以买方需求重塑数据交易 2026年中国数据交易观点报告 © 2026 iResearch Inc. 部门：研究院 署名：李超 1. 数据交易顺利展开遵循的基本框架 1.1. 长期失效的数据交易 从2003年以来，互联网产业发展已经度过了23个春秋，在这期间诞生了无数的数字经济奇迹。在云 服务和数智化等趋势加持下，数据成为经济圈公认的新生产资料，也因此数据作为生产资料的流通 与交易，成为经济增长的重要事项。 当前我国数据交易市场虽已实现形式层面的落地运作，各类数据交易场所、交易行为逐步出现，数 据也被纳入资产范畴完成权属界定与资产并表等基础操作，但整体仍处于名义交易阶段，尚未形成 实质性推动产业发展的模式。交易过程多停留在表层数据的流转，缺乏对产业深层需求的挖掘与匹 配，且未构建起供需双方共赢的商业逻辑。这导致数据交易无法为产业发展提供实际支撑，尚未发 挥数据要素作为新生产资料的核心价值，对产业发展的实质性促进作用尚未显现。 1.2. 从交易基础到交易规则的两步走 数据交易的顺利运转，核心在于"基础构建"与"规则制定"的两步递进，二者缺一不可，但这里 的规则制定并非单纯的交易规则，而是潜藏在交易双方背后，使双方 ...

2024年1月，《企业数据资源相关会计处理暂行规定》正式施行，数据资产第一次被允许纳入企业财务报表。当时5400多家A股公司里，只有100家敢为人 先完成入表——如今一年过去，这些"吃螃蟹的人"究竟吃到了多少红利？ 红利1：数据变"提款机"，融资不用再靠厂房土地 对很多企业来说，数据资产入表最直接的好处就是"融资破冰"。 这些案例背后是银行信贷逻辑的转变：以前只认厂房、设备，现在数据资产能直接当"信用凭证"，100家入表企业中，已有超10家通过数据质押、授信拿 到融资，总额超5500万元。 红利2：财务报表"颜值"飙升，负债率悄悄降了 1. 荟宸数据资产评估AI模型这类技术型企业，将迎来更大发展空间，了解数据资产估值模型（解密"数价锚钉"数据资产估值模型（1）） 数据入表不是"账面游戏"，而是真金白银的财务优化： 反观数据原生企业，虽然70.59%负债率略有上升，但这是主动扩张的"甜蜜烦恼"——中文在线、中国移动等企业加大数据采集和平台建设投入，入表金额 分别暴涨15.59倍、8.8倍，为未来铺路。 红利3：行业分化明显，这些赛道成最大赢家 一年实践证明，不是所有行业都能平等吃到红利，这3个赛道脱颖而出： 更 ...

Core Insights - The article highlights the disparity between individual data valuation and the lack of formal recognition of data as an economic asset at the national level in the U.S. [1][3][10] Group 1: Data Valuation and Recognition - The U.S. has not formally integrated data into its institutional framework despite recognizing its value, contrasting with China's approach of categorizing data as a production factor [3][4] - China's new regulations, effective in 2024, will allow data to be recognized as intangible assets or inventory, marking a significant shift in asset structure and information disclosure [3][9] - The U.S. regulatory framework has historically treated data as privacy and compliance issues, avoiding its economic resource aspect, while China acknowledges data's foundational role in the digital economy [3][4] Group 2: Market Dynamics and Economic Impact - The data brokerage industry generates over $200 billion annually, indicating that the market actively utilizes data despite regulatory uncertainties [4][6] - Historical cases, such as the Toysmart and RadioShack bankruptcy cases, illustrate the recognition of data as a valuable asset, yet the U.S. legal system has not established a stable governance framework for data [6][9] - The absence of a unified valuation framework for data in the U.S. leads to significant governance challenges, especially as data becomes a core element in AI and digital trade [4][9] Group 3: Future Implications and Governance - China's proactive approach to data assetization aims to create a governance framework that will support future developments in AI, digital trade, and public governance [9][10] - The U.S. reliance on market self-regulation is becoming increasingly inadequate as data emerges as a critical production factor, revealing systemic delays in governance [9][10] - The article suggests that the ongoing avoidance of data valuation in the U.S. could lead to significant long-term costs, emphasizing the need for institutional reform [10]

Core Viewpoint - The article emphasizes the significant gap between the United States and China regarding the recognition and valuation of data as an economic asset, with China taking proactive steps to classify data as a production factor and implement data assetization practices [1][5]. Group 1: Data Valuation in the U.S. vs. China - The U.S. has a low valuation of data, while China has recognized data as an asset, implementing regulations that allow companies to classify qualifying data resources as intangible assets or inventory [1][5]. - The U.S. has not formally acknowledged the economic value of data, despite the fact that data brokers generate over $200 billion in revenue annually from information not reflected on corporate balance sheets [2][5]. - The U.S. legal system has recognized the value of customer data in bankruptcy cases, indicating that data can be one of the most valuable assets for companies [2][5]. Group 2: Government Data Governance Challenges - The U.S. federal data policy framework is disorganized, lacking a systematic approach to understanding and protecting data, which complicates the establishment of a data valuation framework [5][6]. - The article highlights the need for a structured approach to data valuation to enhance data protection and privacy, referencing past data breaches as evidence of the risks associated with unvalued data [5][6]. - The comparison between the U.S. and China illustrates a stark difference in data strategy, with China actively pursuing data assetization while the U.S. remains indifferent [5][6]. Group 3: Recommendations for U.S. Data Policy - The article suggests that the U.S. Financial Accounting Standards Board should initiate projects to establish standards for recognizing data assets, and the SEC should enhance research on data asset disclosure requirements [6]. - It calls for Congress to mandate federal agencies to assess the value of their data assets, with state and local governments encouraged to follow suit [6]. - The author argues that the current refusal to quantify data value in the U.S. overlooks a vast economic potential, as illustrated by the example of a $2 reward for survey participation, which could multiply across the entire population [6].

Group 1 - Nanjing Bank Yancheng Branch signed a contract with Yancheng Shurong Zhisheng Technology Co., Ltd. for the first "data intellectual property + data asset" dual pledge financing in the city, providing a special credit of 5 million yuan to the enterprise [1][2] - This innovative financing model marks a significant progress in promoting the market-oriented allocation reform of data elements in the city, opening new channels for the transformation of data element value [1][2] - The dual pledge financing indicates that the value of the company's data resources and intellectual property has been officially recognized by financial institutions, facilitating a complete process from data product registration to bank credit [2] Group 2 - Yancheng Shurong Zhisheng Technology Co., Ltd., established on February 28, 2022, is a high-tech enterprise focused on industrial data value services, aiming to drive the digital transformation of traditional manufacturing enterprises [2] - The company has developed a core model of "data platform + product ecosystem + offline services" and has been recognized as a "double-soft enterprise" in Jiangsu Province [2] - The successful implementation of the dual pledge financing creates a replicable and scalable experience for realizing the value of data elements, broadening financing channels for technology-based enterprises [2]

我们常说"数据是新时代的石油"。但原油未经提炼无法使用,同样,未经"制度确认"的数据也无法流通。 在过去,企业内部沉淀了海量的ERP记录、日志文件,这些被统称为大数据。但在银行信贷、供应链审计 等严肃商业场景中,这些数据的价值往往被大打折扣,因为缺乏公信力。白皮书提出的"交易本体论",深刻 地指出了问题的解法:只有经过税务、法律、会计准则确认的数据,才具备"经济事实"的法律属性。 作为产业研究者以及学院的校企产学研合作负责人,在与百望股份推动创新合作的交流过程中,我非常认 同这一判断。随着国家数字基础设施(如金税工程)的完善,发票、合同、申报记录实际上构成了企业商业 行为的"法律底座"。百望股份等基础设施平台的作用,就是将这些分散的合规行为,转化为企业可自证清 白的"数字资产身份证"。拥有这张"身份证",企业的数据才能从内部的"管理报表"跃迁为跨主体的"交易 通货",经过交易合规认证的数据,才能成为企业的"信用资产"。 2. RaaS 新范式:从"买铲子"到"挖金矿" 如果说"交易本体"解决了信任问题,那么RaaS则解决了价值落地问题。这是我对这份白皮书印象最深的 部分。 香港科技大学金融研究院 助理院长,政 ...

2023年，周林与贵州省社会科学院合作研究《贵州省数据交易流通发展建议》课题；深度参与的国家级 项目《节点要求化的数据流通基础设施建设》获国家发展改革委（国家数据局）批复，成为国内首批数 据流通基础设施。 2023年底，周林团队着手研究数据资产增值市场化路径，成功打造数据资产全生命周期管理解决方案， 验证"数据资源－数据资产－运营－交易流通"闭环模式。在文旅行业，落地省内首个文旅数据资产化项 目——万峰林景区数据资产化；在工业领域，完成化工行业首个资产化案例——新疆天业集团数据资产 化。 2024年，周林被聘为青海省数据要素专家智库成员，将贵州经验推广至青海，还当选新的社会阶层人士 联合会贵安分会副会长。2025年，他受贵州省财政厅邀请，深度参与编写《贵州省数据资产全过程管理 实践蓝皮书》，该蓝皮书在2025数博会上发布后反响热烈。 截至目前，周林累计助力公司运营链接超50家部委的数据资源，形成1800+数据产品，落地400+数据应 用场景；累计参与全国9个交易平台的建设或运营，推动区域数据价值互联互通，累计数据交易金额达 数十亿元。 贵阳日报融媒体记者 汤欣健 在近日召开的贵阳市劳动模范和先进工作者表彰大会 ...

Core Insights - Beijing's Economic and Information Technology Bureau, along with three other departments, has issued measures to promote the development and utilization of commercial satellite remote sensing data resources from 2026 to 2030, aiming to create an innovative service hub for satellite applications that integrates terrestrial and space technologies [1] Group 1: Policy Measures - The measures focus on the full lifecycle development and utilization of remote sensing data resources, proposing 14 specific initiatives to establish a comprehensive policy support system [2] - Policies encourage the cultivation of market entities that integrate "satellite data + industry," support mergers and acquisitions to create globally competitive leading enterprises, and provide R&D tax incentives for companies in AI data processing and space-based computing [2] - The initiative aims to break down data barriers and technological bottlenecks, promoting the construction of multi-source integrated satellite big data platforms and accelerating the development of urban spatial digital foundations [2] Group 2: Industrial Development - Beijing E-Town has emerged as a core area for commercial aerospace, recently announcing nine major capacity projects with a total investment exceeding 15 billion yuan, which will rapidly form new production capacities across the entire industry chain [3] - The region has attracted a dense cluster of commercial aerospace companies, with 75% of commercial rocket enterprises and the highest concentration of commercial internet satellite companies in the country, establishing a comprehensive industrial ecosystem [3] Group 3: Infrastructure and Innovation - The first national common research and production base for commercial aerospace has been completed and is now operational, featuring technology platforms, R&D centers, and high-end manufacturing facilities to support the entire development chain of commercial aerospace [4] - As of now, 70% of China's commercial aerospace achievements originate from Beijing, with over 300 commercial aerospace companies in the city, and the industry scale has surpassed 100 billion yuan [4] Group 4: Future Outlook - Looking ahead to the 14th Five-Year Plan, Beijing aims to provide greater support for the commercial aerospace industry, emphasizing the importance of building a healthy and sustainable industrial ecosystem [5] - The integration of financial innovations, such as insurance products for commercial aerospace, aligns with the policy's goals of optimizing financial support and enhancing risk protection for enterprises [6] - The commercial aerospace industry is entering a golden development period driven by both supply and demand, with significant growth expected as policies continue to strengthen and regional clustering effects become more pronounced [6]

Core Insights - The article discusses the launch of new measures in Beijing aimed at promoting the development and utilization of commercial satellite remote sensing data from 2026 to 2030, addressing the challenges of "abundant data, limited applications, and difficult monetization" in the industry [2][5]. Group 1: Policy Measures - The "Several Measures" aim to enhance the breadth and depth of remote sensing data applications in key industries such as low-altitude industries, smart networking, finance and insurance, cultural tourism, urban governance, agriculture, and space assets [2][6]. - The policy includes financial support and data assetization to reduce costs for companies while expanding revenue sources through scenario development, creating a dual drive of "cost reduction + revenue increase" for profitability in commercial satellite enterprises [2][6]. Group 2: Industry Structure and Support - The measures encourage the development of chain-leading enterprises and multi-source integration platforms, promoting mergers and acquisitions among satellite data companies to strengthen the industry chain and improve data circulation efficiency [3][4]. - The initiative emphasizes building a robust market support, technical support, and resource guarantee for the satellite data industry, enhancing overall utilization efficiency by breaking down data silos [3][4]. Group 3: Data Assetization and Application - The measures mark a new phase in China's commercial space sector, focusing on "application supremacy" and integrating digital and space economies through comprehensive data supply, processing, circulation, and application [5][6]. - Specific actions include promoting satellite data assetization and supporting the creation of innovative application cases in key industries, with incentives for solutions that meet typical application needs [5][6]. Group 4: International Cooperation and Market Expansion - The measures also propose expanding international cooperation in remote sensing data resources, leveraging Beijing's advantages in free trade and service industry openness to enhance satellite data exports and cross-border flow [7]. - The global satellite data service market is rapidly expanding, with a compound annual growth rate of 16% to 21%, indicating a shift from "launching - imaging" to "imaging - processing - productization - industry empowerment" [7][8]. Group 5: Future Development and Recommendations - The article suggests that the future focus should be on creating a "satellite data +" ecosystem, emphasizing the need for unified standards, collaborative mechanisms, and security measures to facilitate data flow and address development concerns [8]. - Recommendations include policies for standard implementation, ecosystem cultivation, and market expansion to support small and medium-sized enterprises in exploring niche demands [8].

Core Insights - AI has transitioned from a laboratory concept to an omnipresent tool that can write articles, compose music, design, program, schedule in enterprises, inspect in factories, teach in classrooms, and diagnose in hospitals, effectively reducing costs for businesses and creating opportunities for entrepreneurs and investors [1][5] Industry Trends - The logic of profitability has shifted from "scaling" to "efficiency," with AI becoming the commercial core that addresses pain points across various industries, supported by a new synergy among policy, capital, industry, and social acceptance [3][7] - The current phase of AI integration into industries is the third stage, where service applications are central to AI's value release [3][7] - Future technological integrations, such as blockchain with AI, quantum computing with AI, and brain-computer interfaces with AI, are expected to create new business opportunities and potentially lead to the emergence of unicorn companies [3][7] AI Demand and Data Trends - Global AI computing power demand is projected to reach 10^23 FLOPS by 2024, which is 1 million times the total global computing power in 2010, and is expected to grow to 10^26 FLOPS by 2027, a 1000-fold increase in three years [3][7] - Data is viewed as the "oil" of AI, with four key trends anticipated: 1. Data assetization will become a core strategy for companies, with over 50% of listed companies expected to disclose data asset values in their financial reports by 2026 2. The data factor market will mature, transitioning from non-standard to standardized trading 3. Privacy computing technologies like federated learning and differential privacy will be widely adopted to address the "data usable but invisible" issue 4. Synthetic data will become a significant supplement, with its share in AI training expected to exceed 25% by 2027 [3][7] AI Implementation Framework - A five-layer architecture for AI implementation has been proposed, encompassing resource access, data management, Data & AI engineering, intelligent applications, and security operations, which has shown significant effectiveness in large and medium-sized enterprises, reducing project delivery cycles by over 50% and greatly increasing customer renewal rates [4][8] - It is anticipated that over 80% of large and medium-sized enterprises will adopt similar architectural frameworks to build AI infrastructure in the next three years [4][8]