Hnin Ei Lwin

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 

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...