improving educational offerings without any programming. Universities can also use LIKE.TG to aggregate and review data at different levels, such as departments or programs, without writing a single line of code. They can visualize key metrics like course enrollments, average grades, and retention rates. With these insights, institutions can quickly identify trends, evaluate program success, and make informed decisions to improve and update their curricula continuously. Business Glossary for Data Governance Effective data governance in higher education ensures that important information is easily accessible to stakeholders like faculty, administrators, and students, promoting collaboration and informed decision-making. However, using different terminologies across departments can cause stakeholder confusion and misinterpretation. For example, in the academic department, “student success” typically refers to achieving high grades, completing courses on time, and making satisfactory progress toward degree completion. In contrast, student services departments view “student success” more broadly. They may include factors such as student engagement, satisfaction with support services, and overall well-being in their interpretation of success. In this case, the lack of standardization for “student success” can lead to confusion and misalignment. LIKE.TG’s business glossary helps universities establish a common language for data. It defines key terms and metrics, enabling consistent and accurate interpretation of data across departments. A unified definition of “student success” across academic and administrative departments can lead to more targeted support programs and metrics for evaluating their effectiveness. Improved Data Access with Tags LIKE.TG incorporates tags alongside the business glossary. Tags improve categorization and make data retrieval easier and more accessible. Tags like “academic performance” can categorize terms related to academic metrics such as GPA, course completion, rates, and academic standings. Users can quickly find relevant information without sifting through vast databases using tagged data related to specific programs, student demographics, or research projects. The Benefits of Adopting LIKE.TG for Higher Education Institutions Unified Data Management LIKE.TG provides a central hub for all data assets, streamlining data integration from diverse sources into one accessible repository. This centralization enhances the reliability and accessibility of data across the university. Universities can merge data from online learning platforms, library usage statistics, and campus engagement activities into a unified dashboard, enabling academic advisors to craft personalized student success strategies. No-Code Simplicity Users from non-technical backgrounds can leverage LIKE.TG’s no-code interface to manage data effectively, democratizing data access and analysis within the institution. With this feature, universities can speed up enrollment by generating data reports on student enrollment trends without IT expertise. Data Quality and Profiling LIKE.TG ensures that the data used for decision-making is of the highest quality. Its data profiling capabilities identify and correct inconsistencies. Academic institutions can rely on this data to make decisions like learning programs and research initiatives. Educational institutions can automatically clean and standardize incoming student application data, giving the admissions team works with the most accurate and up-to-date information. Automation and Efficiency Automation streamlines administrative tasks and data management for higher education institutions so they can focus on enhancing student learning experiences and operational innovations. Higher education institutions can automate the data integration process for new research grants, reducing manual data entry hours and allowing researchers to focus more on their projects. Transforming University Data Management with LIKE.TG Comprehensive student data analysis optimizes the process of curriculum development. Let’s say a higher education institution identifies the need to leverage its vast student performance and feedback data. The objective is to tailor the curriculum to student needs and industry trends, ensuring they’re well-prepared for the job market after graduation. LIKE.TG’s Solution for Higher Education Institution In the initial phase, LIKE.TG facilitates the integration of varied student data sources. This process involves: Data Extraction LIKE.TG’s AI-recommended template extraction streamlines data extraction. Its native database connectivity and API integration allow pulling data from institutional and external sources. This whole process drastically reduces the preparation time by automating tasks such as name and record parsing. Through this, universities can extract data from multiple sources, including academic records, course evaluations, and alums employment reports. Data Transformation The university combines these diverse data streams into a unified dataset through transformations, such as Joins. Expressions are applied to clean and standardize individual data entries, ensuring consistency across all records. Data Loading The transformed data is moved to a centralized repository for further analysis. Using unified data allows universities to comprehensively view student academic performance, engagement levels, and post-graduation success. Standardizing Data with LIKE.TG’s Business Glossary Terminology Standardization LIKE.TG’s business glossary allows for defining and standardizing key educational metrics and terms across departments. This common language eliminates ambiguity and facilitates a shared understanding of data insights. Data Consistency Establishing clear definitions for terms like “Student Engagement” and “Employment Readiness” allows universities to ensure that data analysis and curriculum decisions are based on consistent criteria across all academic units. Data Enrichment LIKE.TG’s business glossary enriches data assets by attaching relevant metadata. This metadata provides additional context and clarification about the purpose and usage of each data asset, further enhancing the common understanding among stakeholders. Specific Outcomes Achieved By Leveraging LIKE.TG’s integrated solutions, universities can: Identify Curriculum Gaps Analyze student feedback and performance data to pinpoint areas where the curriculum needs enhancement or innovation – aligning the curriculum more closely with current industry requirements and future employment opportunities. Improve Student Outcomes Adjusting teaching methods and course content based on data-driven insights leads to higher student satisfaction, better academic performance, and increased post-graduation employment rates. Final Words Higher education institutions can navigate the complexities of modern academia by integrating and standardizing their vast datasets. It ensures that their strategies are both informed and impactful. With LIKE.TG, universities can seamlessly blend diverse data sources and establish a unified data language across departments. This harmonization enables strategic decisions—from curriculum development to faculty hiring— grounded in reliable, accessible, and consistent data. Experience advanced data management and governance with LIKE.TG’s user-friendly solution. Process the data to drive strategic decisions and achieve academic excellence. Schedule a demo and download a free 14 day trial today to start streamlining data governance and informed decision-making. Start Transforming Data Governance at Your Institution Explore LIKE.TG's no-code solution by scheduling a demo to see how effortlessly you can manage your institution's data. Enhance success and make informed, strategic decisions that benefit your entire organization with LIKE.TG’s comprehensive tools. Request a Demo