Abstract:

[Purpose/Significance] In response to the urgent need to cultivate talent in the era of data elements, this study clarifies the core connotations, disciplinary orientation, and practical value of "Data Resources and Data Intelligence" as an emerging undergraduate major. It provides a theoretical reference and practical pathways for transforming, upgrading, and constructing undergraduate programs in the discipline of Information Resources Management. [Methods/Process] After systematically reviewing the rationale and implementation process for establishing the "Data Resources and Data Intelligence" undergraduate major, this study transcends the traditional framework of collection, processing, storage, retrieval, and utilization. With the core objective of realizing or facilitating the release of data value, it establishes the disciplinary foundation and knowledge framework of "Data Resources and Data Intelligence." The study corresponds data resources, data intelligence, and data elements with the first, second, and third releases of data value, respectively, explaining the core connotations and intrinsic relationships among the three concepts, and clarifying their logical connections with the DIKW model. For the first time, it provides a clear theoretical definition of "data intelligence" and reveals the role of artificial intelligence in the three value releases. Finally, through specific cases in typical application scenarios, it further illustrates the external empowerment process and practical value of data resources, data intelligence, and data elements. [Results/Conclusions] The study proposes that data intelligence is a capability system built upon data resources that integrates data and intelligent technologies to achieve systematic and intelligent generalized decision-making. This undergraduate major's core objectives are to transform data into organized resources, extract knowledge from data, and enable intelligent application, while fostering data as a productive factor as its broader value proposition. It integrates data and intelligent technologies, covers the entire process of the first, second, and third releases of data value, addresses current gaps in talent cultivation, represents a significant initiative for the transformation of the discipline of Information Resources Management in the era of data, and serves as a direct response to the national data element strategy.

Key words: data resources, data intelligence, data elements, data value release, artificial intelligence, DIKW model