Data provenance documentation: a dossier buyers can actually check
Provenance is not a sentence in a pitch deck. It is a chain of evidence from how a record was created to the frozen version you offer. Below is a six-part dossier structure, aligned with industry metadata standards.
From record to frozen version
- Sources How each was obtained, and on what basis.
- Lineage Cleaning, filtering, removals.
- Frozen version Name, version, hash, freeze date.
Original source
Who created or collected it?
Rights chain
What contracts and permissions apply?
Proposed AI use
Does the allowed scope cover this use?
Check copyright, contract restrictions and personal-information obligations separately. Access alone is not permission to license.
Dossier structure
- Dataset identity: name, version, content hash, freeze date.
- Sources: for each, how it was obtained, date range and contractual basis.
- Rights chain: assignments, third-party licenses, opt-outs honored.
- Privacy: what notice or consent applied at collection, de-identification method (e.g., HIPAA Safe Harbor or Expert Determination where relevant).
- Processing: cleaning, filtering, removals on request.
- Known limitations and open questions.
Why buyers need it
Developers increasingly have to describe their training data publicly. California AB 2013 is one example, and frameworks such as NIST's AI RMF stress provenance. A dossier mapped to fields like the Data & Trust Alliance's provenance standards saves the buyer work and strengthens your position.
What not to put in it
No raw personal data, credentials or secret keys. Describe processes; store evidence separately with restricted access.
How this affects value
A dossier a buyer can map to its own disclosure duties shortens diligence and strengthens your position.
See what shapes a dataset's value