Stanford Social Innovation Review posted a great article about the importance of data in the non-profit world, and how one specific organization, Data Without Borders (DWB) is making a difference.
DWB’s mission statement begins with the line, “Data Without Borders seeks to match non-profits in need of data analysis with freelance and pro bono data scientists who can work to help them with data collection, analysis, visualization, or decision support.”
A noble task indeed. Of the resources available to non-profits, data visualization and analysis is typically not at the top of their list. Non-profits are mainly concerned with looking for the next batch of funding, and working to drive impact true to their mission.
Jake Porway, co-founder of DWB is quoted in the SSIR article talking about the limitations of data. “He pointed out the limitations of a survey that asks people to opt in, in exchange for mobile minutes. ‘That doesn’t accurately take the mood of a billion people. You need to understand the limits when you talk about data.’”
He’s absolutely right that there is no silver bullet to understanding just how emotions, actions, and reasoning all interact on a regular basis. As Dan Ariely famously pointed out, people are Predictably Irrational. Yet, we should not intentionally limit ourselves to the potential of the interaction of data.
Analyzing data from one organization can be incredibly helpful from the perspective of that organization. Think of the value you could glean from a structured analysis helping you learn about your constituents and trends among the people you work with and for.
But think of the macro value of connecting datasets from various industries. Individuals at the Bottom of the Pyramid who utilize the services of microfinance institutions might be the same consumers who purchase solar powered lanterns to keep their phone charging store lit at night. What if we were able to connect available data between MFIs, solar powered lantern distributors, and mobile operators.
What more can we learn?
Nothing glues the story together better than anecdotal or qualitative inputs; data alone cannot help us learn all there is to learn. But just as DWB is finding out, there is plenty of untapped intelligence in available data.
Data could and should talk to other sources. While indepth industry knowledge is critical, I would argue that the next step out of this data aggregation process is to paint a more complete picture with cross-industry data synchronization.
Don’t stop working to find ways to learn from data.