Understanding and managing international migration relies heavily on the availability of timely, accurate, and detailed data. Traditional migration statistics, though foundational, often suffer from delays, limited granularity, and inconsistencies across countries. In response, researchers and institutions have increasingly turned to big data sources, including social media activity, mobile phone records, satellite imagery, and web-scraped content, to address these gaps and offer new insights into migration dynamics. This narrative review critically examines the role of big data in migration research. Drawing on peer-reviewed literature and grey sources, the study maps how big data has been used to track real-time migration flows, predict emerging trends, and analyse patterns of integration, while also identifying the ethical, methodological, and technical challenges involved. Findings reveal that big data offers significant potential to complement traditional statistics, especially in crisis contexts or for underreported migration flows. However, its use remains largely experimental, with key concerns around data access, representativeness, privacy, and the integration with official statistics. The review concludes that big data can support more responsive, evidence-based migration governance if its limitations are acknowledged and addressed. To that end, it recommends greater interdisciplinary collaboration, the adoption of ethical-by-design frameworks, and hybrid methodological approaches that combine big data with traditional and qualitative methods. Addressing digital inequality and fostering inclusive data practices will be critical to ensuring that big data serves as a tool for empowerment rather than exclusion.
Yar et al. (Fri,) studied this question.
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