Proceedings of the 2nd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2023, May 19–21, 2023, Hangzhou, China

Research Article

Application of Data Governance in Logistics Industry

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  • @INPROCEEDINGS{10.4108/eai.19-5-2023.2334267,
        author={Shuguang  Chen and Zhifeng  Mao},
        title={Application of Data Governance in Logistics Industry},
        proceedings={Proceedings of the 2nd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2023, May 19--21, 2023, Hangzhou, China},
        publisher={EAI},
        proceedings_a={ICBBEM},
        year={2023},
        month={7},
        keywords={data governance big data logistics industry network platform logistics information},
        doi={10.4108/eai.19-5-2023.2334267}
    }
    
  • Shuguang Chen
    Zhifeng Mao
    Year: 2023
    Application of Data Governance in Logistics Industry
    ICBBEM
    EAI
    DOI: 10.4108/eai.19-5-2023.2334267
Shuguang Chen1, Zhifeng Mao1,*
  • 1: Liuzhou City Vocational College
*Contact email: 1225778151@qq.com

Abstract

The rapid development of big data in various industries has attracted the attention of the government. The requirements of strengthening big data governance capabilities and resource status are reflected in the 13 th Five-Year Plan. From warehousing to transportation, the logistics industry has established a large number of business and management systems. While meeting consumer demand, it has also accumulated massive data. However, data governance has not made essential progress for a long time, resulting in these data not supporting the management of enterprises. The main problems are unclear governance ideas, lack of governance systems, and poor data quality. In the future, logistics enterprises should grasp the general trend of the industry, improve the quality of service in an all-round way, and make improvements from the aspects of defining data governance strategy, constructing perfect data governance organization, perfecting data service processing process and ensuring data quality