ISO/IEC TR 5259-6:2026

Technical
Report
ISO/IEC TR 5259-6
First edition
Artificial intelligence — Data
2026-05
quality for analytics and machine
learning (ML) —
Part 6:
Visualization framework for data
quality
Intelligence artificielle — Qualité des données pour les analyses
de données et l’apprentissage automatique —
Partie 6: Cadre de visualisation pour la qualité des données
Reference number
© ISO/IEC 2026
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© ISO/IEC 2026 – 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.1
5 Data quality management . 2
5.1 General .2
5.2 Data life cycle stages .2
5.3 Data quality management life cycle (DQMLC) .3
5.4 Data quality concept framework .3
5.5 Data quality management and visualization .3
6 Visualization framework for data quality .3
6.1 General .3
6.2 Stakeholders and their perspectives .4
6.3 Dataset properties .4
6.4 Data quality management life cycle stages and processes .4
6.5 Data quality model .4
6.5.1 General .4
6.5.2 Data quality characteristics .4
6.6 Data quality measures .5
6.7 Data quality assessment .5
6.8 Applying the visualization framework .5
7 Data visualization . 6
7.1 General .6
7.2 Visualization considerations .7
7.2.1 General .7
7.2.2 Applicable visualization methods .7
7.3 Visualization examples .7
7.3.1 General .7
7.3.2 Dataset characteristics .8
7.3.3 Analytics and ML context and stakeholders .8
7.3.4 Visualization of dataset properties .9
7.3.5 Visualization of data quality characteristics .11
7.4 Summary .14
Annex A (informative) AI stakeholders’ perspectives .15
Annex B (informative) Dataset properties .17
Bibliography . 19

© ISO/IEC 2026 – All rights reserved
iii
Foreword
ISO (the International Organization for Standardization) is a worldwide federation of national standards
bodies (ISO member bodies). The work of preparing International Standards is normally carried out through
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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 ISO documents 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).
Attention is drawn to the possibility that some of the elements of this document may be the subject of patent
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This document was prepared by Technical Committee ISO/IEC JTC 1, Information technology, Subcommittee
SC 42, Artificial intelligence.
This document is intended to be used in conjunction with all parts of the ISO/IEC 5259 series.
A list of all parts in the ISO/IEC 5259 series can be found on the ISO website.
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.

© ISO/IEC 2026 – All rights reserved
iv
Introduction
Visualization can be used to augment data quality management by displaying a data quality measure
generated by the measurement function in a tangible and meaningful manner for assessment by the
stakeholder. Visualization can be used in any data quality management process in a data quality management
life cycle as part of the development and making of the artificial intelligence (AI) system. For example, it is
useful as part of data quality reporting for documenting the data quality management process. It can also
stimulate cognitive responses from the stakeholder in exploratory data analysis which can lead to more
insights (e.g. detection of missing data, outliers, anomalies, deviations, errors, making comparisons and
potential relationships among the observations). On the other hand, visualization also has its pitfalls that
stem from cognitive biases such as pareidolia and apophenia.
Visualization can also help in explaining to stakeholders how the application built from the data makes its
predictions by providing some transparency to the choice of and input to machine learning (ML) algorithms.
This can contribute to the trustworthiness of an AI system by stakeholders who use the AI system and have
different expectations.
The background of data quality management is described in Clause 5. A visualization framework for data
quality based on data quality management concepts is described in Clause 6. Illustration of the application of
the visualization framework with practical use cases is presented in Clause 7. Annex A provides information
on AI stakeholders’ perspectives and Annex B provides information on database properties.

© ISO/IEC 2026 – All rights reserved
v
Technical Report ISO/IEC TR 5259-6:2026(en)
Artificial intelligence — Data quality for analytics and
machine learning (ML) —
Part 6:
Visualization framework for data quality
1 Scope
This document describes a visualization framework for data quality in analytics and machine learning (ML).
The aim is to enable stakeholders using visualization methods to assess the results of data quality measures.
This visualization framework supports data quality goals.
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
ISO/IEC 22989:2022, 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 5259-1 and ISO/IEC 22989
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/
4 Symbols and abbreviated terms
AI artificial intelligence
DQMLC data quality management life cycle
ML machine learning
© ISO/IEC 2026 – All rights reserved
5 Data quality management
5.1 General
Data quality for analytics and ML is described in the ISO/IEC 5259 series. The various components of data
quality management from these International Standards and their relationships are summarized in Figure 1.
Figure 1 also shows how this document is positioned as a companion to the rest of the series.
Key
Data life cycle stage
Process in data quality management
Element in data quality management
Coverage of ISO/IEC 5259 series
Data quality management life cycle (DQMLC)
stage (data motivation and conceptualization not shown here)
Primary development pathway
Feedback pathway
NOTE Data provenance, security and privacy from ISO/IEC 5259-1:2024 Figure 1 are not included in this figure.
Change management and configuration management from ISO/IEC 5259-4:2024, Figure 1 are also not included in this
figure.
Figure 1 — Summary of data quality management for analytics and ML
5.2 Data life cycle stages
The data life cycle stages shown in Figure 1 are described in ISO/IEC 5259-1:2024, Figure 3 and the rationale
with requirements of each stage are described in ISO/IEC 5259-1:2024, 5.3.2.2 to 5.3.2.7 inclusive. The data
life cycles stages are generic in nature and further refinement for the purpose of data quality management
are discussed in 5.3.
© ISO/IEC 2026 – All rights reserved
5.3 Data quality management life cycle (DQMLC)
The generic data life cycle in Figure 1 is refined for data quality management purposes in ISO/IEC 5259-3:2024,
7.2.1. Each DQMLC stage is in synchrony with one or two data life cycle stages. These DQMLC stages provide
feedback to a continuous validation and verification process. Data quality management processes described
in 5.4 are used throughout the stages of the DQMLC.
5.4 Data quality concept framework
A data quality concept framework is described in ISO/IEC 5259-1:2024, 5.2. This framework shows that data
quality management of a dataset involves the following processes: data quality model, data quality measures,
data quality assessment, data quality improvement and data quality reporting. The specific documents from
the ISO/IEC 5259 series for each process are shown in Figure 1 as spanning over all the stages of the data
quality management life cycle.
5.5 Data quality management and visualization
Visualization can be used in any of the data quality management processes in the DQMLC to support
stakeholders’ understanding in assessing the results of data quality measures to achieve data quality goals.
6 Visualization framework for data quality
6.1 General
Based on the summary of data quality management for analytics and ML in Clause 5, Clause 6 describes
a visualization framework for data quality that enables and supports stakeholders’ performance of the
processes in the DQMLC in assessing the results of data quality measures.
The visualization framework is described as follows and illustrated in Figure 2:
For a stakeholder [A] with certain perspective [B] working with a dataset and its properties [C] within
an analytics and ML usage context [D] in performing a data quality management process [E] during a
particular stage of a data quality management life cycle [F], what are the data quality requirements
[G] and associated data quality characteristics of interest [H]? What are the correlated data quality
measures [I] with their visualization considerations [J] and applicable visualization methods [K]?
Figure 2 — Visualization framework for data quality
The relationships between the visualization framework and other International Standards are also shown
in Figure 2. The stakeholders of the AI system are defined in ISO/IEC 22989 and ISO/IEC 5339 together with
their respective “make”, “use” or “impact” perspectives. The data quality components of the visualization
framework are from the ISO/IEC 5259 series on data quality. The stakeholders’ perspectives and the data
quality context, needs, requirements and characteristics are delineated with the appropriate data quality
measures. This document takes the visualization considerations of these data measures and suggests
applicable visualization methods illustrated with examples that reference the [A] to [K] notation used in
Figure 2.
© ISO/IEC 2026 – All rights reserved
6.2 Stakeholders and their perspectives
The visualization framework can be employed by stakeholders ([A] in Figure 2) in performing the processes
in the DQMLC on a dataset within an analytics and ML usage context ([D] in Figure 2). These stakeholders are
the AI producers, AI providers, AI developers and AI partners such as data providers (ISO/IEC 22989:2022,
5.17).
The stakeholders’ perspectives ([B] in Figure 2) are based on the "make", "use" and "impact" perspectives of
stakeholders in AI applications described in ISO/IEC 5339:2024, 6 (see Annex A).
Other stakeholders such as AI customers and AI users have their perspectives on using the AI application.
The visualization framework can be employed by them for improving their understanding of how the AI
system and AI application were built from data and algorithms in an AI application. This can contribute to
the trustworthiness of the AI application.
The deployment and use of an AI application by AI customers and AI users can also have impact on the
stakeholders’ community and its relevant authorities such as policy makers and regulators. The visualization
framework can be of use for these external stakeholders in performing their roles.
6.3 Dataset properties
The visualization framework includes a dataset and its properties [C] because they are important
considerations in how the data are going to be used in the usage context of analytics and ML [D]. These
properties (including statistical properties) and the needs and requirement of the stakeholders are
inputs to the data quality model (see Figure 3). The knowledge of these dataset properties is also needed
in the preparation of the dataset to feed into quality measurement functions. Dataset properties from
ISO/IEC 23751:2022 are detailed with an example in Annex B.
6.4 Data quality management life cycle stages and processes
Stakeholders are aligned with their roles in performing data quality management processes ([E] in Figure 2)
during different stages of the data quality management life cycle ([F] in Figure 2). For example, an AI
producer can define the data quality model with data quality requirements ([G] in Figure 2) during the data
specification and data planning stages.
6.5 Data quality model
6.5.1 General
A data quality model for a dataset is established by stakeholders with a "make" perspective on the basis of
their business objectives and the specific analytics and ML usage context. Fo
...