ISO 8000-61:2016

Data quality — Part 61: Data quality management: Process reference model

ISO 8000-61:2016 specifies the processes required for data quality management. The processes are used as a reference to enhance data quality and assess process capability or organizational maturity for data quality management.

Qualité des données — Partie 61: Gestion de la qualité des données: Modèle de référence des procédés

General Information

• Status: Published
• Publication Date: 16-Nov-2016
• 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
• Completion Date: 03-Aug-2022

Standards Content (Sample)

INTERNATIONAL ISO
STANDARD 8000-61
First edition
2016-11-15
Data quality —
Part 61:
Data quality management: Process
reference model
Qualité des données —
Partie 61: Gestion de la qualité des données: Modèle de référence des
procédés
Reference number
ISO 8000-61:2016(E)
©
ISO 2016

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ISO 8000-61:2016(E)

COPYRIGHT PROTECTED DOCUMENT
© ISO 2016, Published in Switzerland
All rights reserved. Unless otherwise specified, no part of this publication may be reproduced or utilized otherwise in any form
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ISO 8000-61:2016(E)

Contents Page
Foreword .v
Introduction .vi
1 Scope . 1
2 Normative references . 1
3 Terms, definitions and abbreviated terms . 1
3.1 Terms and definitions . 1
3.2 Abbreviated terms . 2
4 Fundamental principles of data quality management . 2
5 The data quality management process . 2
5.1 The basic structure of the data quality management process . 2
5.2 The detailed structure of the data quality management process. 3
5.3 The elements of a process description . 5
6 The Implementation process. 5
6.1 Overview of Implementation . 5
6.2 Data Quality Planning . 6
6.2.1 Overview of Data Quality Planning . 6
6.2.2 Requirements Management . 6
6.2.3 Data Quality Strategy Management . 7
6.2.4 Data Quality Policy/Standards/Procedures Management . 7
6.2.5 Data Quality Implementation Planning . 8
6.3 Data Quality Control . 9
6.3.1 Overview of Data Quality Control . 9
6.3.2 Provision of Data Specifications and Work Instructions . 9
6.3.3 Data Processing . . 9
6.3.4 Data Quality Monitoring and Control .10
6.4 Data Quality Assurance .11
6.4.1 Overview of Data Quality Assurance .11
6.4.2 Review of Data Quality Issues .11
6.4.3 Provision of Measurement Criteria .12
6.4.4 Measurement of Data Quality and Process Performance .12
6.4.5 Evaluation of Measurement Results .12
6.5 Data Quality Improvement .13
6.5.1 Overview of Data Quality Improvement .13
6.5.2 Root Cause Analysis and Solution Development .13
6.5.3 Data Cleansing .14
6.5.4 Process Improvement for Data Nonconformity Prevention .14
7 The Data-Related Support process .15
7.1 Overview of Data-Related Support .15
7.2 Data Architecture Management .15
7.3 Data Transfer Management .15
7.4 Data Operations Management .16
7.5 Data Security Management .17
8 The Resource Provision process .17
8.1 Overview of Resource Provision .17
8.2 Data Quality Organization Management .17
8.3 Human Resource Management .18
9 Relationship between data quality management and data governance .19
10 Implementation requirements .19
Annex A (normative) Document identification .20
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Bibliography .21
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ISO 8000-61:2016(E)

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 on 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 the following URL: www.iso.org/iso/foreword.html.
The committee responsible for this document is Technical Committee ISO/TC 184, Automation systems
and integration, Subcommittee SC 4, Industrial data.
ISO 8000 is organized as a series of parts, each published separately. The structure of ISO 8000 is
described in ISO/TS 8000-1.
Each part of ISO 8000 is a member of one of the following series: general data quality, master data
quality, transactional data quality, and product data quality. This part of ISO 8000 is a member of the
general data quality series but is also applicable to the other series.
A list of all parts in the ISO 8000 series can be found on the ISO website.
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Introduction
The ability to create, collect, store, maintain, transfer, process and present information and data to
support business processes in a timely and cost effective manner requires both an understanding of
the characteristics of the information and data that determine its quality, and an ability to measure,
manage and report on information and data quality.
ISO 8000 defines characteristics of information and data that determine its quality, and provides
methods to manage, measure and improve the quality of information and data.
When assessing the quality of information and data, it is useful to perform the assessment in accordance
with documented methods. It is also important to document the tailoring of standardized methods with
respect to the expectation and requirements pertinent to the business case at hand.
ISO 8000 includes parts applicable to all types of data and parts applicable to specific types of data.
ISO 8000 can be used independently or in conjunction with quality management systems.
There is a limit to data quality improvement when only the nonconformity of data is corrected, since
the nonconformity can recur. However, when the root causes of the data nonconformity and the related
data are traced and corrected through data quality processes, recurrence of the same type of data
nonconformity can be prevented. Therefore, a framework for process-centric data quality management
is required to improve data quality more effectively and efficiently. Furthermore, data quality can be
improved through assessing processes and improving under-performing processes identified by the
assessment.
This part of ISO 8000 specifies the processes required for data quality management. This specification
is used as a reference for assessing and improving the capability of the processes or increasing
organizational maturity with respect to data quality management.
This part of ISO 8000 can be used on its own or in conjunction with other parts of ISO 8000.
This part of ISO 8000 is intended for use by those actors that have a vested interest in information or
data quality, with a focus on one or more information systems both inter- and intra-organization views,
throughout all phases of the data life cycle.
Annex A contains an identifier that unambiguously identifies this part of ISO 8000 in an open
information system.
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INTERNATIONAL STANDARD ISO 8000-61:2016(E)
Data quality —
Part 61:
Data quality management: Process reference model
1 Scope
This part of ISO 8000 specifies the processes required for data quality management. Each process is
defined by a purpose, outcomes and activities that are to be applied for the assurance of data quality.
The following are within the scope of this part of ISO 8000:
— fundamental principles of data quality management;
— the structure of the data quality management process;
— definitions of the lower level processes for data quality management;
— the relationship between data quality management and data governance;
— implementation requirements.
The following is outside the scope of this part of ISO 8000:
— detailed methods or procedures by which to achieve the outcomes of the defined processes.
This part of ISO 8000 is applicable to managing the quality of digital data sets that include not only
structured data stored in databases but also less structured data such as images, audio, video and
electronic documents. This part of ISO 8000 can be used by an organization managing data quality at the
organization level because, for instance, multiple software applications are sharing and exchanging data.
This part of ISO 8000 is used as a process reference model by internal and external parties, including
certification bodies, to assess process capability or organizational maturity for data quality
management and to enhance data quality through process improvement.
This part of ISO 8000 can be used in conjunction with, or independently of, quality management systems
standards (e.g. ISO 9001).
2 Normative references
The following referenced documents are indispensable for the application 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, definitions and abbreviated terms
3.1 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO 8000-2 apply.
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3.2 Abbreviated terms
DBMS database management system
4 Fundamental principles of data quality management
The following fundamental principles apply to managing the quality of data.
— Process approach: the processes that use, create and update data are defined and operated. These
processes become repeatable and reliable by also defining and operating processes for managing
data quality.
— Continuous improvement: data are improved through effective measurement and correction of data
nonconformities that arise from data processing. Such improvements, however, do not prevent the
same nonconformities occurring repeatedly. Sustained improvement arises from analysing, tracing
and removing the root causes of poor data quality, usually requiring the improvement of processes.
— Involvement of people: specific responsibilities for data quality management exist at different levels
of the organization. End users have the greatest direct effect on data quality through data processing
activities. In addition, data quality specialists perform the necessary intervention and control to
implement and embed processes for improvement of data quality across the organization. Finally,
oversight by top management ensures the necessary resources are made available and directs the
organization towards achieving the vision, goals and objectives for data quality.
5 The data quality management process
5.1 The basic structure of the data quality management process
The basic structure of the data quality management process is as follows.
— The data quality management process consists of Implementation, Data-Related Support, and
Resource Provision. This is depicted in Figure 1.
— To achieve continuous improvement of data quality, the Implementation process is performed
following the Plan-Do-Check-Act pattern.
— The Data-Related Support process enables the Implementation process by providing information
and technology related to data management.
— The Resource Provision process improves the effectiveness and efficiency of the Implementation
and the Data-Related Support processes by providing resources and training services at the
organizational level.
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Figure 1 — Basic structure of data quality management
NOTE The structure of Implementation, Data-Related Support, and Resource Provision processes is adapted
from the concept of Primary, Support and Organizational processes in ISO 12207:1995 and from the Plan-Do-
Check-Act cycle from ISO 9001.
The Plan-Do-Check-Act cycle is also applicable to improving the performance of any of the lower level
processes of data quality management. These improvements will contribute to more effective and
efficient data quality. The Plan-Do-Check-Act cycle consists of:
— plan: establish the strategy and implementation plans as necessary to deliver results in accordance
with data requirements;
— do: implement the processes;
— check: monitor and measure data quality and process performance against the strategy and data
requirements and report the results;
— act: take actions to continually improve process performance.
5.2 The detailed structure of the data quality management process
As shown in Figure 2, the data quality management process is a hierarchy of lower level processes, as
follows.
a) The Implementation process consists of four sub-processes based on the “Plan-Do-Check-Act”
pattern:
1) Data Quality Planning, corresponding to “Plan”:
― Requirements Management;
― Data Quality Strategy Management;
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― Data Quality Policy/Standards/Procedures Management;
― Data Quality Implementation Planning;
2) Data Quality Control, corresponding to “Do”:
― Provision of Data Specifications and Work Instructions;
― Data Processing;
― Data Quality Monitoring and Control;
3) Data Quality Assurance, corresponding to “Check”:
― Review of Data Quality Issues;
― Provision of Measurement Criteria;
― Measurement of Data Quality and Process Performance;
― Evaluation of Measurement Results;
4) Data Quality Improvement, corresponding to “Act”:
― Root Cause Analysis and Solution Development;
― Data Cleansing;
― Process Improvement for Data Nonconformity Prevention.
b) The Data-Related Support process provides Implementation with information, constraints and
technology. This process consists of:
1) Data Architecture Management;
2) Data Transfer Management;
3) Data Operations Management;
4) Data Security Management.
c) The Resource Provision process enhances the performance of Implementation and Data-Related
Support by providing resources at the organizational level. This process consists of:
1) Data Quality Organization Management;
2) Human Resource Management.
The sub-processes of Implementation take place in sequential order, while those of Data-Related
Support and Resource Provision take place as and when necessary.
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Figure 2 — Detailed structure of data quality management
5.3 The elements of a process description
The process descriptions in the remainder of this part of ISO 8000 consist of the following elements:
— title, which is a descriptive heading for the process;
— purpose, which describes the goal of performing the process;
— outcomes, which express the observable results expected from successful performance of the
process;
— activities, which is a list of actions that can achieve the outcomes.
NOTE ISO/IEC/TR 24774 provides further details on these elements.
6 The Implementation process
6.1 Overview of Implementation
The Implementation process identifies data requirements corresponding to the needs of stakeholders,
establishes objectives and creates implementation plans to meet those requirements. In line with these
plans, end users perform data processing according to data specifications and work instructions, while
data quality specialists monitor and control the conformance of data to requirements.
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When identical or similar types of data nonconformity are found repeatedly and remain unsolved
by monitoring and control, further action is necessary. This action begins with measuring data and
processes related to the nonconformities. Subsequently, root causes are identified and improvement
solutions are provided. These solutions increase data quality by correcting nonconforming data and
improving processes that cause nonconformities to arise.
The sub-processes of Implementation are Data Quality Planning (see 6.2), Data Quality Control (see
6.3), Data Quality Assurance (see 6.4) and Data Quality Improvement (see 6.5).
6.2 Data Quality Planning
6.2.1 Overview of Data Quality Planning
Data Quality Planning establishes data requirements and objectives for data quality, creating plans to
achieve the objectives and evaluating the performance of the plans. These plans balance current data
quality levels, cost, resources and capabilities across the organization. This process is initiated based
on needs and expectations of stakeholders or the feedback of the process improvements performed in
Data Quality Improvement (see 6.5).
NOTE Stakeholders can include end users, data quality management specialists, top management,
governments, regulatory authorities, suppliers to the organization and customers of the products and services
that the organization delivers.
Data Quality Planning consists of Requirements Management (see 6.2.2), Data Quality Strategy
Management (see 6.2.3), Data Quality Policy/Standards/Procedures Management (see 6.2.4) and Data
Quality Implementation Planning (see 6.2.5).
6.2.2 Requirements Management
a) Purpose
The purpose of Requirements Management is to establish the basis for creating or for refining a
data quality strategy that aligns with the needs and expectations of stakeholders.
b) Outcomes
— The needs and expectations of stakeholders with respect to data are collected.
— The needs and expectations are refined into data requirements.
NOTE This refinement can include structuring and classifying requirements in order to improve
understanding of the interdependencies of those requirements.
— Requirements are analysed to determine their feasibility in terms of technology, cost, manpower,
and schedule.
— Requirements are prioritized and approved.
— The needs of different parts of the organization are balanced and an agreed common set of
requirements is achieved.
c) Activities
— Identification of data requirements: Collect the needs and expectations related to data from
stakeholders and identify and classify the data requirements.
NOTE See ISO 8000-8 and ISO 8000-110 for further detail on data requirements.
— Prioritization of data requirements: Analyse the feasibility of the requirements identified in
terms of technology, cost, timeliness and importance, providing the basis on which to determine
implementation priority.
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— Validation of data requirements: Trace and evaluate the extent to which the requirements have
been satisfied through the data quality management process, and, when necessary, modify
requirements through consultation with stakeholders.
6.2.3 Data Quality Strategy Management
a) Purpose
The purpose of Data Quality Strategy Management is to establish the basis on which subsequently
to develop policies, standards, procedures and implementation plans that apply to data quality
management across the organization and that align with strategic intentions for data quality.
b) Outcomes
— Top management is committed to the improvement of data quality to agreed levels at the
organizational level.
— A data quality strategy is created, describing the vision, long term goals, an implementation
roadmap and short term objectives, which are defined in terms of quantitative outcomes.
— A framework is created for establishing and reviewing the data quality strategy.
— Results are evaluated to determine the performance of the data quality strategy, leading to the
strategy being updated as necessary.
— The data quality strategy is communicated throughout the organization.
c) Activities
— Establishment of data quality strategy: Establish a data quality strategy consisting of the vision,
long-term goals and implementation roadmap to secure data quality across the organization in
accordance with identified data requirements. Create short-term objectives to achieve the long-
term goals. Top management ensures that the quality strategy is appropriate to the goals and
objectives of data management and the overall business of the organization.
— Performance evaluation of the data quality strategy: Evaluate whether the data quality strategy
has been achieved through the data quality management process. Change the strategy through
consultation with stakeholders when necessary.
6.2.4 Data Quality Policy/Standards/Procedures Management
a) Purpose
The purpose of Data Quality Policy/Standards/Procedures Management is to capture rules that
apply to performing Data Quality Control, Data Quality Assurance, Data Quality Improvement,
Data-Related Support and Resource Provision consistently across the organization.
b) Outcomes
— Policies are defined in terms of fundamental intentions and rules that guide the organization
as to which actions are appropriate and which are inappropriate in performing data quality
management.
— Standards are defined to support data quality management.
NOTE These standards include those covering: formats for expressing data requirements; measurement
methods; how to sustain data quality when changing supporting technology; and the infrastructure of
computer hardware and software systems.
— Procedures are defined to specify in detail how the organization performs data quality
management.
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— Policies, standards and procedures are communicated throughout the organization, covering
the consistent application to data quality management.
c) Activities
— Management of quality policies for data quality management: Specify fundamental intentions
and rules for data quality management in the organization. Ensure the data quality policies
are appropriate for the data quality strategy, comply with data requirements and establish
the foundation
...