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Data Catalog and Lineage

Summary​

Data catalog and lineage provide discoverability, ownership, and traceability across enterprise data assets. For AI, they are essential to trust, explainability, and incident response.

Why This Matters​

  • Teams cannot govern what they cannot find.
  • Lineage accelerates root-cause analysis in AI failures.
  • Auditors need evidence of data provenance and usage paths.

Core Concepts​

  • Catalog as operating interface for datasets and data products.
  • End-to-end lineage from source to model output.
  • Business glossary and technical metadata alignment.

Use the flow above to sequence decisions for Data Catalog and Lineage before implementation starts.

Diagram​

Implementation Steps​

  1. Define metadata minimum standards and ownership fields.
  2. Integrate pipeline tooling with lineage capture.
  3. Link business terms to technical assets in catalog.
  4. Add search and impact analysis for change planning.
  5. Monitor metadata completeness and lineage coverage.

Realistic Example​

A policy assistant outage was traced to an upstream field rename. Lineage graph immediately exposed impacted datasets and retrieval indexes, cutting recovery time.

Senior Tech vs Dev Conversation​

Senior Tech: Is a catalog only for data engineers? Dev: No, business users need it to trust AI answers. Senior Tech: What is the first must-have metadata field? Dev: Asset owner with SLA and sensitivity classification.

UX/UI Checklist​

  • Catalog search supports business and technical terms.
  • Asset pages show owner, quality, freshness, and lineage links.
  • Impact analysis is available before schema changes.
  • Access request path is visible from each asset.

Common Pitfalls​

  • Catalog adoption without ownership accountability.
  • Lineage capture limited to batch jobs only.
  • Missing links between glossary and technical assets.

References and Next Steps​