The AI-Ready Data Checklist
Check four things: is there documented governance, an integrated catalog, data lineage, and a named owner for every dataset? If any is missing, your data is not ready, and building AI on it will produce unreliable results no matter how good the model is.
The four checks
Governance, catalog, lineage, ownership. These are the artifacts the ready ~7% of enterprises establish before shipping AI, and their absence predicts failure.
- Governance, can you state who may use each dataset and for what purpose?
- Catalog, is there one inventory where any team can find available data?
- Lineage, can you trace any field back to its origin and transformations?
- Ownership, does every dataset have a named person accountable for its quality?
Why it must come first
These foundations are cheap to build before a pilot and expensive to retrofit after one fails. The ready minority front-load this work; the unprepared majority discover the gap only when the AI produces garbage.
Building governance, catalog, lineage, and ownership before your first pilot costs a fraction of untangling them after a failed deployment has already eroded trust in AI across the organization.
Frequently asked questions.
How long does it take to get data AI-ready?
It varies with the mess, but the foundations, catalog, ownership, basic governance, can often be established in weeks for a focused first use case rather than boiling the ocean. Start with the specific data your first AI project needs, not everything.