Driving Systems Change with Data

Earlier this month, my colleague Matthew Bishop and I co-hosted a wonderful group of thinkers and doers at The Rockefeller Foundation’s Bellagio Center in Italy. We explored the Innovative Frontiers of Development with a focus on achieving 21^st Century Systems change. The ideas flowed through plenary sessions debating our biggest political challenges and in smaller breakout groups shaping specific 30-day deliverables and projects.

This year I’ve been quite focused on the application of data science to realize the Foundation’s vision. With that lens, I heard notes throughout our Bellagio discussions relating to data. They helped me see how data can help overcome many challenges in systems change work. In particular, I saw the importance of data-driven approaches to increasing clarity, adaptation, and innovation in systems change efforts.

Clarity by seeking measurable goals

Those advocating data-driven approaches for systems change often chant “if you can’t measure it, you can’t improve it” only to be rejoined by those dismissive of such approaches with “what matters can’t always be measured”. What really matters in systems change efforts is that people mobilize to common goals, or at least they understand each other’s goals. The process of framing a specific, measurable goal – whether it is ultimately defined or not – forces more precise conversation among actors aspiring to the same systems change, particularly when working across sectors, cultures, and communities.

Adaptation through monitoring and learning 

No one fully understands a system or can predict how it will change. We are all crossing the river one stone at a time. Having a data-driven strategy allows you to be specific in what you’re trying to achieve. You can then more frequently and precisely test and improve your approach. You can take more risk with your strategy because you have broken your plans into more manageable and measurable steps, allowing for course corrections before it’s too late. I believe that one learns more by being specifically wrong than vaguely right. It’s crucial that everyone involved in systems change efforts continuously share fact-based insights about what’s working and what’s not. That’s hard to do without data.

Innovation from outside our current frame

The traditional approach to big scale change efforts is to create a logic model based on a few hypotheses. We then invest in data gathering and monitoring based on those hypotheses. Unfortunately, that means new information or ideas unrelated to our original hypotheses doesn’t get captured and considered. With the advent of big data, we can constantly explore insights that don’t directly relate to our original hypotheses. By scanning horizons, we can spot transformational opportunities outside of our framework. Systems change requires lucky breaks; data science helps us hear more knocks on more doors when opportunity is nearby.

Last week the Foundation gathered leaders from around the world at its Bellagio Center for the Solvable Summit, a forward-looking, action-oriented convening that harnessed expertise and optimism to identify the most promising solutions to tackle the world’s biggest problems. I look forward to hearing new ideas for how we change systems and solve the big challenges of our time. I invite you to share your ideas in the comments below.