Expert interpretation | The advantage of data resources continues to consolidate and solidify the foundation of digital economy development
Jiang Yan, Director and Deputy Secretary of the Party Committee of the National Industrial Information Security Development Research Center
Data, as a new production factor in the digital economy era, is a fundamental strategic resource for the country and an important foundation for developing new quality productive forces. With the deepening of the construction of Digital China, new technologies, models, and formats in the field of data are rapidly emerging, and the multiplier effect on the economy and society continues to be released. China's massive data and rich scene advantages are transforming into new driving forces for economic growth and new competitive advantages for the country.
1、 Data resources are fundamental and strategic resources for economic and social development
One is that data resources help drive technological innovation breakthroughs and seize the high ground of development.Currently, a new round of technological revolution and industrial transformation is accelerating, and data, as a new production factor, is deeply integrated into the technological innovation system, providing key support for seizing the high ground of future development. On the one hand, data elements accelerate the breakthrough of cutting-edge technological innovation. For example, in the field of artificial intelligence, large-scale high-quality datasets accelerate the optimization and upgrading of AI and big model technology, promoting the implementation of scenarios such as intelligent driving and smart cities; In the field of quantum technology, the efficient flow of data elements has laid a solid foundation for optimizing quantum algorithms and building quantum communication networks, helping China seize the initiative in global quantum competition. On the other hand, data resources promote the deep integration of cross domain technologies, enabling efficient utilization of technology resources from different fields. Taking pharmaceutical research and development as an example, traditional models have long cycles, high costs, and low success rates due to data barriers. However, by leveraging data platforms to share genetic sequences, clinical data, and other resources, cross disciplinary collaboration networks are built to promote research and development from "experience trial and error" to "data-driven precision", achieving interdisciplinary technology integration and efficient transformation of results.
Secondly, data resources help optimize the allocation of production factors and stimulate new economic drivers.On the one hand, data resources are constantly infiltrating traditional production factors. Data elements are deeply integrated with traditional production factors such as labor, capital, and technology through processing, analysis, and application, to digitize and intelligentize various aspects of traditional production factors and improve factor utilization efficiency. For example, data elements have changed the traditional one-way order taking mode of labor based on knowledge and experience by promoting human-machine collaboration, and transformed it into an interactive mode of "intelligent prediction task assignment automatic execution", thereby optimizing human labor and transforming the advantage of labor quantity into a quality advantage. On the other hand, data elements achieve targeted aggregation and configuration optimization of traditional elements. Under the traditional model, the utilization, allocation, and innovation capabilities of various factors have clear limits, which are insufficient to cope with complex and changing market environments and better utilize cross-border factor resources. Data elements accelerate the marketization and intelligence process of various factors, promote the effective allocation of various production factors in different fields, enable traditional factors such as labor, capital, and technology to be more effectively aggregated and utilized, and improve the overall quality and efficiency of economic operation.
Thirdly, data resources can help accelerate industrial transformation and upgrading, and reconstruct a new economic pattern.On the one hand, data resources can drive the transformation of industrial organizational methods. The synergy and scale of data resources have promoted the full factor digital upgrading of the upstream and downstream of the industrial chain, driving the transformation of production methods from large-scale standardized production to large-scale personalized production, and achieving significant changes in production organization and social division of labor. On the other hand, data resources are helping industries extend towards the high-end of the value chain. The deep mining and application of data resources have endowed industries with new dimensions of value, helping enterprises to find and discover new sources of value, continuously innovate market space and business models, such as service-oriented extension, platform design, etc., and thus create new points of value growth. The synergistic effect of data resources has given rise to new formats and models with high added value, promoting the upgrading of industries towards knowledge intensive and technology intensive directions, and providing sustained momentum for the high-end extension of the value chain.
2、 The advantage of data resource scale continues to consolidate
The results of the 2024 National Data Resource Survey show that China's advantage in data resource scale continues to expand, its data resource base is more solid, and its advantage in basic resources continues to consolidate, mainly reflected in the following four aspects:
One is that intelligent applications have become the core driving force for the expansion of data resource scale.The survey results show that the national data production volume will reach 41.06 zettabytes (ZB) in 2024, a year-on-year increase of 25%, among which the growth of data volume from smart devices is particularly prominent. In the production field, intelligent processes such as process optimization, quality inspection, and safety production generate massive amounts of data at high frequencies. For example, a standard industrial visual quality inspection equipment can capture thousands of frames per day and produce over 100 terabytes (TB) of data per day. In the consumer field, smart devices such as smart homes and smart connected cars have a leading data growth rate. Taking smart connected cars as an example, the daily data production of a single new vehicle reaches 20 terabytes (TB). The driving behavior data, environmental perception data, and user interaction data during its driving process are becoming important "digital fuels" for the development of smart travel and other fields.
The second is that strategic emerging industries have become the main engine for new growth points in data.With the gradual improvement of low altitude intelligent networking infrastructure and the acceleration of the industrialization process of service robots and industrial robots, the year-on-year growth rate of data production in low altitude economy, robotics and other fields has exceeded 30%, significantly higher than the growth rate of traditional industries. Taking the "5G+Beidou" technology as an example, it breaks through the accuracy limitations of traditional satellite navigation lane level positioning, achieves centimeter level positioning, and is widely applied in industries such as transportation, electricity, and agriculture, directly driving the rapid increase of data scale in related fields. Quantum computers, with their performance reaching millions and billions of times that of supercomputers, can complete the computing power of ordinary computers for 10000 years in just a few hundred seconds. They can quickly decrypt ZB level meteorological data, provide high-precision data support for typhoon path prediction and other scenarios, and promote exponential growth in data processing capabilities.
The third is to improve the efficiency of data resource management and promote the optimization of data storage structure and space.Against the backdrop of explosive growth in data, increasingly diverse data types, and constantly emerging new business demands, enterprises actively optimize their storage strategies and continuously improve the efficiency of storage space development and utilization. According to survey data, the proportion of structured data in total storage has increased by 36% year-on-year, far exceeding the growth rate of unstructured data. In particular, financial enterprises rank first in the industry in terms of structured data storage scale due to strict regulatory requirements. At the same time, cloud services play an important role as the core carrier to meet the demand for flexible data access in various fields. Government data has achieved significant results in promoting efficient and convenient development and utilization of public data, and improving the convenience level of public services. Accelerate the implementation of the "Cloud based Data Empowerment" action for meteorological data, with the cloud adoption rate ranking among the top in the industry.
Fourthly, intelligent computing power has become the core driving force for the growth of computing power scale.The survey results show that the proportion of intelligent computing power in the total scale of computing power has increased to 32%. This structural change marks an accelerated iteration of computing power resources from general to specialized, and the rapid development of intelligent computing power provides an intelligent foundation for the digital transformation of thousands of industries. With the promotion of artificial intelligence applications, the demand for intelligent computing power in the market is exploding. It is worth noting that different market entities are showing differentiated development paths. Among them, the computing power scale of central enterprises has grown nearly three times, and the proportion of intelligent computing power is 40.22%, reflecting the strategic layout of central enterprises in new infrastructure; The scale of computing power in data technology enterprises has nearly doubled year-on-year, with intelligent computing power accounting for 43.63%, verifying the urgent market demand for improving the efficiency of intelligent computing power.
3、 Next policy recommendations
One is to optimize the top-level design and build a regulatory system for data resources.Clarify the phased goals and implementation paths for the development of data resources, continuously improve and optimize top-level design, and accelerate the formation of a systematic, scientific, reasonable, and coordinated data infrastructure system. Accelerate the construction of a legal and regulatory system in the field of data, clarify registration, certification, and rights protection rules, and provide legal guarantees for the healthy development of the data factor market.
The second is to promote standard collaboration and form a global data governance pattern.Focus on key industries such as finance, medical care, transportation, and industrial Internet, improve the information exchange channels of the data market, and accelerate the construction of the data circulation and transaction standard system. Guide enterprises and local platforms to actively adopt national standards, enhance cross platform data interoperability, break down industry data barriers, and promote efficient circulation and utilization of data resources. At the same time, defining data responsibilities, improving interest protection mechanisms, exploring the establishment of profit distribution mechanisms for all parties involved in data circulation transactions, and strengthening data circulation compliance governance.
The third is to strengthen security protection and create a trustworthy data space ecosystem.Strengthen the application of data security technology, promote privacy computing technologies such as federated learning and homomorphic encryption, and promote the construction of a secure data usage environment that is "usable but invisible" and "traceable and auditable". Strengthen the implementation of the action plan for the development of trustworthy data space, actively organize innovative pilot experiments for trustworthy data space in enterprises, industries, cities, etc., and promote the formation of a secure and controllable data ecological pattern.
Information source: Global Times
