“Today, we stand at the edge of possibility.”
That powerful imagery from Dr. Ronda Železný-Green, program director of the Capacity Accelerator Network at data.org, kicked off a globally-attended webinar to release a first-of-its-kind new report, “Workforce Wanted: Data Talent for Social Impact.”
Workforce Wanted is informed by a review of 200 data talent initiatives around the world, 90 articles and reports, and more than 30 expert interviews, led by data.org and The Patrick J. McGovern Foundation, in collaboration with Dalberg and with support from the Mastercard Center for Inclusive Growth.
The report shows the critical need and the tremendous opportunity for global data talent to enable the social sector to harness the power of data to solve the world’s most pressing problems.Mamadou Biteye Executive Secretary African Capacity Building Foundation
Železný-Green points out that while social impact organizations are at the forefront of serving communities and improving lives, they continue to lag behind the private sector when it comes to using the power of data to advance change. Leveraging that power is the motivation behind the report, which speaks to the challenges and opportunities, success stories and gaps that must be addressed to create a stronger and more diverse talent pipeline to lead this work forward.
“The report shows the critical need and the tremendous opportunity for global data talent to enable the social sector to harness the power of data to solve the world’s most pressing problems,” said Mamadou Biteye, executive secretary for the African Capacity Building Foundation and moderator for the event.
In the next decade, there could be as many as 3.5 million job opportunities in data science for social impact in low- and middle-income countries. But to get there, there is a lot of work to be done.
An Emerging Field at a Crossroads
When Claudia Juech, vice president of data and society at The Patrick J. McGovern Foundation, started on this kind of work five years ago, the conversations in the field were still heavily focused on the ‘why.’ Why is data science important? Why should we invest?
The case has been sufficiently made, but social impact organizations continue to struggle with the ‘how.’
“We are now shifting. Now it is about ‘where is the talent that we need to do that work?’” Juech said. “‘How do we implement?’”
In particular, organizations are often missing the ‘who.’ They lack the people power to maximize the use of data. Existing staff don’t have the skills, and attracting talent is difficult when competing with private sector peers that often advertise higher-paying jobs with bigger teams and more extensive records of data science investment.
“Through this report, our goal was really to articulate and better understand where the field is today and the scale of the opportunity ahead,” said Ginger Zielinskie, senior advisor at data.org, a lead author of the report, and a former social impact practitioner who has long seen the looming gap with data science staffing. “Talent continued to come up as an underpinning of each challenge we sought to solve.”
The Workforce Wanted project team identified four pathways to expand the talent pool in data for social impact:
- New talent, or attracting early career professionals into the field through educational and workforce development pathways. Traditional programs often lack exposure to the social impact sector, so forging those connections is key.
- Existing talent, or upskilling and reskilling workers who are already in the sector. They have already opted into mission-driven work. With the challenges around attracting and retaining this talent already solved, what can it look like when you give them opportunities for growth and development in data science?
- Transitional talent, or creating on-ramps that allow for greater exposure and access to employment opportunities. Hands-on fellowships, short courses, volunteer opportunities, and rotational leadership programs are all examples of how to support this pipeline.
- Leadership, or helping those at the top of organizations to commit to the change process and implement meaningful approaches to build data-led cultures.
Early on in the webinar, attendees were asked to identify the most important pathway, and the results were compelling. The four pathways were neck and neck at 25, 24, 23, and 28 percent, respectively.
In other words, they are seen as equally important, illustrating how much work has to be done across the board.
“I was absolutely surprised by how early we are; by how much information and knowledge we still need to uncover and gain and aggregate and share and learn from,” Zielinskie said. “We have not yet used data and data science to our fullest potential. The opportunity is so massive.”
Finding the Right Talent
In an exhaustive review of the data talent landscape, the Workforce Wanted team heard over and over that there were not enough data talent professionals. More educational and training opportunities are needed, including university degree programs. Greater investment is needed to improve a growing number of non-traditional training models, including massive open online courses (MOOCs), which have demonstrated varying degrees of efficacy. Leadership needs the opportunity to train and develop themselves in order to scale up what works and create institutional buy-in. And across all of these efforts, organizations must also consider the digital divide, and which communities do—or do not—have access to foundational tools like the internet and technology.
It isn’t just a question of having enough talent or enough training, either; it’s a question of having the right talent.
“The social impact sector sees the potential to use data and data science more effectively to tackle literally the hardest problems in the globe. The question is how do we use this powerful tool responsibly?” Zielinskie asks, calling out the most important theme of the report: lack of diversity. “If we actually want to build equity-centered data science solutions, we have to be proactive in focusing on building an inclusive and diverse workforce.”
The values of inclusion, diversity, equity, and accessibility (IDEA) are generally acknowledged as important, yet not enough has been done to embed them in the field in a meaningful way. When considering the strategies listed above for expanding workforce development, Juech points out that these strategies must be developed in a way that specifically addresses attracting and retaining more diverse talent.
“It’s important that we think about diversity of data professionals because we know that data fairness is better achievable if the people who work on data, clean data, analyze data, really represent the communities and the contexts where the data originates from,” she said.
In 2021, The Patrick J. McGovern Foundation invested more than $10 million in workforce development initiatives, focusing heavily on working not just with individuals, but with teams and organizations. This nuance is important, as it ensures that capacity building does not live with a single person; it becomes integrated into systems and is therefore more sustainable. It shifts from individual projects to systems-level thinking.
That intentionality, Juech adds, is essential at every stage of the process. She often hears leaders say, for example, that they post data talent jobs but do not receive applications from candidates from historically underrepresented communities. Organizations must take responsibility and hold themselves accountable, expanding outreach efforts and peeling back the onion another layer to ask what other benefits, flexibility, or support could make a position more attractive for both external and internal candidates.
“Be intentional about who you hire, who you promote, and who you offer growth opportunities to in nonprofit organizations,” she said.
Putting Money Where Your Mouth Is
Building capacity requires intentionality. It also requires investment. Funders must ask organizations about the makeup of their teams and begin to distribute funds to signal that this isn’t just a nice to have—it’s a priority.
To train this next generation of talent to develop mechanisms to support the upskilling within organizations, to build clearer and porous pipelines between the technology sector and the social impact sector, and to support leaders in driving this transformation, we have to get real about the capital required.Ginger Zielinskie Chief Growth Officer Federation of American Scientists
At the organizational level, that can be a harder decision than it seems. When presented with a choice between providing direct services to communities or investing in digital infrastructure and data capacity, Zielinskie understands why data science often takes a back-burner.
But an investment today can yield dividends in the future.
“To train this next generation of talent to develop mechanisms to support the upskilling within organizations, to build clearer and porous pipelines between the technology sector and the social impact sector, and to support leaders in driving this transformation, we have to get real about the capital required,” she said.
She and Juech and the rest of the Workforce Wanted team are hopeful that this report will help spark an increased investment, and improve coordination across academia, philanthropy, social impact practitioners, and partners in the tech for good space. There are emerging bright spots, and wherever there is a lesson to be learned, it’s essential that best practices and effective strategies be shared.
The momentum is growing, as long as cross-sector partners are willing to double down and get serious about building a stronger and more diverse talent pipeline.
“Be curious about what the data has to really tell you,”Zielinskie said. “Be agile enough to evolve and opportunistic enough to take advantage, and we can drive this change forward.”
To learn more about building data talent capacity, watch the full webinar online and download and read the full report, “Workforce Wanted: Data Talent for Social Impact.” You can also access the data.org Resource Library, and get your organization started with the data.org Data Maturity Assessment.