Making sense of shaky data in humanitarian crises

TL;DR

This paper discusses how poor-quality data in humanitarian crises can lead to bad decisions, and suggests ways to improve data interpretation.

Contribution

The paper highlights data interpretation as a critical weakness in humanitarian response and offers recommendations for improvement.

Findings

Data interpretation is a key weakness in humanitarian decision-making.

Conflicting and ambiguous data in crises like Darfur, Yemen, and Ethiopia can lead to harmful decisions.

Political and organizational factors often override data in humanitarian responses.

Abstract

Humanitarian decision-making occurs in volatile and politically charged environments where information is often incomplete, outdated, or conflicting. Effective humanitarian response often requires interpreting poor-quality data to guide interventions, allocate resources, and assess impact. Despite advances in evidence generation, knowledge gaps persist, and decisions are frequently influenced by political and organizational factors rather than by data. This paper argues that data interpretation is an area of weakness in humanitarian response. Data availability and quality vary across crises, with methodological challenges and political sensitivities further complicating interpretation. The three examples of Darfur (Sudan), Yemen and Ethiopia illustrate how conflicting information and ambiguous interpretation can negatively impact critical decisions with far-reaching consequences on the affected communities. This paper concludes with suggestions for making better interpretation and use of data in humanitarian crises.