How fit your data is for the decisions made on it — accurate, complete, fresh and consistent.
Reviewed by Francisco Ferreira ·
Data quality is how much you can trust a number when you act on it. Good-quality data is accurate (it reflects reality), complete (no missing rows or columns that should be there), fresh (recent enough to matter), consistent (the same thing means the same thing everywhere) and valid (values stay inside their expected range).
The hard part isn't defining it — it's noticing when it slips. A pipeline stalls, a table fills with nulls, a metric quietly doubles, and the dashboard says nothing because dashboards wait for you to look. Most teams find out after a decision has already been made on the bad number.
Tabkeel learns what normal looks like for each table and tells you in plain language when something drifts — with the query to diagnose it already written. You don't need a data team to run it, and it scales up cleanly when you have one.