On Demand Data
One of the questions we most frequently encounter is "What kind of data do you collect?"
Our learning curriculum is based on teaching data-based problem solving. As part of this process, surveys are integrated into a student's coursework, and customized to collect on-demand data on a wide range of topics. Currently, we focus on collecting data around the following themes:
What spending habits do respondents engage in? What does financial access- to loans and banking services- look like? By understanding financial behavior in rural communities, it is possible to develop metrics such as credit scores to improve financial services for under-served communities.
What practices are small-scale subsistence farmers engaged in? What techniques are being used to increase productivity, and what resources are lacking? What are local preferences for different agricultural inputs? Access to data in the agricultural sector is vital to improving yields and food security for the region.
Mobile Access and Communication
Mobile access is growing faster than ever across the continent- making it the platform of choice for many companies and organizations hoping to reach a rural demographic. However, little data exists regarding what people are using mobile phones for- whether it's calling, mobile money, or information access. Likewise, it is equally as important to understand barriers to mobile access. How far do people have to walk to charge their phones, and how much does it cost them? Understanding mobile access unlocks previously inaccessible markets.
Initiatives to improve education abound, but efforts to quantitatively measure their impact are much less. Do test scores actually improve as a result of an intervention? What are key challenges preventing educational improvement, and how do they differ from community to community?
Rural communities are intuitive markets for solar and off-grid technologies. Understanding potential barriers to uptake, willingness to pay, and existing attitudes towards energy access is key to engineering a successful transition.
In remote areas without clinics and hospitals, it is difficult to identify health concerns and their root causes. For example- are there local beliefs, customs, or behaviors that contribute to the spread of diseases such as HIV? Data metrics can be used to indicate the prevalence of health issues in certain areas, and how to best treat them.
Infrastructure for roads across rural East Africa is still largely lacking, with dirt paths constituting the majority of community highways. Using data, it is possible to evaluate where infrastructure investments are the most necessary, and where infrastructure improvements can yield high returns on trade and productivity.