Often, when people think of data governance, they think of data quality management. It’s true – data governance enables data quality management. However, there are many enterprise data management capabilities that can be so much more successful with a robust data governance operating model in place. Below, I identify several EDM initiatives and describes how data governance supports them.
First of all, what’s a “robust data governance operating model?” The foundational elements for a data governance operating model include confirmed executive sponsorship, a defined organization with roles and responsibilities (and accountabilities) assigned, a charter that defines the scope of the data governance capability (which could expand over time), communication and training plans defined and developed by role, and a set of consistent policies and procedures that describe the people, process and technology aspects of the data governance program in scope.
The data governance operating model can support the capture and management of both business and technical metadata. I’ve talked to people across so many companies who have indicated that they don’t know where their data resides and what it means. Technical metadata can be created using many different tools. Ideally, one that can provide a robust data catalog and lineage. This information is useful, but the business definitions of that data is a critical need.
The data governance office can coordinate the development of common business language and even a business glossary across impacted business areas, leveraging the data domain owners and business data stewards, and ensure that this information is integrated with the technical metadata for a full view of enterprise data, no matter where it resides. The value that this effort can bring to an organization is huge.
Data privacy regulations such as GDPR and CCPA have exacerbated the need for enterprise wide regulatory compliance. A data governance operating model enables many aspects of regulatory compliance. For example, GDPR has highlighted the need for data privacy compliance. Data subjects can ask for the details of any information you have on them. They can ask that you pass along their data to another organization, or even that you permanently delete their personal information, particularly when you no longer have a defined need for it.
A data governance operating model will naturally identify the data environment and landscape through metadata. Developing a data framework through the data governance program can then enable business process mapping, risk analysis, and classification of data quite readily. Policies and procedures that support compliance can be developed by the data domain owners via the council, and the data governance office can develop appropriate training and ensure appropriate documentation resides in the data governance knowledge base.
Many organizations begin an MDM/RDM program with no data governance operating model in place. This situation creates many issues, since all aspects of mastering data require business input, especially ongoing management.
