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GB/T 44109-2024: Information technology - Big data - Guidelines of data governance implementation
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Information technology - Big data - Guidelines of data governance implementation
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GB/T 44109-2024
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Basic data | Standard ID | GB/T 44109-2024 (GB/T44109-2024) | | Description (Translated English) | Information technology - Big data - Guidelines of data governance implementation | | Sector / Industry | National Standard (Recommended) | | Classification of Chinese Standard | L70 | | Word Count Estimation | 22,257 | | Date of Issue | 2024-05-28 | | Date of Implementation | 2024-12-01 | | Issuing agency(ies) | State Administration for Market Regulation, China National Standardization Administration | GB/T 44109-2024: Information technology - Big data - Guidelines of data governance implementation---This is a DRAFT version for illustration, not a final translation. Full copy of true-PDF in English version (including equations, symbols, images, flow-chart, tables, and figures etc.) will be manually/carefully translated upon your order.
ICS 35.240.01
CCSL70
National Standards of People's Republic of China
Information Technology Big Data Data Governance Implementation Guide
Released on 2024-05-28
2024-12-01 implementation
State Administration for Market Regulation
The National Standardization Administration issued
Table of Contents
Preface III
Introduction IV
1 Scope 1
2 Normative references 1
3 Terms and Definitions 1
4 Data Governance Implementation Process 2
5 Planning 3
5.1 Current situation investigation and analysis 3
5.2 Establishing Goals 3
5.3 Clarify governance content 3
5.4 Determine the implementation route 3
6 Execution 4
6.1 Overview 4
6.2 Establishing a governance organization 4
6.3 Formulate system specifications 4
6.4 Conduct governance activities 4
6.4.1 Data Architecture Design 4
6.4.2 Metadata Management 5
6.4.3 Data Standard Management 5
6.4.4 Data Quality Management 6
6.4.5 Master Data Management 6
6.4.6 Data Application 7
6.4.7 Data security management 7
6.4.8 Data life cycle management 8
7 reviews 8
7.1 Clarify the evaluation objectives and scope 8
7.2 Design indicator system 8
7.3 Evaluating governance performance 8
8 Improvements 9
8.1 Performing differential analysis 9
8.2 Develop improvement plans 9
8.3 Execute improvement activities 9
Appendix A (Informative) Data Governance Organizational Structure 11
A.1 Data Governance Organization Establishment Model11
A.2 Data Governance Organizational Hierarchy11
A.3 Data Governance Organization Responsibilities Reference 11
Appendix B (Informative) Data Governance System and Management Specifications 13
B.1 Overview 13
B.2 Data governance organizational management system13
B.3 Data architecture related systems and management specifications 13
B.4 Metadata-related systems and management specifications 13
B.5 Data standard related systems and management specifications 13
B.6 Data quality related systems and management standards 13
B.7 Master data related systems and management specifications 13
B.8 Data application related systems and management specifications 14
B.9 Data security related systems and management specifications 14
B.10 Data life cycle related systems and management specifications 14
B.11 Data governance assessment and evaluation management system 14
Appendix C (Informative) Data Governance Evaluation Index System 15
References 16
Foreword
This document is in accordance with the provisions of GB/T 1.1-2020 "Guidelines for standardization work Part 1.Structure and drafting rules for standardization documents"
Drafting.
Please note that some of the contents of this document may involve patents. The issuing organization of this document does not assume the responsibility for identifying patents.
This document was proposed and coordinated by the National Technical Committee for Information Technology Standardization (SAC/TC28).
This document was drafted by. China Electronics Technology Standardization Institute, Shanghai Computer Software Technology Development Center, Beijing Yixin Huachen Software
Co., Ltd., Merrill Lynch Data Technology Co., Ltd., Founder International Software (Beijing) Co., Ltd., Beijing Sunway World Technology Co., Ltd.
Ltd., Beijing Huayu Information Technology Co., Ltd., Puyuan Information Technology Co., Ltd., Guizhou Tuzhi Information Technology Co., Ltd., Alibaba Cloud
Computing Co., Ltd., Inspur Software Technology Co., Ltd., Chengdu Sifang Weiye Software Co., Ltd., China Petrochemical Corporation,
Shanghai SoftChina Digital Technology Co., Ltd., Shanghai Yixun Information Technology Co., Ltd., Guoneng Information Technology Co., Ltd., Guangdong Power Grid Co., Ltd.
Liability Company, Anhui Railway Investment Co., Ltd., Ningxia Jiaotong Technology Development Co., Ltd., Sichuan Development Digital Jinsha Technology Co., Ltd.
Ltd., Shandong Runyi Intelligent Technology Co., Ltd., Beijing Ehualu Information Technology Co., Ltd., Guangzhou CESI Certification Center Service Co., Ltd.,
China Electronics Technology Group Corporation Big Data Research Institute Co., Ltd., Shanghai Guanan Information Technology Co., Ltd., Northwestern Polytechnical University, Chengdu Jiuzhou Electronic Information Department
Shenzhen Feisu Innovation Technology Co., Ltd., Sanya Hailan Huanyu Ocean Information Technology Co., Ltd., Nanjing Weishu
Software Co., Ltd., Chengdu Shuzhilian Technology Co., Ltd., Guangzhou Fanghe Data Co., Ltd., Jinan University, Beijing Quanlu Communication Information Co., Ltd.
No. 1 Research and Design Institute Group Co., Ltd., Beijing Ditan Hospital Affiliated to Capital Medical University, Hangzhou DreamWorks Technology Co., Ltd., Yunshang Guizhou
Big Data Industry Development Co., Ltd., Zhejiang Digital Economy Development Center, Shandong Diwei Software Co., Ltd., China Unicom
Co., Ltd. Guangdong Branch, China Rongtong Group Information Technology Co., Ltd., Isa Technology Co., Ltd., Aerospace Network Security Technology
(Shenzhen) Co., Ltd., China Southern Power Grid Energy Storage Co., Ltd., Fujian Fuqing Nuclear Power Co., Ltd., Digital Chongqing Big Data Application Development Co., Ltd.
Company, China Southern Power Grid Co., Ltd.
The main drafters of this document are. Zhang Qun, Dai Bingrong, Li Bing, Gao Hongmei, Zi Jinjin, Li Xiaoyan, Zhang Yu, Wang Zhaojun, Jiang Nan, Shi Yuliang, Ying Zhihong,
Liu Qinghui, Xie Jiang, Zhu Song, Liu Chenyu, Wang Jinchao, Li Baodong, Liu Ying, Wang Lan, Bi Shanshan, Wang Hongmei, Yang Lin, Song Jundian, Yu Yongsheng, Zhang Yansheng,
Li Shuchao, Kang Lili, Liu Fu, Liu Junliang, Liu Yufeng, Chen Bin, Yang Qiuyong, Li Jun, Jiang Xiangjun, Yuan Lei, Liu Yanhua, Chen Yingqi, Cao Yiwei, Hao Jianming,
Liu Lida, Kuang Zhiguang, Lü Jiangnan, Tao Ping, Wang Ling, Shouyang, Jin Ronghua, Li Bing, Pang Hui, Huang Haifeng, Yu Kai, Sun Lijuan, Xiao Rang, Zhang Yanning,
Wang Peng, Liu Ting, Liu Chuanjie, Xiang Wei, Shi Guihua, Hu Peng, Fu Yan, Zhou Junlin, Tong Yao, Zhang Haotian, Weng Jian, Xia Zhihua, Lin Pengcheng, Guo Ning, Huang Mingfeng,
Liu Jun, Yang Yuanyuan, Tian Yanxiang, Huang Yong, Lü Liang, Li Yonglu, E Mei, Yan Yuping, Huang Dehui, Li Zhiyu, Li Rui, Xiao Zheng, Yao Wei, Yang Xu, Zhang Liming,
Wang Yujing, Wang Chengliang, Wang Wei, Liu Zhen, Yang Weiwei, and Guo Linyuan.
Introduction
At present, the main data governance standards are.
---GB/T 34960.5-2018 Information technology service governance Part 5.Data governance specification;
---GB/T 36073-2018 Data management capability maturity assessment model.
As one of the data governance standards, this document mainly draws on GB/T 34960.5-2018 for data governance implementation during its development.
The data governance process in the data governance process includes four processes. planning, execution, evaluation and improvement, and further clarifies the implementation activities and content of each process.
The governance activities carried out during the implementation process mainly draw on the capability domains in GB/T 36073-2018, further clarifying the various
The implementation process and content of capability domains.
The correspondence between the data governance implementation process is shown in Table 1.
Table 1 Data governance implementation process correspondence table
GB/T 34960.5-2018 GB/T 36073-2018 This document
Coordination and planning
Build and Run
Data Strategy
Data Governance
Data Architecture
Data Standards
Data quality
Data Application
Data Security
Data life cycle
Current situation investigation and analysis
confirm target
Clarify governance content
Determine the implementation route
Establishing a governance organization
Formulate institutional norms
Conducting governance activities. Data architecture design
Conducting governance activities. Metadata management
Conduct governance activities. Data standards management
Conduct governance activities. Data quality management
Conduct governance activities. Master data management
Conduct governance activities. Data application
Conduct governance activities. Data security management
Conduct governance activities. Data life cycle management
planning
implement
Monitoring and evaluation
- Clarify the evaluation objectives and scope
- Design indicator system
- Evaluate governance performance
evaluate
Improvements and Optimizations
- Perform differential analysis
- Develop improvement plans
- Execute improvement activities
Improve
Information Technology Big Data Data Governance Implementation Guide
1 Scope
This document provides a process guide for implementing data governance in a big data environment, including four processes. planning, execution, evaluation, and improvement.
Related activities and content.
This document is intended to guide organizations in implementing data governance.
2 Normative references
The contents of the following documents constitute the essential clauses of this document through normative references in this document.
For referenced documents without a date, only the version corresponding to that date applies to this document; for referenced documents without a date, the latest version (including all amendments) applies to
This document.
GB/T 35295-2017 Information Technology Big Data Terminology
3 Terms and definitions
The terms and definitions defined in GB/T 35295-2017 and the following apply to this document.
3.1
Data Governance
The set of activities (planning, monitoring and enforcement) that exercise authority and control over the management of data resources.
3.2
Data Management
A collection of activities such as data resource acquisition, control, and value enhancement.
[Source. GB/T 34960.5-2018, 3.2]
3.3
metadata
Data about data or data elements (which may include their data descriptions), and information about data ownership, access paths, access rights and data
According to the volatile data.
[Source. GB/T 34960.5-2018, 3.6]
3.4
metamodel
A data model that specifies one or more other data models.
[Source. GB/T 36073-2018, 3.9]
3.5
Data life cycle data lifecycle
The process of evolution of various forms of data acquisition, storage, integration, analysis, application, presentation, archiving and destruction.
[Source. GB/T 34960.5-2018, 3.7]
3.6
Data quality
The degree to which the characteristics of data meet stated and implied requirements when used under specified conditions.
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