📄 Executive Summary
China's data trading market is still in a nominal trading stage, lacking substantial momentum for industrial development. This report proposes a two-step process for smooth data transactions: first, building the "transaction foundation," and second, establishing "commercial rules." It analyzes three core obstacles in the transaction foundation: data usability, data rights confirmation, and data depth. The report deconstructs the commercial logic and practical workflow of data trading, using financial credit and marketing as typical examples to identify key success factors. Buyer-driven customized data transactions are identified as the core development direction at this stage. Large-scale replication will systematically drive digital infrastructure upgrades across industries, providing strategic insights and practical references for the data trading industry.
📑 Table of Contents
1 Fundamental framework for successful data transactions
1.1 Long-term ineffective data trading
1.2 Two-step approach: from transaction foundation to trading rules
1.3 Core elements of the transaction foundation
1.3.1 Data usability
1.3.2 Data rights confirmation
1.3.3 Data depth
1.3.4 Summary: framework viable but value insufficient
2 Commercial logic of data trading
2.1 Transaction foundation focuses on data assetization
2.2 Commercial thinking behind trading rules
2.2.1 Seller perspective
2.2.2 Buyer perspective
2.3 Commercial logic of data trading
2.3.1 Seller-led data trading
2.3.2 Typical applications in credit and marketing
2.3.3 Why credit and marketing data trading succeed
3 Practical data trading will drive industrial digitalization
📊 Charts & Figures
Figure 1-1 Data trading foundation and trading rules
Table 2-1 Key buyer considerations for purchase intent
Figure 2-1 Seller-led data trading workflow diagram
Building the transaction foundation focuses on three major challenges: data usability, rights confirmation, and data depth. Data usability requires high standardization and regulatory compliance of tradable assets. Rights confirmation demands industry consensus on separating ownership from usage rights. Data depth determines the value ceiling of transactions – raw data alone cannot generate excess returns without processing and modeling.
In the credit sector, data trading succeeds due to high-frequency, strong-demand scenarios and complementary data dimensions. Marketing relies on precise user profiling and third-party data to boost ROI. Seller-driven off-the-shelf trading is gradually giving way to buyer-driven customized solutions. Scalable replication will in turn accelerate digital infrastructure upgrades in healthcare, transportation, manufacturing, and other industries.
* This report is based on iResearch's long-term industry tracking and data trading case studies, highlighting core perspectives and commercial pathways for China's data trading in 2026. Freely accessible to promote industry awareness and digital infrastructure development. Please cite the source when using data.
