How Can Data Science Drive Equitable Growth and Recovery? Our $10M Challenge Offers Some Insights into the Future.

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By Ginger Zielinskie, Interim Executive Director, data.org and Afua Bruce, Chief Program Office, DataKind.

In January of this year, data.org, in partnership with the Mastercard Center for Inclusive Growth and The Rockefeller Foundation, launched an ambitious idea: a $10M Challenge to find and fund the most promising inclusive growth data science projects around the world. Since then, COVID-19 has upended how we work and live, killing over 1.3M people and plunging economies around the world into recession. As a result, we changed the focus of the Challenge not only to uncover new and promising ways to use data science to drive inclusive growth but also to focus on recovery from COVID-19. Together with data scientists from DataKind and Mastercard, data.org spent 3,000 hours reviewing more than 1,250 submissions from 107 countries across six continents. These applications came from a diverse cross-section of organizations covering a broad swath of topics related to inclusive growth. We are excited to share key insights generated from this snapshot of where the field of data science for social impact is in its evolution.

Introduction 

When we first developed the concept of a global Challenge for individuals and organizations that are using data science for social impact, we knew the field was growing fast. Thousands of people and organizations worldwide were trying to find meaningful and feasible ways to use data science to serve their missions. Even so, we could not have imagined just how crucial this field would become in such a brief period of time. Today, data is more essential than ever to tailor critical solutions, mobilize aid, and advance policy change.  

Through the Challenge, we sought inclusive growth proposals from and for anywhere in the world. We were amazed by the deep well of ideas and innovative work, evidenced by the sheer number of applications received—more than 1,250 in total. We were equally encouraged to see incredible geographic diversity, with applications from 108 countries across six continents.

The Applicant Pool

The applicants, proposals, and collaborations submitted varied in many ways, including the organizational size, the role of partnerships, the scope of the challenge they sought to address, the scale of impact they hoped to achieve, and the degree to which data science/data analytics was embedded into the strategy and operation of the organization(s). Applications also varied by the level of maturity of the project proposed, from ideation to full scaling.

Applicants represented a range of organization types and sizes; while nearly 50% of applications came from nonprofits, more than 25% were from for-profit businesses, and nearly 20% from individuals. In terms of organizational size and operating budget, we received more than 400 applications from start-ups and small NGOs with annual budgets of under $500K, as well as more than 300 from very well-established groups with operating budgets above $1 million.

Similar Problems, Different Solutions

The Challenge focused on three areas that are critical drivers of inclusive growth: Jobs of Tomorrow, Access to Capital, Cities and Towns. In each area, we encouraged applicants to intentionally consider critical questions related to workers, entrepreneurs, and/or communities. 

We encountered diverse solutions to similar problems in different communities. For example, many applicants addressed improving financial outcomes for the underbanked; however, solutions ranged from various alternative credit-scoring to direct cash transfers and more. We also received many proposals that sought to support the economic prosperity of smallholder farmers. While some applicants worked to incorporate satellite imagery to assess yearly crop growth, others proposed to equip farmers with market insights or access to tools that could help them increase profitability. 

From ideation through scaling, we saw innovative proposals from groups and individuals who used the Challenge as an opportunity to generate creative new program ideas. On one end of the spectrum, we learned about small-scale pilot projects with the potential for national or even global impact. On the other end, we were introduced to projects that had successfully demonstrated impact in a given region or country and were ready to be replicated or scaled to reach more communities around the world.

Although, at first glance, inclusive growth is most directly related to economic development, the Challenge demonstrated just how many sectors can contribute and are integral to inclusive economic growth and recovery. We received projects from various sectors, including Health, Education, Social Services, and Criminal Justice, to name a few.

Partnerships are Critical

In most cases, partnerships were essential, as the majority of applicants outlined how they would engage with external partners to achieve success. Promising projects included strong data science expertise coupled with policy and/or practice subject matter expertise. Additionally, relationships and strong connections with individuals affected by the problem addressed were important components of successful models. Rarely could a single organization provide the data science capacity, subject matter expertise, and trusted client engagement needed to execute a project on their own successfully. In many projects, partners offered one or more key components, like technical expertise –  sometimes with data science consultancies or critical implementation support; in other cases with government agencies, NGOs, or on the ground civil service organizations.

Data Science Approaches Vary

Applicants proposed a range of data science and machine learning approaches in their submissions, including traditional methodologies like data mining, geospatial analysis, predictive modeling, and natural language processing. We were also excited to see applicants incorporate more advanced approaches, such as deep learning applications to computer vision and image-based pattern recognition. The varying levels of sophistication demonstrated by these methods highlight the wide range of data science capacity within the field. While some applicants would benefit from basic data science capacity-building, others lack the funding to implement breakthrough data science projects. Regardless of current data maturity, it’s clear that the field has identified a tremendous potential for applying data science for social impact.

Conclusion

The applications received through the data.org Inclusive Growth and Recovery Challenge prove that across the world, social sector organizations are ready to adopt data tools, talent, methods, and solutions that are currently concentrated in the private sector. To support a variety of techniques that generate positive impacts on people’s lives, a community of data scientists, funders, social sector organizations, private sector partners, community experts, practitioners, and policymakers must come together to support and grow current efforts. data.org, working together with partners such as DataKind, will continue to showcase examples of what’s possible as we work together to unlock the power of data science for social impact. 

We look forward to sharing the Inclusive Growth and Recovery Challenge awardees with you in 2021. Stay tuned!

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