Long before digital technologies appeared on the horizon, there was a very simple defining value of most impact initiatives. Any nonprofit trying to empower a population that had been devastated by some kind of calamity—war, disease, poverty—would immediately be asked by a funder, “How many feet can you have on the ground?”
“Feet on the ground” meant, of course, having a local presence. But impacting the community involved didn’t just mean a better ability to provide services. It also meant having far better information about the problem at hand.
Case in point: Back in the late 2000s, the organization I worked for was helping to plan a way to provide anti-malarial bed nets to Swaziland, distributed through the Anglican Church. We had a local sponsor to help us with introductions, and funding from an American foundation. But when our project manager reached Swaziland, he was told that the infection rates had dropped precipitously. The country had been spraying DDT for the past two years—and our data was over two years old.
Today, we can use data to drive better decisionmaking. But how can we drive exponentially better decisions?
An exponential process increases at a logarithmic rate, in an accelerating curve, rather than a simple, linear progression. To give an example, back in the early 2000s, many of us focused on the intersection of connectivity and development were trying to figure out how internet access could be accelerated in Africa. At that time about 4.5 million were connected. Along came cell phones, the iPhone and its brethren, and suddenly internet penetration accelerated at a blinding rate. Access on the continent grew 100-fold by the end of 2017, to nearly half a billion users.
But to be exponential in our impact—to truly co-create breakthrough solutions to deep challenges—there are several important ways to think about the use of data for impact.
First, initiative leaders must have a data mindset from the very beginning. If an initiative doesn’t have the tracking, gathering, dissemination, and use of data infused into the DNA of the project, it can be nearly impossible to gather good data after the fact. Ask the question: If we’re successful, how would we know?
Second, recognize that data is always imperfect. We need to think in terms of “optimization”—using the best data we have under the circumstances. Where may the data be flawed, and how can we continually work to improve data quality? Think of data as a fabric, with flaws that need to be continually filled in and modified.
Third, remember that, when it comes to impact, causation and correlation will remain fuzzy for a long time. Just because left-handed people eat more carrots (or maybe they don’t), that doesn’t mean that eating carrots makes you left-handed. In the same way, the actual impact of health and market interventions doesn’t always show up clearly in the data, and the work of any single initiative may not clearly prove its impact.
Fourth, it takes a village. Few organizations have the scale to own all of the data necessary to understand the impact of even the smallest and most local initiatives. It is critical to work with other data stakeholders to continually improve the quantity and quality of data, and to share that data as openly as possible with other stakeholders. Who are the most important? The people you’re helping to empower, of course. If they aren’t part of the process from the beginning, why are you even doing this initiative?
None of this removes the need for on-the-ground presence and collaborative processes for solving key social and economic challenges. But a deep commitment to the wise use of data can help to put a lot more “virtual feet” on the ground.