Volunteering Sparks Innovation in Data for Good

When data scientists from businesses volunteer their time in nonprofits, the result is much more than the sum of the parts

Volunteering Sparks Innovation in Data for Good

May 29, 2015

In our first post on Data for Good, we touched on why it’s surprising that businesses have become early pioneers of Data for Good by donating data. It’s less surprising, however, when you consider the underlying economics of data science. Data scientists are in famously short supply and can command competitive salaries, making their expertise hard to come by outside larger corporations and research institutes. Ironically, this often puts mission-driven organizations like nonprofits, which have unique competencies in social impact, at a particular disadvantage. That’s why another type of donation—the donation of data scientists’ time in the social good sector—is one of the most important developments shaping Data for Good.

Why donating talent broadens the horizons for social impact
DataKind was founded in 2011 to bring together data scientists and mission-driven organizations on projects that harness the power of data science in the service of humanity. The organization connects and engages volunteer data scientists from the for-profit and research worlds with non-profits, foundations and social enterprises working to address tough humanitarian challenges. These pairings have racked up successes as varied as improving the effectiveness of a program supporting Ugandan farmers to helping a UK tutoring charity better serve youth traditionally underrepresented at top universities.

Significantly, it’s not just the social-sector organizations that benefit: The pairings also help data scientists discover new applications for what is still a new and emerging field. “Our work depends just as much on the subject-matter expertise of our partner organizations as it does on the data skills of our volunteers,” says DataKind co-founder and executive director Jake Porway. “Our volunteers are highly skilled data science experts, but they depend on partner organizations to guide their work, provide context and ultimately implement their work to effect real-world change.”

One such example is the partnership with Simpa Networks, a mission-driven for-profit organization with an innovative model for increasing access to renewable energy in India. Simpa’s customers make a small initial down payment for a high-quality solar cell system and then use their mobile phones to top up their accounts as needed in small user-defined increments; these payments eventually cover the full price of the system, allowing customers to produce their own solar energy for free. DataKind’s volunteers are using Simpa Networks’ historical data on customer payment behavior to predict which new applicants are likely to be a good fit for their model. “On their own, our New York-based volunteers would not know how their professional expertise could help support electrification in rural India. However, with a knowledgeable partner like Simpa Networks, they can now apply their skills on a focused project that will ultimately make a big difference,” says Porway.

This program enjoys both financial and volunteer support from MasterCard. Kamalesh Rao, Director of Economic Research at MasterCard who contributed data modeling as a volunteer project, adds that it gave him the opportunity to work with cutting-edge technologies and learn from other scientists at work in the field.

Donating expertise comes of age
Some businesses are now establishing their own programs to share in-house expertise. SumAll, an analytics software company, set aside 10% equity to start its nonprofit arm, which works with groups to use data for social change and to shed light on global issues. Since 2013, SumAll analysts have been working with Humanitarian Tracker to create a dashboard that examines the different causes of death in the Syrian civil war. These visualizations provide a powerful tool with which to push back against “coverage fatigue”’ of this war as the devastating conflict enters its fifth year.

Using advanced analysis, “volunteers are at the cutting edge” of data philanthropy work, says Lucy Bernholz, a senior researcher at the Stanford University Center on Philanthropy and Civil Society, where she is helping to launch the Digital Civil Society Lab. Now, for nonprofits, the major question is, “what data should they be collecting and holding on to?” comments Bernholz. She is currently working with DataKind and other groups to help set industry norms that will aid data scientists in identifying the ethical challenges of working with non-profits and their data sets.

As we’ll see in the next post in our series, such questions are central to the discussions of the next emerging trend in Data for Good: pooling data donations from different sources.