Governance, Catalog & Lineage
A catalog makes data findable, lineage makes errors traceable, and governance makes usage accountable and compliant. Together they turn a pile of data into an asset AI can safely use, which is why the ready ~7% of enterprises build all three before shipping.
What each one does
Catalog answers "where is the data?"; lineage answers "where did this number come from?"; governance answers "who may use it, and how?" Missing any one breaks AI reliability or compliance.
- Catalog, findability. Teams stop rebuilding datasets that already exist and can locate trusted sources fast.
- Lineage, traceability. When an AI output is wrong, you can trace the field to its origin and fix the root cause.
- Governance, accountability. Clear rules on who uses which data for what purpose keep AI usage safe and compliant.
Lineage is how you keep trust
The fastest way to lose organizational trust in AI is an unexplained wrong answer no one can trace. Lineage is the mechanism that lets you find and fix the cause before trust erodes.
One confidently wrong AI answer that no one can explain does more damage to adoption than ten correct ones do good. Lineage, knowing exactly where every field came from, is your defense against that trust collapse.
Frequently asked questions.
Is data governance only for large enterprises?
No. The scale differs, but every business using AI needs to know where its data is (catalog), where it came from (lineage), and who may use it (governance). For a smaller business this can be lightweight, but skipping it entirely still breaks AI reliability.