医疗数据资产流通交易专家 20250926 北京市试点医疗数据流通后，2024 年推广至 22 家直属医院，上海国儿 等医院 2025 年跟进，财政部《数字资产全过程管理的试点方案》推动 卫健委数据资产管理，天坛医院和同济医院联合研究产生影响。 大模型厂商积极购买医疗数据训练模型，如 AI 公司购买胸科医院数据开 发手术规划产品通过欧美标准审核，促使医院认识到数据价值并参与流 通，预计年底新数据交易案例将超过去年。 民营医疗机构对数据交易意愿高，包括检测机构、影像中心和专科医院， 需确保匿名化处理保护患者隐私，已有影像中心完成相关服务，检测中 心数据逐步开放。 医疗数据需求方包括大模型厂商、科研单位、药物/器械研发机构、医药 营销机构，互联网头部机构和垂直领域企业需求较高，通过合作、科研 项目等方式获取数据。 医疗数据流通需患者知情同意，对外流通需匿名化处理，通过第三方数 据交易所交易确保流程透明合法，上市公司已通过交易所完成多次交易。 Q&A 请介绍目前国内医疗数据流通的试点情况及典型案例。 摘要 民营医疗机构，包括检测机构、影像中心以及一些专科医院，对医疗数据交易 表现出较高意愿。例如，一些由知名三甲专家 ...

数据作为第五生产要素由于其非竞争、可复制、无限增长与供给的特性，其价值挖掘流程的复杂程度远高于 其他传统生产要素，从数据来源的合法性、权属划分到后续的价值评估与增值管理，数据要素价值的提升对 于政策明晰的制度框架与实施路径有较高依赖，随着市场化体系的健全，以地方性数据交易机构、数商为代 表的产业模式正逐步成为推动数据要素市场发展的核心，以"政产联动"建立良好的供数、用数生态，促 进"供得出、流得动、用得好、保安全"的阶段性目标达成。 数据要素行业丨研究报告 核心摘要： 近况解读：国内数据要素行业现状分析 在政策指导与产业建设的共同作用下，以数据、科技与基建为核心的数据要素市场体 系逐步完善，推动行业向更高价值的"政产联动"迈进 政策剖析：近年数据要素行业政策解读 数据产业链价值流通体系的政策性完善与地方性数据体系 的健全成为国内数据要素 价值链流通的重要保障，数字产业进入"质量提升"新阶段 规模核算：中国数据要素行业市场规模 数字经济核心产业成为推动国内整体经济体系发展的重要驱动，预计国内数据要素 市场 将以约 20.26% 的复合增长率于 2028 年突破 3000 亿元 近年国内数据要素产业发展呈现稳步增 ...

杜坤伦 许余洁 自2024年财政部《企业数据资源相关会计处理暂行规定》正式施行以来，数据资产"入表"已从一个前沿 的理论概念，迅速转变为数字经济时代企业价值重估的现实命题。然而，广大企业在实践中普遍面 临"想入、盼入，但又怕入、难入"的窘境。数据资产的权属界定、价值计量、成本归集等一系列难题， 构成了从顶层设计到实践落地之间的"最后一公里"。在此背景下，江苏省财政厅近日印发的《企业数据 资源入表指南》（以下简称《指南》），为企业数据资源规范化入表等提供操作手册，标志着地方层面 在推动数据要素价值化进程中，迈出了从"怎么看"到"怎么办"的关键一步。 首先，当前企业数据资产入表的最大障碍之一，是会计核算与传统体系的冲突以及操作流程的缺失。企 业面对"数据"这一新型生产要素，往往不知从何入手进行成本归集与分摊。《指南》第四章系统性地构 建了数据资源成本的归集与分摊框架。它不仅厘清了采购成本、加工成本、开发成本等主要构成，更创 新性地将成本细分为可直接归集的"数据权属鉴证费""质量评估费"等，以及需要分摊的"基础设施成 本""人力资源成本"等间接费用。与此同时，《指南》为间接成本的分摊提供明确的动因指引，如基础 设施 ...

杜坤伦许余洁 自2024年财政部《企业数据资源相关会计处理暂行规定》正式施行以来，数据资产"入表"已从一个前沿 的理论概念，迅速转变为数字经济时代企业价值重估的现实命题。然而，广大企业在实践中普遍面 临"想入、盼入，但又怕入、难入"的窘境。数据资产的权属界定、价值计量、成本归集等一系列难题， 构成了从顶层设计到实践落地之间的"最后一公里"。在此背景下，江苏省财政厅近日印发的《企业数据 资源入表指南》（以下简称《指南》），为企业数据资源规范化入表等提供操作手册，标志着地方层面 在推动数据要素价值化进程中，迈出了从"怎么看"到"怎么办"的关键一步。 首先，当前企业数据资产入表的最大障碍之一，是会计核算与传统体系的冲突以及操作流程的缺失。企 业面对"数据"这一新型生产要素，往往不知从何入手进行成本归集与分摊。《指南》第四章系统性地构 建了数据资源成本的归集与分摊框架。它不仅厘清了采购成本、加工成本、开发成本等主要构成，更创 新性地将成本细分为可直接归集的"数据权属鉴证费""质量评估费"等，以及需要分摊的"基础设施成 本""人力资源成本"等间接费用。与此同时，《指南》为间接成本的分摊提供明确的动因指引，如基础 设施成 ...

Group 1: Company Performance and Financials - As of June 30, 2025, the company has 2,827 personnel in its data service business [2] - The company plans to distribute cash dividends of RMB 15.9361 million for the first half of 2025 [2] - The company's net profit growth rate over the past three years is -3.22%, and the non-recurring net profit growth rate is -9.02% [8] Group 2: Market Strategy and Growth - The company aims to enhance its market share through technological innovation and service model optimization [3] - The company has set a target to expand its data service coverage across eight major industries, including black metals and new materials [8] - The company has 273,800 paid members in its data service business as of the first half of 2025 [8] Group 3: AI and Technology Investment - In the first half of 2025, the company invested over RMB 50 million in R&D, focusing on AI and big data technologies [5] - The company has launched digital assistants "Xiao Gang" and "Xiao Tie" to improve operational efficiency and user experience [5] - Future AI applications will include enhanced data processing and predictive modeling capabilities [5] Group 4: Corporate Governance and Shareholder Relations - The company emphasizes value management and has implemented a share buyback and cancellation plan in 2024 [6] - The company maintains a commitment to transparent communication with investors to enhance confidence in its long-term value [6] - The company has a structured approach to governance, ensuring that major decisions are made through the board and shareholder meetings [3] Group 5: Risk Management and Financial Health - As of mid-2025, the company's accounts receivable balance is RMB 664 million, a decrease of 36.60% from the previous year [7] - The company is actively optimizing its product structure to enhance customer service and risk control capabilities [7] - The company has no current plans for mergers or acquisitions but will disclose any significant developments [6]

Group 1 - The core viewpoint of the article highlights that Beijing Sanwei Tiandi Technology Co., Ltd. has won a procurement project for the construction of a system platform for China-ASEAN Information Harbor Co., Ltd. in 2025, indicating recognition of the company's technology and product strength [1] - The company aims to accumulate implementation experience while completing the project, with aspirations to participate more broadly in various aspects of data asset development, circulation, and trading in the future [1] - The article emphasizes that the strategic value of data assets is becoming a key component of corporate competitiveness as the marketization process of data elements accelerates [1] Group 2 - The recent intensive policy rollout across various regions supports the core logic of accelerating the incorporation of corporate data assets into financial statements [1] - Local governments are employing a dual policy approach of "incentives + guidance" to address the actual needs of enterprises in the data assetization process [1] - This approach aims to create a more comprehensive institutional environment for the full release of data element value [1]

这一趋势在资本市场的表现尤为突出。一方面，数据资产证券化进程加速推进，部分手握核心用户数据、交易数据等优质 资源的企业，通过将数据资产未来收益权证券化的方式，有效拓宽了融资渠道，打破了传统融资模式对实体抵押物的依赖，为 企业发展注入了新的资金活力。 据Wind资讯数据统计，自今年4月份市场首单数据资产ABS（资产证券化）成功发行以来，截至目前，累计已有4单数据资 产（赋能）ABS落地，合计发行规模达17.74亿元，数据资产证券化加速进入常态化发行阶段。 本报记者 田鹏 9月15日，北京三维天地科技股份有限公司在投资者互动平台回应称，此次中标中国—东盟信息港股份有限公司2025年系 统平台建设服务采购项目，代表了客户对公司技术和产品实力的认可，公司将在完成项目的同时积累实施经验，争取未来更广 泛参与各地数据资产开发、流通、交易各环节的建设。 这一企业动态清晰印证，随着数据要素市场化进程加快，数据资产的战略价值已逐步成为企业核心竞争力的关键构成。这 也正是近期各地密集出台政策、加速推进企业数据资产入表工作的核心逻辑支撑。各地通过"激励+指引"的双重政策发力，既 精准呼应了企业在数据资产化进程中的实际需求，也为数据 ...

Core Insights - Data, as the fifth production factor, has unique characteristics such as non-competitiveness, replicability, and infinite growth potential, making its value extraction process more complex than traditional production factors [1] - The development of a market for data elements relies heavily on a clear policy framework and implementation pathways, with local data trading institutions and data merchants becoming key drivers [1][2] - The domestic data element market is expected to grow at a compound annual growth rate (CAGR) of approximately 20.26%, surpassing 300 billion yuan by 2028 [6] Current Situation Analysis - The data element market system is gradually improving, driven by policy guidance and industrial construction, focusing on data, technology, and infrastructure [2] - The digital economy's core industries are becoming significant drivers of the overall economic system, with the digital economy scale increasing from 27.2 trillion yuan in 2017 to 53.9 trillion yuan in 2023, doubling in six years [6] Policy Analysis - The improvement of the policy framework for the data industry value chain and the establishment of local data systems are crucial for the circulation of data element value [4] Market Scale Assessment - The data element industry is projected to reach approximately 200 billion yuan by 2025 and exceed 300 billion yuan by 2028, with data processing and analysis being the largest segment [6] Data Value Chain Construction - The establishment of a data value circulation system is supported by advanced technology and regulatory compliance [8] - The construction of a data ownership system based on the "Data Twenty Articles" is essential for efficient data value circulation [11] Data Registration - Data registration is critical for asset ownership delineation and promoting data value release, with a "1+3" policy framework guiding public data resource management [13] Data Value Assessment - The data valuation policy framework is becoming more refined, with public data resource quantification standards emerging as important benchmarks [16] Data Asset Capitalization - The capitalization of data assets is a core practice for realizing data value, with the implementation of regulations marking a new era for data asset inclusion in financial statements starting January 1, 2024 [19] Data Asset Trading - The data market exhibits a distribution pattern of "internal cold, external hot," with off-market transactions dominating due to their flexibility and customization [21] Industry Practices - The financial sector is expected to see a CAGR of approximately 19.06%, reaching over 100 billion yuan by 2028, driven by data element integration [31] - The industrial manufacturing sector is projected to grow at a CAGR of about 24.22%, with a focus on high-quality data sets and trusted data spaces [34] - The healthcare industry is anticipated to grow at a CAGR of around 23.69%, emphasizing the compliance of personal health data applications [36] Trends - High-quality data set construction is becoming a key factor in advancing the artificial intelligence industry, transitioning from "point breakthroughs" to "holistic development" [39] - The establishment of trusted data spaces will be crucial for ensuring the circulation and high-value application of data elements [42]

Core Insights - The article emphasizes the importance of data as a core production factor in business operations, highlighting the need for effective integration and measurement of data resources to maximize their value [1][20] - The introduction of the "Data Twenty Articles" and the "Interim Regulations on Accounting Treatment of Enterprise Data Resources" provides clear policy guidance and operational frameworks for data asset management [1][20] Policy Framework - The "Data Twenty Articles" establishes the institutional foundation for the data factor market, clarifying data ownership, circulation rules, and security requirements, which are essential for the legal and compliant use of data resources [1] - The "Interim Regulations" further detail accounting treatment methods, ensuring that enterprises can scientifically and reasonably recognize, measure, and report data assets while adhering to accounting standards [1] Data Inventory and Assessment - Conducting a comprehensive data inventory is crucial for enterprises to identify the types of data they possess, where it is stored, and which teams manage it, allowing for precise delineation of data suitable for financial reporting [3] - The process of selecting valuable data for inclusion in financial statements is likened to gold mining, emphasizing the need for careful selection to ensure that only valuable data is reported [3] Ownership and Valuation Challenges - Data ownership remains a significant challenge due to historical reasons and cross-border complexities, necessitating industry guidelines to clarify rights and responsibilities [5] - Choosing appropriate valuation methods for data assets is critical, with cost, income, and market approaches each having specific applicability depending on the data's maturity and revenue generation potential [5] Measurement and Reporting - Once data is included in the balance sheet, ongoing measurement is essential, with inventory-type data requiring regular impairment testing and intangible data needing differentiated treatment based on its useful life [7] - Maintaining consistency in measurement methods is fundamental to ensuring the rigor of financial information [7] Risk Management in Data Asset Financing - When considering data assets for collateralized loans, risk management is paramount, with banks typically setting a collateral ratio not exceeding 50% of the assessed value and requiring compliance with registration procedures [9] - Selecting data with strong resilience to depreciation as collateral can effectively mitigate credit risk associated with rapid asset value decline [9] Asset Securitization Challenges - Asset securitization is a viable method for activating existing assets, but it faces challenges such as complex legal relationships, difficulties in cash flow forecasting, and a lack of historical default data [10] - Overcoming these challenges requires learning from successful domestic and international cases and continuous improvement of relevant laws and regulations [10] Strategic Importance of Data Asset Management - Successful inclusion of data assets in financial statements optimizes corporate financial structures, reduces debt ratios, and enhances asset turnover efficiency, particularly for asset-light technology companies [20] - Strengthening talent development through cross-training between IT and finance teams is essential for improving data asset management capabilities [20] - The process of data asset inclusion is a systematic project involving policy interpretation, resource organization, rights definition, value assessment, accounting treatment, and risk control [20]

Core Insights - Data, as the fifth production factor, has unique characteristics such as non-competitiveness, replicability, and infinite growth potential, making its value extraction process more complex than traditional production factors [1] - The development of a market for data elements relies heavily on a clear policy framework and implementation pathways, with local data trading institutions and data merchants becoming key drivers [1][2] - The integration of government and industry is essential for establishing a robust ecosystem for data supply and usage, aiming for a phased goal of effective supply, fluid movement, good utilization, and security [1] Current Status of the Data Element Industry - The data element market system is gradually improving, driven by policy guidance and industrial construction, focusing on data, technology, and infrastructure [2] Policy Analysis - The improvement of the policy framework for the data industry value chain and the establishment of local data systems are crucial for the circulation of data element value [4] Market Scale Estimation - The domestic data element market is expected to grow at a compound annual growth rate (CAGR) of approximately 20.26%, surpassing 300 billion yuan by 2028 [6] - The digital economy's core industries are projected to contribute significantly to the overall economic development, with the digital economy scale increasing from 27.2 trillion yuan in 2017 to 53.9 trillion yuan in 2023, reflecting a CAGR of about 12.07% [6] Data Value Chain Construction - The construction of a data value circulation system is supported by advanced technology and regulatory compliance [8] Data Compliance and Rights Confirmation - The establishment of a data ownership system based on the "Data Twenty Articles" is crucial for ensuring efficient circulation of data value [11] - The legal framework for data rights confirmation is expected to evolve, addressing challenges such as data classification and compliance standards [11] Data Registration - Data registration is essential for asset ownership division and promoting data value release, with a "1+3" policy framework guiding public data resource management [13] Data Value Assessment - The data valuation policy framework is becoming more refined, with public data resource quantification standards emerging as important benchmarks [16] Data Asset Inclusion in Financial Statements - The inclusion of data assets in financial statements marks a significant step towards capitalizing data elements, with regulations coming into effect in 2024 [19] Data Asset Trading - The data market exhibits a "cold inside, hot outside" distribution pattern, with off-market trading dominating due to its flexibility and customization [21] Capitalization of Data Assets - Capitalization of data assets is becoming a core method for value release, optimizing the asset-liability structure of data-intensive enterprises [23] Data Asset Tokenization - Data asset tokenization represents the highest level of data value application, integrating physical asset digitization with digital asset monetization [25] Industry Practice: Market Size Breakdown - Data resource-intensive industries are central to the data element market, with finance and internet sectors collectively holding about half of the market share [28] Practical Scenarios: Financial Industry - The financial sector is expected to see a CAGR of approximately 19.06%, reaching over 100 billion yuan by 2028, driven by data element integration [31] Practical Scenarios: Industrial Manufacturing - The industrial manufacturing sector is projected to grow at a CAGR of about 24.22%, driven by the demand for high-quality data and cross-industry data resource sharing [34] Practical Scenarios: Healthcare Industry - The healthcare sector's data element scale is expected to grow at a CAGR of approximately 23.69%, surpassing 25 billion yuan by 2028 [36] Trends: High-Quality Data Set Construction - High-quality data sets are becoming key to driving AI industry development, with a focus on systematic data collection and processing [39] Trends: Trusted Data Space Construction - The establishment of trusted data spaces is essential for ensuring the secure circulation and high-value application of data elements [42]