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激活数据潜能,赋能企业新未来——基于政策与实践的注册数据资产管理师之路
Sou Hu Cai Jing· 2025-09-01 04:27
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]
陈刚主持召开书记专题会议,研究部署全区数据和林业产业高质量发展工作
Guang Xi Ri Bao· 2025-08-29 01:47
Group 1: Data Industry Development - The meeting emphasized the importance of leveraging national initiatives like the AI capability construction plan to enhance cooperation with ASEAN countries and promote the development of the AI industry [2] - The focus is on accelerating the market-oriented allocation of data elements and the process of data assetization, with the establishment of a cross-border data trust space aimed at ASEAN [2] - The plan includes the establishment of comprehensive experimental bases for humanoid robots and the introduction of enterprises to create a data processing and labeling industry ecosystem [2] Group 2: Forestry Industry Development - The region's forestry industry has grown into a trillion-yuan industry, but faces challenges in the transformation and upgrading of wood processing [3] - The strategy includes optimizing supply-side structural adjustments, enhancing new productivity, and building forestry industrial parks to improve quality and efficiency [3] - There will be targeted招商 (investment attraction) for leading domestic forestry enterprises and support for local enterprises to expand into overseas markets [3]
2025中国国际大数据产业博览会昨日开幕
Zheng Quan Ri Bao· 2025-08-28 16:10
Core Insights - The 2025 China International Big Data Industry Expo focuses on the value release of data elements and the process of data assetization in the context of a booming digital economy [1][2] Group 1: Data Element Innovation Ecosystem - Since the establishment of the National Data Bureau in October 2023, significant reforms for market-oriented allocation of data elements have been initiated, with nearly 30 related policies introduced [2] - The National Data Bureau is promoting the construction of national data infrastructure and has established 25 city business nodes, with a total computing power of 780,000 Pflops, ranking second globally [2] Group 2: Data Assetization - Data assetization is becoming a powerful engine for enterprises to unlock data value, serving as a key to address the challenge of realizing data value [4][5] - The introduction of the "Interim Provisions on Accounting Treatment of Enterprise Data Resources" effective from January 1, 2024, clarifies the inclusion of data assets on balance sheets, improving financial indicators for companies [5][6] Group 3: Innovations and Applications - Companies showcased new products at the expo, such as Inspur's Haiyue inSuiteONE, which simplifies digital transformation processes for enterprises [3] - The "Quick Claim for Car Insurance Injury" platform by Qulian Technology utilizes blockchain and privacy computing to enhance claim efficiency and customer satisfaction [3] Group 4: Financialization of Data Assets - The first data asset loan of 5 million yuan was issued in Yunnan Province, marking a significant step in the financialization of data assets in the cultural tourism sector [5] - Companies with substantial data assets can enhance investor confidence and improve market perception of their potential in the digital era [5][6]
浙江沪杭甬:上半年实现归母净利润27.87亿元 同比增长4%
Zhong Zheng Wang· 2025-08-25 13:11
报告期内,集团所辖9条高速公路的通行费收益为51.32亿元,同比增长0.4%,整体车流量同比 增长1.5%。 2025年上半年,公司有序推进甬金高速、乍嘉苏高速改扩建项目;中标甬舟高速复线(二期)项 目;完成沪杭甬高速改扩建工程可行性研究招标并全面启动工可研究;通过合营平台收购桂三高速51% 股权,桂三高速是川渝、云贵地区前往珠三角的主要通道之一,亦是著名的旅游线路,该收购进一步拓 展了区域布局。 中证报中证网讯(王珞)日前,浙江沪杭甬高速公路股份有限公司(以下简称"浙江沪杭甬"或"公 司")(港交所编号:0576.HK)公布截至2025年6月30日止6个月未经审计合并经营业绩。 报告期内,集团经营业继续保持良好增长态势,收益与2024年同期相比增长3.8%,为86.85亿元 (人民币,下同);归属于公司拥有人利润为27.87亿元,同比增长4.0%;基本每股盈利为0.4651元, 同比增长4.0%,摊薄每股盈利为0.4651元,同比增长5.6%。 主业聚焦多元提质增效 通行费收益持续增长 绿色低碳加快布局。融合AI大模型升级智慧高速以提升智慧保畅成效,新能源重卡换电站、分布 式光伏发电项目正式运营,新能源建设 ...
国家管网福建公司实现数据产品“资源”变“资产”
Yang Shi Wang· 2025-08-23 07:23
Group 1 - The core viewpoint of the news is that the National Pipeline Network Group Fujian Company has successfully launched a high-quality data set for pipeline fiber optic early warning on August 22, marking a significant transition from "resource" to "value" in the oil and gas pipeline operation sector [1][2] - The data set includes distributed fiber optic acoustic sensing data from the "Zhangzhou to Quanzhou" pipeline segment, with a total data resource scale of 150GB, which is suitable for deep adaptation to AI model training needs [2] - The intelligent analysis of fiber optic vibration anomaly signals can significantly enhance the efficiency and accuracy of risk event type warnings, ensuring the safe and stable operation of oil and gas pipelines [2] Group 2 - The National Pipeline Network Group Fujian Company is primarily responsible for the construction and operation management of the oil and gas pipeline network in Fujian Province, demonstrating a proactive response to the "smart transformation and digital upgrade" strategy of the province [2] - The systematic transformation of data resources into data assets not only enhances the authority of the National Pipeline Network Group's data but also lays a solid foundation for the accelerated integration of artificial intelligence across various industries [2]
西湖财政探索“数据生金”新路径
Hang Zhou Ri Bao· 2025-08-22 03:18
Core Viewpoint - Zhejiang Shiguang Coordinate Technology Co., Ltd. has completed the data asset registration and established a "Digital Content Asset" zone in collaboration with Hangzhou Data Exchange, marking a significant step in the monetization of digital content in the cultural and creative industry [1] Group 1 - The "Digital Content Asset" zone aims to create a capitalized platform for the circulation of cultural and creative assets, enhancing the value of data through industry collaboration [1] - Shiguang Coordinate is integrating its digital asset library with Zhejiang University's Oasis AI Virtual Imaging Laboratory and the Digital Imaging Asset Alliance of Chinese Universities to facilitate the transformation of academic resources into industrial resources [1] - The West Lake District will continue to promote data asset management by enhancing collaboration among various departments, including the Finance Bureau and Data Resource Bureau, to ensure effective connection of data property rights and registration [1] Group 2 - The district is focusing on areas such as smart parking and smart healthcare, forming a professional alliance for data assets with universities, accounting firms, and evaluation agencies to foster deep integration of industry, academia, and research [1] - Initiatives include the launch of the "Data Enterprise Direct Access" service and conducting over three specialized events, training more than a hundred companies to transition from "data dormancy" to "data monetization" [1]
破局“数据盲区”:银行数智生态如何重塑中小微融资新范式?
Sou Hu Cai Jing· 2025-08-20 10:09
Core Insights - The article discusses the challenges faced by small and micro enterprises in securing financing, highlighting the need for a digital transformation in the banking sector to address these issues [1][2][4][6] Group 1: Challenges in Financing - Small and micro enterprises are trapped in a "data fog," leading to difficulties in obtaining financing due to a lack of standardized financial reporting and transparency [4][6] - Over 60% of enterprises have issues with non-compliant financial statements and low information transparency, which exacerbates the information gap between banks and businesses [4] - The reliance of banks on "hard information" such as standardized financial reports and sufficient collateral creates barriers for small enterprises, which often lack these resources [6] Group 2: Technological Solutions - The integration of advanced technologies like AIGC is seen as a potential solution to the financing challenges faced by small and micro enterprises [2][13] - Companies are encouraged to embrace digital platforms and AI to streamline operations and improve data quality, which is essential for securing financing [9][12] - The introduction of the "Micro Wind Smart Selection Digital Enterprise Service System" aims to address the core financing challenges by breaking down data silos and standardizing enterprise data into recognized "financing assets" [10][11] Group 3: Financial Institution Empowerment - Financial institutions can enhance their efficiency and risk management by utilizing automated data collection and analysis technologies, such as RPA and AI [13][14] - The use of advanced algorithms and models can support banks in various financing scenarios, including risk assessment and loan management [14][15] - The collaboration between enterprises and financial institutions through digital platforms is crucial for overcoming the data blind spots that hinder financing for small and micro enterprises [15]
特需儿童康复领域首例!复米健康凭数据资产获2000万元银行授信
Core Insights - The article highlights the transformation of data into assets within the special needs rehabilitation sector, showcasing the pioneering efforts of Fumi Health in this domain [1][2][4] Group 1: Company Overview - Fumi Health, established in 2014, focuses on services for children with autism spectrum disorders and developmental disabilities [1] - The company has accumulated over 30,000 case assessment records, nearly 200 million intervention behavior records, and over 3 million supervisory decision records [1][3] Group 2: Data Assetization Process - Fumi Health has successfully registered its data products on the Zhejiang and Shaanxi data exchange platforms, receiving multiple certifications [1] - In March 2025, Fumi Health achieved the first instance of data asset financing in the industry, with its data assets valued at approximately 30.84 million yuan [2] - The company received a bank credit approval of 20 million yuan from the Shenzhen branch of the Bank of Communications for its data assets [2] Group 3: Data Quality and Utilization - The data collected by Fumi Health is characterized by its completeness, covering the entire lifecycle of special needs children's interventions [3] - The company has implemented a comprehensive data management system that ensures high-quality data collection, storage, and application for AI training [4][5] Group 4: Industry Context and Policy Influence - The establishment of the National Data Bureau in 2023 has accelerated the marketization of data elements, providing a favorable environment for Fumi Health's data assetization efforts [4] - The company aims to address industry challenges such as low standardization and high labor costs by leveraging AI technology and data assetization [4][6]
从“亿元级”到“千亿级”:中国数据交易市场的十年飞跃
Sou Hu Cai Jing· 2025-08-12 09:49
Core Insights - The data exchange market in China has rapidly evolved since 2015, with over 50 data trading institutions established by July 2025, and the trading scale projected to exceed 300 billion yuan by 2025 [2][4][19] - Data exchanges serve as a regulated marketplace for data, allowing for the standardized transformation of data products and facilitating compliance and quality assurance [3][5] - The market is characterized by a diverse range of participants, including government entities, data service providers, and large internet companies, with buyers spanning various industries such as finance, healthcare, and AI [5][6] Group 1: Market Development - The trading scale has grown from "billion-level" in 2015 to "trillion-level" by 2024, indicating significant growth in both quantity and quality of data transactions [2][19] - The market is expected to reach approximately 2.841 trillion yuan by 2025, with a compound annual growth rate of 46.5% from 2021 to 2025 [19][34] - The introduction of policies and frameworks, such as the "Data Element ×" three-year action plan, aims to enhance the coordination between on-site and off-site trading by 2026 [6][10] Group 2: Key Players and Institutions - Major data exchanges include Beijing International Data Exchange, Shanghai Data Exchange, Shenzhen Data Exchange, and Guizhou Data Exchange, which are leading the market [4][7][12][15] - By June 2025, Beijing International Data Exchange had achieved a trading scale of 2,250 TB, with an annual growth rate exceeding 200% [12] - Shanghai Data Exchange reported over 2,000 signed data merchants and a trading amount exceeding 50 billion yuan in 2024 [13] Group 3: Emerging Trends and Technologies - The data trading landscape is expanding into new fields such as AI model training, medical diagnostics, and scientific research, reflecting the increasing importance of data in driving technological and economic development [20][34] - The introduction of advanced technologies like blockchain and privacy computing is enhancing the security and traceability of data transactions [22][28] - Data assetization is becoming a significant trend, with projections indicating that the market for data assets will reach 8,278 billion yuan by 2030 [19][34] Group 4: Case Studies - Notable case studies include the successful assetization and financing of data by companies like BAIC New Energy and the implementation of the "U235" framework by Shanghai Data Exchange, which utilizes blockchain for transparency and efficiency [24][27] - Shenzhen Power Supply Bureau's data product for enterprise electricity behavior was successfully traded, showcasing the application of privacy computing in data transactions [31] - The collaboration between Southern Power Grid and a chemical group demonstrates the practical benefits of data trading in optimizing production and reducing costs [32]
上海数据资产金融创新再提速
Jin Rong Shi Bao· 2025-08-08 07:55
Core Viewpoint - The collaboration between China Construction Bank (CCB) Shanghai Branch and Shanghai Data Exchange aims to innovate financial solutions for data asset financing, providing a credit line of 30 million yuan to Weizhi Technology, marking the highest credit amount for the "Data Easy Loan" program to date [1][2]. Group 1: Financing Innovation - Weizhi Technology, a leading spatiotemporal AI platform, has successfully completed data product certification and trading on Shanghai Data Exchange, with total transaction amounts exceeding 70 million yuan in the first half of the year [2]. - The company utilized its data product "Weizhi Address Search" for data asset pledge financing, receiving a credit line of 30 million yuan from CCB Shanghai Branch after asset evaluation using the "Jinzhun Estimate" tool [2][3]. - The new loan model provides flexible financing channels for enterprises, addressing challenges faced by small and micro enterprises in obtaining loans and equity financing [3]. Group 2: Data Asset Challenges - Data asset financing presents both opportunities and challenges, including difficulties in asset ownership confirmation, valuation, custody, and disposal [4]. - The replication nature of data and multi-party participation complicate asset ownership confirmation, necessitating clearer policies and legal frameworks regarding data asset rights [4]. - Current valuation methods for data assets are not mature enough to fully reflect market value, and the volatility of data asset values increases the difficulty of disposal [4]. Group 3: Market Practices and Regulations - Shanghai Data Exchange provides essential functions such as registration, valuation, trading, disclosure, and disposal of data assets, with the "Jinzhun Estimate" tool offering fair value analysis [5]. - The evaluation of data asset value is a rigorous process based on facts and data, supported by extensive market transaction records that help assess market dynamics and potential risks [5]. - The development of data assetization should focus on empowering the real economy, avoiding hidden liabilities and bubbles, and ensuring that business models and cash flows are closely monitored [5].