Scholar from Our Academy Gives an Exclusive Interview to China Media Group on How Digital Governance Empowers the Science and Technology Innovation Sector

　　With the rapid development of the digital economy, data, as a new factor of production, is becoming an increasingly important force driving scientific and technological innovation and industrial upgrading. However, how data resources can be transformed into data assets, how public data can be opened up in an orderly manner, and how security and efficiency can be balanced in the circulation and use of data remain important issues for enterprises undergoing digital transformation. Focusing on data assets, digital governance, and the development of science and technology innovation enterprises, Wang Peng, Associate Research Fellow at the Institute of Management of the Beijing Academy of Social Sciences, gave an interview in which he provided an in-depth analysis of the underlying logic and practical pathways through which data as a factor of production can empower scientific and technological innovation.

Wang Peng

Associate Research Fellow at the Institute of Management of the Beijing Academy of Social Sciences

　　Recently, Wang Peng, a scholar from our Academy, gave an exclusive interview to China Media Group’s “Voice of the Greater Bay Area” on issues related to data assets and digital governance. Wang Peng noted that making data assets and digital governance core areas of research is necessary to keep pace with the development of the digital economy and address practical challenges in enterprise development. At present, many science and technology innovation enterprises possess abundant data resources but face problems such as inconsistent data standards, barriers to cross-departmental sharing, and difficulties in measuring asset value. Transforming data resources into compliant data assets requires a systematic framework for data asset management and digital governance that connects the entire process, from data resource aggregation and compliant governance to value realization, so that data can truly provide important support for enterprise innovation and development. 

　　Wang Peng noted that data as a factor of production is reshaping the growth dynamics of science and technology innovation enterprises. On the one hand, legally compliant data assets are expected to broaden corporate financing channels and ease financing difficulties and reduce financing costs for asset-light technology enterprises; on the other hand, integrating and using data from multiple sources can optimize R&D processes, shorten R&D cycles, and reduce the costs of trial and error in innovation. At the same time, data as a factor of production can help enterprises participate more deeply in value creation across the industrial chain and cultivate new areas of growth. To fully unlock the value of data, it is also necessary to define the basic conditions for data assets: clear ownership, the ability to create economic value, standardized governance, and measurability. This will lay the foundation for turning data into assets and realizing their value. 

　　Discussing the practical obstacles facing science and technology innovation enterprises in accessing and using data, Wang Peng noted that problems remain, including insufficient alignment between public data supply and enterprise demand, high barriers to data circulation across entities, weak internal data-governance capabilities, and inadequate channels for realizing the value of data products. Promoting the efficient circulation of data as a factor of production requires improving mechanisms for the authorized operation of public data, facilitating data collaboration across upstream and downstream segments of industrial chains, strengthening enterprises’ data-governance capabilities, and further improving mechanisms for data-asset valuation, compliance review, trading and circulation, so as to transform data resources into sustainable drivers of innovation.

　　Wang Peng emphasized that unlocking the value of data as a factor of production must be grounded in security and compliance. Public data, corporate data, and personal information differ in terms of rights and governance requirements and should therefore be managed by category according to data source, ownership, purpose of use, and security risk. In large-scale AI model training, particular attention must also be paid to issues including the ability to trace ownership of training data, authorization costs, personal-information protection, data-asset measurement, and cross-border data compliance. Going forward, data-governance systems and supporting rules should be further refined to advance the coordinated development of data security, compliant circulation, and value creation, thereby providing strong support for the growth of science and technology innovation enterprises and the high-quality development of the digital economy.