What does AI-ready business data mean?
AI-ready is a description of fitness for a defined task, not a universal certification. A dataset can be useful for one evaluation and unsuitable for another.
Read the guide ↗Data quality
Useful data starts
with usable evidence.
Check whether records connect, identities remain consistent and outcomes can be inspected. A quality framework should make uncertainty visible.
Request→ Action→ Decision→ Outcome
Illustrative sequence, not a customer dataset or a verification result.
Six questions to investigate
Make the quality visible.
Do the records connect?
Measure validated links against a defined eligible set. Keep inferred relationships separate.
Do links survive preparation?
Compare authorized before-and-after samples. Explain exclusions and broken relationships.
Are identities consistent?
Check collisions, shared accounts and split identities across systems.
Is the workflow complete?
Inspect requests, decisions, actions and observable results. Label missing steps.
Is there useful context?
Measure deduplicated activity by type and time period, with distributions and exclusions.
Do sources overlap in time?
Confirm date coverage, timestamp meaning and gaps in the shared window.
Quality is not a privacy verdict.
Connected pseudonymous records can still carry identification risk. A complete workflow does not establish permission to share it. Keep rights, privacy and technical fitness as separate review questions.
The quality field guide
How to measure cross-system linkage
A linkage metric should explain which records were eligible, what counts as a valid connection and how correctness was checked.
Read the guide ↗Keeping useful links through de-identification
Privacy transformations can break the relationships that make a workflow intelligible. Test utility and privacy separately before release.
Read the guide ↗Why the sequence of work matters
Isolated records show that activity occurred. Ordered evidence can explain what led to a decision and whether the resulting action worked.
Read the guide ↗Measuring activity density without rewarding noise
Record volume can describe available context, but a larger number is not necessarily a better learning signal.
Read the guide ↗The connection manifest: a map of the dataset
A manifest helps another evaluator navigate a delivery without guessing how records relate. Keep it specific to one dataset version.
Read the guide ↗Preparing a representative data sample
A sample should answer an agreed evaluation question. The easiest records to retrieve are not always representative of the larger collection.
Read the guide ↗How quality evidence supports a licensing discussion
Quality documentation can reduce uncertainty about what is being offered. It does not establish a market price or guarantee buyer interest.
Read the guide ↗How to compare operational datasets
Compare evidence against the intended use rather than choosing the largest archive or the highest single quality score.
Read the guide ↗Seven improvements to make before licensing
Start with changes that make the dataset interpretable and its limitations visible. Not every issue requires a new pipeline.
Read the guide ↗