The construction of the data factor market is a long-term, systematic, and complex project. With the accelerated improvement of policies and mechanisms, the industrial practice related to the development of the data factor market has been deepened, and certain positive progress has been made, but it still faces obstacles, such as insufficient data supply and demand, and imperfect circulation rules and supporting services. With the accelerated implementation of the data-related fundamental institutions, several breakthroughs related to the data market are expected to be made continuously in strengthening the supply and application of public data, accelerating the construction of a rule system for data trading and circulation, and improving the data factor market ecology.
With the rapid development of digital economy, the problem of data factor has gradually become a hot issue in domestic academic research. Based on the keyword analysis of Cite_Space, it can be seen that up to now, domestic academia has basically completed the research on the concept and characteristics of data factor as well as the connotation of its production factors. Research on the development and governance of data factor has gradually become the current hotspot and focus. In the future, the realistic investigation of data factor should be further deepened, the academic research community should be built, and the scientific and comprehensive research in this field should be promoted.
As an emerging factor of production, data has been rapidly integrated into all aspects of production, life and social management, and has great significance and far-reaching impacts on the transformation of social production mode and lifestyle. Standards are the technical support for economic activities and social development, and become an important aspect of the country’s foundational institutions. The establishment of a standard system for data factors will effectively promote the building of a market system for data factors and give full play to the role of data factor.
With a large volume and high value, public data are a significant component of the national system of data factor. Since public data are different from other types of data in terms of producing process, managing method and content characteristics, the ownership structure of public data is much clearer, which makes public data become the key breakthrough in building the data factor market. Local governments and industries have made some progress in exploring the authorized operation of public data, but there are still some bottlenecks in source supply, data operation and income distribution that need to be solved urgently. It is urgent to accelerate the innovation in authorized operation system of public data according to the general approach of developing the data-related fundamental institutions. The government should serve better as the leader, participant and supervisor to promote the prosperity and sustainable development of the data factor market.
With the building of data factor market, the value of public data has been widely concerned. How to develop and utilize public data to benefit people to the maximum extent has become an important issue. First, this paper compares and analyzes the differences between opening and authorized operation of public data in terms of participation subjects, content and methods. It summarizes the actual problems of public data circulation in China, which are reflected in the current supply shortage in terms of quantity, quality and efficiency, and the lack of value mining in terms of demand. Then, it proposes that the authorized operation as a new mode mobilizes the subjectivity and initiative of market entities in mining data value, and provides a new way to realize the public data value based on application scenarios. Finally, this paper compares and analyzes the experience of regional pilot exploration from the practical level, such as the centralized and unified model, the centralized data zone model by industry, and the multi-level decentralized model. Based on this, it believes that the path to realize the public data value will be refined and improved in the practice of various regions.
The property right of data factor is a basic issue in the development of data factor market, which is related to the efficient circulation and value realization of data factor. By analyzing characteristics of the public data factor market, three problems in the public data property rights confirmation are summarized. Based on the operation mechanism of separating ownership of data resources, data processing and use rights, and data product management rights, a three-pronged scheme for data property rights confirmation is constructed by combining with the five links of public data circulation. This scheme combines the catalog of public data resources, data classification, and registration of data property rights.
The policy of building data factor market puts forward higher requirements for enterprise data compliance, so enterprises need to adjust their data compliance strategies accordingly. Considering the trend of data compliance supervision of China in recent years and the challenges faced by enterprises in data compliance, this paper puts forward a few data compliance strategies for enterprises in the new situation from the perspectives of data classification, data source compliance and enterprises’ data compliance capability system.
Privacy-preserving computation (PPC) is an information technology that analyzes and calculates data and provides results to the data demander on the premise that the data provider does not disclose the original data. This paper describes the development background and current situation of PPC, and analyzes the constraints in its development process. Then, it reviews the industry exploration practice of promoting the development of PPC business, contribution incentive and service ecology in combination with the relevant opinions on the construction of basic data system. Finally, several suggestions on data rights confirmation, income distribution and ecological construction are proposed. It is expected to provide a reference for promoting the role of PPC in the construction of data factor market.
As a key technology across the fields of data mining and process modeling, process mining helps to bring the value of data factor into play, but at present, enterprises have insufficient use of process mining technology and data factor. Based on the analysis of factor characteristics and DIKW model, it is found that the application of data factor in process mining has the characteristics of directness, dynamism and externality, which can exert the factor value through the business oriented mechanism, the factor integration mechanism and the technology integration mechanism. Therefore, the enterprises need to further strengthen the application and accumulation of process mining technology, attach importance to the building of digital governance system, and explore to establish the coexistence mechanism for data security and sharing so as to maximize the value of data factor.
Data asset management and application from the perspective of data factor have become the key to improving production and operation efficiency, making the value of data assets more and more obvious. Identifying the value of data assets and making full use of it are the keys to realizing the circulation of data factor and promoting the transformation of data value. Based on the data characteristics and value system of operators, a system for data factor value assessment applicable to operators is proposed, and the process of data factor assessment is elaborated in five aspects: applicability analysis, influencing factor analysis, value assessment process, valuation and pricing methods, and operation strategies.
This paper focuses on the practice of internet industry social responsibility/ESG information disclosure. Then, it puts forward the views that the number of social responsibility/ESG information disclosures in the internet industry is growing steadily, the disclosure topics are diversified and closely follow the hot spots, the disclosure of data performance has begun to be standardized, the agglomeration effect of information disclosure is obvious and positively related to the comprehensive strength of enterprises, the listed reference basis is decentralized while the indexed reference basis is centralized. Those views provide ideas for drawing the multi-governance blueprint of internet industry social responsibility/ESG information disclosure, exploring new social governance paths, and cultivating the sustainable development ecology of the internet.
Starting from the security development status of Internet of Things (IoT) terminals, this paper analyzes the development trend of IoT terminal technology and form, and deeply analyzes the security hidden dangers faced by IoT terminals under the new situation from the aspects of network security and user data security. In order to solve the security problems faced by IoT terminals, this paper puts forward corresponding countermeasures, and provides references for ensuring the healthy development of the industry.
In order to improve the digital level of Small and Medium-sized Enterprises (SMEs) and solve the technical constraints faced by the transformation, China Telecom has explored a digital transformation scheme based on the industrial Passive Optical Network (PON), a network carrying various production equipment of enterprises, and combined edge computing and the distributed AI analysis to create a digital base for enterprises. The case analysis can effectively solve various digital problems faced by enterprises and promote the digital transformation of SMEs.
The feed water pump system is an important auxiliary equipment of the power plant system, so an effective evaluation of its state is helpful to the safe and reliable operation of the power plant. Based on the historical health data of the feed pump system, the fuzzy c-means clustering algorithm is adopted to build the state score model which can automatically predict the equipment state score in real time. The experiment is carried out on the real data of the power plant. The results show that the predicted scores are in line with the actual situation and can effectively characterize the equipment state.