Creating a joint data analysis framework with national authorities
Challenge
Mexico lacks an integrated data analysis framework to support joint analysis and planning by actors engaging with displaced communities. This results in inefficient resource allocation, with duplications in certain settings while many displaced people face barriers accessing basic services.
Solution
The creation of a data analysis framework to collate, study, and interpret information from the national refugee commission, the national population registry, and UNHCR. This will generate information detailing the characteristics and displacement patterns of the affected population, and highlight cases of successful local integration.
Impact
UNHCR will better understand the needs, capacities, motivations and interests of displaced people in Mexico – information that will help decision-making processes, reduce duplication, and enable better coordination to deliver effective interventions.
Project impact
Other information
UNHCR Mexico, COMAR, and RENAPO managed refugee data across more than 20 fragmented systems (proGres, SIRE, Kobo, Cash Assist, Excel, and others), creating duplicate records, slow case management, and missed opportunities to identify vulnerable people. Funded by the Data Innovation Fund, this project delivered the Integrated Data Framework (DAF), Mexico's first AI-powered AutoML platform for refugee analytics, now live at daf-mexico.unhcr.org. The solution was co-designed with refugees, asylum seekers, COMAR, RENAPO, and UNHCR field staff across five locations, ensuring it reflects the operational realities of registration, protection, livelihoods, and durable solutions. By centralising data, enabling profile-based targeting, predictive modelling, and dashboards in plain language, DAF Mexico cuts response times, prevents revictimisation through a unified interview approach, strengthens interoperability between UNHCR and government systems, and is replicable across the Latin America region.