Data governance handles legacy systems by addressing their unique challenges through structured processes that balance modernization with practicality. Legacy systems often lack built-in support for modern governance requirements like data lineage tracking, access controls, or metadata management. To manage this, teams typically start by inventorying legacy data sources, documenting their structure, and mapping how data flows between older systems and newer infrastructure. For example, a legacy mainframe using flat-file storage might require custom scripts to extract metadata or enforce retention policies, while a decades-old database might need wrappers to integrate with modern audit tools. The goal is to minimize disruption while ensuring compliance with current governance standards.
A key strategy is implementing intermediate layers or adapters to bridge legacy systems with governance frameworks. Developers might build APIs to expose legacy data to centralized governance tools, apply tagging or classification rules at the integration point, or use middleware to log access events. For instance, a COBOL-based payroll system could be wrapped with a REST API that enforces role-based access controls before allowing queries. This avoids costly rewrites while enabling governance features like audit trails. Teams might also prioritize incremental updates, such as adding encryption to legacy file transfers or retrofitting basic metadata fields into older databases, rather than attempting full modernization in one step.
Specific examples include using ETL pipelines to migrate legacy data to governed storage (e.g., moving VSAM files to a cloud data lake with tagging) or implementing proxy services to intercept and validate requests to legacy APIs. In one real-world case, a financial institution used a schema-on-read approach to apply governance rules to unstructured legacy data in Hadoop by creating metadata catalogs and access policies during ingestion. Developers working with legacy systems should focus on creating isolation boundaries (like data sandboxes for testing governance changes) and automating compliance checks where manual processes previously existed. The emphasis is on pragmatic solutions that respect technical debt while preventing legacy systems from becoming governance blind spots.