Starting from December 5th, the "Guidelines for Data Asset Confirmation Work" formulated by the Zhejiang Provincial Department of Finance and led by the Zhejiang Provincial Institute of Standardization will be officially implemented. This is the first provincial-level local standard developed in China for data asset confirmation. Liu Xingliang, a member of the Information and Communication Economy Expert Committee of the Ministry of Industry and Information Technology, stated that for listed companies, clear data asset recognition and management processes can enhance investors' confidence in the company's data management capabilities, and have a positive effect on enhancing the company's stock price and market competitiveness. What is the difference between data resources and data assets? What is the impact of the Work Guide on further unlocking the value of data?

According to data released by the Ministry of Commerce, the scale of China's digital economy reached 50.2 trillion yuan in 2022, a year-on-year increase of 10.3%, accounting for 41.5% of GDP. As a key production factor in the digital economy, data is gradually becoming an extremely important new asset. Guo Mingjun, Director of the Planning Department of the Big Data Development Department of the National Information Center, said, "The commercial value of data assets is gradually recognized by enterprises and has become a key element that cannot be ignored in the process of enterprise development. However, only data that meets certain conditions can be recognized as a 'data asset'.", The Accounting Standards for Enterprises - Basic Principles define assets as "resources formed by past transactions or events of an enterprise, owned or controlled by the enterprise, and expected to bring economic benefits to the enterprise." According to relevant standards, if a resource is to be recognized as an asset, in addition to meeting the aforementioned asset definition, it must also meet the following two conditions: first, the economic benefits related to the resource are likely to flow into the enterprise, The second is that the cost or value of the resource can be reliably measured. Only resources that meet both asset definition and asset recognition criteria can be recognized as assets and included in the company's balance sheet. Obviously, there are a series of definitions and usage prerequisites from data to data assets, and then to data assets that can be included in the table.

02 | Technology still needs to be upgraded. Compared to traditional production factors, data presents characteristics such as huge capacity, rich types, and high-speed circulation. In terms of confirming the economic benefits generated by data resources and tracing the survival cycle of data, it is not possible to continue to apply the traditional production factor property rights registration system. Not only does it need to build a new property rights registration system, but the corresponding technology also needs to be upgraded. According to Gao Yingmai's analysis, the technologies involved in data assetization can be divided into four levels: first, data traceability technologies, including blockchain, data watermarking, label recognition (barcode, QR code, RFID), etc; The second is data management technology, including metadata management, databases, etc; The third is data governance technology, including data cleaning, data mining, data aggregation, data visualization, etc; The fourth is data security technology, including identity management, tamper prevention, data security situational awareness, and trusted execution environment. Gao Yingmai said, "The core of data traceability technology is to solve related source problems such as data production, authorization, and operation; the core of data management technology is to solve related management problems such as data access, control, and acquisition; the core of data governance technology is to solve the problem of data asset quality level; the core of data security technology is to ensure the security of data assets." A detailed classification of technical methods such as data resource traceability has been conducted, including data traceability models, data traceability methods, etc. It is proposed to use security algorithms such as blockchain, smart contracts, and artificial intelligence to embed data traceability into lifecycle nodes such as data collection, data rights confirmation, data circulation, data trading, and data supervision. Wang Peng, Executive Director of the Data Asset Management Research Institute, told reporters that data has both intangible and non exclusive properties. Intangible properties make traditional property rights certification methods unsuitable, while non exclusivity makes it difficult to distinguish between original and duplicate data. The same data property rights may be registered multiple times. In this regard, it is necessary to use new technologies such as blockchain to register data property rights more scientifically, accurately evaluate the value of data and the future development potential of enterprises, increase market effectiveness, and provide investors with a more just investment environment.

03 | From "Resources" to "Assets" Liu Xingliang stated that data resources usually refer to various raw data accumulated by enterprises or organizations in the operation process. When data resources are systematically organized, analyzed, and transformed into forms that can directly support decision-making and create economic value, they become data assets. From "resources" to "assets", a series of conditions must be met: data must be accurate and complete, easily accessible and usable, data can be transformed into useful information knowledge after analysis and processing, and the potential value of data can be identified and transformed into actual economic value. The Work Guidelines propose a confirmation work framework consisting of internal control, management system, technical support, work system, workflow, and work execution elements for data asset management. Liu Xingliang believes that the Work Guidelines provide guidance for the process of data asset confirmation, which helps to standardize and standardize data asset confirmation. Through standardized confirmation processes, the value evaluation of data assets will be more accurate and credible, which helps to enhance the recognition of data assets, promote the commercial utilization of data, and help enterprises better manage their data resources. The confirmation of standardized data assets also helps to establish a market mechanism for data trading and sharing, making data "live".

04 Jingtai Discussion | What impact will the acceleration of data assets bring? In recent years, with the acceleration of the development of China's data element market, the capital market's attention to data element concept stocks has been continuously increasing. For enterprises with data assets, especially listed companies, what impact will the gradual advancement of data asset recognition bring? Data asset recognition can help enterprises more accurately evaluate the value of their data assets, which is particularly important for asset evaluation and financial reporting of listed companies. This increases the transparency of the company's financial reports, which helps attract investors and improve market trust. Meanwhile, through data asset recognition, enterprises can better manage their data resources, optimize data storage, processing, and analysis processes, thereby improving data utilization efficiency and value creation capabilities. In addition, with the increasing strictness of data protection regulations, data asset recognition helps enterprises better understand and manage compliance risks related to data, avoiding legal and financial risks arising from data leakage or improper use. Listed companies can form professional teams to be responsible for the confirmation, management, and utilization of data assets, ensuring effective management and maximum value of data assets; Develop clear data asset management strategies based on the company's business needs and data characteristics, including data collection, storage, processing, analysis, and protection; Pay attention to and comply with relevant data asset recognition standards and data protection regulations to ensure the legal and compliant use of data assets; Invest necessary technical resources, adopt advanced data management and analysis tools, and improve the management efficiency and value creation ability of data assets. For listed companies, data asset ownership confirmation can increase their data assets, thereby thickening the asset items on the balance sheet, enhancing the asset amount of the listed company, and thereby boosting investor confidence in data holding enterprises. In addition, data asset recognition can also reduce the inclusion of immediate expenses in enterprises through assetization, thereby increasing the current profits of enterprises. In the long run, this will increase the investment of listed companies in data assetization.