National foreword

This British Standard is the UK implementation of EN ISO/IEC 5259‑2:2025. It is identical to ISO/IEC 5259‑2:2024.

The UK participation in its preparation was entrusted to Technical Committee ART/1, Artificial Intelligence.

A list of organizations represented on this committee can be obtained on request to its committee manager.

Contractual and legal considerations

This publication has been prepared in good faith, however no representation, warranty, assurance or undertaking (express or implied) is or will be made, and no responsibility or liability is or will be accepted by BSI in relation to the adequacy, accuracy, completeness or reasonableness of this publication. All and any such responsibility and liability is expressly disclaimed to the full extent permitted by the law.

This publication is provided as is, and is to be used at the recipient’s own risk.

The recipient is advised to consider seeking professional guidance with respect to its use of this publication.

This publication is not intended to constitute a contract. Users are responsible for its correct application.

Compliance with a British Standard cannot confer immunity from legal obligations.

This British Standard was published under the authority of the Standards Policy and Strategy Committee on 31 May 2025.

Foreword

ISO (the International Organization for Standardization) and IEC (the International Electrotechnical Commission) form the specialized system for worldwide standardization. National bodies that are members of ISO or IEC participate in the development of International Standards through technical committees established by the respective organization to deal with particular fields of technical activity. ISO and IEC technical committees collaborate in fields of mutual interest. Other international organizations, governmental and non-governmental, in liaison with ISO and IEC, also take part in the work.

The procedures used to develop this document and those intended for its further maintenance are described in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the different types of document should be noted. This document was drafted in accordance with the editorial rules of the ISO/IEC Directives, Part 2 (see www.iso.org/directives or www.iec.ch/members_experts/refdocs).

ISO and IEC draw attention to the possibility that the implementation of this document may involve the use of (a) patent(s). ISO and IEC take no position concerning the evidence, validity or applicability of any claimed patent rights in respect thereof. As of the date of publication of this document, ISO and IEC had not received notice of (a) patent(s) which may be required to implement this document. However, implementers are cautioned that this may not represent the latest information, which may be obtained from the patent database available at www.iso.org/patents and https://patents.iec.ch. ISO and IEC shall not be held responsible for identifying any or all such patent rights.

Any trade name used in this document is information given for the convenience of users and does not constitute an endorsement.

For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and expressions related to conformity assessment, as well as information about ISO's adherence to the World Trade Organization (WTO) principles in the Technical Barriers to Trade (TBT) see www.iso.org/iso/foreword.html. In the IEC, see www.iec.ch/understanding-standards.

This document was prepared by Joint Technical Committee ISO/IEC JTC 1, Information technology, Subcommittee SC 42, Artificial Intelligence.

A list of all parts in the ISO/IEC 5259 series can be found on the ISO and IEC websites.

Any feedback or questions on this document should be directed to the user’s national standards body. A complete listing of these bodies can be found at www.iso.org/members.html and www.iec.ch/national-committees.

Introduction

Data-supported decision-making brings new challenges to data quality management in data analytics and artificial intelligence (AI) based on machine learning (ML). Issues in data quality, such as incomplete, false or outdated data, can adversely affect analytics and ML processes and outcomes. Data from various sources, including structured data (e.g. relational databases) and unstructured data (e.g. documents, images, audios), can be directly consumed into the data life cycle for analytics and ML model development. Data are transformed in each stage of the data life cycle of analytics and ML. A holistic standardized approach to control, produce and deliver sufficient high-quality data is necessary for data analytics and ML models to be safe, reliable and interoperable. To develop credible data quality management for analytics and ML, intrinsic data quality International Standards, including concepts and use cases, characteristics and measurements, management requirements, and process framework, can be considered.

This document is a part of the ISO/IEC 5259 series. This document builds upon the ISO 8000 series, ISO/IEC 25012 and ISO/IEC 25024. The purpose of this document is to describe a data quality model through the definition of data quality characteristics and data quality measures based on ISO/IEC 25012 and ISO/IEC 25024. Data quality models can be extended or modified according to this document.

This European Standard was approved by CEN on 18 May 2025.

CEN and CENELEC members are bound to comply with the CEN/CENELEC Internal Regulations which stipulate the conditions for giving this European Standard the status of a national standard without any alteration. Up-to-date lists and bibliographical references concerning such national standards may be obtained on application to the CEN-CENELEC Management Centre or to any CEN and CENELEC member.

This European Standard exists in three official versions (English, French, German). A version in any other language made by translation under the responsibility of a CEN and CENELEC member into its own language and notified to the CEN-CENELEC Management Centre has the same status as the official versions.

CEN and CENELEC members are the national standards bodies and national electrotechnical committees of Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Republic of North Macedonia, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Türkiye and United Kingdom.

European foreword

The text of ISO/IEC 5259‑2:2024 has been prepared by Technical Committee ISO/IEC JTC 1 "Information technology” of the International Organization for Standardization (ISO) and has been taken over as EN ISO/IEC 5259‑2:2025 by Technical Committee CEN-CENELEC/ JTC 21 “Artificial Intelligence” the secretariat of which is held by DS.

This European Standard shall be given the status of a national standard, either by publication of an identical text or by endorsement, at the latest by November 2025, and conflicting national standards shall be withdrawn at the latest by November 2025.

Attention is drawn to the possibility that some of the elements of this document may be the subject of patent rights. CEN-CENELEC shall not be held responsible for identifying any or all such patent rights.

Any feedback and questions on this document should be directed to the users’ national standards body. A complete listing of these bodies can be found on the CEN and CENELEC websites.

According to the CEN-CENELEC Internal Regulations, the national standards organizations of the following countries are bound to implement this European Standard: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Republic of North Macedonia, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Türkiye and the United Kingdom.

Endorsement notice

The text of ISO/IEC 5259‑2:2024 has been approved by CEN-CENELEC as EN ISO/IEC 5259‑2:2025 without any modification.

1 Scope

This document specifies a data quality model, data quality measures and guidance on reporting data quality in the context of analytics and machine learning (ML).

This document is applicable to all types of organizations who want to achieve their data quality objectives.

2 Normative references

3 Terms and definitions

4 Symbols and abbreviated terms

5 Data quality components and data quality models for analytics and machine learning

5.1 Data quality components in data life cycle

5.2 Data quality model

6 Data quality characteristics and quality measures

6.1 General

6.2 Inherent data quality characteristics

6.3 Inherent and system-dependent data quality characteristics

6.4 System-dependent data quality characteristics

6.5 Additional data quality characteristics

7 Implementing a data quality model and data quality measures for an analytics or ML task

8 Data quality reporting

8.1 Data quality reporting framework

8.2 Data quality measure information

8.3 Guidance to organizations

Bibliography

BS EN ISO/IEC 5259-2:2025

Artificial intelligence. Data quality for analytics and machine learning (ML) - Data quality measures

Current

Published:

31 May 2025