ISO/IEC FDIS 5259-2
(Main)Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 2: Data quality measures
Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 2: Data quality measures
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FINAL DRAFT
International
Standard
ISO/IEC FDIS
5259-2
ISO/IEC JTC 1/SC 42
Artificial intelligence — Data
Secretariat: ANSI
quality for analytics and machine
Voting begins on:
learning (ML) —
2024-07-03
Part 2:
Voting terminates on:
2024-08-28
Data quality measures
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 52592:2024(en) © ISO/IEC 2024
FINAL DRAFT
International
Standard
ISO/IEC FDIS
5259-2
ISO/IEC JTC 1/SC 42
Artificial intelligence — Data
Secretariat: ANSI
quality for analytics and machine
Voting begins on:
learning (ML) —
Part 2:
Voting terminates on:
Data quality measures
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.
© 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: +41 22 749 01 11
Email: copyright@iso.org
Website: www.iso.org
Published in Switzerland Reference number
ISO/IEC FDIS 52592:2024(en) © ISO/IEC 2024
© 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. 5
5 Data quality components and data quality models for analytics and machine learning . 5
5.1 Data quality components in data life cycle .5
5.2 Data quality model .6
6 Data quality characteristics and quality measures .8
6.1 General .8
6.2 Inherent data quality characteristics .9
6.2.1 Accuracy .9
6.2.2 Completeness .10
6.2.3 Consistency . 12
6.2.4 Credibility . 13
6.2.5 Currentness .14
6.3 Inherent and system-dependent data quality characteristics . 15
6.3.1 Accessibility . 15
6.3.2 Compliance . 15
6.3.3 Efficiency .16
6.3.4 Precision .16
6.3.5 Traceability .17
6.3.6 Understandability .17
6.4 System-dependent data quality characteristics .18
6.4.1 Availability .18
6.4.2 Portability .18
6.4.3 Recoverability .19
6.5 Additional data quality characteristics .19
6.5.1 Auditability.19
6.5.2 Balance . 20
6.5.3 Diversity . . 22
6.5.4 Effectiveness . 23
6.5.5 Identifiability .24
6.5.6 Relevance . 25
6.5.7 Representativeness . 25
6.5.8 Similarity . . . 26
6.5.9 Timeliness .27
7 Implementing a data quality model and data quality measures for an analytics or ML
task .28
8 Data quality reporting .28
8.1 Data quality reporting framework . 28
8.2 Data quality measure information . 29
8.3 Guidance to organizations . 29
Annex A (informative) Design and document of a measurement function .30
Annex B (informative) UML model of data quality measure framework .32
Annex C (informative) Overview of data quality characteristics .33
Annex D (informative) Alternative groups of data quality characteristics .35
© ISO/IEC 2024 – All rights reserved
iii
Annex E (informative) Comparison between data quality characteristics of ISO/IEC 25012 and
ISO/IEC 5259-2 .36
Bibliography .37
© ISO/IEC 2024 – All rights reserved
iv
Foreword
ISO (the International Organization for Standardization) and IEC (the International Electrotechnical
Commission) form the specialized system for worldwide standardization. National bodies that are
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The procedures used to develop this document and those intended for its further maintenance are described
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IEC Directives, Part 2 (see www.iso.org/directives or www.iec.ch/members_experts/refdocs).
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In the IEC, see www.iec.ch/understanding-standards.
This document was prepared by Joint Technica
...
© ISO/IEC 202X – All rights reserved
ISO/IEC FDIS 5259-2:202X(X)
ISO/IEC JTC 1/SC 42/WG 2
Secretariat: ANSI
Date: 2024-06-18
Artificial intelligence — Data quality for analytics and machine
learning (ML) — —
Part 2:
Data quality measures
FDIS stage
Warning for WDs and CDs
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© ISO/IEC 202X – All rights reserved
© 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: copyright@iso.org
Website: www.iso.orgwww.iso.org
Published in Switzerland
iv © ISO/IEC 202X 2024 – All rights reserved
iv
Contents
Foreword . viii
Introduction . ix
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Symbols and abbreviated terms . 5
5 Data quality components and data quality models for analytics and machine learning . 6
5.1 Data quality components in data life cycle . 6
5.2 Data quality model . 7
6 Data quality characteristics and quality measures . 10
6.1 General . 10
6.2 Inherent data quality characteristics . 11
6.2.1 Accuracy . 11
6.2.2 Completeness . 12
6.2.3 Consistency . 14
6.2.4 Credibility . 15
6.2.5 Currentness . 16
6.3 Inherent and system-dependent data quality characteristics . 17
6.3.1 Accessibility . 17
6.3.2 Compliance . 17
6.3.3 Efficiency . 18
6.3.4 Precision . 18
6.3.5 Traceability . 19
6.3.6 Understandability . 19
6.4 System-dependent data quality characteristics . 20
6.4.1 Availability . 20
6.4.2 Portability . 20
6.4.3 Recoverability . 21
6.5 Additional data quality characteristics . 21
6.5.1 Auditability . 21
6.5.2 Balance . 22
6.5.3 Diversity . 24
6.5.4 Effectiveness . 25
6.5.5 Identifiability . 26
6.5.6 Relevance . 27
6.5.7 Representativeness . 28
6.5.8 Similarity . 28
6.5.9 Timeliness . 30
7 Implementing a data quality model and data quality measures for an analytics or ML task30
8 Data quality reporting . 31
8.1 Data quality reporting framework . 31
8.2 Data quality measure information . 31
8.3 Guidance to organizations . 32
Annex A (informative) Design and document of a measurement function. 34
Annex B (informative) UML model of data quality measure framework . 36
Annex C (informative) Overview of data quality characteristics . 38
© ISO/IEC 202X 2024 – All rights reserved
v
Annex D (informative) Alternative groups of data quality characteristics . 40
Annex E (informative) Comparison between data quality characteristics of ISO/IEC 25012 and
ISO/IEC 5259-2 . 42
Bibliography . 44
Foreword . v
Introduction . vi
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Symbols and abbreviated terms . 4
5 Data quality components and data quality models for analytics and machine learning . 5
5.1 Data quality components in data life cycle . 5
5.2 Data quality model . 6
6 Data quality characteristics and quality measures . 8
6.1 General . 8
6.2 Inherent data quality characteristics . 8
6.2.1 Accuracy . 8
6.2.2 Completeness . 9
6.2.3 Consistency . 11
6.2.4 Credibility . 12
6.2.5 Currentness . 13
6.3 Inherent and system-dependent data quality characteristics . 14
6.3.1 Accessibility . 14
6.3.2 Compliance . 15
6.3.3 Efficiency . 15
6.3.4 Precision . 15
6.3.5 Traceability . 16
6.3.6 Understandability . 16
6.4 System-dependent data quality characteristics . 17
6.4.1 Availability . 17
6.4.2 Portability . 17
6.4.3 Recoverability . 18
6.5 Additional data quality characteristics . 18
6.5.1 Auditability . 18
6.5.2 Balance . 19
6.5.3 Diversity .
...
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