China Policy Think Tank Highlights Industry Integration Over Cash Data Trading

Commercializing data as an economic production factor relies on industrial integration and supply-chain collaboration rather than standalone cash transactions on centralized exchanges, according to Hu Jianbo, head of the National Data Development Research Institute. Speaking in an interview series co-hosted by the National Data Administration and People's Daily Online, Hu pushed back against the view that data circulation is purely monetary trading. Instead, he argued that real economic value emerges when public and corporate data are embedded into concrete use cases, such as manufacturing supply chains, smart transportation, and public governance.

The guidance signals an official shift away from viewing data markets solely through the lens of transaction volume on nascent data exchanges. For enterprises, technology vendors, and local authorities, regulatory priorities favor pragmatic, ecosystem-based data sharing over speculative asset listings or hasty exchange licensing.

Unlocking the economic value of data in China relies primarily on integrating information into real industrial scenarios rather than conducting cash transactions on centralized exchanges, according to Hu Jianbo, head of the National Data Development Research Institute. Speaking in an interview program organized by People's Daily Online and the National Data Administration, Hu addressed what he characterized as a widespread misconception: equating data circulation exclusively with monetary trading. In practice, Hu explained, data circulation encompasses a wide spectrum of mechanisms, including open public access, internal sharing, cross-entity exchange, and commercial trading. Non-monetary arrangements currently outweigh cash-based sales across industrial sectors, Hu noted. These mechanisms include swapping data access for analytical services, algorithmic models, or commercial supply contracts. Across industrial and supply chains, collaborative value creation represents the predominant mode of circulation, rather than high-volume trading on spot markets. To illustrate the model, Hu pointed to Chongqing-based motorcycle manufacturer Loncin General. The company established a trusted data space across its supply chain, connecting research and development, procurement, manufacturing, marketing, and logistics. By adopting an architecture where data remains stored locally while computational value is shared, the company built predictive models for supplier evaluation and demand forecasting without exposing proprietary raw data. According to Hu, data value realization hinges on a 'scenario-driven and integration-enabled' approach. Both public and enterprise data hold distinct application priorities. For public datasets, the primary arenas comprise administrative governance, public welfare services such as healthcare and transportation, and emerging sectors including connected vehicles and the low-altitude economy. By contrast, corporate data assets are commercial in nature. Their deployment progresses from internal cost reduction and operational optimization to supply-chain collaboration led by anchor enterprises, and finally to cross-industry data flows that support broader macroeconomic efficiency. Hu noted that resolving data property rights and pricing mechanisms remains central to the foundational institutional architecture outlined in national policy frameworks, including the 'Data Twenty Articles' guidelines.