Authors :
Caleb Terhemba Ikyernum; Oliver Jütersonke
Volume/Issue :
Volume 8 - 2023, Issue 8 - August
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
http://tinyurl.com/bd8cyva6
DOI :
https://doi.org/10.5281/zenodo.8339985
Abstract :
This study sought to identify possible solutions for more effective translation of data into actionable humanitarian
response in Afghanistan. While the Humanitarian Needs Overview is the primary document highlighting multisectoral
needs in Afghanistan, it does not reflect the variation in needs within a humanitarian response year and at the sub-
provincial level, thereby limiting the extent to which implementing actors’ responses are guided by evidence. Analysis of
IOM’s community needs assessment data showed that up to 40% of districts returned different vulnerability
categorizations when aggregated at district level, compared to when aggregation was done at province level, reflecting the
importance of spatial aggregation on needs analysis. A time series visualization of ACLED data for Afghanistan showed
notable spikes and dips that were seasonal or triggered by major events, such as the spring offensive and significant
reduction in conflicts after the Taliban became the De facto Authority in Afghanistan. Analysis of primary qualitative data
derived through Key Informant Interviews with Afghanistan humanitarian response stakeholders suggests that effective
translation of data to response in Afghanistan will be achieved if it is based on a SMART (Specific, Measurable,
Assignable, Reliable, and Time-bound) framework that is integrated with additional elements of accountability, resource
allocation, and capacity mapping. A SMART+ model, which integrates the SMART components with the additional
elements, was recommended and an example of how to operationalize this model in Afghanistan was presented.
This study sought to identify possible solutions for more effective translation of data into actionable humanitarian
response in Afghanistan. While the Humanitarian Needs Overview is the primary document highlighting multisectoral
needs in Afghanistan, it does not reflect the variation in needs within a humanitarian response year and at the sub-
provincial level, thereby limiting the extent to which implementing actors’ responses are guided by evidence. Analysis of
IOM’s community needs assessment data showed that up to 40% of districts returned different vulnerability
categorizations when aggregated at district level, compared to when aggregation was done at province level, reflecting the
importance of spatial aggregation on needs analysis. A time series visualization of ACLED data for Afghanistan showed
notable spikes and dips that were seasonal or triggered by major events, such as the spring offensive and significant
reduction in conflicts after the Taliban became the De facto Authority in Afghanistan. Analysis of primary qualitative data
derived through Key Informant Interviews with Afghanistan humanitarian response stakeholders suggests that effective
translation of data to response in Afghanistan will be achieved if it is based on a SMART (Specific, Measurable,
Assignable, Reliable, and Time-bound) framework that is integrated with additional elements of accountability, resource
allocation, and capacity mapping. A SMART+ model, which integrates the SMART components with the additional
elements, was recommended and an example of how to operationalize this model in Afghanistan was presented.
