These days, it’s hard to get away from the hype around “big data.” We read articles about how Silicon Valley is using data to drive everything from website traffic to autonomous cars. We hear speakers at social sector conferences talk about how nonprofits can maximize their impact by leveraging new sources of digital information like social media data, open data, and satellite imagery.
Braving this world can be challenging, we know. Creating a data-driven organization can require big changes in culture and process. Some nonprofits, like Crisis Text Line and Watsi, started off boldly by building their own data science teams. But for the many other organizations wondering how to best use data to advance their mission, we’ve found that one ingredient works better than all the software and tech that you can throw at a problem: collaboration.
As a nonprofit dedicated to applying data science for social good, DataKind has run more than 200 projects in collaboration with other nonprofits worldwide by connecting them to teams of volunteer data scientists. What do the most successful ones have in common? Strong collaborations on three levels: with data science experts, within the organization itself, and across the nonprofit sector as a whole.
1. Collaborate with data science experts to define your project. As we often say, finding problems can be harder than finding solutions. If your organization is new to data science, it can be hard to know what to ask for when you aren’t sure what’s even possible. Working with a data scientist and tech expert is critical to uncovering and articulating your needs. Start with the question you want to answer, not the data. While it can be tempting to start by throwing a big dataset at a technologist and asking for input, you can go farther faster by laying out your end goals first. Help the data scientists you’re working with understand your organization’s strategic priorities first, then figure out how data can help you get there.
2. Collaborate across your organization to “build with, not for.” Our projects follow the principles of human-centered design and the philosophy pioneered in the civic tech world of “design with, not for.” Before you even think about what data you need to answer the question you’re focused on, be sure you bring together the right people. Oftentimes, nonprofit data projects are housed within the evaluation team. While those folks should definitely lead or be involved in those initiatives, knowledge of how the organization uses data and algorithms needs to permeate all levels of management. Too often, we see a project with high potential hidden away within one department that then has to advocate for it fiercely, often to no avail. A project’s success depends on strong collaboration across the organization to ensure that the work is a priority and designed for the people that will ultimately use it. Get senior management involved early on, as well as any end users of the solution. Consider first building a prototype or proof of concept as a way to illustrate the power of the work and get your colleagues excited to join in from the very start.
3. Collaborate across your sector to move the needle. Many organizations think about building data science solutions for unique challenges they face, such as predicting the best location for their next field office. However, most of us are fighting common causes shared by many other groups. There’s great opportunity for organizations to work together to amplify their collective impact. For example, we’ve seen our nonprofit project partners share data and subject matter expertise on issue areas, replicate and repurpose each others’ data solutions, and inspire each other about novel approaches and ways of harnessing the power of data for social good. By working together, we can learn from each other, build and improve on past work, and ultimately move the needle together on tough social issues.
If you’re looking to use data and algorithms at your nonprofit, don’t get fooled into focusing on the tech itself or feel daunted about where to start. As one of our heroes, former US Chief Data Scientist DJ Patil, once said, “Data science is a team sport.” No one undertakes a data science journey alone. Whether you work with DataKind or another organization, start by reaching out to technologists with whom you can collaborate. Once they help you identify the end goal, gain buy-in at your organization to prioritize the work. And once a solution is developed, don’t forget to collaborate with peer organizations so as not to recreate wheels and to all grow stronger together.
By focusing on building strong collaborations on these three levels—with data experts, across your organization, and across your sector—you’ll go from merely talking about big data to making big impact.