Data provenance is simply the record of where a piece of data came from and how it was produced: the source, the person, the instructions, the review, the changes. For AI, it is quickly moving from nice-to-have to the thing that decides whether a model is defensible.
Why it matters now
Two forces are converging. Regulation (the EU AI Act) asks you to document data provenance for high-risk systems. And quality pressure means teams need to trace a bad model behaviour back to the batch that caused it. Provenance answers both from the same record.
What a provenance record contains
At minimum: the source of the data, the contributor and their verification status, the instructions or guidelines they followed, the review or override applied, and timestamps. At the batch level, a lineage record ties all of that together and carries quality signals such as agreement or acceptance rate.
The more of this is captured automatically, the more trustworthy it is, because automatic records are far harder to curate than a summary written before a review.
Building it in, not bolting it on
Provenance recorded after the fact is weak. The strong version is captured as work happens: every output linked to a contributor and a review step, every batch carrying its own lineage. That is what lets you quarantine one batch instead of distrusting a whole dataset, and what turns an audit from a scramble into a lookup.
Provenance vs data cataloguing
Provenance is often confused with data cataloguing. A catalogue tells you what datasets you have and where they live. Provenance tells you how each piece of data was produced and reviewed, down to the human judgment behind it. You need both, but only provenance answers the questions that decide quality disputes and regulatory reviews.
Provable beats asserted
Pathwize records provenance at every step so data quality becomes a matter of record, not trust. Book a demo to see the lineage on a real batch.