公共数据要素价值的深度挖掘与释放——基于DCU模型框架

Li Qingqing,Shao Xiangdong(School of Management,Shenyang Normal University Shenyang 110034,China)

在数字化时代浪潮下,数据已逐步跃升为关键生产要素,其内在价值正受到前所未有的关注与挖掘。特别是公共数据领域,其潜在价值尤为显著,涵盖经济价值与社会效益。然而,当前的管理与制度框架尚无法充分释放公共数据要素的全面价值。鉴于此,聚焦公共数据要素的价值挖掘,旨在构建一个科学且实用的公共数据要素价值释放DCU(Development, Circulation, and Utilization of Data)模型,其中包括公共数据分类分级的AB分类法与S3模式。该模型将围绕公共数据的开发、流通与使用三大核心环节展开深入分析,进而为公共数据的确权、分类与分级、共享与开放等关键问题提供切实可行的策略与建议。通过此研究,期望能够推动公共数据要素的优化管理与高效利用,为公共数据的价值持续释放注入新的活力。

Amidst the wave of the digital era,data has gradually emerged as a crucial production factor,and its inherent value is attracting unprecedented attention and exploration.In particular,the potential value of public data is especially prominent,encompassing both economic and social dimensions.However,the current management and institutional frameworks are yet to fully unleash the comprehensive value of public data elements.In light of this,this paper focuses on the value mining of public data elements,aiming to construct a scientific and practical DCU(Development,Circulation,and Utilization of Data)model for releasing the value of public data elements.This model incorporates the AB classification method and S3 model for categorizing and grading public data.Centering on the three core aspects of public data development,circulation,and utilization,the model delves into in-depth analysis,thereby providing practical strategies and recommendations for key issues such as the confirmation of rights,categorization and grading,sharing,and opening of public data.Through this research,it is expected to drive the optimal management and efficient utilization of public data elements,injecting new vitality into the continuous release of their value.