ISO/IEC 5259-3:2024
2024-07
What is ISO/IEC 5259-3?
ISO/IEC 5259-3 defines the requirements and guidance for managing the quality of data used in analytics and machine learning (ML). It provides a flexible framework for setting up a data quality management system (DQMS) that can adapt to various AI life cycles, ensuring that the data powering ML systems is reliable, auditable and aligned with stakeholder expectations.
Why is ISO/IEC 5259-3 important?
Analytics and ML outcomes are only as trustworthy as the data they rely on. Without proper data quality management, even the most advanced algorithms can produce biased or flawed results. ISO/IEC 5259-3 helps organisations structure how they plan, maintain and improve data quality across the full data life cycle. It promotes transparency, accountability and long-term performance, all while integrating smoothly with broader quality, performance or governance efforts.
Benefits
- Enables consistent and auditable data quality management
- Improves trust in AI and ML systems by ensuring reliable inputs
- Supports alignment with stakeholders and regulatory expectations
- Allows flexible integration with various AI development life cycles
- Provides a foundation for third-party or self-assessed data quality assurance
FAQ
Any organisation using data for analytics or ML that wants to establish or improve a structured approach to managing data quality.
Yes. The standard is designed to work across various AI life cycle models as long as they can be mapped to the data life cycle processes.
While focused on data quality, it can be aligned with other ISO management system standards, enabling integrated governance of data, security and AI performance.
Buy together
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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