A specific way of measuring how good data is — for example, the percentage of records with a complete address field, or the proportion of duplicate entries.
A specific way of measuring how good data is — for example, the percentage of records with a complete address field, or the proportion of duplicate entries.
ISO/IEC 5259 Part 2 provides a framework for defining and applying data quality measures to AI data. Measures are associated with one or more quality dimensions (accuracy, completeness, consistency, etc.) and are specified in terms of the measurement function, the data element or dataset to which it applies, and the target or threshold value. In AI applications, measures may include completeness rates for feature vectors, label agreement rates across annotators, demographic representation ratios, or temporal coverage metrics. Measures are the operational tools that turn abstract quality dimensions into assessable, actionable indicators.
Data and AI teams should define a set of standard quality measures for each key training dataset, making quality assessments reproducible and comparable across dataset versions and AI model iterations.
Like clinical trial endpoints — specific, pre-defined metrics (blood pressure reduction, survival rate at 12 months) against which a treatment's effectiveness is assessed, not vague statements of 'good health outcomes'.
A specific way of measuring how good data is — for example, the percentage of records with a complete address field, or the proportion of duplicate entries.
ISO/IEC 5259 Part 2 provides a framework for defining and applying data quality measures to AI data. Measures are associated with one or more quality dimensions (accuracy, completeness, consistency, etc.) and are specified in terms of the measurement function, the data element or dataset to which it applies, and the target or threshold value. In AI applications, measures may include completeness rates for feature vectors, label agreement rates across annotators, demographic representation ratios, or temporal coverage metrics. Measures are the operational tools that turn abstract quality dimensions into assessable, actionable indicators.
Data and AI teams should define a set of standard quality measures for each key training dataset, making quality assessments reproducible and comparable across dataset versions and AI model iterations.