ISO/IEC 5259-3:2024

Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 3: Data quality management requirements and guidelines

ISO/IEC 5259-3:2024

Name:ISO/IEC 5259-3:2024   Standard name:Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 3: Data quality management requirements and guidelines
Standard number:ISO/IEC 5259-3:2024   language:English language
Release Date:01-Jul-2024   technical committee:ISO/IEC JTC 1/SC 42 - Artificial intelligence
Drafting committee:ISO/IEC JTC 1/SC 42/WG 2 - Data   ICS number:35.020 - Information technology (IT) in general

International
Standard
ISO/IEC 5259-3
First edition
Artificial intelligence — Data
2024-07
quality for analytics and machine
learning (ML) —
Part 3:
Data quality management
requirements and guidelines
Intelligence artificielle — Qualité des données pour les analyses
de données et l’apprentissage automatique —
Partie 3: Exigences et lignes directrices pour la gestion de la
qualité des données
Reference number
© ISO/IEC 2024
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on
the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address below
or ISO’s member body in the country of the requester.
ISO copyright office
CP 401 • Ch. de Blandonnet 8
CH-1214 Vernier, Geneva
Phone: +41 22 749 01 11
Email: [email protected]
Website: www.iso.org
Published in Switzerland
© ISO/IEC 2024 – All rights reserved
ii
Contents Page
Foreword .v
Introduction .vi
1 Scope .1
2 Normative references .1
3 Terms and definitions .1
4 Symbols and abbreviated terms. 2
5 Intended usage . 2
6 Overall data quality management . 2
6.1 Objective.2
6.2 General .2
6.3 Requirements and recommendations .3
6.3.1 General .3
6.3.2 Data quality culture . .3
6.3.3 Management of data quality issues . .3
6.3.4 Competence management .3
6.3.5 Resource management .4
6.3.6 Management system integration .4
6.3.7 Documentation.4
6.3.8 Data quality audit and assessment.4
6.3.9 Confirmation review and data quality measures .5
6.3.10 Project-specific data quality management .5
6.4 Work products .5
7 Life cycle-specific data quality management .6
7.1 Objective.6
7.2 General .6
7.2.1 Data quality management life cycle .6
7.2.2 Data quality management life cycle stages .7
7.2.3 Project-independent tailoring of the data quality management life cycle .8
7.2.4 Horizontal aspects of the data quality management life cycle .8
7.3 Requirements and recommendations .9
7.3.1 Data motivation and conceptualization . .9
7.3.2 Data specification .9
7.3.3 Data planning .11
7.3.4 Data acquisition . . .11
7.3.5 Data preprocessing . 13
7.3.6 Data augmentation . 13
7.3.7 Data provisioning .14
7.3.8 Data decommissioning .16
7.4 Work products .17
7.4.1 Work products of data motivation and conceptualization stage .17
7.4.2 Work products of data specification stage .17
7.4.3 Work products of data planning stage .17
7.4.4 Work products of data acquisition stage .17
7.4.5 Work products of data preprocessing stage .17
7.4.6 Work products of data augmentation stage .18
7.4.7 Work products of data provisioning stage .18
7.4.8 Work products of data decommissioning stage .18
8 Horizontal processes .18
8.1 Objective.18
8.2 General .18
8.3 Requirements and recommendations .18
8.3.1 Verification and validation .18

© ISO/IEC 2024 – All rights reserved
iii
8.3.2 Configuration management .19
8.3.3 Change management .19
8.3.4 Risk management . 20
8.4 Work products .21
8.4.1 Work products of verification and validation .21
8.4.2 Work products of configuration management .21
8.4.3 Work products of change management.21
8.4.4 Work products for risk management .21
9 Management of data quality in supply chains .22
9.1 Objective. 22
9.2 Requirements and recommendations . 22
9.3 Work products . 22
10 Management of data processing tools .23
10.1 Objective. 23
10.2 Requirements and recommendations . 23
10.3 Work products . 23
11 Management of data quality dependencies .23
11.1 Objective. 23
11.2 Requirements and recommendations . 23
11.3 Work products .
...

  • Relates Information
  • ISO 8130-9:1992

    ISO 8130-9:1992 - Coating powders
    09-28
  • EN 352-2:2020/FprA1

    EN 352-2:2021/oprA1:2023
    09-28
  • IEC TS 61158-4:1999

    IEC TS 61158-4:1999 - Digital data communications for measurement and control - Fieldbus for use in industrial control systems - Part 4: Data Link protocol specification Released:3/24/1999 Isbn:2831847656
    09-28
  • HD 566 S1:1990

    HD 566 S1:1998
    09-28
  • ISO 5131:1982/Amd 1:1992

    ISO 5131:1982/Amd 1:1992
    09-28
  • EN 60598-2-22:1990

    EN 60598-2-22:1996
    09-27
  • ISO 8504-2:1992

    ISO 8504-2:1992 - Preparation of steel substrates before application of paints and related products -- Surface preparation methods
    09-27
  • EN 12165:2024

    prEN 12165:2022
    09-27
  • IEC TS 61158-6:1999

    IEC TS 61158-6:1999 - Digital data communications for measurement and control - Fieldbus for use in industrial control systems - Part 6: Application Layer protocol specification Released:3/24/1999 Isbn:2831847613
    09-27
  • ISO 4252:1992

    ISO 4252:1992 - Agricultural tractors -- Operator's workplace, access and exit -- Dimensions
    09-27