Digital News

　　Digital talent is characterized by innovation, strong practical orientation, interdisciplinarity, openness, and cross-sector integration. It is critical to advancing reform toward the market-oriented allocation of data elements, achieving innovation in data technologies, promoting the construction and operation of national data infrastructure, and fostering the high-quality development of the data industry. Digital talent is not only at the core of international competition in the digital economy, but also the cornerstone of building Digital China. Recently, five central government bodies—the National Development and Reform Commission, the National Data Administration, the Ministry of Education, the Ministry of Science and Technology, and the Organization Department of the CPC Central Committee—jointly issued the Opinions on Strengthening the Development of Disciplines and Majors Related to Data Elements and Building the Digital Talent Workforce (hereinafter referred to as the Opinions). The Opinions accurately identify key challenges in current digital talent cultivation, including incomplete coverage of disciplines and majors, insufficient practical capabilities among data professionals, the relative immaturity of academic research in the data field, and the urgent need to strengthen industry–academia–research–application collaboration. In response, the Opinions put forward major measures such as improving discipline and major systems related to data elements, advancing vocational education for the data industry, invigorating academic research in the data field, and promoting industry–academia–research–application collaboration. Through coordinated efforts across discipline development, vocational education, academic research, and application-driven collaboration, a new ecosystem for the independent cultivation of digital talent will be built, laying a solid talent foundation for the building of Digital China.

　　I. Reshaping Discipline and Major Structures to Create a New Paradigm of Categorized Talent Cultivation

　　Disciplines and majors related to data elements form the foundation of digital talent development. At present, as the market-oriented development of data elements continues to accelerate and large-scale artificial intelligence (AI) models advance rapidly, applications of intelligent upgrading, digital transformation, and network connectivity across industries are being further deepened. This has generated strong demand for digital talent in areas such as the development and utilization of data resources, high-quality datasets, data infrastructure, and comprehensive digital transformation. However, the current disciplinary system related to data elements has yet to cover all stages of data element value realization, and the number of specialized universities and vocational institutions offering distinctive data-related programs remains limited. As a result, both the scale and quality of digital talent cultivation fall far short of the needs of the building of Digital China.

　　China possesses the world’s largest higher education system, giving it unique advantages in cultivating talent at multiple levels. To address the problems of the limited number and narrow coverage of disciplines and majors related to data elements, the Opinions set out a forward-looking plan along three dimensions: “expanding in number,” “enhancing specialization,” and “strengthening quality.” 

　　In terms of “expanding in number,” universities and colleges at all levels are encouraged to establish a broader range of disciplines and majors related to data elements. This includes not only supporting comprehensive universities in establishing schools or colleges of digital studies, but also encouraging specialized institutions to strengthen their competitive data-related programs, as well as guiding vocational institutions to introduce data-related majors that are closely aligned with market demand. With regard to “enhancing specialization,” higher education institutions with a solid foundation, together with local governments, are encouraged to explore practice-oriented data-element majors. On the one hand, universities are supported in offering data-related “micro-majors.” For example, institutions with strengths in communications networks, data security, or data science are encouraged to pilot micro-majors in data infrastructure. On the other hand, local governments are encouraged to establish data-focused institutions and to guide vocational colleges in developing distinctive programs. As for “strengthening quality,” multiple channels are encouraged to explore pathways for cultivating high-level digital talent. First, a well-coordinated talent development mechanism linking undergraduate, master’s, and doctoral programs in data-element disciplines is to be established and improved. Second, universities are encouraged to engage industry experts in collaborative talent cultivation. For instance, Nanjing University of Posts and Telecommunications has jointly established a Data Element Innovation Laboratory with DBAPPSecurity and appointed a leading industry expert as the laboratory director, thereby promoting joint university–enterprise research, practice-oriented teaching, and an integrated industry–academia–research–application talent cultivation model. Third, greater use is to be made of the role of teaching guidance committees related to data elements.

　　With the effective implementation of these measures, a diversified, broad-coverage, and high-standard system of disciplines and majors related to data elements will be established, laying a solid foundation for the large-scale, high-quality cultivation of digital talent across different fields and levels.

　　II. Vigorously Developing Vocational Education to Meet the Needs of the Data Industry

　　The data industry is one of the main pillars of the building of Digital China and a key driver and practitioner of the elementization and value realization of data. It encompasses diverse segments, including data resources, data technologies, data applications, data services, data security, and data infrastructure. China is vigorously fostering data enterprises, concentrating efforts on building a data industry chain oriented toward AI applications, accelerating the development of data industry clusters, and exploring distinctive digital talent training programs that leverage regional and industrial strengths. By advancing the high-quality development of the data industry across the three dimensions of “points, lines, and planes,” China faces an urgent need for a large number of digital professionals with strong hands-on and practical capabilities.

　　The cultivation of practice-oriented and operational digital talent is a vital guarantee for the high-quality development of the data industry. For a long time, digital talent cultivation in China has focused primarily on economic and technical fields such as the digital economy, data science, big data, and artificial intelligence, while paying relatively limited attention to practice- and operation-oriented talent in areas such as data collection and aggregation, processing, circulation and application, and security governance. To address the insufficient practical capabilities of data professionals, the Opinions set out a comprehensive framework from the perspectives of “ecosystem building,” “co-construction,” and “hands-on practice.” 

　　In terms of “ecosystem building,” plans are made to establish a new industry–education integration ecosystem, including a National Teaching Guidance Committee for Vocational Education in the Data Industry, industry–education integration consortia, industry–education integration communities, and competency standards for practitioners. With respect to “co-construction,” the Opinions emphasize joint efforts by enterprises and educational institutions, such as building training bases in the forms of “factories on campus” and “campuses in factories,” and supporting enterprises and vocational colleges in jointly developing digital curricula, workbook-style teaching materials, and teaching cases. As for “hands-on practice,” the Opinions propose accelerating the development of “dual-qualified” teaching teams and integrated work–study models, encouraging participation in the World Vocational Colleges Skills Competition, promoting the transformation of competition outcomes into teaching resources, and encouraging programs across disciplines to incorporate data-related content into their curricula. 

　　Through the implementation of these measures, a comprehensive model for cultivating practice-oriented and operational digital talent aligned with the requirements of the high-quality development of the data industry will be fully established.

　　III. Invigorating Academic Research in the Data Field to Unleash the Momentum of Data Innovation

　　Innovation in data technologies is a core component of the building of Digital China, while academic research in the data field serves as the driving force behind such innovation. Only through a thriving and dynamic academic research ecosystem can the momentum for data technology innovation be fully unleashed, providing stronger scientific and technological support for the building of Digital China. At the heart of data technology innovation lies the enhancement of governance capacity for the secure circulation of data—ensuring that high-value data can flow efficiently while remaining secure, and addressing the long-standing challenge of balancing data circulation with data security. This requires the establishment of high-level research teams and platforms, the strengthening of the scientific data foundation, and the pursuit of organized, mission-oriented research targeting major issues in the data field.

　　Conducting large-scale, organized research on major frontier theoretical and technological issues in the data field is an important pathway for achieving breakthroughs in key core data technologies. To address the problems of fragmented, weak, and insufficiently organized academic research in this field, the Opinions propose solutions at three levels: “organization,” “system,” and “authority.”

　　At the “organization” level, measures include establishing scientific and technological societies, implementing high-level digital talent development initiatives, improving channels for research output publication, and organizing academic exchanges. At the “system” level, the Opinions call for systematic research spanning strategic issues, fundamental theories, policy and legal frameworks, foundational and common theories, and applied technologies, with the goal of building an independent, China-oriented knowledge system and scientific research framework for data elements. For example, in the process of developing national data infrastructure, coordinated efforts are to be advanced across policy, technology, engineering, and standards, with particular emphasis on a standards-first approach. In this regard, a set of standards and technical documents on the hierarchical architecture and interoperability of the national data infrastructure has already been formulated at an early stage. At the “authority” level, the Opinions emphasize the creation of authoritative scientific databases, the development of data infrastructure for science and technology, and the construction of high-quality datasets.

　　With the full implementation of these measures, academic research in China’s data field will be significantly strengthened, greatly stimulating momentum for data technology innovation.

　　IV. Promoting Industry–Academia–Research–Application Collaboration in the Data Field and Giving Full Play to the Guiding Role of Application Scenarios

　　The multiplier effect of data elements is reflected in synergistic optimization, value enhancement through reuse, and integrated innovation. The more data from different sources and of different types are coordinated, reused, and integrated across diverse application scenarios, the greater the value that can be generated. Application scenarios in different fields not only serve as venues where multi-source data can achieve these multiplier effects, but also produce derivative data with additional value, thereby facilitating the full realization of the value of data elements. This calls for an application-scenario-driven approach to strengthening industry–academia–research–application collaboration in the data field and exploring collaborative training models for digital talent that are composite, innovative, and practice-oriented.

　　Building innovation platforms for the integration of industry and education around data elements, and better leveraging the role of enterprises in digital talent development, is an effective way to innovate collaborative talent cultivation models and fully realize the multiplier effect of data elements. To address the insufficient level of industry–academia–research–application collaboration and the limited number of scenarios for collaborative digital talent training, the Opinions propose implementation pathways across three dimensions: “scenarios,” “collaboration,” and “innovation.”

　　With respect to “scenarios,” the Opinions call for the development of a number of exemplary application scenarios for digital talent cultivation in which enterprises play a leading role. In terms of “collaboration,” first, universities and enterprises are supported in jointly establishing specialized colleges or schools in the digital field. Second, digital talent training programs with distinctive features are to be jointly explored in coordination with pilot and demonstration projects related to Digital China, the digital economy, data elements, and the data industry. Third, universities are encouraged to build cross-disciplinary platforms for data elements, industry–education integration research institutes, and other innovative organizational forms. Regarding “innovation,” the Opinions propose establishing and operating innovation platforms for industry–education integration around data elements, accelerating R&D on frontier, key, and common technologies in the data field, encouraging enterprises to open their technology platforms and application scenarios, and commissioning relevant national platforms to undertake digital talent training programs—thereby jointly cultivating composite, application-oriented talent urgently needed for national strategic priorities.

　　Through the implementation of these measures, industry–academia–research–application collaboration in the data field will be significantly strengthened, and collaborative digital talent development models will continue to be iteratively optimized.