National foreword

This British Standard is the UK implementation of ISO 8000‑150:2022.

The UK participation in its preparation was entrusted to Technical Committee AMT/4, Industrial data and manufacturing interfaces.

A list of organizations represented on this committee can be obtained on request to its committee manager.

Contractual and legal considerations

This publication has been prepared in good faith, however no representation, warranty, assurance or undertaking (express or implied) is or will be made, and no responsibility or liability is or will be accepted by BSI in relation to the adequacy, accuracy, completeness or reasonableness of this publication. All and any such responsibility and liability is expressly disclaimed to the full extent permitted by the law.

This publication is provided as is, and is to be used at the recipient’s own risk.

The recipient is advised to consider seeking professional guidance with respect to its use of this publication.

This publication is not intended to constitute a contract. Users are responsible for its correct application.

Compliance with a British Standard cannot confer immunity from legal obligations.

This British Standard was published under the authority of the Standards Policy and Strategy Committee on 31 July 2022.

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 cancels and replaces ISO/TS 8000‑150:2011, which has been technically revised.

The main changes are as follows:

increased emphasis on roles and responsibilities for data quality management;
removal of being specifically only applicable to master data;
clarification of the differentiation of this document with ISO 8000‑61 (including removing apparent overlaps).

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.

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 Nations Sustainable Development Goals1.

The influence on performance originates from data being the formalized representation of information2. 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 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 that are made by each organization.

EXAMPLE 4 ISO 8000‑1 identifies that data have syntactic (format), semantic (meaning) and pragmatic (usefulness) characteristics.

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 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. This document 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.

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, 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;
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.

ISO 8000‑23 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 (for example, terms relating to quality and terms relating to data and information).

Annex A contains an identifier that conforms to ISO/IEC 8824‑1. The identifier unambiguously identifies this document in an open information system.

1 Scope

This document specifies the key considerations for organizations that are establishing appropriate roles and responsibilities for data quality management.

The following are within the scope of this document:

implementing roles and responsibilities for data quality management;
providing documentary evidence of this implementation;
a framework for roles and responsibilities;
a functional model of roles and responsibilities;
example deployment scenarios for the framework of roles and responsibilities;
comparison with the processes specified by ISO 8000‑61.

The following are outside the scope of this document:

process reference models for data quality management (ISO 8000‑61 specifies a process reference model for data quality management);
methods for data quality evaluation and certification;
models for assessing the maturity of data quality management (ISO 8000‑62 and ISO 8000‑64 specify approaches to assessing the maturity of data quality management).

This document can be used in conjunction with or independently of standards for quality management systems.

2 Normative references

3 Terms and definitions

4 Principles of roles and responsibilities for data quality management

5 Implementation requirements

6 Conformance

Bibliography

BS ISO 8000-150:2022

Data quality - Data quality management: Roles and responsibilities

Current

Published:

31 Jul 2022