Data Catalog

A practical example

Example: an analyst searching 'churn rate' should find the certified metric definition, the table it comes from, its owner, and downstream dashboards that would be affected by a change.

What to evaluate before investing

• Confirm the connector list covers your actual stack: warehouse, BI layer, orchestration and dbt or equivalent, not just generic JDBC.
• Test whether column-level metadata and usage stats are harvested automatically or require manual tagging that will decay.
• Check how the catalog handles popularity and certification signals so analysts can distinguish trusted assets from abandoned ones.

Limitations and tradeoffs

Catalogs depend on metadata quality: if descriptions and ownership are sparse at rollout, search results look complete but answer nothing, and adoption stalls.