Research on the construction of automobile big data governance system based on data 5W2H analysis model

Sisi Chu; Jian Ma

doi:10.1117/12.3033073

12 July 2024 Research on the construction of automobile big data governance system based on data 5W2H analysis model

Sisi Chu, Jian Ma

Proceedings Volume 13185, International Conference on Communication, Information, and Digital Technologies (CIDT2024) ; 1318507 (2024) https://doi.org/10.1117/12.3033073
Event: International Conference on Communication, Information and Digital Technologies, 2024, Wuhan, China

Abstract

With the deep integration of big data in the automotive industry, and the increasingly prominent position of big data as a strategic resource, the value of data as a key element becomes more and more important, data circulation, data quality, data security and other issues are attracting more and more attention. For automobile enterprises, with the rapid growth of massive, multi-source and heterogeneous automobile big data, how to promote the efficient use of data and promote the digital transformation of enterprises through data governance is the current research focus. This paper uses "data5W2H analysis model" to summarize and analyze how automobile enterprises carry out systematic data governance, and proposes the construction of automobile big data governance system from four aspects of data governance activities, organizational structure, institutional process and platform building. Based on the comprehensive inventory of the status quo of data assets, unified data standards and norms are adopted to improve the quality of data, ensure data security, make the data from disorderly to standardized and orderly.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Sisi Chu and Jian Ma "Research on the construction of automobile big data governance system based on data 5W2H analysis model", Proc. SPIE 13185, International Conference on Communication, Information, and Digital Technologies (CIDT2024) , 1318507 (12 July 2024); https://doi.org/10.1117/12.3033073

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