Terms related to data · 3.2.2
data quality checking
examining data for completeness, bias and other qualities that affect how useful it is to an AI system
What this term settles
The definition is deliberately narrow. It tells the reader what counts as data quality checking and leaves the engineering choices outside the word.
Who it binds
The clause does not instruct anyone. It settles what data quality checking means so that an instruction written elsewhere can be read the same way by both sides.
What to do in your practice
Write the entry into the glossary the firm actually reads, not a shelf document. Check the two artefacts where the word does most work and note any sentence that stretches the meaning.
Evidence an auditor or regulator asks for
- A controlled vocabulary entry for data quality checking with the citation 3.2.2, an owner and a review date.An auditor can check that the term is controlled rather than assumed.
- A data inventory field that records the entry for each dataset used in training or operation.Shows data quality checking in use in the artefact the firm relies on, not only in a glossary.
- A data quality record with the check, the date and the person who signed it off.The data inventory is organised by system rather than by dataset, so the same data is described twice with different labels.
Common gaps this page catches
- data quality checking is used in the data record behind every AI system without a written definition, so two teams can mean different things.
- The term appears in a template with no citation, so the reader cannot tell which sense was intended.
What to prepare before the amendment lands
Brief the owners who use data quality checking most often, then ask each to return one artefact that shows the entry in use. File the artefacts against the register row.
How to test it here
Take one live artefact rather than a template, find the sentence or field where data quality checking is used, and put this page beside it. Record the answer, the artefact and the date, because that record is the evidence and a glossary entry on its own is only an intention.
What this page does not claim
The page does not reproduce the standard, certify the firm or predict the wording the amendment will use; it states data quality checking at the claim level "definition" and stops where the public record stops.
Common questions
- What does data quality checking mean in ISO 22989?
- examining data for completeness, bias and other qualities that affect how useful it is to an AI system
- What evidence should we keep for 3.2.2?
- A controlled vocabulary entry for data quality checking with the citation 3.2.2, an owner and a review date. A data inventory field that records the entry for each dataset used in training or operation.
- Is data quality checking a requirement?
- No: data quality checking is a definition or clause of a terminology standard, and it becomes binding only when a firm writes it into a policy, a contract or a procedure, or when another standard incorporates it.
Related units
Source. ISO/IEC 22989:2022, Clause 3.2.2, Terms and definitions. Open the ISO public entry. Retrieved 21 October 2026.
https://www.iso.org/obp/ui/en/#iso:std:iso-iec:22989:ed-1:v1:en
ISO catalogue record 88145, the amendment status, stage and dates. Catalogue record 74296, the base standard.
Claim level: definition. The definition is in the public preview. This page states it in our own words and does not reproduce the text.
Last updated: 2026-10-21 · Markdown version · Check your vocabulary