Table of Contents

1. Definition of data assetization

1.1 Differences between data resources and data assets

1.2 The significance of data assetization

1.3 Data asset value-added model

2. The path to realize the assetization of enterprise data

2.1 Data resource stage

2.2 Data assetization stage

2.3 Data asset operation stage

Third, the inclusion of data assets in the table opens the reconstruction of enterprise value

3.1 Overview of data assets of listed companies

3.2 Data assets are included in the table to reconstruct the enterprise value

IV. Conclusion

Body

With the in-depth development of digital transformation, data has become a new focus of competition for enterprises and even countries. As a key component of the digital economy, data assetization is of great significance to promote the circulation of data value and promote the high-quality development of the digital economy.

This report will explain the definition and significance of data assetization, and discuss the practical path of data assetization and its role in enterprise value revaluation.

1. Definition of data assetization

1.1 Differences between data resources and data assets

In the digital era, the marketization path of data resources involves the process of gradually transforming raw data into data assets. This process includes four stages: data resourcing, data productization, data assetization, and data capitalization. Data resources and data assets are two concepts that are often mentioned, and there are significant differences between them.

Data resources refer to the raw data generated by a business or organization in the course of operations, which is often unprocessed and analyzed. The goal of data resourcing is to improve data quality and transform these chaotic raw data into valuable data resources. This process involves cleaning, integrating, and standardizing data so that it can be further analyzed and utilized.

Data assets refer to data that has been deeply analyzed, processed, and governed, and has clear usage scenarios and value. The goal of data assetization is to transform data resources into data assets with clear use scenarios and higher value through data analysis, data orchestration, and data governance. This process includes not only technical processing, but also the mining and realization of the business value of data.

Data resources focus more on the collection of raw data and information, while data assets are assets that can bring economic benefits to enterprises after processing, management, and value realization from these data. The marketization path of data resources is a gradual value-added process, from data resource to data productization, to data assetization, and finally to data capitalization. Data transaction is the key link in this path to realize the value conversion and circulation of data assets. Through these processes, data resources are transformed into data assets that can bring clear economic value to business and society.

1.2 The significance of data assetization

Data assetization is an important part of the digital economy. In the digital economy, data is regarded as an important factor of production, and the realization and circulation of its value are of great significance to economic development. Data assetization promotes the development of the digital economy by improving the value and liquidity of data, and has a profound impact on economic development and social progress from macro to micro.

1.2.1 Macro perspective: Promote the circulation of data value and promote the high-quality development of the digital economy

Data assetization is a prerequisite for data capitalization. At the macro level, data assetization helps build a more open and interconnected data market and promotes the rational allocation and effective use of data resources. Through data assetization, the liquidity of data can be improved, transaction costs can be reduced, and the rapid development of the digital economy can be promoted.

Through data capitalization, the efficiency and value of data utilization can be improved, thereby driving economic innovation and transformation. In addition, data assetization can also promote the sustainable development of the digital economy, and achieve green economic development by optimizing resource allocation and improving production efficiency.

1.2.2 Micro perspective: re-evaluate the value of the enterprise

At the micro level, data assetization is of great significance to the revaluation of enterprise value. The value of data assets for data-resourced and data-driven enterprises will be visible and revalued. Through data assetization, these enterprises can better explore the business value of data and improve their competitiveness and market position.

Data assetization makes an enterprise's data assets more transparent and quantifiable. Through data governance and standardization, organizations can more accurately assess the value of their data assets, thereby increasing the overall value of the enterprise. This value enhancement is not only reflected in the financial statements, but also in the market competitiveness and brand influence of the enterprise.

Data assetization can improve the operational efficiency of enterprises. Through data analysis and optimization, businesses can manage their business processes more effectively, reduce costs, and increase efficiency. In addition, data assetization can also promote enterprise innovation. Through in-depth analysis of data, enterprises can discover new business models and market opportunities, so as to achieve business innovation and transformation.

Data assetization can motivate enterprises to increase R&D investment. In a data-driven business model, enterprises need to continuously analyze and mine data to discover new business value. This requires enterprises to invest more resources in data research and development and innovation. Data assetization makes enterprises pay more attention to the value of data, so they are willing to invest more resources in data research and development.

Data assetization also drives enterprises to increase the demand for data purchases. While enterprises are increasing data R&D and innovation, they also need to buy more data to support their business development. This increase in investment and purchase demand will further promote the process of data assetization and form a virtuous circle.

1.3 Data asset value-added model

How exactly is the value of data realized? This may seem like a simple problem, but it has plagued many businesses and organizations. Data is valuable, and its value is realized in the use of data. To put it simply, there are two ways to realize the value of data: one is to empower the business, which corresponds to the DIKW pyramid, such as using data to reduce costs and increase efficiency, precision marketing, etc., which is the traditional method, and the value of data is indirectly realized by empowering the business; The second is through the entry of data resources into the table and data transactions, corresponding to the DRAC pyramid, where data realizes its value through data resources, assets and capitalization.

(1) DIKW model

In the DIKW pyramid system, each layer of the DIKW model, i.e., the data-information-knowledge-wisdom model, is built on top of the previous layer and given higher value and meaning. At the bottom of the pyramid is raw data, these raw facts or figures are the basis on which everything is built. When the data is processed and given context, information is formed – such as reports, statistical charts, etc. Further, through the understanding and analysis of information, people are able to extract knowledge from it, which is the result of a deep understanding of a particular topic. At the top of the pyramid is intelligence, which represents the ability to apply knowledge to solve problems, including highly intelligent applications through artificial intelligence.

In the DIKW model, data is still focused on the realization of value within the organization, so the value of data is an indirect value, that is, value cannot be directly monetized and measured, but must be realized through the promotion of business results.

While countries across the globe recognize the importance of data, there are differences in how they are used. In the United States, for example, the use of data follows the pyramid of DIKW, and ultimately the combination of data and AI. To put it simply, the U.S. values data for the sake of All In AI.

(2) DRAC model

Different from DIKW's AI orientation, the DRAC model is used for data resource entry and data transactions, as well as the final data capitalization operation. This reflects the direct value of data, and it is also the direction of China's current key development.

Similar to DIKW, the DRAC model is also divided into four layers. The lowest level is the data as the original material, which refers to the original and unprocessed data collection of the enterprise, which is a record of objective facts or events; Further up is Resource, which refers to data resources that have been organized and processed; The third layer is Asset, to which data resources become the data assets of the enterprise through table entry or transactions. At the top is Capital, which refers to the operation of data capital.

The

concept of Data Monetization was first introduced in September 2013 when Peter Aiken, the current Chairman of DAMA International, published "Monetizing Data Management: Finding the Value in your Organizationps Most Important Asset, First Edition". For the first time, the concept of "data monetization" was proposed.

In 2019, based on DAMA's business wheel diagram, DAMA Greater China Chairman Wang Guangsheng and Peter mentioned DRAC for the first time, and drew a DRAC triangular pyramid diagram according to DIKW. The DRAC model is intrinsically consistent with China's data element theory, and is more in line with the actual work and development process.

Wang Guangsheng said that the DIKW model provides a basic framework for understanding the value level of data, while the DRAC model further refines the management and value-added path of data assets on this basis. In practical applications, enterprises can flexibly build a comprehensive data asset management framework based on their own situation and development stage, guided by the DRAC model, so as to more efficiently explore and utilize the potential value of data.

2. The path to realize the assetization of enterprise data

The realization of data assetization can be divided into three main stages: data resource stage, data asset stage, and data asset operation stage. In the data resource stage, enterprises transform raw data into high-quality data resources through data acquisition, cleaning, preprocessing, and standardization. In the data assetization stage, enterprises transform data resources into data assets with clear use scenarios and economic value through steps such as data governance system construction, data product development and innovation, cost collection and accounting treatment, pricing and valuation. In the operation stage of data assets, enterprises manage and operate these data assets through data transaction mechanisms and platforms, data capitalization paths and strategies, etc., to realize their economic value.

2.1 Data resourcing stage

2.1.1 Data Acquisition

Data acquisition is the first step in the data resourcing phase, which involves collecting valuable data from an enterprise's internal systems and external sources. Correct and comprehensive data acquisition is key to ensuring data quality and the validity of subsequent analysis.

(1) Internal data acquisition

Business system data: Typical enterprise management systems include ERP, CRM, SCM, etc., and the transaction data, customer data, and supply chain data generated in these systems are the core of enterprise operations, reflecting the actual business situation and market dynamics of the enterprise.

Log data: such as server logs, application logs, user behavior logs, etc., which record details such as system running status and user interaction behavior, which is of great value for analyzing user habits and optimizing product functions.

Internal documents and knowledge: including internal reports, meeting minutes, training materials, patent documents, etc., which are important sources of unstructured data, containing the company's knowledge assets and experience.

(2) External data acquisition

Market and industry data: Market trends, competitor analysis, consumer behavior and other data obtained by purchasing third-party research reports, attending industry conferences, subscribing to industry news, etc., can help enterprises grasp market dynamics and formulate competitive strategies.

Open data sources: such as open government data, social media data, public web crawler data, etc., although these data may be scattered, but after integration and analysis, it can often provide new insights and opportunities for enterprises.

Partner data: Through data sharing or exchange with business partners, we can obtain complementary data resources, such as upstream and downstream data of the supply chain, customer sharing data, etc., which will help improve the collaborative efficiency and customer satisfaction of the supply chain.

After obtaining internal and external data, enterprises also need to carry out data integration work, converting, merging and standardizing data from different sources and different formats to form a unified and high-quality data set. This step is critical to eliminating data redundancy, resolving data conflicts, and improving data quality. Through data integration, enterprises can build a comprehensive and accurate data foundation to provide strong support for subsequent data governance, product development, and operations.

2.1.2 Data cleaning, preprocessing and standardization

Data cleaning, preprocessing, and standardization are the key links to ensure data quality and improve the effectiveness of data analysis, which directly determines the efficiency and accuracy of subsequent data governance, product development, and operation.

(1) Data cleaning: The purpose of data cleaning is to remove errors, anomalies, duplicates, or irrelevant information from the data to ensure the accuracy and consistency of the data. The cleaning process includes, but is not limited to, missing value processing, outlier detection and processing, duplicate value processing, etc.

(2) Data preprocessing: Data preprocessing is a series of transformations and processing of the original data to make it more suitable for subsequent analysis and modeling. Includes data type transformation, data normalization/normalization, feature engineering based on business understanding and data analysis.

(3) Data standardization: Data standardization is an important means to ensure the consistency and comparability of data between different systems and departments. It involves the unification of data formats, data naming conventions, data dictionary establishment, etc.

Through this phase, enterprises can obtain clean, neat, and high-quality data sets, laying a solid foundation for subsequent data governance, product development, operations, and advanced analytics efforts. At the same time, this process also helps to reduce data errors and biases, improve the accuracy and reliability of data analysis, and thus provide more powerful support for enterprise decision-making.

2.2 Data assetization stage

After completing the initial accumulation of data resources, enterprises have initially mastered standardized data resources with a certain level of quality. On this basis, data assetization has become a new strategic focus, aiming to transform these data resources into data assets with clear application scenarios and significant economic value. This process not only covers data governance and product innovation at the technical level, but also goes to the heart of finance and compliance management.

With the promulgation of the Interim Provisions on the Accounting Treatment of Enterprise Data Resources (Cai Kuai [2023] No. 11) (hereinafter referred to as the "Interim Provisions") and its full implementation on January 1, 2024, the accounting treatment and information disclosure of enterprise data resources in China have entered a new stage. This milestone provision not only lays a clear path for enterprise data resources to be "entered into the table" into real assets, but also marks a major breakthrough in China's data assetization process from zero to one.

In order to comprehensively analyze the practice path of data assetization, this section will focus on the construction of data governance system, data product development and iteration, data compliance and security assurance, accurate cost aggregation and accounting treatment, and scientific pricing and valuation of data assets. The promulgation of the Interim Provisions undoubtedly provides clearer financial treatment guidelines for these links, which not only improves the accounting transparency and credibility of data assets, but also provides new imagination for enterprises to explore the diversified application of data assets and business model innovation.

2.2.1 Construction of data governance system

The construction of data governance system is the key to ensure the success of data assetization, which includes two major aspects: the top-level design part and the specific data management task, and each part can be subdivided into different activities.

First, there is the need to set clear data governance goals, which point the way to data assetization. These goals should not only be closely linked to the company's grand strategy, but also be specific and measurable, such as improving data quality, strengthening data security, and optimizing data processes. In this way, you can be targeted and ensure that every step is in the right direction.

Next, the organizational structure and division of responsibilities are particularly important. Enterprises should set up a "think tank" for data governance, that is, a data governance committee, so that they can control the overall situation and make informed decisions. At the same time, it is also necessary to clarify the "role positioning" of each department in data governance to ensure that everyone can perform their duties and work together.

Policy and institutional development are also indispensable. Enterprises should formulate a complete set of data governance "rules of the game", including the principles, norms, and processes of data management, so that everyone has rules to follow and evidence to follow, and lay the foundation for the efficient operation of data assets.

The data governance system also needs to be continuously improved and optimized. Enterprises should establish a continuous improvement mechanism, so that the data governance system can keep pace with the times, always maintain the best state, and provide continuous motivation and guarantee for the enterprise's data assetization road.

The data governance system plays a vital role in the process of data assetization. It is not only the basic support for data assetization, but also the core engine to promote the process of data assetization. Only by carefully building and maintaining a data governance system can we ensure that the road to data assetization is more stable and long-term.

2.2.2 Data product development and innovation

The

value of data assets is closely related to application scenarios, and data can play a completely different value in different application scenarios. Therefore, it is necessary to develop data resources for different use scenarios, and data products are an important form to transform data resources into clear use scenarios and economic value.

(1) The form of the data product

According to the Practice and Operation Guide for Data Asset Inclusion and Valuation of Data Assets of the Shanghai Data Exchange, data products can be divided into three forms: datasets, data information services, and data applications based on different demand characteristics and service methods, as shown in the following table

(2) Data product development process

Demand research and analysis: First, it is necessary to have an in-depth understanding of business requirements and clarify the specific problems or goals to be solved by the data product. This includes working closely with business units, data analysts, product managers, etc., to understand their pain points and expectations, as well as analyzing market trends and competitor dynamics.

Data source integration and cleaning: Collect relevant data from inside or outside the enterprise as needed, and carry out pre-processing work such as cleaning, deduplication, and formatting to ensure data quality. This step is the foundation for building a high-quality data product.

Data modeling and analysis: Statistics, machine learning and other technologies are used to conduct in-depth analysis of preprocessed data and build prediction models, classification models, or clustering models to extract valuable information and patterns.

Product design and development: Based on the analysis results, design the interface, function, and interaction mode of the data product to ensure that the product is easy to use, intuitive, and can meet the needs of users. At the same time, the corresponding software or platform is developed to realize the functions of visual display, interactive query, and intelligent recommendation of data.

Testing and optimization: Before the product is launched, comprehensive testing is carried out, including functional testing, performance testing, security testing, etc., to ensure that the product is stable and reliable. After going live, we will continue to optimize product functions and user experience based on user feedback and usage data.

(3) Data product innovation strategy

Convergence of new technologies: With the continuous development of technologies such as artificial intelligence, big data, and blockchain, data products should keep up with the frontiers of technology and integrate new technologies to improve product performance and enhance user experience.

Customized services: Provide customized data products and services according to the specific needs of different industries and enterprises. With a deep understanding of your customers' businesses, we can tailor data solutions to their unique needs.

Cross-border cooperation: cross-border cooperation with enterprises in other industries or fields to jointly develop data products, realize the sharing and complementarity of data resources, and broaden the application scenarios and market space of data products.

Continuous iterative upgrades: Data products should be a continuous iterative upgrade process. By continuously collecting user feedback, tracking market changes, and introducing new technologies and algorithms, we continuously optimize product functions and performance to maintain product competitiveness.

2.2.3 Data Compliance and Review

The compliance of data sources is a prerequisite for data assets to be included in the table. The Interim Provisions stipulate that one of the conditions for data resources to be included in the table to form data assets is that they are "legally owned or controlled by an enterprise". Therefore, data resources from illegal sources or cannot be used in compliance cannot be included in the balance sheet to form data assets. Enterprises should conduct corresponding compliance reviews from two aspects: data source and data utilization.

(1) Compliance review of data sources

Internally generated data:

The data generated within the enterprise covers all kinds of business data generated in the process of production, operation, management, etc., such as customer order data, equipment operation data, employee management data, etc. For the compliance of internally generated data, enterprises need to pay attention to the following aspects: first, the legitimacy of the data, that is, whether the data generation meets the requirements of relevant laws and regulations, such as whether it may infringe the trade secrets of other enterprises; Second, privacy protection, especially data involving the personal information of employees or customers, must comply with the requirements of relevant laws and regulations such as the Personal Information Protection Law; The third is the authenticity and verifiability of the data, whether the data generated internally has been objectively recorded and has reliability. When data is entered into a table, enterprises need to provide sufficient evidence to prove the authenticity of the data, such as internal management process documents, data storage logs, etc.

Externally Collected Data:

Data obtained externally requires more complex and rigorous scrutiny. External data is often derived from publicly sourced data, directly collected data, and marketplace procurement data.

Public data refers to data that is provided and displayed to the public without permission, and anyone has the right to access it. The collection of publicly available data is known as public collection. There are many ways to collect data publicly, such as manual data collection such as manual entry and search query, as well as automated data collection such as crawlers. When reviewing the legality of public data collection, companies need to confirm whether the data is collected in a lawful manner, such as whether it has been authorized by relevant parties such as the website owner.

In the way of public data collection, the compliance risk is mostly concentrated in the data crawled through crawler technology. It should be noted that data crawling itself is not illegal, but due to its technical characteristics, if it is used improperly or beyond reasonable limits in actual operation, it is likely to involve violations of laws and regulations, resulting in risks of unfair competition, intellectual property infringement, personal information infringement and even criminal risks.

Direct collection, that is, the enterprise directly obtains the relevant information of the data source (such as the user). When collecting data directly, enterprises should mainly pay attention to the legal compliance of personal information and enterprise information collection, and must strictly comply with relevant laws and regulations. For the collection of personal information, enterprises shall clearly inform the purpose, method and scope of collection in accordance with the requirements of the Personal Information Protection Law and other laws and regulations, and obtain the consent of users. For the collection of enterprise information, especially when it involves the trade secrets of competitors, enterprises should strictly follow the relevant provisions of the Anti-Unfair Competition Law to avoid unfair competition acts such as infringement of trade secrets, and ensure that the collection process is legal and compliant.

Indirect acquisition refers to the acquisition of data through agreement purchase. When purchasing data through an agreement, although the data provider usually bears the initial legality review obligation, as the buyer of the data, it still needs to take the initiative to conduct an in-depth and comprehensive review to prevent potential risks.

The buyer should ensure that the transaction data is free of ownership defects by reviewing the data source, acquisition method and relevant ownership certification materials. In addition, when signing a data transaction agreement, companies need to carefully review the integrity and compliance of the terms of the contract. In general, the contract should clearly stipulate the accuracy, completeness and timeliness of the data, as well as the scope and purpose of the data, so as to avoid data defects affecting the normal development of business. At the same time, the agreement should contain detailed privacy protection clauses, especially if the data involves personal information or sensitive information, and ensure that the transaction and subsequent use process strictly comply with the requirements of the Personal Information Protection Law and other relevant laws and regulations.

(2) Compliance review of data utilization

After confirming the compliance of the data source, the enterprise conducts substantive processing and innovative labor on the original data through the processes of desensitization, cleaning, annotation, integration, and analysis, and forms creative results, and finally produces data assets that meet the company's business and R&D needs, and stores them in the enterprise's internal or third-party cloud storage. The utilization of data assets by enterprises mainly includes internal use and external transactions. When an enterprise mainly uses data resources for internal production, operation or management, it can be managed as intangible assets in accounting. When a company mainly uses data resources for external transactions, it can be managed as inventory in accounting. Regardless of internal or external transactions, enterprises should still pay attention to ensuring compliance in the process of processing data resources and operating data products according to different utilization methods.

The key points of the compliance review of the internal use of data assets include: first, the legality of the data use. When using data, enterprises should review whether they comply with laws, regulations and relevant industry standards, whether they meet the compliance standards required by regulatory authorities, and whether they infringe on the rights and interests of personal information or the trade secrets of others according to the specific data utilization behavior and process, and for the data content involved in each specific node. Secondly, the security of data storage. Enterprises must establish a corresponding policy system and adopt appropriate security technical measures to ensure the confidentiality and security of internal data utilization and avoid compliance risks caused by data leakage.

The

compliance review involved in external trading of data assets is more complex than that of internal use, mainly involving aspects including but not limited to data tradability, data protection capabilities, and compliance with data exports.

Data tradability: The premise of data trading is that data assets are tradable. The tradability of data assets refers to the ability to form data products that can be bought, sold and exchanged in the market like commodities through substantive processing and innovative labor. According to existing laws and regulations, data products must not contain illegal information that endangers national security, violates public order and good customs, or infringes upon the lawful rights and interests of others. In addition to the relevant national laws that clearly stipulate the prohibition of transactions, some industries also stipulate the qualifications of data transaction parties, such as financial institutions purchasing personal credit information, they shall purchase it from institutions with the qualifications to engage in personal credit reporting business. Therefore, enterprises should first pay attention to whether the data assets involve the scope of transactions restricted or prohibited by national laws and regulations.

Data protection capabilities: Before data transactions, the data provider and the data demander also need to assess each other's data security capabilities, and both parties shall comply with the requirements of the Cybersecurity Law, the Data Security Law and other laws and regulations, establish a data security protection system, and for data involving personal information, they shall also comply with the requirements of the Personal Information Protection Law, and take technical measures to avoid the occurrence of personal information leakage, tampering or loss. Before the transaction, due diligence can be carried out to evaluate the data security protection capabilities of the counterparty in terms of technical capabilities, staffing, reputation and qualifications.

Compliance with cross-border data transfer: If the data demander is an overseas enterprise, the enterprise needs to pay special attention to the compliance of cross-border data transfer, and in accordance with the provisions of laws and regulations such as the Measures for Security Assessment of Cross-border Data Transfer, the Provisions on Promoting and Regulating Cross-border Data Flow, and other laws and regulations, and according to the specific circumstances of the subject, the nature of the data, the amount of personal information, etc., respectively, the compliance requirements such as applying for the security assessment of data export, entering into a standard contract for personal information export, and passing the personal information protection certification are performed. Where the export of personal information is involved, compliance obligations such as obtaining the individual's separate consent and conducting a personal information protection impact assessment are also required.

In summary, the management of enterprise data resources and data assets will involve laws and regulations in many different fields, and it is necessary to carry out effective compliance management in combination with the enterprise's own business processes and business characteristics. For enterprises that need to manage data resources and enter data resources into tables, they should pay attention to the importance of enterprise data compliance management, including the establishment of internal management systems and the construction of internal management personnel, so as to improve the enterprise's internal data compliance governance capabilities. In addition, when it comes to major matters related to data compliance management, it is recommended to promptly engage a professional third-party organization such as a law firm to assist the enterprise in handling the corresponding matters.

2.2.4 Cost collection and accounting treatment

Data assets are included in the table, that is, the process of accounting for data assets. By determining the business scenario, determining the ownership and income of the data, determining the cost, etc., the accounting basis for each stage of the data asset entry table is submitted, and then the accounting treatment is carried out to meet the financial requirements. Cost collection and corresponding accounting treatment is a key point and a difficult point.

(1) Cost collection

Cost aggregation refers to the process of identifying, measuring, and summarizing all costs associated with a data resource. These costs include, but are not limited to, the costs of data collection, storage, processing, analysis, maintenance, etc.

(2) Accounting treatment

Accounting treatment refers to the process of recording and reporting the cost of aggregated data resources in accordance with accounting standards. According to the Interim Provisions, enterprises are required to reflect the cost and value of data resources in their financial statements.

(3) Difficulties in cost collection and accounting treatment

Although cost collection and accounting are crucial in the process of data asset entry, enterprises often face a series of difficulties and challenges in operation.

Complexity of cost identification and measurement:

The cost components of data resources are diverse and complex, including direct and indirect costs, as well as possible hidden costs. How to accurately identify and measure these costs, especially those that are closely related to data governance and data product development but are difficult to directly quantify, such as time investment in labor costs and R&D expenditure in technology costs, is a huge challenge.

Determination of data asset ownership and revenue:

As an emerging intangible asset, data is difficult to directly apply due to its non-exclusivity, reproducibility and sensitivity, and there is no clear law on data ownership, which makes it difficult to clearly define the ownership and benefits of data assets, which directly affects the accuracy of cost collection and the rationality of accounting treatment. In a multi-party data sharing or cooperation project, how to reasonably allocate costs and determine the rights and interests of all parties is an issue that needs to be handled carefully.

Selection of amortization method:

The "useful life" of data assets is not as clear as that of physical assets, and businesses may need to determine the amortization period based on the actual use of data, which can lead to uncertainty in accounting treatment. The value of data assets can change rapidly over time, and traditional amortization methods may not accurately reflect changes in their value.

Accuracy of cost allocation:

In the case of multiple data products or projects sharing data resources, how to accurately allocate costs to each product or project is a highly technical and subjective problem. The selection and application of cost allocation methods need to take into account various factors, such as the amount of data, the frequency of use, the importance of the project, etc., which increases the complexity and uncertainty of cost allocation.

Enterprises need to strengthen their awareness of cost management, improve their professional capabilities in cost collection and accounting treatment, and establish a sound internal control system to ensure the accuracy and completeness of cost information and provide strong support for their financial decision-making and strategic planning. In practice, the time log method can be used to determine the presentation of project members, such as merging hours and rate records; For costs that are difficult to quantify, all parties need to make reasonable apportionment on the premise of agreeing on the principle of apportionment. At the same time, enterprises also need to pay close attention to changes in accounting standards and regulations, and adjust and optimize their cost collection and accounting processes in a timely manner.

2.2.5 Valuation and pricing of data assets

A data asset is a type of asset. In September 2023, the Ministry of Finance and the China Data Asset Appraisal Association issued the Guiding Opinions on Data Asset Assessment, which sets out detailed provisions on the assessment objects, operational requirements, assessment methods and disclosure requirements of data assets, and emphasizes that quality factors should be taken into account in data asset assessment. Article 19 clearly states that the valuation methods of data assets include the cost approach, the income approach and the market approach and their derivative methods.

For the above three methods, considering the characteristics of the data itself, it is necessary to optimize and adjust the calculation results to a certain extent, and the influencing factors mainly include data quality, data security, data application, etc. By constructing scoring rules for data quality and data security, as well as statistical indicators such as the scope of data application scenarios, the number of users, and the use effect, the differences in the needs of data in different usage scenarios and groups are fully considered to improve the accuracy of data value assessment.

2.3 Data asset operation phase

2.3.1 Data Transactions

Data transaction refers to the exchange of rights to use, access or process data between data providers and data demanders through specific trading platforms or mechanisms. Such an exchange may involve money, services, technology, or other forms of consideration. The core of data transaction is to realize the circulation and transformation of data value, and promote the rational use and sharing of data.

As data becomes the new factor of production, the scale of data transactions is growing. According to the 2024 China Data Exchange Market Research and Analysis Report, the global data transaction volume will be about US$126.1 billion in 2023 and is expected to reach US$370.8 billion by 2030. In 2023, the size of China's data trading market will be about 153.7 billion yuan, and it is expected to grow to about 284.1 billion yuan by 2025, with an average annual compound growth rate of 46.5% from 2021 to 2025, and is expected to reach 715.9 billion yuan by 2030

At present, China's data trading forms are mainly divided into on-exchange transactions and over-the-counter transactions. Floor transactions are carried out through data exchanges or trading centers, which are centralized and standardized, easy to supervise and trace, and have compliance requirements for the subject of data transactions, data products and data transactions. OTC transactions, on the other hand, are generated by enterprises or individuals, which are more flexible and free, leaving no traces between the two parties to the transaction, but they are very prone to non-compliant transactions and data leaks, and are relatively difficult to supervise.

According to the China Academy of Information and Communications Technology's "Data Valueization and Data Element Market Development Report (2023)", at present, China's data circulation transactions are still dominated by over-the-counter transactions, with on-site data transactions accounting for only 4% of the total size of the data trading market, and the rest are scattered "peer-to-peer" transactions outside the market.

Based on the characteristics of centralized, transparent, electronic, standardized and easy supervision of on-site data transactions, the above-mentioned imbalance is expected to be improved in the future. At the national level, in January 2024, the "Three-Year Action Plan for the × of Data Elements" (2024~2026) jointly issued by 17 departments including the National Data Bureau takes "the coordinated development of on-exchange transactions and over-the-counter transactions, and the doubling of the scale of data transactions" as the goal, indicating that the country attaches importance to the coordinated development of the data trading market.

Local data exchanges have also introduced policies and issued subsidies to encourage on-site data trading. On August 21, 2023, the Shanghai Data Exchange released the "Data Element Market Prosperity Plan", which plans to set up a special incentive fund of RMB 100 million to prosper the digital business ecosystem and activate on-exchange transactions. In 2023 and 2024, Zhengzhou Data Exchange will launch the "Data Broker Incentive Plan" for two consecutive years, which will provide incentives for data brokers at the rate of 1% of the total transaction amount after matching the supply and demand sides to complete the whole process of on-site transactions, and for the filing of OTC data transaction contracts, ladder incentives will also be implemented according to the range where the transaction volume is located; On April 18, 2024, the Beijing Municipal Bureau of Economy and Information Technology and the Beijing Municipal Bureau of Finance issued the "Organizing and Carrying out the Application for Funds for the Development of High-tech Industries in Beijing in 2024 (Second Batch)", which clarified that incentives and subsidies will be given to enterprises that register and trade data assets on the Beijing International Big Data Exchange, and encourage data resources to be included in the table, with a subsidy amount of up to 500,000 yuan.

In 2023, the on-exchange data transaction volume in Guangdong Province will be nearly 8 billion yuan, of which the annual transaction volume of the Shenzhen Data Exchange will exceed 5 billion yuan, and the on-exchange transaction volume in Beijing and Guiyang will exceed 2 billion yuan. In 2024, the data trading scale of the Shenzhen Data Exchange will exceed 14 billion yuan, and the trading scale of the Shanghai Data Exchange is also expected to exceed 4 billion yuan.

2.3.2 Data Capitalization

Data assetization mainly solves the problem of productization of data resources and the formation of market circulation. After that, the corresponding "data capitalization" work can be carried out, such as investment and financing. Data capitalization is related to the comprehensive upgrade of data value, which is the key to realizing the market-oriented allocation of data elements. There are multiple implementation paths for data capitalization, including data asset pledge financing, data equity, data securitization, etc.

(1) Data asset pledge financing

Data asset pledge financing usually refers to a financing method in which enterprises or individuals use data as collateral and borrow funds through the evaluation and approval of banks or financial institutions, and use the future income and market value of data as collateral. It is similar to a traditional mortgage, except that the pledge is against data rather than real estate or other physical assets.

With the development of data asset entry in 2024, a number of cases of data asset pledge financing have appeared successively. In July 2024, the country's first state-owned enterprise data asset notarization and confirmation pledge financing was implemented in Gongqingcheng, Jiangxi. Through the supervision of the notary agency, Gongqing City Financial Services Group has completed the closed-loop market of data resource registration, right confirmation to data asset evaluation, valuation, table entry and financing under the full-link compliance notarization mode, and obtained 66 million yuan of pledge financing from Shangrao Bank.

(2) Credit financing of data assets

Use the data of the enterprise (such as operational data, financial data, etc.) for credit evaluation or credit enhancement, and provide loan support for the enterprise. This type of financing does not rely on the data itself as collateral, but through the in-depth analysis of the company's data, it relies on the data to assess the credit status of the enterprise and provide financing for the enterprise.

This model of data asset credit financing is suitable for small and micro enterprises, science and technology enterprises, innovative enterprises, etc., which lack traditional assets, and it gives full play to the advantages of data as a core production factor.

A number of domestic data exchanges have launched products based on data asset credit financing, such as the "Digital Easy Loan" product of the Shanghai Data Exchange, the "Credit Data Treasure" of Wenzhou Big Data Operation Co., Ltd., and the "Digital Business Loan" product of the Suzhou Big Data Exchange.

Taking "Digital Easy Loan" as an example, it can provide credit services for enterprises by analyzing the big data of enterprises (including but not limited to the financial status of enterprises, transaction data, customer credit data, etc.). The platform uses big data technology to conduct risk assessment, so that enterprises do not need traditional collateral or guarantees, but only need to prove their credit through their business data, and the loan amount is determined according to the credit and business scale of the enterprise. The platform reduces the manual review and process links in the traditional loan process, and improves the speed and accuracy of loan approval.

Since the launch of the "Digital Easy Loan" product, there have been cases in many industries. At the beginning of 2024, the Shanghai Data Exchange and CCB Shanghai Branch worked together to successfully issue the first data asset pledge loan based on the "Digital Easy Loan" product, which opened up a new path for data asset financing. The beneficiary of the loan is Shanghai Sibuge Network Technology Co., Ltd., a subsidiary of Shanghai Huandong Robot Co., Ltd. (Suteng Data). Due to the short establishment time and lack of material assets, Shanghai Sibuge Network Technology Co., Ltd. has been facing the problem of limited financing channels. Fortunately, the company has previously completed the listing of the "Big Data for Data Center Operation and Maintenance" series of data products on the Shanghai Data Exchange and realized on-exchange transactions, which has laid a solid foundation for its subsequent data asset pledge financing.

In June 2024, the Shanghai Data Exchange and Bank of Shanghai once again innovated and cooperated to promote the successful implementation of the first "Digital Easy Loan" in the chemical industry. The financing was provided by Shanghai Xinhua and Cloud Data Technology Co., Ltd., which successfully obtained 1.5 million yuan of credit support by pledging its data assets. This case not only provides a practical sample for the "monetization" of data assets in the chemical industry, but also further verifies the effectiveness and feasibility of the "Digital Easy Loan" product in broadening the financing channels of enterprises.

(3) Data equity

Data equity refers to the data holder's legally owned and clear property rights that can be transferred in accordance with the law as "capital and shares", convert them into equity, and participate in the distribution in accordance with the principle of equity equality and the degree of contribution.

Legally, as a new form of assets, the provisions of the new Company Law on non-monetary property contributions are applicable to the contribution of data assets. In November 2022, the Standing Committee of the Beijing Municipal People's Congress issued the Regulations on the Promotion of the Digital Economy of Beijing, Article 21 of which proposes to "support the development of digital economy innovations such as data shareholding, data credit, data trust, and data asset securitization". In January 2024, the State-owned Assets Supervision and Administration Commission (SASAC) issued the Notice on Matters Concerning the Optimization of Asset Appraisal and Management of Central Enterprises, confirming that data assets can be valued for capital contributions.

In August 2023, there was an innovative case of data asset valuation in China. Qingdao Huatong Intelligent Technology Research Institute Co., Ltd., Qingdao North Shore Digital Technology Group Co., Ltd. and Yifang Jianshu (Shandong) Information Technology Co., Ltd. jointly signed an agreement on investment and shareholding of data assets. Prior to this, the relevant data products have been evaluated by a third-party professional organization and a detailed valuation report has been issued, which provides a conclusive shareholding certificate for the data assets to be valued and the joint venture company established. This move not only successfully realizes the deep integration of data elements, technology and capital, but also explores a new model and path for the value release and market-oriented allocation of data assets.

(4) Data securitization

Any product that can generate stable cash flow in the future can be securitized. In the operation of data assets, the future cash flow of data assets can be used as a source of repayment to issue securitization products, that is, the future income of data assets can be realized at the spot, which can maximize the enthusiasm of data owners to participate in data circulation transactions.

There are also cases in the field of data securitization in China. On July 5, 2023, Hangzhou High-tech Financial Investment Holding Group Co., Ltd. successfully booked the country's first asset-backed note (ABN) containing data intellectual property rights, with an issue amount of RMB 102 million, a coupon rate of 2.80%, and a maturity of 358 days. The project was led by the Hangzhou High-tech Zone (Binjiang) Market Supervision Administration (Intellectual Property Office) and other units, and received the support and participation of a number of institutions.

As collateral, the project collected 145 intellectual property rights from 12 companies such as Siwei Ecology, Unisplendour Communication, and Digital Cloud, including 26 invention patents, 54 utility model patents, 63 software copyrights, and 2 data intellectual property rights, with a total appraised value of 143 million yuan. Through this innovative model, these companies have received RMB 102 million in financial support, opening up a new way of securitization financing based on intangible assets.

(5) Other attempts at data capitalization

Digital asset insurance: On April 21, 2023, China's first digital asset insurance was released in Xi'an. The insurance project is led by the Digital Asset Insurance Innovation Center and underwritten by the Xi'an Branch of Chinese People's Property Insurance Co., Ltd., providing a total of 10 million yuan of protection for the digital assets of 10 enterprises.

Data trust products: In April 2023, the country's first personal data trust case took shape in the Guiyang big data trading venue. Individuals can entrust their resume data to Guiyang Big Data Exchange through data trust, and then Guiyang Big Data Exchange entrusts the data intermediary Haohuo (Guizhou) Network Technology Co., Ltd. to operate. The latter obtains intermediary fees from data sales by assisting individuals in data governance, desensitization and encryption, product packaging, sales, etc.

Third, the inclusion of data assets in the table opens the reconstruction of enterprise value

With the vigorous development of the digital economy, the data element market is rapidly becoming a new engine of economic growth. According to a report by the China Academy of Information and Communications Technology, in 2023, the contribution of China's data economy will be 2.05%, an increase of 0.99 percentage points over 2022. This growth trend not only reflects the centrality of data in the modern economy, but also indicates that the investment value of the data element market will continue to rise in the coming years.

Since the Interim Provisions came into effect in early 2024, various enterprises across the country have responded positively and gradually promoted the entry of data assets into the table. The compliance of data resources is the basis and basis for enterprises to participate in social and economic allocation with data assets. After being included in the table, data resources become assets, and data assets are the embodiment of the owner's equity, which will expand the total assets of the enterprise. The inclusion of data assets in the table marks a substantial step forward in the capitalization of data elements in China, which will greatly promote the process of data assetization.

3.1 Overview of data assets of listed companies

According to the statistics of Yicai, a total of 54 A-share listed companies disclosed the entry of enterprise data assets into the table in the third quarterly report, with the amount of data assets in the statement being 1.094 billion yuan, and the ratio of the amount in the statement to the total assets was 0.01857%.

Overall, looking back at the data assets of A-share listed companies in the first three quarters of 2024, the following characteristics are mainly presented.

(1) The number of entities in the table increases, and the scale of data assets in the table decreases

In terms of the number of entities included in the table, the number of entities in the first three quarters of 2024 will continue to grow, from 18 in the first quarter to 54 in the third quarter; The total amount of data assets in the statement increased significantly from 103 million yuan in the first quarter report to 1.364 billion yuan in the interim report, but the total amount of data assets in the third quarter decreased to 1.094 billion yuan in the overall number of disclosures.

Comparing the information disclosed in the interim report and the third quarterly report, 6 of the 41 companies that disclosed data assets in the third quarterly report failed to continue to disclose the relevant information of data assets in the third quarterly report, including Haike Xinyuan (301292. SZ), Valin Seiko (603356. SH) and so on. According to the financial report, Haike Xinyuan, Valin Seiko, Ruyi Group (002193. SZ) reported that the scale of data assets included in the statement exceeded 100 million yuan, of which Haike Xinyuan was the highest, and the data assets included in intangible assets and inventories were 163 million yuan and 249 million yuan respectively, totaling more than 400 million yuan, accounting for 8.66% of the total assets.

It is reported that data resources, as an emerging asset class, due to insufficient understanding of the Interim Provisions and lax compliance review, some companies have withdrawn modifications after releasing relevant information about data assets into the table, and the accounting treatment rules for data assets entering the statement need to be further clarified and detailed to help enterprises correctly identify, measure and report this part of the assets.

In addition, 19 companies with new data assets in the third quarter report were added to the table, except for Nanjing Panda (600775. SH) has a total amount 688615 of more than 200 million yuan, and the data assets of the rest of the enterprises are small. SH) data assets are only 33.1687 million yuan, accounting for less than 1% of the total asset scale.

The

increase in the overall number of disclosers shows that more and more listed companies have realized the importance of data assets and actively responded to the requirements of relevant laws and regulations to carry out data asset entry practices, but due to the differences in the maturity of data asset management among different enterprises, some enterprises have insufficient or difficult evaluation and confirmation of data assets, resulting in the failure of their data assets to be included in the table in a timely or complete manner.

Some enterprises also said in an interview with Yicai that due to the need to meet data security and regulatory requirements for data assets to be entered into the table, enterprises are relatively cautious about the entry of data assets into the table, and with the continuous improvement of the data asset entry mechanism and the further development of the market, it is expected to increase the scale of data resources in the table in the future.

(2) The three major operators have become the "main force" of data assets in the table

In terms of the scale of data assets included in the table, the three major operators have become the main force in the table. In the 2024 interim report, the total data assets of the three major operators exceeded 260 million yuan, accounting for 18.5% of the total scale of disclosed enterprises. In the third quarter of 2024, the total amount of data assets of the three major operators further increased to 451 million yuan, a year-on-year increase of 73.46%, accounting for 41.2% of the total scale of disclosed enterprises. Among them, China Unicom (600050. SH) had the highest amount of 204 million yuan, China Telecom (601728.SH) had 151 million yuan, and China Mobile (600941.SH) had 96 million yuan.

The three major communication operators have a large amount of data, good consistency and high activity, which is a high-quality resource for data transactions. The inclusion of data assets in the table of communication operators has released a signal for the industry to improve its data management and application capabilities, and has demonstrated the active exploration of the potential of data assetization, which has a leading demonstration effect.

From the perspective of the industry distribution of enterprises whose data assets are included in the table, among the 54 enterprises whose data assets are included in the table in the third quarter, the computer industry accounts for the highest proportion, including Tongfang Co., Ltd. (600100. SH), Aerospace Grand Plan (688066. SH), INTSIG, Daily Interaction (300766. SZ) accounted for 27.8% of the total, followed by the transportation industry and the media industry, with 8 and 6 respectively.

The computer industry is leading the way in data asset tabulation practices. The computer industry itself is a data-intensive industry, generating and processing a large amount of data in daily operations, strong dependence on data analysis and application, strong technical strength and research and development capabilities, and more effective data collection, storage, processing and analysis, which enables computer enterprises to more accurately identify and use data assets, give full play to the value of data, and then effectively promote the systematic management and entry of data assets, and also ensure that enterprises maintain a sustainable competitive advantage in the era of digital economy.

At the same time, traditional industries such as transportation and media have begun to recognize the value of data assets and actively participate in the "table" work of data resources, such as Chinese online (300364. SZ), Shandong High-speed (600350. SH) data assets also increased significantly, with a year-on-year increase of 1808% and 737%.

In the transportation industry, the application of data elements can improve the efficiency of multimodal transportation, promote the sharing and mutual recognition of freight data, waybill data, settlement data, etc., and achieve logistics cost reduction and efficiency increase. In the media industry, data elements can provide user behavior analysis, market trend prediction, etc., to help media companies better understand audience needs and optimize content creation and distribution strategies. Traditional industries may face challenges in terms of technology, talent, and security in the process of data asset entry, but they also see the opportunities brought by data assetization, such as improving enterprise competitiveness and innovating business models.

From the perspective of the market value distribution of enterprises with data assets in the table, most of the first quarterly reports are small and medium-sized enterprises with a market value of no more than 50 billion yuan, and more and more large enterprises with a market value of more than 50 billion yuan participate in the practice of data assets in the middle report and the third quarterly report.

Among the companies that disclosed data assets in the third quarterly report, in addition to the three major operators, Haitong Securities (600837. SH), Shandong Gold (600547. SH) two companies with a market value of more than 100 billion yuan disclosed the entry of data assets into the table. On the one hand, data asset management can improve the internal management efficiency of these large enterprises, enhance their competitiveness and efficiency through technological innovation and digital transformation, and at the same time, as leading enterprises in the industry, these enterprises are qualified to provide rich application scenarios for data resources, which is conducive to the release of the value of data elements.

Unlike large-capitalization enterprises, such enterprises rely more on the direct commercialization of data assets, such as providing data services and solutions, and the inclusion of data assets in the table can better reflect their data technology advantages, which is of great significance to enhance their asset value and market competitiveness.

(3) Intangible assets and development expenditures are the main items listed in the table

Judging from the distribution of the items listed in the data assets table, most listed companies include data resources in intangible assets or development expenditures, and the proportion of inventory is small. In the third quarter of 2024, the total scale of intangible assets and development expenditure included in the statement is about 878 million yuan, accounting for 80.26% of the total scale of the statement; Only Nanjing Panda, ST Guandian (688287.SH), Haitian Ruisheng (688787. SH), Guangzhou Port (601228. SH) 4, of which the amount of data assets included in the inventory of Nanjing Panda is 201 million yuan, and the remaining three are less than 10 million yuan.

Inventory usually refers to the materials and commodities that a business uses for sale or consumes in the production process in the normal course of business activities, such as data products, such as customized data packages, real-time data analysis services, etc., otherwise it is generally not classified as inventory. However, most enterprises include data resources in intangible assets or development expenditures, hoping to better utilize the competitive advantages brought by data assets through active management and in-depth mining of data, rather than treating them as a one-time source of sales profits.

The inclusion of data assets in the statement will have a certain impact on the company's asset scale, cost, tax and net profit, and further affect some indicators such as asset-liability ratio, profit margin and return on net assets. Investors should not only pay attention to the inclusion of data assets in the statement of listed companies, but also pay attention to the changes in financial data brought about by the subsequent measurement of data assets.

(4) The proportion of companies that disclose the subsequent measurement of data assets is relatively small

According to the relevant regulations, the inclusion of data resources as assets in the financial statements requires reliable follow-up measurement of data resources and disclosure of relevant information.

Data resources recognized as intangible assets shall be disclosed separately in the case of the increase or decrease in the original book value, the increase or decrease in accumulated amortization, the increase or decrease in impairment provisions, and the book value of data resources obtained by outsourcing data resources, self-developed data resources, and other means of obtaining data resources. In addition, information such as the determination of the useful life of data resources, the amortization period, the amortization method, and the disposal of data resources need to be disclosed.

Data resources that are confirmed as inventories shall be disclosed separately in the case of the increase or decrease in the original book value, the increase or decrease in the provision for inventory decline, and the book value of the data resources obtained by purchasing data resources, self-processing data resources, and obtaining data resources by other means. At the same time, it is also necessary to disclose information such as the method of issuing data resources, the method of inventory cost, the provision and reversal of price decline provisions, and the restricted situation.

Specifically, after the data assets are included in the table, the data assets are included in the intangible assets or inventory, the total asset size of the enterprise will increase, and the expenses related to data resources that were originally used as expenses can be capitalized, which will reduce the expenses in the current period and thus increase the current profit. At the same time, after data resources are formed into intangible assets, they need to be amortized within a certain period of time, which will lead to a gradual increase in the cost or expense of the enterprise in the later stage, and the profit may show a trend of high first and then low.

By combing the enterprises that have disclosed the inclusion of data assets in the table, only 10 companies have made clear disclosures on the subsequent measurement of data assets, and there are large differences in amortization methods and years. It can be seen that the follow-up measurement of data assets has become a major problem that hinders more enterprises from entering data assets into the table. Among them, how to determine the useful life of data assets is a major issue. In this regard, the advice given by experts and relevant departments is to choose a reasonable amortization method and determine a reasonable service life according to the different application scenarios on which the data assets are attached, combined with the relevant amortization regulations of the intangible assets of their own enterprises.

(5) At present, the proportion of data assets in total assets is small, which has little impact on the company's financial situation

The proportion of data assets to total assets is a measure of the importance of data assets relative to the overall asset size of an enterprise. This proportion reflects the status and role of data assets in the overall assets of the enterprise.

For the 54 A-share listed companies that disclosed the entry of data assets in the third quarterly report, the total amount of data assets included in the statement was 1.094 billion yuan, and the ratio of the amount entered to the statement to the total assets was 0.01857%. Among them, only 4 data assets accounted for more than 1% of the total assets, namely Nanjing Panda, ST Guandian, and Zhuochuang Information (301299. SZ), daily interactions. Nearly 7 percent of enterprises have data assets accounted for less than 0.01% of total assets.

The scale of data assets in the statement is relatively small, but its improvement function for financial statements has begun to appear. Especially in small and medium-sized enterprises, the inclusion of data assets in the balance sheet has a certain effect on the improvement of their asset-liability ratio and profit margin. However, for listed companies with large market capitalization, the financial structure after inclusion in the balance sheet has not been significantly optimized. This shows that although the value of data assets is gradually recognized, its proportion of total enterprise assets is still limited and has not yet had a significant impact on the overall financial health of the enterprise.

3.2 Data assets are included in the table to reconstruct the enterprise value

Driven by the wave of digitalization, data assets have become a new highland of enterprise competition, and the importance of data assets is becoming increasingly prominent, although its current proportion in the total assets of enterprises is still small, and the impact on the financial data of enterprises has not yet been fully revealed, but the potential impact of data assets on enterprise valuation and the changes it may bring in the future cannot be ignored.

The

reconstruction of enterprise value by data asset entry is mainly reflected in the following aspects:

(1) Increase in asset scale: The inclusion of data assets in the table directly increases the total asset scale of the enterprise, which will increase the market valuation of the enterprise to a certain extent. However, due to the small proportion of data assets in total assets, this impact has not yet been fully felt.

(2) Improvement of profitability: The capitalization of data assets reduces current expenses, thereby improving the profit level of enterprises. This improvement is reflected in the financial statements as an increase in profit margins.

(3) Value re-mining: The inclusion of data resources in the table and other forms of information communication such as disclosure can make the market realize that the enterprise has data resources and the corresponding expected economic benefits, which will have a positive impact on the market value of the enterprise.

(4) Capitalization potential: The inclusion of data assets in the table provides enterprises with the potential for data capitalization, such as data asset monetization and securitization, giving data assets financial attributes. This will provide new financing channels and investment opportunities for companies, thereby increasing their valuation potential.

IV. Conclusion

Entering a new era of digital economy, the rules, order, and value investment methods of the capital market have opened a page of challenging innovation. Investors and economists are actively exploring value investment systems and valuation methods in the era of digital economy. In general, with the continuous changes in the economic form in recent years, the value investment valuation system in the era of industrial economy can no longer evaluate listed companies in the era of digital economy, and it will be the general trend to use the digital value investment system for analysis.

Special expert for this report

Li Xiang is a partner at Global Law Office

Zhao Huaxin is a researcher at the International Data Management Association in Greater China

Hu Yue is a teacher at Shanghai National Accounting Institute

References

"2023-2024 China Data Asset Development Research Report", China Electronic Information Industry Development Research Institute, CCID (Qingdao) Blockchain Research Institute

Blue Book of Data Asset Inclusion Practice of Chinese Listed Companies, Jinan Big Data Association

"Data Resources into the Table, Asset Gold Mine + Valuation Blue Ocean", Debang Securities

Research Support Unit

Global Law Office, International Data Management Association Greater China, Rongliang Data Technology (Shanghai) Co., Ltd., Zhongchuang Digital Economic Information Service (Shanghai) Co., Ltd

Data description

Data, Cases, Opinion Sources

Unless otherwise specified, the data and content in the report are based on CBN's research, interviews and public information.