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A professional-level summary covering key definitions, frameworks, and exam-relevant points.
Metadata Management: Definition and Framework
Metadata management is the planning, implementation, and control of activities that enable access to high-quality, integrated metadata. The DAMA DMBOK v2 defines metadata as "data that describes data" and identifies it as one of the four highest-weight knowledge areas in the CDMP exam (11%), reflecting its foundational importance to all other data management activities.
The Three Types of Metadata
| Type | Definition | Examples |
|---|---|---|
| Business Metadata | Describes data in business terms — meaning, context, and usage | Data definitions, business rules, data ownership, data lineage descriptions |
| Technical Metadata | Describes the technical properties of data structures | Table names, column names, data types, field lengths, database schemas, ETL mappings |
| Operational Metadata | Describes how data has been processed and maintained | Last updated timestamp, process that updated it, record counts, job execution logs |
The Metadata Repository and Data Catalogue
A metadata repository is the technical store for metadata. A data catalogue is the business-facing interface that makes metadata accessible and searchable to data consumers. Modern data catalogues (Collibra, Alation, Microsoft Purview, Atlan) combine both functions — storing metadata and providing a user-friendly interface for data discovery, data lineage visualisation, and data quality monitoring.
Data Lineage
Data lineage is a subset of metadata that tracks the origin of data and all transformations it has undergone as it moves through systems. It answers the question: "Where did this data come from, and how did it get here?" Data lineage is critical for regulatory compliance (GDPR Article 30 requires records of processing activities), impact analysis (understanding what is affected when a source system changes), and root cause analysis of data quality issues.
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