In the digital economy,data assets have come to be regarded as the new oil,underscoring their critical role in modern business models and decision-making processes.In response,the Chinese government has prioritized th...In the digital economy,data assets have come to be regarded as the new oil,underscoring their critical role in modern business models and decision-making processes.In response,the Chinese government has prioritized the formalization and management of data assets,introducing policies aimed at enhancing their value.Given the unique nature of data assets,characterized by the potential for both depreciation and appreciation,precise methods for assessing value changes and realizing the appreciation of data assets are urgently needed.Effective data governance techniques,including data cleaning,acquisition,and integration,are essential for maximizing the economic potential of data assets.Against this backdrop,this survey explores two key issues from a data governance perspective:the enhancement of data asset value and the quantification of its changes.It is structured around two primary dimensions:first,by examining data assets'inherent properties and quality indicators,and second,by utilizing an“on-demand evaluation”approach that assesses value of data assets in response to the performance of downstream machine learning models.By advancing understanding of these issues,this study seeks to optimize strategies for maximizing the economic impact of data assets through refined data governance practices.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62202126 and 62232005)the Natural Science Foundation Project of Heilongjiang Province of China(No.YQ2024F005).
文摘In the digital economy,data assets have come to be regarded as the new oil,underscoring their critical role in modern business models and decision-making processes.In response,the Chinese government has prioritized the formalization and management of data assets,introducing policies aimed at enhancing their value.Given the unique nature of data assets,characterized by the potential for both depreciation and appreciation,precise methods for assessing value changes and realizing the appreciation of data assets are urgently needed.Effective data governance techniques,including data cleaning,acquisition,and integration,are essential for maximizing the economic potential of data assets.Against this backdrop,this survey explores two key issues from a data governance perspective:the enhancement of data asset value and the quantification of its changes.It is structured around two primary dimensions:first,by examining data assets'inherent properties and quality indicators,and second,by utilizing an“on-demand evaluation”approach that assesses value of data assets in response to the performance of downstream machine learning models.By advancing understanding of these issues,this study seeks to optimize strategies for maximizing the economic impact of data assets through refined data governance practices.
