“We need to stop and admit it: we have a prediction problem. We love to predict things — and we aren’t very good at it.” — Nate Silver
In a world riddled with poverty, nearly every government, non-profit and aid agency struggles with the issue of finding the poor, which requires making decisions on pinpointing areas of greatest needs.
Until very recently, the data commonly used to answer these questions came almost exclusively from countrywide surveys, which are expensive and logistically challenging. Now, recent innovation is beginning to change how we combat poverty around the world.
Satellite photos provide a level of geographic specificity that national accounts do not. Another set of researchers used visual algorithms (related to those that recognize your face on Facebook or help navigate cars) to analyse these images pixel by pixel. Through this process, they could quantify poverty in each square kilometre of Uganda.
Also, there are many important, unconventional sources of data. Consider cell phones, for instance. For most of the world’s poor, each call and text has a very noticeable and real monetary cost.
Of course, all these data sources that is, satellites, cell phones and many others work even better in harmony and we are beginning to see tangible benefits. For example, The World Bank recently held its Big Data Innovation Challenge. Many of the winners used novel data sets to improve measurement and decision making; in essence, changing how we use old sources of data.
What other ways are there in fast-tracking the SDGs through technology? Share your big idea with us!