research-article

Research and Application of Enterprise Big Data Governance

Authors:

Kuisheng WangAuthors Info & Claims

CSAE '18: Proceedings of the 2nd International Conference on Computer Science and Application Engineering

Article No.: 29, Pages 1 - 5

https://doi.org/10.1145/3207677.3278000

Published: 22 October 2018 Publication History

Abstract

With1 the further development of information technology, data has become one of the core resources of enterprises. In the current era of big data, data governance has gradually become an important means for enterprises to make intelligent decisions, helping enterprises to occupy a favorable position in a highly competitive market environment. This paper briefly describes the current situation of enterprise big data governance, and analyzes the problems of current data governance from the perspectives of enterprise management and data itself. To deal with these problems, this paper proposes several suggestions and strategies from the perspectives of organization and management system construction, construction of a data standard system, improve the level of data quality management, data technology surpport, etc. Fully combining the advanced theories and methods of domestic data governance, this paper designs a data governance framework from the perspective of data application and innovation. The framework includes supervision and control modules, data governance staff organization modules, application and service modules, and data processing and integration modules. And the framework is applied to the data governance of electric power enterprises, which has important reference significance and value for enterprises to carry out data governance.

References

[1]

Sunil Soares. 2014. Big Data Governance. Tsinghua University Press, Beijing.

[2]

Zhao Xingfeng. 2016. Enterprise Data Management Change Data Governance and Coordination Plan. Electronic Industry Press, Beijing.

[3]

Zhang Shaohua, Pan Rong, and Zong Yuwei. 2015. Big Data Governance and Services. Shanghai Science and Technology Press, Shanghai.

[4]

Walker R. 2015. From big data to big profits: Success with data and analytics. Oxford University Press, New York.

Digital Library

[5]

Liu Xiaoyin, Li Yu, and Cheng Guangming. 2017. Cloud Data Governance. Publishing House of Electronics Industry, Beijing.

[6]

Liu Chi, Hu Baiqing, and Xie Yi. 2017. Big Data Governance and Security from Theory to Implementation. China Machine press, Beijing.

[7]

Zhu Lin, Zhao Hanjing, Wang Yongkun, and Jin Yaohui. 2016. Global Data: A New Paradigm for Data Governance in the Era of Big Data. J. E-GOVERNMENT, 01, 34--42.

[8]

Li Ming, Zhang Yangyang, and Cai Zhenyu. 2016. Research on IT Governance Standards. J. Information Technology and Standardization, 03, 29--33.

[9]

Qin Jiongcong. 2016. Data Governance Model in Big Data Background. J. Information and Computers China Computer & Communication, 16, 155--156.

[10]

Su Bo, Chen Xiu, and Tang Chengli. 2012. Study on ERP Data Quality Assessment and Data Governance Method. J. Academic Research, 08, 140--144.

[11]

Zheng Daqing, Huang Lihua, Zhang Chenghong, and Zhang Shaohua. 2017. The concept of big data governance and its reference framework. J. Research and Development Management R&D MANAGEMENT, 29, 04, 65--72.

[12]

Su Yujuan. 2016. Big Data Technology and Data Management Innovation of High-tech Enterprises. J. Science and Technology Progress and Policy, 33, 06, 47--52.

[13]

Yang Li. 2015. Problems and solutions for enterprise information management in the era of big data. J. Internet Technology, 18, 22, 63.

[14]

Li Linyu. 2010. Problems and Solutions in Enterprise Information Management. J. Cooperative Economy and Technology, 18, 30--32.

[15]

Liu Shimin, Liu Yuyang, and Fan Rui. 2017. Historical Data Management Based on Big Data Technology. J. Technology Application, 07, 77--79.

[16]

Wang Yude. 2014. Research on Enterprise Big Data Governance. China Internet, 01, 20--24.

[17]

Yang Qingyun, Zhao Peiying, Yang Dongqing, Tang Shizhen, and Tong Yunhai. 2004. Research on Data Quality Assessment Method. Computer Engineering and Technology, 09, 3--4+15.

[18]

Malik P. 2013. Governing big data: Principles and practices. IBM Journal of Research and Development 57, 3/4, 1:13.

Digital Library

[19]

Otto B. 2011. Data governance. J. Business & Information Systems Engineering.

[20]

Data Governance Institute. 2009. The DGI data governance frame-work {R}. USA:DGI.

Cited By

Zhao X(2024)Design and Implementation of Campus Data Governance Platform Based on Big Data Algorithm2024 Asia-Pacific Conference on Software Engineering, Social Network Analysis and Intelligent Computing (SSAIC)10.1109/SSAIC61213.2024.00080(391-396)Online publication date: 10-Jan-2024
https://doi.org/10.1109/SSAIC61213.2024.00080
Alsaad A(2023)Governmental Data Governance Frameworks: A Systematic Literature Review2023 International Conference on Computing, Electronics & Communications Engineering (iCCECE)10.1109/iCCECE59400.2023.10238504(150-156)Online publication date: 14-Aug-2023
https://doi.org/10.1109/iCCECE59400.2023.10238504
Chandra YPrabowo HGaol FPurwandari B(2023)Control and Data Integrity are Important Factors of Data Governance Technology2023 10th International Conference on ICT for Smart Society (ICISS)10.1109/ICISS59129.2023.10291563(1-6)Online publication date: 6-Sep-2023
https://doi.org/10.1109/ICISS59129.2023.10291563

Index Terms

Research and Application of Enterprise Big Data Governance
1. Information systems
  1. Data management systems
    1. Information integration
      1. Deduplication

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cover image ACM Other conferences

CSAE '18: Proceedings of the 2nd International Conference on Computer Science and Application Engineering

October 2018

1083 pages

ISBN:9781450365123

DOI:10.1145/3207677

Conference Chair:
Ali Emrouznejad,
Program Chair:
Zhihong Qian

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 22 October 2018

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CSAE '18

CSAE '18: The 2nd International Conference on Computer Science and Application Engineering

October 22 - 24, 2018

Hohhot, China

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CSAE '18 Paper Acceptance Rate 189 of 383 submissions, 49%;

Overall Acceptance Rate 368 of 770 submissions, 48%

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Cited By

Zhao X(2024)Design and Implementation of Campus Data Governance Platform Based on Big Data Algorithm2024 Asia-Pacific Conference on Software Engineering, Social Network Analysis and Intelligent Computing (SSAIC)10.1109/SSAIC61213.2024.00080(391-396)Online publication date: 10-Jan-2024
https://doi.org/10.1109/SSAIC61213.2024.00080
Alsaad A(2023)Governmental Data Governance Frameworks: A Systematic Literature Review2023 International Conference on Computing, Electronics & Communications Engineering (iCCECE)10.1109/iCCECE59400.2023.10238504(150-156)Online publication date: 14-Aug-2023
https://doi.org/10.1109/iCCECE59400.2023.10238504
Chandra YPrabowo HGaol FPurwandari B(2023)Control and Data Integrity are Important Factors of Data Governance Technology2023 10th International Conference on ICT for Smart Society (ICISS)10.1109/ICISS59129.2023.10291563(1-6)Online publication date: 6-Sep-2023
https://doi.org/10.1109/ICISS59129.2023.10291563

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