He Siyuan: Accelerating the Innovative Utilization and Social Value Transformation of Public Data Resources | Marxism Research Network

In the current tide of the digital era, data has become a pivotal force driving social development and transformation, with public data constituting an indispensable and vital component. Public data carries a multi-dimensional mission: it inherently serves the public and promotes social equity and justice, while also demonstrating immense operational potential amid the flourishing development of the digital economy. Clear guidance from national strategies and active local experimentation have laid the foundation and charted the course for the development of public data. However, achieving its innovative utilization and the transformation of its social value still requires an in-depth exploration of balancing strategies across multiple dimensions, including openness, authorized operations, incentive guarantees, the expansion of data spaces, and institutional innovation.

I. The Connotation and Epochal Mission of Public Data

(1) Analysis of the Essence of Data

The foundation of public welfare attributes. Public data originates from the operational practices of the public sphere; its generation is intrinsically linked to public management and services. It naturally bears the mission of serving the public and promoting social equity and justice. Taking the field of public education as an example, schools and educational departments collect data on student learning and the allocation of educational resources to optimize educational decision-making and improve teaching quality, thereby ensuring that every student enjoys equitable and high-quality educational resources. This process profoundly reflects the public welfare attribute of public data, which is the fundamental value of its existence.

Exploration of operational potential. With the vigorous development of the digital economy, the potential commercial value of public data is gradually being uncovered. Through rational data analysis and innovative applications, public data can create new development opportunities and economic benefits for enterprises. Taking urban traffic data as an example, the analysis and utilization of data regarding traffic flow and travel patterns can provide decision-making support for smart transportation enterprises, enabling the development of more efficient transportation solutions. This also brings positive impacts to urban traffic optimization and economic development, showcasing the enormous potential of public data.

(2) Policy Drivers

Guidance from national strategy. The Third Plenary Session of the 20th CPC Central Committee ^[1] emphasized "cultivating a unified national market for technology and data," and "building and operating national data infrastructure to promote data sharing." This strategic deployment has pointed the way for the development of public data and provided a solid policy foundation for its widespread application and deep mining, promoting the efficient circulation and rational allocation of public data on a national scale.

Progress in local practice. In recent years, localities have actively responded to the call of national policies by introducing measures to promote the opening of public data. For instance, Chongqing was a pioneer in formulating detailed rules for the management of public data openness and constructing an advanced public data openness platform. This has attracted a large number of enterprises and innovation teams to participate in data application development, achieving significant results in fields such as smart government affairs and urban governance. This provides replicable experiences and demonstrations for other regions, promoting the effective utilization and innovative development of public data at the local level.

II. Core Elements and Practical Paths of the Balancing Strategy

(1) Meticulous Planning of the Openness Dimension

1. Strategies for improving mechanisms

Precise setting of management norms. The various elements of public data openness should be clearly regulated. This includes accurately defining the boundaries of data openness, formulating unified and rigorous data standards, and rationally classifying data types. For example, regarding the opening of data in the public security field, it should be explicitly stipulated which public surveillance data can be opened to specific institutions after processing, in what format, and under what specifications. A strict data quality audit mechanism must be established to ensure accuracy and reliability. Simultaneously, confidentiality review and security management processes must be perfected to prevent the leakage of sensitive data and ensure the standardized and secure opening of public data.
Optimization and upgrading of supervision and feedback. Constructing a transparent and efficient supervision and feedback system for public data openness is crucial. Specialized online supervision platforms can be established to facilitate public feedback on issues and suggestions during data usage. Timely processing of public demands will create a virtuous cycle of interaction between data supply and demand, promote the balanced development of data public welfare services, and enhance the quality and effectiveness of public data openness. For example, by establishing complaint channels, investigations into data quality issues reported by the public can be conducted promptly to continuously optimize data openness work.

2. Strategies for the construction and application of high-quality datasets

Domain focus and fine-processing of data. Advanced data processing technologies can be applied in areas concerning people's livelihoods—such as healthcare, elderly care services, and employment security—to meticulously create a series of high-quality and influential demonstration datasets. In the medical and health field, deep analysis and integration of patient medical records and the distribution of medical resources can provide robust data support for medical research and disease prevention, driving innovative development in the healthcare industry.
Collaborative promotion of innovative cooperation. Universities, research institutes, and enterprises should be encouraged to participate in collaborative innovation. Universities can provide theoretical support and technological R&D through their deep scientific research capabilities, while enterprises can use their market sensitivity to transform technology into actual products and services. For example, in the field of elderly care, cooperation between universities and enterprises using public elderly care data to develop smart service platforms can provide more convenient and personalized services for the elderly, jointly promoting the development of the elderly care industry.

(2) Explorations of Diversified Models for Authorized Operations

1. Strategies for the integration of operations and public welfare

Problem-solving approaches and the construction of incentive mechanisms. In the initial stage, high-value government data can be selected for open pilot programs to break new ground. Once a virtuous cycle is formed between the effective supply of data and the rational distribution of returns, market mechanisms can be used to incentivize more public institutions to participate in the marketized operation of data. For instance, Shanghai has guided enterprises to develop industrial analysis and prediction models by opening government industrial and economic data. This creates economic benefits for enterprises while providing a scientific basis for government industrial planning and policy formulation, achieving an organic integration of commercial operations and public welfare.

2. Expansion strategies for public-service-oriented operations

Professional cultivation of domain entities. In key areas such as smart city construction and ecological environment protection, professional entities for authorized operations should be cultivated. These entities should possess strong technological R&D and service capabilities, enabling them to dive deep into data value and provide innovative solutions for public services. For example, cultivating an operating entity focused on smart city water resource management can optimize urban water supply systems and improve water use efficiency, providing a guarantee for the sustainable development of the city.
Practical principle of "breaking even with micro-profits" ^[2]. Relevant enterprises should develop public welfare scenarios according to the principle of "breaking even with micro-profits." In the field of ecological environmental protection, enterprises can provide environmental monitoring and analysis services for environmental departments through authorized operations of public environmental data, charging only reasonable operating costs and a slim profit. This ensures that public welfare goals are prioritized while maintaining the sustainable operation of the enterprise and promoting the improvement of public service quality.

(3) Comprehensive Construction of Incentive and Guarantee Mechanisms

1. Incentive measures for the R&D of public welfare services

Selection activities and organization of competitions. Selection activities for high-quality public welfare scenarios should be organized regularly to discover potential and innovative projects. Data application innovation competitions can stimulate enthusiasm across society for the R&D of public welfare services. For example, organizing a "Public Data Public Welfare Innovation Challenge" can attract tech companies, social organizations, and individuals to create excellent public welfare applications, such as community mutual-aid platforms based on public data.
Reward/subsidy policies and data archive construction. Refined reward and subsidy policies should be formulated to reward social entities and public institutions that perform outstandingly in public welfare scenario R&D. Simultaneously, the traceability and analysis of public data should be strengthened to build comprehensive archives for data products or services. These archives should record data sources, processing procedures, application scenarios, and information on the various parties involved, providing a scientific basis for the rational distribution of benefits and ensuring the sustained promotion and healthy development of public welfare service R&D.

2. Support strategies for infrastructure and technological innovation

Optimization of platform functions and strengthening of data management. Investment in infrastructure for public data openness should be increased to optimize the functions of open data platforms. Improving search precision and the level of intelligent matching will facilitate quick and accurate data acquisition for users. Strengthening the hierarchical and categorical management of data and the integration of related data will increase data usability and value.
Breakthroughs in security technology innovation. New security technologies, such as quantum encryption and blockchain data security, should be actively explored to protect public data. Innovative security measures should be used to fully release the value of data under the premise of ensuring security. For instance, using quantum encryption for the transmission of critical public data can effectively prevent data theft and tampering.

(4) Practice of Data Space Expansion and Institutional Innovation

1. Expansion paths for personal and corporate data spaces

Participation models and the role of government certification. Individuals and enterprises should be supported in actively participating in the management and application of their own data, with the government playing an authoritative role in certification and supervision. Individuals can independently manage and rationally authorize the use of their health and consumption data, while enterprises can optimize the integration and innovative application of their production and operation data. Through certification mechanisms, the government ensures the legality, security, and authenticity of data, creating a favorable environment for data circulation.
Collaborative strategies for system architecture. Research into technologies related to evidence-storage chains (blockchain) and data spaces should be conducted to build a collaborative system where "individuals and enterprises lead data management and application, the government certifies data quality and compliance, and government platforms provide secure and reliable authorized access services." This will break down data silos, realize efficient data circulation and sharing, stimulate the innovative vitality of data, and promote the digital transformation and high-quality development of the economy and society.

2. Institutional innovation measures for data public welfare

Integration of the concept of "data philanthropy." Drawing on the development models of charitable enterprises, data should be included in the scope of social public welfare. Enterprises and individuals should be encouraged to donate valuable data resources for public welfare research and social services. For example, an enterprise donating data on user consumption habits could help public welfare organizations carry out precision poverty alleviation and social assistance projects, improving the efficiency of resource allocation.
Establishment of public welfare funds and implementation of preferential policies. Specialized data public welfare funds should be established to support projects for the public welfare development and utilization of public data. At the same time, income tax incentives should be implemented for expenditures that meet the conditions for public welfare donations, incentivizing more entities to participate in data philanthropy. Through policy guidance and incentives, the factor of data ^[3] can be guided to play a positive role in social distribution, promoting social equity, justice, and sustainable development.

(About the Author: He Siyuan is a researcher at the Chongqing Center for the Study of the Theoretical System of Socialism with Chinese Characteristics.)

Source: China Theory Online (Theory.com.cn) Web Editor: Hui Hui