What is Data Governance?

Data governance (DG) is the process of managing the availability, usability, integrity and security of the data in enterprise systems, based on internal data standards and policies that also control data usage. Effective data governance ensures that data is consistent and trustworthy and doesn't get misused. It's increasingly critical as organizations face new data privacy regulations and rely more and more on data analytics to help optimize operations and drive business decision-making.

A well-designed data governance program typically includes a governance team, a steering committee that acts as the governing body, and a group of data stewards. They work together to create the standards and policies for governing data, as well as implementation and enforcement procedures that are primarily carried out by the data stewards. Ideally, executives and other representatives from an organization's business operations take part, in addition to the IT and data management teams.

While data governance is a core component of an overall data management strategy, organizations need to focus on the expected business benefits of a governance program for it to be successful, independent consultant Nicola Askham wrote in a January 2022 blog post. Eric Hirschhorn, chief data officer at The Bank of New York Mellon Corp., made the same point in a session during the 2022 Enterprise Data World Digital conference. "Outcomes can't just be good governance," he said. "Outcomes have to be running better businesses."

This comprehensive guide to data governance further explains what it is, how it works, the business benefits it provides, best practices and the challenges of governing data. You'll also find an overview of data governance software and related technologies that can aid in the governance process. Throughout the guide, hyperlinks point to related articles that cover the topics being addressed in more depth.

Why data governance matters

Without effective data governance, data inconsistencies in different systems across an organization might not get resolved. For example, customer names may be listed differently in sales, logistics and customer service systems. That could complicate data integration efforts and create data integrity issues that affect the accuracy of business intelligence (BI), enterprise reporting and analytics applications. In addition, data errors might not be identified and fixed, further affecting BI and analytics accuracy.