Data Governance Framework: What is it? Importance, Pillars and Best Practices

What is a Data Governance Framework? 

A data governance framework is a structured way of managing and controlling the use of data in an organization. It helps establish policies, assign roles and responsibilities, and maintain data quality and security in compliance with relevant regulatory standards. The framework, therefore, provides detailed documentation about the organization’s data architecture, which is necessary to govern its data assets.

Moreover, the framework supports self-service tools, which help technical and non-technical users find and use the data they need for governance and analytics. With these tools, organizations can manage, change, and share data across all their systems, whether in the cloud or on-site.

Why is a Data Governance Framework Needed? 

The data governance framework is important for applying consistent policies and rules to all organizational data, ensuring data reliability for various roles, such as business leaders, data stewards, and developers.

Data Inaccuracy

A data governance framework sets standards and processes to ensure data accuracy and consistency. Many organizations face challenges with inaccurate, inconsistent, or outdated data affecting insights and decision-making processes. The data governance framework enhances the quality and reliability of the organization’s data.

Data Security

It sets guidelines and controls to protect sensitive information, including encryption, access controls, and compliance measures to reduce security risks and comply with regulations.

Data Silos

A data governance framework simplifies data integration and standardization, breaks down silos by establishing control, and enables seamless data exchange and collaboration across the organization.

Lack of Accountability and Ownership

It emphasizes accountability by defining roles and responsibilities and assigning data stewards, owners, and custodians to oversee data management practices and enforce governance policies effectively.

Inefficient Data Management

This framework establishes structured processes and workflows for data management. It automates repetitive tasks, streamlines workflows, and improves operational efficiency.

Benefits of a Data Governance Framework  

It provides a structured way to keep data across all business operations accurate and secure while ensuring it’s used responsibly.

Sets Standards & Accountability

The initial phase of data governance is the establishment of clear standards, norms, responsibilities, ownership, and roles. This process ensures all members of the organization, from new hires to top executives, are aware of their duties in data management. The organization can avoid data mismanagement by setting defined accountabilities and uphold data quality and security.

Establishes Key Indicators & Metrics

Indicators and metrics offer a detailed view of the data’s condition and safety, guiding strategic decisions.

• Key Quality Indicators (KQIs) gauge data quality, helping businesses ensure their data meets the required standards.
• Key Data Elements (KDEs) identify the most critical data within the organization, guiding data management priorities.
• Key Performance Indicators (KPIs) track data performance against business objectives, ensuring data aligns with company goals.
  • Data Risk and Privacy Metrics measure the organization’s exposure to data breaches and compliance with privacy laws, which is essential for the regulatory environment.

Builds a Unified Data Language

A common business language and data quality rules help everyone in the organization understand data terms and standards similarly. This approach avoids confusion and errors in data management and use, making communication across the company more straightforward.

Enhances Data Discovery & Management

The framework sets up methods for finding and understanding all the company’s data. This process involves figuring out how data is related and its history, handling different kinds of information and conducting data profiling and classification. These steps make it easier to manage data well, ensuring it’s correct and dependable for making decisions.

A robust data governance framework helps in:

• Improving and managing data quality to ensure data is accurate and useful.
• Addressing data issues promptly to maintain data integrity.
• Identifying data owners to establish accountability for data assets.
• Building a data catalog to organize and classify data, making it easily accessible.
• Creating reference and master data to provide consistent data across the organization.
• Driving data literacy across the organization to enhance understanding and use of data.
• Provisioning and delivering data efficiently ensures it is available when and where needed.

Pillars of a Data Governance Framework 

A comprehensive data governance framework relies upon fundamental pillars that ensure an organization’s effective and secure use of data assets. They combine the foundational elements with essential strategic approaches, creating a well-rounded framework for managing data across all enterprise levels.

Data Quality

Data quality is essential to a data governance framework as it implements the rules and processes for data validation, cleansing, and organization. It guarantees decision-makers access to accurate and consistent information, improving decision-making.

Data Integration

Data integration simplifies data governance by aggregating and harmonizing data from various sources across an organization into a unified view. This approach supports improved analysis, reporting, and decision-making by providing stakeholders with a complete business picture. Consequently, it ensures all organizational data adheres to set policies and compliance standards, reducing inconsistencies and errors.

Data integration also improves data governance by allowing for centralized management, which makes it easier to control data quality, security, and access. It standardizes data handling practices throughout the organization, ensuring consistent implementation and monitoring of governance policies.

Integrated data systems clean and correct data during consolidation, enhancing data quality and reliability for decision-making. This streamlined approach helps meet regulatory compliance and increases reporting efficiency, as all data is consistent and readily available.

Data Privacy and Security

This pillar is essential for keeping data assets confidential and available within the governance framework. It refers to managing and protecting data in a way that aligns with the organization’s data governance policies. Privacy means ensuring that data is secure from unauthorized access and readily available to authorized users, all under the oversight and rules established by the governance framework. Organizations can protect data by using strong encryption, setting access controls, and following compliance mechanisms.

It builds trust among customers, partners, and stakeholders, protects the organization’s reputation, and reduces the risk of legal and financial fallout from data breaches.

Data Architecture

The role of data architecture is to provide a structured framework for designing, organizing, and managing data assets. Data architecture ensures data is stored, accessed, and used consistently by defining data models, schemas, and storage mechanisms.

A clear data architecture aids data governance by setting straightforward guidelines for managing data, ensuring it complies with organizational policies and standards.

Data Asset Governance

It highlights the need to treat all data elements, including dashboards, code, and data science models, as essential assets within the governance framework throughout their lifecycle.

It suggests adopting a comprehensive view of governance covering data and analytics to ensure thorough management and oversight of all data assets.

Bottom-Up Approach

As data use increases, companies should share the role of data governance across creators and users of data. This decentralized approach gives everyone responsibility for data governance, aligning them with more inclusive and collaborative data management models like data mesh.

It means that governance is spread across different business areas, with each domain owner looking after their data while following the broader organizational guidelines.

Routine Workflow Governance

Data governance shouldn’t just be about meeting compliance or seen as an extra task. Instead, teams should integrate it into everyday work processes to make sure they base decisions on accurate data. It then becomes an essential element of strategic decision-making within the organization.

A data governance framework is incomplete without its core elements: people, processes, and technology. Each plays a critical role in making the framework effective.

People: The Core of Governance

Successful data governance relies on collaboration and careful planning by people across the organization.

Key considerations to implement and establish data governance include:

• Commitment: Assessing whether individuals are dedicated to the principles of data governance.
• Roles and Responsibilities: Clearly defining and formally documenting the roles and responsibilities of all involved in data governance.
• Skills and Data Literacy: Ensuring team members possess the necessary skills and understanding of data to implement governance strategies effectively.
• Change Management: Developing a plan with sponsors to support alignment and encourage organization-wide buy-in.
• Stakeholder Identification: Recognizing data governance stakeholders across the organization.
• Maintaining Expertise: Understanding where expertise resides within the organization to leverage it for governance initiatives.

Processes

Processes like data collection, storage, integration, and quality assurance are vital for verifying that data management practices are consistent across the enterprise.

Important aspects to consider while handling these processes are:

• Realistic Definitions and Goals: Establishing practical and achievable data definitions, rules, and objectives.
• Modernization of Business Processes: Updating business processes to integrate data governance, enhancing the ability to deliver meaningful results.
• Integration of Governance: Reviewing and adjusting business rules to incorporate data governance seamlessly.

Contributors include business and IT experts who provide the necessary context for data governance efforts.

They are essential in:

• Technology: Enabling Effective Governance

Technology provides the tools and platforms for implementing reliable data governance processes, such as data profiling, lineage, and metadata tools, and utilizing technology to automate, scale, and accelerate the data governance processes.

How do Data Governance Frameworks Work? 

Data governance frameworks differ from one business to another, but they generally include several core components discussed above that guide how data is managed and used within an organization.

Let us discuss further how these frameworks typically work.

Understanding Ownership

Ownership is identifying who sets up the rules, policies, definitions, and standards for managing data. For instance, a company can establish a Data Governance Office (DGO) to lead the data governance initiative. The initiative could include a dedicated team, a group of stakeholders, or even a single person, such as a data architect, depending on the size and needs of the organization.

Data stewards are appointed across various departments to ensure everyone aligns with these standards and to offer recommendations. In larger companies, several councils can focus on different aspects of data management, such as data storage, quality, and protection.

Setting Goals

Defining clear goals and metrics is key to measuring the success of a data governance initiative. The Data Governance Institute suggests focusing on the impact of data governance initiatives on:

• How well they are aligned with the organizational objectives.
• The effectiveness in improving data quality and compliance in projects.
• The integration of governance principles into professional disciplines.
• The adoption of data governance practices among individuals within the organization.

For example, a healthcare provider can set a goal to reduce patient data errors by 20% in the first year to improve patient care and reduce billing mistakes.

Monitoring Performance

Keeping track of how well the data governance framework is working involves setting accountabilities for tasks necessary for compliance, like assigning a data steward or appointing a data security officer. These tasks should be well-documented, repeatable, and cover various aspects of data governance, such as assigning decision rights, managing changes, resolving issues, and ensuring data quality.

For instance, a financial institution can implement a system to regularly review and update access rights to sensitive financial data, ensuring only authorized personnel can view or modify this information.

Approving Technology

Choosing and approving technology is another important aspect, where stakeholders decide on the tools and systems used to process, store, and secure data. It also guarantees these technologies have the necessary controls to prevent data breaches.

For example, relevant personnel in an e-commerce company may decide to approve using a cloud storage solution that offers encryption and multi-factor authentication to protect customer data.

Establishing Collaboration Standards

Deciding how data stakeholders create, use, or regulate data within the organization will collaborate, including determining who should be involved in decision-making processes and who needs to be informed about decisions.

For instance, a manufacturing firm can include engineers, production managers, and quality control specialists in the discussions on collecting and using production line data to improve efficiency and product quality.

Data Governance Framework Examples  

There are various tried and tested data governance frameworks in use. Each framework meets the needs and requirements of managing data effectively.

The DAMA International Data Management Body of Knowledge (DMBOK)

DAMA-DMBOK is a comprehensive framework that outlines the standard industry practices for data management. It covers many topics, including data governance, architecture, and modeling, providing a holistic approach to managing and governing data assets.

The Data Governance Institute Framework (DGI)

The Data Governance Institute (DGI) offers a framework focusing on governance practices. It emphasizes establishing roles, responsibilities, and processes to ensure data is managed as an asset. This framework is particularly beneficial for organizations looking to implement data governance with a clear emphasis on accountability and stewardship.

The CMMI Data Management Maturity (DMM) Model

The Data Management Maturity (DMM) model provides a comprehensive approach to enhancing an organization’s data governance practices. It outlines the essential components of data governance, including data quality, operations, and platforms, offering a path toward achieving higher levels of data management maturity.

The EDM Council’s Data Management Capability Assessment Model (DCAM)

The EDM Council’s DCAM is an industry-standard framework for evaluating and improving data management and governance practices. DCAM provides a structured approach with defined principles and capabilities across various aspects of data governance, including data architecture, quality management, and security.

ISO/IEC 38500 IT Governance Framework

Although ISO/IEC 38500 is primarily an IT governance framework, it has significant implications for data governance. It guides organizations using IT (including data assets) to achieve business goals, ensure regulatory compliance, and manage risks.

How to Create a Data Governance Framework?  

Building and implementing a data governance framework involves a strategic process that prioritizes properly managing an organization’s data assets. This framework is essential for enhancing data governance across businesses. The following step-by-step guide simplifies the approach:

1. Understanding Data Governance

a. Define the scope and objectives of the data governance initiative.

b. Align stakeholders with data governance goals, such as improving decision-making, ensuring regulatory compliance, and enhancing operational efficiency.

2. Identifying Data Domains

a. Categorize data into domains based on their relevance to the business, such as customer data, product data, financial data, etc.

b. Understand the data landscape to manage data across the organization effectively.

3. Identifying Ownership & Consumption

a. Determine data owners responsible for the accuracy, privacy, and security of each data domain. 

b. Identify data consumers who need access to the data for their work. 

c. Establish accountability and clarity on roles and responsibilities. 

4. Validating & Documenting Data

a. Document the data lifecycle, including its origin, internal flow, user interactions, and application purposes.

b. Verify data accuracy and consistency.

c. Record governance policies, standards, and procedures for reference and clarity in data management.

5. Assessing Data Security

a. Conduct security and risk assessments on various data types to identify vulnerabilities and threats. 

b. Prioritize risks and implement protective measures for sensitive information. 

c. Ensure compliance with legal requirements and build trust with clients and partners by demonstrating a commitment to data security.  

Data Governance Framework Best Practices 

Businesses should embrace certain best practices to create a practical data governance framework that ensures long-term success. These practices aim to meet current needs while also adapting to future challenges.

Engage Stakeholders Early in the Process 

Involve key stakeholders from various departments in defining the scope and objectives of data governance to align all activities with organizational goals and priorities.

Utilize Data Catalogs 

Implement data catalogs to categorize and document data assets. This approach is about organizing data efficiently, making it easily accessible and understandable to users within the organization. It facilitates easy identification of data domains and ownership responsibilities by providing visibility into data assets and their associated metadata.

Appoint Data Stewards 

Appoint dedicated data stewards for each data domain to oversee data quality and serve as contact points for all data-related queries. These stewards enforce data governance policies, maintaining the quality and security of the data within their respective domains.

Automate Documentation Processes 

Leverage automation tools to streamline the documentation of data lifecycles, governance policies, and procedures. These tools enhance efficiency and accuracy in maintaining up-to-date records of data management activities.

Conduct Regular Training

Provide ongoing training sessions to educate stakeholders on data governance principles, best practices, and compliance requirements. This training ensures all participants understand and effectively implement the organization’s data governance policies.

Implement Data Security Measures 

Use security mechanism such as encryption, access controls, and monitoring systems to protect sensitive data from unauthorized access, breaches, and compliance violations. 

Final Thoughts  

A strong data governance framework equips organizations to manage and derive maximum value from their data assets proactively. Companies can implement a framework by carefully engaging stakeholders, defining roles, and extensive planning. Implementing a data governance framework demands a commitment to continuous improvement and flexibility in response to new business and technology shifts.

For successful implementation, using data governance tools or data management tools with governance features is essential. LIKE.TG’s platform simplifies data governance for businesses, offering tools for comprehensive data management. It allows for clearly assigning roles and responsibilities, ensuring data stewards and owners can easily oversee and control their data. This transparency aids in implementing governance policies and upholding data integrity organization-wide.

LIKE.TG enables you to use your data more effectively and achieve better business results, all while minimizing data complexities.

Start a free trial or book a demo today to explore LIKE.TG’s data management and governance solutions suite.