Open Data Infrastructure
Foundation for AI Catalog Coverage Gaps
Why uncataloged tables, missing owners, stale metadata, and incomplete lineage create AI failure modes that model evaluation misses.
A model can pass an evaluation and still fail because the data catalog did not know the table existed.
Models cannot compensate for missing catalog coverage
AI programs often focus on model behavior, prompt design, and evaluation sets. Those matter. They do not fix a data foundation with missing owners, stale metadata, incomplete lineage, or uncataloged tables.
NIST AI RMF emphasizes governance and measurement. Catalog coverage is part of that measurement. If an AI system depends on data products, the platform needs to know which products exist, who owns them, how fresh they are, and how they connect.
Coverage gaps become AI gaps
A catalog coverage gap can show up as wrong retrieval, unsafe access, stale context, duplicate definitions, or missing incident ownership. The model may look like the problem because the failure appears in the answer. The root cause lives in the data layer.
DataHub and OpenMetadata both document lineage and metadata capabilities. OpenLineage defines an event model for jobs and datasets. Those systems are useful only when coverage is treated as a production requirement.
Core idea: foundation for AI work starts by measuring what the data platform cannot see yet.
The foundation has to be inspectable
Open Data Infrastructure should track catalog coverage by domain, product, owner, lineage depth, freshness signal, policy tag, and AI consumption path. Coverage should be visible enough that leaders can see where AI risk is hiding.
For adjacent context, read Open Data Infrastructure as the foundation for AI, foundation for AI data product ownership, and DataHub and OpenMetadata in the metadata layer.
What breaks first
- Agents retrieve from tables that have no visible owner.
- Metadata freshness is unknown, so stale context looks authoritative.
- Lineage exists for pipelines but not for AI retrieval paths.
- Evaluation failures are fixed in prompts while catalog gaps remain untouched.
Questions to ask
Ask which tables are uncataloged, which cataloged assets lack owners, and which AI tools consume assets without lineage. Ask whether catalog coverage is measured before an agent is allowed to depend on a domain.
Sources to start with
These primary sources anchor the technical claims in this guide.
- NIST AI Risk Management Framework
- DataHub lineage documentation
- OpenMetadata lineage documentation
- OpenLineage object model documentation
AI cannot stand on a foundation the catalog cannot see.