Democratizing data science to create social impact worldwide

22/07/2022, por Angel

Democratizing data science to create social impact worldwide

Ronda Železný-Green

Digital changemaker and Program Director, Capacity Accelerator Network (CAN) at DataDotOrg

Empodera Impact Stories talks with Dr. Ronda Železný-Green, a digital changemaker creating social learning systems to empower Black people, women, people with disabilities, and other marginalized populations in the technology and education sectors. Serving as a mobile technologist, trainer, and researcher, Ronda’s two decades of professional experience spans five continents and the public, private, and civil society sectors. A Black and Native American-British cis woman excelling with ADHD, Ronda also has extensive experience delivering action-oriented training that integrates the themes of racial equity and justice and gender with global perspectives. Hallmarks of her career include:

  • learning/educating with tech;
  • championing, coaching, and mentoring women in tech; and
  • empowering people to use technology as transformative tools to live their best lives.


Hello Ronda, we would love to know more about the work of DataDotOrg, What’s the story behind this platform shaping social impact though data science? seeks to democratize and reimagine the use of data science to tackle society’s greatest challenges and improve lives across the globe. Our current initiatives aim to advance inclusive growth, build capacity, mitigate and adapt to climate change and fight pandemics.’s Capacity Accelerator Network (CAN) initiative was created to change the game where participation and representation in data science for social impact are concerned. Many social impact organizations (SIOs)[1] work to serve communities who have been historically marginalized and/or excluded and these SIOs don’t have the data capacity in-house (whether found in individual employees or the leadership) to more effectively make data-driven decisions about how they support their constituents and allocate resources. By working to build data capacity of individuals as well as SIOs through strategic inclusion of people with diverse and intersectional backgrounds, our aim is to address these challenges by helping the people and communities affected by orchestrated inequalities acquire the data skills needed to make a difference for the people who share their lived experiences.

To achieve this outcome, our approach is grounded in interdisciplinary and inclusive capacity building approaches that are accessible, experiential, and speak to real world problems and datasets. With this background, has an audacious goal to train 1 million purpose-driven data professionals by 2032, and with the explicit ambition to ensure that these data professionals are drawn from people who previously have not been empowered to participate in data science in large numbers.

[1] We define a social impact organization as being a non-profit organization, B corporation, government agency, or a social enterprise.


You are growing a global community to collaborate on problems with a social impact. How do you incubate ideas, tools, and solutions to scale the impact of data science for social good?

We are a community-oriented, platform for partnerships and so we start there when incubating ideas, tools, and/or solutions to scale the impact of data science for social good. The first product we built and launched is the Data Maturity Assessment (DMA), which we use as a pulse check on our community to understand where they are on their data journeys. Once people complete the DMA, they are then recommended resources from our library that serves to help propel them further in their data journey. This has become a mechanism to help us understand the gaps and needs of the ecosystem and informs the curation of tools and solutions we make available on the platform. We also facilitate problem-solving by hosting challenges, such as the Inclusive Growth and Recovery Challenge, where with generous support from our founding partners The Rockefeller Foundation and the Mastercard Center for Inclusive Growth (CFIG), we offered financial resources, technical assistance, and funder matchmaking to stimulate innovation to help address persistent societal problems that could be solved with data. Finally, we also convene like-minded individuals and organizations for webinars as well as attend events that focus on our organizational interests.


“In the near future, we will be launching further hubs on themes related to health, climate, and social justice in several Low and Medium Income Countries, amplifying our reach further as we work towards our goal of 1 million purpose-driven data professionals.”



For the social sector to benefit from today’s data revolution, we need both more data talent and more data capacity within organizations. What is the impact of the Capacity Accelerator Network (CAN)?

The greatest impact of the Capacity Accelerator Network (CAN) to date is without a doubt the sizing of the need for data talent through our recently released Workforce Wanted: Data Talent for Social Impact report. Created in partnership with the Patrick J. McGovern Foundation and Dalberg, this first-of-its-kind report confronts systemic challenges, highlights both immediate and big-picture opportunities, reveals the current landscape, and identifies four pathways forward for building purpose-driven data professionals. Workforce Wanted provides an understanding of the growing need for data talent within SIOs, including the leadership to help SIOs take advantage of the data opportunity. Through the report research, we identify an opportunity to shape and support a pool of 3.5 million data professionals focused on social impact in low- and middle-income countries (LMICs) over the next ten years.

We know that business cannot continue as usual, and we need to be intersectional in how we conduct our outreach and capacity building for this next generation of data professionals. We are calling on interested individuals and organizations to get in touch to see how we can work together to cultivate data professionals who care not about shareholders but about the people who most stand to benefit from data-driven approaches that can enhance and transform lives.


“Mediante la investigación del informe, identificamos una oportunidad para dar forma y apoyo a un grupo de 3,5 millones de profesionales de los datos centrados en el impacto social en los países de renta baja y media (PRMB) durante los próximos diez años”


Workforce Wanted: Data Talent for Social Impact. Photo by


How many organizations have you invested in and trained in data-driven advocacy so far? How the network collaborates?

We partner with organizations around the globe who are creating social impact through data. With support from CFIG, CAN recently initiated a collaboration with the University of Chicago along with seven colleges and universities in the United States representing diverse citizens including historically Black colleges and universities, as well as Hispanic and Minority serving institutions. This collaboration reaches hundreds of tertiary-level students who come from backgrounds of often lower socioeconomic statuses and wish to do more in data science than just create and increase shareholder wealth. These partners bring broad, interdisciplinary expertise—spanning from social policy to data science—to the challenge of financial inclusion.

Collectively, this financial inclusion hub will create an open curriculum, tools, and a model for experiential data science education that scales across diverse higher education institutions across the globe. While UChicago will play a key leadership and organizational role in this initiative, all consortium members will have an equal and powerful voice and make essential individual contributions, such as drafting data science apprenticeship models, developing co-curricular experiences for students, piloting experiential learning courses, and collaborating with local organizations to apply data science skills to local issues. This approach builds on the findings of our RECoDE report, which emphasized the critical need for data-driven solutions to be co-created with the communities they serve. All partners will engage regularly to share their work, ideas, successes, and failures as they drive data science for social impact at their own institutions. As each partner works within different communities and contexts, the learnings from their collective experiences will be shared and integrated into the open curriculum.

In the near future, we will be launching further hubs on themes related to health and climate in several LMICs, amplifying our reach further as we work towards our goal of 1 million purpose-driven data professionals. We will also be establishing programs to support the data transformation of SIOs in LMICs and other developing contexts, along with the launch of a digital learning platform where our open-source and experiential learning curricula developed from these collaborations will become available to the world. Our ambitions for our digital learning platform include the launch of a data for social impact certification based on the curricula we’re currently developing in collaboration with our existing and future network of partners.


Results page of’s Data Maturity Assessment. Photo by


“We need to transform how data professionals are educated, trained, and (re/up)skilled by ensuring an intersectional approach to inclusion, diversity, equity, and access remain at the heart of our interdisciplinary capacity building approaches and data initiatives”


How can data scientists apply data to serve and overcome global challenges related to health, sustainability, poverty or climate change?

To serve and overcome some of the world’s greatest challenges (and no, getting your groceries delivered in 15 minutes or less is not one of them!), data professionals must take an interdisciplinary approach. Addressing issues in health, sustainability, poverty, and/or climate change requires expertise to be gathered not only among people with different areas of expertise within a particular sector (medical doctors, data scientists, epidemiologists, data analysts, public health professionals, community health workers, data collectors, etc. as an example from health) but also the expertise of the communities for whom data-driven solutions are created. Lived experiences of the people most affected by substantial, and often perennial, challenges must be a part of any potential solutions developed if such solutions are to be socially sustainable and to have the greatest chance of meeting a community’s nee.

Being able to achieve the ideal mix of expertise, community-based knowledge, and data-led innovation requires appropriate data learning and training that are most appropriate and effective in the context(s) where such challenges are encountered. This is where I believe will make a substantial contribution in the next decade and beyond since our goal is to completely transform how data professionals are educated, trained, and (re/up)skilled by ensuring that an intersectional approach to inclusion, diversity, equity, and access remain at the heart of our interdisciplinary capacity building approaches.


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


Are Nonprofits using the power of data science to achieve their goals? Any success examples?

While we are not pioneering data science for social impact, we are quickly becoming the platform for facilitating the growth of activity and actors in this space. So far, we have seen that there are several social impact organizations that are already having success by using data to transform lives. In sub-Saharan Africa, Solar Sister is addressing energy poverty by empowering female entrepreneurs and providing them with data insights that help them understand their current and potential market of customers so that products and services can be better tailored to their needs. GiveDirectly is using data to help get cash transfers into the hands of the world’s poorest households. Similarly, Fundación Capital is helping people living in poverty in Mozambique to become more economically resilient by using data mining, visualization techniques, and a machine learning-powered recommendation system to deliver real-time labor market insights directly to informal workers. Finally, but not exhaustively, Benefits Data Trust is leveraging data to identify and connect people in the United States with billions of dollars’ worth of benefits for which they qualify but were unaware they could access.

All these organizations are using more advanced data techniques to help address significant social challenges in the countries where they operate. I am very encouraged that, as we advance our work at, we will begin to see even more individuals and SIOs harnessing data in a manner that is both more sophisticated and effective for the purposes of making a substantial and positive difference in the lives of people who need it the most.


You are an Edtech and Gender expert. What are the challenges right now to include more women and other underrepresented populations in data science so that the opportunities data provides can benefit everyone?

Historically marginalized and excluded groups in data science include women, ethnic minorities, people from different castes, socioeconomic statuses, and several other identity-linked groups. Of course, how the intersection of one’s identity impacts their ability to take part in data science (and data work more broadly) varies based on several factors, some of the barriers I have consistently seen include a lack of awareness about the opportunities that exist in data work, financial and time poverty, and a lack of mentors with a shared identity who are already established in the field. These barriers have proven to be persistent since data emerged as one of the “next big things”. Because of this, there is still a lot of catch up being played to ensure that fair and proportional representation is achieved for historically marginalized and excluded groups.


For these reasons, is intentional about its work to help ensure that we bring as many new and capable people into the sector as possible through both traditional and innovative ways. Capacity building for an inclusive and diverse data-led generation will not occur by chance – on the contrary, every day we will need to collaborate to ensure that data opportunities can benefit everyone who wishes to take advantage of them and that these skills are applied to contribute to the societal greater good.


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