ISO 8000-1:2022

Data quality - Part 1: Overview

This document provides an overview of the ISO 8000 series. The following are within the scope of this document: - stating the scope of the ISO 8000 series as a whole; - establishing the principles of information and data quality; - describing the path to data quality; - describing the structure of the ISO 8000 series; - providing a summary of the content of each part in the ISO 8000 series; - establishing the relationship of the ISO 8000 series to other international standards.

Qualité des données — Partie 1: Aperçu

General Information

• Status: Published
• Publication Date: 19-Apr-2022
• ICS: 25.040.40 - Industrial process measurement and control
• Technical Committee: ISO/TC 184/SC 4 - Industrial data
• Drafting Committee: ISO/TC 184/SC 4/WG 13 - Industrial Data Quality
• Current Stage: 9093 - International Standard confirmed
• Start Date: 10-Aug-2024
• Completion Date: 13-Dec-2025

Relations

• Revises: ISO/TS 8000-1:2011 - Data quality - Part 1: Overview
• Effective Date: 10-Jul-2021

Overview - ISO 8000-1:2022 (Data quality - Part 1: Overview)

ISO 8000-1:2022 provides an authoritative overview of the ISO 8000 series on data quality. Published in 2022 as the first edition, it replaces ISO/TS 8000-1:2011 and sets out the scope, principles and structure of the whole series. This part explains the series’ intent to help organizations treat digital data as a reliable asset that supports decision-making, interoperability, digital transformation and regulatory compliance.

Key topics and requirements

• Scope of the ISO 8000 series - Defines what the series covers and how parts relate to one another.
• Principles of information and data quality - Establishes foundational concepts (e.g., syntactic, semantic and pragmatic characteristics of data) and the idea that quality is conformance to requirements for a given purpose.
• Path to data quality - Describes an organizational approach and lifecycle considerations for improving and sustaining data quality.
• Structure of the series - Summarizes individual parts and groups: general aspects, data governance, data quality management, data quality assessment, quality of master data and quality of industrial data.
• Relationship to other standards - Explains how ISO 8000 aligns with standards for quality management systems, software quality, industrial data and other data types.
• Referenced parts - ISO 8000-2, -8, -51, -61, -100, -110, ISO/TS 8000-81, ISO/TS 8000-311 and others are described as constituents of the series (overview only in this part).

Note: ISO 8000-1 is an overview document - detailed technical requirements and methods are provided in the individual parts referenced.

Practical applications and who uses it

• Data governance teams and data stewards - to frame policies, roles and responsibilities for data quality.
• Quality managers and CIOs - to integrate data quality into management systems and digital transformation strategies.
• IT architects and system integrators - to design interoperable systems that preserve data portability and machine-readability.
• Supply chain and procurement professionals - to improve master data exchange and trust across trading partners.
• Engineers and industrial data specialists - to align product and shape-data quality practices (see ISO/TS 8000-311).
• Compliance and audit functions - to demonstrate evidence-based data reliability and regulatory readiness.

Related standards

ISO 8000-1 positions the ISO 8000 series relative to ISO management system standards (e.g., ISO 9001), software quality standards, and domain-specific industrial-data standards. For practical implementation, refer to the specific ISO 8000 parts (data governance, assessment, master data exchange, profiling, etc.) that contain prescriptive guidance and measurable approaches.

Keywords: ISO 8000-1, data quality, data governance, data quality management, master data, data standards, data quality assessment, data lifecycle, interoperability.

Standards Content (Sample)

INTERNATIONAL ISO
STANDARD 8000-1
First edition
2022-04
Data quality —
Part 1:
Overview
Qualité des données —
Partie 1: Aperçu
Reference number
© ISO 2022
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: [phone]
Email: [e-mail]
Website: www.iso.org
Published in Switzerland
ii
Contents Page
Foreword .v
Introduction . vi
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Scope of the ISO 8000 series .1
5 Principles of information and data quality . 2
6 The ISO 8000 series path to data quality . 3
7 Structure of the ISO 8000 series . 5
7.1 Overview of the structure . 5
7.2 General aspects of information and data quality . 6
7.2.1 Group purpose and constituents . 6
7.2.2 ISO 8000-2 . 7
7.2.3 ISO 8000-8 . 7
7.3 Data governance . 7
7.3.1 Group purpose and constituents . 7
7.3.2 ISO 8000-51. 7
7.4 Data quality management. 8
7.4.1 Group purpose and constituents . 8
7.4.2 ISO/TS 8000-60 . 8
7.4.3 ISO 8000-61 . 8
7.4.4 ISO 8000-62 . 8
7.4.5 ISO 8000-63 . 8
7.4.6 ISO 8000-64 . 9
7.4.7 ISO/TS 8000-65 . 9
7.4.8 ISO 8000-66 . 9
7.4.9 ISO 8000-150 . 9
7.5 Data quality assessment . 9
7.5.1 Group purpose and constituents . 9
7.5.2 ISO/TS 8000-81 . 10
7.5.3 ISO/TS 8000-82 . 10
7.6 Quality of master data . 10
7.6.1 Group purpose and constituents . 10
7.6.2 ISO 8000-100 . 11
7.6.3 ISO 8000-110 . 11
7.6.4 ISO 8000-115.12
7.6.5 ISO 8000-116 .12
7.6.6 ISO 8000-120 .12
7.6.7 ISO 8000-130 .12
7.6.8 ISO 8000-140 . . .12
7.7 Quality of industrial data .13
7.7.1 Group purpose and constituents . 13
7.7.2 ISO/TS 8000-311 .13
8 Relationship of the ISO 8000 series to other international standards .13
8.1 Overview of the relationship to other international standards .13
8.2 Standards for quality management systems . 13
8.3 Standards for management systems other than quality management systems . 14
8.4 Standards for software quality . . . 14
8.5 Standards for industrial data .15
8.6 Standards for types of data other than industrial data . 16
Annex A (informative) Document identification .17
iii
Bibliography .18
iv
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 ISO technical committees. Each member body interested in a subject for which a technical
committee has been established has the right to be represented on that committee. International
organizations, governmental and non-governmental, in liaison with ISO, also take part in the work.
ISO collaborates closely with the International Electrotechnical Commission (IEC) on all matters of
electrotechnical standardization.
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 rights. ISO shall not be held responsible for identifying any or all such patent rights. Details of
any patent rights identified during the development of the document will be in the Introduction and/or
on the ISO list of patent declarations received (see www.iso.org/patents).
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.
This document was prepared by Technical Committee ISO/TC 184, Automation systems and integration,
Subcommittee SC 4, Industrial data.
This first edition of ISO 8000-1 cancels and replaces ISO/TS 8000-1:2011, which has been technically
revised.
The main changes are as follows:
— updates to cover the complete published parts in the ISO 8000 series;
— general editorial changes.
A list of all parts in the ISO 8000 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.
v
Introduction
Digital data deliver value by enhancing all aspects of organizational performance including:
— operational effectiveness and efficiency;
— safety;
— reputation with customers and the wider public;
— compliance with statutory regulations;
— innovation;
— consumer costs, revenues and stock prices.
In addition, many organizations are now addressing these considerations with reference to the United
1)
Nations Sustainable Development Goals .
The influence on performance originates from data being the formalized representation of
2)
information . This information enables organizations to make reliable decisions. Such decision-making
can be performed by human beings directly and also by automated data processing including artificial
intelligence systems.
Through widespread adoption of digital computing and associated communication technologies,
organizations become dependent on digital data. This dependency amplifies the negative consequences
of lack of quality in these data. These consequences are the decrease of organizational performance.
The biggest impact of digital data comes from two key factors:
— the data having a structure that reflects the nature of the subject matter;
EXAMPLE 1 A research scientist writes a report using a software application for word processing. This report
includes a table that uses a clear, logical layout to show results from an experiment. These results indicate how
material properties vary with temperature. The report is read by a designer, who uses the results to create a
product that works in a range of different operating temperatures.
— the data being computer processable (machine readable) rather than just being for a person to read
and understand.
EXAMPLE 2 A research scientist uses a database system to store the results of experiments on a material.
This system controls the format of different values in the data set. The system generates an output file of digital
data. This file is processed by a software application for engineering analysis. The application determines the
optimum geometry when using the material to make a product.
ISO 9000 explains that quality is not an abstract concept of absolute perfection. Quality is actually
the conformance of characteristics to requirements. This actuality means that any item of data can
be of high quality for one purpose but not for a different purpose. The quality is different because the
requirements are different between the two purposes.
EXAMPLE 3 Time data are processed by calendar applications and also by control systems for propulsion
units on spacecraft. These data include start times for meetings in a calendar application and activation times in
a control system. These start times require less precision than the activation times.
The nature of digital data is fundamental to establishing requirements that are relevant to the specific
decisions made by an organization.
EXAMPLE 4 ISO 8000-8 identifies that data have syntactic (format), semantic (meaning) and pragmatic
(usefulness) characteristics.
1) https://sdgs.un.org/goals
2) ISO 8000-2 defines information as “knowledge concerning objects, such as facts, events, things, processes, or
ideas, including concepts, that within a certain context has a particular meaning”.
vi
To support the delivery of high-quality data, the ISO 8000 series addresses:
— data governance, data quality management and maturity assessment;
EXAMPLE 5 ISO 8000-61 specifies a process reference model for data quality management.
— creating and applying requirements for data and information;
EXAMPLE 6 ISO 8000-110 specifies how to exchange characteristic data that are master data.
— monitoring and measuring information and data quality;
EXAMPLE 7 ISO 8000-8 specifies approaches to measuring information and data quality.
— improving data and, consequently, information quality;
EXAMPLE 8 ISO/TS 8000-81 specifies an approach to data profiling, which identifies opportunities to improve
data quality.
— issues that are specific to the type of content in a data set.
EXAMPLE 9 ISO/TS 8000-311 specifies how to address quality considerations for product shape data.
Data quality management covers all aspects of data processing, including creating, collecting, storing,
maintaining, transferring, exploiting and presenting data to deliver information.
Effective data quality management is systemic and systematic, requiring an understanding of the
root causes of data quality issues. This understanding is the basis for not just correcting existing
nonconformities but also implementing solutions that prevent future reoccurrence of those
nonconformities.
EXAMPLE 10 If a data set includes dates in multiple formats including “yyyy-mm-dd”, “mm-dd-yy” and
“dd-mm-yy”, then data cleansing can correct the consistency of the values. Such cleansing requires additional
information, however, to resolve ambiguous entries (such as, “04-05-20”). The cleansing also cannot address any
process issues and people issues, including training, that have caused the inconsistency.
As a contribution to this overall capability of the ISO 8000 series, this document provides a detailed
explanation of the structure and scope of the whole ISO 8000 series.
Organizations can use this document on its own or in conjunction with other parts of the ISO 8000
series.
This document supports activities that affect:
— one or more information systems;
— data flows within the organization and with external organizations;
— any phase of the data life cycle.
By implementing parts of the ISO 8000 series to improve organizational performance, an organization
achieves the following benefits:
— objective validation of the foundations for digital transformation of the organization;
— a sustainable basis for data in digital form becoming a fundamental asset class the organization
relies on to deliver value;
— securing evidence-based trust from other parties (including supply chain partners and regulators)
about the repeatability and reliability of data and information processing in the organization;
— portability of data with resulting protection against loss of intellectual property and reusability
across the organization and applications;
vii
— effective and efficient interoperability between all parties in a supply chain to achieve traceability
of data back to original sources;
— readiness to acquire or supply services where the other party expects to work with common
understanding of explicit data requirements.
3)
ISO 8000-2 specifies the single, common vocabulary for the ISO 8000 series. This vocabulary is
a foundation for understanding the overall subject matter of data quality. ISO 8000-2 presents the
vocabulary structured by a series of topic areas (for example, terms relating to quality and terms
relating to data and information).
4)
ISO has identified this document, ISO 8000-2 and ISO 8000-8 as horizontal deliverables .
Annex A contains an identifier that conforms to ISO/IEC 8824-1. The identifier unambiguously identifies
this document in an open information system.
3) The content is available on the ISO Online Browsing Platform. https://www.iso.org/obp
4) Deliverable dealing with a subject relevant to a number of committees or sectors or of crucial importance to
ensure coherence across standardization deliverables.
viii
INTERNATIONAL STANDARD ISO 8000-1:2022(E)
Data quality —
Part 1:
Overview
1 Scope
This document provides an overview of the ISO 8000 series.
The following are within the scope of this document:
— stating the scope of the ISO 8000 series as a whole;
— establishing the principles of information and data quality;
— describing the path to data quality;
— describing the structure of the ISO 8000 series;
— providing a summary of the content of each part in the ISO 8000 series;
— establishing the relationship of the ISO 8000 series to other international standards.
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 8000-2, Data quality — Part 2: Vocabulary
3 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO 8000-2 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 Scope of the ISO 8000 series
The ISO 8000 series provides frameworks for improving data quality for specific kinds of data. The
series defines which characteristics of data are relevant to data quality, specifies requirements
applicable to those characteristics, and provides guidelines for improving data quality. The series is
applicable within all stages of the data life cycle.
NOTE The ISO 8000 series can be used either in conjunction with or independently of standards for quality
management systems.
The following are within the scope of the ISO 8000 series:
— general aspects of data quality, including principles, vocabulary and measurement of information
and data quality;
— data governance;
— data quality management, including processes, roles, responsibilities and maturity assessment;
— data quality assessment, including profiling and data rules;
— quality of master data, including exchange of characteristic data and identifiers;
— quality of industrial data, including product shape data.
The following are outside the scope of the ISO 8000 series:
— quality of the things represented by data;
EXAMPLE 1 AF Industries makes fasteners. AF publishes an electronic catalogue of its products. The quality
of the catalogue (the data) is within the scope of the ISO 8000 series. The quality of the fasteners (the things
represented by the data) is outside of the scope of the ISO 8000 series.
— quality management principles;
EXAMPLE 2 ISO 9000 identifies eight quality management principles: customer focus, leadership, engagement
of people, process approach, improvement, evidence-based decision making and relationship management.
— systems and software product quality.
EXAMPLE 3 ISO/IEC 25000, ISO/IEC 25010, ISO/IEC 25012 and ISO/IEC 25024 address systems and software
product quality requirements and evaluation.
The ISO 8000 series contains requirements that are intended to be applicable to:
— all organizations, regardless of type and size;
— organizations at each point in the data supply chain.
5 Principles of information and data quality
The following principles of information and data quality underlie the ISO 8000 series:
— data are the reinterpretable representation of information in a formalized manner suitable for
communication, interpretation, or processing;
— an agreed level of data quality enables the right people to make the right decisions at the right time;
NOTE 1 Making the right decisions validates that the data are fit for purpose. Decision making can depend on
many different characteristics including location and sequence.
NOTE 2 Agreement is necessary between the stakeholders participating in and affected by the decision.
— effective data quality management builds on the fundamental concepts and principles of ISO 9000;
— data quality is a function of the inherent characteristics of the data under consideration;
EXAMPLE 1 A data set contains only characters from the character set specified by ISO/IEC 10646. This fact is
an inherent characteristic of the data.
EXAMPLE 2 One item in a data set has a length of 13 (i.e. is represented by 13 characters). This length is an
inherent characteristic of the data.
EXAMPLE 3 One column in a database table contains only values that are members of the set {"cm", "inches",
"mm", "m"}. This fact is an inherent characteristic of the data.
EXAMPLE 4 ISO/IEC 27001 addresses confidentiality, integrity and availability of data as the primary
considerations for management of information security. Confidentially, integrity and availability are functions
of the whole system (including hardware, software and people) and, thus, are not inherent characteristics of the
data.
— organizations gain greater value when digital data are computer processable;
— sustained co-ordinated activity by an organization protects and realises the value of data as an
asset.
NOTE 3 This activity requires levels of investment that are appropriate to the value delivered by the data. The
co-ordination addresses the influence that everyone in the organization has on the quality and value of data.
6 The ISO 8000 series path to data quality
The ISO 8000 series specifies a comprehensive capability to achieve and sustain high quality data. This
capability (see Figure 1):
— builds on the principles of data quality (see Clause 5);
— responds to the information need arising from the decision-making requirements of an organization;
— consists of processes that deliver effective data quality management and achieve governance and
assurance;
NOTE 1 These processes follow the fundamental structure of the Plan-Do-Check-Act cycle as also adopted by
ISO 9001.
— makes use of data specifications to address information need and to establish a rigorous basis for
determining that data meet applicable requirements in respect of syntactic, semantic and pragmatic
considerations;
— supports verification and validation of conformance to requirements;
— ensures alignment with the strategic objectives of the organization;
— delivers sustainable organizational and behavioural changes to improve data quality;
— is applicable to any type of data.
Figure 1 — ISO 8000 series capability to achieve and sustain high quality data
Although the ISO 8000 series is applicable to any type of data, the series does address some of the
different considerations that distinguish between types. These considerations include the:
— role of the data;
EXAMPLE 1 Roles include, but are not limited to: master data; transaction data; reference data.
EXAMPLE 2 The role of master data is to describe the entities that are both independent and fundamental
for an organization. The data are referenced in order to perform transactions. The entities include customers,
products, employees, materials, suppliers, services, shareholders, facilities, equipment, rules and regulations.
— component within the data architecture;
EXAMPLE 3 Components include, but are not limited to: dictionary; exchange file; database, data schema.
EXAMPLE 4 A dictionary consists of data that uniquely identify and describe entities such as part
classifications, types of property and units of measurement. This dictionary is a component of the data
architecture within an organization. This component enables consistent understanding of other data managed
by the organization.
— scope of information represented by the data;
EXAMPLE 5 Scope includes, but is not limited to: products (for example, product shape data); projects;
financial records; identifiers; performance characteristics; physical characteristics; safety. These scopes are not
necessarily distinct from each other.
EXAMPLE 6 The subject matter of product data is all the different aspects that determine the functions,
performance and how to conduct activities on the product (including creation, maintenance and disposal).
— intended area of application of the data;
EXAMPLE 7 Areas of application include, but are not limited to: industry; healthcare; banking.
EXAMPLE 8 Industrial data enables an organization to develop, manufacture and distribute a range of
products to fulfil customer demand.
— structure of the data.
NOTE 2 The terms “structured data” and “unstructured data” are sometimes used to suggest a clear, binary
distinction in the structure of data. This distinction is, however, a simplification of typical digital data sets, which
actually combine structured and unstructured elements.
EXAMPLE 9 In an equipment database with less structure, an organization describes each item of equipment
with a single free-text entry that covers all the characteristics of the item and is unable to enforce any
standardisation of how different persons choose to describe the item. In an equipment database with more
structure, the organization is able to record discrete values for individual characteristics of each item. These
characteristics could include length, width and height, each with an explicit unit of measurement to ensure
consistency and interpretability of the database entry for each item.
In the above list of considerations, each item is independent of the other items.
EXAMPLE 10 An item of master data (role) that is highly structured product data (scope) and forms part of a
wider set of industrial data (intended application).
7 Structure of the ISO 8000 series
7.1 Overview of the structure
The ISO 8000 series consists of groups of standards that address the following:
— general aspects of data quality (see 7.2);
— data governance (see 7.3);
— data quality management (see 7.4);
— data quality assessment (see 7.5);
— quality of master data (see 7.6);
— quality of industrial data (see 7.7).
Each group consists of one or more parts, which address specific aspects of data quality.
Table 1 lists the current parts of the ISO 8000 series.
Organizations can:
— use individual parts of the ISO 8000 series to address specific issues;
— implement multiple parts to achieve a more comprehensive capability for data quality.
Table 1 — Parts of the ISO 8000 series
Group of parts Part Description
ISO 8000-1 Overview of information and data quality
General aspects of
information and ISO 8000-2 Vocabulary for information and data quality
data quality
ISO 8000-8 Measuring and reporting information and data quality
a
Data governance ISO 8000-51 Exchange of data policy statements
ISO/TS 8000-60 Overview of data quality management
ISO 8000-61 Process reference model for data quality management
Determining maturity of data quality management by apply-
ISO 8000-62
ing standards relating to process assessment
ISO 8000-63 Process measurement approach
Data quality
Determining maturity of data quality management by apply-
ISO 8000-64
management
ing the Test Process Improvement Method
Process measurement questionnaire for data quality man-
ISO/TS 8000-65
agement
Maturity of data quality management for manufacturing
ISO 8000-66
operations
ISO 8000-150 Roles and responsibilities for data quality management
ISO/TS 8000-81 Data profiling
Data quality
b
assessment
ISO/TS 8000-82 Creating data rules
ISO 8000-100 Overview of master data quality
ISO 8000-110 Requirements for the exchange of characteristic data
ISO 8000-115 Exchange of identifiers
Quality of master
ISO 8000-116 Authoritative legal entity identifiers
data
ISO 8000-120 Stating provenance when exchanging characteristic data
ISO 8000-130 Stating accuracy when exchanging characteristic data
ISO 8000-140 Stating completeness when exchanging characteristic data
Quality of
ISO/TS 8000-311 Product data quality for shape
industrial data
a
Under preparation. Stage at the time of publication: ISO/DIS 8000-51.
b
Under preparation. Stage at the time of publication: ISO/PRF TS 8000-82.
7.2 General aspects of information and data quality
7.2.1 Group purpose and constituents
The purpose of the general aspects group of parts is to enable organizations to understand the purpose
and scope of the ISO 8000 series. This understanding ensures each organization is able to choose which
parts of the ISO 8000 series are relevant to the circumstances of the organization. This choice supports
the implementation of sustainable information and data quality.
The purpose of the general aspects group of parts also includes enabling organizations to implement
effective identification of the characteristics that determine the quality of information and data. This
effectiveness ensures organizations achieve coherent and repeatable quantification of quality issues in
data sets.
The following topics are addressed by the parts in the general aspects group:
— an overview of information and data quality and how the ISO 8000 series deals with the topic (the
contents of this document);
— a vocabulary for information and data quality (see 7.2.2 and ISO 8000-2);
— measuring and reporting information and data quality (see 7.2.3 and ISO 8000-8).
7.2.2 ISO 8000-2
ISO 8000-2 specifies the single, common vocabulary for the ISO 8000 series. This vocabulary is ideal
reading material by which to understand the overall subject matter of data quality. ISO 8000-2 presents
the vocabulary structured by a series of topic areas.
EXAMPLE Two of the subclauses in ISO 8000-2 are “Terms relating to quality” and “Terms relating to data
and information”.
7.2.3 ISO 8000-8
ISO 8000-8 specifies prerequisites for measuring information and data quality when executed within
processes and systems for quality management. These prerequisites include:
— understanding the nature of information and data quality, including the existence of syntactic,
semantic and pragmatic quality;
— a structured way to plan and perform information and data quality measurements;
— an approach to reporting information and data quality measurements.
ISO 8000-8 is applicable to all types of:
— organization;
— information and data;
— data processing;
— technology used for that processing.
7.3 Data governance
7.3.1 Group purpose and constituents
The purpose of the data governance group of parts is to enable an organization to implement effective
development and enforcement of policies related to the management of data. This effectiveness ensures
organizations perform data processing that:
— aligns with strategic objectives of the organization;
— satisfies the requirements of all stakeholders, including those external to the organization.
[54]
EXAMPLE The General Data Protection Regulation specifies data protection principles, rights and
obligations. This regulation applies to most organizations operating within the territories of the European Union.
The following topics are addressed by the parts in the data governance group:
— exchange of data policy statements (see 7.3.2 and ISO 8000-51).
7.3.2 ISO 8000-51
ISO 8000-51 specifies requirements that support the exchange of data governance policy statements
and the automation of testing the compliance of data sets to applicable policy statements. These
requirements cover the syntax and semantics of identifiers for organizations issuing data governance
policy statements and for those statements.
7.4 Data quality management
7.4.1 Group purpose and constituents
The purpose of the data quality management group of parts is to enable organizations to implement
effective planning, control, assurance and improvement of the quality of data. This effectiveness
ensures organizations perform data processing that:
— consistently generates or sustains data that meet requirements;
— delivers value to one or more classes of stakeholder.
NOTE Some stakeholders will be direct users of the data. Other stakeholders will benefit because someone
else has exploited the data.
EXAMPLE A government analyses data to improve delivery of social services to citizens.
The following topics are addressed by the parts in the data quality management group:
— an overview of data quality management (see 7.4.2 and ISO/TS 8000-60);
— a process reference model for data quality management (see 7.4.3 and ISO 8000-61);
— applying standards relating to process assessment to perform the process maturity assessment of
data quality management in an organization (see 7.4.4 and ISO 8000-62);
— a process measurement approach to data quality management (see 7.4.5 and ISO 8000-63);
— application of the Test Process Improvement method (see 7.4.6 and ISO 8000-64);
— a process measurement questionnaire for data quality management (see 7.4.7 and ISO/TS 8000-65);
— maturity of data quality management for manufacturing operations (see 7.4.8 and ISO 8000-66);
— roles and responsibilities for data quality management (see 7.4.9 and ISO 8000-150).
7.4.2 ISO/TS 8000-60
ISO/TS 8000-60 is an introduction to the group of standards that specify requirements applicable
to data quality management. The introduction describes the core concepts applicable to data quality
management and gives an overview of each part in the group. Those parts enable the implementation,
assessment and improvement of data quality management.
7.4.3 ISO 8000-61
ISO 8000-61 specifies a process reference model for data quality management. This model supports
assessing and improving the capability of the processes and increasing organizational maturity with
respect to data quality management. The model describes each process in terms of a purpose, outcomes
and activities. ISO 8000-61 also lists the fundamental principles of data quality management.
7.4.4 ISO 8000-62
ISO 8000-62 specifies how organizations can use a maturity model in assessing their process maturity
with respect to data quality management as specified by ISO 8000-61 (see 7.4.3). This assessment
requires the use of assessment indicators and can use the measurement stack specified by ISO 8000-63
(see 7.4.5) to determine these indicators. The maturity model conforms to ISO/IEC 33004.
7.4.5 ISO 8000-63
ISO 8000-63 specifies a process measurement approach that is appropriate for use when assessing
process maturity. This approach can serve when an organization is looking to improve the maturity of
data quality management. The approach makes use of a structure for process measurement stacks that
organizations can instantiate to measure the characteristics of processes for data quality management.
This structure consists of goal, sub goal, question, indicator and metric. The instantiated stack consists
of content that is determined by a chosen model for assessing the maturity of the processes under
consideration.
7.4.6 ISO 8000-64
ISO 8000-64 specifies a procedure by which an organization can assess process maturity according to
the specific priorities of the organization. This procedure provides a capability to assess and improve
data quality management as specified by ISO 8000-61 (see 7.4.3). The procedure makes use of the Test
[55]
Process Improvement method and the measurement stack specified by ISO 8000-63 (see 7.4.5).
7.4.7 ISO/TS 8000-65
ISO/TS 8000-65 establishes a simple measurement method to evaluate the implementation of data
quality management implementation by organizations. This method uses the processes from the
reference model specified by ISO 8000-61 (see 7.4.3). The method poses a series of questions, each of
which addresses one of the outcomes of the corresponding process from ISO 8000-61. The questions are
applicable to all types of business process, technology, information system, data and data processing.
NOTE The term "business process" refers to the wider process within which the specific data processing
is taking place. This wider process involves decision making that depends on the data. The process typically
generates a product or service, although this can be for the internal purposes of an organization. This is also not
necessarily for commercial gain in the case of government or other types of organization.
7.4.8 ISO 8000-66
ISO 8000-66 supports the application of ISO 8000-62 (see 7.4.4) to determine the process maturity
of data quality management in manufacturing organizations. This support is provided by specifying
assessment indicators for data processing in manufacturing operations management that is specified
by IEC 62264-1. These indicators conform to ISO/IEC 33004.
7.4.9 ISO 8000-150
ISO 8000-150 addresses key considerations when establishing the roles and responsibilities necessary
to deliver effective and efficient data quality management. These considerations are supported by a
framework that links role levels to structured groups of responsibility and a model of operations to
deliver data quality management. ISO 8000-150 also provides example scenarios for deployment of the
framework. The role levels and responsibility groups are appropriate for all types of data and all types
of organization.
7.5 Data quality assessment
7.5.1 Group purpose and constituents
The purpose of the data quality assessment group of parts is to enable organizations to implement
effective identification and resolution of quality issues in data sets. This effectiveness ensures
organizations:
— uncover previously hidden quality issues;
— prevent future recurrence of the same quality issues.
The following topics are addressed by the parts in the data quality assessment group of standards:
— an approach to data profiling (see 7.5.2 and ISO/TS 8000-81);
— creating data rules (see 7.5.3 and ISO/TS 8000-82).
7.5.2 ISO/TS 8000-81
ISO/TS 8000-81 specifies an approach to data profiling, which involves applying analysis techniques
to data in actual use. This analysis generates a profile consisting of the structure, columns and
relationships of the data. The profile provides the basis for identifying opportunities to improve data
quality by establishing new explicit rules for the data. The approach also typically produces greater
effect from repeated application to uncover issues progressively.
7.5.3 ISO/TS 8000-82
ISO/TS 8000-82 specifies how different data rules apply to various types of data. Such rules exist to
sustain the integrity and reliability of data by capturing requirements into a form that can be processed
by databases and other information systems. Each rule is able to support data quality assessment.
7.6 Quality of master data
7.6.1 Group purpose and constituents
The purpose of the master data group of parts is to enable organizations to implement effective
processing of master data. This effectiveness ensures organizations:
— exploit or supply master data that meet a relevant range of applicable requirements;
EXAMPLE 1 Master data identifies and describes individuals, organizations, locations,
...