Data Volunteerism Can Reshape the Nonprofit World

Nonprofits in the United States currently face four seemingly unrelated but stark realities.


First, volunteerism appears to be on the decline. At least based on the most recent statistics, which were published by the Census Bureau in 2016. More recent but less specific statistics from the American Time Use Survey suggest a less pessimistic but still not rosy picture: the fraction of Americans 15 years and over volunteering on an average day increased from 5.9% in 2016 to 6.1% in 2017, but remains below the 7.1% figure posted in 2007 and the 7.2% in 2009.


Second, the tools through which organizations can effectively engage the world are rapidly changing. In the advertising sphere, new mediums, like mobile advertising, have exponential growth rates. Younger generations interact in significantly different ways than their older counterparts: as Generational Kinetics puts it, Generation Z is using YouTube the way Millennials use Google.


Third, while the availability of data continues to grow, nonprofits continue to underutilize it to make decisions. According to a 2016 survey by Every Action, only six percent of nonprofit staff surveyed said they were effectively using their data, while 33 percent admitted they were not using it at all. This is in contrast to an explosion in the usage of big data and advanced analytics within the for-profit sector: NewVantage Partners reports that 84.1% of the executives they surveyed are investing in big data initiatives.


Fourth, nonprofit organizations have very little extra cash for additional operating expenses. The National Council of Nonprofits State of the Sector Report found the “majority of the nonprofits responding reported that they had less than three months of operating reserves on hand.” Even when nonprofits do have extra cash on hand, spending it on administrative costs can be self-destructive. Behavioral experiments by Dr. Keenan at Harvard Business School revealed donors are extremely averse to their donations going to overhead. And with the proliferation of websites like Charity Watch, the public can easily see the percentage of revenue spent on overhead.


These four pieces form the full picture: nonprofits exist in a rapidly changing environment, but they have a new tool to help them navigate: data analytics. But data science work is not cheap, and it enters the scene at a time when technology itself reinforces the need to minimize administrative expenses. The main source of free help, volunteerism, is either on the decline or stagnated.


Enter: data science volunteers; individuals who possess some level of data or statistics know-how.  These volunteers can easily be the missing piece that solves the four faceted problem laid out earlier. The data science community already has a strong volunteer ethic, evidenced by a plethora of competitions, nonprofit groups, and fellowships targeted at solving social problems. These volunteers possess the potential to solve the problems plaguing nonprofits – specifically the moving landscape of online engagement and the pressure to operate efficiently.


Within the ecosystem working to use data science for good, the focus is on “hot” problems: understanding student outcomes, identifying diseases from images, etc. These are all worthy goals. They are, in truth, why nonprofits exist in the first place. Data science volunteers want to see their code solve visible problems, and this leaves an unfortunate neglect of operational problems. If a nonprofit is boat, everyone wants to be on the bridge. But then who will make sure the engine operates correctly? The bottom line: we need to encourage data science volunteers to lend their talents to operational problems. We need less captains on the deck with a spyglass and more mechanics in the depths with a wrench.


Success in shifting data volunteerism onto operational problems will have three positive side effects. First, we will waste less talent on competitive prediction problems. At the end of the day, only the winning few of the thousands of Kaggle submissions get used by the person with a problem. If we have more data analysts seeking local volunteer work fixing operational problems, we have more problems fixed and less excess solutions. Second, we broaden the toolkit of the average data scientist. Local charities and non-profits often lack a middleman to translate technical findings into real world decisions. The volunteer will need to learn to seek out simple methods delivered in a user-friendly manner. Third, we generate opportunities for aspiring or entry-level data scientists to showcase their skills by starting out small. By redirecting interested high school volunteers from painting fences to cleaning data, we have not only solved a need but generated a career path.


Moving from vision to realization requires a series of practical steps on both sides.


On the nonprofit side, the first step is announcing a need for data science volunteers. The second step is identifying either a qualified staff member or volunteer to manage the analysis; this person can sift through the work and make sure findings presented by volunteers are meaningful. The third step is being ready to make actionable changes based on the resulting analysis.


For data analysts looking to volunteer, the first step is to contact a nonprofit and offer to help them improve their operations by analyzing their data. The second step is being willing to do the data grunt work. It is likely that data, if it exists, will be in shabby condition. Developing a pipeline to clean and merge data is critical service to many organizations, who may have never looked at a metric because the data was in an ugly format.


If you are a data scientist, analyst or programmer looking for a flexible way to give back, check out our Contributor program. Contributors assist Intrepid Insight directors with pro bono work for nonprofits and local governments. They also are free to run their own projects and publish on the blog. This gives budding data scientists a way to improve their skills and showcase their talent. It also lets experienced analysts give back by donating their most valuable professional asset: their hard-earned talent.


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