- Global disruptions are becoming increasingly complex, testing the capacities of social impact organizations and governments on the front lines.
- Innovative companies are using data science to improve every aspect of their operations, but these sophisticated tools are unavailable to many organizations and governments.
- Companies, funders and global institutions can help organizations and governments build a robust data science infrastructure, to deal with future crises more equitably.
Around the world, local leaders and NGO workers are responding heroically to complex and overlapping crises, whether fighting new waves of the COVID-19 pandemic or delivering aid after natural disasters. The energy and determination of these frontline organizations are unmatched, but their technical capacity may be critically insufficient.
In particular, many lack the tools, teams, and resources to effectively harness the power of data science. For cutting-edge companies integrating data science into all levels of their operations, this is an opportunity to help. For example, governments in Eastern Europe worked quickly to accommodate a historic exodus of Ukrainians fleeing the Russian invasion. A Polish city turned to Mastercard to help plan for the influx of newcomers. Within 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 and traumatized families.
But one-off partnerships should not be our goal. If the private sector and other funders, including foundations and development organizations, work together, we can help underfunded NGOs and governments build their own more sophisticated data science infrastructure, thereby strengthening their ability to combat crises and build global resilience.
That’s why the Mastercard Center for Inclusive Growth, which I lead, makes impact data science a top priority. Two years ago in Davos, alongside the Rockefeller Foundation, Mastercard launched data.org, a growing platform that works with organizations around the world to infuse data science into social sector decision-making.
Impact data science can improve frontline crisis response
Building the technical capacity of small NGOs or remote government offices is not easy. But three recent examples show why it matters.
First, look at Community Solutions, an organization that fights homelessness in the United States. Even before the pandemic, homelessness was on the rise across the country and resources were stretched thin. To improve its efficiency, Community Solutions worked with experts to improve its data analysis capabilities. This work paid off when the pandemic hit and he was able to identify people in shelters at high risk of COVID-19 and help move them to safer environments.
As the pandemic spread halfway around the world, the Togolese government also turned to data science. Togo’s Ministry of Digital Economy and GiveDirectly, a non-profit organization that sends money to people experiencing poverty, have launched a pilot program to quickly distribute cash assistance to the country’s poorest residents. They worked with the Center for Effective Global Action to use machine learning and survey data to identify residents and provide assistance. This method has reached more of the country’s most needy people, and so far the program has given nearly $10 million to around 137,000 people.
The third example illustrates how the initial investment in impact data science can pay off in a crisis. In India, around a third of farmers’ produce is wasted, in part because it is difficult to determine how long to store particular products and how to extend their shelf life. Swiss experts have developed an app to help solve both of these 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, with Russia’s invasion of Ukraine causing a grain shortage that threatens to trigger a serious food crisis, we can clearly see how investments like this can make a big difference.
Governments and NGOs struggle to integrate data science
Unfortunately, these examples are exceptions. Far too often, organizations and aid workers on the ground are crippled by a lack of data, tools and staff to analyze and deploy it.
Too many governments and NGOs have been unable to recruit enough data scientists. A forthcoming report by the McGovern Foundation and data.org estimates that the social sector alone needs 3.5 million more data scientists over the next 10 years. Mastercard just announced a $4.6 million grant to help train 1 million data scientists, with a focus on diversifying the field, but we need to do more.
The World Economic Forum Center’s Healthcare Data Project for Japan’s Fourth Industrial Revolution questions how societies should balance the interests of citizens, businesses and the general public when it comes to sensitive health issues. An improved approach to governance during a number of health crises, including pandemics, can help build trust and perhaps even save lives.
The Center for the Fourth Industrial Revolution has developed an approach to data governance – Authorized Public Purpose Access (APPA) – which seeks to balance human rights such as privacy with the interests of data collection organizations and the public interest – that is, the needs of entire societies.
Furthermore, a recent white paper examining existing data governance models found that most are biased towards the interests of one of three main stakeholder groups. The white paper revealed the need for a balanced governance model designed to maximize the socially beneficial potential of data while protecting individual rights such as privacy and the legitimate interests of data holders.
The lack of analytical tools and internal structure is also a problem, even for organizations that have a lot of data. In contrast, many companies have spent years developing data and analytics strategies, investing in interconnected and user-friendly platforms, building teams with deep expertise, and cultivating a culture of culture. Datas.
Companies and funders can help build capacity to roll out impact data science
Companies and funders can help bridge this “data science gap” faced by governments and NGOs by dedicating attention, technical skills and funding to it. We know this because organizations like DataKind successfully connect volunteer data scientists with social organizations, helping them unlock the power of data in an ethical and responsible way.
The challenge is to move from individual partnerships and volunteer efforts to building impact data science as a field – at the scale needed. Last fall, at the request of G7 governments, data.org began bringing together the private, public and not-for-profit sectors to build the Epiverse – an open digital infrastructure that will analyze data streams from around the world to help prevent the next pandemic.
The broader goal is to strengthen collaboration across sectors so we can use data science to address other social challenges in a systematic way, while prioritizing privacy and security concerns.
No initiative will help frontline responders build the teams and tools to use data science as easily and effectively as the best-funded private sector companies. But we can and must increase our investments. Businesses can start by embracing data responsibility principles that include innovation and social impact as well as security and privacy, then take steps like joining Mastercard to make data.org an even stronger platform for businesses. data science partnerships.
This work may not make headlines, but it will help the world weather tomorrow’s crises fairer and more effectively.