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Discuss data licensing

Free tool · five minutes

Could this dataset support a licensing process?

Think of one dataset. Ten questions on who can authorize it, where it came from, what it contains, and how well it is documented. The check reads only your answers, never the data.

Discuss data licensing

The check

Answer for one dataset.

“Partly or unsure” is a fine answer. The point is to find the next step. This is not a valuation or an eligibility decision.

  1. 01 Can you name the person who could authorize a license for this data?
  2. 02 Do you know the rights you hold in each source: created, generated, contributed, or licensed in?
  3. 03 Are prior licenses, exclusivity, and customer or contract restrictions known and written down?
  4. 04 Could you describe how the data was created, when, and by whom?
  5. 05 Is the data distinctive: a domain, workflow, or outcome that is hard to find elsewhere?
  6. 06 Do you know whether it contains personal, customer, or confidential third-party information?
  7. 07 Is there a data dictionary or schema that explains the fields and formats?
  8. 08 Can you state its size in consistent units: records, files, hours, or tokens?
  9. 09 Has anyone measured its quality: completeness, duplicates, and broken records?
  10. 10 Could the data be exported in a stable format, with updates on a schedule if needed?

Answer all ten questions. Your answers stay in your browser.

How the score works

Yes scores 2, Partly or unsure scores 1, and No scores 0.

16–20 Well documented
The basics a licensing process needs appear to be in place. A fit review can test the description against buyer requirements. This is not a valuation or an eligibility decision.
10–15 Some gaps
Several questions a buyer or counsel will ask are still open. Most can be closed with documentation work before any outreach.
0–9 Early
Authority, provenance, or sensitivity is still unknown. Settle who can authorize a license and what the data contains before anything else.

The check reads only your answers. It does not see the data, and it gives no valuation, sale probability, or buyer match. Eligibility needs a documented review.

Your next actions list every No first, then every Partly, in question order.

After your score

What usually comes next.

  • Authority unclear: name the data owner and the person who can approve a license
  • Provenance unclear: write a one-paragraph origin note for each source
  • Sensitivity unknown: inventory personal and confidential content before anything else
  • Documentation thin: start a data dictionary, or scope a readiness assessment
  • Well documented: describe the dataset to us for a fit review

Start with your dataset and your preferred route.

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