SIST EN ISO/IEC 5259-3:2025

SLOVENSKI STANDARD
01-julij-2025
Umetna inteligenca - Kakovost podatkov za analizo in strojno učenje - 3. del:
Zahteve in smernice za vodenje kakovosti podatkov (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)
Künstliche Intelligenz - Datenqualität für Analytik und maschinelles Lernen (ML) - Teil 3:
Anforderungen und Leitlinien für das Datenqualitätsmanagement (ISO/IEC 5259-3:2024)
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 (ISO/IEC 5259-3:2024)
Ta slovenski standard je istoveten z: EN ISO/IEC 5259-3:2025
ICS:
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 5259-3

NORME EUROPÉENNE
EUROPÄISCHE NORM
May 2025
ICS 35.020
English version
Artificial intelligence - Data quality for analytics and
machine learning (ML) - Part 3: Data quality management
requirements and guidelines (ISO/IEC 5259-3:2024)
Intelligence artificielle - Qualité des données pour les Künstliche Intelligenz - Datenqualität für Analytik und
analyses de données et l'apprentissage automatique - maschinelles Lernen (ML) - Teil 3: Anforderungen und
Partie 3: Exigences et lignes directrices pour la gestion Leitlinien für das Datenqualitätsmanagement (ISO/IEC
de la qualité des données (ISO/IEC 5259-3:2024) 5259-3:2024)
This European Standard was approved by CEN on 18 May 2025.

CEN and CENELEC members are bound to comply with the CEN/CENELEC Internal Regulations which stipulate the conditions for
giving this European Standard the status of a national standard without any alteration. Up-to-date lists and bibliographical
references concerning such national standards may be obtained on application to the CEN-CENELEC Management Centre or to
any CEN and CENELEC member.
This European Standard exists in three official versions (English, French, German). A version in any other language made by
translation under the responsibility of a CEN and CENELEC member into its own language and notified to the CEN-CENELEC
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© 2025 CEN/CENELEC All rights of exploitation in any form and by any means
Ref. No. EN ISO/IEC 5259-3:2025 E
reserved worldwide for CEN national Members and for
CENELEC Members.
Contents Page
European foreword . 3

European foreword
The text of ISO/IEC 5259-3:2024 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 5259-3:2025 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 5259-3:2024 has been approved by CEN-CENELEC as EN ISO/IEC 5259-3:2025
without any modification.
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 5259-3:2024(en) © ISO/IEC 2024

ISO/IEC 5259-3:2024(en)
© ISO/IEC 2024
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
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© ISO/IEC 2024 – All rights reserved
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ISO/IEC 5259-3:2024(en)
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

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ISO/IEC 5259-3:2024(en)
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 . 23
12 Project-specific data quality management .24
12.1 Objective.24
12.2 Requirements and recommendations .24
12.2.1 Context and intended use .24
12.2.2 Objective .24
12.2.3 Requirements and recommendations .24
12.3 Specification and management of data quality requirements .24
12.3.1 Objective .24
12.3.2 Requirements and recommendations . 25
12.4 Roles and responsibilities in data quality management . 25
12.4.1 Objective . 25
12.4.2 Requirements and recommendations . 25
12.4.3 Work products . 25
12.5 Tailoring of the data quality activities . 25
12.6 Planning and coordination of the data quality activities . 26
12.6.1 General . 26
12.6.2 Data quality plan . 26
12.6.3 Planning of processes. 26
12.7 Progression of the data quality life cycle . 26
12.8 Data quality justification . 26
12.9 Decommissioning .27
12.10 Work products .27
Bibliography .28

© ISO/IEC 2024 – All rights reserved
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ISO/IEC 5259-3:2024(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
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ISO/IEC 5259-3:2024(en)
Introduction
The quality of analytics and machine learning (ML) based products and services depends on the quality of
data used to train ML models. Hence, data quality management is essential as it often helps to ensure the
success of analytics and ML technology.
The adoption of a data quality management system facilitates managing the quality of products and services
that employ analytics and ML technologies. This document defines vocabulary, requirements and guidelines
for communication, alignment and agreement for managing data quality. The data quality management
system provides transparency and auditability, either through self-assessment or third party assessment.
It facilitates achieving relevant stakeholder satisfaction and managing quality, performance and self-
declaration requirements. Specifically, this document defines requirements for a data quality management
system with references to data quality measures that are relevant for the most commonly used analytics
and ML technologies.
As data quality requirements vary with context and application domain, this document provides a generic set
of requirements and recommendations relating to common data life cycle stages. A data life cycle is typically
tightly integrated with the accompanying AI system life cycle and therefore has several dependencies. This
document does not prescribe what AI system life cycle to use. Instead, it provides generic interfaces that
allow users of this document the flexibility to interface with several life cycle models as long as the life cycle
processes can be mapped.
ISO/IEC 5259-1 describes the data quality terminology and concepts used in this document.
1)
ISO/IEC 5259-2 describes the data quality model and data quality measures used in this document.
ISO/IEC 5259-4 describes the data quality process framework used in this document.
2)
ISO/IEC 5259-5 provides a data quality governance framework as guidance for governing bodies.
3)
ISO/IEC TR 5259-6 describes a visualization framework for data quality in analytics and ML.
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
vi
International Standard ISO/IEC 5259-3:2024(en)
Artificial intelligence — Data quality for analytics and
machine learning (ML) —
Part 3:
Data quality management requirements and guidelines
1 Scope
This document specifies requirements and provides guidance for establishing, implementing, maintaining
and continually improving the quality of data used in the areas of analytics and machine learning.
This document does not define a detailed process, methods or metrics. 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 this document.
The requirements and recommendations set out in this document are generic and are intended to be
applicable to all organizations, regardless of type, size or nature.
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 5259-1:2024, Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 1:
Overview, terminology, and examples
4)
ISO/IEC 5259-2 , Artificial Intelligence — Data quality for analytics and machine learning (ML) — Part 2: Data
quality measures
ISO/IEC 22989, Information technology — Artificial intelligence — Artificial intelligence concepts and
terminology
3 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO/IEC 22989, ISO/IEC 5259-1 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 quality claim
statement to what degree data satisfy a data quality requirement
4) Under preparation. Stage at the time of publication: ISO/IEC FDIS 5259-2:2024.

© ISO/IEC 2024 – All rights reserved
ISO/IEC 5259-3:2024(en)
3.2
data quality plan
specification of practices, processes and allocation of resources to achieve data quality objectives as the
outcome of data quality planning
3.3
data quality planning
part of data quality management focused on setting data quality objectives and specifying necessary
operational processes and related resources to achieve the quality objectives
[SOURCE: ISO 8000-2:2022, modified — example removed]
3.4
development interface agreement
DIA
agreement between customer and supplier in which the responsibilities for activities to be performed,
evidence to be reviewed, or work products to be exchanged by each party related to the development of
items or elements are specified
Note 1 to entry: While DIA applies to the development phase, supply agreement applies to production.
[SOURCE: ISO 26262-1:2018]
4 Symbols and abbreviated terms
DQMLC data quality management life cycle
5 Intended usage
This document may be used in one or more of the following modes:
— by an organization to establish and tailor a data quality management process for the use of data in
analytics and ML, and continually improve processes;
— by an ML project to define, trace and evaluate data quality requirements;
— by a data user and data holder to establish a common understanding of data quality characteristics, and
to ensure that agreed requirements have been met, facilitating an agreement for transacting data.
NOTE The organization can request assurances of confidentiality and proper use for supporting evidence.
6 Overall data quality management
6.1 Objective
The objective of a data quality management process is to establish appropriate (i.e. repeatable and auditable)
processes to manage the quality of data and reliably meet a given set of requirements set by the organization.
6.2 General
Data quality impacts outcomes of analytics and ML algorithms. Data quality has an inherent constituent and
a system-dependent constituent. Data can be suitable for one application but not suitable for another. This
document helps to establish and maintain data quality for each analytics and ML application.

© ISO/IEC 2024 – All rights reserved
ISO/IEC 5259-3:2024(en)
6.3 Requirements and recommendations
6.3.1 General
The following requirements and recommendations apply to the whole organization.
6.3.2 Data quality culture
The organization should sustain a data quality culture.
The organization shall:
a) have rules and processes to achieve quality (according to this document) taking into account the data
quality model as applied to the applicable products and services;
b) define and implement data quality management processes, and perform related data quality activities;
c) integrate the data quality management processes and activities, to the extent appropriate, into other
management processes and activities, such as general quality management and risk management;
d) document the performed activities;
e) provide resources sufficient to perform data quality management;
f) monitor, and to the extent necessary, review and improve the data quality management processes;
g) provide the required authority to involved personnel;
h) communicate data quality policies within the organization.
6.3.3 Management of data quality issues
The organization shall meet data quality requirements by:
a) having processes for communicating, analysing, evaluating, resolving and closing data quality issues;
b) documenting closed issues;
c) escalating or delegating issues that cannot be closed.
NOTE 1 Resolving and closing issues of data quality can include limiting or adjusting the scope of the ML project.
NOTE 2 A data quality issue can be closed by implementing a resolution or determining a resolution based on
defined acceptance criteria.
6.3.4 Competence management
The organization shall manage competence by:
a) documenting required skills and tools to process the data;
b) ensuring that involved personnel have sufficient skills to perform their activities and duties;
c) maintaining records of persons and their proficiency on the required skills and tools;
d) keeping appropriate records of training and experience that substantiate claims of appropriate skills.
The organization can use external sources of competencies.

© ISO/IEC 2024 – All rights reserved
ISO/IEC 5259-3:2024(en)
6.3.5 Resource management
The organization shall provide the resources required for data quality management, including:
a) software applications, training and support necessary to perform data quality management;
b) IT infrastructure or services necessary to perform data quality management (e.g. compute, storage,
networking);
c) personnel with the skills required to perform data quality management.
6.3.6 Management system integration
The organization should integrate its data quality management activities into its existing management
system, including its management systems for product or service quality, and for the development and use
of AI systems. Implications from dual roles of stakeholders should be managed by the quality management
system, including mitigation of any conflicts of interest.
NOTE 1 Stakeholder management can consider the potential of multiple roles for an individual. A user of analytics
and ML based products or services can also be an owner or contributor of data.
NOTE 2 Organizations can use ISO/IEC 42001 to define a management system for the development or use of AI
systems.
NOTE 3 Organizations can use ISO 9001 or other sector-specific quality management systems to define their
quality management system.
6.3.7 Documentation
Documentation shall be intelligible to relevant stakeholders of the project in accordance with their role.
Resources in a language that is not understood by a relevant stakeholder should be accompanied by a
summary in a language that the stakeholder can understand.
The documentation shall be accessible to relevant stakeholders as appropriate and authorized. Access
overhead should be minimized.
Documentation should include the context or references necessary to make it intelligible to future relevant
stakeholders who are not part of the current project. This practice can enable these stakeholders to evaluate
a dataset for potential reuse, partially or in total.
6.3.8 Data quality audit and assessment
The implemented processes shall be audited when appropriate, which shall be based on an evaluation of:
a) the data quality plan against organizational rules and processes;
b) arguments and justifications detailing how the requirements of the data quality model have been
applied;
c) arguments detailing how the objectives of data quality plan have been achieved;
d) whether the data quality plan and all work products are complete, consistent and correct according to
this document;
e) recommendations for improvement of data quality.
The data shall be assessed using a data quality assessment, which shall be based on an evaluation of
whether the data achieve the objectives of this document, the current state-of-the-art in technology and the
applicable engineering domain knowledge.

© ISO/IEC 2024 – All rights reserved
ISO/IEC 5259-3:2024(en)
The data quality assessment plan shall be included in the specification stage. The data quality assessment
shall be performed before data provisioning (see Figure 1, Stage 7: Data provisioning) or at an appropriate
interval when using continuous learning or when using streaming data.
The data quality assessment may be performed on a subset of the data when it can be demonstrated that the
quality of the subset is representative of the quality of the complete dataset.
6.3.9 Confirmation review and data quality measures
Data quality shall be confirmed by appropriate data quality measures in accordance with ISO/IEC 5259-2. A
data quality review shall at least cover:
a) confirmation reviews of key work products. Every confirmation review:
1) shall be finalized before data provisioning;
2) should be based on whether the objectives of this document are achieved;
b) quality audits of the implemented processes;
c) quality assessment of the data.
All work products shall undergo confirmation reviews.
The personnel performing these reviews shall have access to the involved personnel, relevant information
and required resources.
NOTE Confirmation reviews of key work products can be delegated, but the responsibility stays with the
designated person.
6.3.10 Project-specific data quality management
The organization shall manage project-specific data by:
a) establishing a suitable project-specific data quality management process that meets all requirements of
the specific ML project;
b) maintaining a list of relevant data quality claims. Where applicable, quantitative and qualitative
benchmarks for data quality measures shall be documented;
c) adopting appropriate processes to identify and manage all data quality measures relevant for the
project.
The project-specific data quality management process shall fulfil the requirements of Clause 12.
6.4 Work products
Work products of the data quality management process shall include:
a) organization-specific rules and processes for data quality (e.g. according to ISO/IEC 5259-4);
b) evidence of competence management;
c) evidence of a data quality management system;
d) identification of the used data quality measures;
e) documentation of applicable data quality measure benchmarks;
f) identified quality anomaly reports.

© ISO/IEC 2024 – All rights reserved
ISO/IEC 5259-3:2024(en)
7 Life cycle-specific data quality management
7.1 Objective
The objective of a data quality management life cycle (DQMLC) is to establish and maintain data quality
throughout the data life cycle. An example of a data life cycle model is described in ISO/IEC 5259-1:2024,
Figure 3.
7.2 General
7.2.1 Data quality management life cycle
Data quality shall be managed in all stages of the data life cycle. The data quality management life cycle
(DQMLC) model shown in Figure 1 provides guidance towards meeting the quality requirements of data for
use in an analytics and ML context. It derives discrete stages that are relevant for the management of data
quality and facilitates grouping and organizing of requirements and guidelines to consider for managing
data quality. The DQMLC model is not prescriptive of the temporal ordering of stages. The various stages of
the DQMLC are described in 7.2.2.

Key
Primary development pathway
Feedback pathway
Instantiation of DLC model for 5259 series (see ISO/IEC 5259-1:2024, Figure 3)
Data quality management life cycle stage
Horizontal processes of the data quality management life cycle
Indicating iteration within the life cycle
Grouping of data quality management life cycle stages to clarify mapping to DCL model
Figure 1 — Data quality management life cycle

© ISO/IEC 2024 – All rights reserved
ISO/IEC 5259-3:2024(en)
Issues in data quality can originate at any stage throughout the data life cycle. Data quality management
shall establish and maintain data quality processes at the beginning of the data life cycle. If the organization
delegates the responsibility for a process, that delegation shall be documented and be traceable.
NOTE It is generally more difficult to detect and correct data quality issues after the fact than to manage data
quality risks when they first occur. For example, errors introduced while collecting data are more easily avoided by
appropriate quality management rather than attempting to detect and correct errors later in the data l
...