ISO IEC 5259-1-2024 PDF

Full title and description

ISO/IEC 5259-1:2024 — Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 1: Overview, terminology, and examples. This foundational part of the ISO/IEC 5259 series provides an overview of data-quality concepts, establishes common terminology and gives illustrative examples and use cases to support consistent application across analytics and ML projects.

Abstract

Part 1 of ISO/IEC 5259 introduces the conceptual framework for data quality in the context of analytics and machine learning, defines key terms used across the series and presents examples and scenarios to clarify how data-quality concepts apply across the data life cycle. It is intended to be used alongside the other parts of the ISO/IEC 5259 series (which address measures, management, operational guidance and governance).

General information

• Status: Published
• Publication date: July 2024
• Publisher: ISO/IEC (International Organization for Standardization and International Electrotechnical Commission)
• ICS / categories: 35.020; 01.040.35
• Edition / version: Edition 1 (2024)
• Number of pages: 19
• Technical committee: ISO/IEC JTC 1/SC 42

Scope

This document provides the means for understanding and associating the individual documents of the ISO/IEC "Artificial intelligence — Data quality for analytics and ML" series. It establishes foundational concepts and terminology and gives examples (use cases and usage scenarios) to support consistent interpretation and application of the series across different sectors and lifecycle stages of data used for analytics and ML.

Key topics and requirements

• Definitions and core terminology for data quality in analytics and ML (to ensure a common vocabulary across the series).
• Overview of the data-quality framework that applies across the data life cycle for analytics and ML.
• Illustrative examples and use cases demonstrating how data-quality concepts and terms apply in practice.
• Positioning and relationship to other parts of ISO/IEC 5259 (measures, management, operational guidance and governance) and related standards (for example ISO/IEC 25012 and ISO 8000).
• Guidance to help organizations interpret and adopt the more specific requirements and measures given in subsequent parts of the series.

Typical use and users

Intended users include data scientists, data engineers, ML practitioners, AI governance and compliance teams, quality managers, and organizational leaders who need a common conceptual basis and terminology for assessing and improving data quality used in analytics and ML initiatives. It is useful both as an introductory reference and as a normative vocabulary for multidisciplinary teams.

Related standards

ISO/IEC 5259-1 is the introductory part of the ISO/IEC 5259 series. Related published and forthcoming parts include: ISO/IEC 5259-2 (Data quality measures), ISO/IEC 5259-3 (Data quality management requirements and guidelines), and ISO/IEC 5259-5 (Data quality governance framework). It is also aligned with data-quality standards such as ISO/IEC 25012 and other data-quality guidance (e.g., ISO 8000). These related parts and standards provide the measurable characteristics, management requirements, operational guidance and governance needed to implement the concepts introduced in Part 1.

Keywords

data quality, analytics, machine learning, ML, data lifecycle, data governance, data-quality measures, terminology, use cases, ISO/IEC 5259

FAQ

Q: What is this standard?

A: ISO/IEC 5259-1:2024 is the first part of the ISO/IEC 5259 series addressing data quality for analytics and machine learning; it provides an overview, common terminology and examples to support consistent application of the series.

Q: What does it cover?

A: It covers foundational concepts and vocabulary for data quality in analytics and ML, plus illustrative examples and scenarios that clarify how terms and concepts apply across the data life cycle; it does not itself prescribe all measures or management requirements (those are in later parts of the series).

Q: Who typically uses it?

A: Data scientists, data engineers, ML/AI teams, data-governance and quality managers, compliance officers and organizational decision‑makers who need a shared conceptual basis and terminology for data-quality activities in analytics and ML projects.

Q: Is it current or superseded?

A: As published by ISO, ISO/IEC 5259-1 is current (published July 2024) and is the first edition; users should check national adoption dates or subsequent revisions for their country, but as of the ISO publication it is the active international standard.

Q: Is it part of a series?

A: Yes — it is Part 1 of the ISO/IEC 5259 series on data quality for analytics and ML; subsequent parts provide measures (Part 2), management requirements and guidelines (Part 3), operational guidance (Part 4) and governance framework (Part 5).

Q: What are the key keywords?

A: Key keywords include: data quality, analytics, machine learning, ML, data lifecycle, data governance, data-quality measures, terminology and use cases.