GB/T 36073-2018 English PDF (GBT36073-2018)

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GB/T 36073-2018: Data management capability maturity assessment model
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
sta...