Our opportunity seemed obvious: demonstrate what’s possible, create space to bring organizations together and make the case for greater investment in data for good.

We were a new organization testing a different approach. Would anyone notice?

In May 2020, as one of our first major initiatives and in response to the incredible challenges of a global pandemic, we launched the $10 million Inclusive Growth and Recovery Challenge. The goal? Help individuals and communities rebound from the economic uncertainty created by COVID-19. We wanted to support breakthrough innovations that could scale and replicate.

From Idea to Innovation: Awardees Illustrate the Power of Data for Social Impact

More than 1,260 applications later, we were wading through a pile of ideas for how to apply data science to problems that were only growing more urgent. From tiny social enterprises to massive international NGOs, we read about how people would use data science for the first time or expand existing efforts if given the proper resources.

At the same time, we had an outpouring of support from across sectors to aid our effort — from expert judges that helped evaluate proposals to organizations that offered technical assistance for awardees.

Our ambitions for data.org were starting to materialize — we had strong ideas to choose from and an emerging community taking shape around us.

It wasn’t easy, but we identified nine diverse organizations with some of the most promising ideas we’d come across. Over the past two years, it’s been thrilling to watch and support these awardees as they brought their projects to life.

Scaling Impact: Collaboration and Partnerships Provide Capacity to Accelerate Solutions

The achievements of our Challenge awardees remind me of the enormous potential of data to solve intractable problems. And what’s more — they remind me that no one can tackle these challenges alone.

I truly was amazed at how the Challenge illustrated just how powerful partnerships can be and how we must do a better job prioritizing cross-sector collaboration.

Take the University of Chicago for example. Local partnerships have been essential for gaining insights about individual households in order to achieve equitable internet delivery, while ongoing engagement with state broadband offices and federal agencies is ensuring that this work informs the national conversation around public investment in broadband infrastructure and digital equity in the United States.

Or GiveDirectly, which has revolutionized how aid is delivered. This would not be possible without navigating a complex web of partnerships with governments and private sector entities and establishing trust among end beneficiaries in order to reach those that need aid the most, faster and more effectively.

Further, several organizations used the opportunity of their Challenge partnerships to train people throughout the organization — not just those in data roles — strengthening their overall organizational data culture.

For example, the Solar Sister leadership team was trained to use the project’s data dashboard, which will be critical as they make data-driven decisions about where to expand operations.

As part of their technical support for D4WN, Data Elevates trained Fundación Capital and UX to independently manage the platform at the close of the project. Beyond hard skills, teams were trained in data decision-making, data ethics, and principles of data visualization.

Beyond strengthening existing capacity, many awardees grew their data maturity so significantly during the Challenge — thanks in large part to their partnerships with technical experts — that they recognized the need to add permanent in-house data roles to support the sustainability of their data strategies and increase the potential for future innovation. 

Watching these projects unfold over the past three years furthered our understanding of how essential partnerships are to implementing proposed solutions. And we continue to witness the lasting impact of those partnerships for building capacity across different skill sets.

Beyond the Challenge: Lessons Learned Can Be Applied to the Emerging DSI Sector

The competition for data talent is fierce and the supply does not match the demand. Many organizations experienced this firsthand as they tried to hire new roles, often getting creative to keep projects moving in the absence of in-house talent.

data.org is vocal about the opportunity — and urgency — to build the purpose-driven data practitioner workforce. Building this talent pool will take time, so we must stay creative about what we can do now while working towards a more robust sector.

We also observed that organizations are investing in responsible data practices as they balance how to do the most good while reducing potential harms — especially those working in areas where lives and livelihoods are on the line. Some have even established ethics committees to formalize decision-making around gray areas. Still, there are opportunities to learn how to do better together as a sector.

data.org is committed to promoting open source through licenses that make tools and source code freely available when possible and the publication of project outputs like reports and toolkits. Many awardees agreed with this in principle, but a learning curve remains, especially when it comes to navigating nuances of software licenses, platforms to use, and the communication package that will aid the wider community in finding and using project outputs.

Spending time with each of these organizations individually and as a cohort over the past two years, I was struck by these common pain points — points that illustrate that we are all still learning together as we advance our work in data for social impact. Our recently released Accelerate report outlines the work we still need to tackle to achieve systems change.

Solar Sister Entrepreneur Taibat Zakariyah, Ibadan, Nigeria

A family in Houston, Texas, United States

The Biscate team in Maputo, Mozambique

Female manufacturing worker in Australia

Young woman accessing her financial institution through her mobile phone.

Womens’ Savings Group, Malawi

A BASE cold room and pilot site, set up by Koel Fresh in Keonjhar, Odisha, India.

The University of Chicago Data Science students. Photo by Colin Lyons.

Anna and Widzy at Lilongwe District, Malawi getting first $50 monthly UBI payment from Project Canva

Building a Field: It’s Time to Harness Collaboration, Innovative Thinking, and a Willingness to Share

When we launched data.org and the Inclusive Growth and Recovery Challenge, we were hopeful but unsure of what we would find. Nearly three years later, we continue to be amazed by people and organizations we have met. They are driving impact and demonstrating what’s possible within this growing field.

We are especially proud of the work of these nine awardees, who collectively have launched over 20 new products, raised $30.8M in direct follow-on funding, and expanded into several new geographies within just two years. I am grateful for the openness shown by the awardees in letting us peek under the hood to better understand the challenges they faced as well as the secrets to their success.

As a team, we will continue to highlight organizations and projects that show what’s possible and we will deliver on efforts to address persistent challenges, like building a workforce and digital public goods that provide critical infrastructure for this emerging sector.

But we can’t do it alone.

If this Challenge highlights anything, it’s that working together—harnessing collaboration, innovative thinking, and willingness to share—is truly the only way to accelerate our aspirations to build a thriving field of data for good.

The post We’re Proving There’s Power in Partnerships appeared first on data.org.

To deliver on these ambitious but critical targets, countries met to discuss the phase-out of coal, expansion of renewables, ecosystem restoration, and smarter investments, among other priorities. Also essential, conference leaders say, is the need for global cooperation and collaboration. 

That, and better data. 

At the SDG7 Pavilion at COP26, data.org, a platform for partnerships to build data science for social impact, convened a panel of experts for an engaging conversation on how data-driven approaches can get us closer to achieving Sustainable Development Goal 7, increasing global access to clean, affordable energy. 

This compelling session, held in the Pavilion supported by The Rockefeller Foundation and IKEA Foundation, puts data at the center of sustainable climate solutions.

SDG7 Data Science Success in Action

Approximately 840 million people still live without access to electricity. And according to The Rockefeller Foundation’s analysis, at the current rate of growth and power distribution, only 650 million people will be on the grid by 2030.

But increasingly, investments on the ground – in both tools and capacity building – are charting a path forward, including through data.org’s network of Inclusive Growth and Recovery Challenge awardees, like Solar Sister and the Basel Agency for Sustainable Energy (BASE).  

In Sub-Saharan Africa, Solar Sister has empowered more than 6,000 women entrepreneurs to build clean energy businesses, generating family-sustaining income while expanding the use of solar-powered products like lighting and clean cookstoves. Over 2 million people in Nigeria and Tanzania have access to clean energy thanks to this network.

In India, where lack of refrigeration leads to 40 million tons of food wasted annually, worth $13 billion per year in that country alone, BASE has created a mobile application that makes it easier for smallholder farmers to access sustainable cooling facilities. Their data-informed tools, like a virtual coaching assistant, help level the playing field for these small businesses, providing critical insights into how to store and when to sell foods.

And around the world, the Global Partnership for Sustainable Development Data continues to grow its network of partners. With more than 300 members and counting – including data.org – this community shares a commitment to leveraging data and data technology in pursuit of fulfilling the SDGs, the world’s shared plan to end extreme poverty, reduce inequality, and protect the planet by 2030.

Three organizations, three stories that showcase how grassroots, data-driven approaches can make a difference across contexts.  

“At data.org, we are committed to helping social impact organizations in the energy sector to build a sustainable future that grants affordable and reliable energy access to people around the world,” said panel host Danil Mikhailov, the Executive Director of data.org. “You can’t just kind of translate the same solution and plant it in different countries without understanding how it might change to serve the needs of the communities.”

Why is Data Important for Climate Justice?

Dr. Claire Melamed, CEO of Global Partnership for Sustainable Development Data, says that data can be a game-changer when it comes to scaling up solutions, particularly when reaching rural and remote communities.

Take satellite data, for example. This information can quickly illustrate where opportunities exist to maximize the efficacy of solar panels. Instead of manual surveying, developers or government planners save time and money and improve the accuracy of information by integrating data that already exists.

“We’re often used to data giving us bad news, telling us all the things that are going wrong. But data can also sometimes give us good news stories. And those are absolutely critical in making the changes that we all need to see,” Melamed said. “We see data as being critical at every point in the decision-making chain.”

Utilizing high-quality data also establishes trust in the process and can build greater consensus around decision-making, says Thomas Motmans, the Senior Sustainable Energy Finance Specialist at BASE. Data can serve as the foundation that keeps policymakers anchored in the real needs of a community, and can likewise empower the community to lobby more effectively for responsive policies and programs. 

Quality is key, though, and the panelists recognized that challenges remain in building better data systems. 

How Can We Accelerate the Use of Data? 

There is a growing awareness of how data science can be leveraged in the fight against climate change. But problems persist with data collection and analysis and must be overcome to accelerate progress. 

First, Melamed points out that more collaboration is needed. Partners need to share data and make information accessible and transparent whenever they can, so datasets can be combined to tell a more holistic story. Motmans agrees, noting that data silos continue to be a challenge. Even well-intentioned data providers, like governments or civil service organizations, sometimes collect information without a vision for how it will be used, and then stop short of making the information public in a usable format. 

“In the world of data, we tend to see that once we have the data, then the problem is solved,” Melamed added. 

Second, all data should not be treated equally.

“I’m passionate about this,” said Katherine Lucey, Founder and Chief Executive Officer of Solar Sister. “Whenever we are working with data and trying to understand it and learn from it, we need to apply a gender lens. We need to make sure that we’re disaggregating data, that we’re understanding our populations are not monolithic and that averages don’t really tell us much.”

And perhaps one of the most significant and consistent barriers that the panelists see is lack of capacity. For data to be truly inclusive, accessible, and actionable, people at every level of decision-making need the skills to understand it, from the community level up through heads of government. Those skills must also be distributed across diverse groups to ensure that the voices and perspectives of communities are represented at the table. 

“People can only do the things that they know how to do. Skills are absolutely important, not just to help us get better decisions, which is critical, but also I think to democratize the world of data,” Melamed said. “We need to democratize this world and to have more people who feel confident understanding data.”

Building capacity and closing the skills gap takes investment, and investment grows from demand. The panelists pledged to do their part to increase capacity, demand, and investment for data for good, and through convenings focused on actionable outcomes, a growing network of partnerships, and support for force-multiplying efforts like the Inclusive Growth and Recovery Challenge, so will data.org.

Learn about data.org’s work to democratize data for good, read more stories of impact, and sign up for the newsletter for the latest news and upcoming events.

The post Data for (Climate) Good at COP26 appeared first on data.org.

In advance of COP26, the next UN Climate Change Conference scheduled for the end of October in Glasgow, data.org, a platform for partnerships to build data science for social impact, hosted a panel of experts to outline climate concerns on the ground and model programs to help address them. 

Welcoming panelists and a global online audience to the September 29, 2021 session called “Building Environmental Resiliency: The Road to COP26 and Beyond,” Perry Hewitt, chief marketing officer of data.org, outlined the unfolding crisis vividly, saying, “Today, babies will grow up to experience twice as many droughts and wildfires and three times more river floods and crop failures than someone who’s 60 years old.” 

Moderator Bruno Sánchez-Andrade Nuño, head of AI for Earth at Microsoft, introduced the panelists, all representing awardees of data.org’s Inclusive Growth and Recovery Challenge. Setting the table for discussion, he said, “Data could be our extremely powerful ally, not only to see what’s happening [on climate change], but also to help us decide what we could do.”  

An opening audience poll asked which COP26 goals aligned most closely with the viewers’ own work. Thirty-eight percent checked “Adapt to protect communities and natural habits” or “Work together to deliver” environmental solutions. Another question asked viewers how they were using or thinking of using data against climate change. Seventy-seven percent said for “research and development,” and 46 percent cited “policy and advocacy.” 

From those findings, Nuño asked the panelists what roadblocks they face in reaching their goals and how data science has helped them get there.  

Danielle Getsinger, chief executive officer of Community Lattice, which helps local groups to manage environmental risk in restoring contaminated brownfield sites, said her overarching concern is “We haven’t prioritized climate change. We [as a society] have the answers. We just haven’t … incentivized doing this work.” 

“The systems that we have in place, especially the incentive programs and the government programs that distill a lot of what we do are not answering the real-world, real-time conditions and concerns of communities. And that is especially true of overburdened and underserved communities.” She said Community Lattice is using data analysis to ensure improved community access.  

Olasimbo Sojinrin, Nigeria country director of Solar Sister, which is building a system of women entrepreneurs to distribute clean-energy products in underserved African communities, said, “For us, the major challenge is how we can combat energy poverty. This creates a multitude of negative consequences on the health, education, and incomes of the communities that we work in.”  

Thomas Motmans, sustainable-energy finance specialist for the Basel Agency for Sustainable Energy (BASE), which develops business models to promote investment in energy and climate solutions, said one major obstacle concerns resistance from banks and businesses wary of investing in green projects, since they offer novel approaches not readily defined by their risks and returns. To counter that perception, “Data science can play a big role because all of these or most of these [issues] are related to trust. So data science can really help to bring trust and confidence.” 

Echoing how data can move the needle on key issues, Getsinger cited Community Lattice’s Inclusive Growth and Recovery Challenge project that organizes disparate environmental public records, which can vary by state into a usable, searchable platform, allowing for easier and accurate analysis.  

Sojinrin said Solar Sister is “finding through data how to scale and help entrepreneurs maximize potential,” including by identifying future markets, mapping expansion opportunities, and cutting costs.  

BASE is using data, Motmans said, to reduce farmers’ post-harvest crop losses, in part by perfecting cooling systems that reduce spoilage. “Data science allows service providers to shift from being reactive to being proactive and actually offer optimal services.” 

While advocating the democratization of data for communities, Getsinger noted how residents of a Houston neighborhood complained about what they saw as a surge in cancer cases there, and a follow-up study confirmed there was a cancer cluster.  

In that spirit, Sojinrin said, “We need to be driving policy change from the ground up.”  

“There needs to be a commitment to a conversation between those who generate the actual data and the people on the ground who are also the change-makers,” Motmans added. “So there should be a match, and this content should be made available to these change-makers in local communities because they’re the ones who can actually do something with that data. Data for the sake of data won’t really have much actual impact.” 

Nuño also asked the panel about the obstacles they encounter working with data. “The challenge is not knowing what you don’t know,” Sojinrin said, adding that training and buy-in can help change that. “People have been doing something one way forever” and need to learn that data isn’t some sort of mystical art.  

“Data is not an identity,” Nuño agreed. “It’s a method. And just like you don’t need to be a mechanic to use a car, you don’t need to be a data scientist to use data” effectively. “We need to be including everybody in that journey of data literacy.”  

“Everybody’s business models at this point should be data-driven,” Getsinger suggested, including in “our policies, our regulations — any decision-making at a community level.”  

In closing, Nuño asked the panelists to summarize their takeaways:  

• “Let’s demystify data science and make it more practical,” Sojinrin suggested.  

• “Use the power of data to make agriculture more resilient,” Motmans said.  
• “We have the solutions for climate action,” Getsinger added. “Those solutions exist. We need to find how to use data to drive the real-time, real-world needs to make those solutions a reality.”  

For these panelists, who spend their days tackling local and regional concerns, data research is helping them to craft policies, fuel growth, and gain trust. The resultant solutions, when they work, could provide useful guidelines on the national and even international level. It’s likely to take thousands of such answers, woven together, to stop climate change. 

The post Localized Data: The Pathway to Climate Change Solutions appeared first on data.org.

This service, called MobileAid, is now a reality in Togo. And it will soon be scaling internationally thanks to new funding from the data.org Inclusive Growth and Recovery Challenge supported by founding partners the Mastercard Center for Inclusive Growth and The Rockefeller Foundation.

MobileAid is just one example of data-driven global transformation among many being considered by more than 1,500 people from 52 countries at Good Tech Fest 2021. The conference attendees attended numerous workshops to engage with compelling case studies that illustrate what data and data science for social good can do.

GiveDirectly, one of the fastest growing NGOs focused on international issues, and its academic partner, the Center for Effective Global Action (CEGA) at UC Berkeley, have used data science and machine learning to identify and send cash with unprecedented speed to 95,000 people living in poverty in Togo during the COVID-19 pandemic. Governmental advisors in Togo have described the project as “foundational” in terms of setting up the country’s social protection system. It’s one of eight projects chosen to receive a combined $10 million in funding and technical assistance in the Challenge. The Challenge drew ideas and proposals from 108 countries around the world to tackle social problems with data and technology.

On the road to recovery from COVID-19, many countries lack reliable and up-to date information about economic conditions on the ground and have no way of collecting it. Traditional aid modalities relying on in-person enrollment and delivery are no longer safe or scalable, with governments and NGOs lacking personnel and relief taking weeks to arrive.

GiveDirectly and CEGA proposed addressing the challenge by developing and testing a new model for humanitarian support that enables cash transfers to be deployed effectively, accurately, and at scale to those who need them most. The project incorporates new data and computational technologies to identify people and places in economic distress and integrates data from mobile phones, satellite imagery, and traditional surveys. The plan piloted in Togo during the pandemic and aims to develop a transparent framework to scale globally.

Han Sheng Chia, Vice President, Innovation at GiveDirectly, and Emily Aiken, Data Scientist and UC Berkeley PhD candidate working at CEGA, gave a talk titled “Dial 999 to Receive Emergency Cash” during the 2021 Good Tech Fest.

Han Sheng and Emily explained in detail how they ran the project.

Direct Cash Payments

“GiveDirectly is a non-profit that focuses on just one thing, which is providing unconditional cash to families living in extreme poverty around the world,” said Han Sheng.

The organization has so far delivered direct payments to 695,000 recipients in 10 countries. GiveDirectly manages end-to-end processes for its programs, designing each program, determining its location, targeting criteria and payment amounts, and then executing on it with targeting to identify eligible recipients. GiveDirectly then enrolls and audits people for the programs, paying them and monitoring their spending, evaluating each project through surveys and controlled trials.

Han Sheng said that a common concern raised when he talks about his work, is that people might spend the money on so-called temptation goods such as alcohol or tobacco. But there are more than 165 studies that have shown the efficacy of cash transfers in helping people in poverty, including 15 randomized controlled trials of GiveDirectly’s programs. These studies have shown that cash transfers consistently lead to increases in positive outcomes such as school attendance, use of health facilities, and crop production. Cash transfers also lead to decreases in negative outcomes including child labor and intimate partner violence. And a review of 19 studies found that cash payments led to no increase in spending on temptation goods such as alcohol or tobacco.

Algorithmic Targeting of Aid

What was new about GiveDirectly’s partnership with CEGA on this project was the rapid identification of recipients using machine learning.

CEGA “thinks about how to implement these programs at a massive scale, entirely remotely,” said Emily.

Good income data doesn’t exist in many developing countries. In Togo, for example, the last census didn’t contain any information on poverty. Traditionally, poverty information could be ascertained only through a door-to-door survey, which takes time and resources. Emily and the team at CEGA and their partners have focused on two nontraditional “big data” sources to identify poverty in developing countries. The first source, satellite imagery, is used to produce hyper-granular poverty maps by identifying infrastructure and building materials.

“The intuition here is that wealthy places and poor places look different from above,” said Han Sheng. “By and large, wealthy places have more organized urban planning. Roads are made of more long-lasting material, homes may have roofs that are more durable. And these are patterns you can pick out from satellite data.”

The second source, mobile phone data, is used to identify individual-level poverty by looking at the number of international calls, data purchases, and size of contact networks, Emily said. The organizations work with cellphone companies to create a list of predicted poor cellphone subscribers. If one makes a lot of international phone calls, buys a lot of data, then the intuition is that one is classified as wealthier and not eligible for the programs.

“We start with mobile phone metadata, which basically details every transaction occurring on the cell network. We don’t get anything like the content of text messages, but who is calling who, at what time,” said Emily. “We calculate a bunch of statistics about mobile phone use, things like how many days are they using their phone out of the year, how many transactions are they making in each part of the country based on cell tower locations, how many unique contacts do they have, and so on.”

The two organizations then combine that information with survey data, matching actual data about wealth outcomes with the data from the cellphones, and using that, can predict poverty estimates for each phone user.

The team at CEGA has been researching improved targeting using nontraditional big data sources for more than a decade.

COVID-19 Relief in Togo

“[COVID-19] has led to an increase in poverty for the first time in 20 years, and an increase at tremendous scale,” said Han Sheng. “The prediction for the increase in extreme poverty in 2020 was 120 million new people in poverty. As we stared at this giant problem, we needed a new way to respond at tremendous speed and scale and to distribute cash.”

The tenets of a successful response to this unprecedented crisis include the ability to scale globally, to give help now, having a secure model that is replicable at scale, and to be able to precisely identify and prioritize those who need aid most, and because of the nature of COVID-19 and the need to avoid in-person contact, a fully remote approach.

GiveDirectly and the team at CEGA worked in partnership with the Ministry of Digital Economy and Innovation in Togo to choose the beneficiaries for cash payments. They looked at the 400 cantons, or administrative regions, in the country and used satellite imagery combined with algorithmic learning to identify the 100 most poverty-stricken cantons. They then used cellphone data to identify potential program recipients.

To reach those recipients, they used catchy radio ads combined with in-person outreach to community leaders in two enrollment phases. The gender split for recipients was 48% male and 52% female, and the groups found that the machine learning targeting approach was more accurate compared to available alternatives such as distribution by income or mere geographic targeting by canton alone.

Measurement also showed more beneficiaries are living in poverty, and that a better proportion of people living in poverty were paid versus other identification methods.

Scaling in Togo and Around the World 

“What we’re super excited about is the fact that we have tremendous support, at the policy level and at foundations with really strong partners, to transform how the sector thinks about not just social protection but about emergency response as well,” said Han Sheng.

The new approach can augment regular safety net programs. The use of machine learning and data science can increase the amount going to regular beneficiaries and identify new beneficiaries. It also frees up in-person capacity to identify more hard-to-find beneficiaries for benefits.

Increased enrollment can come from combining the approach with ecosystem partner screening, government social protection lists, and in-person office-based and home-based screening, Han Sheng said. He also pointed out that while mobile phone use in Togo exceeds 80 percent, there are other ways to reach recipients including radio ads, community leader meetings, and loud trucks in neighborhoods announcing the opportunity. Supplementing text-based service with measures like these along with voice-based support and in-person visits is the likeliest way forward based on the experience in Togo.

The approach has drawn partner support and press coverage around the world in addition to the support of the data.org Inclusive Growth and Recovery Challenge.

As a next step, GiveDirectly and CEGA will be looking to improve rates of uptake by improving the pathways to getting people money using their technology. In Togo, for example, not everybody has a phone, and keeping phones charged is difficult. Literacy can also pose a challenge, making it more difficult for people to navigate the questions to get accepted for benefits. There are also issues with consistent cell coverage. Han Shen and Emily said that while countries with more established infrastructure make it easier to swiftly scale a program like theirs, it is vital to figure out how to serve the most vulnerable people in the world. And that is where they plan to focus their efforts, using data science to deliver life-changing social impact.

Find out more about GiveDirectly, CEGA and all the data.org Challenge awardees here.

The post Dial 999 to Receive Fast Cash: How Data Science and Machine Learning Can Identify and Aid People Living in Poverty appeared first on data.org.

The Challenge originated from the vision of data.org’s founding partners, the Mastercard Center for Inclusive Growth and The Rockefeller Foundation. Both organizations saw an opportunity to harness the power of data science to tackle the world’s most pressing problems, and worked with data.org to craft a Challenge that would draw ideas and proposals from and for everywhere in the world.

The scale of the data.org Challenge was huge: more than 1,260 individuals and organizations applied for $10 million in funding and technical assistance, and 400 judges from 49 countries spent more than 3,000 hours in technical review to find the projects best aligned with the Challenge’s goals and resources. Technical partner DataKind led the review, working closely with the data.org team.

You can read more about the groundbreaking work of the inspiring round of eight Inclusive Growth and Recovery Challenge awardees by reading the report.

What lessons were learned? And how might those lessons stretch beyond the final awardees to everyone who applied?

The Challenge Encouraged New Partnerships

The Challenge focused on democratizing data and data science to make a positive social impact. With “Inclusive Growth” in the title, a majority of applicants submitted proposals addressing impact through an economic lens. But applicants also partnered with organizations in other areas like peace and justice and on building strong institutions. These partnerships were a positive surprise for Afua Bruce, Chief Program Officer at DataKind. She enjoyed seeing so many new and creative partnerships as she reviewed the applications. “It’s exciting to see that there are so many different ways that the social sector can come together,” she said.

Partnerships Make Better Products

Partnering as grantees to effect sustainable solutions with smallholder farmers in India, The Basel Agency for Sustainable Energy (BASE) & the Federal Laboratories for Materials Science and Technology (EMPA) built a project neither group could have delivered alone. BASE, which is focused on sustainable energy and climate change solutions, and EMPA, a Swiss materials science and research institute, are working with smallholder farmers in India to use data and deliver insights that provide farmers access to more solar-powered refrigeration. Their mobile application, called Your Virtual Cold Chain Assistant, will help farmers better understand their options when it comes to product refrigeration, like where and how to store and sell their produce to make the most profit. The organizations estimate that farmers who use the app will be able to increase their income by up to 30% and will discard 20% less fruit and vegetables. What’s more, moving food through solar-powered cooling systems will cut cooling-related greenhouse gas emissions in half.

“The partnership has been essential because of the complementary skill sets,” said Thomas Motmans, project lead at BASE. “In acknowledging our strengths and weaknesses, we were able to identify and address potential gaps or challenges and work together as one broad team.”

The Challenge Fostered Global Collaboration and Community

As a platform for partnerships, data.org serves as the connective tissue for these kinds of collaborations. But its goals are broader than making project-based connections, said the organization’s Chief Strategy Officer Ginger Zielinskie. data.org is committed to building a global community for shared learning, and this community was able to help applicants as they put together their proposals for the Challenge. There are six criteria for a successful application, including smart problem statements, funding, data sets, data scientists, subject matter experts, and of course, a keen grasp of the people who will use the solutions that are being proposed.

“Some of the applicants wanted support in funding. Some of them wanted to execute work they already envisioned. Some needed support in finding and cleaning data sets. Some needed to know, ‘how do I find a data scientist to help me execute on this?’” she said. “And that’s the role of this community, to help each other address those needs.”

The Challenge Helped Zero-In on the Biggest Problems

Afua Bruce said the most successful applications focused intently on defining the problem before they started to look at how to solve it. Questions to ask included: “How do they define the problem? What are the biggest problems? What are the wicked problems that keep them up at night? And then, what are the tools that need to be in place to solve those problems? What are the data sources that need to be in place to help support that problem? And then from there, how do you actually scope a project?”

To facilitate the application process, data.org provided training webinars to address these needs. “Scoping 101,” for example, helps applicants scope projects once they’ve identified their problems. It’s available along with the other training webinars, here.

“I watched some of them twice because they’re so good and well structured,” said Thijs Defraeye of EMPA, one of the Swiss organizations partnering on the produce refrigeration project. “And you learn a lot from them. It was a big help in scoping the project. The webinars gave us more steps, which let us achieve a higher quality level.”

Zeroing-in on Problems Encouraged Applicants to Listen to the People They Want to Serve

The teams working on the Your Virtual Cold Chain Assistant are interviewing 1,000 stakeholder farmers, one by one, while scoping their project. They asked the farmers what they needed so that they could design something they would actually use and benefit from.

“Doing the research avoids a situation in which we design a very fancy machine learning model that doesn’t actually end up saving food,” said Thomas Motmans. “It’s about consulting with the people who will actually use the product. In our case, asking farmers.”

This close collaboration with the communities served was a hallmark of many of the strongest applications in the Challenge.

The Challenge Encouraged Applicants to Root Out Bias

When organizations are intentional about identifying and mitigating bias in their data sets, their results are more accurate and effective. That’s why the Challenge encourages applicants to screen for bias and to utilize additional or alternative data sources when they do identify bias in their data.

Afua Bruce of DataKind explained how bias presents itself in data, and how organizations can address it. For a project on benefits administration, for example, one might look at eligibility and location. But, she said, “eligibility criteria in benefits data affects people of color, and zip codes also include bias. So should we be using benefits data or zip codes? Is there something else that we can correct for? Or add in? That is the question they should be asking.”

The Challenge Helped Tell the Story of Why Data Science for Social Impact Matters

The Challenge’s primary aim was identifying projects with the best chance of achieving meaningful progress in inclusive growth and recovery. A secondary result, made clear in the Challenge report, is that the broad and innovative applicants tell an effective story of why the opportunity for data science is in the social sector.

By surfacing these applications, the Challenge shows that “data science is about more than helping [people] buy the right turtleneck before they put down [their] phone at night,” said Ginger Zielinskie. The next challenge for partners of the Challenge: “How do we tell that story of what’s going on in ways that resonate and inspire similar efforts around the world?”

You can read more about breadth of the issues being tackled by Inclusive Growth and Recovery Challenge applicants and awardees by reading the report. It shows the range of opportunities that exist to use data science to drive social impact for workers, entrepreneurs, and communities.

Cities and Towns — Leave No Place Behind

Community Lattice will create a platform to predict the cost and risk of brownfield redevelopment projects in the United States to transform a community’s ability to secure redevelopment funding, improve community health, and create economic opportunities.

GiveDirectly and Center for Effective Global Action will develop and implement a new model for humanitarian and development aid that enables cash transfers to be deployed effectively, accurately, and at scale to those who need them most.

To address the digital divide, the University of Chicago will create open-source maps and toolkits that can be used to highlight inequities in broadband access and advocate for more equitable policies and investments.

Using a rich, longitudinal population dataset, Aalborg University will create interactive and actionable maps for policy-makers and urban planners that identify the geographical places in Denmark that are most vulnerable to out-migration and economic instability.

Access to Capital — Leave No Entrepreneur Behind

BASE will use machine learning and physics-based food modelling to enable smallholder farmers in India to access sustainable cooling facilities, thereby reducing food loss and dramatically improving livelihoods.

In Nigeria and Tanzania, Solar Sister will share market insights with its network of women entrepreneurs to grow their renewable energy businesses.

Addressing gender bias in lending algorithms, Women’s World Banking will work with financial service providers around the world to increase credit access for low-income female entrepreneurs.

Jobs of Tomorrow: Leave No Worker Behind

Using data mining and machine learning, Fundación Capital will arm informal workers in Mozambique with essential labor-market insights to increase income and employment opportunities. 

The Challenge report shows that mission-driven individuals and organizations of all types, sizes, and maturity that seek to transform communities and realize lasting social change have recognized the potential of data science to achieve their goals. The Challenge clearly highlighted the high and varied nature of the demand for data-driven solutions in service of the social sector. As data.org we will take this learning, and use it as we develop programs, partnerships, and a platform to build the field of data science for social impact alongside this committed global community.

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Winning projects from around the world include the use of AI to increase credit access for female entrepreneurs, data models to track how internet connectivity disproportionately impacts low-income communities in Chicago, and an open-source quality of life map index to highlight where investment is needed most, among others.

“The Challenge was created to kickstart breakthrough data science ideas and also to provide financial support to existing innovative social impact projects,” said ​Danil Mikhailov, executive director of data.org​. “We have been enormously inspired by the innovative thinking of these projects that implement programs and policies to lift up all segments of society.”

Awardees were selected based on exceptional use of data science to advance inclusive growth. They are located in the U.S., Chile, Mozambique, Togo, Australia, Mexico, India, Denmark, Switzerland, Nigeria, and Tanzania.

“Other than impact, we also selected our awardees based on their potential for replicability, scalability, and practicality,” continued Danil Mikhailov. “The eight projects recognized by The Challenge not only are visionary, but also practical. They are the type of project we expect to one day fundamentally – and positively – impact the way we live our lives.”

“A year ago, data.org was launched with the belief that the world’s most pressing challenges and the lives of vulnerable people could be improved with data-driven insights,” said ​Mike Froman,​ vice chairman of Mastercard​. “As we begin to recover from the global pandemic, today’s awardees are providing innovative approaches to ensuring economic growth is inclusive and sustainable for everyone. ​We’re committed to building a global testnetwork of data scientists solving these problems – because that’s what is needed to build the field of data science for social impact.”

“As we recover from the devastation of Covid-19, a status quo approach to rebuilding our communities won’t work,” said ​Dr. Rajiv J. Shah, president of The Rockefeller Foundation​. “The awardees of the Challenge leverage the latest data science techniques to build a world where entrepreneurs have fair access to credit regardless of their gender, where workers have analytical tools to chart their own course, and where wage theft is a thing of the past. We founded data.org to reclaim data science as a tool for social impact. This cohort of projects makes that ambition real at a time when the need for an inclusive recovery couldn’t be greater.”

The Rockefeller Foundation and the Mastercard Center for Inclusive Growth launched data.org with the vision of creating partnerships to build the field of data science for social impact. Its current partners include ​DataKind​, ​Benefits Data Trust​,​ ​Community Solutions​, and ​Tableau Foundation.​ ​All of these organizations are leveraging data science to help solve complex societal challenges. data.org is also thrilled to welcome Paul Ramsay Foundation (PRF) to our list of committed partners who, through this Challenge, will be supporting an awardee from Australia.

The Awardees

Aalborg University, Department of the Built Environment (BUILD)​ – Denmark

BUILD will create an interactive open-source map of their Quality-of-Life Index to create awareness of the places that are most vulnerable to economic instability in order to improve social service access and drive more equitable policies.

Basel Agency for Sustainable Energy (BASE)​ – Switzerland, India

BASE will create Your Virtual Cold-Chain Assistant, an open access, data-science-based mobile application for smallholders that aims to increase the percentage of food moved through the cold chain.

Community Lattice​ – USA

Community Lattice will provide equitable access to critical information about environmental conditions, and provide communities a tool to transform revitalization efforts, address potential sources of health issues, and ensure the inclusion of the underserved in recovery.

Fundación Capital​ – Chile, Panamá, Mozambique

This project will combine Mozambique’s largest digital job platform for informal workers with an AI-powered virtual assistant, and apply algorithms to provide workers with targeted insights into the labor market to improve job opportunities and financial outcomes. Over 54% of Mozambique’s adult workforce is in the informal sector, and information gaps on-demand trends mean that opportunities for workers to improve their livelihoods are limited.

GiveDirectly​ ​& CEGA​ – USA, Togo

GiveDirectly and CEGA will demonstrate a new model for rapid assistance where machine learning-based targeting will guide the delivery of digital cash transfers. Data science will enable GiveDirectly to better assess communities that are most in need of cash transfers.

Solar Sister​ – USA, Nigeria, Tanzania

Solar Sister supports local women in Africa to create clean energy businesses. Through data science Solar Sister will gather insights, analyze customer data, and predict market characteristics to inform focal areas as they scale into additional emerging markets.

University of Chicago​ – USA

The University of Chicago is building public maps, datasets, and open-source tools to improve internet connectivity assessments and influence investment for high speed connectivity infrastructure. By highlighting the areas most in need of targeted intervention, the project looks to address the barrier of internet connectivity that disproportionately impacts low income families and minorities.

Women’s World Banking​ – USA and Switzerland, with partners in Mexico, Nigeria, and India

Women’s World Banking will increase credit access for low-income female entrepreneurs by building a technical assistance facility that uses innovative lending methodologies.

In addition to the eight awardees selected, The Paul Ramsay Foundation has selected and will be supporting the Melbourne School of Government’s data science project as part of the $10M Inclusive Growth and Recovery Challenge​.

Paul Ramsay Foundation Award

Melbourne School of Government – Australia

This project will bring together business, government, academia, unions and NGOs to explore the opportunities that data science poses to address the complex issue of wage theft.

To learn more about our awardees visit: https://www.data.org/challenge-awardees/

About data.org

data.org is a platform for partnerships to build the field of data science for social impact. data.org works with organizations from all over the world to increase the use of data science to improve the lives of millions of people, and to tackle society’s most significant challenges. For more information and to keep in touch, follow data.org on Twitter ​@DataDotOrg​ or visit ​data.org​.

About the Mastercard Center for Inclusive Growth

The​ ​Mastercard Center for Inclusive Growth​ advances equitable and sustainable economic growth and financial inclusion around the world. The Center leverages the company’s core assets and competencies, including data insights, expertise and technology, while administering the philanthropic Mastercard Impact Fund, to produce independent research, scale global programs and empower a community of thinkers, leaders and doers on the front lines of inclusive growth. For more information and to receive its latest insights, follow the Center on Twitter​ ​@CNTR4growth​ and​ ​LinkedIn​, and​ ​subscribe​ to its newsletter.

About The Rockefeller Foundation

The Rockefeller Foundation advances new frontiers of science, data, and innovation to solve global challenges related to health, food, power, and economic mobility. As a science-driven philanthropy focused on building collaborative relationships with partners and grantees, The Rockefeller Foundation seeks to inspire and foster large-scale human impact that promotes the well-being of humanity throughout the world by identifying and accelerating breakthrough solutions, ideas, and conversations. For more information, sign up for our newsletter at ​rockefellerfoundation.org​ and follow us on Twitter @RockefellerFdn​.

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