Establishing a Robust Data Governance Framework


Data Governance Framework

The exponentially increasing enterprise data volumes, alongside the need for organizing and protecting internal data, make data governance an urgent strategic initiative. This ensures that each data element collected by your organization is properly stored and distributed. A good data governance framework provides you with a comprehensive approach to managing your data, ensuring it is secure and readily accessible to authorized users.

If you’re yet to establish one, get started by understanding data governance frameworks, the different types, benefits, and how to proceed.

What is Data Governance Framework?

An organization’s data governance framework is a set of processes, rules, and responsibilities that enables it to manage its data effectively. This is especially important as organizations face challenges in data management, quality, security, and compliance.

The Significance of a Data Governance Framework

Without data governance, data can become invalid or unreliable, possibly posing a significant threat to your organization. Additionally, it isn’t possible to know when inaccurate data enters your systems, where it came from, or who uses it. This can result in poor data quality, eventually reducing your stakeholders’ trust in the data.

Here are some benefits of a data governance framework:

  • Improved Operational Efficiency: A data governance framework standardizes the data management process, letting data analysts know how to collect and where to find important data. This eliminates redundancies, improving data accuracy and efficiency. It also leads to better decision-making and strategic planning.
  • Data Democratization: It provides employees of all technical skill sets with the ability to access and use data. This promotes collaboration and innovation within the organization.
  • Regulatory Compliance: A data governance framework helps your organization comply with various privacy and security regulations. It ensures transparency in data handling and minimizes the risk of non-compliance or potential fines.
  • Improved Data Quality: Good data governance provides clear guidelines on how to label and categorize data, allowing you to standardize organizational data. It also enforces protocols for data quality management, ensuring the data meets the quality thresholds.
  • Risk Reduction: By promoting regulatory compliance, data governance reduces the risks of reputational damage, data breaches, leaks, and non-compliance.

Data Governance vs. Data Management

A data governance framework is often referred to as a “data management framework.” Although the terms are used interchangeably, there is a difference.

Data Governance focuses on policies and procedures defining how data is processed across the organization to ensure compliance and privacy. It often revolves around documentation and outlines:

  • Who has ownership of which data
  • Who can access the data
  • What data sources exist
  • How data is stored
  • Whether the given data is subject to privacy laws or other regulatory requirements
  • What security measures are in place to make the data more secure
  • Associated data retention and deletion policies

On the other hand, data management is more about action or executing the policies and rules outlined in the data governance policy framework. It is an IT practice encompassing an organization’s practices across the data life cycle and encompasses:

  • Data creation, processing, and storage
  • Data governance implementation
  • Data model development
  • Data architecture deployment
  • Error resolution
  • Data quality checks
  • Integration of data into a data warehouse or data lake

While you can have data management without data governance policies (although it isn’t recommended), you can’t have data governance without data management.

Core Elements of a Data Governance Framework

While data governance frameworks will vary depending on the business, here are the essential components to include in your framework:

  • Data Lifecycle Management: Includes setting policies and procedures for data archiving, disposal, and retention. It directs the oversight of data from creation and collection through use and end of life.
  • Data Quality: Focuses on establishing and maintaining data accuracy, completeness, consistency, and reliability. It includes identifying the processes, tools, and automation required to identify inaccuracies, minimize errors, and direct corrections throughout the data lifecycle.
  • Data Stewardship: Data stewards will be responsible for establishing the rules and processes within the data governance framework. They also help address data issues, define data elements, ensure compliance, and establish and maintain data quality standards.
  • Data Security: This element influences most of the other elements of a data governance framework. It includes all the measures taken to protect data from unauthorized access, cyber-attacks, data breaches, and theft.
  • Regulatory Compliance: Helps organizations with processes to identify and stay updated with changes to data compliance standards, like GDPR, HIPAA, or CCPA.
  • Technological Infrastructure: Data governance tools that will support your organization’s data governance policies. 
  • Performance Monitoring: Key performance indicators (KPIs) that measure the business impact. KPIs should be measurable and tracked over time to give stakeholders an accurate return on investment (ROI).

Types of Data Governance Frameworks

Data Governance Framework: Types
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To design a data governance framework, you must consider which operational model suits the size and structure of your business. The model should detail operational guidelines, establish oversight, and provide the infrastructure for data ownership and decision-making.

The different framework models differ in how data governance decisions flow through your organization:

  • Bottom-up: Employees at lower levels of the organization implement data governance policies, such as standardizing naming conventions, which spread to higher levels.
  • Centralized: A single individual in the organization is designated as the governance lead, responsible for making decisions and providing direction for the program.
  • Decentralized: A committee develops and manages the organization’s data governance strategy. However, functional areas of the business will create and manage their datasets and control the distribution of information to their users.
  • Top-down: The organization’s leadership team implements data governance policies that transition to individual business units and the rest of the company.
  • Hybrid or Federated: Data governance decisions are made at different levels of the organization, following a centralized data governance framework. Each area of the business operates semi-autonomously, developing procedures, standards, and policies that suit their business needs.

Data Governance Framework Examples

Here are some established, tried, and tested data governance framework examples:

  • DGI
  • McKinsey
  • PwC


Data Governance Framework: DGI

An appropriate framework for larger organizations and enterprises with complex data systems, DGI comprises ten universal components addressing the why-what-who-how of data governance. It divides each of its components into core areas—rules, people, and processes—to simplify the concepts.

Some of the components include:

  • A Data Governance Office (DGO): Oversees the entire governance program, collaborates with other stakeholders, maintains detailed records on the program, and aligns data-related policies and standards.
  • Controls: These can be corrective or preventive and are for risk management. They are applied at various levels of the framework to support the program’s goals.
  • Goals, Metrics, and Funding: Elaborates how the program would optimize costs, increase revenue, and ensure business resilience despite disruptions or risks.


Data Governance Framework: DAMA DMBOK
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Another popular data governance framework, the DAMA-DMBOK template, portrays data management as a wheel with data governance at the center, with nine surrounding knowledge areas. Data governance is considered the high-level planning that is required for effective data management.

The framework further defines the environment elements providing structure to each knowledge area. They define the underlying roles, processes, deliverables, and technology that guide the planning and execution of each area. The elements also cover how an organization’s culture must evolve for its data governance initiatives to work.


Data Governance Framework: McKinsey
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McKinsey believes that to ensure success with data governance, it is essential to reconsider the entire organizational design. Their data governance framework template comprises three core components: a central data management office (DMO), a data council, and data leadership by domain.

While the DMO consists of leaders who set data governance standards, the data council resolves issues and ensures compliance with previously set standards. Data leadership by domain is responsible for data quality in their respective domains (such as product or transactional data) and takes part in the data council.


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The PwC enterprise data governance framework goes one step further than the conventional models, such as DGI and DAMA DMBOK, to account for next-generation data landscapes.

It includes five components, starting with a data governance strategy layer. A management layer encompasses all the aspects of a data ecosystem. The lifecycle management layer comprises all the policies that ensure a streamlined flow of data throughout its lifecycle. And the stewardship layer focuses on enforcing governance.

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Best Practices to Create a Data Governance Framework

Here are some best practices that can guide you in creating an enterprise data governance framework:

Start Small

Rather than starting with a long-term or complex project, consider a smaller, more manageable one, like analyzing the data of a single team. Assess the types of data, its collection, storage, and usage, and any existing data management practices.

Set Clear Goals

Set actionable objectives for your data governance program to ensure long-term success. This can include protecting top-level data, lowering data management costs, reducing team friction, and improving data entry speed. Such objectives must align with the overall business strategy and define a clear roadmap to success.

Develop the Governance Model

Based on the data landscape assessment and goals, create a data governance model. This will determine who can view and distribute different data types, ensuring that sensitive data is accessed only by trusted employees and preventing unauthorized access.

Additionally, describe the rules and regulations for data collection. Outline the standards for securing data and which channels you’ll use to obtain it. This will help create consistency in the data collection process.

Create a Dedicated Team

Ensuring data governance requires dedicated roles within the program; assign deliverables and ownership levels across your organization. The leader must have strong communication skills to convey the importance of the program to the company. Clearly define each team member’s responsibilities to ensure smooth and efficient governance initiatives.

The data governance office should have employees who excel in managing customer data and can independently determine the best process for organizing information. Some standard roles include:

  • Data Governance Board: The team developing tactical practices and policies for leveraging data as a business asset.
  • Data Owners: Individuals responsible for the data for specific functions within the business.
  • Data Stewards: Individuals responsible for enacting data governance processes, ensuring the quality of data elements, and bringing order to unstructured data.
  • Data Consumers: Individuals who enter and use data to perform their assigned duties.

Integrate Data Governance Into Every Department

Integrating the data governance framework into every department will help ensure consistent data collection and provide insights for marketing, sales, customer service, and product development teams.

The framework must be sensibly embedded in your enterprise architecture, the IT landscape, and the tools needed.

Measure Progress with Metrics

Continuously measure the effectiveness of data governance and identify the areas for improvement. Test the data, discuss it with your team, and readjust or streamline the processes accordingly.

Consistently Refine the Compliance Framework

Businesses are constantly evolving; adapt your data governance framework to accommodate organizational changes and prevent data oversight or accidental leakage of sensitive information.

Data Governance Framework Tools

After you have your data governance framework in place, consider the tools that will help you accomplish your goals. Here are some popular data governance tools:

  • SAP Master Data Governance: You have the flexibility to govern your data in two distinct ways. You can decentrally own and consolidate your data across your enterprise system landscape. Alternatively, you can centrally create, modify, and distribute master data throughout your landscape.

This tool allows you to implement bulk changes, streamline your databases, evaluate the impact of your data governance processes, and establish, monitor, and validate rules for data quality.

  • IBM Cloud Pak for Data: This is specifically designed to help your organization discover, organize, analyze, prepare, share, and safeguard data within complex enterprise environments. It helps enhance data accessibility, enforce comprehensive data and usage policies, and automate tasks.

IBM’s solution is beneficial for streamlining the development, implementation, and responsible scaling of AI in business operations.

  • Egnyte: The only unified cloud content governance platform in the market, Egnyte is ideal for collaboration, data security, compliance, and threat detection. It allows multi-cloud businesses to get a 360-degree insight into their apps, devices, and repositories.
  • Lyftrondata: Ideal for companies on a budget, Lyftrondata comes with a built-in enterprise data governance framework. This allows you to ensure your data is secure, accessible, up-to-date, and accurate. It offers features such as data catalog, data model, data definition, etc., to enable data governance.


A robust data governance framework has become crucial in the data-driven business world. It helps safeguard your organization’s data assets and position yourself to harness the full potential of the data for a competitive advantage.

Understanding the different data governance model types, elements, tools, and best practices will help you establish an effective framework for your organizational needs. As your business evolves, such a framework will prove vital to maintaining the integrity, security, and accessibility of data.

Hevo Data Inc. ensures the privacy and confidentiality of all user data. For data compliance, Hevo is GDPR, HIPAA, SOC 2 Type 2, and CCPA compliant. This ensures the security and privacy of data that is personal or sensitive.

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Freelance Technical Content Writer, Hevo Data

Suchitra's profound enthusiasm for data science and passion for writing drives her to produce high-quality content on software architecture, and data integration

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