An overview of data governance frameworks

When companies consider data governance, they often focus on the role of data quality. However, governance goes far beyond data quality. This article provides a framework for data governance.

Image: Anything Possible/Adobe Stock

Organizations are collecting and storing more data than ever. This data can be used to improve business processes, but it can also be a liability if mismanaged. To protect the privacy of their customers and comply with the latest privacy laws, organizations must implement a data governance framework that goes beyond basic data quality and management.

Jump to:

What is a data governance framework?

Data governance is the process of managing data throughout its lifecycle, from acquisition to archiving. This includes setting policies and standards for handling data and ensuring compliance with those policies. On the other hand, data management is the process of storing, organizing and retrieving data. While data governance frameworks are about overall data management, data management focuses on the more practical tasks of day-to-day administration.

SEE: Recruitment Kit: Database Engineer (TechRepublic Premium)

In order to effectively govern data, organizations must have a clear understanding of their data landscape. They need to know where their data comes from, who owns it, how it is used and where it is stored. For data to be reliable for decision-making, it must be relevant, trustworthy, accurate, of high quality and easy to understand. This requires close collaboration between different departments and business units when creating a data governance framework. Data governance frameworks must also consider regulations and compliance requirements.

Types of data governance frameworks

There are two opposing philosophies to creating data governance frameworks that offer different advantages and disadvantages depending on an organization’s specific goals:

Bottom-up philosophy

The bottom-up approach, popularized by the growing big data movement, starts with raw data. Data is first ingested, then structures, or schemas, are built on top of the data after it has been read. Governance rules, policies and quality checks are also added to the dataset at this time. The advantage of this approach is its scalability; however, it can be difficult to maintain consistent quality control over a large volume of data.

Top-down philosophy

In the top-down approach, modeling and data governance take priority and are the first steps in developing a data governance framework. The process begins with data professionals applying well-defined methodologies and best practices to data. The advantage of this approach is that it emphasizes quality control; however, it can be difficult to apply in organizations with a large volume of data.

Components of a data governance framework

There are four main components of a data governance framework:

Data Stewardship

Data stewards ensure that an organization’s data assets are accurate, consistent, and compliant with all relevant regulations, especially during company projects.

Data quality management

Data quality management includes all processes and procedures used to ensure that an organization’s data assets are free from errors and inaccuracies. It also includes methods for identifying and correcting any errors or inaccuracies.

Data management process

These processes define how an organization’s data assets are created, stored, accessed, and used. They also establish the rules for sharing these assets with internal and external stakeholders.

Technological infrastructure

It refers to the hardware and software systems used to collect, store and manage data. It includes databases, enterprise resource planning systems, and data warehouses. It also includes network connections that facilitate the exchange of information between stakeholders.

Examples of data governance frameworks

Below is a list of some commonly referenced data governance frameworks:

Each of these frameworks has its own advantages and disadvantages. Organizations should select the data governance framework that best fits their unique needs and goals.

Why is a data governance framework necessary?

A data governance framework is necessary because it provides a standard set of policies and procedures for managing an organization’s critical data assets. Without such a framework, these assets risk becoming fragmented, inaccurate and non-compliant with applicable regulations.

Additionally, a lack of governance can lead to confusion and duplication of effort as different departments or individual users attempt to manage data with their own methods. A well-designed data governance framework ensures that all users understand data management rules and that there is a clear process for making changes or additions to data. A good governance framework unifies teams, improves communication between different teams and allows different departments to share best practices.

Finally, a data governance framework helps ensure compliance with laws and regulations. From HIPAA to GDPR, there are a multitude of data privacy laws and regulations around the world. Violating these legal provisions is costly in terms of fines and settlement costs and can damage an organization’s reputation.

Best practices for creating a data governance framework

There is no single solution for data governance frameworks. The best approach for an organization will depend on its specific needs and goals. However, there are some best practices that all organizations should keep in mind:

Define the purpose of the framework

The first step in creating a data governance framework is to define the purpose of the framework. What goals does the organization want to achieve by implementing such a framework? Understanding enterprise-wide data management goals is an important first step in developing a data governance framework.

Understand the current state of the organization

It is also important to understand the current state of an organization’s data management processes and technology infrastructure before designing the framework. Apply a data maturity model serve as a benchmark and guide for improvement. This will help identify gaps that need to be filled by the framework.

Engage stakeholders early and often

One of the most important things to remember when creating a governance framework is to engage stakeholders early and often throughout the process. This ensures that everyone understands the goals of the framework and is on board with its implementation. It can also ensure that all current best practices for using and managing data are considered and optimized for the new framework, regardless of which department is using the data.

keep it simple

Trying to cram too many rules and procedures into a governance framework can be tempting. However, keeping things simple is key to promoting organization-wide adoption and compliance.

Allow for flexibility

No matter how carefully a governance framework is designed, there will always be unforeseen circumstances that arise. As such, it is important to create a flexible framework that can evolve with organizational needs over time.

Apply data governance frameworks and best practices to your business

Every organization wants to reap the benefits of being more data-driven, but to achieve this, collecting data alone is not enough. It also requires a well-designed data governance framework to ensure data is managed effectively and remains compliant with applicable laws and regulations. By following the best practices outlined above, organizations can create a data governance framework that meets their specific needs and industry requirements to help them achieve desired business outcomes.

The 3 best GRC solutions


Visit the website

Build a modern, data-driven enterprise. Connect to any data source to bring your data together in a unified view, then make analytics available to take action based on insights, while maintaining security and control. Domo serves enterprise customers from all industries looking to manage their entire organization from a single platform.

Learn more about Domo


Visit the website

RSA Archer removes silos from the risk management process so that all efforts are streamlined and information is accurate, consolidated and complete. The platform’s configurability allows users to quickly make changes with no coding or database development required. Archer was named a Leader in the 2020 Gartner Magic Quadrant for IT Risk Management and IT Vendor Risk Management Tools. Additionally, Forrester named it Contender in its Q1 2020 GRC wave.

Learn more about RSA


Visit the website

StandardFusion is a cloud-based GRC platform designed for information security teams of any size organization, large or small, to easily manage risk, compliance, audits and vendors with an intuitive user experience and top notch customer service. Their mission is to make CRM simple and accessible for businesses of all sizes.

Learn more about StandardFusion