Core Viewpoint - Yiyun Information has been recognized for its leading role in the standardization of data asset management, showcasing its commitment to digital innovation and application [1] Group 1: Company Initiatives - Yiyun Information adheres to national and group standards to promote the standardized practice of data asset management [1] - The company has developed a "Data Asset Steward" product that integrates various functions such as data asset identification, value assessment, accounting, and disclosure reporting [1] - This product provides a comprehensive professional tool for enterprises to manage their data assets [1] Group 2: Future Focus - Yiyun Information plans to continue focusing on technological innovation and practical applications in various scenarios [1] - The company aims to deepen the application of cutting-edge technologies such as big data and artificial intelligence, expanding the breadth of its services [1] - Yiyun Information intends to contribute more practical examples for the construction of a digitally strong province [1]

Summary of Medical Data Asset Circulation and Trading Industry Overview - The focus is on the medical data circulation and trading industry in China, particularly in Beijing and Shanghai, with a pilot program initiated by the Beijing Health Commission and Economic and Information Technology Bureau [1][2]. Key Points and Arguments - **Pilot Programs and Expansion**: In 2023, six municipal hospitals in Beijing began data sharing trials, with plans to expand to 22 hospitals in 2024. Shanghai's Guo'er Hospital will follow in 2025 [2]. - **Government Support**: The Ministry of Finance's "Pilot Program for Digital Asset Management" emphasizes the management of data assets, including registration, authorization, and revenue distribution [2]. - **Market Demand**: Major AI companies are actively purchasing medical data to train models, with a notable case of an AI company developing a surgical planning product using data from a chest hospital [2][4]. - **High Willingness from Private Institutions**: Private medical institutions, including testing centers and specialty hospitals, show a strong willingness to engage in data trading, provided that patient privacy is protected through anonymization [5][6]. - **Data Demand Sources**: Key demand sources for medical data include AI companies, research institutions, pharmaceutical/device developers, and marketing agencies, with significant interest from leading internet firms [5][6]. - **Compliance and Anonymization**: Ensuring compliance is critical, requiring patient consent and anonymization of data before external circulation. Third-party data exchanges are used to maintain transparency and legality [7][12]. - **CT Imaging Data Demand**: There is a strong demand for CT imaging data for model training, with applications categorized into B2B (hospital services) and B2C (individual users) [3][8]. - **Challenges in Data Circulation**: The industry faces challenges such as varying levels of hospital information technology, lack of internal revenue mechanisms, and the need for technical service providers [11][12]. - **Future Market Potential**: The medical data circulation and trading industry is expected to grow significantly, driven by increasing hospital digitalization and supportive policies [13][14]. - **Cross-Border Data Transactions**: There is potential for cross-border data transactions, particularly with foreign pharmaceutical companies interested in purchasing data from Chinese hospitals [15][16]. - **Pricing Models**: A pricing model for hospital data has been developed in collaboration with Tsinghua University, relying on market references for data valuation [18]. Additional Important Content - **Data Standardization**: The lack of unified data standards poses a challenge for data integration across hospitals, necessitating the establishment of anonymization protocols and standards [11][12]. - **Role of Data Trading Platforms**: Platforms like the Beijing Stock Exchange facilitate data circulation by managing transactions and ensuring compliance with regulations [21]. - **Inclusion of Rural Data Resources**: Smaller hospitals are less likely to provide data but may serve as buyers of AI diagnostic products, indicating a shift in their role within the data ecosystem [22][23].

Core Insights - Data is recognized as the fifth production factor, with its value extraction process being more complex than traditional production factors due to its non-competitive, replicable, and infinite growth characteristics [1] - The development of a market-oriented system, represented by local data trading institutions and data merchants, is becoming the core driver for the growth of the data factor market [1][2] - The establishment of a clear policy framework and implementation path is crucial for enhancing the value of data elements, aiming for a well-functioning ecosystem of data supply and usage [1][4] Current Situation Analysis - The data factor 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 for the overall economic development in China, with the data factor market expected to grow at a compound annual growth rate (CAGR) of approximately 20.26% to exceed 300 billion by 2028 [6] Policy Analysis - The improvement of the policy framework for the data industry value chain and the establishment of local data systems are essential for the circulation of data factor value [4] Market Size Calculation - China's digital economy has grown from 27.2 trillion in 2017 to 53.9 trillion in 2023, with a CAGR of about 12.07% [6] - The data processing segment, focusing on data processing and analysis, is expected to become the largest sub-industry within the data factor market, reaching approximately 144 billion by 2028 [6] Data Value Chain Circulation - The establishment of a data ownership system based on the "Data Twenty Articles" is crucial for ensuring efficient circulation of data value [11] - 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] - The data valuation policy framework is becoming more refined, with public data resource quantification standards emerging as important benchmarks [16] Capitalization of Data Assets - The entry of data assets into financial statements marks a significant step in the capitalization of data elements, with regulations coming into effect in 2024 [19] - The market for data asset transactions is characterized by a "cold inside, hot outside" distribution pattern, 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 by 2028, driven by the integration of diverse data [30][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 sector's data element scale is expected to grow steadily, with a CAGR of about 23.69%, emphasizing the importance of data compliance and security [36] Trends - High-quality data sets are becoming key to driving the artificial intelligence industry, with a shift from "single-point breakthroughs" to "holistic development" [39][40] - The construction of trusted data spaces will be crucial for ensuring the circulation and high-value application of data elements [42]

Core Viewpoint - The implementation of the Ministry of Finance's "Interim Provisions on Accounting Treatment of Enterprise Data Resources" in 2024 marks a significant shift in recognizing data assets as a vital component of corporate value in the digital economy, while companies face challenges in integrating these assets into their financial statements [1] Group 1: Challenges in Data Asset Integration - One of the main obstacles for companies in recognizing data assets is the conflict between accounting practices and traditional systems, along with a lack of operational processes for cost allocation [2] - The "Guidelines" provide a systematic framework for cost collection and allocation related to data resources, clarifying the main components such as procurement, processing, and development costs, and innovatively categorizing costs into directly attributable and indirect expenses [2] Group 2: Legal and Compliance Framework - The "Guidelines" bridge the gap between compliance and accounting recognition by addressing the complexities of data asset ownership, which is more intricate than traditional assets [3] - A comprehensive solution is provided for compliance and ownership verification, requiring companies to conduct thorough self-assessments and implement measures to clarify their rights to data resources [3] Group 3: Strategic Management and Value Creation - The "Guidelines" elevate the discussion of data asset recognition beyond mere accounting, emphasizing the importance of aligning data integration with corporate strategic goals to activate data value and drive high-quality development [4] - Companies are encouraged to disclose non-financial information regarding data resource applications and value creation, enhancing financial report transparency and promoting a shift from viewing data as a cost to recognizing it as a value driver [4] Group 4: Broader Implications and Future Outlook - The "Guidelines" reflect the Ministry of Finance's macro policy leadership while providing a scientific summary of regional practices, offering a model for replication in other areas [5] - The establishment of a robust accounting ecosystem for data assets will depend on cross-industry and cross-regional data circulation and value assessment, requiring higher-level institutional coordination [5]

Core Viewpoint - The implementation of the "Interim Provisions on Accounting Treatment of Enterprise Data Resources" by the Ministry of Finance in 2024 marks a significant shift in recognizing data assets as a vital component of corporate value in the digital economy, while companies face challenges in practical application due to issues like ownership definition and value measurement [1][5]. Group 1: Challenges in Data Asset Accounting - One of the main obstacles for companies in accounting for data assets is the conflict with traditional accounting systems and the lack of operational processes. The "Guide" provides a framework for cost collection and allocation related to data resources, clarifying the main components such as procurement, processing, and development costs [2]. - The "Guide" innovatively categorizes costs into directly attributable costs like "data ownership verification fees" and "quality assessment fees," as well as indirect costs such as "infrastructure costs" and "human resource costs," offering clear guidelines for their allocation [2]. Group 2: Bridging Compliance and Accounting - The "Guide" effectively addresses the gap between compliance verification and accounting recognition, emphasizing that the ambiguity in ownership is a fundamental barrier to data assetization. It outlines a systematic solution for compliance and ownership verification, requiring companies to conduct comprehensive self-checks [3]. - The "Guide" translates the "three rights separation" concept from the "Twenty Articles on Data" into actionable steps, detailing how companies can clarify their ownership rights through contracts, technical means, and registration [3]. Group 3: Strategic Integration of Data Assets - The "Guide" transcends mere accounting techniques by positioning data asset accounting within the broader context of corporate strategic management and value creation. It emphasizes that data asset recognition should align with corporate strategic goals and encourages analysis of the commercial value and financial contribution of data resources [4]. - Companies are urged to consider factors such as business models, update frequency, and technological iterations when estimating the useful life of intangible assets, linking accounting treatment closely with economic substance and business lifecycle [4]. Group 4: Broader Implications and Future Directions - The "Guide" reflects the Ministry of Finance's macro policy leadership while providing a scientific summary of regional practices, offering a replicable model for other areas. However, it acknowledges that a local guideline cannot resolve all issues in the marketization of data elements [5]. - The formation of fair value for data assets relies on cross-industry and cross-regional data circulation and value assessment, necessitating higher-level institutional coordination. Future local explorations may help build a data asset accounting ecosystem that aligns with national conditions and international standards, positioning data as a new engine for high-quality economic development [5].

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]

Core Insights - The strategic value of data assets is increasingly recognized as a key component of corporate competitiveness, driven by the acceleration of the marketization process of data elements [1][3] - The transition of data from elements to assets requires overcoming three core barriers: legal, accounting, and market challenges, making data asset incorporation into financial statements a necessary prerequisite for unlocking data value [2] - The development and utilization of data assets by companies have significantly increased, extending beyond single business segments to include financing, industry integration, and business innovation [3] Company Developments - Beijing Sanwei Tiandi Technology Co., Ltd. recently won a procurement project from China-ASEAN Information Harbor Co., Ltd., indicating recognition of its technology and product strength [1] - Shenzhen Yushun Electronics Co., Ltd. plans to acquire shares in Zhong'en Cloud (Beijing) Data Technology Co., Ltd. to enter the promising data center market, enhancing future growth and profitability [4] Policy Support - Local governments are intensifying policy support to facilitate the incorporation of data assets into financial statements, with measures such as cash incentives and operational guidelines [4][6] - Dongguan City has implemented measures to support enterprises in the initial assessment and incorporation of data elements, while Jiangsu Province has provided clear operational paths for data resource standardization [4] Market Trends - The process of data asset securitization is accelerating, with four data asset-backed securities (ABS) issued since April, totaling 1.774 billion yuan, indicating a shift towards normalized issuance [3][6] - A total of 102 A-share listed companies disclosed data asset information in their 2025 semi-annual reports, with a combined data asset scale of 5.637 billion yuan, reflecting a year-on-year increase of 137.21% in the number of companies and 74.79% in scale [6] Challenges and Recommendations - Companies face challenges in data asset incorporation, including issues with data compliance, ownership definition, and valuation difficulties [2][6] - Experts suggest the establishment of a robust evaluation standard and methodology for data assets, considering factors such as data quality, scarcity, and potential commercial value [7][8] - Companies are encouraged to strengthen data governance capabilities and ensure compliance and security in data management processes [8]

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]