Interest in topics like “data science,” “analytics,” and “big data” is growing in the nonprofit and education sectors. Attendance at training programs, salons, and conferences like NTEN’s annual conference, Do Good Data or Stanford Social Innovation Review’s Data on Purpose is booming.
And it’s no surprise – we are in the midst of a data revolution that is fundamentally changing the world. Data is everywhere, and the same algorithms and techniques that help Fortune 500 companies boost profits can help nonprofits and social businesses increase their impact.
However, most social change organizations don’t have the same budget or staff capacity as these companies do. They assume this constraint prevents them from leveraging advanced data analytics and data science to inform their good work.
That simply isn’t true. At DataKind and Tableau Foundation, we work with these inspiring change-makers to develop new ways for organizations at every level to inform their efforts through data. For example, Tableau now makes free software available to most small nonprofits in the U.S. (and soon to be global), and the company’s employees have created the Tableau Service Corps as a way to help organizations get started with big data projects. DataKind helps organizations at all stages, whether they are new to data science or looking to refine their approach, by helping them scope and articulate their needs and work with a team of pro bono data experts to design data-driven solutions.
‘We are in the midst of a data revolution that is fundamentally changing the world.’
Seeing a need to overcome this preconceived notion, we asked ourselves, “how can we start to remove the barriers for organizations trying to use advanced data analytics the same way companies do?” Two recommendations immediately came to mind, and we hope they will start a broader conversation about expanding the use of data to address social issues.
Looking to the Past… and the Future
What comes to mind when you think of data in the social good sector? Impact evaluation, measurement & evaluation (M&E), or reporting? How about terms like analytics, dashboards, or big data? Maybe you just picture a blue fog of zeros and ones.
Our challenge is that “data” is not a new term. Undoing or expanding existing definitions about what data is, its purpose, and how it should be collected is the first step toward change.
‘Undoing or expanding existing definitions about what data is, its purpose, and how it should be collected is the first step toward change.’
For example, at this year’s Do Good Data conference, we found a wide range of perspectives on “analytics” and “data.” At one extreme, we heard advocates for classification systems for student behavior built upon forward-facing test score models. That stood in stark contrast to the others simply looking for a way to forensically evaluate IRS form 990 information or survey results in search of historical insights.
Even the experts disagreed on some core concepts. There was a clear dichotomy between the data scientists working with unclean, unstructured data to find real-time or predictive meaning and the analysts working with agreed-upon standards for evaluating past actions.
It’s time for more gatherings to encourage collaboration between these two extremes. This can only happen if tools and techniques from data science and M&E are employed to look both backward and forward.
Breaking Barriers and Getting Help
At Do Good Data, it was clear that even the most data-savvy organizations were just starting to see the power of advanced analytics. They maybe had some experience in data manipulation or analysis, but were very new to some of the advanced concepts being presented on the stage by groups like Datascope Analytics and Foundation Center.
Data scientists are still hard to find in the wild. And even if a nonprofit can find one, being able to afford hiring one is another story. But does every nonprofit need a data scientist on staff? Probably not. Many nonprofits don’t have lawyers on staff, but they know legal expertise is needed.
‘But does every nonprofit need a data scientist on staff? Probably not.’
If organizations want to leverage data to inform their work, they will need to increase data literacy among their entire staff. Equipping executives, program staff, development staff, and others with the skills to read and respond to usable data (charts, graphs, maps) is critical to data-informed decision-making in this – and really any – professional context.
The good news is that groups like Data Analysts for Social Good and NTEN’s Data Community of Practice are thriving peer learning communities that help organizations increase their own data literacy skills. We’re also seeing many organizations hiring data analysts, and even data scientists. Funders are increasingly supporting organizations’ data efforts in search of more reliable metrics for their own use.
And let’s not forget the many data professionals around the world we see raising their hands to donate their skills and work pro bono – including DataKind’s own growing network of pro bono data scientists worldwide or the members of the Tableau Service Corps.
It will take a thriving, coordinated ecosystem of people who care about using data to drive greater impact to keep this momentum going.
We must grow our community of data scientists to help wrangle huge, complicated sets of data into usable information.
We must encourage data analysts to help nonprofit organizations use data effectively every day.
We must find funders willing to go beyond using data to simply streamline nonprofit reporting and be connectors to rally everyone together to tackle larger challenges.
And, most importantly, we must have committed social sector professionals and researchers ready to use data to tackle some of the world’s toughest challenges.
If we talk to each other, share learnings, share perspective and create new solutions, we can break barriers and unleash the full potential of the Data for Good movement.
In the spirit of sharing perspectives and breaking barriers, we would love to hear your thoughts! What other hurdles are you seeing preventing social change organizations from applying advanced analytics and data science to their work? What other resources did we miss?