We need more data talent in the nonprofit sector. Data has the power to revolutionize our organizations yet we lack the workforce to launch the revolution. So where should this workforce come from?
There are two strategies we can employ to increase the data talent in the sector; attract or develop.
Many great organizations are working to attract knowledgeable and skilled data scientists to the social sector. DataKind and Bayes Impact both get data scientists to work on pro bono projects for nonprofits. The Data Science for Social Good summer fellowship I help organize in Chicago has students work on civic projects with the hope of seeing some choose the social sector full-time. Code for America sends brilliant young people to work within the halls of city government and many have gone on to become Chief Data Officers.
Attracting the amount of data talent the social sector is going to be hard, really hard. While most people will give up some compensation to perform meaningful work, there is a limit to this generosity. Asking someone who just got a starting offer of $200k at Facebook to work for $80k at your nonprofit is unlikely to be successful often enough to meet the needs of nonprofits. Additionally, the private sector can’t even fill all of the data jobs it needs with as many as 1.2 million going unfilled. With competition for talent ever increasing, attracting it to the social sector will be difficult.
So we need to develop this talent.
We need to find ways of getting people who are already passionate about our work (and used to our salaries) to gain important analytical skills. Luckily more and more opportunities are emerging to gain these kinds of skills without too much expense.
I started Data Analysts for Social Good with the idea of providing a home for those of us in the sector who use analysis in our day to day jobs. It provides basic training on a variety of topics targeted at the kinds of skills best used in this work and contextualized for the social sector.
“The private sector can’t even fill all of the data jobs it needs with as many as 1.2 million going unfilled.”
The biggest thing we do every year is the Do Good Data conference. Two days of training, inspiration, and networking with others who are looking to gain and apply analytical skills in their work. This year’s conference features a spectacular faculty including Dean Karlan (Economist at Yale University and Founder of Innovations for Poverty Action), Michael Weinstein (Chief Program Officer at the Robin Hood Foundation), Ned Breslin (CEO of Water for People), and Jake Porway (Founder of DataKind).
For those interested in more traditional education, The University of Chicago’s Harris School launched a Masters in Computational Analysis and Public Policy degree this year. It’s a program intended for those with no formal computer science training and brings them through the core curriculum in both the computer science department and public policy school. It’s an innovative, forward-thinking program I wish was available when I went to grad school.
What’s great though is that the options to learn some of these skills are seemingly endless. Coursera has a Data Science specialization in partnership with Johns Hopkins, the Open Source Data Science Masters organizes lots of free online courses, or bootcamps like Metis and Zipfian.
Our sector is ripe for a data revolution. We just need to create some revolutionaries.
Many thanks to Andrew Means for his insights into the sector’s need for data talent, and how we can develop it. Be sure to follow him on Twitter @meansandrew, and of course, check out their annual event, Do Good Data conference.