Data Provenance
Definition
Data provenance is a verifiable record of where data came from and how it was produced, including who created each piece of it, when, and under what process.
For AI training and evaluation data, provenance answers questions a reviewer or regulator will ask: who made this judgment, were they qualified, when was it done, and can it be shown. Without provenance, a dataset is a black box and its quality cannot be defended after the fact.
Provenance is becoming a compliance requirement as well as a quality one. Governance frameworks increasingly expect organisations to document how their training and evaluation data was created, not just what the data contains.
Strong provenance ties each label or judgment to an identity, a timestamp and the process that produced it, so the record can be audited rather than taken on trust.
How Pathwize relates
Provenance is built into Pathwize: every judgment carries who made it, when, and how long it took, which is the record AI labs and regulated enterprises need to prove how their data was made.
Frequently asked
A verifiable record of where data came from and how it was produced, including who made it and when.
So the quality can be defended and the process documented for reviewers and regulators, not taken on trust.
The identity of who produced each item, a timestamp, and the process or guidelines used, in a form that can be audited.
Related terms