ISO 8000-150:2022

INTERNATIONAL ISO
STANDARD 8000-150
First edition
2022-05
Data quality —
Part 150:
Data quality management: Roles and
responsibilities
Reference number
© ISO 2022
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Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Principles of roles and responsibilities for data quality management .2
5 Implementation requirements .2
6 Conformance . 3
Annex A (informative) Document identification . 4
Annex B (informative) Framework of role levels and responsibility groups for data quality
management . 5
Annex C (informative) Functional model of roles and responsibilities.18
Annex D (informative) Example deployment scenarios for the framework of roles and
responsibilities .23
Annex E (informative) Comparison with processes specified by ISO 8000-61 .26
Bibliography .28
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
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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.
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
2)
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 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.
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”.
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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;
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— 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.
3)
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 (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.
3) The content is available on the ISO Online Browsing Platform. https://www.iso.org/obp
vii
INTERNATIONAL STANDARD ISO 8000-150:2022(E)
Data quality —
Part 150:
Data quality management: Roles and responsibilities
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
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 8000-61, Data quality — Part 61: Data quality management: Process reference model
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 Principles of roles and responsibilities for data quality management
The following key principles apply when an organization implements roles and responsibilities for
managing and improving data quality through systemic and systematic processes.
— An integrated approach across the organization. The data managed by each organization are
scattered throughout the organization. These data also flow to various places through the hands
of many persons. This scattering and flow make difficulties for trying to control data quality by
concentrating efforts in a single area of the organization. Data nonconformities can occur anywhere
data are processed and from any type of such processing (including inserting, updating, and
retrieving). It is, therefore, important to identify exactly what is causing any such nonconformity
and to perform data quality management across the entire organization. This, in turn, depends on
the allocation, communication and understanding of appropriate roles and responsibilities across
the entire organization to ensure processes are delivering their intended outputs.
— High-level commitment for empowerment. Data quality management takes significant time and
effort because of the frequency, severity and complexity of actual and potential data nonconformities.
Furthermore, organizations can only prevent recurrence of such nonconformities by changing ways
of working. At all levels of an organization, persons who have responsibilities for data quality require
resource and authority assigned to achieve such change. This resource and authority is supported
by strong commitment from top management, who are responsible for establishing co-ordinated
governance to monitor and control management of data quality across the organization.
— Value-adding cycle of changes. Requirements for data evolve dynamically, driven by internal and
external changes. Appropriate roles and responsibilities identified for data quality management
enable a value-adding cycle to respond to the changes quickly.
These principles can be delivered by an organization:
— using a framework of role levels and responsibility groups (see Annex B);
— using appropriate roles to perform each of the responsibility groups as part of a coherent whole (see
Annex C);
— addressing a wide range of deployment scenarios (see Annex D).
5 Implementation requirements
An organization shall implement effective roles and responsibilities for data quality management
through the following:
— executing processes within the scope of data quality management specified by ISO 8000-61 (see
Annex E for a mapping of those processes to the responsibility groups specified by this document);
— identifying one or more roles that will perform the responsibilities for data quality management;
NOTE 1 As appropriate to the size of the organization, organizations can implement roles either by assigning
one or more persons to each role and by assigning one or more roles to each person. Any person can also fulfil
other roles that are not responsible for data quality management.
EXAMPLE 1 A small organization appoints a single individual, who is responsible for performing all nine
responsibility groups in Annex B in addition to other organizational roles.
EXAMPLE 2 A large organization appoints multiple data diagnosis planners, who are each responsible for
performing different aspects of the responsibility group data diagnosis planning. These aspects are the diagnosis
in different systems across the organization, such as one planner being responsible for customer relationship
data and another being responsible for product catalogue data.
— embedding the processes for data quality management within the business processes that are core
to the purposes for which the organization exists;
NOTE 2 An individual process for data quality management can be embedded into many different core
processes of an organization, where each one of those core processes involves the creation or use of data.
EXAMPLE 3 A manufacturer embeds data quality management into manufacturing processes.
— implementing appropriate other parts of ISO 8000 to address specific requirements of the
organization.
EXAMPLE 4 When an organization exchanges master data, ISO 8000-110 addresses requirements for syntax,
semantics and conformance to data specification, ISO 8000-115 addresses requirements for identifiers and
ISO 8000-120 addresses requirements for representing information about the provenance of the master data.
6 Conformance
To achieve conformance to this document, an organization shall prepare documentary evidence of the
following:
— identification of roles for data quality management;
— assignment of those roles to specific persons within the organization;
— allocation of responsibilities to those roles;
EXAMPLE 1 A job description is documentary evidence of a role and responsibility assignment.
— embedding processes for data quality management within other business processes across the
organization;
EXAMPLE 2 An organization-wide process model is documentary evidence of embedding data quality
management into other business processes.
— execution of the processes for data quality management;
EXAMPLE 3 Documentary evidence for this execution includes specifications of data requirements, results
of data quality measurements, a log of nonconformities and a log of root-cause analysis and corresponding
corrective actions.
— auditing assigned roles and responsibilities to check performance and, as necessary, to initiate the
development and implementation of improvements.
Annex A
(informative)
Document identification
To provide for unambiguous identification of an information object in an open system, the following
object identifier is assigned to this document. The meaning of this value is defined in ISO 10303-1.
{ iso standard 8000 part(150) version(2) }
Annex B
(informative)
Framework of role levels and responsibility groups for data
quality management
B.1 Overview of the framework
The fundamental structure of the framework is a 3x3 matrix (see Figure B.1). The framework includes
three responsibility groups collections: data implementation, data diagnosis and data improvement
(see B.2). Each responsibility group collection consists of three responsibility groups at the next lower
level; one responsibility group for each of the three role levels in the framework. These role levels are:
managerial, operational and technical (see B.3).
Figure B.1 — Framework of roles and responsibilities for data quality management
In contrast to this framework, ISO 8000-61 specifies a process reference model for data quality
management and does not identify responsibility groups and role levels as part of that model.
While the framework shows the three role levels in the order managerial, operational and technical,
actual interactions between these levels is not necessarily top to bottom or linear. The framework is
supported by a functional model for delivering the responsibility groups (see Annex C). This model
provides further detail on information flows between the individual lower-level responsibility groups.
These responsibility groups take place in a context governed by a data quality policy, which aligns with
the overall organizational policy for data and information and is the basis for data quality objectives.
The data quality policy is the controlling factor that drives performance of the whole framework.
B.2 Responsibility group collections of the framework
B.2.1 General
The three responsibility group collections are:
— data implementation (see B.2.2);
— data diagnosis (see B.2.3);
— data improvement (see B.2.4).
B.2.2 Data implementation
The data implementation responsibility group identifies factors that affect data quality and ensures
data are available at the right place in a timely manner.
This responsibility group collection consists of the following responsibility groups.
— Data architecting (see B.4.2): manage organization-wide data architecture from an integrated
perspective to use data in distributed information systems with consistency and, therefore, ensure
data quality.
— Data design (see B.4.3): create data schemas and implement databases to support users processing
data without nonconformities and to ensure data quality.
— Data processing (see B.4.4): create, search, update and delete data in accordance with applicable
requirements.
B.2.3 Data diagnosis
The data diagnosis responsibility group identifies data nonconformity through a systematic approach.
This responsibility group collection consists of the following responsibility groups.
— Data diagnosis planning (see B.4.5): set up objectives of data quality in alignment with the strategies
of an organization, identify factors to be managed and perform actions to accomplish the objectives.
This responsibility group also includes assurance of data quality and adjustment of objectives in
response to assurance results.
— Data quality criteria setup (see B.4.6): set criteria including characteristics of data, target data and
methods to measure data quality.
— Data quality measurement (see B.4.7): measure target data with the criteria set by the process data
quality criteria setup on a real-time basis or periodically.
B.2.4 Data improvement
The data improvement responsibility group corrects data nonconformities and eliminates the root
causes of those nonconformities. Improvement often depends on appropriate alignment of data
stewardship with flow of data within and beyond the organization. The responsibility group not only
addresses issues with data but also delivers appropriate process improvement, which at the managerial
level covers core business processes of the organization and at the operational level covers data
management processes.
This responsibility group collection consists of the following responsibility groups.
— Data stewardship / flow management (see B.4.8): analyse data operations and data flows within and
between organizations, identify responsible parties and the data processing systems influencing
data quality and manage the stewardship of data operations.
— Data nonconformity cause analysis (see B.4.9): analyse root causes of data nonconformities and
identify solutions to prevent recurrence of those nonconformities.
— Data nonconformity correction (see B.4.10): modify data that do not conform to requirements.
B.3 Role levels in the framework
B.3.1 General
The following three role levels perform the responsibility groups in the framework:
— managerial role level (see B.3.2);
EXAMPLE 1 A data manager is a role at the managerial role level.
— operational role level (see B.3.3);
EXAMPLE 2 A data administrator is a role at the operational role level.
— technical role level (see B.3.4).
EXAMPLE 3 A data technician is a role at the technical role level.
B.3.2 Managerial role level
The managerial level performs the following responsibility groups:
— data architecting (see B.4.2);
— data diagnosis planning (see B.4.5);
— data stewardship / flow management (see B.4.8).
The managerial level directs the execution of data quality management across an organization, with
the primary purpose to set the context within which the operational level performs corresponding
responsibility groups. This direction aligns with the overall objectives of the organization.
In particular, the managerial level:
— establishes the basis for data consistency between information systems (data architecting);
— identifies key factors that can affect data quality (data diagnosis planning);
— grants authority to the operational level to trace and correct data.
EXAMPLE An enterprise data architect (managerial level) selects one of the several Product Identification
Code (PIC) systems available. While a data administrator (operational level) or a data technician (technical level)
operates the PIC system, this enterprise data architect oversees and manages whether the use of the PIC aligns
with the objectives of data quality management.
B.3.3 Operational role level
The operational level performs the following responsibility groups:
— data design (see B.4.3);
— data quality criteria setup (see B.4.6);
— data nonconformity cause analysis (see B.4.9).
The operational level acts on the direction from the managerial level, with the primary purpose to
set the context within which the technical level performs corresponding responsibility groups. This
context consists of:
— appropriate data schemas (data design);
— criteria for use when measuring the quality of data (data quality criteria setup);
— solutions to the root causes of data quality issues (data nonconformity cause analysis).
EXAMPLE Product Identification Codes exist in several systems across an organization, requiring a data
administrator (operational level) to identify all the locations of repeated data and to re-design data schemas
to prevent this redundancy. In this example, the role of data administrator could equally be replaced by a
programme steering committee or a database administrator.
B.3.4 Technical role level
The technical level performs the following responsibility groups:
— data processing (see B.4.4);
— data quality measurement (see B.4.7);
— data nonconformity correction (see B.4.10).
In particular, the technical level:
— creates, reads, modifies, transfers and deletes data (data processing) to meet the requirements
applying to data quality management and created by the operational level;
— measures data quality (data quality measurement);
— corrects any nonconformities that are identified by the measurement activities (data nonconformity
correction)
— only handles data within an allocated scope of responsibility, while, in contrast, the managerial or
operational level can face situations in which data from external sources are flowing into the local
environment and these data then require attention.
The technical level covers the activities of either a data user (actually inputting and exploiting data) or
a data operator (periodically measuring whether data conform with requirements and correcting any
identified data nonconformities).
EXAMPLE A data user applies Product Identification Codes when creating a new record for a product in
inventory at an organization, while a data operator periodically examines these codes to verify conformance
with the applicable requirements for formatting. This examination can be by using an automated tool or by visual
review of the codes.
B.4 Lower-level responsibility groups in the framework
B.4.1 General
The framework includes nine lower-level responsibility groups:
— data architecting (see B.4.2);
— data design (see B.4.3);
— data processing (see B.4.4);
— data diagnosis planning (see B.4.5);
— data quality criteria setup (see B.4.6);
— data quality measurement (see B.4.7);
— data stewardship / flow management (see B.4.8);
— data nonconformity cause analysis (see B.4.9);
— data nonconformity correction (see B.4.10).
The following descriptions of each responsibility group include an overview, the tasks within the
responsibility group, the responsibilities of corresponding role levels and any relationships with other
responsibility groups in the framework.
These lower-level responsibility groups also appear in a functional model for performing the
responsibility groups (see Annex C) and are the basis for deployment scenarios for the framework (see
Annex D).
B.4.2 Data architecting
B.4.2.1 Overview of data architecting
In general, data are distributed across any organization and, thus, data quality cannot be ensured
without systematic management.
The data architecting responsibility group identifies the key data existing across the organization and
defines a data schema to drive data quality inside and outside the organization. This responsibility
group prevents discrepancy between the same data that appear in different information systems. The
responsibility group also manages the lifecycle of the data identified by the data architecture.
B.4.2.2 Constituent tasks of data architecting
This responsibility group consists of the following tasks.
— Management of organization-wide conceptual data models: these models cover the data that appear
in more than one information system across the organization or that are of significant value to the
organization and require management. The models enable data mapping and tracing.
NOTE This task includes agreeing and communicating to all stakeholders the existence of master data
sources.
— Management of organization-wide data standards: identifies the standards and other rules that
apply to data across the organization. These standards and rules govern the scope and content of
the data architecture.
B.4.2.3 Required responsibilities for data architecting
To perform this responsibility group, the managerial role level (see B.3.2) has the following specific
responsibilities.
— Organization-wide co-ordination: seeking convergence of goals and plans for data quality
management amongst those parties who are responsible for those goals and plans. This convergence
is then followed by assuring conformance by those parties. As data quality depends heavily on data
users across the organization, the managerial level has authority to control and co-ordinate parties
in organizational units beyond those that are purely technical in focus.
— Organization-wide sharing and maintenance: issuing conceptual data models and data standards to
ensure data quality, while continually maintaining the consistency of those artefacts (for example,
modifying data mappings whenever a data schema changes).
NOTE This task includes promptly updating master data sources when changes are necessary and then
propagating these changes to slave data stores.
B.4.2.4 Relationship of data architecting to other responsibility groups
This responsibility group has the following relationships to other responsibility groups.
— Between data architecting and data diagnosis planning: the organization-wide data architecture
enables development of the plan for data diagnosis. This plan drives the content of conceptual data
models and the selection of appropriate data standards.
— Between data architecting and data stewardship / flow management: the organization-wide
data architecture drives assignment of appropriate data stewardship. When resolution of data
conformities requires change to data stewardship or to data flows then this also requires an update
to the data architecture.
— From data architecting to data design: the conceptual data models and data standards are the
foundation for developing appropriate data schemas.
NOTE These conceptual data models and data standards establish foundations for master data management
and all types of data processing.
B.4.3 Data design
B.4.3.1 Overview of data design
Data quality issues arise from either end-user tasks or from structural inadequacies in the tools and
methods for data processing. The former issues are not generally subject to systematic resolution but
when the latter cause data nonconformities then re-design of data schemas is the way forward. Such
re-design does not, however, easily integrate into in-service information systems, so the data design
responsibility group requires a focus on data quality from the very beginning. This focus does not
address the needs of individual systems in isolation to avoid creating a data quality approach that is
not sustainable across the whole organization. The schemas take account of the balanced requirements
from all relevant information systems.
EXAMPLE To support the exchange of master data, ISO 8000-110 addresses requirements for syntax,
semantics and conformance to data specification, ISO 8000-115 addresses requirements for identifiers,
ISO 8000-120 addresses requirements for statements about provenance, ISO 8000-130 addresses the
representation of statements about accuracy and ISO 8000-140 addresses the representation of statements about
completeness.
B.4.3.2 Constituent tasks of data design
This responsibility group consists of the following tasks.
— Design to address data characteristics: creation of schemas that identify the types and value ranges
of data. These schemas address the needs of the data processing performed by the technical level.
Some of this data processing involves physical data structures in existing information systems and,
thus, schemas reflect the configuration of those structures.
— Design to address the organization-wide data architecture: creation and change of data schemas
to satisfy the needs of the organization-wide data architecture. These needs evolve every time the
organization decides to implement a new information system.
B.4.3.3 Required responsibilities for data design
To perform this responsibility group, the operational role level (see B.3.3) has the following specific
responsibilities:
— to identify data requirements across the organization by consulting with stakeholders (including
users) for internal information systems;
— to identify data dependencies with external information systems by consulting with stakeholders
responsible for those systems.
B.4.3.4 Relationship of data design to other responsibility groups
This responsibility group has the following relationships to other responsibility groups.
— From data design to data architecting: detailed data schemas provide feedback on any discrepancies
in the outputs of data architecting.
— From data design to data processing: detailed data schemas control the scope and content of outputs
from data processing.
— From data design to data quality criteria setup: detailed data schemas are the basis for appropriate
data quality criteria.
B.4.4 Data processing
B.4.4.1 Overview of data processing
End users are the most numerous of persons at the technical role level but are also the ones who
generally have a narrow focus on the data processing responsibility group to support a core process
for which they have responsibility. Such users are less likely to understand the wider implication on
data quality from taking shortcuts and often fail to spot the more subtle types of data nonconformity.
These limitations drive the need for robust and comprehensive requirements to apply data quality
considerations to all data transactions.
Data processing includes manual transactions and also those performed by information systems.
B.4.4.2 Constituent tasks of data processing
This responsibility group consists of the following tasks.
— Data transactions: this task involves creating, reading, updating, transferring or deleting data in
accordance with applicable requirements. The task requires competence from end users and this
competence is delivered by interactive support from software or by formal training programmes.
When this task is an automated function of an information system then requirements are still
applicable and the design of such systems involves explicit audit of the correct implementation of
those requirements.
— Data logging: to trace every data transaction, this task creates a record of who performs what data
processing at what time.
B.4.4.3 Required responsibilities for data processing
To perform this responsibility group, the technical role level (see B.3.4) has the following specific
responsibilities.
— applying specialist knowledge of systematic considerations to maximize the extent to which
high-quality data propagate across the organization;
— paying close attention to the quality of any data that are critical to the organization.
B.4.4.4 Relationship of data processing to other responsibility groups
This responsibility group has the following relationships to other responsibility groups.
— From data processing to data design: data quality issues provide feedback in the form of opportunities
to improve data schemas.
— From data processing to data quality measurement: output data are subject to measurement to
assess data quality.
B.4.5 Data diagnosis planning
B.4.5.1 Overview of data diagnosis planning
The data diagnosis plan unifies the approach to satisfying the various perspectives within and beyond
an organization on how to deliver effective, relevant data quality. These perspectives are the foundation
for specific objectives and demand that the organization seeks a structured policy for data quality
management.
B.4.5.2 Constituent tasks of data diagnosis planning
This responsibility group consists of the following tasks.
— Identification and management of objectives: this task collects data requirements from all
stakeholders including consumers of the data inside and outside the organization. These
requirements form the basis for a coherent set of objectives and lead to assurance that the plan
properly implements the objectives.
— Identification and planning of quality management tasks: this task identifies the tasks necessary to
achieve the objectives. These tasks sit within a detailed plan of action that also includes scheduling,
allocated resources and appropriate identified methods.
B.4.5.3 Required responsibilities for data diagnosis planning
To perform this responsibility group, the managerial role level (see B.3.2) has the following specific
responsibilities:
— identification of all applicable quality management factors and sufficient resources to address those
factors to ensure a relevant, feasible plan;
— securing the support of top management through effective dialogue to align the plan with
overall governance objectives and, thus, achieve consistent implementation of the plan across the
organization.
B.4.5.4 Relationship of data diagnosis planning to other responsibility groups
This responsibility group has the following relationships to other responsibility groups.
— From data diagnosis planning to data architecting: scope, tasks, scheduling, required resources and
the methods for data diagnosis provide feedback in the form of opportunities to improve the data
architecture.
— From data diagnosis planning to data stewardship / flow management: scope, tasks, scheduling,
required resources and the methods for data diagnosis establish the basis for identifying data
stewards and data flows.
— From data diagnosis planning to data quality criteria setup: scope, tasks, scheduling, required
resources and the methods for data diagnosis establish the basis for creating appropriate data
quality criteria.
B.4.6 Data quality criteria setup
B.4.6.1 Overview of data quality criteria setup
To deliver a data diagnosis plan, data quality criteria are necessary. These criteria cover the specific
relevant considerations of data quality (i
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