It's tempting to think a dataset's value comes from its size -- more rows, more columns, more coverage. In practice, a 200-row dataset with a clean schema and a clear source beats a 200,000-row scrape nobody can verify.

Four things drive real value: structure (a defined schema instead of free-form columns), documentation (field definitions, source, and license), freshness (a verifiable last-updated date), and uniqueness (data that isn't trivially available everywhere else for free).

Uniqueness doesn't require proprietary data collection. Aggregating, normalizing, and documenting publicly available information into a structure nobody else has bothered to build is itself valuable -- that's most of what a dataset template actually is.

If you're deciding whether to package something as a dataset, ask: would a stranger who has never seen this data understand exactly what each field means and how current it is, just from the file and its documentation? If yes, it's ready to be a product.