Abstract：The assetization of enterprise data in China holds a strategic position in the process of Chinese-style modernization, becoming a key driver to enhance enterprise competitiveness and market status. Based on the theory of the digital economy and utilizing the database of the “Urban Statistical Yearbook” for 2024 in conjunction with the database of China’s big data asset exchanges, an analytical framework for the entitlement, valuation system, and accounting recognition of enterprise data assets was constructed. The study finds that: the construction of the enterprise data assetization system is the cornerstone of developing a “digital economy”; the property rights framework of “three rights separation” for data assets clarifies the rights of data resource holders, data processors and users, and data product operators, delineating the rights and responsibilities of different entities throughout the lifecycle of enterprise data assets; enterprise data assets select quantitative valuation methods or evaluative valuation methods adaptively according to different classification criteria and application scenarios; the recognition of enterprise data assets on the balance sheet in China has a practical foundation, with the framework system of the “ fourth financial report” divided into three parts: “Input Value Column”, “Business Value Column”, and “External Value Column”; empirical evidence shows that the assetization of enterprise data has a significant positive effect on regional economic growth using methods such as Tobit regression analysis, difference-in-differences, and robustness tests. The research conclusions provide a theoretical basis and practical methods for the management and optimal allocation of enterprise data assets, offering references for corporate strategic planning and policy formulation in the era of the digital economy.
汪小龙. 确权、估值与入表：我国企业数据资产化及其实践成效[J]. 《深圳大学学报》（人文社科版）, 2024, 41(6): 82-92.
WANG Xiao-long. Entitlement, Valuation and On-balance-sheet Recognition: Based on the Assetization of Enterprise Data. , 2024, 41(6): 82-92.
2024, Vol. 41 
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