TS ISO/IEC 5259-1

TS ISO/IEC 5259-1 is the first part of the ISO/IEC 5259 series on data quality for analytics and machine learning, and it helps readers understand how the documents in the series fit together. Rather than focusing on a single metric or a narrow procedure, this standard builds the conceptual foundation for discussing data quality in AI-related work through overview material, terminology, and examples.

Conceptual foundation for data quality

In general technical terms, data quality for analytics and machine learning is about whether data is fit for a intended analytical or model-building purpose. Common quality dimensions in this field include accuracy, consistency, completeness, timeliness, and representativeness. The role of this document, based on the supplied scope, is to support conceptual understanding and help users associate the individual documents in the series; it should not be read as a standalone source of detailed requirements or prescribed test methods.

Examples, use cases, and usage scenarios

The standard also discusses associated technologies and examples, including use cases and usage scenarios. That is valuable because data quality is not abstract in machine learning: it affects data collection, preprocessing, training, evaluation, and ongoing monitoring. In practice, clear terminology helps different stakeholders speak consistently about data defects, suitability for use, and the relationship between data characteristics and model outcomes.

• An introductory part that connects the ISO/IEC 5259 series conceptually
• Terminology for data quality in analytics and machine learning
• Examples of related technologies, use cases, and usage scenarios
• A foundation for understanding data quality within the AI lifecycle