Across the world, local officials and NGO workers are heroically responding to overlapping, complex crises – whether they’re battling new waves of the COVID-19 pandemic or delivering aid after natural disasters. The energy and determination of these frontline organizations is unmatched, but their technical capacity can fall short in crucial ways.
In particular, many lack the tools, teams and resources to effectively harness the power of data science. For cutting-edge companies that incorporate data science into all levels of their operations, this is an opportunity to help. For example, Eastern European governments have worked rapidly to welcome a historic exodus of Ukrainians fleeing the Russian invasion. One Polish city turned to Mastercard for help in planning for the influx of the new arrivals. Within a matter of days, our team analyzed regional spending patterns to provide near real-time insights that helped city officials better prepare to meet the needs of exhausted, traumatized families.
But one-off partnerships shouldn’t be our goal. If the private sector and other funders, including foundations and development organizations, work together, we can help NGOs and under-resourced governments build more sophisticated data science infrastructure of their own – strengthening their ability to battle crises and bolstering global resiliency.
That’s why the Mastercard Center for Inclusive Growth, which I run, makes impact data science a top priority. Two years ago at Davos, alongside The Rockefeller Foundation, Mastercard launched data.org, a growing platform that works with organizations across the world to infuse data science into social sector decision making.
Impact data science can enhance frontline crisis response
It is not easy work to build technical capacity at small NGOs or in far-flung government offices. But three recent examples underscore why it matters.
First, look at Community Solutions, an organization combating homelessness in the United States. Even before the pandemic, homelessness was on the rise across the country and resources were stretched. To improve efficiency, Community Solutions worked with experts to enhance its data analytics capabilities. That work paid off when the pandemic hit and it was able to identify individuals in shelters at high risk for COVID-19 and help move them into safer settings.
As the pandemic spread on the other side of the world, the Togolese government also turned to data science. Togo’s Ministry of Digital Economy and GiveDirectly, a nonprofit that sends cash to people experiencing poverty, launched a pilot programme to quickly distribute cash assistance to the country’s lowest-income residents. They worked with the Center for Effective Global Action to use machine learning and survey data to identify residents and deliver aid. This method reached more of the country’s neediest people, and so far the programme has given nearly $10 million to about 137,000 people.
The third example typifies how front-end investment in impact data science can pay dividends when crises strike. In India, about a third of the food farmers’ produce goes to waste, in part because it’s difficult to determine how long to store particular products and how to extend shelf life. Experts from Switzerland developed an app to help solve both problems, equipping farmers with modeling to help them keep more food fresh for longer. Six months ago, helping rural Indian farmers access data science tools was hardly a global priority. But now, as Russia’s invasion of Ukraine causes a grain shortage that risks sparking a severe food crisis, we can clearly see how investments like this can make a big difference.
Governments and NGOs struggle …….