Lack of data professionals in the impact sector

Arthan organizes a fireside chat to highlight the need for more data talent for social impact
15 Oct 2022
0 mins read
There is a need to integrate data science into the existing education system (Image: Mohamed Mahmoud Hassan, Public Domain Pictures)
There is a need to integrate data science into the existing education system (Image: Mohamed Mahmoud Hassan, Public Domain Pictures)

Arthan, a social enterprise, recently organized a Fireside Chat Session - Data Talent for Social Good to bridge the awareness gap of the lack of data professionals in the impact sector. Priyank Hirani, Associate Director, Capacity Accelerator Network at Data.Org, was a speaker at the session, with Satyam Vyas, Founder, and CEO of Arthan and Climate Asia, moderating the event. The virtual event was held on 28th September 2022.

As per the report titled ‘Workforce Wanted: Data Talent for Social Impact’ released by in association with Patrick J. McGovern Foundation and Dalberg, about 50% of social impact organizations are unaware of the exponential impact of data usage on their work. Another UNESCO report highlighted that 107 out of 114 countries have fewer women than men graduating with STEM degrees, with merely 26% of women as data and AI professionals.

Speaking about the report’s genesis, Priyank Hirani said, “At Data.Org, we believe in a future where everyone can use data to solve society’s greatest challenges. And we realized that there isn’t much material globally about the landscape of data talent. We surveyed 30+experts, scanned 90 articles and reports, and 200 talent initiatives in the data science space to learn how to train the data talent workforce for social impact.”

Adding further, Satyam Vyas said, “It is important to integrate data science into the existing education system in India. This would further imbibe the values of data science across different fields, and then if this talent decides to work in the impact space, it would be the best possible scenario.” 

Throwing light on the gender gap, Priyank opined, “Across industries, there is a fundamental gender data gap, and its consequences are detrimental and even deadly. Historical discrimination based on caste and race can be bridged through representation and diversity among the data talent across the board. The malicious algorithms and reinforced bias in the data space must be highlighted, and there is a need to ensure the IDEA (inclusion, diversity, equity, and accessibility) principle in the data space.” 

According to statistics highlighted during the session, women are 17% more likely to be killed, 47% more likely to be seriously injured, and 17% more likely to be moderately injured in car crashes than men. This is due to the utilization of male anatomy dummies for crash tests. 

Answering one of the questions from the audience on enhancing data skills and getting a foot in the social impact space, Priyank said, “We have J-PAL South Asia as our partner in India, and they are working with us in creating opportunities in terms of data fellowships for people to have short term and long-term engagements with non-profits, government partners. They will be curating the opportunities and doing the matchmaking.” 

The session ended with a bunch of questions about the usage of data in the academic world, opportunities for professionals in the data space, and the key highlights of the report by Data.Org titled: Workforce Wanted: Data Talent for Social Impact.

Key findings of this report

A review of nearly 200 data talent initiatives, a literature review of approximately 90 articles and reports, and expert interviews with more than 30 leaders in the field suggest that training and talent initiatives struggle with several systemic issues, including low levels of organizational awareness of how data can be valuable and a need for increased, sustained financing to drive shared growth, as well as:

  • Limited capacity of traditional institutions. Traditional education programs—specifically, university science, technology, engineering, and mathematics (STEM) programs—are insufficient in terms of both the number of institutions and volume of qualified data professionals; they also lack social impact orientation.

Proliferation of non-traditional training models, including massive open online courses (MOOCs) and other online training platforms, lack evidence of efficacy. Program outcomes are often disconnected from longer-term results such as job placements, and many programs demonstrate a bias towards technical training rather than integrated translational skills and work readiness.

  • Mid- and senior-level talent as both a gap and driver of growth. Intermediate and advanced skills are underserved relative to the need for growing the talent base; training or bringing in mid- and senior-level data talent can have a multiplier effect based on leadership’s ability to shape ecosystems.
  • Ecosystem constraints. The ability of training programs to adjust their business models is hindered by a lack of accurate market demand data for skills. While there is a growing appreciation of the value of data for social impact across public, private, and social sectors, the understanding of demand and sourcing of talent remains limited.
  • Need for leadership programs that focus on supporting leaders’ evolution rather than one-off interventions. Leadership programs and fellowships often focus on individuals rather than holistic interventions that affect the broader data ecosystem; lessons of leadership programs are not embedded into the existing activities of the professional environment through workplace experiments and nudges.


We believe there is an opportunity to shape and develop 3.5 million data professionals focused on social impact in developing countries over the next 10 years. This opportunity is based on multiple factors and includes variation based on different scenarios.

The current landscape for purpose-driven data professionals is nascent in terms of its overall size and organization, but shows momentum and growth, fueled by a number of intrinsic and extrinsic factors—including increasing access to the internet and data itself; acceleration of digital transformation efforts around the world; recognition of the value of inclusion, diversity, equity, and access; acceleration of digitization forced by COVID-19; elevation of and investment in advancing social issues like the SDGs, climate, public health, etc.; exceedingly high demand for data professionals globally; and many others.

Alignment of efforts, partnership, and resilient digital infrastructure is required. There are several complementary efforts that offer the potential to galvanize shared goals when it comes to unlocking data for social impact (DSI) talent.

The momentum and investment in supporting digital transformation strategies around the world—particularly in low- and middle-income countries (LMICs)—offers opportunity to further advance structural investments in shaping labor markets for data-driven skills and professionals.

The advancement of research institutions and academia in understanding the opportunities and realities of IDEA in the data ecosystem can help push the data field, including DSI, toward a systematic emphasis on IDEA.

Digital transformation efforts focus on holistic, meaningful connectivity solutions (including cost of data, devices, and enabling infrastructure such as identity, payments, and asset.

The opportunities highlighted above can be realized through the high-level prioritization of data, including long-term financing, investments in human capital, and laws conducive to the safe production, exchange, and use of data. Some investments in better data have paid for themselves. Capacity There are a number of ways in which organizations can access and build data skills, teams, and organizational strategy.

As a framework for considering the different approaches to building and growing skills and organizational strength, we identified four pathways:

  • New talent. Expanding exposure of learners through development of DSI use cases; integration of hands-on, practical learning; incorporation of applied learning into curriculum; and stronger alignment of training models with the needs and demands of the social impact sector.
  • Existing talent. Models for upskilling and reskilling— such as in-house, outsourcing, and sponsorship models—that recognize the value of existing talent committed to social impact and SIOs.
  • Transitional talent. Greater exposure and access to opportunities that allow for more agile flow of talent across sectors; examples include hands-on fellowships, short courses, volunteer opportunities, and rotational leadership programs.
  • Leadership. Enhancing and shaping new models to support design, experimentation, and advancement of data-driven strategies, initiatives, and talent acquisition; investment in allies, such as boards and funders, to advance understanding of data-driven solutions.

The DSI field is competing within the broader data skills ecosystem, meaning that DSI professionals are often disincentivized to choose the social impact pathway, particularly when considering public-private wage gaps and limited visibility into career growth opportunities registries), which are critical to addressing the digital divide and unlocking meaningful access to opportunities.

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