The demand for data asset accounting in China has become increasingly urgent. Photo: TUCHONG

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With the rapid development of the digital economy, the data resource has become a crucial engine driving the high-quality development of the global economy. Data asset accounting is a key step in fully unlocking the value and potential of the data resource. Establishing and improving a data asset accounting system is not only a strategic measure to address global data governance challenges but also a critical safeguard for promoting the sustainable and healthy development of the digital economy.

 

Data as a new resource with potential

With the rapid advancement and widespread application of new-generation information technologies such as big data, the Internet of Things, and artificial intelligence (AI), the data factor has permeated every sector of the social economy. From optimizing industries internally to promoting cross-industry integration and accelerating the digital twin process, this new factor is experiencing explosive growth and demonstrating diverse application potential, positioning it as a new resource in the digital economy era.

First, it empowers the optimization and upgrading of industries. By collecting and analyzing extensive production and operational data, companies can precisely identify bottlenecks and inefficiencies in their processes, enabling targeted optimization measures.

Second, it can promote the integrated development of industries. An efficient data sharing and exchange mechanism helps dismantle information barriers across sectors, fostering the cross-border integration of technology and resources.

Third, it accelerates the digital twin process. Through high-precision collection, efficient processing, and intelligent analysis, the data factor can create an accurate digital mirror of the real world, facilitating the efficient simulation and optimization of physical laws in the virtual realm.

The role of the data factor in driving modern economic development has been fully demonstrated, and its value is widely acknowledged across society. However, to fully realize its potential, it is essential to address critical challenges such as inefficient data circulation, inequitable revenue distribution, and inadequate security governance. As a scientific evaluation and management tool, data asset accounting plays a pivotal role in unlocking the data factor’s potential and advancing economic growth.

First, data asset accounting promotes the standardization of data circulation. A robust, scientific accounting system establishes unified evaluation standards, offering a clear framework for measuring data circulation. This standardization not only enhances the tradability of data but also creates favorable conditions for its flow across industries and sectors. 

Second, data asset accounting promotes equitable revenue distribution. By quantifying the economic value that data generates for various stakeholders during its use, a scientific value accounting mechanism provides a fair and objective foundation for revenue allocation. This approach ensures that data holders, processors, users, and product operators receive economic returns proportional to their actual contributions to data value.

Third, data asset accounting enhances the precision of security governance. By precisely assessing the value of data assets, it helps stakeholders identify and quantify the investments in and requirements for data security. Leveraging this refined management model, stakeholders can develop tailored security strategies based on the varying values of data assets, ensuring optimal resource allocation and maximizing the effectiveness of security measures. 

Global references   

Theories and practices of data asset accounting have garnered widespread attention and significant recognition from international organizations and various countries. The 51st Session of the United Nations Statistical Commission explicitly included the topic of “how to incorporate data into the System of National Accounts” in its research agenda. The Organization for Economic Cooperation and Development (OECD) has conducted in-depth discussions on quantifying data asset value within macroeconomic statistics as part of its digitalization framework. Furthermore, numerous countries have actively engaged in assessing data asset value. These explorations not only provide valuable references for other nations but also offer critical experience for advancing data asset accounting theory and practice. From these efforts, three key lessons can be drawn.

First, data classification is essential for ensuring the comprehensiveness of data asset accounting. International practices reveal that a rational and systematic classification of data assets serves as a prerequisite for comprehensive and precise accounting. A well-designed classification system helps to uncover the potential value and roles of different types of data assets in economic activities, thereby enhancing the systematicity, transparency, and consistency of data asset accounting. This approach also facilitates international comparisons and analyses of data assets under unified standards, strengthening the global comparability of accounting results. By adhering to a scientific classification framework, countries can not only improve the precision and scientific basis of data asset accounting but also support policy-making and optimize resource allocation. Moreover, systematic classification promotes standardized data asset management by clarifying their attributes, uses, and roles in the economy and society. This foundation not only aids in subsequent data valuation and pricing but also strengthens the systematic and standardized management of data assets.

Second, the cost approach serves as a foundational method for assessing data asset value. Given that the global data market is still in its infancy, data assets are often self-produced and self-used, making it challenging to derive market prices through trading mechanisms. As a result, many countries favor the cost approach for valuing data assets, which considers their value as the total cost incurred during their creation. In practice, some nations estimate direct labor costs, indirect labor costs, and other related costs separately, summing these to calculate the asset’s value. Others estimate the labor costs involved in data asset creation and apply a markup coefficient to account for additional costs, such as fixed capital consumption and intermediate inputs. While the cost approach has limitations in evaluating future returns and added value, its foundational role in data asset accounting is undeniable. It provides a practical framework for initial valuation, offering governments and enterprises a workable methodology for quantifying data asset value.

Third, technological applications are critical for improving the precision of data asset value accounting. With the rapid advancement and widespread adoption of big data, AI, and machine learning, the precision of data asset accounting has significantly improved. The integration of these technologies not only enhances the efficiency of the valuation process but also improves its objectivity and systematicity. Advanced tools enable deeper analysis and processing of large datasets, reducing the errors caused by subjective assumptions in traditional methods. These technologies optimize data handling, improve accuracy, and ensure consistency in value accounting. The effective use of such tools is indispensable for enhancing accounting precision and represents a core driver of methodological innovation in data asset valuation. By leveraging technologies like machine learning and big data analytics, global data asset valuation is moving toward greater precision. This technological progress lays a robust foundation for the scientific management and efficient utilization of data assets, offering nations a competitive edge in the digital economy era. 

Chinese data asset accounting  

As a major player in the digital economy, China has achieved remarkable progress in this field. With the rapid development of the digital economy, the demand for data asset accounting in China has become increasingly urgent. However, significant challenges persist, including unclear data ownership, an incomplete data classification system, insufficient foundational data resources, and an underdeveloped statistical survey system. To address these issues and accelerate the development of data asset accounting with Chinese characteristics, innovation and reform should focus on the following areas.

First, it is suggested to establish a data ownership mechanism. Laws and regulations clarifying the ownership of different types of data should be developed. Additionally, technologies such as blockchain and privacy computing should be employed to implement data ownership at the technical level.

Second, it is advised to accelerate the management of data classification. Factors such as data sensitivity, importance, and usage frequency should be taken into account to devise scientific, rational, and unified standards for data classification and grading. These standards should comprehensively address the diverse types of data across various industries and fields.

Third, the sources of foundational data should be diversified. Cooperation among government entities, enterprises, social organizations, and public platforms should be strengthened to enhance the variety and scope of foundational data through multi-channel collection efforts.

Fourth, it is suggested to improve the statistical survey system. A comprehensive statistical survey framework should be established to ensure seamless integration between statistical accounting methods and foundational data for accounting purposes.

Ping Weiying is a professor from the School of Statistics and Data Science at Jiangxi University of Finance and Economics.

Edited by WANG YOURAN