Scenario-Driven, Trustworthy Data, AI Empowerment

Data elements, epitomizing new qualitative productivity characterized by high technology, high efficiency, and high quality, have become a focal point of attention across various sectors. The discourse revolves around maximizing the fueling and multiplier effects of data as a production factor, leveraging artificial intelligence (AI) as an engine to amplify the multiplicative impact of data elements, thereby facilitating the high-quality growth of urban digital economies.

Dr. Lin Zhenyang, from the perspectives of constructing the national data element market and scenario empowerment of data elements, outlines a multi-level, multi-stakeholder development path for the data element market, also furnishing invaluable guidance to governments and enterprises navigating the complexities of data challenges.

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1. How to comprehend the concept of data elements and their value?

Dr. Lin Zhenyang:Let us commence by referencing the Ten Questions on Data Elements posed by Academician Mei Hong last May, which I   have synthesized as follows.

Firstly, it is crucial to understand why data is classified as an asset. This classification inevitably leads to inquiries about the ownership attributes of data, how its value is manifested and quantified, and how we assess the worth of this intangible asset. Assessing value necessitatesunderstanding the unit of data, how to divide it reasonably, and how to ensure its returns. These questions frame the fundamental definition ofdata as a factor and its basic quantitative metrics. At the core of data’s value realization lies its circulation.

To harness the value of data as a new form of productive force across diverse scenarios, several key relationships in the data flow process must be grasped: firstly, the relationship between responsibilities, rights, and interests among data providers, users, platforms, and the government, all dynamically negotiating to strike a balance. Secondly, the balance between security and development must be considered, especially given the novel business models and industrial forms this new production factor spawns, along with the inherent tension between unknown data security risks and economic progress. Lastly, under the national strategy for data elements, how can technology and institutionalreforms serve as dual drivers propelling the development of the data element market?

2. What are the main challenges and dilemmas facing the current data element market in its development?

Dr. Lin Zhenyang: The development of the data element market confronts four major challenges. Firstly, the issue of scattered data resources arises from the legacy of past IT infrastructure developments, which has resulted in data resources being dispersed across multiple systems and departments, creating data silos. This leads to an insufficient total volume of data resources available for integration and development. Consequently, there is a pressing need to accelerate data governance under the national integrated big data system, dismantle barriers, and establish broader coverage data resource pools ready for processing.

Second, data quality issues are particularly prominent, with China's overall data quality being relatively low, especially concerning completeness, accuracy, consistency, timeliness, usability, and security. This significantly impacts the application and value-added effects of data resources in later stages.

Third, regarding data circulation, there's a need to facilitate the flow of data. Due to data security concerns and intricate approval processes, public administrative data and others struggle to circulate effectively, urgently requiring the establishment of a rule system for data separationand categorization to streamline processes and enhance data flow efficiency.

Fourth, a comprehensive institutional framework is needed throughout the process to provide guarantees. While we have guiding principles like the '20 Articles on Data,' however, at the practical implementation level, the construction of institutional systems and the complementary regulatory safeguards for security, as well as the technology for monitoring data flow, remain inadequate.

In the overall planning and implementation path of data elements, especially for local governments in building city-level data element markets, there persists to some extent a "three-no" predicament: no clue, no resources, no leverage. "No clue" refers to the highly complex systemic project involving multiple entities, stages, and policies that transforms data element values from national macro policies into local industrial applications, often leaving local authorities unsure where to begin. "No resources" indicates the challenge of professional demands indata elements, with uneven local resources unable to form a cohesive force without specialized operational organizations. "No leverage" signifies that initial steps taken by local governments commonly face issues of high investment with slow returns, lacking specific operational ororganizational handles, resulting in insufficient on both supply and demand sides and an underactive industry.

Developing the data element market not only requires addressing the aggregation of data resources, quality enhancement, and facilitationof circulation but also identifying breakthrough points based on regional characteristics to overcome these "three-no" bottlenecks, thereby vigorously promoting the healthy development of China's data element market and the transformation of urban digital economies.

In line with national policy trends, the overarching design of the data element market can adopt an innovative model combining "Scene-Driven, Trusted Data, AI Empowerment." It necessitates pulling trusted data supply from the demand side, i.e., scenarios, and realizing corresponding data products and services through AI algorithms, ultimately empowering real industry scenes. "Data Element ×" and "Artificial Intelligence +" are the two core elements and engines.

3. How to comprehend the overall framework of data element market construction?

Dr. Lin Zhenyang:The data element market, a trillion-level, multi-layered, and multi-entity sector, encompasses a three-tiered, interconnected layout comprising zero-level, first-level, and second-level markets.

The zero-level market represents the nascent stage of data, where internal circulation initially occurs, primarily encompassing public data generated during digital government construction and enterprise data accumulated through digital transformations. These data resources formthe foundation of the data element market.

The first-level market is the pivotal transition phase from internal to external circulation, where data enters the market. Local data groups act as bridges between government and market, responsible for initial data development and standardized governance, making data market-ready by separating ownership from usage rights and converting data resources into pools for urban digital economy development.

The second-level market marks the critical stage where data truly enters the market. Many data merchants develop and utilize data extensively, generating a wealth of data products and services that empower various industry scenarios, fostering a thriving "Data Element ×" ecosystem. Only with a sufficiently prosperous second-level market can the entire data element market advance. The "Data Element ×" Three-Year Action Plan released by our National Data Bureau focuses on pulling from the demand and scenario sides of the second-level market to ensure zero-level market data supply, first-level market data flow, and effective utilization in the second-level market.

4. How should the construction of the data element market be practically implemented?

Dr. Lin Zhenyang:Broadly, our tasks can be summarized in a three-step approach: consolidating the zero-level market, focusing on the first-level market, and responding to the second-level market.

In the zero-level market stage, we need to take stock of our data resources, strengthen data supply, and pay special attention to data quality. Through the construction of digital government and a nationally integrated e-governance big data system, we aim to achieve a census of data, its access, sharing, processing, governance, and future open development.

In the first-level market, the focus is on the transformation of data resources into assets and their compliant circulation, along with the transfer of data ownership. Via processes such as data registration, rights confirmation, valuation, authorized operation, and inclusion on balance sheets, a data asset management system is established to facilitate the scalable growth of data assets. Data groups established in various regions play a pivotal role as gatekeepers between the zero-level and first-level markets.

The second-level market emphasizes the development, customization of data products, and the implementation of application scenarios. This stage underscores the diversified development of data products, personalized customization, and intelligent governance and value discovery based on AI technology, all in service of various industry scenarios in the real economy. Ultimately, it realizes the effect of data transactions and interactions both within and outside the market.

Throughout the construction of the entire data element market, we must also establish four systems: the institutional system, the platform technology support system, the operational system, and the ecosystem. The institutional system, starting from top-level design, necessitates the establishment of laws, regulations, supervision systems, and trading rules related to data elements, ensuring clear data asset operation mechanisms, organizational structures, and management systems. The platform technology support system focuses on constructing platforms and related technical infrastructure for data registration, rights confirmation, data governance, and transaction facilitation, ensuring the safety, compliance, and efficiency of data resource management and trading processes. The operational system revolves around value discovery of data resources, innovation in application scenarios, and the construction of service systems, promoting the continuous appreciation of data value and building a data industry chain and ecosystem. The ecosystem aims to foster a benign interactive environment for the data element market with the participation of multiple parties, including government, enterprises, individuals, and third-party service institutions.

5. How does data transform from being a resource into an asset?

Dr. Lin Zhenyang:The assetization of data resources is a key aspect of the first-level market. Prior to entering the first-level market, the crucial task is data registration and rights confirmation. To this end, localities are exploring and establishing data registration and management centers, adhering to the principle of "no registration, no rights confirmation, no illegal data entry," ensuring clear data ownership and laying the groundwork for compliant data circulation among governments, enterprises, and individuals. Many places, including Guangdong, Shandong, Shenzhen, Zhejiang, Shanghai, Anhui, Henan, and Guizhou, have already issued exploratory documents and initiated pilots for data registration certificates, hoping for a unified national data asset registration and management approach.

Following data registration, data assets require evaluation, traditionally done through methods such as market comparison, income approach, and cost approach. A rigorous asset appraisal process facilitates the entry of data assets into market circulation. With the advent of the era of corporate data assets on balance sheets, companies should account for data assets according to relevant accounting provisions. Data asset listing improves corporate profits, increases asset accumulation, and drives digital transformation, further generating synergistic effects inparticipating in the data trading market.

In the process of data asset measurement and listing, there are complex stages including initial measurement, subsequent measurement, and termination recognition. Enterprises can refer to frameworks like PwC’s “Five Steps Method for Data Asset Listing,” covering compliance, rights confirmation, effective governance, economic feasibility analysis, cost allocation, and disclosure. Here, I propose a dual-cycle for data asset listing or enterprise data asset value circulation: the first is an internal cycle within the enterprise involving data governance, compliance, rights confirmation, and listing; the second is an external value cycle realized through data asset registration, evaluation, and trading, thereby promoting the appreciation and market circulation of data assets. The hope is that, with a sound legal framework, data assets will serve as a new form of collateral, providing more development momentum for enterprises.

6. In which high-quality development scenarios has Data Element × already played a facilitating role?

Dr. Lin Zhenyang:Let me highlight a few cases for reference. The first involves Data Element × Belt and Road Initiative (BRI), specifically a case of data elements empowering the healthcare sector. On the African CDC data operations platform we participated in, data elements played a pivotal role. Africa was also grappling with infectious diseases, and they required a function akin to our domestic health code, necessitating theaggregation of multidimensional and diverse data to build a basic information source during the pandemic and provide strong support for public emergency monitoring, early warning, and infectious disease control strategies. These data were not only used for epidemic prevention and control but were also closely integrated with the pharmaceutical industry's supply chain, driving innovative development in Africa's healthcare industry. This is a typical case of data integration enabling regional medical and public health development, showcasing the typicality and replicability of data element applications.

The second case is Data Element × Trade & Logistics, exemplified by the Shuibei Jewelry Index in Shenzhen. Shuibei accounts for 70% of the global gold jewelry supply. The Shuibei Index, which includes value index, prosperity index, industry development index, and more, serves as a benchmark for the overall market development, demand situation, operational status, and urgency. Although it doesn't directly generate economic value, the enhanced international discourse power and the invigoration of the domestic jewelry market it fosters are distinctive examples of how data flows drive trade and logistics in commerce.

Additionally, there is a case involving the Shenzhen Meteorological Bureau. Meteorological data, being relatively less sensitive and without the high privacy concerns of personal data, can also be harnessed after effective processing to empower agriculture, industry, and the energy sector, enhancing the smart level of these traditional industries.

7. What is the envisioned development of data fiscal policy in the future data element market?

Dr. Lin Zhenyang:Since data elements were included as a factor of production, they have remained a hot topic, much like when land was classified as a production factor in the past. Land fiscal policy indeed brought significant economic growth to the country, and the question arises whether data can bring about similar benefits and ultimately lead to a data fiscal system. This remains uncertain and requires a series of policy systems and technological means to advance.

We can explore the similarities and unique aspects between data fiscal policy and land fiscal policy. Starting from our understanding of value logic, the first step is the transformation of data property rights and the co-construction of an ecosystem by multiple stakeholders. The vision for data fiscal policy involves aggregating local government data, industry data, and personal data into local data conglomerates, leveraging the compliant authorization of data processing rights and product operation rights to build platforms and monetize data. While there is ongoing debate over the existence of something akin to a "data land grant fee," the general trend leans towards indirect revenue sources through taxation on the data industry. Specifically, this refers to taxes generated from the two economic activities that occur when datacirculates among different entities for governance, development, and trading, which could become one viable path for data fiscal policy.

We also observe attempts and explorations in various regions to leverage franchise rights for data mortgage financing, granting paid authorization to local data groups. However, this approach is controversial and requires careful consideration. From a rigorous academic perspective, several logical conditions must be met, including the aggregation of public data, granting franchise rights to a platform company, the platform company commercializing at least one application using data, third-party data asset appraisal, coordination with a guarantor institution, engaging willing banks, and having a platform for data asset registration and rights confirmation. Currently, this practice is immature, and local governments need to proceed cautiously.

Another approach involves mortgaging data assets after they are listed on the balance sheet. If the operating entity's business is stable, it can leverage clear data asset ownership for balance-sheet financing through data assets. Thus, the entity can secure bank financing based on these data assets.

8. How will personal data be valorized in the future data element market?

Dr. Lin Zhenyang:When discussing the valorization of personal data elements, we note that current systems do not adequately address the issue of personal data rights, a matter of great public concern. To ensure individuals benefit from the data economy, we can envision building apersonal data account system based on digital identity, returning to individuals their portion of government, public, and social data –essentially "returning data to the people." This ensures individuals have full control and knowledge of their data.

For example, in healthcare, if medical institutions require personal medical records for drug development, a future scenario might involve individuals consolidating all their public and personal data within a personal data account, visualized in a super app. When institutions wish to use this data, they must obtain individual authorization, potentially through a compensated model where fees are paid for each data access. This way, individuals directly profit from the use of their data, while their data security and privacy are safeguarded.

Achieving this goal necessitates establishing a comprehensive personal data infrastructure and management system, covering data aggregation, storage, governance, and authorized operation markets, ensuring safe and compliant data usage. Only by establishing a completesystem of rules and standards can we effectively manage personal data security, operations, and value realization.

Ultimately, the future data market will form a three-tiered, stratified structure of data element markets coexisting at the central, industrial, and regional levels. The central level leverages a nationally integrated big data system to accumulate core data resources; the industrial level builds data markets tailored to specific industry characteristics; and at the regional level, localities formulate corresponding data regulations and innovative practices, fostering vertical linkages and horizontal collaboration in data element markets.

Throughout this process, there needs to be ongoing exploration of a full-service system for the resourceization, assetization, and capitalization of data resources, addressing these issues across different market tiers. Empowering various industries and sectors with data, unleashing the value of data elements, and ultimately creating a prosperous, diversified, multi-layered, and multi-participant data element market are key objectives. This aims to transcend the limitations of traditional economic development in the digital economy era, promoting common prosperity for society through a fair and reasonable mechanism for data value distribution.