2026年中国数据交易观点报告：以买方需求重塑数据交易-20260303

Group 1: Data Trading Framework - The current data trading market in China is still in a nominal trading phase, lacking substantial models to drive industrial development[3] - The successful operation of data trading relies on two steps: "foundation construction" and "rule formulation," both essential for creating a win-win commercial logic[4] - The core obstacles in establishing a trading foundation are data availability, data ownership, and data depth[7] Group 2: Data Availability Challenges - Different industries exhibit varying levels of digitalization, leading to gaps in data usability; some industries have data in incompatible formats, creating data silos[8] - Data often lacks clear ownership due to its non-physical nature and complex rights boundaries, complicating the trading process[9] - The scarcity of deep data insights, which are essential for decision-making, arises from difficulties in data collection and transformation[10] Group 3: Commercialization Logic - Data trading is primarily driven by the relationship between buyers and sellers, with the buyer's willingness to pay influenced by their business objectives[15] - The pricing of data usage is determined by the difference in buyer's revenue before and after data application, divided by the usage amount[16] - In the financial and marketing sectors, data trading has matured due to a solid digital foundation and established business models, facilitating easier consensus in negotiations[24] Group 4: Future Implications - Customized data trading driven by buyer demand is crucial for transitioning from concept to practical application, similar to the SaaS model[25] - The successful implementation of data trading can promote systematic upgrades in digital infrastructure across various industries[26]