ISO/IEC 5259-2:2024 provides a technical reference for Artificial intelligence - Data quality for analytics and machine learning (ML) - Part 2: Data quality measures, helping organizations evaluate whether data is suitable for analytics and ML use. As a derived document connected to ISO/IEC 5259, it is most relevant where teams need a structured basis for documented evaluation, technical review, and quality workflows around data used in model development, validation, and operational deployment. It supports more consistent assessment of data quality in engineering and compliance-oriented environments.

What is ISO/IEC 5259-2:2024?

ISO/IEC 5259-2:2024 focuses on data quality measures within the broader ISO/IEC 5259 series. In practical terms, it is intended to support technical assessment of data characteristics that affect analytics and machine learning outcomes, such as how data is measured, reviewed, and compared during verification activities. For procurement, compliance preparation, and engineering documentation, it can serve as a supporting reference when defining acceptance criteria, traceability expectations, and quality controls for AI-related data pipelines.

Applications of ISO/IEC 5259-2:2024

This document is typically used in workflows where data quality must be assessed before training, testing, or operational use of ML systems. It may be relevant to data engineering teams, laboratories, system integrators, and compliance groups that need a common basis for product evaluation and technical validation. Organizations working on regulated or high-assurance AI deployments can use it to align internal review methods, strengthen conformity assessment preparation, and improve consistency across data collection, preprocessing, and model governance activities.

Why is ISO/IEC 5259-2:2024 important?

Reliable data quality measures help reduce risk in analytics and machine learning programs by making review criteria more transparent and repeatable. ISO/IEC 5259-2:2024 matters because it can improve testing consistency, support operational consistency across teams, and provide a clearer compliance reference during audits or supplier qualification. For engineering and procurement decisions, it offers a structured way to evaluate whether data-related controls are adequate for technical validation, quality assurance, and broader conformity assessment preparation.

• Supports documented evaluation of data quality measures for analytics and machine learning use cases
• Useful for technical review, acceptance planning, and internal quality workflows
• Helps align data assessment practices with conformity assessment and compliance preparation needs
• Relevant for teams managing model inputs, data governance, and verification activities
• Provides a supporting reference within the ISO/IEC 5259 series for structured AI data quality work