National foreword

This British Standard is the UK implementation of ISO/IEC 5259‑5:2025.

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 28 February 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 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

To address data quality properly without wasting critical resources, the organization’s governing body can set the strategic direction for the use of analytics and machine learning (ML) and can oversee the quality of the needed data.

The data quality governance framework for analytics and ML assists the governing body in establishing a data quality governance within its organization with adequate controls across different layers of the organization throughout the data life cycle (DLC).

The framework can be used by both the governing body and management to interact and ensure the establishment of an effective data quality governance for analytics and ML at all levels in the organization.

The framework can be applicable regardless of an organization’s size and type; and used in conjunction with other parts of the ISO/IEC 5259 series.

1 Scope

This document provides a data quality governance framework for analytics and machine learning (ML) to enable governing bodies of organizations to direct and oversee the implementation and operation of data quality measures, management, and related processes with adequate controls throughout the data life cycle (DLC) model according to ISO/IEC 5259‑1.

This document can be applied to any analytics and ML. This document does not define specific management requirements or process requirements according to ISO/IEC 5259‑3 and ISO/IEC 5259‑4 respectively.

2 Normative references

3 Terms and definitions

4 Abbreviated terms

5 Data quality governance in the context of analytics and ML

5.1 Foundation

5.2 Ambiguous responsibilities for data

5.3 Purpose and justification

6 Data quality governance framework

6.1 General

6.2 DQ guiding principles

6.3 Strategies and policies for DQ

6.4 Business planning for DQ

6.5 DQ accountabilities

6.6 DQ risk management

6.7 Management processes for DQ

7 Responsibilities of governing body

7.1 Understand the strategic importance of data quality

7.2 Establish enabling environment for data quality governance

7.3 Formulate data quality strategies

7.4 Business planning for data quality

7.5 Data quality risk management capability

7.6 Set policies to ensure data quality

7.7 Establish oversight mechanisms

8 Responsibilities of management

8.1 Implement data quality strategies

8.2 Establish and enforce comprehensive data quality policies

8.3 Implement data quality management processes

8.4 Establishing internal risk control as part of management process

Bibliography

BS ISO/IEC 5259-5:2025

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

Current

Published:

28 Feb 2025