Global competition for high-skilled talent has made immigration decision-making increasingly complex due to fragmented information, policy variability, and uneven evaluation standards. This paper reviews the conceptual foundations and practical value of data-driven immigration decision systems in improving global talent mobility. It proposes a three-layer analytical framework consisting of a Data Layer, an Evaluation Layer, and a Decision Layer. The Data Layer integrates applicant profiles, pathway requirements, and contextual case information; the Evaluation Layer transforms these inputs into assessments of feasibility, risk, and competitiveness; and the Decision Layer generates pathway recommendations and strategy options. Based on this framework, the paper discusses how structured decision systems can improve decision efficiency, strengthen case preparation, and support talent allocation across sectors of strategic importance. In addition, the study highlights the role of systematic comparison and structured evaluation in reducing information asymmetry and improving the transparency of immigration decision processes. The study also identifies key limitations, including data quality issues, policy change, model bias, and the continuing need for human judgment. It argues that data-driven immigration decision systems should be understood as decision-support tools rather than deterministic mechanisms. The analysis further suggests that integrating analytical frameworks into immigration practice can facilitate more consistent decision-making across different cases and institutional contexts. As global competition for talent intensifies, improving such systems will become increasingly important for both individual applicants and broader talent governance.
Dong Mengni (2026) studied this question.