Effective market key to data element ecosystem

China's data element ecosystem is developing rapidly on strong policy support, but faces challenges including insufficient technical infrastructure and inactive market transactions.

Several problems still wait to be solved, such as high compliance costs on the supply side, slow digital transformation on the demand side, mismatched supply and demand and an incomplete data pricing mechanism.

To address these challenges, it is essential to understand the value of data as a production factor, find efficient circulation models, protect data investment incentives, and develop technological and innovation systems.

Since 2014, dozens of data trading institutions have been established across the country. China's data element circulation market has significantly increased since then, and the overall scale is continuously expanding, with an estimated market size of 159.2 billion yuan ($21 billion) by 2024.

The data element market can be analyzed from three perspectives — support, value and policy. Support means infrastructure and technical support for data elements. In terms of value, it involves data suppliers, data trading institutions, and analysis and application groups. Policy means establishment of unified data standards, promotion of public data openness, incentivizing market participants to share data, and scientific definition of data property rights.

In terms of support, technologies such as blockchain, privacy computing and multiparty computation can be applied to the circulation and trading of data elements.

However, in reality, there is a significant gap between the infrastructure and technical environment, and between national strategic goals and the needs of data element circulation practices.

In terms of value, there are high compliance costs on the supply side, due to the stringent and comprehensive compliance assessments required on the data supply side.

For instance, there are high costs for obtaining personal authorization, and difficulty in obtaining authorization from groups, lack of clear standards for the anonymization of personal data and insufficient motivation for individuals to share their data as they do not receive benefits from sharing their personal data.

There is also a research data fragmentation and a lack of incentive for public data development. Currently, the government and public institutions have not clearly defined the fees and standards for authorizing public data to operating units.

From the demand side, some enterprises have a slow digital transformation process and lack deep understanding and exploration of data value, and fail to fully utilize data for business decision-making and innovation.

Others lack the corresponding data analysis technology and capability, meaning the data cannot be transformed into actual business value. More than 80 percent of enterprises have developed or utilized only a small portion of their data.

In terms of matching data supply and demand, there exists an incomplete data element pricing mechanism. There is also significant information asymmetry between buyers and sellers regarding price negotiations, data compliance and security risks in data transactions.

At the policy level, related systems and regulations are still not perfect. There are four major imperfections in data ownership and rights allocation, data security compliance cost, data circulation and definition of data monopolies.

The causes of such data-related issues are due to market and policy reasons, for example, a redundant construction of data exchanges.

There is also a binary policy conflict between development and security, leading to unclear and unstable policies that cause enterprises to lack vitality due to policy uncertainty. Additionally, there is a lack of incentive mechanisms for public data sharing.

Monopoly is also prevalent, alongside coordination failures among various enterprises in the industry chain and between different departments within a single conglomerate.

To address the aforementioned problems, it is necessary to first understand the value of data as a production factor. The value of data as a production factor lies in its ability to improve quality, reduce costs, increase efficiency and promote innovation, with the core being the development and utilization of data.

The design of foundational data systems should facilitate the full development and utilization of data, rather than maximize the volume and value of data transactions. Additionally, data should be cautiously treated as an asset for balance sheets, collateral and financing.

Second, it is important to find efficient circulation models for data elements to balance data transactions and interactions. This involves nurturing professional talent in the data element market and actively providing supporting services like quality assessment to promote data traceability and trustworthy transactions.

Third, more efforts should be made to effectively protect data investment incentives. It is necessary to scrutinize the standards used to judge whether data sharing is insufficient, to have a reasonable level of sharing to enhance social well-being.

Moreover, it is crucial to circulate and use data. In the face of competition from data giants like Alibaba, JD and Tencent, companies like Pinduoduo and ByteDance have successfully risen. The success of ChatGPT is also the result of the combined technology and economic factors.

My suggestions are as follows. First, it is essential to recognize that an effective market is the foundation for the development of the data element ecosystem, and the government's role is to supplement and guide in case of market failure, and policy formulation needs to follow market rules and principles.

Second, it is important to focus on the development of data trading platforms to further improve the data element market ecosystem. Data trading platforms should position themselves as comprehensive service providers, leverage their intermediary value to build trust mechanisms, connect various links in the data industry chain, and form a closed loop of data production and transactions.

Third, greater efforts should be made to explore a more refined system for data element pricing and revenue distribution.

Finally, data sources are divided into public data and enterprise data, and uses are divided into commercial and public welfare purposes. Different pricing methods should be applied based on these different sources and uses. Simultaneously, data trading platforms should continuously explore rules and methods for data transaction pricing to enhance the market's role in price discovery.

The writer is a professor at the School of Economics and director of the digital economy research center at Renmin University of China. This article is a translation of his speech published on the official WeChat account of the China Macroeconomy Forum, a think tank. The views don't necessarily reflect those of China Daily.