From “Data for Good” to “Data for Impact”

Despite our best intentions, some efforts in the data for good sector end up short-sighted. How can we come together as a community to problem solve while keeping long-term impact in mind?

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Image courtesy of NationSwell

Data science has the power to accelerate social and environmental progress. Yet according to Salesforce.org’s Nonprofit Trends Report, only 22% of social impact organizations have achieved high data maturity today.

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December2
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From Mapping to Action: Advancing the Data for Good Sector

What does it mean to do ‘data for good’? And how do we take the sector from artisanal to industrial? At data.org, we strive to solidify the field of data science for social impact. As we embark on this journey, we want to start by creating a shared understanding of…

In order to actually deliver impact through data science at scale, what needs to change across our sector?

At a recent data.org event, we convened social impact organizations, funders, and data science leaders to explore ways to address this challenge. We sought participants’ insights and gained a clearer sense of what it will take for data to be accessed and applied for good.

What follows are three calls to action that emerged from our conversation. We believe that realizing these calls would catalyze a shift toward scalable, sustainable, and genuinely community-driven projects that help the social good sector use data science to realize impact.

Deepen our commitment to understanding the problem

It’s easy to fall for the flash and glimmer of a new AI solution — but we can’t stop there. We have to deepen our understanding of the problems that we are trying to solve, and our commitment to working with the people and communities that experience real challenges every day.

This might seem like a small shift, but it’s seismic. It pushes us beyond thinking only about the mechanics of a technical solution and instead challenges us to ask how new technology can change the balance of power in favor of people and communities that have been systematically excluded or harmed.

To be clear, passion for new technical solutions isn’t bad. Many problems we face in the social impact sector do require innovation and creativity. But simply having a new approach doesn’t guarantee actual impact. Our metric for success cannot simply be that we delivered a solution.  That solution must meaningfully contribute to reducing suffering or improving equity. 

Doing this isn’t easy. It requires technical experts to diversify their networks and engage with humility. True understanding of social issues cannot be done without community experience and partnership. Creating technology far from the community it purports to benefit rarely works. Instead, we must partner with communities to develop solutions that are responsive and designed to scale in the real world.

Funders play a critical role in shifting the focus from novel solutions to actual impact. Much of the innovation funding ecosystem currently focuses on building new things instead of investing in long-term capacity building and problem-solving. As solution builders, it can be easy to lose focus on the impact you seek in favor of amplifying what will be most attractive to funders. Changemakers and funders bear a joint responsibility to honor the complexity and context of the problem at hand and continually seek to deliver impact, not getting distracted by a desire to over-index on what might be considered the shiny, data-driven technology of the moment. Disciplined focus on what specific problem data science is helping you understand or address at any one moment in time is essential when unlocking the power of this technology. Without a disciplined approach, the use of data science can be distracting and potentially dilute or derail your impact.

So, we must follow the problem. And one of the things we might learn as we follow it is that the problem is not solvable because of a single data science method. For people coming from data science backgrounds and engineering backgrounds, that means that you might actually have to admit that you maybe aren’t the biggest part of the solution. And that reflection, and the maturity around that reflection, is absolutely critical for figuring out what you can do, for figuring out an angle in, for figuring out an approach or an impact model that actually does speak to the real problem. You have to identify what problem it is that you are capable of solving and find true product-impact fit.

While following the problem seems intuitive, it is inherently very difficult. But it’s urgently necessary if we want to advance and truly use data to drive impact — rather than just giving rise to pilots that explore emerging technologies. As social impact officers, implementers, and funders, we must honor the complexity of the problems that we seek to solve, and be committed enough to fall in love with the actual problems themselves.

Build the muscle for iteration

Advancing our sector also means seeing and supporting projects through to the very end, to where people are applying it to their everyday lives or organizations. It is much easier to build a new product and get it to a Minimal Viable Product stage. But then, to deliver on the impact, you have to actually use the product over time. You have to build the muscle for iteration.

Embracing iteration helps to solve one key challenge social impact organizations face: a lack of clarity around the metric for which they are optimizing. In profit-driven business, it’s much more straightforward: Does a new recommender algorithm, for example, increase engagement, conversions, and then revenue?

But for social impact organizations, measurement and agreement on what the key metrics actually are can make this messier. Building a muscle for iteration means you commit to actually looking at the outcomes of deploying a new method, and that you’re able to regularly and reliably measure those outcomes at a reasonable cost. And like building muscle in the gym, this process requires trial and error — and an ongoing commitment.

Funders have traditionally taken a very linear, more short-term approach to supporting solutions — providing resources to get to the end of an initial pilot, for instance — but the messy nature of achieving impact goals demands that we should be embracing a more iterative mindset and approach. Common case studies for success — like BlueConduit’s data driven approach to helping Flint with its water crisis or GiveDirectly’s efforts to use data science to target cash transfers for COVID-19 relief — all reflect an iterative narrative, reinforcing the ideal process of idea, implementation, and success, with funding and governmental support at every step of the journey. However, those seamless journeys are the exception, not the rule.

The reality of driving impact outcomes is more like life: unpredictable and requiring constant course correction. Imagine an exciting new algorithm that promises to solve hunger in a community. We might expect there to be funding to build the algorithm, have the paper written about it, get press published; but, when it comes to working through the application of it with 20 nonprofits with different use cases, we may realize that the algorithm will need continuous refining and that the exercise of testing and refining will take us in new and unexpected directions around how to effectively serve diverse neighborhoods — or, at worst, that no one needs the technology in its initial form, and we’ll have to go back to the drawing board and build something fundamentally different from the initial solution.

That’s where our current systems for funding and support can fall apart. So, we need solution builders and funders to anticipate and embrace the 2.0’s of the project, the 3.0’s, and beyond. Only through the creation of Minimum Viable Products and its testing phase can we understand that component of the problem statement that we can effectively influence, improve, predict, or make more efficient.

Build capacity and human systems — not just new tech

Sustaining and scaling data science for impact requires a deep commitment to capacity building and technical education. This capacity building must happen across the ecosystem, from implementing organizations, through to funders.

At this stage investing in the capacity of humans is probably the most powerful thing that we can do to move along the transformation curve. Because humans and systems are what actually move the needle on solving problems, investments in human systems ensure that innovation happens at scale, rather than just one thing at a time.

Katharine Lucey, who leads Solar Sister, is a perfect example of what you unlock when you invest in the humans and internal capacity behind a solution. With data.org’s support through the Inclusive Growth and Recovery Challenge, she invested in making sure she had data experts on her team and the budget to support them in the long term. As a result, her work in supporting local women entrepreneurs in Africa who work with clean energy has become a model for how data science can help steer social impact. That evolution is the direct result of investments in capacity.

As another example of building the capacity of partners: The Center for Global Action devises a system for locating and measuring poverty. But the step that actually helped people in poverty was getting money to them, and having policymakers who understood this system and could adapt it and move it through. So the CEGA system of data measurements for poverty was important, but only in as much as it enabled a sophisticated, human-driven administrative process that was actually distributing money.

At the end of the day, it will be our subject matter experts who understand the complexity and the context of the challenges faced by the communities seeking to solve problems in their neighborhoods. We have a responsibility to make sure that this type of thinking, learning, and tooling is available.

How do we train more? How do we implement more for more people?

As problem solvers, and funders of problem solvers, there needs to be more consideration of the patience of capital — especially when we’re talking about product-impact fit — and learning around how to fund product roadmaps. We need to be asking not just, “What can the technology do?” but, “How do we train more people? How long can they sustain this work? What else do the people doing this work need? How do we build interdisciplinary teams that have the data skills, technical skills, community insight and subject matter expertise of the problem?”

Funders or impact partners shouldn’t be afraid if any of this sounds overly ambitious or daunting: it’s just a different mindset, and different set of knowledge to acquire. We can all do this together — but to do it, we must change how we build, fund, train, support, and lead the sector moving forward. We must move from being solutions-focused to being problem-focused, from launch-focused to iteration-focused, and from tech-focused to capacity-focused.

These challenges require all of us —innovator, funder, and implementer alike — to contribute. They’re complex challenges, but it’s exactly what data.org was set up to do. For practical information and inspirational ideas to help social impact organizations use data science to solve the world’s biggest problems, check out data.org’s public resource library.

About the Authors

Evan Tachovsky is the former lead data scientist and a director at The Rockefeller Foundation where he builds, and funds data and technology initiatives. He is currently focused on using data to help stop the spread of COVID-19 and building analytical tools to improve the efficacy of international development.

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Ginger Zielinskie is the Senior Advisor at data.org, where she works to bring the power of data science to the world’s most challenging social problems. With over twenty years of experience, Ginger serves as an action-oriented executive leader focused on building strong partnerships to achieve systematic and meaningful change.

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