Data engineering·Quality, tests and knowing it is wrong
you split 'the data is bad' into six specific claims, and it turns out only one of them is actually true.
Data quality dimensions
Also calledcompleteness accuracy timeliness, quality dimensions
The standard axes for saying what is wrong: completeness, accuracy, consistency, timeliness, validity, uniqueness. Their value is entirely in decomposition — 'bad data' is not actionable and 'twelve percent of rows have a null postcode' is. Each dimension also maps to a different test and a different owner, which is what makes the vocabulary worth having in a meeting.