With a PhD in statistical astrophysics, David Corliss leads a data science team at Fiat Chrysler. He serves on the steering committee for the Conference on Statistical Practice and is the founder of Peace-Work, a volunteer cooperative of statisticians and data scientists providing analytic support for charitable groups and applying statistical methods in issue-driven advocacy.

Pro bono—literally “for good”—service has become more common in statistics and data science in recent years. Often called “Data for Good,” this growing movement has been embraced by individuals, companies, and professional associations. A number of organizations have sprung up to design and manage statistical volunteering projects, recruiting volunteers and connecting them with projects and organizations that will benefit from analytic support. This addresses a prominent need in organizations pursuing good causes: they often have data but lack statistical skills and resources to perform analytics and cannot afford to hire highly skilled consultants. In these situations, statistical volunteers have the opportunity to make a real difference in the world doing what they love most.

There are so many ways to get involved! Data for Good organizations seek volunteers to join new and ongoing projects. Student organizations connect people to research projects. Many hackathons and other events welcome first-timers with analytic skills. Many people involved in statistical volunteering remain unaffiliated, preferring the flexibility and close personal relationships of individual consulting. Also, many companies offer employees volunteer opportunities through work—although statistical projects are still few and far between, with more advocacy, volunteers, and projects needed.

Data for Good Organizations

Within the growing Data for Good movement, there are a number of volunteer organizations, each with a distinctive character. All of them are eager to work with new volunteers and can be a big help in getting connected to a first project.

Statistics Without Borders (SWB) is an outreach group of the American Statistical Association. Established in 2008, SWB is one of the first Data for Good organizations. To learn about Statistics Without Borders, see their article in this special issue, check out their website or talk to one of their volunteers.

Another prominent Data for Good organization is DataKind. Founded in 2011 by data scientist Jake Porway, DataKind has grown rapidly to become a global organization making a great impact for social change, with thousands of volunteers working on dozens of projects. Headquartered in New York, DataKind has chapters and hosts events around the world. The organization combines the resources of a large number of volunteers with a smaller cadre of professional staff. With substantial financial support from foundations, grants, and other donations supporting a multitude of projects, DataKind is a powerful engine for using data and analytic science to make a difference in communities and around the world.

‘Adding Analytics’: A Case History

Being able to make an impact for good requires a relationship of trust between the analytic volunteers and the people and organizations who will use the results. This makes “adding analytics”—helping an organization where you are already volunteering in a nonstatistical capacity—one of the best ways to get started in Data for Good. Let me tell you about one of my first projects as an example.

After volunteering for Habitat for Humanity on a number of construction projects and getting to know some of the local leadership, the question of how data science could be used to support the organization was raised. When asked what they needed the most, they said finding more donors and volunteers. Every civic organization I have spoken with in the 15 years since has said the same thing.

I suggested doing a cluster of the communities in their area. Three clusters of communities were identified—one with lots of donors, another with many volunteers but fewer large donors, and a third dominated by the people they were trying to help. This identified communities likely to have many interested contributors where the Habitat chapter had little presence.

This project tells the story of how a lot of people get started in Data for Good and often continue going forward. A person can already be helping an organization in a nonstatistical way—working at the public library; walking dogs at the local animal shelter; volunteering at a school, house of worship, or community center. Data for Good so often begins when people with analytic skills use them to help the organizations and causes they already support. We possess a rare ability so needed by groups across our communities that it can make a huge impact right where we live.

Doing Well by Doing Good

Statistical volunteering offers many advantages. In addition to the primary advantage of using our technical skills to support causes and organizations we care about, pro bono work offers opportunities for career development. While volunteering certainly looks good on a résumé—especially to desirable employers who encourage contribution to the larger community as an ordinary part of one’s career—there is much more. People early in their career gain important practice and learn new analytic techniques. Volunteer consultants are able to broaden their experiences and gain practical experience in new techniques—something that can be limited in professional situations, which often focus on the same handful of methods used over and over for a commercial product. Statistical volunteering is often the best way to try out and enjoy a wide variety analytic tools and emerging technology.

Statistical volunteering often pairs more experienced data scientists and analysts with those having less experience. This naturally leads to mentoring relationships than benefit both parties. All participants benefit from networking with people from different backgrounds, skill sets, and work situations. More junior volunteers gain practical experience and the chance to work directly with the people needing the analysis—a quality often restricted at work, where newer team members rarely see anyone who directly uses their analysis. Statistical volunteers gain presentation skills, practical working experience, and confidence in their work. As experienced is gained, taking a larger role on volunteer projects creates opportunities for leadership, furthering career development.

The most important benefits of statistical volunteering, however, will never appear on a résumé but in the lives impacted by our projects. As statistical and data science professionals, the need for what we do every day on the job is so great, but volunteers are still far too few. The Data for Good movement has grown so rapidly because it makes so much more than a good résumé; it makes a career by making a real and tangible difference in our profession, our society, and our world.