ISO/TS 8000-150:2011

TECHNICAL ISO/TS
SPECIFICATION 8000-150
First edition
2011-12-15
Data quality —
Part 150:
Master data: Quality management
framework
Qualité des données —
Partie 150: Données permanentes: Cadre de management de la qualité

Reference number
©
ISO 2011
©  ISO 2011
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ii © ISO 2011 – All rights reserved

Contents Page
Foreword .v
Introduction .vi
1 Scope.1
2 Normative references.1
3 Terms, definitions and abbreviated terms.2
3.1 Terms and definitions.2
3.2 Abbreviated terms .2
4 Fundamental principles of master data quality management.2
5 Requirements .3
5.1 Implementation requirements.3
5.2 Data exchange requirements .3
5.3 Provenance requirements .3
6 Conformance.4
Annex A (normative) Document identification.5
Annex B (informative) Master data quality management framework.6
B.1 Overview of the master data quality management framework.6
B.2 The top-level processes of the framework.7
B.2.1 The three top-level processes.7
B.2.2 Data operations .7
B.2.3 Data quality monitoring .8
B.2.4 Data quality improvement.8
B.3 The roles in the framework .8
B.3.1 The three roles.8
B.3.2 Data manager .8
B.3.3 Data administrator.9
B.3.4 Data technician.9
B.4 The lower level processes in the framework .10
B.4.1 The nine lower level processes .10
B.4.2 Data architecture management.10
B.4.2.1 Overview of data architecture management .10
B.4.2.2 Constituent activities of data architecture management .11
B.4.2.3 Required responsibilities of data manger for data architecture management.11
B.4.2.4 Relationship of data architecture management to other processes .11
B.4.3 Data design.11
B.4.3.1 Overview of data design.11
B.4.3.2 Constituent activities of data design.12
B.4.3.3 Required responsibilities of the data administrator for data design .12
B.4.3.4 Relationship of data design to other processes.12
B.4.4 Data processing.12
B.4.4.1 Overview of data processing .12
B.4.4.2 Constituent activities of data processing .12
B.4.4.3 Required responsibilities of data technician for data processing.13
B.4.4.4 Relationships of data processing to other processes.13
B.4.5 Data quality planning.13
B.4.5.1 Overview of data quality planning .13
B.4.5.2 Constituent activities of data quality planning .13
B.4.5.3 Required responsibilities of data manager for data quality planning .13
B.4.5.4 Relationship of data quality plan to other processes .14
B.4.6 Data quality criteria setup .14
B.4.6.1 Overview of data quality criteria setup .14

B.4.6.2 Constituent activities of data quality criteria setup .14

B.4.6.3 Required responsibilities of data administrator for data quality criteria setup.14

B.4.6.4 Relationship of data quality criteria setup to other processes.14

B.4.7 Data quality measurement.15

B.4.7.1 Overview of data quality measurement.15

B.4.7.2 Constituent activities of data quality measurement.15

B.4.7.3 Required responsibilities of data technician for data quality measurement .15
B.4.7.4 Relationship of data quality measurement to other processes.15

B.4.8 Data stewardship and flow management .15

B.4.8.1 Overview of data stewardship and flow management.15

B.4.8.2 Constituent activities of data stewardship and flow management.16

B.4.8.3 Required responsibilities of data manager for data stewardship and flow
management.16

B.4.8.4 Relationship of data stewardship and flow management to other processes.16

B.4.9 Data error cause analysis .16

B.4.9.1 Overview of data error cause analysis.16

B.4.9.2 Constituent activities of data error cause analysis.17

B.4.9.3 Required responsibilities of data administrator for data error cause analysis .17

B.4.9.4 Relationship of data error cause analysis to other processes.17

B.4.10 Data error correction.17

B.4.10.1 Overview of data error correction .17

B.4.10.2 Constituent activities of data error correction .18

B.4.10.3 Required responsibilities of data technician for data error correction.18

B.4.10.4 Relationship of data error correction to other processes .18

Annex C (informative) The functional model of the framework .19

Annex D (informative) Business scenario with examples for the framework .22

D.1 Business environment and situation .22

D.2 Data architecture management .22

D.3 Data design.22

D.4 Data processing .22

D.5 Data quality plan .22

D.6 Data quality criteria setup.23

D.7 Data quality measurement .23

D.8 Data stewardship and flow .23

D.9 Data error cause analysis.23

D.10 Data error correction .24

Bibliography.25

Index.26

iv © ISO 2011 – All rights reserved

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.
International Standards are drafted in accordance with the rules given in the ISO/IEC Directives,
Part 2.
The main task of technical committees is to prepare International Standards. Draft International
Standards adopted by the technical committees are circulated to the member bodies for voting.
Publication as an International Standard requires approval by at least 75% of the member bodies
casting a vote.
In other circumstances, particularly when there is an urgent market requirement for such documents, a
technical committee may decide to publish other types of normative document:
— an ISO Publicly Available Specification (ISO/PAS) represents an agreement between technical
experts in an ISO working group and is accepted for publication if it is approved by more than
50% of the members of the parent committee casting a vote;
— an ISO Technical Specification (ISO/TS) represents an agreement between the members of a
technical committee and is accepted for publication if it is approved by 2/3 of the members of
the committee casting a vote.
An ISO/PAS or ISO/TS is reviewed every three years with a view to deciding whether it can be
transformed into an International Standard.
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.
ISO/TS 8000-150 was prepared by 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 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
master data quality series.
A complete list of parts of ISO 8000 is available from the Internet:
http://www.tc184-sc4.org/titles/DATA_QUALITY_Titles.htm

Introduction
The ability to create, collect, store, maintain, transfer, process and present data to support business
processes in a timely and cost effective manner requires both an understanding of the characteristics
of the data that determine its quality, and an ability to measure, manage and report on data quality.
ISO 8000 defines characteristics that can be tested by any organization in the data supply chain to
objectively determine conformance of the data to ISO 8000.
ISO 8000 provides a framework for improving data quality that can be used independently or in
conjunction with quality management systems.
There is a limit to master data quality improvement with the data-centric approach where only the data
found defective is corrected. When data errors and their related data are traced and corrected, or root
causes of data errors are removed through processes for data quality management, recurrence of the
same data errors can be prevented. Therefore, a framework for process-centric data quality
management is required to improve data quality.
For this purpose, this part of ISO 8000 specifies fundamental principles of a master data quality
management, and requirements for implementation, data exchange and provenance. This standard also
contains an informative framework that identifies processes for data quality management. For reader’s
better understanding, the framework in detail, its functional model and a business scenario with
examples are provided in Annexes B, C and D, respectively. This framework can be used in
conjunction with or independently of quality management systems standards, for example, ISO 9001.
This part of ISO 8000 is intended for use by organizations that have multiple systems that share
master data and/or that share and exchange data with other organizations and therefore need to
manage the quality of their master data.
Although the framework has been developed based on the experience of data quality management
applied in industries such as finance, telecommunication, and public institutions, it is expected that
this framework, with appropriate extension, can also be applied to mechanical design or
manufacturing data.
vi © ISO 2011 – All rights reserved

TECHNICAL SPECIFICATION ISO/TS 8000-150:2011(E)

Data quality —
Part 150:
Master data: Quality management framework
1 Scope
This part of ISO 8000 provides fundamental principles of a process-centred approach to master data
quality management and requirements that can be used by an organization to implement master data
quality management. It also contains an informative framework that identifies processes for master
data quality management. This part of ISO 8000 can be used in conjunction with or independently of
quality management systems standards, for example, ISO 9001.
The following are within the scope of this part of ISO 8000:
— fundamental principles of master data quality management;
— requirements
— implementation requirements;
— data exchange requirements;
— provenance requirements;
— master data quality management framework
— top-level and lower level processes;
— roles.
The following are outside the scope of this part of ISO 8000:
— data quality evaluation and certification methods;
— taxonomy of data;
— data quality maturity model.
This part of ISO 8000 is intended for use by organizations that have multiple systems that share
master data and/or that share and exchange data with other organizations and therefore need to
manage the quality of their master data.
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
ISO 8000-110, Data quality — Part 110: Master data: Exchange of characteristic data: Syntax,
semantic encoding, and conformance to data specification
ISO/TS 8000-120, Data quality — Part 120: Master data: Exchange of characteristic data:
Provenance
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.
3.2 Abbreviated terms
For the purposes of this document, the following abbreviated terms apply.
UNSPSC United Nations standard products and services code
GTIN global trade item number
SQL structured query language
4 Fundamental principles of master data quality management
To manage master data quality successfully, organizations shall keep the following fundamental
principles.
— Involvement of people: people at all levels who have roles for data quality management are
involved to improve data quality of an organization. Although data processing of end users with
lower-level role has the most direct effect on data quality, intervention or control of data
administrators with middle-level role is required to implement and settle down processes for
data quality improvement in the organization. In addition, involvement of managers who are in
charge of organization-wide data quality with high-level role is inevitable to change and
optimize roles, authority, and processes of the organization.
— Process approach: data-centric measurement and correction is not enough to improve data
quality of the whole organization. Desired data quality is achieved more efficiently when
activities and related resources for data quality are managed by processes.
— Continual improvement: data quality is improved continuously through the processes of data
processing, data quality measurement and data error correction. However, with these processes
only, identical data errors that occur repeatedly cannot be prevented. Recurrence of data errors
can be prevented when the processes to analyze, trace and improve root causes which hinder
data quality goes with these processes. For this, management processes concerned with data
architecture/schema, data stewardship and data flow shall also be supported. In addition,
organizations shall improve not only processes for data quality management but also business
processes where data are directly operated.
— Master data exchange: all processes to manage master data quality comply with requirements
that can be checked by computer for the exchange, between organizations and systems, of
master data that consists of characteristic data.
2 © ISO 2011 – All rights reserved

The framework of master data quality management, the functional model of the framework and a
business scenario with examples are provided as informative in Annexes C, D and E, respectively.
5 Requirements
5.1 Implementation requirements
An organization that implements this part of ISO 8000 shall perform the following actions:
— perform processes for data quality management that include at least data processing, data quality
measurement and correction, data schema design, measurement criteria setup, error cause
analysis, data quality planning and data architecture/stewardship/flow management;
— assign roles for data quality management within their organization;
NOTE 1 Each role can be assigned to multiple persons, or multiple roles assigned to one person or
position. The roles assigned can be one of many other roles assigned to a person or position.
— embed processes for data quality management within the organizations business processes.
NOTE 2 The processes defined can be embedded at multiple places within the business processes of
organizations, specifically anywhere master data is created and used.
5.2 Data exchange requirements
An organization shall:
— be capable of sending and receiving master data messages that conform to ISO 8000-110;
— specify a data dictionary to be used for semantic coding of master data messages sent to and
from external organizations. The data dictionary shall meet the requirements of ISO 8000-110
for use in semantic coding;
— maintain a registry of data specifications that document the organization’s data requirements for
master data messages.
All master data messages that the organization sends to external organizations shall conform to
ISO 8000-110.
The organization shall require that all master data messages sent to it conform to ISO 8000-110.
A data supplier claiming conformance to this part of ISO 8000 shall maintain a suitable electronic
means for receiving queries for master data.
EXAMPLE An email address is published on the company’s website or in a registry maintained by a third party.
5.3 Provenance requirements
This clause contains requirements that are optional for data exchange in addition to those in 5.2.
Any master data message that the organization sends to external organizations shall conform to
ISO/TS 8000-120.
6 Conformance
An organization conforms to this part of ISO 8000 when it can present documentary evidence of the
following:
— Roles for data quality management are assigned within their organization.
EXAMPLE 1 A job description is documentary evidence of a role assignment.
— Processes for data quality management are incorporated within the organizations business
processes.
EXAMPLE 2 A business process model that includes the processes at appropriate places would be evidence of
incorporation.
— The processes for data quality management are being executed.
EXAMPLE 3 Specifications of master data quality requirements, results of master data quality measurements,
a log of defects and non-conformance, and a log of root cause analysis and corrective actions are evidence of the
business processes being executed.
This part of ISO 8000 also provides for a number of options that may be supported by an
implementation. These options have been grouped into the following conformance classes:
— free decoding;
— fee-based decoding;
— free decoding with provenance;
— fee-based decoding with provenance.
Conformance to the free decoding conformance class requires:
— the data dictionary, data specifications, and any incoming or outgoing master data messages
conform to the free decoding conformance class of ISO 8000-110;
— all requirements of 5.2 are met.
In addition to the above, conformance to the free decoding with provenance conformance class
requires that all requirements of 5.3 are met.
Conformance to the fee-based decoding conformance class requires:
— the data dictionary, data specifications, and any incoming or outgoing master data messages
conform to the fee-based decoding conformance class of ISO 8000-110;
— all requirements of 5.2 are met.
In addition to the above, conformance to the fee-based decoding with provenance conformance class
requires that all requirements of 5.3 are met.
Any claim of conformance to this part of ISO 8000 that does not explicitly state the conformance
class shall be a claim of conformance to one of the free decoding conformance classes.
4 © ISO 2011 – All rights reserved

Annex A
(normative)
Document identification
To provide for unambiguous identification of an information object in an open system, the object
identifier
{ iso standard 8000 part(150) version(1) }
is assigned to this part of ISO 8000. The meaning of this value is defined in ISO/IEC 8824-1, and is
described in ISO 10303-1.
Annex B
(informative)
Master data quality management framework
B.1 Overview of the master data quality management framework
The structure of the framework is graphically represented in a 3x3 matrix as shown in Figure B.1. The
framework consists of three top-level processes: data operations, data quality monitoring, and data
quality improvement. Each top-level process is segmented into three processes by the role of the
person performing the process. The processes are related to one another according to the order of
processes and input/output of data.
Processes grouped under the role of data manager support those under the role of a data administrator.
The process of data architecture management presents the guidelines necessary to design data
structures. The process of data stewardship and flow management provides the necessary information
to analyze error causes and the process of data quality plan offers objectives or guidelines for data
quality that assist in setting up criteria.
Processes grouped under the role of data administrator are comprised of the followings: data design,
data quality criteria setup, and data error cause analysis. These three processes control and coordinate
data so as to support processes under the role of data technician. The process data design helps to
ensure data operation quality by improving data schema. The process, data quality criteria setup
provides with criteria and methodologies to assess data quality. The process of data error cause
analysis prevents recurrence of the same data errors by analyzing root causes of the data errors.
Processes grouped under the role of data technician are split into the followings: data processing, data
quality measurement, and data error correction. The three processes are performed successively: first,
the process of data processing that creates, reads, modifies, transfers, and deletes data is implemented
in accordance with data guidelines. In order to find unnoted data errors during the process, the process
of data quality measurement is executed on a real time basis or periodically. In case that data errors
are found in the process, the process of data error correction enters the execution mode.
Thus far, processes were explained in the order of data manager, data administrator and data
technician. However, in certain cases results of the processes are fed back in reverse order.
Generally the data quality policy is consistent with the overall information technology (IT) policy of
the organization and provides a basis for the setting of data quality objectives. Therefore, the data
quality policy is considered one of control factors which affect the performance of the whole
framework.
6 © ISO 2011 – All rights reserved

Data Data quality Data quality
operations monitoring improvement
Data
Data
Data Data quality stewardship
architecture
manager planning /flow
management
management
Data Data quality Data error
Data design
administrator criteria setup cause analysis
Data Data Data quality Data error
technician processing measurement correction
= role = process
Figure B.1 — Master data quality management framework
B.2 The top-level processes of the framework
B.2.1 The three top-level processes
The three top-level processes in the framework shall be:
— data operations (see B.2.2);
— data quality monitoring (see B.2.3);
— data quality improvement (see B.2.4).
B.2.2 Data operations
The data operations process identifies factors that affect data quality and ensures data is available at
the right place in a timely manner. This top-level process shall consist of the following processes:
— data architecture management; the process that manages organization-wide data architecture
from the integrated perspective to use data in distributed information systems with consistency
and therefore ensure data quality(see B.4.2).
— data design; the process that designs data schema, and implements a database to make data users
apply data without mistake and ensure data quality(see B.4.3).
— data processing; the process that creates, searches, updates, deletes data in accordance with
guidelines of data operations (see B.4.4).
B.2.3 Data quality monitoring
The data quality monitoring process identifies data errors through a systematic approach. This top-
level process shall consist of the following processes:
— data quality planning; the process that sets up objectives of data quality in alignment with the
strategies of an organization, identifies factors to be managed, and performs actions in order to
accomplish objectives. This process also includes assurance of data quality and adjustment of
objectives on the back of assurance results (see B.4.5).
— data quality criteria setup; the process that sets criteria that include characteristics of data, target
data, and methods to measure(see B.4.6).
— data quality measurement; the process that measures target data with the criteria set in the
process of data quality criteria setup on a real time basis or periodically (see B.4.7).
B.2.4 Data quality improvement
The data quality improvement process corrects data errors detected and eliminates root causes of the
data errors by tracing and identifying them. In order to support the top-level process effectively,
adjustment of data stewardship in accordance with data flows tracing is required. This process has the
function of process improvement not only data quality improvement. Processes for data management
are improved at the data administrator level while business processes at the data manager level. This
top-level process shall consist of the following processes:
— data stewardship and flow management; the process that analyses data operations and data flows
among businesses or organizations, identifies responsible parties and their data operation
systems which influence data quality, and manages the stewardship of data operations(see
B.4.8).
— data error cause analysis; the process that analyses root causes of data errors and prevents a
recurrence of the same errors fundamentally (see B.4.9).
— data error correction; the process that corrects the data that turns out erroneous (see B.4.10).
B.3 The roles in the framework
B.3.1 The three roles
The three roles in the framework are responsible for performing the processes in the framework.
These roles shall be:
— data manager (see B.3.2);
— data administrator (see B.3.3);
— data technician (see B.3.4).
B.3.2 Data manager
The data manager shall perform the following processes within the framework:
— data architecture management (see B.4.2);
8 © ISO 2011 – All rights reserved

— data quality planning (see B.4.5);
— data stewardship and flow management (see B.4.8).
The data manager performs the role that directs a guideline for master data quality management in
compliance with objectives of an organization, manages factors that impact data quality at an
organization level, and establishes the plans for performing data quality activities in the organization.
Along with each major top-level process, the data manager maintains data consistency in individual
information systems through the organization-wide data architecture management, and analyzes
factors that affect data quality in data quality planning. In addition, the data manager takes a role of
granting data administrators authority to trace and correct data over the information systems or
organization.
EXAMPLE A data manager selects one out of multiple PIC (Product Identification Code) systems used in
industry. While a data administrator or a data technician operates the PIC system, a data manager oversees and
manages whether they use the PIC in alignment with objectives of data quality management. A position entitled
enterprise data architect (EDA) can be an example of this type of role.
B.3.3 Data administrator
The data administrator shall perform the following processes within the framework:
— data design (see B.4.3);
— data quality criteria setup (see B.4.6);
— data error cause analysis (see B.4.9).
The data administrator controls and coordinates over data technicians by defining criteria required to
maintain the quality of master data, and prevents a recurrence of the same data errors by analyzing the
causes of errors or designing data schema. In general, supporting resources and guidelines to data
technicians, the data administrator carries the data quality plan into practice to achieve the objectives
set by the data manager.
EXAMPLE A data administrator designs data schema for data technicians to ensure data quality (for example,
data consistency, standards, etc.). Additionally, the data administrator is dedicated to setting criteria to manage
the data quality in alignment with organizations’ objectives, and controlling data quality so that the measured
data reach the level of the objectives. Furthermore, the data administrator takes an important role that searches
for the root causes of data errors and removes them. In the example of PIC(Product Identification Code), if the
PICs are dispersed in many individual systems, the data administrator has to identify where the representative
master data is located, and re-design data schema to prevent the master data of PIC from redundancy. The
program steering committee or a database administrator can take this role.
B.3.4 Data technician
The data technician shall perform the following sub-processes within the framework:
— data processing (see B.4.4);
— data quality measurement (see B.4.7);
— data error correction (see B.4.10).
The data technician creates, reads, modifies, and deletes data as per guidelines of data quality
management set by the data administrator, and measures data quality and corrects erroneous data as a
result of the measurement. While the data manager or administrator can handle data even across its
own business scope in accordance with data flows, the data technician handles data within its business
scope.
EXAMPLE While data users actually input and use data, data operators periodically measure whether data in
use complies with business rules and correct data when errors are found. (Data technician may be called data
user or data operator depending upon its detailed role.) In the example of PIC (Product Identification Code),
data users apply PICs to their business directly, and data operators examine PICs with check digit checking rules
periodically by an inspection tool or manually.
B.4 The lower level processes in the framework
B.4.1 The nine lower level processes
The framework shall include nine lower level processes:
— data architecture management (see B.4.2);
— data design (see B.4.3);
— data processing (see B.4.4);
— data quality planning (see B.4.5);
— data quality criteria setup (see B.4.6);
— data quality measurement (see B.4.7);
— data stewardship and flow management (see B.4.8);
— data error cause analysis (see B.4.9);
— data error correction (see B.4.10).
In this section, each process in the framework is specified in terms of its necessity, activities required
at the least, responsibilities required to improve data quality from the role perspective, and
relationships among processes. Processes are described in order of top-level processes, data
operations, data quality monitoring and data quality improvement.
The functional model for the framework and a business scenario with examples are provided in
Annexes C and D, respectively.
B.4.2 Data architecture management
B.4.2.1 Overview of data architecture management
As data is distributed in the organization, data quality cannot be ensured without systematic
management. The process identifies the data commonly used throughout the organization and defines
data schema that secures data quality inside and outside the organization. In addition, identifying in
what type of schema the data is distributed throughout the whole information systems, the process can
prevent discrepancy among the same data distributed in different information systems. The process
can also manage the lifecycle of the data identified in the data architecture.
10 © ISO 2011 – All rights reserved

B.4.2.2 Constituent activities of data architecture management
Data architecture management shall consist of the following activities:
— Management of organization-wide conceptual data models: the activity that manages conceptual
data models represented by factors which need to be shared or managed at an organization level.
Data mapping or tracing can be done through the models.
— Management of organization-wide data standards: the activity that manages standards and
business rules that should be observed in the organization-wide data architecture management.
B.4.2.3 Required responsibilities of data manger for data architecture management
From the role perspective, a data manager in this process shall have the following responsibilities.
— Organization-wide coordination: the responsibility that seeks convergence on the goals and
plans for data quality management amongst responsible parties and assures conformance by
responsible parties. Since data quality heavily depends on data users in business units, this role
should have authority to control and coordinate responsible parties not only in technical units
but also in business units.
— Organization-wide sharing and maintenance: the responsibility that shares conceptual data
models and data standards to secure data quality, and maintain them consistently, performing
activities such as modification of data mapping whenever a data schema changes.
B.4.2.4 Relationship of data architecture management to other processes
Relationships to other processes:
— Between data architecture management and data quality planning: Based on organization-wide
data architecture, which shows the entire data composition of an organization, a plan for data
quality can be developed. Critical findings from the plan should be reflected to conceptual data
models and data standards.
— Between data architecture management and data stewardship/flow: Data stewardship is assigned
based on the organization-wide data architecture. In practice, when data stewardship and flow
changes in the process of data error resolution, the change should be reflected to the data
architecture management.
— Between data architecture management and data design: The data architecture management
provides the process of data design with information such as conceptual data models and data
standards.
B.4.3 Data design
B.4.3.1 Overview of data design
Data quality errors are separated into two categories, errors by user and errors by structure. Whereas
errors by user have limitations to be resolved systematically, errors by structure that mean data errors
caused by wrong data schema, can be resolved by data schema redesign. On the other hand, when data
is in service, correcting errors by structure is not easy. Hence, data quality should be considered from
the initial stage of data design. Especially, if data is designed for a specific application system only,
the quality of the data shared through the organization cannot be maintained. Therefore, data
relationship with other application systems should be considered at the organization level when data is
designed.
B.4.3.2 Constituent activities of data design
Data design shall consist of the following activities:
— Data design in consideration of quality: the activity that identifies data schema such as required
types of data and ranges in data values. In addition, this activity should reflect requirements of
data technicians sufficiently. This includes the specification of the configuration and use of data
structures in software packages where the physical data structure is already defined.
— Data design in connection with the organization-wide data architecture management: the activity
that implements data schema design and change to maintain the relationship with the
organization-wide data architecture management, when a database is built or the need for data
schema changes arise.
B.4.3.3 Required responsibilities of the data administrator for data design
From the role perspective, a data administrator in the process shall have the following responsibilities.
— An internal responsibility that reflects data quality requirements to data design through in-depth
consultation with users and responsible parties of application systems.
— An external responsibility that consults with responsible parties about the relationship of data
with other application systems.
B.4.3.4 Relationship of data design to other processes
Relationships to other processes:
— Between data design and data architecture management: Results of the process of data design
feed back to the process of data architecture management.
— Between data design and data processing: Data processing is performed based on data schema
created in the process of data design.
— Between data design and data quality criteria setup: Data quality criteria are defined based on
the data schema created in the process of data design.
B.4.4 Data processing
B.4.4.1 Overview of data processing
Carelessness and lack of understandi
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