The fundamental outcome of well-executed Metadata Management is a consistent understanding of data resources. DAMA-DMBOK2 states this directly: effective Metadata Management enables consistent understanding of data resources and supports more efficient development across organizational boundaries.

Metadata supplies context that raw data cannot provide by itself. It explains business meaning, technical structures, valid domains, calculations, transformations, ownership, lineage, systems of record, access restrictions, known quality issues, and update schedules. DMBOK2 also identifies Data Quality rules and measurement results as forms of business metadata.

Consequently, Metadata Management is broader than database design, dimensional reporting, authorization, or Data Modeling. Each of those activities consumes metadata, but none represents its complete objective.

This capability is essential to Data Quality. A dataset cannot be meaningfully evaluated for fitness for purpose unless users know what its fields mean, where the data originated, how values were transformed, which business rules apply, and which source is authoritative. Metadata therefore provides the semantic and technical framework required to define and interpret Data Quality measurements consistently.

DAMA's current DMBOK2 revision also explicitly strengthens the relationship between Data Quality and Metadata Management.

Reference Topics: DAMA-DMBOK2 Metadata Management — Goals and Principles; Business Metadata; Technical Metadata; Data Lineage; Chapter 13 — Data Quality and Metadata Management.

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