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Data dictionary vs business glossary

A data dictionary explains fields as they exist in data. A business glossary explains concepts as the business uses them.

A data dictionary explains how data is represented technically. A business glossary explains what an organization means. Governance needs both, linked: one business term may use several fields, and one field may support several business concepts.

Keeping the layers distinct makes review easier. Engineers can update observed types and formats without rewriting business policy; Stewards can refine a term without copying the same definition into every column description.

Build the link, not two isolated lists

  1. Profile the implementation Record table, field, type, format, nullability, key evidence and source in the dictionary.
  2. Define the concept Record business boundary, calculation, synonyms, domain and Steward in the glossary.
  3. Create explicit links Connect terms to the fields and tables that implement them, including filters or derivations.
  4. Review changes separately A detected type change and a revised business definition are different governance events.
Linked records for Revenue
LayerExample recordAnswers
Business glossaryRevenue = net amount for completed salesWhat do we mean, who decides, what is included?
Data dictionarysales_transactions.net_amount · decimal(12,2)Where is it, how is it stored, what was observed?
Data dictionarysales_transactions.status · textWhich field supplies the inclusion condition?
Semantic linkRevenue → net_amount where status = completedHow does the concept map to implementation?

What good looks like

If a definition cannot be traced to implementation, it is hard to test. If a field has no business context, it is hard to use responsibly.

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