专访原海南省大数据管理局局长董学耕：2026年数据要素价值规模化释放可期，全国一体化数据市场或成破局关键

Core Viewpoint - The designation of 2026 as the "Year of Data Factor Value Release" marks a significant shift in China's data factor market reform, transitioning from foundational institutional development to a focus on value creation and verification [1][3]. Group 1: Data Factor Value Release - The release of data factor value has been a consistent theme since the concept was introduced, indicating that data must be integrated into production and circulation processes to unlock its value [3]. - The national data work conference's designation of 2026 as the year for value release signifies that prior efforts have reached a critical point for qualitative change [3][4]. - Policies implemented since late 2024, including those on public data resource utilization and data infrastructure, have laid the groundwork for this transition [3]. Group 2: National Integrated Data Market - The establishment of a national integrated data market is essential for overcoming challenges related to data factor value release, aiming to eliminate barriers to data circulation across regions and industries [7][8]. - Key infrastructure for this market includes a nationwide interconnected data platform, a unified data standard system, and operational norms for data interoperability [7][8]. - The "Five Unifications and One Openness" framework is considered foundational for building this integrated market, focusing on unified data property rights, circulation, trading, security, and governance [8][9]. Group 3: Data Product Development - The transition from "data resources" to "standardized data products" is crucial for public data holders, particularly government departments, which must be driven by accountability and performance metrics [11]. - Large enterprises with abundant data resources should adopt market-driven principles to encourage the release of data through paid usage and the development of metadata for better accessibility [11][12]. - The expected first wave of mature data products includes analytical data products and those driven by strong market incentives, such as in finance and healthcare [12]. Group 4: AI and Data Value - The integration of data with AI is essential for enhancing the quality and compliance of data used in AI models, which in turn influences the standards for measuring data value [13][14]. - AI's role in reshaping data value standards includes providing new measurement possibilities beyond traditional metrics, allowing for a more nuanced understanding of data's worth [14]. - The evolution of data products towards intelligent applications (Data Product 2.0) is anticipated, driven by the need for high-quality data to support AI applications [14][16]. Group 5: Business Model Transformation - The data industry's business model is shifting from project-based development to recurring data services, emphasizing the importance of data application in realizing value [15][16]. - This transformation is supported by the convergence of data and AI infrastructure, enabling rapid development of data products tailored to specific application needs [16]. - The core activities of the data industry will revolve around the elements of data, data platforms, data products, and data applications, fostering a diverse range of data applications and significantly enhancing data value [16].