Metadata

Intro

Metadata is data about data—descriptions that define characteristics like meaning, structure, lineage, and usage. In EA, good metadata improves discoverability, quality, and governance.

Key points:

• Enables consistent definitions and shared understanding.
• Supports lineage, impact analysis, and compliance.
• Common use cases across EA/BPM/Data/App/Tech include glossaries, data catalogs, API specs, and model repositories.
• Pitfall: leaving metadata unmanaged or outdated, causing confusion.

Examples:

• Business glossary entries defining critical data elements.
• Data catalog tags for sensitivity and retention.
• Model attributes documenting ownership and versions.

In practice:

Establish stewardship and a metadata catalog to keep definitions current and traceable.

Related terms: Data; Information; Data Governance

FAQs:

Q: Is metadata only technical?
A: No; it includes business definitions, ownership, and policies.

Q: How often should metadata be updated?
A: Whenever structures, rules, or ownership change.

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