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Metadata management processes are crucial for efficiently managing information in organizations. They help ensure data quality standards and optimize data availability.
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Executable approach: can be applied and produces an outcome.
What organizes, connects, or makes decisions possible.
Metadata management processes keep descriptive data about data complete, understandable, and reliable throughout its lifecycle.
They grew from data administration, librarianship, and enterprise data management. Data-quality practice treats metadata as a basis for discoverability, meaning, and assessment; no single originator is established.
A process defines which metadata are created, who owns them, how they are checked, and when they change or are retired. A glossary clarifies terms, catalogs improve discovery, and lineage builds trust. Rules alone are insufficient: responsibilities and quality checks must operate in daily work.
Metadata describe the structure, meaning, origin, and use of a dataset.
Named roles maintain metadata and decide on changes.
Completeness, consistency, timeliness, and understandability are checked.
Metadata management improves data discovery, interpretation, and governance. Effort grows with system count and change rate, so priorities and automatable checks matter.
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