Big data has become a buzzword across many research domains, and migration studies is no exception. Once centred on a handful of “Vs” (volume, velocity, variety, veracity), the conversation about big data now spans countless dimensions, value, variability, visibility, and beyond. The term refers to huge, often real-time datasets, ranging from social media posts and mobile phone logs to satellite images and online search trends. These sources can provide granular, up-to-date insights into human mobility that traditional methods, such as surveys and censuses, might miss. Yet big data is neither a quick fix nor a replacement for the rich perspectives offered by established approaches in our field
One clear advantage of big data is its ability to detect rapid changes in migration patterns. For instance, anonymised mobile phone records can help identify sudden population displacements after a natural disaster or conflict. By mapping call-detail records, researchers may spot unusual surges of movement well before official statistics are updated. Similarly, social media analytics can reveal shifts in public sentiment toward migration or track emerging diaspora networks in real time. The EU-funded Humming Bird project is one example of how diverse big data types, from social media to satellite data, have been assessed to identify trends that conventional data alone might overlook.
Yet, big data’s real value emerges when it complements existing methodologies rather than attempts to replace them. Surveys, censuses, and ethnographic research remain indispensable for understanding the lived experiences of migrants. For instance, while digital traces can show where people are moving, qualitative interviews can shed light on the motivations and social relationships that drive those movements. In other words, combining computational analytics with established social science methods yields a more holistic view of migration.
Beyond simply describing where and when people move, big data can also broaden our theoretical insights. Classic frameworks in migration research, such as push-pull models or transnationalism, have long guided our thinking on why and how people migrate. Big data allows us to test and refine these theories at finer scales. For example, aggregated geolocation data might challenge assumptions that migrants only follow economic opportunities, highlighting the role of social networks or digital connectivity in shaping journeys. By weaving new evidence into established theories, we can expand our knowledge of how migration patterns emerge and evolve.
Nevertheless, we must remain mindful of ethical and representativeness challenges. Migrants are often socio-economically and legally vulnerable, making privacy protection crucial. Regulations like the General Data Protection Regulation (GDPR) in the European Union offer a starting framework, but ethical responsibility goes beyond legal compliance. Datasets must be anonymised and handled securely to avoid unintended surveillance or discrimination. Furthermore, big data can under-represent communities with limited digital access, such as those in remote regions or precarious circumstances, necessitating cross-checks with more inclusive or traditional sources.
Concrete applications of big data in migration research are already shaping policies and interventions. The Displacement Tracking Matrix (DTM) developed by the International Organization for Migration leverages various data sources to monitor mobility in crisis settings, enabling faster, targeted humanitarian responses. Meanwhile, the European Commission’s Joint Research Centre (JRC) collaborates with national statistical offices to produce near real-time mobility indicators from call-detail records, revealing subtle population shifts. Some agencies also experiment with web-scraped data and online job advertisements to refine labour migration estimates or anticipate potential outflows, while initiatives like the Big Data for Migration (BD4M) Alliance bring together governments, businesses, and academia to explore internet usage patterns and geospatial imagery. By weaving these novel data inputs into existing frameworks, researchers can illuminate how people move, why they choose particular destinations, and how social networks evolve, ultimately informing more agile, evidence-based migration policies.
To unlock this potential responsibly, migration scholars should develop at least a foundational literacy in computational methods. Not everyone must become a data scientist, but familiarity with how data is collected, processed, and analysed, and an awareness of potential biases, is key to evaluating studies that use big data. Interdisciplinary teams, bringing together computer scientists, statisticians, and social theorists, can also foster shared infrastructures, such as secure data repositories and transparent ethics protocols, ensuring that innovation does not come at the expense of accountability or the nuanced understanding that social science demands.
Ultimately, big data in migration research is neither a magic bullet nor a passing fad. Its worth lies in providing timely evidence on evolving mobility patterns, evidence that can shape policy, support humanitarian interventions, and enrich theoretical debates. By pairing big data with established concepts and qualitative insights, we can deepen our collective understanding of why and how people move, without sacrificing the ethical and inclusive values that underpin our field.
Prof. Tuba Bircan
Director of BRISPO, Head of AIMS Lab, Vrije Universiteit Brussel
Tuba Bircan works as a research professor of sociology at the Dept. of Sociology, at the Free University of Brussels (VUB), besides her appointment as a senior social scientists at Kavli Research Centre for Ethics, Science and the Public at University of Cambridge and Wellcome Connecting Science. As an interdisciplinary computational social scientist, her research interests cover a wide range from migration, inequalities, social and public policies to new methodologies and use of Big Data and AI for studying socio-political challenges. She is currently involved in the Horizon Europe project Measuring irregular migration and related policies (MIrreM).