Since the beginning of this year, the market's exploration of data assetization has accelerated significantly. For example, PwC has launched an integrated platform for accounting and processing of enterprise data resources to help enterprises strengthen data resource management and achieve convenient "entry of data resources into the table", while Shanxi Yuanda Zongheng Technology Co., Ltd., a private big data company in Shanxi, announced the implementation of the chief data officer system, focusing on breaking the fragmented model of data resource development and utilization. Making data an asset has become the conscious pursuit of more business entities for future development.
How to walk steadily on this road of coexistence of opportunities and challenges?
The institutional foundation has been gradually consolidated
The "Opinions of the Central Committee of the Communist Party of China and the State Council on Building a Data Basic System to Better Play the Role of Data Elements" issued in December 2022 proposes to establish a "data property rights system that protects rights and interests and is used in compliance", "a data element circulation and trading system that is compliant and efficient, and combines inside and outside the market", and "a system for the distribution of data element income that reflects efficiency and promotes fairness". These deployments have further consolidated the institutional foundation for data assetization.
In addition, the Ministry of Finance has formulated and issued the Interim Provisions on the Accounting Treatment of Enterprise Data Resources, which will come into force on January 2024, 1. On the basis of full argumentation, the Interim Provisions clarify that enterprise data resources are applicable to the current accounting standards for business enterprises, and do not change the accounting recognition and measurement requirements of the current standards. By formulating special and unified regulations for data resources, the doubts about whether data resources can be recognized as accounting assets and what type of assets can be 'included in the table' in practice are resolved, and the basis for measurement is clarified. The relevant person in charge of the Accounting Department of the Ministry of Finance said.
In order to standardize the practice of data asset appraisal and protect the legitimate rights and interests of the parties involved in asset appraisal and the public interest, the China Asset Appraisal Association issued the Guiding Opinions on Data Asset Valuation, which will come into force on October 2023, 10. In this document, there is a clearer definition of data assets: data resources that are legally owned or controlled by a specific entity, can be measured monetarily, and can bring direct or indirect economic benefits.
"Data resources are different from physical resources and traditional intangible assets, because of their non-entity, dependence, sharing, processability and other characteristics, especially the volatility of value, which has a significant impact on the reliability of the recorded value of data resources and the judgment of enterprise value. Cui Zhijuan, a professor at Beijing National Accounting Institute and director of the Digital Audit and Risk Management Research Center, said that standardizing the evaluation of data assets and guiding the exploration of valuation methods suitable for data assets is of great significance to promote the classification of data and the construction of high standards of data, promote the circulation and market transactions of data assets, and optimize the valuation of enterprise market value.
On the one hand, the continuous improvement of digital infrastructure and the rapid development of the digital economy have further opened up the space for data assetization. By the end of 2022, China has built the world's largest optical fiber network, with a total optical fiber mileage of nearly 6000 million kilometers, a total of nearly 600 million standard racks for data centers, and more than 5.230 million 2012G base stations in the country, all of which rank among the top in the world. In recent years, the scale of the core industries of the digital economy has accelerated, and the national software business revenue has increased from 2.5 trillion yuan in 2022 to 10.8 trillion yuan in <>.
On the other hand, the demand for data circulation transactions is also stronger. According to incomplete statistics, as of the end of June 2023, there are 6 data exchanges initiated, led or approved by the government across the country, and the trading scale of the top data exchanges has reached 44 million yuan, and it is showing an explosive growth trend. For example, the monthly trading volume of the Shanghai Data Exchange has exceeded 1 million yuan, and the annual trading volume is expected to exceed 2023 billion yuan in 10. In 2022, the market size of data elements in Beijing will be about 350 billion yuan, accounting for about 39% of the country's total.
Experts believe that in the short term, the data basic system will give birth to a data trading market with a scale of 3000 billion yuan to 5000 billion yuan, and in the medium and long term, the potential scale of the data asset-related market will be more than 60 trillion yuan, and the pricing of data elements is the "golden key" to open a new 10 trillion yuan market.
Valuation is difficult
The characteristics of data resources determine that the road to assetization is not smooth.
"Data asset value analysis is difficult in many ways. Liu Wutang, deputy director of the professional technical committee of the Beijing Asset Appraisal Association, said for example, for example, the value of data assets will change with continuous processing, changes in the number of times and number of users, differences in users, etc., and the analysis of data asset ownership is more complex, and the data quality will be the same but may produce different values. At the same time, as an intangible asset, data assets should be owned or controlled by specific entities, but due to the characteristics of data assets themselves, they are easy to be stolen, sometimes difficult to control and use, and lack legal protection.
Cui Zhijuan said: "The difficulty of data asset valuation mainly lies in the identification and judgment of factors that affect the value of data assets, as well as the reasonable choice of asset valuation methods. The "Guiding Opinions on Data Asset Valuation" gives the cost factors, scenario factors, market factors and quality factors that affect the value of data assets, and also gives three basic methods and their derivative methods of data asset valuation: income method, cost method and market method. However, there are many variable elements that require professional judgment among the influencing factors of data assets, such as the opportunity cost that affects the value of data assets, market prospects, and the accuracy and timeliness of data, which require high prediction and prediction capabilities. In addition, the key to data asset valuation is data quality assessment, which requires high professional ability.
In practice, it is not easy to enter data resources into a table. "According to the relevant regulations, data resources are generally classified as intangible assets or inventory. Compared with outsourced data resources, enterprises are more concerned about how endogenous data resources are transformed into assets. Cao Yang, a partner of Grant Thornton Certified Public Accountants (Special General Partnership), told reporters that many of these endogenous data resources are closely related to the daily business activities of enterprises, and it is a difficult task to distinguish the expenditure of data resources which are research and development activities and which are production and operation activities. Traditional enterprises generally lack a clear way to realize the economic benefits of data resources. These measurement and profitability model difficulties create challenges in accounting for the recognition of endogenous data resources as assets.
The registration of data elements is an important part of data assetization, and relevant explorations have continued to advance since the beginning of this year. For example, the Beijing International Big Data Exchange issued the first batch of data asset registration certificates, covering energy, transportation, meteorology and other fields. Wenzhou Big Data Operation Co., Ltd.'s data product "Credit Data Treasure" has completed the confirmation and registration of data assets, which is also the first confirmation and registration of data assets in Wenzhou. Although the relevant exploration is constantly advancing, a number of interviewed experts said that the current registration of data elements is still in the stage of small-scale practice, and there are many problems such as inconsistent platform construction standards, imperfect institutional systems, and low enthusiasm of participants.
The data transaction model also needs to be improved. Experts believe that at present, data trading institutions are still in the exploration stage, and most of them are service providers for matchmaking business, and lack experience in the marketization of data elements such as data rights confirmation, data pricing, and data transactions, and the design of circulation mechanisms, and cannot solve the problems of unstable data sources, data interception and leakage, uncontrollable data use, and difficult to accurately assess data value in the process of data transactions.
Innovation is accelerating
Experts believe that it is necessary to further strengthen macro research and rule design, clarify the ideas, principles and methods of data asset management, and take multiple measures to promote data asset management to a benign development track. Liu Wutang suggested that in order for data assets to be truly popularized and utilized, expand the trading market, and broaden the application prospects, it is also necessary for relevant departments to further strengthen overall coordination and further form a sound asset confirmation, evaluation, accounting, auditing, and taxation system. At the same time, it is necessary to further accelerate the construction of the rule of law for data assets.
The assessment method should also be more precise. "In terms of enhancing the ability to judge the rationality of the value of data assets, it is recommended that the evaluation agency decompose the influencing factors of the value of data assets into value elements, establish a data asset value database, and improve value judgment with the help of science and technology. Selecting the appropriate data asset valuation method requires analyzing the characteristics of data assets and exploring valuation methods that are compatible with the characteristics of data assets in addition to the income method, cost method and market method. Cui Zhijuan thinks.
For the inclusion of data resources in the table, Cao Yang suggested that enterprises should further focus on the development and utilization of data resources, consciously establish and improve the internal control system, and better distinguish the cost of research and development of data resources from project costs and operating costs by optimizing process and system design. At the same time, it is necessary to further expand the application field and profit model of data resources, and explore ways to monetize data resources that are more suitable for their own characteristics.
In response to difficult problems, innovation and exploration are accelerating. Guizhou has issued an implementation plan for the reform of the market-oriented allocation of data elements this year, proposing to innovate the data property rights system, explore new ways of data property rights registration, strengthen the high-quality supply of data elements, and standardize data circulation and transactions, etc., by the end of 2025, a major breakthrough has been made in the reform of data resources and assets, and the data element market system has been basically completed.
Guangdong unites government departments and leading enterprises in the artificial intelligence (large model) industry to aggregate multimodal data covering text, images, video, audio and other multimodal data in stages and batches, and builds a high-quality Chinese corpus through governance processes such as data collection, cleaning, hierarchical classification, and annotation, and actively promotes the transaction of artificial intelligence data products. Up to now, the cumulative transaction volume of artificial intelligence-related data products is nearly 5000 million yuan.
Local data trading centers also pay more attention to expanding the trading field in combination with market demand, and develop in the direction of more specialization and segmentation. For example, in February this year, the Beijing International Big Data Exchange launched an industrial data trading area to provide industrial enterprises with services such as data asset registration, data product development, and data asset trading. In June, the Western Digital Trading Center launched the automotive data trading area, relying on the "platform + resource + service" capability system to create a characteristic transaction model and a characteristic operation model, and strive to break the "island" of automotive data and improve the efficiency of automotive data circulation.
In addition, in August this year, Zhejiang Big Data Trading Center launched the industrial data circulation and trading area, which covers industrial big data, industrial financial big data, industrial chain big data and other fields, and can serve industrial manufacturing, urban governance, financial technology and other application scenarios. "With the continuous development of practice, more innovative practices will be born to promote the assetization of data resources. Cao Yang said.
Focusing on application, theoretical research is also deepening. Recently, the Beijing Asset Appraisal Association held a special forum on the theory and practice of data assets in the asset appraisal industry, and invited industry experts to conduct in-depth discussions on hot issues such as the current data market system, data element ownership confirmation and registration, data asset quality evaluation, data asset evaluation, and digital asset entry into the table from multiple angles and levels. Experts at the meeting said that value evaluation is the core in the process of data assetization, and evaluation institutions should give full play to the advantages of scenario analysis and financial analysis, reshape industry logic, expand competitiveness, carry out big consulting, and actively lead or participate in the design and implementation of data assetization programs.