ISO/IEC 5259 Part 1: Terminology and Overview
The definitions part of the ISO/IEC 5259 standard, establishing the shared vocabulary for talking about data quality in AI and analytics contexts.
The Simple Version
The definitions part of the ISO/IEC 5259 standard, establishing the shared vocabulary for talking about data quality in AI and analytics contexts.
Detailed Explanation
ISO/IEC 5259-1 defines core data quality concepts including quality dimensions (accuracy, completeness, consistency, currentness, representativeness), quality measures, fitness for purpose, and the relationship between data quality and AI system performance. It provides the definitional foundation on which Parts 2–5 build their more operational and governance-oriented requirements. The terminology aligns with ISO/IEC 25000 (SQuaRE) where applicable and introduces AI-specific extensions.
Key Characteristics
- Establishes shared vocabulary for the full ISO/IEC 5259 series
- Defines data quality dimensions for AI contexts
- Aligns with and extends ISO/IEC 25000 SQuaRE quality vocabulary
- Foundational reference for EU AI Act data governance documentation
Why It Matters
Organisations building data quality programmes for AI should adopt ISO/IEC 5259-1 vocabulary as a common language across technical, business, and governance teams, reducing the communication friction that undermines quality management.
Real-World Analogy
Like a glossary at the front of a legal contract, establishing agreed definitions before specifying requirements prevents misinterpretation throughout the document.
Common Misconceptions
- Part 1 alone is sufficient for data quality management, it provides vocabulary only; the management and governance requirements are in Parts 3 and 5.
- The definitions are identical to ISO/IEC 25012. Part 1 extends and adapts existing definitions for AI-specific quality contexts.