Data assetization spurs NQPFs. Photo: TUCHONG

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China is the first country in the world to recognize data as a new type of production factor and the first to promote the inclusion of enterprise data assets in financial statements. These pioneering practices not only provide rich, innovative scenarios for developing digital economy theory but also set higher requirements for theoretical innovations in this field. The concept of new quality productive forces (NQPFs) has manifested theoretical innovations related to digital economy practices such as data as a production factor and the inclusion of data assets in financial statements. This will help advance the development of an independent digital economy knowledge system in China.

Facilitating digital transformation 

To clarify the relationship between the data factor and NQPFs, it is necessary to identify mechanisms through which the data factor influences a firm’s output. Data or data resources alone do not constitute a factor of production; pure data alone do not affect a firm’s output. Only when data are combined with the labor factor do they have an impact on a firm’s output. Research on the role of data indicates that organizational change is the primary path through which the data factor influences a firm’s output. This means that if firms do not undergo corresponding organizational changes, merely collecting big data is unlikely to significantly increase total output. Therefore, although digital technologies are seemingly ubiquitous in daily life, they often do not lead to a significant increase in total output, and total factor productivity (TFP) may even decline. An important reason for this could be that firms or organizations have not implemented the necessary changes in tandem with data usage.

This helps explain why data collection is prevalent in various sectors, such as tourist attractions, restaurants, hotels, and transportation. However, consumers do not experience a noticeable improvement in efficiency, and large-scale congestion can occur. This is because organizational changes needed to make the data factor effective are hard to realize. Existing research has found that improving digital infrastructure can unlock the value of the data factor. However, if organizations or departments do not make corresponding changes, the contribution of digital infrastructure investment to the total economic output remains limited. Meanwhile, when core firms improve their efficiency through digital transformation, their labor force often moves to other related firms, which may not have comparable input-output efficiency. As a result, at a certain stage, the overall input-output efficiency of society may not increase but could actually decrease.

The role of the data factor in facilitating enterprises’ high-quality digital transformation is an important topic for further study. Enterprise digital transformation refers to the series of adaptive management changes or states triggered by the shift from informatization to digitalization, involving comprehensive management changes across multiple dimensions and levels. In contrast to general digital transformation, high-quality digital transformation focuses on improving TFP through the efficient allocation of the data factor during the transition. Thus, the distinction between high-quality digital transformation and general digital transformation lies in the former’s emphasis on data as a production factor, rather than simply a resource, with a focus on improving TFP. At present, the digital transformation of enterprises in China is generally at a relatively low level, and enterprises face many challenges in achieving a high-quality digital transformation. If an investment in digital transformation cannot produce a high-quality data factor, it will be more difficult to drive transformations and upgrading via this factor.

In the digital transformation of enterprises, the data factor plays a role in reconfiguring the production function by acting as a new input and by forming new combinations with existing factors. Among these new combinations, the data factor stands out due to its innovativeness. This distinguishes it from traditional factors such as capital. The value generated by the new combination of the data factor with other factors is not merely the sum of the two, but could potentially yield entirely new and unforseen value. This is a random and far-reaching process. As a result, the previous relationship between production functions and total output becomes increasingly blurred. Thus, it is necessary to re-examine essential changes stemming from the data factor’s new input and its combining with existing factors from a fresh productivity perspective. Research within enterprises shows that these essential changes primarily manifest in two ways: first, transformations in management decision-making driven by data, and second, innovations in the adaptive interaction between enterprises and users, which results in higher output efficiency. This serves as a concrete manifestation of NQPFs at the micro level.

Realizing optimal structure 

Data assets refer to data resources that are legally owned or controlled, measurable, and of socioeconomic value to an organization. While the data factor does not necessarily become data assets, data assets originate from the data factor. Therefore, the data factor forms the foundation of data assets. How the data factor is transformed into data assets—i.e., the process of data assetization—largely determines the quality of data assets. As such, the quality of the data factor directly affects the quality of data assets, but the process of data assetization also has a direct impact on the quality of data assets. Through the realization of data assets’ value, data shifts from a potential production factor to a real one, ultimately forming new productive forces that influence enterprise output efficiency. The management of this process constitutes data asset management.

Enterprise data asset management refers to activities through which enterprises rationally arrange the structure of their data assets via the data factorization process and related business activities. Optimizing the structure of enterprise data assets involves aspects such as data liquidity and security, use value and transaction value, and the allocation of data assets alongside existing assets. The goal is to achieve the optimal structure within a given scale of data assets. Therefore, the improvement in social productivity resulting from the optimization of data asset structure also serves as a concrete manifestation of NQPFs.

Enterprise data asset management primarily focuses on managing the use value and transaction value of data assets. Research indicates that the data factor has both immediate and potential value. Similarly, the use value and transaction value of data assets also exhibit immediate and potential value characteristics and can be managed within a 2×2 framework. This framework includes the following four models: {immediate use value, immediate transaction value}, {potential use value, potential transaction value}, {immediate use value, potential transaction value}, and {potential use value, immediate transaction value}. In contrast to previous asset management models, the highest value in data asset management may lie in the immediate value domain. However, it is often difficult to include it in the measurement of gross national product (GNP) or production efficiency, and consequently, it is hard to measure it in national wealth statistical estimates. Therefore, it is essential to conduct more comprehensive and accurate statistical estimates of total output of the economic value of data to better capture NQPFs and their impacts.

Allocating data assets  

Digital technologies such as the Internet of Things, big data, artificial intelligence (AI), and blockchain appear to have placed workers, labor subjects, and labor tools all within the digitalization process. With the accumulation of big data knowledge, enhanced computing power, and the improved reliability and generalization of large models in AI, the allocation of data assets through privacy computing holds great significance for the emergence and development of NQPFs. 

From a technical implementation perspective, digital technologies like privacy computing enable societies or enterprises to achieve “data availability without visibility.” Suppose, for example, that both Company A and Company B hold their own private data but are unwilling to share their data with one another. If their data could be combined and processed, the combined data might generate information that Company C is willing to pay for. In this scenario, by using privacy computing technology to process the data, neither Company A nor Company B can access each other’s data. Yet, digital products derived from this “data availability without visibility” can be sold to Company C, thereby increasing overall social welfare. Such transactions could generate immense value within the global industrial chain, although accurately calculating this value remains challenging at present. Clearly, the level of intelligence and its capability directly affect the ability to transform the data factor into data assets, as well as the efficiency with which data asset value is realized and converted. This is also closely linked to AI capabilities, the efficiency of data asset allocation, and even the efficiency of resource allocation across society. Greater intelligence correlates with better resource allocation efficiency for societies, industries, departments, or enterprises. Therefore, the level of intelligence and its capability are central to the embodiment of NQPFs. 

As the digital economy becomes more intelligent, the digitalization of industries increasingly influences industrial structure upgrades through the accumulation of data assets, leading to a digital leap in the industrial structure. Similar to a quantum transition, where a particle may tunnel through an impermeable barrier with a certain probability, once the digitalization and intelligence of industries reach a critical threshold, traditional industries or enterprises will be able to achieve higher TFP across various dimensions such as product processes, operational systems, and business models. This will result in an innovation leap that was previously difficult to achieve. This digital leap embodies the innovativeness of NQPFs. 

Xie Kang is a professor from the School of Business at Sun Yat-sen University.

Edited by ZHAO YUAN