What is ISO/IEC 5259-4?
ISO/IEC 5259-4 defines a standardised process framework to manage data quality in analytics and machine learning (ML). It provides guidance for organisations to implement reliable, structured approaches across different ML types — including supervised, unsupervised, semi-supervised and reinforcement learning — with a particular focus on data labelling, evaluation and lifecycle management.
Why is ISO/IEC 5259-4 important?
The performance of ML models depends heavily on the quality of the data they are trained and tested on. This is especially true in supervised learning, where inaccurate labelling can introduce bias or critical errors. ISO/IEC 5259-4 offers practical process-level guidance to ensure data used for ML is consistently managed, labelled and evaluated according to industry best practice. It supports a reliable ecosystem for both industrial data labelling services and in-house AI development.
Benefits
- Supports high-quality training and evaluation data for ML
- Provides structured guidance on data labelling practices
- Applicable across all ML types and analytics use cases
- Strengthens traceability and reliability in AI systems
- Aligns data processes with broader quality and lifecycle standards
FAQ
Organisations developing or using ML systems, as well as data labelling service providers and teams managing AI data workflows.
No — while it includes detailed guidance on supervised learning and labelling, it also covers unsupervised, semi-supervised and reinforcement learning approaches.
ISO/IEC 5259-4 provides the operational process framework to help meet the quality management requirements set out in ISO/IEC 5259-3.
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AI data quality management bundle
Ensure high-quality data for analytics and machine learning with our comprehensive ISO/IEC 5259 standards bundle.
- ISO 5259-1:2024
- IEC 5259-2:2024
- IEC 5259-3:2024
- IEC 5259-4:2024
- IEC 5259-5:2024
General information
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Status: PublishedPublication date: 2024-07Stage: International Standard published [60.60]
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Edition: 1Number of pages: 28
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Technical Committee :ISO/IEC JTC 1/SC 42ICS :35.020
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