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吕先竞. “5WHR”数据治理概念框架及其应用—针对重大突发公共卫生事件[J]. 西华大学学报(哲学社会科学版),2020,39(5):54 − 66 . doi: 10.12189/j.issn.1672-8505.2020.05.007
引用本文: 吕先竞. “5WHR”数据治理概念框架及其应用—针对重大突发公共卫生事件[J]. 西华大学学报(哲学社会科学版),2020,39(5):54 − 66 . doi: 10.12189/j.issn.1672-8505.2020.05.007
LYU Xian-jing. “5WHR” Data Governance Conceptual Framework and Its Applications in Major Public Health Emergencies[J]. Journal of Xihua University (Philosophy & Social Sciences) , 2020, 39(5): 54-66. DOI: 10.12189/j.issn.1672-8505.2020.05.007
Citation: LYU Xian-jing. “5WHR” Data Governance Conceptual Framework and Its Applications in Major Public Health Emergencies[J]. Journal of Xihua University (Philosophy & Social Sciences) , 2020, 39(5): 54-66. DOI: 10.12189/j.issn.1672-8505.2020.05.007

“5WHR”数据治理概念框架及其应用针对重大突发公共卫生事件

“5WHR” Data Governance Conceptual Framework and Its Applications in Major Public Health Emergencies

  • 摘要: 应对重大突发公共卫生事件离不开数据治理。针对重大突发公共卫生事件有效地进行数据治理,需要有一个概念框架来反映数据治理的内涵特征及外延全貌。文章参考借鉴国内外相关成果,构建了针对重大突发公共卫生事件的“5WHR”数据治理概念框架,该框架由Who—组织机构、What—数据、Where—域、How—机制、Why—影响因素、When—时间及Results—结果七个部分组成。在此基础上,文章还从分析数据治理影响因素、选择数据治理时间、确定参与数据治理的组织机构、明确需要重点治理的数据、提出数据治理的重点问题、建立良好的数据治理机制、评价数据治理效果七个方面,探讨了数据治理概念框架的应用,并提出了当前数据治理亟待探索及解决的问题。文章为开展相关数据治理研究及实践提供了理论支撑及应用示例,有助于学界及业界全面把握当前数据治理亟待研究的议题,提升数据治理能力。

     

    Abstract: Dealing with major public health emergencies requires a conceptual framework to reflect the connotation and extension of data governance. This paper constructs a conceptual framework of "5WHR" data governance for major public health emergencies with reference to relevant achievements at home and abroad. The framework is composed of seven parts: organization (Who), data (What), domain (Where), mechanism (How), influencing factors (Why), time (When) and conclusion (Results). On this basis, the paper firstly discusses the application of the conceptual framework of data governance from seven aspects: analyzing the influencing factors of data governance, confirming the time of data governance, determining the organizations involved in data governance, making clear the data that need to be focused on governance, putting forward the key issues of data governance, establishing a good data governance mechanism and evaluating the effect of data governance, and then puts forward the problems that need to be explored and solved urgently in current data governance. The achievements of this study provide theoretical support and application examples for carrying out relevant data governance research and practice, and help the academic circles and the industry to fully grasp the issues that need to be studied in current data governance, and improve data governance ability.

     

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