You Don’t Need a Data Scientist, You Need a Data Culture

Most of the larger non-profit organizations we work with are scrambling to figure out how to deploy complex technologies like machine learning and “AI” in service of the social good. These include inspiring examples that range from poverty alleviation, to home fire prevention, to self-harm risk reduction.  These stories have spread widely and have come to define what a data-centric organization should be doing – namely complicated data science.  However, if you’re an organization thinking about how to use data better, this is not where you should start.  You don’t need a data scientist, you need a data culture.

Catherine D’Ignazio and I have built the DataBasic.io tools to focus on helping people creatively build their data literacy.  As more and more organizations have started using them, we’ve been pushed to think more deeply about what it means to take this approach to building a data culture.  This post lays out our latest thinking abut the building a data culture, and how to overcome barriers you’re likely to run into.

The key problem we see is that organizations working for the social good don’t feel empowered to work with data in a variety of ways. This is a rank-and-file staff problem, not a data scientist problem. We’ve come to work on this in three ways:

Organizations don’t feel confident that they can work with data at all, so to build a data culture we prioritize building confidence through small, focused activities. The technology that they think they need to work with data is daunting, expensive, and requires technical expertise, so our approach focuses on approaches that don’t rely on complex technology.  Organizations don’t have a good process for starting to work with data, so we introduce a step-by-step approach with hands-on activities.

We’re trying to help here by creating the “Data Culture Project” – you can expect to hear more about that early next year.  This gives organizations a lightweight, self-service curriculum or video-facilitated activities.  We’re piloting that with 30 organizations right now, to learn from how they approach running these over three months within their organizations.

This phrase is becoming a bit of a buzz-word right now. So what does it mean? After lots of conversations, with organizations big and small, we’ve narrowed down to this list:

This four-part definition focuses on leadership and staff responsibility very intentionally.  You need buy in across the organization to really make this work. We also focus on making sure data doesn’t get siloed into one department or another. Working with data is a core skill that can be valuable across an organization.

Why bother with building a data culture?  Over the last 10 years we’ve seen a lot of data projects in our workshops and partners. These tend to cluster around three purposes.

Data is most often used to improve operations;  doing things like measuring delivery performance, changing how it works, and them measuring it again to see if it improved.  One the last years we see more and more uses of data to spread a message, giving rise to infographics and other formats where data is used to show impact of programs.  Data is less-often used to bring people together, which is the focus of my work on arts-based hands-on activities, data murals, and more.  We think this third purpose is central to building a strong data culture across your organization.

Of course, like any organizational change, there are barriers. We’ve listed 6 that we think are useful to have in mind while thinking about any efforts you are taking to build a data culture.

Most introductions to data are confusing and overly technical.

Complicated words can alienate people that are just entering the field of working with data.  Pick your words carefully to welcome them.  For instance, you could introduce the idea of “correlation” by talking about “connections” between pieces of data that move together.