GB/T 36073-2018 PDF in English
GB/T 36073-2018 (GB/T36073-2018, GBT 36073-2018, GBT36073-2018)
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Data management capability maturity assessment model
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GB/T 36073-2018: PDF in English (GBT 36073-2018) GB/T 36073-2018
GB
NATIONAL STANDARD OF THE
PEOPLE’S REPUBLIC OF CHINA
ICS 35.240.70
L 67
Data management capability maturity assessment
mode
ISSUED ON: MARCH 15, 2018
IMPLEMENTED ON: OCTOBER 01, 2018
Issued by: General Administration of Quality Supervision, Inspection and
Quarantine;
Standardization Administration of the People's Republic of
China.
Table of Contents
Foreword ... 4
1 Scope ... 5
2 Normative references ... 5
3 Terms and definitions ... 5
4 Abbreviations ... 7
5 Summary... 7
5.1 Capability area and capability item ... 7
5.2 Maturity assessment level ... 8
6 Data strategy ... 11
6.1 Data strategy planning ... 11
6.2 Data strategy implementation ... 13
6.3 Data strategy assessment ... 15
7 Data governance ... 18
7.1 Data governance organization ... 18
7.2 Data system construction ... 20
7.3 Data governance communication ... 23
8 Data architecture ... 25
8.1 Data model ... 25
8.2 Data distribution ... 28
8.3 Data integration and sharing ... 31
8.4 Metadata management ... 33
9 Data application ... 35
9.1 Data analysis ... 35
9.2 Data opening and sharing ... 38
9.3 Data service ... 40
10 Data security ... 42
10.1 Data security strategy... 42
10.2 Data security management ... 44
10.3 Data security audit... 46
11 Data quality ... 49
11.1 Data quality requirements ... 49
11.2 Data quality check ... 51
11.3 Data quality analysis ... 53
11.4 Data quality improvement ... 55
12 Data standards ... 57
12.1 Business term... 57
12.2 Reference data and master data ... 59
12.3 Data element ... 61
12.4 Indicator data ... 64
13 Data lifecycle ... 66
13.1 Data needs ... 66
13.2 Data design and development... 69
13.3 Data operation and maintenance ... 71
13.4 Data retirement... 73
Bibliography ... 77
Data management capability maturity assessment
mode
1 Scope
This Standard provides data management capability maturity assessment
mode as well as the corresponding maturity level. It defines 8 capability areas:
data strategy, data governance, data architecture, data application, data
security, data quality, data standards and data life cycle.
This Standard is applicable to the assessment on data management capability
maturity by organizations and institutions.
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.
GB/T 35295, Information technology - Big data - Term
3 Terms and definitions
For the purposes of this document, the terms and definitions defined in GB/T
35295 as well as the followings apply.
3.1 data management capability
the ability of an organizations and institution to manage and apply data
3.2 data management capability maturity assessment model
a model used to assess the maturity of an organization's data management
capability
3.3 capability area
a collection of data management related activities, processes and a collection
of related data capability sub-domains
3.12 master data
core business entity data that needs to be shared across systems and
departments in an organization
3.13 reference data
the data to classify and standardize other data
3.14 data lifecycle
a set of processes that transform raw data into knowledge that can be used for
action
[GB/T 35295-2017, definition 2.1.2]
3.15 data element
a data unit whose definition, identification, representation and allowable value
are specified by a set of attributes
[GB/T 18391.1-2009, definition 3.3.8]
4 Abbreviations
The following abbreviations apply to this Standard.
DCMM: Data management Capability Maturity assessment Model
ETL: Extraction-Transformation-Loading
KPI: Key Performance Indicator
SOR: System of Record
TCO: Total Cost of Ownership
5 Summary
5.1 Capability area and capability item
DCMM contains 8 data management capability areas. Each capability area
includes several capability items in the data management domain, 29 in total.
See Table 1 for capability areas and capability items.
c) Each business system manages its own data. There are inconsistencies
in data between various business systems. The organization does not
realize the importance of data management or data quality;
d) Data management is only carried out according to the cycle of project
implementation. It is unable to calculate the cost of data maintenance and
management.
5.2.2 Managed level
The organization has realized that data is an asset. According to the
requirements of the management strategy, it has developed the management
process and designated relevant personnel for preliminary management. The
specific features are as follows:
a) Be aware of the importance of data. Develop some data management
specifications. Set up relevant positions;
b) Realize that data quality and data silos are an important management
issue. But there is currently no solution to the problem;
c) The organization has carried out preliminary data integration work, tried to
integrate data from various business systems and designed relevant data
models and management positions;
d) Start the documentation of some important data. Design relevant
management measures for the safety and risk of important data.
5.2.3 Stable level
Data has been regarded as an important asset to achieve organizational
performance goals. It has developed a series of standardized management
processes at the organizational level to promote the standardization of data
management. The specific features are as follows:
a) Realize the value of data. It has established data management rules and
systems within the organization;
b) Data management and application can be combined with the
organization's business strategy, business management requirements
and external supervision requirements;
c) It has established relevant data management organization and
management process, which can promote all departments in the
organization to carry out work according to the process;
d) The organization can obtain data support in the daily decision-making and
business development process. It has significantly improved work
6 Data strategy
6.1 Data strategy planning
6.1.1 Overview
Data strategy planning is the result of consensus among all stakeholders. It
determines the motivation for data management and application from the macro
and micro levels. It comprehensively reflects the needs of data providers and
consumers.
6.1.2 Process description
The process is described as follows:
a) Identify stakeholders. Identify the needs of stakeholders;
b) Data strategy needs assessment: Organize to assess the status quo of
business and information. Understand the needs of business and
informatization for data;
c) Data strategy formulation, including but not limited to:
1) Vision statement, which contains data management principles, goals
and objectives;
2) Planning scope, which includes important business areas, data scope
and data management priorities;
3) Selected data management model and construction method;
4) Main gaps in current data management;
5) Management officials and responsibilities, as well as a list of
stakeholders;
6) Preparation of management methods for data management plans;
7) Continuous optimization of roadmap.
d) Data strategy release: formally release the approved data strategy through
documents, websites, emails and so on;
e) Data strategy revision: Regularly revise the data strategy according to the
requirements of business strategy, information development and so on.
6.1.3 Process goals
6) Regularly revise the published data strategy.
d) Level 4: quantitative management level
1) Perform quantitative analysis and timely optimization of the
management process of the organization's data strategy;
2) Be able to quantitatively analyze the implementation of the data
strategy roadmap. Continuously optimize data strategy.
e) Level 5: optimization level
1) Data strategy can effectively enhance the competitiveness of
enterprises;
2) Share the best practices in the industry and become industry
benchmarks.
6.2 Data strategy implementation
6.2.1 Overview
It is a process that organizes and completes data strategy planning and
gradually realizes the data functional framework. Assess the current status of
the organization's data management and data application during
implementation. Determine the gap between vision and goals. Develop periodic
data mission goals based on the data function framework. Determine the
implementation steps.
6.2.2 Process description
The process is described as follows:
a) Assessment criteria: Establish assessment standards for the
implementation of data strategy planning. Standardize the assessment
process and methods;
b) Current situation assessment: Analyze the implementation of the
organization's current data strategy. Assess the progress of various tasks;
c) Gap assessment: Compare the results of the status quo assessment with
the organization's data strategy plan. Analyze the differences;
d) Implementation path: Stakeholders prioritize data function tasks based on
the organization’s common goals and actual business value;
e) Guarantee plan: According to the implementation path, formulate the
budget required to carry out various activities;
2) Perform comprehensive assessment of the actual situation within the
organization. Determine the gap between various data functions and
vision, goals;
3) Develop a work report template of data strategy promotion. Release it
regularly, so as to enable stakeholders to understand the
implementation of the data strategy and existing problems;
4) Based on the organizational business strategy, use business value-
driven methods to assess the priority of data management and data
application. Develop an implementation plan. Provide guarantees in
terms of resources, funds and so on;
5) Track and assess the implementation of various data tasks. Adjust and
update the implementation plan based on work progress.
d) Level 4: quantitative management level
1) Quantitative analysis can be used to analyze the progress of the data
strategy;
2) Accumulate a large amount of data to improve the accuracy of data task
schedule planning;
3) The arrangement of data management tasks can meet the needs of
business development in time. It has established a standardized
prioritization method.
e) Level 5: optimization level
Share the best practices in the industry and become industry benchmarks.
6.3 Data strategy assessment
6.3.1 Overview
Corresponding business cases and investment models shall be established in
the process of data strategy assessment. Track the progress throughout the
implementation of the data strategy. At the same time, keep records for audit
and assessment.
6.3.2 Process description
The process is described as follows:
a) Establish a task benefit assessment model. Establish a benefit
assessment model for tasks related to data strategy in terms of time, cost,
benefit and so on;
b) Level 2: managed level
1) Within a single department or data functional area, it has established a
business case and task benefit evaluation model based on business
needs;
2) Within a single department or data functional area, establish a standard
decision process for business cases. It has clarified the responsibilities
of stakeholders;
3) Within a single department or data functional area, stakeholders
participate in the formulation of investment models for data
management and data application projects;
4) Within a single department or data functional area, the related data
tasks are assessed according to the task benefit assessment model.
c) Level 3: stable level
1) Within the organization scope, establish relevant business cases for
data management and application according to standard workflows and
methods;
2) Within the organization scope, it has developed a data task benefit
evaluation model and related management methods;
3) Within the organization scope, the formulation of business cases can
obtain the support and participation of senior managers and business
departments;
4) Within the organization scope, guide the implementation priority
arrangement of data function projects through cost-benefit criteria;
5) Within the organization scope, assess and manage data strategy
implementation tasks through the task benefit evaluation model.
Include them in the scope of the audit.
d) Level 4: quantitative management level
1) Build a dedicated data management and data application TCO method.
Measure and assess the changes in data management implementation
entry points and basic implementation. Adjust the funding budget;
2) Use statistical methods or other quantitative methods to analyze the
cost evaluation criteria of data management;
3) Use statistical methods or other quantitative methods to analyze the
effectiveness and accuracy of fund budgets to meet organizational
a) Establish a complete organizational structure and corresponding workflow
mechanism;
b) Data management clarifies the jurisdiction management and set up
enough full-time and part-time positions. Continue to promote team
building;
c) Establish a performance evaluation system that supports data
management and data application strategies.
7.1.4 Capability level standards
The capability level standards are as follows:
a) Level 1: initial level
1) Reflect the positions, roles and responsibilities of data management
and data application in specific projects;
2) Solve data problems based on personal ability. A professional
organization has not been established.
b) Level 2: managed level
1) It has developed data-related training plans, but they are not formed as
a system;
2) In a single data functional area or business unit, set up data governance
part-time or full-time positions. Job responsibilities are clear;
3) The importance of data governance is recognized by management
officials;
4) Clarify the management responsibilities of data governance positions
in new projects.
c) Level 3: stable level
1) Management officials are responsible for decision-making related to
data governance and participate in data management related work;
2) The data governance and jurisdiction department clarified within the
organization scope is responsible for organizing and coordinating
various data functions;
3) The job responsibilities of data governance personnel are clear, which
can be reflected in the job description;
4) It has established evaluation standards for data management. It has
divided into three levels: policies, methods, and rules. The framework
stipulates the specific areas of data management and data application,
the goals in each data functional area, the action principles to be followed,
the clear tasks to be completed, the work methods to be implemented, the
general steps and specific measures;
b) Organize the content of the data system. Data management policies, data
management methods, and data management rules together constitute
an organizational data system. The basic content is as follows:
1) The data policy describes the purpose of data management and data
application. Clarify its organization and scope;
2) Data management methods are related rules and procedures for the
development of activities in various fields of data management and data
application;
3) Data management rules are related documents formulated to ensure
the implementation of various data methods;
c) Data system release: The internal organization publishes the approved
data system through documents, emails and so on;
d) Data system promotion: Regularly carry out training and publicity work
related to the data system;
e) Data system implementation: Combining with the settings of the data
governance organization, promote the implementation of the data system.
7.2.3 Process goals
The process goals are as follows:
a) Establish a data system. Release it after extensive consultation within the
organization;
b) Establish a systematic management process to carry out system
inspection, update, release and promotion.
7.2.4 Capability level standards
The capability level standards are as follows:
a) Level 1: initial level
1) Establish data-related specifications or rules for each project;
2) The realization and implementation of the data management system
are determined by the project personnel themselves.
the data system management process.
e) Level 5: optimization level
Share the best practices in the industry and become industry benchmarks.
7.3 Data governance communication
7.3.1 Overview
Data governance communication aims to ensure that all stakeholders in the
organization can keep abreast of relevant policies, standards, processes, roles,
responsibilities, and plans; carry out data management and application related
training; master the knowledge and skills related to data management. Data
governance communication aims to establish and improve cross-departmental
and internal data management capabilities, raise awareness of data assets and
build a data culture.
7.3.2 Process description
The process is described as follows:
a) Communication path: Identify stakeholders for data management and
application. Analyze the needs of all parties. Understand the key points of
communication;
b) Communication plan: Establish a regular or irregular communication plan.
Reach consensus among stakeholders;
c) Communication execution: Arrange and implement specific
communication activities in accordance with the communication plan. At
the same time, record the communication;
d) Problem negotiation mechanism: Introduce senior managers and other
methods to solve the differences;
e) Establish communication channels. Clarify the main channels of
communication within the organization, such as emails, documents,
websites, self-media, seminars;
f) Develop a training promotion plan. According to the needs of the
organization's personnel and business development, develop relevant
training and publicity plans;
g) Training: according to the requirements of the training plan, regularly carry
out relevant training.
7.3.3 Process goals
3) Clarify the internal communication and publicity methods of the
organization. Regularly publish the development situation inside and
outside the organization;
4) Regularly carry out data-related training. Improve the capabilities of
personnel;
5) Communicate the relevant policies, methods and norms of data
management within the organization. Cover most data management
and data application related departments. Update based on the
feedbacks;
6) Clarify the content composition of the comprehensive report on data
work. Regularly release the organization's comprehensive report on
data work.
d) Level 4: quantitative management level
1) Establish a communication mechanism with external organizations.
Expand the scope of communication;
2) Collect and sort out relevant cases of internal and external data
management in the industry, including the best practices, experience
summaries. Publish them regularly;
3) Organize personnel to understand the business value of data
management and application. All employees agree that data is an
important asset of the organization.
e) Level 5: optimization level
1) Through data governance communication, establish a good corporate
data culture. Facilitate the internal and external application of data;
2) Share the best practices in the industry and become industry
benchmarks.
8 Data architecture
8.1 Data model
8.1.1 Overview
The data model is to use a structured language to comprehensively analyze the
collected data requirements used in the organization's business operations,
management and decision-making. Reorganize requirements according to
model design specifications.
a) Establish and maintain organization-level data models and system
application-level data models;
b) Establish a set of development specifications for the organization to follow
the data model design;
c) Use organization-level data models to guide the construction of application
systems.
8.1.4 Capability level standards
The capability level standards are as follows:
a) Level 1: initial level
1) At the application system level, the specifications for data model
development and management have been compiled;
2) Guide application system data structure design according to relevant
specifications.
b) Level 2: managed level
1) Based on organizational management requirements, it has formulated
a data model management specification;
2) Sort out the data status of some application systems in the organization.
Understand current problems;
3) According to the data status quo, based on the needs of the
organization's business development, an organization-level data model
is established;
4) The construction of the application system refers to the organization-
level data model.
c) Level 3: stable level
1) Comprehensively sort out the data status of the application system in
the organization. Understand current problems and propose solutions;
2) Analyze the existing data model reference architecture in the industry;
Learn related methods and experiences;
3) Compile organization-level data model development specifications.
Guide the development and management of organization-level data
models;
4) Understand organizational strategy and business development
8.2.2 Process description
The process is described as follows:
a) Sort out data status. Sort out the data in the application system.
Understand the role of data. Identify existing data problems;
b) Identify the data type. Sort and manage the data in the organization
according to its characteristics. General types include but are not limited
to master data, reference data, transaction data, statistical analysis data,
document data, metadata;
c) Sort out data distribution relationship. According to the definition of the
organization-level data model, based on the results of business process
combing, define the distribution relationship between data and process,
data and organization, data and system in the organization;
d) Sort out the authoritative source of data. For each type of data, clarify the
relatively reasonable unique information collection and storage system;
e) Application of data distribution relationship: According to the combing of
data distribution relationship, standardize the work related to
organizational data, including defining data work priorities and optimizing
data integration;
f) Maintenance and management of data distribution relationship: According
to the business process and system construction in the organization,
regularly maintain and update the data distribution relationship in the
organization. Keep timeliness.
8.2.3 Process goals
The process goals are as follows:
a) Establish a classification management mechanism for the organization's
data assets. Determine the authoritative data source of the data;
b) Sort out the relationship between data and business processes,
organizations, and systems;
c) Standardize the construction of data-related work.
8.2.4 Capability level standards
The capability level standards are as follows:
a) Level 1: initial level
Manage part of the data distribution relationship in the project, such as the
......
Source: Above contents are excerpted from the PDF -- translated/reviewed by: www.chinesestandard.net / Wayne Zheng et al.
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