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Concepts

The metadata map: every piece, one example

Catalog, glossary, dictionary, lineage, profiling, quality, contracts, semantic layer: what each one is, shown on a single loan-balance column.

draft v1 Updated 2026-10-07 personalbusinessenterprise
By Shan UmasankarDrafted with Claude

Governance vocabulary is a pile of overlapping words. The fastest way to untangle it is to hold one piece of data still and look at it through each lens in turn.

The example: a lending table LOAN_DTL with a column OUTSTANDING_BAL_AMT.

Every piece, on one column

Piece What it is On our column
technical metadata Facts about the physical data LOAN_DTL.OUTSTANDING_BAL_AMT, DECIMAL(18,2), Oracle, schema LND
data dictionary One system's column-by-column reference "Unpaid principal, USD, ≥ 0"
business glossary The enterprise's words and meanings Outstanding Balance: unpaid principal as of the reporting date
business element Governed data carrying that meaning in context Mortgage Outstanding Balance
critical data element (CDE) A flag: this one matters most Yes, because it feeds regulatory reporting
data catalog Searchable inventory of everything Search "loan balance" → this column, its owner, its reports
data lineage Where it comes from and goes Core banking → batch job → LOAN_DTL → exposure report
data profiling What the data actually looks like 12M rows, min 0, max 4.2M, 0.3% null
data quality Rules it must pass "≥ 0 and not null": 99.7% pass
data contract Producer's promise to consumers Stays DECIMAL, lands by 06:00, nulls under 1%
reference data Shared code lists Status codes AC / CL / CO on the same table
master data The golden version of an entity The loan's borrower resolves to Customer #123
semantic layer Calculations defined once Total Exposure = SUM(OUTSTANDING_BAL_AMT)

How they connect

  1. Harvest the technical metadata and the source data dictionary.
  2. Map columns to business elements and business terms. This is the business ↔ technical link, and it is the real product.
  3. Govern the elements: owners, critical data element (CDE) flags, policies, data quality rules.
  4. Trace them with data lineage.
  5. Package them as data products, contracts or a semantic layer.
  6. Serve them to people, systems and AI agents.

One line each

  • Catalog: what exists.
  • Glossary: what it means.
  • Dictionary: what it means here.
  • Lineage: where it flows.
  • Profiling: what it is.
  • Quality: whether it's what it should be.
  • Contract: what was promised.
  • Semantic layer: how to calculate it.

Three scales

  • Personal: your spreadsheet of accounts has columns (technical metadata), you know what "balance" means (glossary), and you'd notice if a number went negative (data quality). You're already doing this informally.
  • Business: a 20-person company's CRM and accounting system disagree on what an "active customer" is. A one-page glossary and two quality rules fix more than any tool purchase.
  • Enterprise: millions of columns across hundreds of systems. None of this works by hand; the mapping is proposed by machines and approved by people.