ISO/TS 8000-82:2022

Data quality - Part 82: Data quality assessment: Creating data rules

This document describes how 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. The following are within the scope of this document: - fundamental concepts of data rules; - key characteristics of data rules for common types of data, where these types are identifier, currency value, quantity, date or time, rate, free‑text entry, code and key; - how data profiling contributes to formulating effective data rules. The following is outside the scope of this document: - specific rules for specific sets of data. This document can be used in conjunction with or independently of standards for quality management systems. EXAMPLE 1 ISO 9001 specifies requirements for quality management systems. This document can also be used in conjunction with or independently of standards for more detailed definitions of data types. EXAMPLE 2 ISO/IEC 11404 specifies the nomenclature and shared semantics for a collection of datatypes commonly occurring in programming languages and software interfaces. EXAMPLE 3 IEC 61360‑1 specifies principles for the definition of the properties and associated attributes and explains the methods for representing verbally defined concepts.

Qualité des données — Partie 82: Titre manque

General Information

• Status: Published
• Publication Date: 13-Jun-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: 9060 - Close of review
• Completion Date: 02-Dec-2028

Overview

ISO/TS 8000-82:2022 - Data quality - Part 82: Data quality assessment: Creating data rules - defines how to express and assess data rules that sustain the integrity and reliability of digital data. The Technical Specification explains fundamental concepts and the key characteristics of machine‑processable rules for common data types (identifier, currency value, quantity, date/time, rate, free‑text entry, code and key) and describes how data profiling (per ISO/TS 8000‑81) informs effective rule creation. The specification is intentionally not a catalogue of specific rules for particular data sets; rather it provides guidance for formulating rules that reflect organizational requirements and system design.

Key topics and technical requirements

• Fundamental concepts of data rules: definition of a data rule as a specification that applies to instances of a data attribute within a data set; rules capture design analysis and operational requirements.
• Data rule characteristics by data type: guidance on what to control for identifiers, currency values, quantities, dates/times, rates, free text, codes and keys (for example: mandatory/optional status, allowed value sets, format constraints, units of measure, precision and min/max ranges).
• Role of data profiling: profiling is required to gather evidence and inform the formulation and validation of rules; ISO/TS 8000‑81 is the recommended profiling reference.
• Lifecycle and governance considerations: rules can apply across the data lifecycle (creation to destruction) and should be managed as an integrated, coherent set originating from policy, design, or system functionality.
• Assessment focus: rules enable objective assessment of data conformity and support proactive data quality management.

Practical applications and users

Who benefits:

• Data stewards and data governance teams - to define enforceable rules that align with business requirements.
• Data architects and database designers - to translate requirements into schema constraints and validation logic.
• Data quality analysts and profilers - to derive rules from profiling results and measure conformance.
• Software developers and integrators - to implement validation, parsing and transformation logic in applications and ETL pipelines.
• Compliance officers and auditors - to demonstrate repeatable, auditable controls over critical data.

Typical uses:

• Creating machine‑readable validation rules for master data and transactional systems.
• Designing unit/precision and format rules for engineering, finance, and logistics data.
• Integrating rule sets into data pipelines, APIs and data quality monitoring tools.
• Supporting digital transformation, interoperability and evidence‑based trust across supply chains.

Related standards

• ISO 8000‑2 (Vocabulary)
• ISO/TS 8000‑81 (Data profiling)
• ISO 8000 series (data quality management)
• ISO 9001 (quality management systems) - for governance alignment
• ISO/IEC 11404 and IEC 61360‑1 - for datatype nomenclature and property definition

Keywords: ISO/TS 8000‑82:2022, data quality, data rules, data profiling, data quality assessment, data governance, identifiers, currency value, quantity, date/time, codes, keys.

Standards Content (Sample)

TECHNICAL ISO/TS
SPECIFICATION 8000-82
First edition
2022-06
Data quality —
Part 82:
Data quality assessment: Creating
data rules
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
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or ISO’s member body in the country of the requester.
ISO copyright office
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Phone: [phone]
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Website: www.iso.org
Published in Switzerland
ii
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Basic concepts for data rules .2
5 Data rules . . 2
5.1 Overview . 2
5.2 Data rules for identifiers . 3
5.3 Data rules for currency values . 3
5.4 Data rules for quantities . 4
5.5 Data rules for dates and times . 4
5.6 Data rules for rates . 5
5.7 Data rules for free text entries . 5
5.8 Data rules for codes . . 5
5.9 Data rules for keys . . 6
Annex A (informative) Document identification . 7
Bibliography . 8
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.
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 and security;
— 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”.
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 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 (e.g. “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 specifies the
characteristics of data rules that can support data quality assessment.
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;
vi
— 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.
ISO 8000-1 provides a detailed explanation of the structure and scope of the whole ISO 8000 series.
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 ISO 8000-1, 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.
vii
TECHNICAL SPECIFICATION ISO/TS 8000-82:2022(E)
Data quality —
Part 82:
Data quality assessment: Creating data rules
1 Scope
This document describes how 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.
The following are within the scope of this document:
— fundamental concepts of data rules;
— key characteristics of data rules for common types of data, where these types are identifier, currency
value, quantity, date or time, rate, free-text entry, code and key;
— how data profiling contributes to formulating effective data rules.
The following is outside the scope of this document:
— specific rules for specific sets of data.
This document can be used in conjunction with or independently of standards for quality management
systems.
EXAMPLE 1 ISO 9001 specifies requirements for quality management systems.
This document can also be used in conjunction with or independently of standards for more detailed
definitions of data types.
EXAMPLE 2 ISO/IEC 11404 specifies the nomenclature and shared semantics for a collection of datatypes
commonly occurring in programming languages and software interfaces.
EXAMPLE 3 IEC 61360-1 specifies principles for the definition of the properties and associated attributes and
explains the methods for representing verbally defined concepts.
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
ISO/TS 8000-81, Data quality — Part 81: Data quality assessment: Profiling
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
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