21专访｜复旦大学黄蓉：数据资产赋能企业新增长点

Core Insights - AI technology is rapidly evolving, and companies across various industries are embracing digital transformation, making it essential for businesses to reshape their organizational structures through digitalization [3][4] - Data resource management is identified as the primary element in data-driven management, encompassing data collection, architecture, governance, cleaning, and value extraction [3] - As of 2025, only 101 listed companies in China are expected to report data resource values totaling 2.475 billion yuan [3] Data Resource Challenges - Companies face challenges in data ownership verification, especially for externally sourced data, which raises questions about data rights and usage [4] - Cost tracing is another challenge, as companies need a clear system to measure and trace the costs associated with data resource development [4] - Information disclosure regarding data resources is limited, with only a small number of companies currently reporting data assets, indicating a need for more practical exploration and policy guidance [4] Data Asset Valuation Methods - Companies can evaluate data asset value using three methods: cost method (initial development costs), income method (net benefits and cash flow generated), and market method (comparable market values) [5] - Key indicators for valuation include cost tracking for the cost method, estimating lifecycle benefits for the income method, and assessing market activity for the market method [5] Utilizing Data Assets for Business Value - Companies should recognize data assets as strategic resources and integrate them into core asset management, necessitating a shift in management philosophy [6] - Establishing a data-driven decision-making system is crucial, requiring the breaking down of departmental silos and creating a unified data platform for sharing and integration [6] - Organizations must adapt their structures and talent pools to support data-driven management, fostering a culture of innovation and creativity [6] Risk Management and Ethical Considerations - While leveraging AI, companies must also address potential risks such as data security and privacy, necessitating robust risk management frameworks and adherence to legal and ethical standards [6] Innovation in Business Models - Digital empowerment should focus not only on operational efficiency but also on creating new business models and value growth opportunities, such as personalized products and services through AI [6] Data Utilization in the Greater Bay Area - The Greater Bay Area, particularly Shenzhen, has many tech companies that can leverage internal and external data for AI algorithm training, necessitating effective data management and privacy protection policies [7] - Financial support for data-driven companies is essential, including policies that enhance data asset credibility for loans and insurance [7] - Utilizing data exchanges can facilitate active trading of data products, promoting overall market development for data assets [7]