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DataKind’s Approach, Weaving Together Disparate Worlds, More Needed Than Ever

Passion is laced through his words when Jake Porway talks about data, demonstrating how far he’s traveled from the years when he nearly flunked out of college statistics and considered backpacking through Europe in search of a revolution. “Data is the fuel that can drive some of our greatest dreams,” says the founder of the award-winning DataKind, a global nonprofit and Rockefeller Foundation partner that links pro bono data scientists with visionary changemakers to design innovative solutions to tough social challenges.

The challenges that are keeping him awake at night now are how data scientists can help the world deal with the impact of the coronavirus pandemic—specifically, how to help community health workers do their jobs in a time of social distancing, and how to bolster social support systems trying to gear up for a demand overload.


[1] Photo Credit: DataKind
DataKind, founded in 2012, views itself as a weaver of worlds, bringing together social change organizations tackling big social problems and data scientists who are generally paid to help companies boost profits.

The global non-profit began by doing one-off projects—some 300 of them accessing an army of more than 20,000 volunteers—and launching five global chapters. But a year ago, with help from the Foundation and its partner Mastercard Center for Inclusive Growth, DataKind shifted its strategy. The funding gave DataKind the freedom to focus on larger systematic problems and work with data scientists to implement what Porway calls “impact practices.”

“Doing many small projects divvies up something very valuable—these data scientists’ time,” says Evan Tachovsky, the Foundation’s lead Data Scientist and Director of Data & Technology. “If problems are considered more broadly, then the results can be scaled up more easily and effectively.”


[2] Photo Credit: DataKind
The support to DataKind is $20 million over five years, and part of, the collaborative effort between the Foundation and Mastercard. It was announced last year and includes an initial commitment of $50 million over five years and an invitation to other companies and philanthropies to join.

Increasing Healthcare Quality and Efficiency in Vulnerable Communities

Riders for Health worker treating a small child.

As its first effort under the new strategy, DataKind began last year focused on community health care in line with the collaborative’s goal of capacity building within critical existing systems.

Frontline health workers play a critical role in administering vaccinations, delivering healthy babies and tamping down pandemics. Data science and machine learning can help them achieve their missions, but it can be difficult to know where to start.

“Most organizations can’t articulate their AI needs,” Porway says. “So we talk to folks, hear their challenges.” DataKind spoke to some 50 organizations and selected about 20 where data science, AI and machine learning could help not only the organizations but also deliver insights to drive sector-wide improvements, potentially impacting health outcomes for millions of people.

Connecting Data Scientists with Digital Health Tools

Laboratory Staff and couriers for Riders for Health smiling.

“We’ve teamed up with amazing partners,” Porway says. Initial work began last year with three organizations in Africa, Jacaranda Health, Medic Mobile and Riders for Health, all delivering life-saving primary healthcare to the hardest-to-reach communities and most vulnerable populations.

For example, Riders for Health is an international nonprofit that serves rural villagers in five African countries by using motorcycles to deliver critical care. One of their key operations is transporting medical samples of infectious diseases from rural health care centers to labs. To maintain quality and integrity of samples from the point of collection through transportation, storage and analysis, Riders for Health couriers capture details by hand in a logbook.


[5] Photo Credit: Riders for Health, photography by Tom Oldham. Outreach Health Worker in Bong County, Liberia, carrying out public health assessments in the community.
[6] Photo Credit: Riders for Health, photography by Tom Oldham. Sample transporters.

Some couriers transport over a thousand samples a day. The laborious and time-consuming process of log entry deeply impacts their ability to reach more health facilities more frequently, causing degradation of samples and delays in diagnosis and treatment. Currently, the average time to digitize logbook entries and sample details is 30 to 60 days.

DataKind’s data scientists teamed up with Riders for Health to develop a prototype tool that will automate key handwritten entries of sample collection forms so that downstream workflows are dramatically sped up and lives are saved. In addition to building these character recognition systems to quickly digitize written records, volunteer data scientists are also improving predictive models for better maternal and child health outcomes, assessing frontline health worker training, and developing data quality and assurance algorithms for better patient tracking and care.


[7] Photo Credit: Riders for Health, photography by Tom Oldham. Sample transport courier Thamae using mobile data collection at the National Reference Laboratory in Maseru, Lesotho.
“Informal health networks are going to be critical for preventing and treating pandemics,” Porway says. “But community health workers going door-to-door—that may not be the same model you want to use during a contagious disease outbreak like COVID-19.”

“So, knowing that isolation is probably coming to African countries within the next month, some groups are thinking about tech solutions to help provide remote care. Could community health workers deliver medically needed supplies? How do we get good health information into rural communities? In other words, how do we repurpose these existing networks for the new COVID-19 challenges?”

Social Services Planning

For the nearly 40 million Americans struggling with food insecurity, disruptions in the food system could have catastrophic consequences. Supporting our emergency food supply channels has become critical. Brooklyn-based DataKind is committed to solving global challenges, but is equally as committed to addressing needs right here in their home community.

So Porway has been thinking about how DataKind might expand a recently completed project to help optimize an app called Plentiful that was launched in 2016 as an initiative of the NYC Food Assistance Collaborative to help New York City food pantries manage their resources and communicate with clients.

The app allows users to locate a food bank and make an appointment to pick up the donations instead of waiting in a line for an unpredictable amount of time—even more important during the coronavirus pandemic. DataKind scientists partnered with Plentiful to create an interactive dashboard that tracks and monitors trends, allowing the organizations to respond to issues faster and connect more people to food.

“Now that the food banks have this data,” Porway says, “we can probably expand it to tell them in advance when they are going to start to run low on a particular item or when there is likely to be an influx of people. Plentiful has these alerts that pop up on your phone, so one could say, for instance, ‘hey, brown rice is about to be in short supply.’

He’s also in early discussions about programs to support workers in house foreclosures and unemployment claims. “Like everyone else, we’re working to align our work with people’s needs as a result of COVID-19,” he says.


Strengthening The Social Safety Net with Data

Woman checking something on an iPhone.

The Foundation/Mastercard collaborative is founded on the belief that improved and targeted data science capabilities of non-profit, civic and government organizations can help local leaders uncover new insights and trends from their data and build more impactful programs for the communities they serve.

Porway agrees. “There is an urgency now to strengthen the social safety net. There are lots of things that are huge systemic problems, not data machine learning problems, so we need to be humble about where these data-driven tools are useful. We have to find the right problem, given the new reality. But decision makers need data to make decisions in game-time situations, and data can also make folks more efficient, and make resources go further.”

Porway may be at home like many of us these days, but he’s definitely working, trying to figure out what’s next for DataKind. He once thought he had to backpack through Europe in search of a revolution, but instead he found his revolution here among APIs and a like-minded community of data scientists.

  • The whole reason DataKind exists, is because we do believe data science and AI have these great opportunities to contribute to human well-being.
    Jake Porway
    Founder and Executive Director, DataKind



[1] Photo Credit: DataKind
[2] Photo Credit: DataKind
[3] Photo Credit: Riders for Health, photography by Tom Oldham. Midwife mobilized by Riders for Health, Letlatsa Mokokoane, vaccinates 2-day old baby in Mafeteng district, Lesotho.
[4] Photo Credit: Riders for Health, photography by Tom Oldham. Ministry of health staff at the Mapoteng Laboratory, Berea District, Lesotho, with Riders for Health sample transport couriers.
[5] Photo Credit: Riders for Health, photography by Tom Oldham. Outreach Health Worker in Bong County, Liberia, carrying out public health assessments in the community.
[6] Photo Credit: Riders for Health, photography by Tom Oldham. Sample transporters.
[7] Photo Credit: Riders for Health, photography by Tom Oldham. Sample transport courier Thamae using mobile data collection at the National Reference Laboratory in Maseru, Lesotho.
[8] Photo Credit: Medic Mobile photo archives