数据资产入表驱动金融信息服务行业商业模式变革研究

Core Viewpoint - The financial information services industry is evolving with the integration of data as a production factor, driven by government policies and advancements in AI technology, positioning digital economy as a new growth engine [1][2]. Group 1: Industry Development and Trends - The financial information services industry has transitioned from merely aggregating data to providing intelligent insights, leveraging big data and AI technologies [3]. - The introduction of data as an asset in accounting practices marks a significant shift, allowing companies to recognize and manage data resources effectively [2][4]. - The industry is currently facing saturation in traditional markets, prompting firms to explore mergers and acquisitions to expand their customer base and enhance profitability [4][5]. Group 2: New Business Models and Challenges - As traditional markets become saturated, financial information service providers are shifting towards data asset-driven business models, utilizing big data and AI to uncover new market demands [5][6]. - Despite the potential of data asset-driven models, many companies struggle with the practical implementation of data as an asset, with only 2% of A-share listed companies disclosing relevant information [6][7]. - The challenges include a lack of understanding of data asset management, the cautious nature of financial personnel, and the inherent complexities of standardizing financial data [7][8]. Group 3: Impact of Data Asset Recognition - Recognizing data as an asset provides a new pathway for business model transformation, enhancing the visibility and governance of data resources [8][9]. - The rapid development of AI technology is increasing the demand for high-quality data, creating opportunities for the financial information services industry to innovate and expand [9][10]. - Companies like Zhongdai Valuation Center are exemplifying how to effectively manage data assets, transitioning from qualitative to quantitative assessments of data value [11][12]. Group 4: Practical Applications and Innovations - Zhongdai Valuation Center's approach to data asset management includes standardizing data asset recognition and implementing a two-step value allocation model to quantify data contributions [12][13]. - The shift from a reactive to a proactive product development strategy allows firms to focus on their unique data assets, leading to innovative and differentiated offerings [14][15]. - Continuous iteration and expansion of data products enable companies to evolve from providing single data services to comprehensive solution outputs, enhancing their competitive edge [15][16].