Core Viewpoint - The article emphasizes the importance of establishing a robust data factor market to promote high-quality economic development in the context of the booming digital economy [1] Group 1: Development of Data Factor Market - The data factor market in China is rapidly developing, with ongoing construction of data trading venues and a steady expansion of market scale [1] - Legal frameworks are increasingly being improved to support the data factor market [1] Group 2: Challenges in Data Factor Market - The value potential of data factors has not been fully realized, and there are significant challenges in the current market construction [1] - There is an urgent need to improve transaction rules and regulatory mechanisms at the institutional level [1] - Market operational efficiency needs enhancement, and there are shortcomings in infrastructure and technological support [1] Group 3: Recommendations for Market Improvement - To address the challenges in the data factor market, a multi-dimensional collaborative approach and precise policy measures are necessary [1] - In terms of institutional support, it is essential to improve the rule system and strengthen governance foundations [1] - For market operations, enhancing operational mechanisms to improve allocation efficiency is crucial [1] - Strengthening technological support is vital to unleash the potential of data [1]

Core Viewpoint - The "Ningbo Data Application Promotion Regulation" aims to cultivate a data factor market, promote the legal and efficient application of data, and drive the development of the digital economy in Ningbo [1][2] Group 1: Regulation Features - The regulation emphasizes three main features: strengthening data application and liquidity, deepening industrial cultivation with a focus on uniqueness, and improving institutional guarantees with an emphasis on innovation [1] - The regulation's goal is to ensure data is "available, flowing, and effectively used," focusing on the entire lifecycle management of data application, including sharing, circulation, and utilization [1] Group 2: Public Data Management - Public data is to be shared as a principle, with non-sharing being the exception; data authorities at municipal and district levels are required to manage public data uniformly and create a public data directory [1] - The regulation includes provisions for protecting rights related to data processing and derived data, establishing a foundation for promoting data flow and value realization [1] Group 3: Industrial Cultivation - The regulation highlights the importance of industrial cultivation, detailing policies for nurturing enterprises, establishing an enterprise cultivation database, and promoting multi-dimensional data integration across industries [2] - It supports the construction of high-quality data sets and corpora, as well as the development and training of artificial intelligence large models [2] Group 4: Trusted Data Space - The regulation mandates the establishment of a trusted data space that is manageable, interconnected, and co-creates value, encouraging enterprises to participate in its construction [2] - It aims to leverage public data to facilitate compliant use, secure development, and trustworthy delivery of data, thereby maximizing data value [2]

Core Insights - The article emphasizes the importance of establishing a robust data market in China, advocating for a market-oriented approach to data resource development and utilization, which is crucial for unlocking data value and fostering a culture of paying for high-quality data [1] Group 1: Current State of High-Quality Data Payment Market - The high-quality data payment market is facing structural contradictions that hinder its development, including issues on both the supply and demand sides, as well as the lack of a core function in the public trading market [2] - On the supply side, traditional data companies often rely on automated browsing techniques to gather public data, leading to skepticism about the actual costs incurred in product development [2] - On the demand side, many small and medium-sized enterprises struggle to participate effectively in the data market due to high technical barriers and limited application capabilities [2] Group 2: Structural Challenges in Data Trading - The absence of key functions in the public trading market, particularly data exchanges, is a significant structural issue, with a low proportion of tradable data products and insufficient supply [3] - Information asymmetry in the market leads to stronger bargaining power for a few buyers, creating distrust between supply and demand sides [3] - Standardized data trading is often embedded in information projects, which restricts the formation of a payment mechanism and awareness for data [3] Group 3: Positive Trends in High-Quality Data Payment Market - Despite existing structural challenges, the data payment market is showing positive development trends due to national efforts in top-level design, institutional supply, technological evolution, and business model innovation [4] - Companies are increasingly recognizing the value of AI technology, leading to more deployments of AI models and intelligent design [4] - The success of models like DeepSeek is lowering the barriers and costs for companies to adopt AI technology, promoting a "technology equality" era [4] Group 4: Institutional and Commercial Developments - The National Data Bureau has issued several top-level planning documents to accelerate the development of high-quality data resources and foster a culture of paying for such data [6] - Local data groups and exchanges are exploring commercial models for data circulation, enhancing the supply of high-quality data and encouraging enterprises to pay for it [6] - Data exchanges are providing professional services that reduce costs for individual companies, thereby increasing trust in data transactions [6] Group 5: Directions for Building a High-Quality Data Payment Mechanism - Strengthening the supply of high-quality data and accelerating the construction of data markets centered around data exchanges are key focus areas for developing the high-quality data payment market [7] - The role of data exchanges as central hubs is crucial for facilitating the flow of high-quality data across the nation, enhancing the capabilities of society to utilize data [8] - Establishing a favorable data market environment through mechanisms for price discovery, property registration, and security will help reduce costs and improve trust in the data market [9]

Core Insights - The rapid development of technology has made data a new production factor driving economic growth, playing a crucial role in optimizing decision-making, enhancing efficiency, and fostering new business models [1] - The exploration of data asset securitization is significant for unlocking the value of data elements, broadening corporate financing channels, and deepening the reform of the data element market [1] Group 1: Definition of Data Asset Securitization - Data assets are defined as data resources that organizations legally own or control, which can be measured and bring economic or social value [2] - Data asset securitization involves financing through the issuance of asset-backed securities, supported by the stable cash flows generated from data assets [3] Group 2: Development Foundations and Practices - There is a solid policy and market foundation for promoting data asset securitization, with a framework established for data ownership rights and various policies addressing data asset management and valuation [5] - The demand for data asset securitization is growing as data-driven companies face funding pressures, providing a solution for converting future revenues into immediate cash [5] - Infrastructure for data trading is developing, with a nationwide network of data trading venues and the application of technologies like privacy computing and blockchain enhancing security and trust [6] Group 3: Domestic Innovation Cases - Various models of data asset securitization are emerging, including indirect credit enhancement through data asset pledges and direct monetization of data [7][8] - These cases illustrate the evolution of data from a supportive role to a core component of financial applications, providing valuable insights for future practices [8] Group 4: Challenges and Risks - Data asset securitization faces challenges related to the improvement of foundational systems, including property rights and regulatory frameworks [9] - Technical bottlenecks exist in areas such as data ownership verification and dynamic valuation, which hinder the scalability of securitization [9] Group 5: Pathways for Steady Development - Continuous improvement of data foundational systems is essential, including accelerating the legislative process for data property rights and promoting unified market standards [10] - Encouraging orderly innovation in the market through coordinated efforts between financial regulators and data management authorities is crucial [10] - Strengthening the collaborative ecosystem among various stakeholders, including data asset evaluation and legal services, will enhance the standardization of professional services [10] Group 6: Conclusion and Outlook - Data asset securitization is a vital innovation connecting data elements with capital markets, with a promising outlook as foundational policies and market practices evolve [11] - The ongoing development of a unified data market will gradually standardize core processes such as ownership verification and valuation, unlocking the potential value of data resources [11]

Core Insights - The article emphasizes the importance of public data development and utilization as a key breakthrough for activating factor value, with Qingdao Data Group positioned as a primary developer of public data [2][3] Group 1: Company Overview - Qingdao Data Group was established in February 2025, evolving from Huatuo Zhiyan Institute, which had two years of experience in public data operations [3] - The company has three core business segments: public data operation, data asset investment, and AI infrastructure development [3] Group 2: Data Operations - The company has integrated social data through a trusted data space and data hosting model, creating nine specialized areas including finance and healthcare, serving over 20 application scenarios for Qingdao municipal departments [4] - Qingdao Data Group has facilitated cross-regional medical services and established a comprehensive data asset management process for administrative units [4] Group 3: Data Services - The company focuses on various scenarios for data value release, including data accounting, equity participation, and financial leverage through securitization and pledging [5] - Qingdao Data Group has attracted over 2,000 companies and 100,000 individuals through its branding initiatives and established a data asset securitization alliance with 26 financial institutions [5] Group 4: Market Impact - Qingdao Data Group has completed data asset registration for 119 non-listed companies, accounting for nearly one-third of the national total [6] - The company has pioneered a standardized approach for data asset registration and valuation, which is now being referenced by over 50 cities [6] Group 5: Internal Collaboration - The company has developed a unique internal collaboration advantage through four platforms, enhancing the efficiency of data development and utilization [7] - Qingdao Data Group is focusing on empowering industries through high-quality data sets for AI applications in marine and health industries [7]

Core Insights - The data factor market is empowering various industries, with a shift towards data-driven business models and services [2][3] Group 1: Industry Trends - The digital transformation of think tank services is leading to the creation of high-quality datasets, such as HiTech data, which includes 40 billion effective data points and integrates advanced technologies like natural language processing and machine learning [3] - The think tank has developed over 100 industry analysis models and constructed a knowledge graph with 780 million entities and billions of relationships, serving more than 50 government departments and over a thousand enterprise users [3] Group 2: Data Integration and Application - There is a need for a market-oriented public data platform to facilitate the integration of public data with real operational data from enterprises, enhancing decision-making in R&D, production, supply chain management, and marketing [3][4] - The think tank emphasizes the importance of understanding specific industry contexts to effectively embed data capabilities and unlock value, highlighting the necessity of cross-industry integration and scenario innovation for successful data business implementation [4] Group 3: Product Development - The think tank has created two types of products: SaaS products for B2B and B2C empowerment, and DaaS services that directly cater to data users [4] - The focus on scenario-based applications has led to the development of knowledge models that assist in industry analysis, monitoring, and talent acquisition [4]

专题：2025中国企业竞争力年会 "2025中国企业竞争力年会"于12月9日至10日在北京举行。中经传媒智库特聘研究员、道衍数科（杭 州）联合创始人梁超杰，华科融资租赁有限公司常务副总裁杨星统一股份总经理，统一石化CEO李嘉， 深圳国家高技术产业创新中心大数据平台与信息部部长、中国科学技术情报学会创新情报专业委员会主 任委员卢春江， 景区交易数据要素化文化和旅游部技术创新中心主任、票付通创始人CEO苏万生， 北 京清竞数智科技有限公司CTO王吴越等共同探讨"数据要素市场如何赋能千行百业"。 王吴越强调，数据供给的核心是适配人工智能发展需求，"当前数据热点已从传统数据库转向人工智能 原生数据（AINet），供得出的先决条件是将数据加工为标准化高质量数据集"。他介绍，团队通过参与 国家数据局高质量数据集评测平台建设、与深圳数据交易所合作可信数据空间项目，实现了大模型语料 训练数据的合规交付，而可信数据空间技术则成为加速数据流通的关键工具。在政务数据应用中，其技 术支撑已落地海淀区公共数据智能体评测场景，验证了标准化数据与技术工具的协同价值。 票付通苏万生聚焦文旅场景的公共数据应用痛点，分享了特殊人群优惠购票的数据 ...

Core Insights - The "2025 China Enterprise Competitiveness Conference" was held in Beijing on December 9-10, highlighting the importance of data as a core production factor alongside land and capital [3][8] - Li Wei, Chairman of QuChain Technology, emphasized the role of "Blockchain + Privacy Computing" as foundational technology to bridge data innovation services with application scenarios [3][8] Healthcare Sector - QuChain Technology aims to address issues in China's healthcare system, such as data fragmentation and discipline separation, by creating a trusted medical data space that integrates resources from health authorities, hospitals, and data bureaus [3][8] - In the smart healthcare domain, the company is developing high-quality datasets and AI multimodal fusion models to enable intelligent diagnosis and imaging functions, exemplified by a collaboration with Peking University Dental Hospital to launch China's first dental AI medical registration certificate [3][8] Insurance Sector - The company has implemented a rapid claims project for accident insurance in Wenzhou, which connects data across health insurance, health commissions, hospitals, traffic police, and insurance companies, allowing patients to claim online without upfront costs [4][9] - This initiative has reduced insurance companies' annual costs by 80%, creating a win-win situation for users, companies, and data providers, with potential for nationwide expansion [4][9] Pharmaceutical and AI Innovation - QuChain Technology leverages integrated high-quality medical data to support drug development and foster more medical AI innovations, driving digital transformation in the healthcare industry [4][9] Financial Sector - The financial industry has high demands for data credibility and security, and QuChain Technology utilizes "Blockchain + Privacy Computing" to enable compliant sharing and efficient use of financial data, addressing privacy risks in multi-party data collaboration [4][10] - The company has established multiple replicable commercial cases in the financial sector, which is characterized by strong willingness to pay and clear demand for technology [10] Future Directions - Li Wei indicated the need to further explore data value across industries and promote more scenarios that achieve cost reduction and efficiency enhancement, aiming to build a standardized, vibrant, and integrated national data market [5][10]

Core Insights - The "2025 China Enterprise Competitiveness Conference" highlighted the extension of the "smile curve" theory from industrial manufacturing to the data factor market, indicating that the highest added value is found at both ends of the value chain [2][5] - AI technology is reshaping the data value chain, with diminishing value in data transmission and simple processing, while emphasizing the importance of high-quality data supply and innovative application scenarios [2][5] - The traditional data supply model has been restructured, driven by the proliferation of computing power, which shifts the upstream data industry from "quantity" to "quality" [2][5] Data Supply and Application - The upstream focuses on high-quality data collection and governance, while the downstream emphasizes scenario monetization and value realization [2][5] - Technologies such as trusted data spaces are addressing challenges in data circulation and control, leading to the emergence of diverse intelligent integration scenarios like model factories and MaaS services [2][5] Data Engineering and AI Integration - Data engineering is identified as the core of future R&D for AI companies, information technology firms, and traditional manufacturing enterprises [2][5] - The integration of business, AI, and data is essential for releasing data value, transitioning from traditional data capabilities to AI-driven data construction [2][5] Intelligent Transformation Logic - The core logic of enterprise digital transformation is to build knowledge engineering based on data, utilizing AI large model training to supply high-quality datasets [6] - This process aims to create a business closed loop of "data - model - application - data," facilitating iterative improvements in data engineering [6] - The transformation not only reduces costs and enhances efficiency for enterprises but also supports the high-quality construction of data assets in the zero-level market of the data factor market [6]

Core Insights - The Guizhou Data Exchange has defined three core roles: as a core service provider for public data value realization, a builder of a market trust system, and an active collaborator in a unified national market [2][7] Group 1: Core Roles - The exchange aims to provide comprehensive services for public data, managing the entire chain from resource directory management to compliant product development [2][7] - It seeks to establish a trustworthy infrastructure that reduces costs and risks for all parties involved in transactions, rather than maximizing its own commercial interests [2][7] - The exchange contributes to a unified and orderly market by participating in cross-regional and cross-level rule alignment and standard recognition [2][7] Group 2: Implementation Measures - The exchange is focusing on solidifying a basic public service system, adhering to principles of fairness and openness, and providing one-stop public services to lower participation barriers [8] - It employs a "five-in-one" approach to cultivate a comprehensive ecosystem, involving rule definition, compliance strengthening, pricing promotion, safety assurance, and ecosystem nurturing [3][8] - The exchange collaborates with the Guizhou Big Data Group to transform public data into tradable products, focusing on product operation, compliance review, and market matching [3][8] Group 3: Future Outlook - The exchange aims to elevate data products to standardized assets, gaining broad recognition in financial markets [9] - It plans to enhance trading mechanisms to be more intelligent and scalable, aiming for breakthroughs in specific trading areas [9] - The exchange seeks to improve cross-domain collaboration through trusted data spaces, facilitating rule recognition and system interconnectivity [9]