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. 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 should focus on the desired business outcomes of a governance program instead of the data itself, Gartner analyst Andrew White wrote in a December 2019 blog post. This comprehensive guide to data governance further explains what it is, how it works, the?business benefits it provides?and the challenges of governing data. You'll also find an overview of data governance software and related tools. Click through the hyperlinks to get expert advice and read about data governance trends and best practices.
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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.