ISO/IEC FDIS 5259-1

Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 1: Overview, terminology, and examples

Intelligence artificielle — Qualité des données pour les analyses de données et l’apprentissage automatique — Partie 1: Vue d'ensemble, terminologie et exemples

General Information

• Status: Not Published
• ICS: 01.040.35 - Information technology (Vocabularies)
• ICS: 35.020 - Information technology (IT) in general
• Technical Committee: ISO/IEC JTC 1/SC 42 - Artificial intelligence
• Drafting Committee: ISO/IEC JTC 1/SC 42/WG 2 - Data
• Current Stage: 5020 - FDIS ballot initiated: 2 months. Proof sent to secretariat
• Start Date: 01-Apr-2024
• Completion Date: 01-Apr-2024

Standards Content (Sample)

FINAL DRAFT
International
Standard
ISO/IEC FDIS
5259-1
ISO/IEC JTC 1/SC 42
Artificial intelligence — Data
Secretariat: ANSI
quality for analytics and machine
Voting begins on:
learning (ML) —
2024-04-01
Part 1:
Voting terminates on:
2024-05-27
Overview, terminology, and
examples
RECIPIENTS OF THIS DRAFT ARE INVITED TO SUBMIT,
WITH THEIR COMMENTS, NOTIFICATION OF ANY
RELEVANT PATENT RIGHTS OF WHICH THEY ARE AWARE
AND TO PROVIDE SUPPOR TING DOCUMENTATION.
IN ADDITION TO THEIR EVALUATION AS
BEING ACCEPTABLE FOR INDUSTRIAL, TECHNO­
LOGICAL, COMMERCIAL AND USER PURPOSES, DRAFT
INTERNATIONAL STANDARDS MAY ON OCCASION HAVE
TO BE CONSIDERED IN THE LIGHT OF THEIR POTENTIAL
TO BECOME STAN DARDS TO WHICH REFERENCE MAY BE
MADE IN NATIONAL REGULATIONS.
Reference number
ISO/IEC FDIS 5259­1:2024(en) © ISO/IEC 2024

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FINAL DRAFT
ISO/IEC FDIS 5259-1:2024(en)
International
Standard
ISO/IEC FDIS
5259-1
ISO/IEC JTC 1/SC 42
Artificial intelligence — Data
Secretariat: ANSI
quality for analytics and machine
Voting begins on:
learning (ML) —
Part 1:
Voting terminates on:
Overview, terminology, and
examples
RECIPIENTS OF THIS DRAFT ARE INVITED TO SUBMIT,
COPYRIGHT PROTECTED DOCUMENT
WITH THEIR COMMENTS, NOTIFICATION OF ANY
RELEVANT PATENT RIGHTS OF WHICH THEY ARE AWARE
AND TO PROVIDE SUPPOR TING DOCUMENTATION.
© ISO/IEC 2024
IN ADDITION TO THEIR EVALUATION AS
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
BEING ACCEPTABLE FOR INDUSTRIAL, TECHNO­
LOGICAL, COMMERCIAL AND USER PURPOSES, DRAFT
be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on
INTERNATIONAL STANDARDS MAY ON OCCASION HAVE
the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address below
TO BE CONSIDERED IN THE LIGHT OF THEIR POTENTIAL
or ISO’s member body in the country of the requester.
TO BECOME STAN DARDS TO WHICH REFERENCE MAY BE
MADE IN NATIONAL REGULATIONS.
ISO copyright office
CP 401 • Ch. de Blandonnet 8
CH-1214 Vernier, Geneva
Phone: [phone]
Email: [e-mail]
Website: www.iso.org
Published in Switzerland Reference number
ISO/IEC FDIS 5259­1:2024(en) © ISO/IEC 2024

© ISO/IEC 2024 – All rights reserved
ii

---------------------- Page: 2 ----------------------
ISO/IEC FDIS 5259-1:2024(en)
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Symbols and abbreviated terms. 5
5 Data quality concepts for analytics and machine learning . 5
5.1 Data quality considerations for analytics and machine learning .5
5.1.1 General .5
5.1.2 Machine learning and data quality .5
5.1.3 Data characteristics that pose quality challenges for analytics and machine
learning .6
5.1.4 Data sharing, data re-use and data quality for analytics and machine learning .
...

© ISO/IEC 202X – All rights reserved
ISO/IEC FDIS 5259-1:202X(E)
ISO/IEC JTC 1/SC 42/WG 2
Secretariat: ANSI
Date: 2024-03-15
Artificial intelligence — Data quality for analytics and machine
learning (ML) — —
Part 1:
Overview, terminology, and examples

FDIS stage

Warning for WDs and CDs
This document is not an ISO International Standard. It is distributed for review and comment. It is subject to
change without notice and may not be referred to as an International Standard.
Recipients of this draft are invited to submit, with their comments, notification of any relevant patent rights of
which they are aware and to provide supporting documentation.

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ISO #####-#:####(X)
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2 © ISO #### – All rights reserved

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© ISO/IEC 202X – All rights reserved
© ISO 202X

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ISO/IEC FDIS 5259-1:202X(E2024(en)
© 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
EmailE-mail: [e-mail]
Website: www.iso.orgwww.iso.org
Published in Switzerland
iv © ISO/IEC 202X 2024 – All rights reserved

iv

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ISO/IEC FDIS 5259-1:202X(E2024(en)
Contents
Foreword . vi
Introduction .vi i
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Symbols and abbreviated terms . 5
5 Data quality concepts for analytics and machine learning . 5
5.1 Data quality considerations for analytics and machine learning . 5
5.1.1 General . 5
5.1.2 Machine learning and data quality . 6
5.1.3 Data characteristics that pose quality challenges for analytics and machine learning . 6
5.1.4 Data sharing, data re-use and data quality for analytics and machine learning . 7
5.2 Data quality concept framework for analytics and machine learning . 7
5.2.1 Overview . 7
5.2.2 Data quality management . 8
5.2.3 Data quality governance . 11
5.2.4 Data provenance .
...