Analysis On The Demand For Top Talent In Big Data 630799

Analysis on the Demand of Top Talent Introduction in Big Data and Cloud Computing Field in China Based on 3-F Method

Analyse the demand for top talent in the field of big data and cloud computing in China using the 3-F method, which involves identifying research hotspots through high-impact literature, locating top talents based on keywords in the Web of Science, and calculating the brain gain index to determine whether China needs to introduce top talents from abroad. The method evaluates whether China's current talent pool is sufficient by comparing the number and distribution of top talents internationally and assessing the need for importing talent to support industry development.

The paper provides a detailed case study, illustrating the process of collecting and analyzing high-impact literature from 2006 to 2016. It highlights the frequency of keywords related to cloud computing and big data, and examines the geographic distribution of top talents, revealing that China ranks second worldwide after the United States but still lags significantly behind in the absolute number of top talents. The computed brain gain index of 2.61 indicates a substantial need for China to attract top international talents to bolster its big data and cloud computing industry.

The analysis underscores the importance of strategic talent acquisition policies, considering the global competition for top talents. The paper concludes that to achieve technological and industrial advancement, China should formulate policies to attract highly skilled experts from abroad and enhance domestic research capabilities. The 3-F method proves to be an effective tool for policymakers and industry leaders to evaluate talent shortages and formulate targeted strategies in emerging high-tech fields.

Paper For Above instruction

Introduction

The rapid development of big data and cloud computing has revolutionized industrial processes and digital infrastructures worldwide. As digital transformation accelerates in China, the demand for top talent in these fields has become critical for maintaining competitive advantage and technological innovation. Recognizing this, researchers have developed analytical methods to evaluate whether a nation needs to import or nurture top talents domestically. The 3-F method is a comprehensive approach that combines bibliometrics, research hotspot analysis, and talent distribution assessment to inform strategic talent policies.

Methodology

The 3-F method comprises three major steps: First, high-impact literature analysis identifies research hotspots by focusing on highly cited articles published within a set period. This helps determine technological trends and key areas of focus, such as cloud computing and big data. Second, by retrieving keywords from authoritative databases like Web of Science, the method pinpoints the current geographical distribution of leading researchers, including their affiliations and countries of origin. Third, the brain gain index quantifies whether a country needs to import top talents based on the relative number of top researchers, population size, and global distribution.

Case Study and Data Analysis

This study applied the 3-F method to analyze the Chinese big data and cloud computing industry's talent landscape from 2006 to 2016. The literature collection yielded 546 high-impact articles, from which research hotspots were identified, such as cloud computing, virtualization, IoT, and bioinformatics. The keyword analysis revealed that the United States leads in the number of top talents, with 268 identified professionals, compared to 48 in China, indicating a significant gap.

The geographic distribution of top researchers showed Chinese institutions such as the Chinese Academy of Sciences and top universities like Tsinghua and Peking University hosting many contributors, but still not matching the scale of U.S. institutions like Harvard and UC Berkeley. The calculation of the brain gain index yielded a value of 2.61 for China, far exceeding the threshold of 1, signaling a high need for importing top talents to sustain industry growth.

In contrast, the United States had a brain gain index of 0.11, reflecting its status as a net exporter of top talent in the field. The disparity suggests that China should focus on establishing policies to attract highly skilled international researchers, such as offering favorable immigration, research funding, and collaboration opportunities. Additionally, domestic talent cultivation needs reinforcement through specialized education programs.

Implications and Recommendations

The findings highlight the necessity for strategic policies aimed at talent acquisition and retention in China’s burgeoning big data and cloud computing sectors. Developing international cooperation programs, establishing innovation clusters, and improving research infrastructure can help attract top talent. Moreover, promoting domestic education and research excellence is vital for reducing reliance on foreign expertise in the long term.

The 3-F bibliometric approach offers a practical, data-driven framework for policymakers to assess talent gaps regularly and to tailor intervention strategies accordingly. As the global competition for digital talent intensifies, countries must adopt multi-faceted strategies encompassing both attraction of foreign experts and domestic talent development to sustain industry growth and innovation.

Conclusion

In the era of the knowledge economy, the global flow of top talent is a decisive factor for technological competitiveness. China's analysis using the 3-F method demonstrates a clear need to import high-level talents in big data and cloud computing. The calculated brain gain index confirms this requirement, emphasizing the importance of targeted talent acquisition policies. Strengthening domestic research capacities combined with strategic international recruitment will be essential for China to meet the demands of this high-growth industry and to position itself as a global leader in digital innovation.

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