What an exploratory data asset review should establish
Harvest Intelligence editorial
An exploratory data asset review identifies candidate datasets, readiness gaps, and the next commercial decision. Before approaching a licensing intermediary, establish what the asset contains and what needs improvement.
Start with an inventory
Describe the datasets your company creates through its ordinary operations. Record where they reside, who maintains them, how frequently they change, and which business activities they reflect. The first inventory can use descriptions and metadata; it need not expose the underlying records.
Separate possession from permission
Holding a dataset does not by itself establish permission to license it. Map the contracts, consents, third-party inputs, and internal restrictions that may affect commercial use. Identify unresolved questions for qualified legal and privacy advisers before sharing samples.
Assess usefulness and readiness
Evaluate completeness, consistency, provenance, and the work needed to produce a usable delivery. Consider potential buyer use cases as hypotheses to investigate. A dataset can be useful internally without being commercially attractive to an external AI developer.
Prepare for intermediary evaluation
A useful brief describes coverage, provenance, quality limitations, update frequency, and proposed uses. Distinguish work for the data owner from questions for the intermediary. Harvest Intelligence helps the owner identify and improve the asset and, under a separate engagement, represents the owner with intermediaries that handle licensing.
Define the next decision
An exploratory review should support a practical decision: investigate further, improve readiness, narrow the proposed scope, or stop. Its value is a better basis for that decision, rather than a promise of licensing revenue.
