SIST EN ISO/IEC 5259-1:2025

SLOVENSKI STANDARD
01-julij-2025
Umetna inteligenca - Kakovost podatkov za analizo in strojno učenje - 1. del:
Pregled, terminologija in primeri (ISO/IEC 5259-1:2024)
Artificial intelligence - Data quality for analytics and machine learning (ML) - Part 1:
Overview, terminology, and examples (ISO/IEC 5259-1:2024)
Künstliche Intelligenz - Datenqualität für Analytik und maschinelles Lernen (ML) - Teil 1:
Überblick, Terminologie und Beispiele (ISO/IEC 5259-1:2024)
Intelligence artificielle - Qualité des données pour les analyses de données et
l’apprentissage automatique - Partie 1: Vue d'ensemble, terminologie et exemples
(ISO/IEC 5259-1:2024)
Ta slovenski standard je istoveten z: EN ISO/IEC 5259-1:2025
ICS:
01.040.35 Informacijska tehnologija. Information technology
(Slovarji) (Vocabularies)
35.020 Informacijska tehnika in Information technology (IT) in
tehnologija na splošno general
2003-01.Slovenski inštitut za standardizacijo. Razmnoževanje celote ali delov tega standarda ni dovoljeno.

EUROPEAN STANDARD EN ISO/IEC 5259-1

NORME EUROPÉENNE
EUROPÄISCHE NORM
May 2025
ICS 35.020; 01.040.35
English version
Artificial intelligence - Data quality for analytics and
machine learning (ML) - Part 1: Overview, terminology,
and examples (ISO/IEC 5259-1:2024)
Intelligence artificielle - Qualité des données pour les Künstliche Intelligenz - Datenqualität für Analytik und
analyses de données et l'apprentissage automatique - maschinelles Lernen (ML) - Teil 1: Überblick,
Partie 1: Vue d'ensemble, terminologie et exemples Terminologie und Beispiele (ISO/IEC 5259-1:2024)
(ISO/IEC 5259-1:2024)
This European Standard was approved by CEN on 18 May 2025.

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© 2025 CEN/CENELEC All rights of exploitation in any form and by any means
Ref. No. EN ISO/IEC 5259-1:2025 E
reserved worldwide for CEN national Members and for
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Contents Page
European foreword . 3

European foreword
The text of ISO/IEC 5259-1:2024 has been prepared by Technical Committee ISO/IEC JTC 1
"Information technology” of the International Organization for Standardization (ISO) and has been
taken over as EN ISO/IEC 5259-1:2025 by Technical Committee CEN-CENELEC/ JTC 21 “Artificial
Intelligence” the secretariat of which is held by DS.
This European Standard shall be given the status of a national standard, either by publication of an
identical text or by endorsement, at the latest by November 2025, and conflicting national standards
shall be withdrawn at the latest by November 2025.
Attention is drawn to the possibility that some of the elements of this document may be the subject of
patent rights. CEN-CENELEC shall not be held responsible for identifying any or all such patent rights.
Any feedback and questions on this document should be directed to the users’ national standards body.
A complete listing of these bodies can be found on the CEN and CENELEC websites.
According to the CEN-CENELEC Internal Regulations, the national standards organizations of the
following countries are bound to implement this European Standard: Austria, Belgium, Bulgaria,
Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland,
Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Republic of
North Macedonia, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Türkiye and the
United Kingdom.
Endorsement notice
The text of ISO/IEC 5259-1:2024 has been approved by CEN-CENELEC as EN ISO/IEC 5259-1:2025
without any modification.
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 5259-1:2024(en) © ISO/IEC 2024

ISO/IEC 5259-1:2024(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
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Email: copyright@iso.org
Website: www.iso.org
Published in Switzerland
© ISO/IEC 2024 – All rights reserved
ii
ISO/IEC 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 .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
ISO/IEC 5259-1:2024(en)
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 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
www.iec.ch/national-committees.

© ISO/IEC 2024 – All rights reserved
iv
ISO/IEC 5259-1:2024(en)
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
ISO/IEC 5259-1:2024(en)
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
evaluating data quality (3.5)
[SOURCE: ISO/IEC 25012:2008, 4.6]
3.8
data quality measure
variable to which a value is assigned as the result of measurement (3.10) of a data quality characteristic (3.6)
[SOURCE: ISO/IEC 25012:2008, 4.5, modified — Note to entry removed.]
3.9
quality requirement
requirement for quality properties or attributes (3.13) of an information and communications technology
(ICT) product, data or service that satisfy needs which ensue from the purpose for which that ICT product,
data or service is to be used
[SOURCE: ISO/IEC 25030:2019, 3.15, modified — Note to entry removed.]
3.10
measurement
set of operations having the object of determining a value of a measure
[SOURCE: ISO/IEC 25024:2015, 4.27]
3.11
measurement scale
quantity-value scale
ordered set of quantity values of quantities of a given kind of quantity used in ranking, according to
magnitude, quantities of that kind
EXAMPLE 1
Celsius temperature scale.
EXAMPLE 2
Time scale.
© ISO/IEC 2024 – All rights reserved
ISO/IEC 5259-1:2024(en)
EXAMPLE 3
Rockwell C hardness scale.
[SOURCE: ISO/IEC Guide 99: 2007, 1.28, modified — Preferred term swapped with admitted term.]
3.12
analytics
data analytics
composite concept consisting of data acquisition, data collection, data validation, data processing, including
data quantification, data visualization, data documentation and data interpretation
Note 1 to entry: Analytics is used to understand objects or events represented by data, to make predictions for a given
situation and to recommend steps to achieve objectives. The insights obtained from analytics are used for various
purposes such as decision-making, research, sustainable development, design and planning.
[SOURCE: ISO/IEC 20546:2019, 3.1.6, modified — The term "analytics" added as a preferred term, definition
and note to entry revised.]
3.13
attribute
property or characteristic of an object that can be distinguished quantitatively or qualitatively by human or
automated means
[SOURCE: ISO/IEC/IEEE 15939:2017, 3.2, modified — Definition revised.]
3.14
feature
measurable property of an object or event with respect to a set of characteristics
Note 1 to entry: Features play a role in training and prediction.
Note 2 to entry: Features provide a machine-readable way to describe the relevant objects. As the algorithm will not
go back to the objects or events themselves, feature representations are designed to contain all useful information.
[SOURCE: ISO/IEC 23053: 2022, 3.3.3]
3.15
data quality management
coordinated activities to direct and control an organization with regard to data quality (3.5)
[SOURCE: ISO 8000-2:2020, 3.8.2]
3.16
data governance
governance of data
system by which the current and future use of data is governed
3.17
data provenance
provenance
information on the place and time of origin, derivation or generation of a dataset, proof of authenticity of the
dataset, or a record of past and present ownership of the dataset
[SOURCE: ISO/IEC 11179-33:2023, 3.11, modified — The term "data provenance" added as a preferred term,
definition revised.]
© ISO/IEC 2024 – All rights reserved
ISO/IEC 5259-1:2024(en)
3.18
visualization
scientific visualization
use of computer graphics and image processing to present models or characteristics of
processes or objects for supporting human understanding
EXAMPLE A display image created by combining magnetic resonance scans of a tumour; volumetric top and side
views of a lake showing temperature data; a two-dimensional model of electrical waves in the heart.
[SOURCE: ISO/IEC 2382:2015, 2125942, modified — Preferred term swapped with admitted term, note to
entry removed]
3.19
machine learning project
ML project
project that utilizes analytics (3.12) and machine learning and is responsible for the associated data
throughout the data’s entire life cycle
3.20
data architecture
description of the structure and interaction of the enterprise's major types and sources of data, logical data
assets, physical data assets and data management resources
Note 1 to entry: Logical data entities can be tied to applications, repositories and services and may be structured
according to implementation considerations.
Note 2 to entry: The concept of “data” is intentionally not defined here, as it is part of the data architecture definition
for each application scenario. It is according to the specific requirements of that scenario.
[SOURCE: ISO TR 21965:2019, 3.2.6]
3.21
data item
smallest identifiable unit of data within a certain context for which the definition, identification, permissible
values and other information is specified by means of a set of properties
Note 1 to entry: "Field" is considered a synonym of data item.
Note 2 to entry: Data item is a physical object “container” of data values.
[SOURCE: ISO/IEC 25024:2015, 4.9]
3.22
data record
set of related data items (3.21) treated as a unit
[SOURCE: ISO/IEC 25024:2015, 4.15]
3.23
metadata
data that define and describe other data
Note 1 to entry: In the context of analytics (3.12) and machine learning, metadata provides information on data items
(3.21) or data records (3.22) such as their properties, structure, type, context, intended use, ownership, access and
volatility.
[SOURCE: ISO/IEC 11179-1:2023, 3.2.26, modified — Note to entry added.]

© ISO/IEC 2024 – All rights reserved
ISO/IEC 5259-1:2024(en)
4 Symbols and abbreviated terms
AI artificial intelligence
DL deep learning
DLC data life cycle
DQ data quality
ETL extract, transform and load
ML machine learning
PII personal identifiable information
5 Data quality concepts for analytics and machine learning
5.1 Data quality considerations for analytics and machine learning
5.1.1 General
Existing data quality standards, such as the ISO 8000 series, were developed from the perspectives of data
production and management. This is because data producers (or data collectors) were traditionally the
largest consumers of data. Since most of the data were used for a predetermined purpose and associated
data quality standards focused on only the characteristics necessary for the defined purpose, data produced
in that manner can require additional processing for use in other contexts.
In the field of data analysis and ML, data users are generally not producing data. They search, collect
and process data they believe are necessary and suitable for their analytics and ML project. In this case,
data quality has an impact on the quality of the analysis results and the performance of the ML model.
No matter how good the data analysis or ML model is, the results can be unreliable when using data that
does not meet requirements. Even when data meets requirements for a particular application or context,
it does not necessarily meet requirements for other applications or contexts. Using data that does not
meet requirements for a specific purpose can result in ML models that are inaccurate and prone to failure.
Therefore, to help organization ensure that data for analytics and ML meet requirements, the ISO/IEC 5259
series identifies data quality characteristics, data quality measures, data quality management requirements
and a representative process to manage data quality over the data life cycle along with the concepts data
record and data item for applying to data quality management, in addition to a governance framework to
direct and oversee the implementation and
...