|
International
Standard
ISO/IEC 5259-1
First edition
Artificial intelligence — Data
2024-07
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
Reference number
© ISO/IEC 2024
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© ISO/IEC 2024 – All rights reserved
ii
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 .6
5.2 Data quality concept framework for analytics and machine learning .6
5.2.1 Overview .6
5.2.2 Data quality management .7
5.2.3 Data quality governance .10
5.2.4 Data provenance .10
5.3 Data life cycle for analytics and ML .10
5.3.1 Overview .10
5.3.2 Data life cycle model .10
5.3.3 Processes across the multiple stages . 13
Annex A (informative) Examples and scenarios .15
Bibliography .18
© ISO/IEC 2024 – All rights reserved
iii
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,
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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 /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.
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For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and expressions
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In the IEC, see /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
/national-committees.
© ISO/IEC 2024 – All rights reserved
iv
Introduction
Data are the raw material for analytics and machine learning (ML) and data quality is a critical aspect for
related analytics and ML projects and systems. The aim of the ISO/IEC 5259 series is to provide tools and
methods to assess and improve the quality of data used for analytics and ML.
Other parts of the ISO/IEC 5259 series include:
1)
— ISO/IEC 5259-2 provides a data quality model, data quality measures and guidance on reporting data
quality in the context of analytics and ML. ISO/IEC 5259-2 builds on the ISO 8000 series, ISO/IEC 25012
and ISO/IEC 25024.
The aim of ISO/IEC 5259-2 is to enable organizations to achieve their data quality objectives and is
applicable to all types of organizations.
— ISO/IEC 5259-3 specifies requirements and provides guidance for establishing, implementing,
maintaining and continually improving the quality for data used in the areas of analytics and ML.
ISO/IEC 5259-3 does not define detailed processes, methods or measurement. Rather it defines the
requirements and guidance for a quality management process along with a reference process and
methods that can be tailored to meet the requirements in ISO/IEC 5259-3.
The requirements and recommendations set out in ISO/IEC 5259-3 are generic and are intended to be
applicable to all organizations, regardless of type, size or nature.
— ISO/IEC 5259-4 provides general common organizational approaches, regardless of type, size or nature
of the applying organization, to ensure data quality for training and evaluation in analytics and ML. It
includes guidelines on the data quality process for:
— supervised ML with regard to the labelling of data used for training ML systems, including common
organizational approaches for training data labelling;
— unsupervised ML;
— semi-supervised ML;
— reinforcement learning;
— analytics.
ISO/IEC 5259-4 is applicable to training and evaluation data that come from different sources, including
data acquisition and data composition, data pre-processing, data labelling, evaluation and data use.
ISO/IEC 5259-4 does not define specific services, platforms or tools.
2)
— ISO/IEC 5259-5 provides a data quality governance framework for analytics and machine learning to
enable the governing bodies of organization to direct and oversee the implementation and operation of
data quality measures, management, and related processes with adequate controls throughout the DLC
model according to ISO/IEC 5259-1.
3)
— ISO/IEC TR 5259-6 describes a visualization framework for data quality in analytics and ML. The aim is
to enable stakeholders using visualization methods to access the results of data quality measures. This
visualization framework supports data quality goals.
1) Under preparation. Stage at the time of publication: ISO/IEC FDIS 5259-2:2024.
2) Under preparation. Stage at the time of publication: ISO/IEC DIS 5259-5:2023.
3) Under preparation. Stage at the time of publication: ISO/IEC CD TR 5259-6:2023.
© ISO/IEC 2024 – All rights reserved
v
International Standard ISO/IEC 5259-1:2024(en)
Artificial intelligence — Data quality for analytics and
machine learning (ML) —
Part 1:
Overview, terminology, and examples
1 Scope
This document provides the means for understanding and associating the individual documents of the
ISO/IEC 5259 series and is the foundation for conceptual understanding of data quality for analytics and
machine learning. It also discusses associated technologies and examples (e.g. use cases and usage scenarios).
2 Normative references
The following documents are referred to in the text in such a way that some or all of their content constitutes
requirements of this document. For dated references, only the edition cited applies. For undated references,
the latest edition of the referenced document (including any amendments) applies.
ISO/IEC 22989, Information technology — Artificial intelligence — Concepts and terminology
ISO/IEC 23053, Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)
3 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO/IEC 22989 and ISO/IEC 23053 and
the following apply.
ISO and IEC maintain terminology databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org/
3.1
data life cycle
life cycle of data
stages in the process of data usage from idea conception to its discontinuation
3.2
data originator
party that created the data and that can have rights
Note 1 to entry: A data originator can be an individual person.
Note 2 to entry: The data originator can be distinct from the natural or legal person(s) mentioned in, described by, or
implicitly or explicitly associated with the data. For example, PII can be collected by a data originator that identifies
other individuals. Those data subjects (PII Principals) can also have rights, in relation to the data set.
Note 3 to entry: Rights can include the right to publicity, right to display name, right to identity, right to prohibit data
use in a way that offends honourable mention.
[SOURCE: ISO/IEC 23751:2022, 3.2]
© ISO/IEC 2024 – All rights reserved
3.3
data holder
party that has legal control to authorize data processing of the data by other parties
Note 1 to entry: A data originator (3.2) can be a data holder.
[SOURCE: ISO/IEC 23751:2022, 3.4]
3.4
data user
party that is authorized to perform processing of data under the legal control of a data holder (3.3)
[SOURCE: ISO/IEC 23751:2022, 3.5]
3.5
data quality
characteristic of data that the data meet the organization's data requirements for a specified context
3.6
data quality characteristic
category of data quality attributes (3.13) that has a bearing on data quality (3.5)
[SOURCE: ISO/IEC 25012:2008, 4.4, modified — Definition revised.]
3.7
data quality model
defined set of characteristics which provides a framework for specifying data quality requirements (3.9) and
eval
...