One of the key aspects of data quality — like accuracy (is it correct?) or completeness (is it all there?) — used to frame what makes data good or bad for a given purpose.
One of the key aspects of data quality — like accuracy (is it correct?) or completeness (is it all there?) — used to frame what makes data good or bad for a given purpose.
ISO/IEC 5259 Part 1 defines and organises data quality dimensions relevant to AI applications. Core dimensions include accuracy (correctness relative to a reference), completeness (absence of missing values), consistency (absence of contradictions), currentness/timeliness (data is not stale), and representativeness (data reflects the population to which it will be applied). Different AI applications prioritise different dimensions — a fraud detection model may prioritise recency, while a demographic prediction model may prioritise representativeness. The EU AI Act references quality dimensions implicitly in its data governance requirements for high-risk systems.
AI product teams should specify required data quality dimensions as acceptance criteria when commissioning training data — preventing quality problems from being discovered only after model development begins.
Like nutritional dimensions on a food label (calories, protein, fat, vitamins) — each dimension captures a different aspect of the food's quality profile, and which dimensions matter most depends on the dietary goal.
One of the key aspects of data quality — like accuracy (is it correct?) or completeness (is it all there?) — used to frame what makes data good or bad for a given purpose.
ISO/IEC 5259 Part 1 defines and organises data quality dimensions relevant to AI applications. Core dimensions include accuracy (correctness relative to a reference), completeness (absence of missing values), consistency (absence of contradictions), currentness/timeliness (data is not stale), and representativeness (data reflects the population to which it will be applied). Different AI applications prioritise different dimensions — a fraud detection model may prioritise recency, while a demographic prediction model may prioritise representativeness. The EU AI Act references quality dimensions implicitly in its data governance requirements for high-risk systems.
AI product teams should specify required data quality dimensions as acceptance criteria when commissioning training data — preventing quality problems from being discovered only after model development begins.