Addressing Critical Gaps in Data Collection and Analysis in Atrocities Prevention Early Warning Systems

Abstract

This qualitative study examines the workflows, challenges, and successes of third-generation conflict Early Warning Early Action (EWEA) systems for mass atrocity prevention. Through 26 semi-structured interviews with practitioners from 21 organizations across Africa, Middle East, and Southeast Asia, the research identifies three key gaps: data availability and quality, resource limitations, and data-to-action challenges. EWEA organizations employ diverse data collection methods, including remote messaging, field-based qualitative data collection, and media monitoring. However, data verification and analysis processes vary significantly, with most organizations relying on ad-hoc mixed methods. The study reveals a disconnect between academic research on conflict prediction and the practical application of EWEA systems. While academic studies utilize complex statistical models and machine learning techniques, community-based EWEA organizations often lack access to such advanced analytics. The findings underscore the need for increased collaboration between funders, academic institutions, technology developers, and EWEA organizations to enhance the effectiveness of atrocity prevention efforts.