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.
I once received clean sales data in which a major brand had disappeared from the category hierarchy. The rows reconciled and the file passed the usual technical checks, but the category total understated the market because the definition had changed.
That is why a definition check comes before the fuller checklist I use before trusting a dataset. The same discipline caught the projection error that made Australia double in size, and it sits at the centre of how I approach data quality and analytical judgement.
Clarifying those meanings early is fast, and it often exposes the largest risk before any code is written.