Data quality

Data quality describes how well data serves the purpose it is held for. A record can be well protected and still be wrong, incomplete, out of date, or inconsistent with its copy in another system. Data governance practice measures quality against a set of dimensions, and sources differ on the list; one widely cited set, from DAMA UK, is accuracy, completeness, consistency, validity, timeliness and uniqueness. ISO 8000 and ISO/IEC 25012 are standards that address data quality in more depth.

Quality overlaps with the integrity property of the CIA triad but is not the same thing. Integrity controls, such as hashing and authorisation checks, guard against unauthorised or improper change. Quality asks whether the data was right in the first place. Data entered incorrectly by an authorised user may pass every integrity control and still fail on quality. The data steward commonly looks after quality day to day under requirements set by the data owner, and data maintenance is the activity that stops quality decaying. For personal data, the accuracy principle in GDPR Article 5(1)(d) turns part of this into a legal obligation.

Exam relevance: a scenario is likely to describe duplicated, stale or inconsistent records and ask which role or activity addresses them. Candidates are expected to link quality problems to the steward rather than the custodian, and to keep quality distinct from integrity controls.