Big Data in Humanitarian Projects: Power of Insight
Originally posted on datapolicywonk
Big data is like a three-eyed raven of any professional setting! It’s super power extends to splicing up past information in numerous ways, use it to learn and predict future trends! It provides clear insights in any setting, but considering how new big data is in humanitarian relief efforts it provides that much more insight in scope and type of services provided to the ones in need.
Big Data is influencing how projects are implemented
Big Data has significant implications for NGOs providing humanitarian and disaster relief, in the same manner it is transforming how businesses and governments work. That impact is still not as significant as in corporations and public administration, but consecutive incremental adjustments are slowly but surely changing the landscape in which projects are implemented.
Yet already, big data changes depth of accountability, flexibility and insight in project implementation.
Talking about big data in deed does sound a bit unattached from real life interactions front line workers have with people in need every day, providing assistance in food, psychosocial aid and other sectors. These effort require a lot of man power, resources and dedication. But big data provides a lot of advantages and far more insight in quality of programing that is being implemented.
Better Insight – Moving from a Log Frame to a Data Set
In the minds of many, first association related to data in humanitarian projects, are (dreaded) log frames!
Aaaand… log frame!
These sometimes tedious tables contain project data. These data points are insightful but in the same time very inflexible for further analysis. Covered by a log frame are only info on reporting periods that were pre – designed for the data collection (those are usually monthly, but can also be quarterly or annual periods), but it is not possible to look at data stream in real time.
Digital data collection tools, allow for real time collection and analysis of received data. When data is collected in one place, as is the case in humanitarian efforts when digital data collection tools are applied, it is easy to create data sets for one specific project or for overall program one NGO has. (Note: special care has to be attributed to collection and protection of private information)
Raw KoBo data
In such case it is easy to make different queries and splice up data in any way that is beyond what is reported in the log frame. You can easily go through all the perils of double counting by looking at services provided to each of the individuals for example, a major issue always considered by NGOs especially in protection efforts.
Splicing up data generated by digital data collection tools
Project managers, or senior managers sometimes have requests that are not straightforward to accommodate and best way to be able to provide data they might be interested in is to have more data on hand. For data and learning needs project teams have, it is crucial to have raw data next to what is reported in the log frame. And only way to do that is to have all the data in one place, in a data set.
Better yet… In a data set that is in a machine readable format.