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QIN Zhi-mei, ZHANG Yao-yao, LI Wei-wei, YANG Yi-fan, WANG Bin, LI Yu-chen. Data Quality Management in Smart Governance: Core Elements, Mechanism Construction, and Support Guarantee[J]. Journal of University of Electronic Science and Technology of China(SOCIAL SCIENCES EDITION), 2024, 26(4): 45-56. DOI: 10.14071/j.1008-8105(2024)-5003
Citation: QIN Zhi-mei, ZHANG Yao-yao, LI Wei-wei, YANG Yi-fan, WANG Bin, LI Yu-chen. Data Quality Management in Smart Governance: Core Elements, Mechanism Construction, and Support Guarantee[J]. Journal of University of Electronic Science and Technology of China(SOCIAL SCIENCES EDITION), 2024, 26(4): 45-56. DOI: 10.14071/j.1008-8105(2024)-5003

Data Quality Management in Smart Governance: Core Elements, Mechanism Construction, and Support Guarantee

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  • Received Date: December 24, 2023
  • Available Online: May 05, 2024
  • This paper is to explore the path of data quality management in smart governance, promote the improvement of data quality, and highlight the effectiveness of smart governance. It builds an analytical framework for data quality management in smart governance from three dimensions: core elements, mechanism construction, and support guarantee,with infectious disease monitoring and early warning management as a case study. Data quality is one of the important factors affecting the effectiveness of smart governance. The source and stakeholders of data, as well as the data flow process, are the core elements of data quality management in smart governance. Classic methods for quality improvement and process improvement can be effectively applied to smart governance. Policy guarantees, technological tools, and mechanism iterations are important guarantees for the long-term implementation of data quality management. The phased nature of smart governance and the demand for governance effectiveness determine the requirements for data quality management. Data quality management can achieve the improvement of smart governance efficiency, high-quality development of the data element market, and further promote the practical implementation of data quality management concepts and methods in all aspects of the data cycle.

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