SIST EN ISO/IEC 5259-1:2025 PDF

Name: SIST EN ISO/IEC 5259-1:2025 — Artificial intelligence - Data quality for analytics and machine learning (ML) - Part 1: Overview, terminology, and examples (ISO/IEC 5259-1:2024)
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Artificial intelligence - Data quality for analytics and machine learning (ML) - Part 1: Overview, terminology, and examples (ISO/IEC 5259-1:2024)

Название (английский):

SIST EN ISO/IEC 5259-1:2025

Artificial intelligence - Data quality for analytics and machine learning (ML) - Part 1: Overview, terminology, and examples (ISO/IEC 5259-1:2024)

Статус документа:: Действующий
Формат:: Электронный (PDF)
Количество страниц:: 27
Дата публикации:: 17 июня 2025 г.
Издание:: CEN/CENELEC EN 5259 edition 1 version 1
ICS:: 01.040.35

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Описание (английский):

This document provides the means for understanding and associating the individual documents of the ISO/IEC 5259 series and is the foundation for conceptual understanding of data quality for analytics and machine learning. It also discusses associated technologies and examples (e.g. use cases and usage scenarios).

Abstract

Похожие стандарты

Стандарты, упомянутые в описании

Overview

EN ISO/IEC 5259-1:2025 (ISO/IEC 5259-1:2024) - “Artificial intelligence - Data quality for analytics and machine learning (ML) - Part 1: Overview, terminology, and examples” provides a foundational, conceptual framework for understanding data quality in analytics and machine learning. Published by ISO/IEC and adopted by CEN, this Part 1 defines scope, core terminology, data-life-cycle concepts, and illustrative examples and use cases to help practitioners apply consistent data-quality thinking across AI projects.

Key topics

Terminology and definitions: Standardized terms (aligned with ISO/IEC 22989 and ISO/IEC 23053) to reduce ambiguity in data quality, data originator, data life cycle and related concepts.

Data quality concepts for analytics and ML: Fundamental considerations for how data characteristics affect model performance, reuse, sharing and downstream analytics.

Data quality concept framework:

Data quality management (processes to assess and improve data quality)
Data quality governance (oversight, roles and responsibilities)
Data provenance (lineage and origin tracking)

Data life cycle for analytics and ML: Stages from conception to discontinuation, including cross-stage processes relevant to data preparation, labeling, evaluation and reuse.

Examples and scenarios: Informative annex with use cases and usage scenarios to illustrate practical application of the concepts.

Context within the ISO/IEC 5259 series: Part 1 links to other parts (5259-2: measures and reporting; 5259-3: management requirements; 5259-4: training/evaluation data guidance; 5259-5: governance framework; TR 5259-6: visualization).

Practical applications

Establishing a common vocabulary for multidisciplinary AI teams (data scientists, engineers, analysts, auditors).

Guiding data-quality planning across the data life cycle-from acquisition and labeling to model evaluation and data retirement.

Informing data governance policies, provenance tracking and compliance checks for reuse and sharing.

Supporting development of quality assurance processes for training and evaluation datasets used in supervised, unsupervised, semi‑supervised and reinforcement learning.

Serving as a reference for tooling, reporting and data-quality visualization efforts in enterprise ML pipelines.

Related standards

ISO/IEC 22989, ISO/IEC 23053 (AI concepts and frameworks)

Other parts of ISO/IEC 5259 series (5259-2, 5259-3, 5259-4, 5259-5, TR 5259-6) for measures, requirements, governance, training data guidance and visualization.

This standard is essential for organizations aiming to formalize data quality practices for robust, trustworthy analytics and machine learning outcomes.

SIST EN ISO/IEC 5259-1:2025 PDF

Abstract

Похожие стандарты

Overview

Key topics

Practical applications

Who should use this standard

Related standards

Технические детали

BS EN ISO/IEC 22989:2023

Information technology. Artificial intelligence. Artificial intelligence concepts and terminology.

BS EN ISO/IEC 23053:2023

Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML).

SIST EN ISO/IEC 5259-4:2025

Artificial intelligence - Data quality for analytics and machine learning (ML) - Part 4: Data quality process…