T/SDDITAI 9022-2024 Craftsman-style data talent evaluation standard
T/SDDITAI 9022-2024 Craftsman-style data talent evaluation standard
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Scope
Main technical content: In March 2020, the Central Committee of the Communist Party of China and the State Council issued the "Opinions on Building a More Perfect System and Mechanism for Element Marketization Allocation", which included data as a new type of production factor. The document classified and proposed reform directions for five element fields including land, labor, capital, technology, and data, clarified specific measures for improving element marketization allocation, and emphasized the need to accelerate the cultivation of the data element market. In December 2022, the Central Committee of the Communist Party of China and the State Council issued the "Opinions on Building a Data Basic System to Better Play the Role of Data Elements", providing guidance and direction for accelerating the construction of a data basic system, activating the potential of data elements, and strengthening and optimizing the digital economy. The document emphasized maintaining national data security, protecting personal information and commercial secrets, and focusing on data ownership, circulation and transactions, income distribution, and safety governance to promote the compliant and efficient circulation and use of data, empower the real economy, and accelerate the development of data element markets and data element-based enterprises. Data has become a new and increasingly critical production factor. As the foundation of digitalization, networking, and intelligence, data has rapidly integrated into all aspects of production, distribution, circulation, consumption, and social service management, profoundly transforming production methods, lifestyles, and social governance. Data elementization has become an inevitable trend for enterprise development in the new era. To further leverage the role of data as a production factor, fully realize the value of public data, enterprise data, and personal data, and deepen data-driven innovation demonstrations, it is essential to re