Your data catalog is lying to you
Why manually maintained glossaries lose trust so quickly, and how active metadata keeps definitions connected to the systems they describe.
Why manually maintained glossaries lose trust so quickly, and how active metadata keeps definitions connected to the systems they describe.
A catalog is not automatically a source of truth
Most catalog programs begin with a burst of documentation. Teams define important terms, assign owners, and describe datasets. Then schemas change, pipelines move, and business logic evolves while the catalog remains frozen in time.
The result is more dangerous than missing documentation: polished definitions that look authoritative but no longer describe production reality.
Metadata has to move with the system
Reliable catalogs harvest technical metadata continuously, connect it to lineage, and surface ownership inside the tools where teams already work. Changes to a critical table should trigger review of the terms and reports that depend on it.
Business context still requires human judgment, but automation can identify what needs attention and prevent reviewers from searching blindly through thousands of assets.
Design for trust, not completeness
A smaller collection of current, owned, and observable definitions is more valuable than a complete-looking catalog nobody trusts. Start with high-impact domains and publish freshness, lineage, and ownership alongside every definition.
A catalog becomes useful when it behaves like part of the data platform—not when it becomes another documentation project.
