Navigating the complexities of migration data management: Challenges and opportunities
Year: 2025
Type: article
Abstract: Migration is a complex global phenomenon with profound socioeconomic, political, and humanitarian implications. Effective migration data management is essential for evidence-based policymaking, efficient resource allocation, and the protection of migrant rights. However, several challenges — such as data fragmentation, privacy concerns, rapidly evolving migration patterns, technological limitations, and policy inconsistencies — hinder the comprehensive use of migration data. At the same time, emerging technologies, including artificial intelligence (AI) and big data analytics, offer transformative opportunities to enhance migration governance. Initiatives such as the Big Data for Migration Alliance (BD4M) exemplify how collaborative, data-driven approaches can improve migration management and lead to better outcomes for migrants. This paper seeks to explore both the challenges and opportunities associated with migration data management, with a particular focus on the role of innovative technologies in advancing migration research and policymaking. The analysis revealed that, with significant barriers exist, such as fragmentation, privacy concerns, dynamic patterns, technological constraints, and misaligned policies, there are also substantial opportunities for enhanced collaboration, informed policymaking, efficient resource allocation, research advancements, and increased public engagement. Furthermore, the author highlights AI and the BD4M as key components of a leading collaborative effort to leverage advanced analytics for a deeper understanding and more effective management of global migration. By analyzing diverse datasets through innovative analytical techniques, such initiatives aim to improve outcomes for migrant populations worldwide.
DOI: 10.36922/ijps.6846
Author: Abduvaliev Mubinzhon
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: hybrid
OpenAlex ID: W4410144414