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China’s data element ecosystem rapid growth and challenges

PHOTO: ENVATO

ANN/CHINA DAILY – Experiencing rapid growth, buoyed by robust policy support, China’s data element ecosystem is, however, facing several significant challenges, including insufficient technical infrastructure and inactive market transactions.

There are numerous issues that still need to be addressed, such as high compliance costs for suppliers, a sluggish pace of digital transformation among users, mismatched supply and demand, and an incomplete data pricing mechanism.

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

Since 2014, numerous data trading institutions have been established nationwide. The data element circulation market in China has expanded significantly, with an estimated market size of CNY159.2 billion (GBP21 billion) by 2024.

The data element market can be analysed from three perspectives: support, value, and policy. Support encompasses infrastructure and technical backing for data elements. Value involves data suppliers, data trading institutions, and analysis and application groups. 

Policy includes establishing unified data standards, promoting public data openness, incentivising market participants to share data, and scientifically defining 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, there is a significant gap between the infrastructure and technical environment and between national strategic goals and the needs of data element circulation practices.

Regarding value, there are high compliance costs on the supply side due to stringent and comprehensive compliance assessments. 

For instance, the high costs of obtaining personal authorisation and the difficulty in obtaining authorisation from groups, lack of clear standards for the anonymisation of personal data, and insufficient motivation for individuals to share their data as they do not receive benefits from doing so.

Research data fragmentation and a lack of incentive for public data development are also issues. Currently, the government and public institutions have not clearly defined the fees and standards for authorising public data to operating units.

On the demand side, some enterprises experience a slow digital transformation process and lack a deep understanding and exploration of data value, failing to fully utilise 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 per cent of enterprises have developed or utilised only a small portion of their data.

Regarding 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 imperfect. There are four major imperfections in data ownership and rights allocation, data security compliance costs, data circulation, and the definition of data monopolies.

These data-related issues are due to market and policy reasons, such as 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 these problems, it is necessary to first understand the value of data as a production factor. The value of data lies in its ability to improve quality, reduce costs, increase efficiency, and promote innovation, with the core being the development and utilisation of data. 

The design of foundational data systems should facilitate the full development and utilisation of data, rather than maximise 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 scrutinise 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 combined technology and economic factors.

Suggestions for improvement include recognising that an effective market is the foundation for the development of the data element ecosystem, with the government’s role being to supplement and guide in case of market failure, and policy formulation needs to follow market rules and principles.

Focus on developing data trading platforms to further improve the data element market ecosystem. Data trading platforms should position themselves as comprehensive service providers, leveraging 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.

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.

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