AI Readiness & Interoperability
Metadata Density: Why Descriptions Matter More Than You Think
Metadata density makes models interpretable by AI and humans.
TL;DR
- • Descriptions are not optional for AI.
- • Metadata improves accuracy and consistency.
The problem (layman)
- • Most models have sparse or missing descriptions.
- • AI lacks the context needed to answer accurately.
Why it matters
- • Metadata enables correct interpretation of fields.
- • It reduces the need for prompt engineering.
Symptoms
- • AI mislabels metrics or uses wrong units.
- • Explanations are vague.
Root causes
- • Metadata considered “nice to have.”
- • No ownership of documentation.
What good looks like
- • High coverage of descriptions across tables, columns, measures.
- • Metadata includes business definitions and units.
How to fix (steps)
- • Set metadata coverage targets.
- • Add descriptions for top metrics and dimensions first.
- • Review metadata in model changes.
Pitfalls
- • Bulk‑filling metadata with generic text.
- • Ignoring updates when logic changes.
Checklist
- • Metadata coverage measured.
- • Top KPIs documented.
- • Review process exists.