365 million yuan! The largest-ever data asset valuation in the country has been released! Data Asset Valuation Report

On July 31, 2024, Zhejiang Lianxin Technology Co., Ltd., a wholly-owned subsidiary of Lianxin Digital, successfully completed the valuation of its “Dongjian Renhe Large Model” data asset portfolio. According to a professional assessment, the value of these data assets is RMB 365 million.

In accordance with the “Guiding Opinions on Data Asset Valuation,” we conducted a thorough review and inventory of the “Dongjian Renhe Large Model” data portfolio, delving deeply into its functions and value. We selected appropriate valuation methods from multiple dimensions—including data structure and business attributes—and performed a rational valuation of the data assets. As a result, we established a comprehensive and clear data asset valuation system, objectively assessed the value of the data assets owned by Lianxin Technology, and thereby supported the fulfillment of its financing needs.

This “Insights into Human and Large Model” data asset valuation project is of great significance for Lianxin to further enhance its core technological competitiveness and improve its financial statements. By conducting data asset valuation, Lianxin is also accelerating its data capitalization process, which will effectively boost the company’s credit rating, broaden its financing channels, and strengthen its financing capabilities.
 

Data Asset Valuation Report

I. Introduction

With the development of the digital economy, data assets—as a new type of production factor—are increasingly demonstrating their growing value. To standardize accounting treatment related to corporate data resources and strengthen data asset management, the Ministry of Finance has issued the “Provisional Regulations on Accounting Treatment Related to Corporate Data Resources,” while the China Association of Asset Appraisers has also formulated the “Guiding Opinions on Data Asset Valuation.” This report aims to conduct an in-depth exploration and practical analysis of data asset valuation, drawing upon these policy documents and relevant theoretical research.

II. Background of the Development of Data Assetization

(1) Policy Background

1. In 2019, the Fourth Plenary Session of the 19th Central Committee of the Communist Party of China for the first time listed data alongside labor, capital, land, knowledge, technology, and management as important factors of production.

2. In March 2021, the “14th Five-Year Plan for National Economic and Social Development of the People’s Republic of China and the Long-Range Objectives for 2035” designated “accelerating digital development and building a Digital China” as a national strategic development goal.

3. In December 2022, the CPC Central Committee and the State Council issued the “Opinions on Establishing a Data Basic System to Better Leverage the Role of Data Elements,” proposing the orderly development of third-party professional service institutions such as asset valuation and risk assessment, and enhancing their capacity to provide end-to-end services for data circulation and trading.

4. In August 2023, the Ministry of Finance issued the “Provisional Regulations on Accounting Treatment Related to Enterprise Data Resources,” providing operational guidance for including enterprise data assets on financial statements.

5. In September 2023, the China Association of Asset Appraisers issued the “Guiding Opinions on Data Asset Valuation” to standardize professional practices in data asset valuation.

(2) Industry Status Quo

1. Enterprises are increasingly recognizing the importance of data assets, yet disclosure in the context of data innovation remains relatively limited. Most enterprises have not yet fully grasped the significant value of data assets.

2. Data assetization in China is relatively widely adopted by platform companies, banks, and other entities, but the related supporting measures still need further improvement.

3. In areas such as data strategy and governance, security and compliance, ecosystem and transactions, as well as asset and operations management, enterprises mostly entrust the management responsibilities for various data-related tasks to functional departments like IT, strategy, operations, and the President’s Office. As a result, data departments have not yet played their intended leading role and still have considerable room for improvement.

III. Analysis of Related Concepts

(1) Definition of Data Assets: Data assets refer to data resources that are legally owned or controlled by a specific entity, can be measured in monetary terms, and can generate direct or indirect economic benefits.

(2) Characteristics of Data Assets

1. Non-physical and non-consumptive: Data assets are non-physical and non-consumptive; their value does not erode or deplete due to increased normal usage frequency.

2. Processability: Data assets can be processed from multiple dimensions, and the processed data can become a new type of data asset.

3. Diversity of Forms: Data assets come in a wide variety of presentation formats and can be showcased, applied, and transformed across multiple forms by integrating with various data processing technologies.

4. Multiple Derivations: The same data subject can be processed across multiple levels and dimensions, thereby generating data values of varying degrees.

5. Shareability: Data assets can be infinitely exchanged, transferred, and utilized, and shared with others.

6. Zero-Cost Replicability: The cost of data assets primarily lies in the initial stages of data acquisition and research & development. The cost of establishing a new data asset is extremely high at the outset; however, once the product is developed, its marginal cost tends toward zero due to its ability to be infinitely replicated.

7. Dependency: Data assets cannot exist independently and must rely on a certain medium for storage and processing.

8. Value Volatility: The value of data assets is more susceptible to the influence of various factors and thus exhibits value volatility.

(3) Classification of Data Assets

1. Raw Data Assets: Detailed data obtained from external sources or collected internally, which can be categorized into externally acquired data and internally collected data.

2. Process-level data assets: These assets represent a transitional stage between raw data assets and application-level data assets, providing cleaned, standardized, and lightly aggregated data that is ready for further development and application.

3. Application-oriented data assets: These are personalized statistical data or data products derived from raw and process-based data assets, tailored to meet specific data needs through data aggregation, data mining, and other processing methods. They can be categorized into two types: revenue-enhancing and statistical-supporting.

IV. Implementation Path for Data Assetization

(1) Data Assetization Roadmap

According to the industry’s stage classification of the assetization path, it can be divided into three stages: resource utilization, assetization, and capitalization.

1. Resourceization: Understand the various ways in which data can be utilized, identify and label data to create a sense of scarcity, thereby highlighting the need for optimal resource allocation.

2. Assetization: The process by which data is gradually transformed from raw data into data assets, encompassing four stages: business digitalization, data resourceization, data productization, and data assetization.

3. Capitalization: The process of socially allocating data resources through data trading, circulation, and other means, such as contributing data as equity.

(2) Steps for Data Assetization

1. Data resource exploration: Based on the data modeling infrastructure, carry out data ingestion and inventory identification of data resources.

2. Data asset registration: Assign resource identifiers to data and properly categorize and register them.

3. Data Compliance Assessment: By engaging a data asset evaluation firm, complete the compliance review of data assets and issue a compliance certification certificate.

4. Data quality assessment: Evaluate data for accuracy, standardization, timeliness, accessibility, completeness, and consistency.

5. Data Asset Assessment: Includes accuracy corresponding to the data and model levels, accuracy corresponding to data and model nodes, completeness and accuracy of model attribute data, and more. 18911835315

6. Data value-added services: Through data asset valuation, we will complete the reconciliation between book records and actual assets, make professional judgments based on the outcomes of various specialized services prior to recording data assets, and thereby achieve accurate accounting of data assets.

(3) Data Evaluation Methods

According to the "Information Technology—Data Quality Evaluation Metrics" (National Standard GB/T 36344-2018), jointly released on June 7, 2018 by the State Administration for Market Regulation and the China National Standardization Administration Committee, the framework for data quality evaluation metrics comprises six dimensions: normativity, completeness, accuracy, consistency, timeliness, and accessibility.

V. Scenario-Based Assessment of Data Assets

(1) Data Asset Valuation in the Power Industry

1. Overview of the Industry’s Current Situation: The digital transformation of power grid enterprises is gaining momentum, and electricity data has become a strategic resource and core production factor for these enterprises.

2. Case Details: Taking XX Power Grid Co., Ltd. as an example, the company possesses data assets including electricity consumption data from large-scale enterprises, power credit rating data, and power loan data.

3. Data product offerings: These include electricity monitoring products for enterprises above designated size, electricity credit rating products, and electricity-based loan products, among others.

4. Purpose of the assessment: To provide a value reference basis for the inclusion of XX Power Grid Co., Ltd. in the financial statements.

5. Data asset type determination: Confirmed as intangible asset-class data assets.

6. Valuation type: Net replacement cost.

7. Valuation Method: Cost method is adopted.

8. Valuation Conclusion: Based on the valuation, the assessed value of the company’s electricity data assets as of the valuation base date is XXX ten thousand yuan.

(2) Valuation of Data Asset Value in the Tourism Industry

1. Overview of the Industry’s Current Status: The widespread adoption of technologies such as the internet, mobile internet, and big data in China has provided an ideal nurturing environment for the development of tourism big data.

2. Case Details: Taking XXX Information Technology Co., Ltd. as an example, the company possesses data assets such as statistical data on the tourism industry and visitor flow statistics for cultural venues.

3. Purpose of the assessment: To provide a value reference for determining the transaction pricing of data products.

4. Application scenarios for data products: Providing tourism companies, platform enterprises, investment firms, and others with statistical data on tourism information as well as statistical data related to tourism product development and sales.

5. Data asset type determination: Confirmed as intangible asset-class data assets.

6. Valuation type: Market value.

7. Valuation Method: The income approach is adopted.

8. Valuation Conclusion: Based on the valuation, the tourism data assets held by the company have a valuation of XXX ten thousand yuan as of the valuation base date.

(3) Valuation of Data Asset Value in the Customs Data Services Industry

1. Overview of the Industry Status: The customs data services industry, supported by digital technologies, leverages the acquisition, processing, and utilization of customs data to assist enterprises and government agencies in activities such as expanding foreign trade customer bases, conducting market analysis, enforcing trade regulations, and controlling risks.

2. Case Details: Taking XXX Information Technology Co., Ltd. as an example, the company possesses international customs data, including bill of lading data, customs declaration data, statistical data, and more.

3. Purpose of the assessment: To include in the financial statements the internationally recognized customs data assets developed and processed by the company itself.

4. Data asset type determination: Confirmed as inventory-type data asset.

5. Valuation type: Net replacement cost.

6. Valuation Method: The cost method is adopted, and the calculation formula is as follows, or.

7. Valuation Conclusion: Based on the valuation, the assessed value of the company’s international customs data assets as of the valuation base date is XXX ten thousand yuan.

(4) Assessment of International Trade Data Assets

1. Overview of the Industry’s Current Situation: With the limitless expansion of global information and the vigorous development of the digital economy, digital trade has become the mainstay of international trade.

2. Case Details: Taking XXX Information Technology Co., Ltd. as an example, the company possesses data on import and export customs declarations from various countries, as well as data on global import and export bills of lading.

3. Purpose of the assessment: To provide a value reference for determining the transaction pricing of international trade data assets.

4. Determination of Data Asset Type: Confirmed as intangible asset-class data asset.

5. Valuation type: Market value.

6. Valuation Method: The income approach is adopted, and the basic calculation model is as follows.

7. Valuation Conclusion: According to the valuation, the value of the company’s international trade data assets as of the valuation base date is XXX ten thousand yuan.

(5) Assessment of Meteorological and Soil Data Assets

1. Overview of the Industry’s Current Situation: Meteorological data primarily includes meteorological observation data and climate change data. Meteorological data is an essential component of the meteorological services industry and has a significant impact on economic and social development as well as people’s daily lives.

2. Case Details: Taking XXX Agricultural Technology Co., Ltd. as an example, the company possesses meteorological forecast data, nationwide gridded real-time meteorological data, soil data, and more.

3. Purpose of the assessment: To provide a value reference for determining the transaction pricing of meteorological and soil data assets.

4. Data asset type determination: Confirmed as inventory-type data asset.

5. Valuation type: Market value.

6. Valuation methods: Either the cost approach or the income approach can be used.

7. Valuation Conclusion: Based on the assessment, using the cost approach, the assessed value of the company’s meteorological and soil data assets as of the valuation base date is XXX ten thousand yuan; using the income approach, the assessed value is XXX ten thousand yuan. The final assessed value is XXX ten thousand yuan.

(6) Intelligent Industrial Design Data Asset Valuation

1. Overview of the Industry’s Current Status: Industrial big data represents a new service model and information technology, and data assets can fully demonstrate the commercial value of data.

2. Case Details: Taking XXX Intelligent Technology Co., Ltd. as an example, the company has developed a highly effective methodology—resulting from the digital transformation of its production processes—that significantly boosts manufacturing enterprises’ productivity. This methodology has been further refined into a standardized SaaS service.

3. Purpose of the assessment: To provide a value reference basis for including intelligent industrial design data assets on the balance sheet.

4. Determination of Data Asset Type: Confirmed as intangible asset-class data asset.

5. Valuation type: Net replacement cost.

6. Valuation Method: The cost method is adopted, and the calculation formula is as follows, or.

7. Valuation Conclusion: According to the valuation, the value of the company’s intelligent industrial design data assets as of the valuation base date is XXX ten thousand yuan.

VI. Challenges in Data Asset Valuation and Proposed Solutions

(1) Main difficulties

1. Enterprises have limited awareness of data assets, a vague definition of data assets, and an unclear scope for identifying data assets.

2. Enterprises lack a sufficient understanding of the pathways for developing data assets, and there are challenges in establishing ownership rights over data resources and distributing the resulting benefits.

3. The company’s early organizational structure is not conducive to cost allocation in the process of forming data assets, posing challenges to the accounting treatment of data assets when they are recognized on the balance sheet.

4. The standards and mechanisms for disclosing data asset information are unclear, and there are difficulties in determining how to establish the disclosure platforms, standards, mechanisms, and content.

5. Professional service providers lack uniform standards for data asset valuation methodologies and have inconsistent understandings of data assets, making it difficult to effectively identify and unlock their market value.

(2) Proposed Solutions

1. Strengthen enterprises’ awareness and training on data assets, and clearly define the definition and scope of data assets.

2. Clearly define the conditions for the formation of enterprise data assets, with a focus on ensuring that data assets can be legally recognized. Further guarantee the legitimacy of how all relevant parties in the data element circulation market obtain rights to hold data resources, rights to process and use data, rights to operate data products, and derivative rights.

3. Optimize the enterprise’s organizational structure, rationally plan business processes, and enhance the accuracy of accounting for data assets on the balance sheet.

4. Standardize the disclosure methods for data assets, clarify the content and mechanisms for disclosure, and promote the establishment of a unified approach for inclusion in financial statements.

5. Strengthen the cultivation and regulation of professional service institutions, and promote the establishment of a systematic, standardized, normative, and convenient workflow for the recognition, valuation, and oversight of data assets by digital commerce entities.
 

VII. Conclusion

The inclusion of data assets on balance sheets and their valuation are crucial components of the digital economy’s development. They play a significant role in enhancing enterprises’ digital transformation, optimizing financial structures, and driving the growth of the digital economy. However, currently, enterprises face numerous challenges in the process of including data assets on balance sheets and valuing them. To address these challenges, governments, enterprises, and professional service providers must work together to strengthen policy guidance, establish standardized norms, and improve enterprises’ awareness and management capabilities, thereby promoting the effective utilization and value realization of data assets.

This report, through case studies of data assets across multiple industries, proposes theoretical and practical approaches for recognizing and valuing data assets, providing a reference framework for enterprises and asset valuation institutions. In the future, as the data-factor market continues to develop and mature, the value of data assets will be more fully realized, injecting new momentum into economic and social development.

The above report is for reference only; the evaluation of specific data assets should be based on a detailed analysis and judgment tailored to the actual situation.