Data Quality in Emergencies: Field-Level Challenges and Mitigation Strategies
In emergency response, the quality of our data and reporting plays a critical role in how we plan, allocate resources, and respond to people in urgent need. High-quality data helps ensure our actions are informed, timely, and equitable.
However, when data collection methodologies are weak or inconsistent, the consequences can be significant:
- Data may not be valid, leading to unreliable conclusions.
- Assessments may be inaccurate or unnecessarily repeated.
- Accessible areas are often oversampled, while harder-to-reach communities are left out.
- Vulnerable groups risk being excluded.
- Programs may rely on assumptions rather than actual evidence.
To support field teams and strengthen MEARL practices in these complex environments, I developed a contextualized resource that outlines key challenges and practical mitigation strategies for rapid needs assessments in conflict-affected emergencies. While grounded in the Myanmar context, I believe the insights are relevant across many humanitarian settings.
This resource includes 1. A detailed guide to common field-level challenges and mitigation strategies, 2. A concise, actionable checklist, and 3. A visual summary of key takeaways for quick reference.
I hope this tool will support more ethical, inclusive, and evidence-based decision-making by placing people and their needs at the center of emergency response efforts.
Hnin Ei Lwin
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