Data Finance: Realization of Public Interests in the Development of Digital Economy

Data Finance: Realization of Public Interests in the Development of Digital Economy

Zhu Yangyong, Xie Bofeng

Paper citation format:

Zhu Yangyong, Xie Bofeng. Data Finance: Realization of Public Interests in the Development of Digital Economy [J]. Big Data, 2023, 9(2): 163-166.

ZHU Y Y, XIE B F. Data finance as the public advantages in the development of digital economy[J]. Big Data Research, 2023, 9(2): 163-166.

Data is a key element of the digital economy, and the most valuable and extensive data is individual (enterprise or individual) behavior data. However, the value of a single individual behavior data is difficult to measure (it can even be said to have no value, of course it can also be said to be priceless), and individuals basically cannot obtain corresponding benefits in the value development process of their individual behavior data. interests, it will cause individuals to oppose any form of behavioral data development and utilization. In addition, on December 2, 2022, 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" proposed to fully realize the value of data elements and promote all people to share the dividends of digital economic development. In the era of digital economy, when data is used as a production factor to generate value, its natural public attributes must require public interests, and national finance is the most direct way to realize public interests and share development dividends. Therefore, in the current era of digital China construction and digital economy development, it is very meaningful to explore the use of fiscal and taxation tools and means to obtain a certain proportion of fiscal revenue from the key elements of the digital economy, and to play the basic and pillar role of finance in national governance. of. The author calls this system "data finance".

1 The concept of data finance

Data finance is a general term for the fiscal and taxation system adapted in the process of data development and circulation, and can be divided into narrow data finance and broad data finance. Data finance in the narrow sense refers to the fiscal revenue and expenditure activities involved in the entry of public data into the market; data finance in the broad sense includes more entities in the government and the market, and fiscal activities in the production, use and circulation of all data on a larger scale, involving various Fiscal tools and means.

Looking further, taking the (static) life cycle of data as the observation dimension, data finance in a narrow sense can be divided into two stages. The first stage is before data enters the market, and the main body of its production and trading activities is the government. Data finance dominates this stage, including the core process of reserve, production and authorized use of data; the second stage is after data enters the market, its production The main body of business and transaction activities is the enterprise, and data finance is on the periphery of the process of data development, service, and reproduction. It tries not to interfere with the process of data utilization by enterprises and individuals, and mainly realizes the incentive and management of the data market through fiscal and taxation means. In the broad sense of data finance, the first stage is mainly the financial activities accompanied by the supervision before data enters the market, such as fiscal and taxation policies and management in the links of digital business establishment, data product development, registration, etc.; the general content of the second stage is the same as Data finance in the narrow sense is similar.

Data finance is not simply "selling data". "Selling data" is just a common name for a certain circulation method of data elements. In fact, according to the different ways in which data functions, the data products formed by the conversion of data resources include data itself, algorithm programs for processing data, and data services based on data, and other diverse forms. Around the rich data products, multiple links have been formed. , multi-subject, multi-level, and multi-mode data market system. Therefore, data financial issues cannot be limited to fiscal and tax issues in a certain link. It is necessary to jump out of finance and taxation, comprehensive data basic institutional arrangements, follow the future trend that data will become one of the main production factors, and systematically consider data-based financial operations. Including but not limited to the cultivation of financial resources, the design of income system, etc. The concept of data finance at this level needs to pay more attention to and examine the public interests brought about by the digital economy with data as a key element.

2 Significance of establishing data finance

The establishment of data finance is the need to improve the basic data system. Data finance is based on the data-based system to reflect the public interests of the digital economy and ensure that all people share the development dividends of the digital economy. On the one hand, many data resources are public to a certain extent. The current institutional innovation of data rights separation has broadened the space for data circulation and use. The operator of the collection point has become the de facto owner of data rights and interests. The transfer of property rights to the government or enterprises (platforms) highlights the urgency of establishing a data fiscal system to reflect the public interest needs of the digital economy. On the other hand, the way in which the value of data (or data products) is generated is public. The more universal value of data should be generated by aggregation, that is, the value of big data formed by the aggregation of massive data. Among them, the value-added data formed by aggregation needs to be adjusted by establishing a data finance. In addition, the new demand for providing relevant public goods that matches the digital economy and the data market also puts forward corresponding requirements for data finance.

The establishment of data finance is the need to play the important role of finance in modern national governance. As a factor of production, "data" will have a significant impact on social and economic operations and national governance. In modern national governance, while finance plays a fundamental and pillar role, it is undoubtedly necessary to use the element of "data" as an important tool and means. On the one hand, data finance should promote data elements to become new production factors on par with traditional elements, laying the foundation for the further development of data elements and the digital economy; on the other hand, data finance should innovate financial tools and mechanisms and embed them into new elements The social production and cycle form a good environment for the market-oriented allocation of factors, play the pillar role of national governance in the era of digital economy, and inject a new meaning of the times into the role of finance.

In short, data finance is the need to improve the system and promote the role of data. It is also the need to improve national governance in the construction of digital economy, digital society, and digital China. It is an important starting point to promote high-quality development and realize Chinese-style modernization through data elements.

3 Main tasks of implementing data finance

First of all, in principle, data finance mainly uses fiscal expenditure and revenue as means to give full play to the function of governing the data element market. In the initial stage of data marketization that is not yet mature, financial incentives and appropriate light taxes will be the mainstay, and the formation and development of the data supply and demand market will be promoted through data finance. With the improvement of the system and the expansion of the scale, strive to form a benign data finance as soon as possible cycle.

Second, establish the ways and means of data finance. At a certain financial level, combined with the layout of the data exchange, the public data is merged and processed according to the principle of territoriality. In the first stage, data is provided through public services and paid services; in the second stage of transaction circulation, according to different transaction links and different identities such as data vendors (transaction middlemen), data suppliers, and data demanders, According to the requirements of fairness, unity and industrial development, corresponding fiscal and taxation policies and management requirements are applied respectively.

Third, establish a data fiscal system and policy. On top of the basic data system, the data fiscal system includes public data asset management evaluation, public data operation authorization, public data product (or service) pricing, data taxation, data fiscal revenue distribution, and incentives, coordination, and regulations for the development of the data market and other policies. Of course, the above institutional requirements are only assumptions at the stage of proposing the concept of data finance, and the various systems involved need further research.

Fourth, carry out the operation of data finance, mainly including the subject access of data finance, the selection of financial operation levels, the operation system, the tax collection and management system of data transactions, etc. The main body access of data finance adopts the license system in the first stage, and the transaction circulation market in the second stage can register and record the main body qualification of the data market. The characteristics and feasibility of data should be considered in the selection of data financial operation levels. From the current point of view, it is more appropriate to take the prefecture-level city (municipalities directly under the Central Government as the main body) as the main body of operation. In terms of the operating system, there are mainly 7 steps: data ownership confirmation, data collection and storage, data maturation, security review, data storage, data use, and circulation monitoring. Tax collection and management can be based on the existing collection and management system.

4 Policy Recommendations

First, on the basis of the basic system of data, promote the concept of data finance to obtain a legal status. On the basis of the existing consensus on data state-owned assets and authorized operation of public data, conduct more systematic scientific research, clarify misunderstandings, demonstrate the feasibility of data finance, standardize the concept of data finance, and strive to be in the existing data regulations and other related When the law is revised, the data fiscal regime can be given the proper status and formulation.

Second, improve the data financial system. On the basis of establishing the concept of data finance, systematically focus on various links such as data transactions and flows, study data asset finance, accounting, taxation, budget, performance, information disclosure and other systems, and formulate corresponding policies and management methods .

Third, choose a pilot to explore. On the basis of the general consensus and the basic formation of the system, some industries or regions with better conditions are selected to formulate feasible plans, carry out pilot verification, and improve and explore data finance in practice.

About the Author

Zhu Yangyong (1963-), male, doctor, professor of School of Computer Science and Technology, Fudan University, deputy director of Data Industry Research Center. Deputy editor-in-chief of the journal "Big Data", vice-chairman and chief scientist of the Agricultural Big Data Industrial Technology Innovation Strategic Alliance, vice-chairman of the National Engineering Research Center for Big Data Collaborative Security Technology, and deputy director of the National Defense Big Data Professional Committee of the Chinese Society of Automation. An international data science advocate who proposes concepts and systems such as data community, data science, data body, data autonomy, and data finance. Published more than 200 academic papers, published monographs such as "Data Science", "Charming Data", "Specific Group Mining", "Data Autonomy", and served as the editor-in-chief of "Big Data Technology and Application Series" (22 volumes) and "Big Data Resources" editor in chief. The main research direction is data science and digital economy. Recent research focuses on digital transformation, data finance, data assets, data autonomy and data cross-border, etc.

Xie Bofeng (1976-), male, Ph.D., associate professor of the School of Finance and Finance of Renmin University of China, deputy director of the Institute of Digital Taxation of Renmin University of China, secretary-general of the "Internet + Finance and Taxation" alliance, and data scientist of Shanghai Key Laboratory of Data Science, Editorial board member of "Big Data" journal. In 2013, he was selected into the first batch of taxation leading talent training programs of the State Administration of Taxation. In 2020, he was selected as an expert in the expert studio of the Ministry of Finance. Dissertation Award and other prestigious academic honors. The main research directions are the in-depth application of artificial intelligence and big data in the field of finance and taxation, the theory and practice of smart taxation, the digital economy, and the fiscal and taxation system matching data elements.

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