企业数据资产入表规模增长、增速放缓，高市值公司入表增多

Core Insights - The implementation of data asset recognition for companies has shown steady growth, but the growth rate has slowed down since its initiation on January 1, 2024 [1][2] Summary by Category Listed Companies - The number of A-share listed companies recognizing data resources increased from 40 in mid-2024 to 109 by mid-2025, with total recognized amounts rising from 1.36 billion to 2.64 billion yuan [1] - The "intangible assets" category remains the primary disclosure method, with 101 companies reporting data resources as intangible assets totaling 1.71 billion yuan [1] - Most data resources in intangible assets are generated through self-development, with straight-line amortization over 3-5 years being the mainstream practice [1] Industry Distribution - The financial sector saw a significant increase in the number of listed companies recognizing data assets, up 33% from the end of 2024, with a total amount of 0.54 million yuan, primarily from banks and securities firms [2] - The three major telecom operators contributed significantly, with a total recognition amount of 1.6 billion yuan, accounting for 60.7% of the market total [2] - Data asset recognition has expanded to cover 25 provincial administrative regions, with an increasing number of high-market-cap companies participating [2] Non-Listed Companies - As of September 30, 2025, 375 non-listed companies have disclosed data asset recognition, with a total financing amount of 1.899 billion yuan [2][3] - Local state-owned enterprises are the main contributors, accounting for 82.67% of the total recognized companies, with a cumulative financing amount of 1.71 billion yuan [3] - The leading industries for non-listed companies recognizing data assets include information transmission, software and IT services, and transportation [3] Challenges and Considerations - There is a gap between top-level design and practical implementation, with many ambiguities in specific rights confirmation, valuation standards, and auditing criteria [4] - Companies face challenges in matching investments with expected returns, as the complexity of data asset recognition requires significant resources [5] - Balancing information disclosure with commercial confidentiality is crucial, as excessive disclosure may risk revealing core algorithms and user data [5]