Guotai Haitong Securities has released a research report stating that the National Data Work Conference, held in Beijing from December 29 to 30, 2025, conducted specialized deployments focusing on clarifying the functional positioning of data circulation service institutions, enhancing the efficiency of data circulation and trading services, and fostering a thriving data market ecosystem. The conference explicitly designated 2026 as the "Value Release Year for Data Elements," emphasizing the need to vigorously smooth channels for data flow and resource allocation, activate both supply and demand in the data market, and enrich the market ecosystem. This aims to further promote the principles of data being "available for supply, capable of flowing, usable effectively, and secure," thereby integrating data elements comprehensively into the economic value creation process to better empower economic and social development. Guotai Haitong expressed that the combination of a complete institutional framework and AI-driven catalysts is expected to accelerate the release of data element value and industrial blossoming in 2026. It recommends focusing on various segments of the industrial chain, including data supply, data trading and circulation, data application development, data services, data security, and computing and network infrastructure. The main viewpoints of Guotai Haitong are as follows: 2026 is designated as the "Value Release Year for Data Elements." The National Data Work Conference, held in Beijing on December 29-30, 2025, conducted specialized deployments focusing on clarifying the functional positioning of data circulation service institutions, enhancing the efficiency of data circulation and trading services, and fostering a thriving data market ecosystem. The conference explicitly designated 2026 as the "Value Release Year for Data Elements," calling for efforts to unblock data flow and resource allocation channels, activate data market supply and demand, prosper the market ecosystem, and further promote the principles of data being "available for supply, capable of flowing, usable effectively, and secure." This is to facilitate the full integration of data elements into the economic value creation process and better empower economic and social development. Since 2024, the pace of policy implementation has accelerated, building a closed-loop institutional framework for data elements. In 2020, the Central Committee of the Communist Party of China and the State Council issued the "Opinions on Building a More Improved Market-Based Allocation System for Factors of Production," officially listing data as the "fifth major factor of production," alongside labor, capital, land, and technology. Guided by this top-level design, the implementation rhythm of China's data element policies has quickened since 2024, transitioning from top-level design documents to practically implemented policies. This process is gradually constructing an institutional closed-loop encompassing data supply, trading circulation, scenario development, and infrastructure, laying a solid institutional foundation for the industrialization of data elements. The demand from AI applications is catalyzing a massive supply gap in the data market. According to data from the National Data Bureau, China's daily Token consumption for AI applications surged from 100 billion at the beginning of 2024 to over 30 trillion by the end of June 2025, representing a more than 300-fold increase in just a year and a half. This reflects the rapid growth in the scale of AI applications in China, which has created a significant supply gap in the data market. Investment recommendation: The combination of a complete institutional framework and AI-driven catalysts is expected to accelerate the release of data element value and industrial blossoming in 2026. It is advisable to focus on industrial chain segments such as data supply, data trading and circulation, data application development, data services, data security, and computing and network infrastructure. Risk提示. Risks include policy volatility, technology implementation falling short of expectations, and data application development underperforming.
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