The process blueprint part of ISO/IEC 5259 — describing the specific activities organisations should follow to build and maintain high-quality data for AI.
The process blueprint part of ISO/IEC 5259 — describing the specific activities organisations should follow to build and maintain high-quality data for AI.
ISO/IEC 5259-4 describes data quality processes across the AI data lifecycle: data quality requirements definition, data profiling and assessment, data quality issue identification, remediation planning and execution, and ongoing monitoring. It maps process activities to the quality dimensions and measures from Parts 1 and 2, creating an end-to-end process framework that links strategic quality objectives to operational data handling activities. The framework is use-case agnostic but includes AI-specific process considerations for training, validation, and test dataset management.
Data engineering and MLOps teams can use Part 4 as a process reference architecture for AI data pipelines — ensuring that quality activities are built into the workflow rather than applied as post-hoc checks.
Like a Good Manufacturing Practice (GMP) guide that specifies the process steps for pharmaceutical production — it does not tell you what the final product must be, but how it must be made.
The process blueprint part of ISO/IEC 5259 — describing the specific activities organisations should follow to build and maintain high-quality data for AI.
ISO/IEC 5259-4 describes data quality processes across the AI data lifecycle: data quality requirements definition, data profiling and assessment, data quality issue identification, remediation planning and execution, and ongoing monitoring. It maps process activities to the quality dimensions and measures from Parts 1 and 2, creating an end-to-end process framework that links strategic quality objectives to operational data handling activities. The framework is use-case agnostic but includes AI-specific process considerations for training, validation, and test dataset management.
Data engineering and MLOps teams can use Part 4 as a process reference architecture for AI data pipelines — ensuring that quality activities are built into the workflow rather than applied as post-hoc checks.