Documenting data at rest involves looking at where data is stored, such as in databases, data lakes, data warehouses and flat files.
To better understand and align data governance and enterprise architecture, let’s look at data at rest and data in motion and why they both have to be documented. You can collect complete application ecosystem information objectively identify connections/interfaces between applications, using data provide accurate compliance assessments and quickly identify security risks and other issues.ĭata governance and EA also provide many of the same benefits of enterprise architecture or business process modeling projects: reducing risk, optimizing operations, and increasing the use of trusted data. In the context of EA, data governance helps you understand what information you have where it came from if it’s secure who’s accountable for it who accessed it and in which systems and applications it’s located and moves between. It documents your data assets from end to end for business understanding and clear data lineage with traceability. Two of the biggest challenges in creating a successful enterprise architecture initiative are: collecting accurate information on application ecosystems and maintaining the information as application ecosystems change.ĭata governance provides time-sensitive, current-state architecture information with a high level of quality. Why should you integrate data governance (DG) and enterprise architecture (EA)? It’s time to think about EA beyond IT. Aligning these practices for regulatory compliance and other benefits
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