Data quality checks often begin with the table: missing values, duplicates, outliers, and broken joins. Those checks matter, but they can all pass while the analysis is still wrong.
A definition check asks what each measure actually includes. Is revenue gross or net? Is a customer a person, account, household, or transaction? Does the date represent an order, shipment, or payment?
It also checks the population and time period. A clean comparison can become misleading if one side excludes closed stores, partial weeks, or customers without a recorded attribute.
Clarifying those meanings early is fast, and it often exposes the largest risk before any code is written.