2024年中国数据要素流通行业研究报告：多举措齐头并进，共同探索数据资源创新应用

Industry Overview - The Chinese big data industry achieved significant growth during the "13th Five-Year Plan" period, with an average annual compound growth rate exceeding 30%, surpassing 1 trillion yuan in 2020 [2] - Policies such as the "Data Twenty Articles" and the "Data Elements × Three-Year Action Plan (2024-2026)" have positioned data as a key factor in economic development, promoting the healthy development of data circulation and application [2] - The report analyzes the industry from three dimensions: macro development, practical paths, and vendor research, providing insights into future development priorities and paths [2] Macro Development Environment - Emerging technologies have driven explosive growth in data, establishing data as a key factor in resource allocation [3] - Data security and privacy protection have spurred new explorations into secure data circulation and reuse [3] - The data elements industry chain is divided into three segments: data resource elementization, market circulation, and data element application, with third-party service providers playing a crucial role in facilitating the transformation from resource to application [3] Market Size and Business Models - The data elements circulation and application market is in its infrastructure phase, with two main business models: infrastructure-focused in government and public sectors, and data operation and service models in finance, healthcare, and transportation [3] - The market size of China's data elements is projected to reach 166.2 billion yuan in 2024, with a compound annual growth rate of 25.7%, expected to reach 204.29 billion yuan by 2025 [3] Data Elements Industry Chain - The data elements industry chain includes five key stages: data collection and processing, data storage and governance, data assetization and productization, data connection and circulation, and data trading [5] - The report focuses on the "external circulation" of data elements, discussing the progress, feasibility, and application scenarios across various industries [5] Public Data Authorization and Operation - Since 2022, China has been exploring data elements circulation through three main directions: public data authorization systems, industry-specific application scenarios, and accounting treatment methods for enterprise data resources [28] - The construction of public data authorization platforms has accelerated, with 21 provinces and cities already launching public data open platforms [34] Challenges in Data Elements Circulation - The industry faces challenges such as insufficient data and infrastructure, lack of motivation in various sectors, and slow progress in multi-party collaboration [4] - Public data quality varies significantly, with issues like missing data, inconsistent formats, and difficulties in cleaning and organizing data [40] - The lack of unified technical standards and the complexity of integrating existing systems further complicate the implementation of public data authorization [41][44] Industry Applications - Financial, transportation, healthcare, and energy sectors are leading in data elements circulation applications, with significant demand for cross-industry data sharing and reuse [51] - In the financial sector, privacy-preserving technologies are driving the construction of digital financial infrastructure, with applications in risk control and marketing [55][56] - The transportation sector leverages public data for applications like urban traffic monitoring, smart city development, and autonomous driving model training [64][65] - The healthcare sector faces challenges in data sharing due to privacy concerns, but is making progress in medical research and drug development through secure data sharing platforms [69][74] Data Assetization and Valuation - As of June 2024, there are over 35 cases of data assetization in China, primarily using the "cost method" for valuation, with limited exploration of long-term data value [81][82] - Enterprises face challenges in data assetization due to unclear data ownership, insufficient internal data governance, and difficulties in accurately assessing data value [85]