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Showing posts with the label Challenges

Real-World Data Quality Challenges in Emergency Settings

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When we look at data, we should not only ask what data we have, but also how it was collected. Research is not just about completing a study or producing results. It is also about ensuring that the data behind those results is reliable and credible.  If we do not take data quality seriously now, there may come a time when human-led data collection is seen as unnecessary, especially as AI can gather data a million times faster than we can. Paying attention to data quality today is what helps human research continue to add value in the future. Hnin Ei Lwin #Monitoring #Evaluation #Reporting #Research #MEARL #social #development #humanitarian #publichealth 

A PRACTICAL FIELD CHECKLIST FOR DATA COLLECTION IN CHALLENGING ENVIRONMENTS

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To support stronger MEARL practices in complex and often unpredictable field settings, I  am  resharing this practical checklist. It  is  a simple yet valuable tool I created to help teams quickly reflect, adapt, and respond more effectively on the ground. This checklist is part of the whole package I developed, which also includes a detailed guide on common field-level challenges and practical strategies to address them. If you  are  interested in exploring more, I  have  included the link below. https://hnineilwinnotes.blogspot.com/2025/04/data-quality-in-emergencies-field-level.html Hnin Ei Lwin #Monitoring   #Evaluation   #Reporting   #Research   #MEARL #social   #development   #humanitarian   #publichealth  

Data Quality in Emergencies: Field-Level Challenges and Mitigation Strategies

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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 hum...