ISO 8000-110:2021

INTERNATIONAL ISO
STANDARD 8000-110
Second edition
2021-11
Data quality —
Part 110:
Master data: Exchange of
characteristic data: Syntax, semantic
encoding, and conformance to data
specification
Qualité des données —
Partie 110: Données permanentes: Échange des données
caractéristiques: Syntaxe, sémantique, encodage et conformité aux
spécifications de données
Reference number
© ISO 2021
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Published in Switzerland
ii
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 2
3 Terms and definitions . 2
4 Fundamental concepts and assumptions . 2
5 Objectives . 4
6 Syntax . 5
7 Semantic encoding .5
7.1 General requirements . 5
7.1.1 Level 1 requirements. 5
7.1.2 Level 2 requirements. 6
7.2 Requirements for all property-value tuples . 7
7.3 Requirements for values of properties that are quantities . 7
7.3.1 Scope of requirements. 7
7.3.2 Representation of units of measurement . 8
7.3.3 Representation of qualifiers of measurement. 10
7.4 Requirements for currency amounts . 13
8 Conformance to the data specification .14
9 Conformance requirements .16
Annex A (normative) Document identification .17
Annex B (informative) Example of a schema that can be used to exchange master data that
are characteristic data .18
Bibliography .19
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 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 second edition cancels and replaces the first edition (ISO 8000-110:2009), which has been
technically revised.
The main changes are as follows:
— removing broken Uniform Resource Locators;
— updating normative references, figures and tables;
— replacing the term “data value” with the term “value tuple”;
— replacing the terms “property value” and “property value pair” with the term “property-value
tuple”;
— adding an Annex B referencing an example of a schema for exchanging master data that are
characteristic data;
— editorial corrections to language, grammar and document layout.
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.
iv
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 information.
This information enables organizations to make reliable decisions. This 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 use but not for another. This difference occurs when the requirements are different
between the two uses.
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 8000-1 identifies that data have syntactic (format), semantic (meaning) and pragmatic
(usefulness) characteristics.
1) https://sdgs.un.org/goals
v
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 This document specifies how to exchange characteristic data that are master data.
— monitoring and measuring data and information quality;
EXAMPLE 7 ISO 8000-8 specifies approaches to measuring data and information 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, however, requires
additional information 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 supports the creation
and exchange of high-quality data. This document contains requirements necessary but not sufficient
to achieve data quality with respect to the exchange of master data. The requirements do not cover
issues such as addressing the accuracy, provenance and completeness of master data. These issues need
to be part of an overall data quality strategy adopted by each organization.
"Organization" does not necessarily mean a single, complete company or corporation. The organization
can be a subdivision or branch that covers some distinct area of business operation.
When different business units of a company exchange master data or when a business unit exchanges
master data with headquarters, these business units are organizations for the purposes of this
document.
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 o
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