Dataset quality is not a single score. It is a combination of traits that either hold up under use or fail in ways that only show up downstream.
Accuracy
Every record should reflect reality at the time it was captured. A dataset with a five percent error rate looks fine in a spreadsheet and causes real damage in a model.
Completeness
Missing fields are not neutral. A dataset that is eighty percent complete forces every downstream user to make assumptions about the other twenty percent.
Consistency
The same field should mean the same thing in every row. Mixed date formats, inconsistent category names, and varying units are the most common silent quality failures.
Provenance
You should be able to answer where each record came from and when it was last verified. Datasets without provenance cannot be trusted at scale, no matter how clean they look.
Structure
A high quality dataset is organized for the tool that will read it... predictable field names, stable schemas, and formats that do not require manual repair before use.
Update discipline
Static data ages. The best datasets have a stated update frequency and a visible last-verified date, so users know exactly how current the information is.
None of these traits require exotic tooling. They require discipline applied consistently, which is exactly why most datasets in the wild fail at least one of them.