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Metadata management

The practice of keeping all the descriptions and context about data accurate, organised, and up to date across an organisation.

The Simple Version

The practice of keeping all the descriptions and context about data accurate, organised, and up to date across an organisation.

Detailed Explanation

Metadata management encompasses metadata standards definition (what fields and vocabularies to use), metadata capture (manual entry and automated discovery), metadata quality (keeping descriptions accurate and current), metadata integration (linking metadata across systems), and metadata governance (defining who is responsible for which metadata). Effective metadata management underpins data catalogs, data lineage tools, and AI documentation requirements. ISO/IEC 11179 provides a metadata registry standard; W3C SKOS enables vocabulary management.

Key Characteristics

  • Includes standards, capture, quality, integration, and governance dimensions
  • Underpins data catalog and lineage capabilities
  • Requires a combination of automated tooling and human curation
  • Critical for AI documentation and auditability

Why It Matters

Organisations investing in metadata management capabilities significantly reduce the cost of AI compliance documentation, automated lineage and provenance capture converts governance from a project-level effort to a platform-level capability.

Real-World Analogy

Like the cataloguing system of a museum, each artefact has a record documenting its provenance, condition, location, and exhibition history; without it, the collection is chaotic and unmanageable.

Common Misconceptions

  • A data catalog implements metadata management, a catalog is one tool; management is the full discipline including standards, governance, and processes.
  • Metadata management is a one-time setup exercise, metadata must be actively maintained as data assets evolve.

Related Terms

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Sources & Further Reading