In this rapidly evolving AI landscape, what was the “aha moment” when you realized the opportunity and the necessity to train AI on low-resource languages to unlock and accelerate Africa’s AI potential?

My “aha” moment came while searching for a text-to-​s​peech api and finding out that Yoruba was still not supported in the ​​Google Cloud Text-to-Speech. With all of Google’s massive technology, it utterly failed in Yoruba. It became evident that language diversity in Africa was not just underrepresented but was essentially missing in mainstream AIs. I realized that we have to move quickly before we have a massive reservoir of cultures invisible to AI. The need became very evident. The opportunity for big tech to bridge this gap and support efforts to train AI on low-resource languages is both urgent and transformative. This is not just to save language heritage but to support inclusive innovation in healthcare, education, governance​,​ ​and beyond​. The future of Africa’s AI depends on language equity as a foundation, not an option. 

How does your work with low-resource languages move the needle for data and AI for social impact work? What are some of the biggest challenges you have faced in doing so?

My focus is on developing ethically sourced and linguistically valid speech data for low-resource languages​,​ specifically Yoruba and Hausa. This enables voice tools for different sectors (healthcare, education, citizenship engagement​,​ etc)​,​ particularly in underserved communities. One of our most significant challenges is infrastructure. Low-resource languages often have no digitized data, no standard orthographies, and variable speaker representation. ​There are​ institutional challenges​, ​​such as​​ ​under-resourced research and low levels of collaboration among technologists and linguists. However, by filling in the gaps, we empower the voices of the locals to shape AI as an instrument of inclusion. 

What are the diverse, interdisciplinary skills that are required to do this work effectively? Which one surprised you the most?

Effective work in AI for low-resource languages demands a fusion of skills​,​ which may include machine learning, computational linguistics, cultural anthropology, community organizing, trust building​,​ and ethics. What surprised me the most was realizing the importance of trust building during interactions with language speakers ​, ​as co-creators and not as merely data providers​ ,​ in ensuring quality data. It reminded me that the future of AI isn’t just about code and compute​;​​ ​it’s ​about ​the people. And unless we prioritize the people, our models will always remain incomplete. 

What key responsible practices should AI practitioners prioritize when developing and training AI systems in African—or other low-resource languages?

When developing and training responsible AI for African and other low-resource language communities, practitioners must give ​priority​ ​to​ community-centered data collection, transparent ​use​ of ​models​, and long-term benefit sharing. Practices such as participatory dataset design, multilingual documentation, and culturally sensitive model assessments must be adopted by practitioners. Some other guardrails include strict consent protocols and preventing models from perpetuating negative stereotypes. Trust from the community is a requirement. Without trust, communities will not cooperate, and the resulting data will ​be both​ ethically and technically imperfect. This trust is earned ​through​ respect, feedback loops, and respecting the rights of speakers as not merely data points but as rights-holders to the data. 

What is the importance of cross-sector collaborations in building inclusive AI? What advice would you offer to people interested in this work?

Inclusive AI cannot be built in silos. Governments offer policy frameworks, technologists bring tools, NGOs offer ​a ​ground-level perspective, and communities provide lived experience. Cross-sector collaboration ensures that the development of AI systems is linguistically fair, culturally relevant, and scalable. My advice to aspiring AI equity advocates is that they should start locally, stay humble, and collaborate widely. They should learn from linguists, community elders, and social scientists. They should also prioritize impact over novelty and remember that language is identity. Working in AI language equity is not just a technical challenge​,​ but a social justice mission. ​You must​​ build for​ and with​ the communities you aim to serve. 

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In this rapidly evolving AI landscape, what was the “aha moment” when you realized the opportunity and the necessity to train AI on low-resource languages to unlock and accelerate Africa’s AI potential?

I realized how often low-resource African languages are left out of global natural language processing (NLP) advancements, as most machine translation models underperform for these languages, not because they are less important, but because the data, infrastructure, and high computing are not readily available. The gap created by this divide doesn’t just limit innovation but marginalizes millions of people, hindering access to critical sectors like primary health care, education, and agriculture, where AI is needed to bridge the gap.

The “aha moment” for me is that if we are serious about AI being a force for inclusive growth, we can no longer overlook the languages our people in Africa speak daily as a developmental imperative. Imagine AI-driven conversational agents that can offer basic medical advice in the Hausa language for a rural village in Northern Nigeria, bridging the gap created by the shortage of health professionals. We can democratize access to technology by enabling localized solutions that empower communities across the continent.

Projections suggest AI can contribute over $1.2 trillion to Africa’s GDP by 2030, which shows that we have a massive opportunity and an urgent responsibility. The necessity becomes clear: without AI models trained on Africa’s linguistic diversity, the continent risks being left behind in the global revolution. Training AI on low-resource languages is not just about catching up but creating truly inclusive and scalable solutions. The vision of AI that genuinely reflects the continent’s contexts drives my work to help accelerate Africa’s AI future.

How does your work with low-resource languages move the needle for data and AI for social impact work? What are some of the biggest challenges you have faced in doing so?

Nigeria has over 500 languages, making it one of the most linguistically diverse countries in the world. However, over 90 percent of these languages are considered low-resource in Natural Language Processing (NLP), meaning they lack the digital resources, corpora, computational infrastructure, and datasets needed to build effective language models. And that’s a problem because, without language inclusion, we’re building technology that doesn’t serve everyone. My work focuses on closing that gap by training AI in local African languages and building localized AI solutions to unlock access to critical services in education, healthcare, agriculture, and finance for communities that have historically been left out. When a student in a rural area can learn in their mother tongue or a patient can describe symptoms to a chatbot that understands them, that’s impact.

But it has not been easy. One of the biggest challenges we faced was acquiring locally nuanced datasets. Community-driven data collection, such as crowdsourcing, is promising but slow and resource-intensive. Additionally, limited access to computational infrastructure hinders model training. These barriers slow progress and prevent low-resource communities from accessing effectively trained AI models in their local languages. Despite the hurdles, we’re seeing progress. Our homegrown Equalyz Crowd allows you to collect multi-modal datasets and be incentivized. Through our startup, equalyzAI, we have built a language-inclusive product that drives health, education, and financial inclusion. We move the needle by making inclusion the foundation, not an afterthought, fostering equitable development, preserving cultural heritage, and driving socioeconomic progress.

What are the diverse, interdisciplinary skills that are required to do this work effectively? Which one surprised you the most?

Developing effective low-resource language models that authentically reflect Indigenous communities’ natural conversational style, cultural nuances, and religious contexts requires an interdisciplinary blend of skills. Of course, you need strong technical skills in machine learning, speech recognition, and model optimization, especially for real-time applications like speech-to-text systems. But what often gets overlooked is just how crucial linguistic expertise is, particularly from native speakers who are also trained linguists. Their ability to capture subtle tonal shifts, idiomatic expressions, and grammatical structures is non-negotiable for accuracy in low-resource language processing.

Beyond linguistics and engineering, we also needed cultural and anthropological insight, with ethical data governance, because we’re representing people’s identities, histories, and worldviews. That’s why community engagement is at the center of the process. We’ve had to co-design data collection methods with local communities to build trust and ensure the outputs are validated in contexts (meaningful and respectful).

The identity element challenged me to think beyond the algorithm and focus on inclusive, ethical AI development that reflects the people it serves.

What key responsible practices should AI practitioners prioritize when developing and training AI systems in African—or other low-resource languages?

Developing AI for African and other low-resource languages demands responsible practices to ensure ethical and inclusive outcomes. Firstly, I strongly recommend Privacy-by-Design principles and robust consent protocols. Prioritizing participant sovereignty and culturally sensitive data is responsible AI development. Interdisciplinary teams, including data governance experts and legal compliance specialists, must enforce these guardrails to align with local regulations.

Secondly, it is important to address linguistic biases in training data. These biases can distort cultural representation and reduce model accuracy. Data Collectors should curate diverse datasets and account for dialectal variations to preserve meaning across contexts.

I attest that community trust is foundational. Engaging local communities fosters linguistic authenticity, improves data quality, and builds confidence in AI systems. Transparent collaboration, including co-designing data collection with indigenous stakeholders, ensures models reflect cultural nuances and meet community needs. Communities may resist participation without trust, undermining data integrity and model effectiveness. By prioritizing ethical stewardship and community trust, AI products can drive equitable impact that preserves cultural heritage and drives social progress in low-resource settings.

What is the importance of cross-sector collaborations in building inclusive AI? What advice would you offer to people interested in this work?

I advocate for partnerships among AI startups, tech companies, academic institutions, governments, and local communities. This pool of expertise, resources, and perspectives addresses linguistic and cultural gaps in AI systems.

These partnerships minimize challenges like scarce datasets and limited infrastructure by leveraging shared resources, such as community-driven data collection or government-funded computing facilities. They also promote ethical practices, balancing technological advancement and cultural preservation.

I advise those interested in AI language equity to prioritize interdisciplinary learning and community engagement. Gain NLP, linguistics, and ethics skills and develop cultural competence to collaborate effectively with diverse stakeholders. Seek mentorship from experts in low-resource language AI and contribute to open-source projects to build practical experience. Finally, it is important to engage communities actively; their insights are critical for creating relevant, trustworthy AI systems.

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In this rapidly evolving AI landscape, what was the “aha moment” when you realized the opportunity and the necessity to train AI on low-resource languages to unlock and accelerate Africa’s AI potential?

My “aha moment” came when I realised that my intensive cultural and creative sectors background actually qualified me for this opportunity.  For so long, many of us working in language and the arts believed that AI belonged solely to engineers and data scientists. We excluded ourselves from conversations that deeply affect the futures of our languages and cultures. But then I recognised that our absence was the gap and our inclusion is the opportunity. Working with indigenous African languages, I saw how AI systems often mistranslate, misrepresent, or ignore them entirely. Training AI on these languages isn’t just a technical task — it’s a cultural necessity. Without it, Africa’s digital future risks being shaped by systems trained on foreign values. Inclusive AI can empower communities to define themselves in digital spaces not as data points, but as agents of meaning.

How does your work with low-resource languages move the needle for data and AI for social impact work? What are some of the biggest challenges you have faced in doing so?

My work with low-resource African languages advances AI for social impact by centering people, not just data. I come from a literary and linguistic background, and I approach this work by asking: What’s the best way to engage with these languages meaningfully? That question continues to guide me. One of my biggest challenges is holding deep conversations with data scientists and asking hard questions like: Who is this for? My role is making sure African communities are not reduced to data sources, that our cultural nuances are respected, and that this work is not treated as a niche for profit. I see myself as a bridge helping to facilitate relationships between communities and AI practitioners. For me, social impact in AI means ensuring that African languages and the people who speak them are central to the design and purpose of these systems.

What are the diverse, interdisciplinary skills that are required to do this work effectively? Which one surprised you the most?

Linguistic expertise, community engagement, ethical research practices, technical literacy, machine translation, project management, advocacy, and policy awareness are diverse interdisciplinary skills required to do this work effectively. Linguistic and cultural knowledge is foundational, especially when working with indigenous languages that carry deep histories and nuanced meanings. At the same time, you need the technical ability to navigate the language of AI, machine translation, and data ethics — even if you’re not building the models yourself.

The skill that surprised me the most was community engagement. I had underestimated how central it would be to the success of AI projects involving low-resource languages. Building trust, working ethically with people, and communicating across power dynamics are not side tasks — they are the core of the work. Without community participation, even the most accurate models fall flat in impact and relevance. This work doesn’t sit neatly in one discipline. It thrives in the space between them, and that’s where I’ve found my purpose. Being able to connect the dots, sit at multiple tables, and bridge knowledge systems is what allows me to push for more inclusive, culturally grounded AI in Africa.

What key responsible practices should AI practitioners prioritize when developing and training AI systems in African—or other low-resource languages?

Key responsible practices include transparency about how data will be used, co-designing projects with language speakers, and ensuring that communities benefit from the tools being developed. AI practitioners must also avoid extractive data collection, where languages are sourced for model training with little regard for who owns, controls, or understands the outcomes. Community trust isn’t just important – it’s essential. Without it, you may get data, but not meaning. Communities need to see themselves reflected in the process, have access to the outputs, and feel respected in how their languages and stories are handled. This is especially true in African contexts where colonial histories have left deep scars around knowledge extraction. Guardrails should include ethical review processes tailored to cultural contexts, open dialogue between technologists and language practitioners, and mechanisms to track and respond to potential harm. Inclusion must be more than representation; it must be active collaboration. Ultimately, AI systems built for low-resource languages will only be sustainable if they are built with the people who speak them.

What is the importance of cross-sector collaborations in building inclusive AI? What advice would you offer to people interested in this work?

Cross-sector collaboration is essential to building inclusive AI because language equity cannot be solved by one field alone. Technologists bring the tools, but linguists, cultural workers, educators, and communities bring the context. Without that blend, we risk building systems that are technically impressive but socially disconnected. In my work, I have seen how the most meaningful AI projects emerge when people from different sectors come together to listen, challenge assumptions, and co-create new approaches. To those interested in AI language equity, my advice is simple: start where you are, and bring your full skillset. You don’t need to be a coder to matter. You need curiosity, humility, and a deep respect for the languages and people you’re working with. Learn to speak across disciplines. Ask hard questions about ethics, power, and access. And most importantly, remember that inclusion is not just about who’s in the room, but about who gets to shape the outcome.

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Tell us about your work with the Capacity Accelerator Network. What impact or outcome are you most excited or encouraged by? How do you measure your success?

I worked with the Research and Insights team at an organization called Janaagraha, a well-known foundation in India that works with governments and citizens to improve the delivery of infrastructure and services in urban areas.

The project I worked on focused on identifying pathways for cleaner energy transitions in household fuel usage among the urban poor in the state of Odisha. My primary responsibility was analyzing a survey dataset of over 5,000 respondents, which provided insights into household fuel usage and behaviour in low-income settlements. The goal was to identify potential cleaner energy sources, with a specific focus on cooking fuels.

Additionally, I worked on developing tools to:

1. Link cooking fuel usage to health outcomes, one of the key evaluation criteria for cleaner fuels.
2. Understand the costs associated with transitioning to cleaner fuels.

Given the substantial body of research linking adverse health outcomes to indoor air pollution caused by traditional cooking fuels, I was particularly excited about quantifying the costs of transitioning to cleaner cooking fuels. I was also excited to work with spatial data, and provide a visual map of fuel usage across Odisha. 

For me, success meant two things: first, completing the project deliverables to meet my team’s expectations and achieving the outcomes I had envisioned at the outset. Second, and more challenging in the short term, was creating outputs that could be used by relevant stakeholders to inform their decision-making processes.

How has your approach and work evolved based on what you have learned and observed from your colleagues across the CAN network?

As the point person for data analysis on this project, I relied on the CAN network to identify spatial datasets at the district and sub-district levels and to navigate reliable publicly available health datasets. It pushed me to seek help – reaching out to colleagues from other projects, clearly explaining the outputs I wanted to achieve, and leveraging their expertise and contacts to support my work.

From my colleagues at Janaagraha, I learned how to meaningfully integrate different research methods and analyses with inputs from stakeholders across community, government, and industry, creating a cohesive and comprehensive framework for the study. Specifically, I gained valuable experience in co-creating energy pathways with inputs from local community members, and in designing a representative sampling approach in the absence of administrative data.

There can be a disconnect between academia or government institutions and social impact organizations doing the work on the ground. How do you build trust and increase adoption?

From my experience, the following approaches have proven effective:

1. Involving relevant stakeholders in the design process. It is essential to take time to understand specific objectives and activities of the government and other implementation or policy partners, and try to engage with them at each stage of the project. This could be done by providing regular status updates, incorporating stakeholder feedback, and having a primary point of contact, among others. It is not possible to achieve this for every project, though, as it really depends on the scope of work and nature of your partnerships. 
2. Recognizing that communication and advocacy are integral to the research process. In many cases, research efforts end with the publication of a paper or presentation. However, building trust and fostering adoption requires actively promoting your research and tailoring communication to meet the needs of each stakeholder. This process demands significant time, resources, and persistent effort but is crucial to ensuring that your work is meaningfully utilized.

It is important to note that despite identifying the best approaches to minimize disconnect, you can still be constrained by the initial theory of change design, funding, organizational capacity, and your own connections with the government and other stakeholders. Transparency about those kinds of limitations can help maintain trust and confidence.

How is your data-driven work driving impact at the intersection of climate and health? What is the importance of an interdisciplinary approach to data training?

Using the National Family and Health Survey, I explored correlations between household cooking fuel choice and related health impacts, such as heart disease and respiratory issues on adult women, caused by indoor air pollution. I also spatially mapped this data at the sub-district and cluster level in Odisha to identify areas where this correlation was strong. 

In the case of my project, it became clear that we needed more robust data to measure these linkages, and that the sample size at smaller geographic units was not representative enough to draw localized insights. 

An interdisciplinary approach to data training is very important as it helps you ask the right questions you want from your data. If you are a generalist in the data space, you are usually connected with domain experts to advise you. In the absence of such experts, it is essential to have training in reading policy reports and research papers to help you understand a particular sector or linkages between two or more sectors. For example, such an approach can enrich your insights by contextualizing your analyses based on various demographic cuts, such as geography, caste, gender etc., that you may apply having had such training.

What advice do you have for data practitioners as they begin purpose-driven careers? Why should they apply their skills in the social impact sector?

Having worked previously in the corporate sector, I think there is a difference in rigor applied to designing and achieving goals using data between the private and public sector. While results and impact-driven work is normalized in the private sector, it is often less mature in the government and social impact sector. This is why we need skilled data personnel in the social impact sector who can improve service delivery through monitoring and reporting, and who can help develop quantifiable goals and measure the impact of programs so that funds are channeled to the most effective and efficient policies. 

My advice to data practitioners is that there is a trade off to working in the social impact sector, and to not be discouraged by the bureaucracy and limited resource capacity that is more common in this space. It is important to remember that you are working towards social and economic good in a sector that is meaningful to you, and that your skills are helping to improve livelihoods and address these structural challenges. 

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Tell us about your work with the Capacity Accelerator Network. What impact or outcome are you most excited or encouraged by? How do you measure your success?

My work with the Capacity Accelerator Network (CAN) focuses on leveraging data science to drive climate and health research through interdisciplinary, data-driven approaches. A significant outcome of this work is translating research findings into actionable insights. One of my key initiatives involved linking climate variability with malnutrition rates and birth outcomes across Indian districts. Using two rounds of nationally representative National Family Health Survey datasets, the project establishes a robust baseline assessment of climate change’s impacts on children’s nutritional outcomes in India.

This research provides a spatial baseline of the health infrastructure’s capacity to deliver essential care for women and children in drought-prone districts. Identifying hotspot areas where health systems need strengthening helps address the projected impacts of climate change effectively.

I measure success by how well my research translates into actionable insights and how these learnings contribute to future projects. Moving forward, I aim to further my career in data-driven policymaking, focusing on sustainable and impactful solutions.

How has your approach and work evolved based on what you have learned and observed from your colleagues across the CAN network?

Collaboration within the CAN network has profoundly influenced my approach and broadened my perspective. Engaging with colleagues from diverse disciplines has highlighted the importance of adapting global frameworks to regional contexts. 

For instance, insights from the network encouraged me to incorporate additional indicators into climate vulnerability assessments, creating a more comprehensive understanding of how climate change affects health. Initially, my work took a single-lens approach, but collaboration exposed me to innovative datasets and methods, helping me analyze climate and health pathways through multiple lenses. This interdisciplinary mindset has significantly enhanced my ability to generate actionable insights.

The collaborative environment has also enriched my technical expertise in data science, equipping me with innovative methods and practical strategies to tackle real-world challenges. This ongoing exchange of knowledge and capacity-building training sessions have allowed me to continuously improve the impact of my work.

There can be a disconnect between academia or government institutions and social impact organizations doing the work on the ground. How do you build trust and increase adoption?

To bridge the disconnect between academia, government institutions, and social impact organizations, it is crucial to establish a robust evidence base that serves as a shared foundation. Involving all stakeholders—government institutions, academia, and social impact organizations—at every stage of the process is essential, from evidence generation to decision-making and implementation.

Building trust requires transparency and consistent communication. A participatory approach ensures that stakeholders feel valued and are more likely to adopt and sustain proposed solutions. This collaboration not only aligns goals across groups but also enhances the relevance and scalability of interventions, fostering long-term trust and impact.

How is your data-driven work driving impact at the intersection of climate and health? What is the importance of an interdisciplinary approach to data training?

My data-driven work identifies and addresses vulnerabilities at the intersection of climate and health, focusing on the needs of vulnerable communities. By integrating climate data such as rainfall variability and droughts with health indicators like malnutrition rates and maternal health, I identify hotspots and prioritize interventions for regions most at risk.

I believe an interdisciplinary approach is critical to understanding the complexity of climate-health linkages. These interconnected issues require perspectives from various fields to develop nuanced insights and effective solutions. This holistic understanding is pivotal for sustainable interventions.

Interdisciplinary training plays a vital role by equipping practitioners with the ability to analyze complex datasets while understanding their broader societal implications. For instance, training in tools like Geographic Information Systems (GIS) empowers professionals to visualize and act on the intricate connections between climate and health, fostering both technical competence and impactful decision-making.

What advice do you have for data practitioners as they begin purpose-driven careers? Why should they apply their skills in the social impact sector?

For data practitioners starting their careers, my advice is to align your technical expertise with a clear social purpose. Understand the needs of underserved communities and design solutions that incorporate their priorities and feedback.

The social impact sector provides unique opportunities to witness the tangible benefits of your work, whether improving public health systems or addressing climate risks. Applying data for social good allows practitioners to address systemic inequities and contribute to solving urgent societal challenges. The work is deeply fulfilling, offering both a sense of purpose and a chance to make lasting contributions to the public good.

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The APAC Data Capacity Accelerator will catalyze the application of data to address systemic financial inclusion challenges – including the critical need to build the data for social impact workforce. In partnership with the Asian Institute of Digital Finance (AIDF) – a university-level institute at the National University of Singapore (NUS) – and the Association of Pacific Rim Universities, this accelerator will produce a cohort of data practitioners and a training model to scale across the region.  

“Digital transformation, AI and data all have a role to play in shaping society and driving economies towards financial health and resilience,” said Shamina Singh, founder and president, Mastercard Center for Inclusive Growth. “At Mastercard, we are committed to driving financial inclusion for small businesses, workers, and communities all around the world. We are proud to work with partners such as data.org, the Asian Institute of Digital Finance at the National University of Singapore, and the Association of Pacific Rim Universities to reach the next generation of data practitioners, so they can harness the power of data and AI to support inclusive economic growth in the APAC region.” 

The latest Capacity Accelerator Network (CAN) launch announcement came at an event held at NUS. Domain leaders across academia, industry, government, and NGOs came together to discuss shared goals and coordination around developing and upskilling purpose-driven data capacity for inclusive growth. 

“data.org works at the intersection of what is possible and what is practical, as increasingly illustrated by the impact of our CAN network partners,” said Danil Mikhailov, executive director of data.org. “We will only reach our goal of training one million purpose-driven data practitioners by 2032 through interdisciplinary, locally-led programs. Our growing and diverse network of partners—including now five Capacity Accelerator Network hubs worldwide—is making connections across sectors and across borders, inspiring a new generation of problem solvers.” 

The APAC Data Capacity Accelerator builds on the work being done at hubs in Africa, India, Latin America, and the United States. To date, data.org programs have engaged more than 20 academic partners around the world, applying the power of research and academic expertise to enable social impact organizations to unlock the power of data to meet their missions. 

For the APAC Accelerator, AIDF and the Association of Pacific Rim Universities are the primary higher education partners.  

“AIDF is proud to host today’s event together with data.org and the Association of Pacific Rim Universities. It’s very exciting to be a part of a movement to empower young people and underprivileged communities, such as small business owners, around the world with the skills they need to be competitive in an increasingly tech-driven workforce,” said Professor Huang Ke-Wei, Executive Director of AIDF. “Our students, regardless of their disciplines, can benefit from exposure to and understanding of data and AI. We hope to create more opportunities for them to apply such critical skills in ways that would be beneficial to the community, society, and the world.” 

“This partnership is about tapping into the power of higher education to ensure that our workforce and our communities are not left behind,” said Thomas Schneider, chief executive of the Association of Pacific Rim Universities. “Data science for social impact has the potential of significant societal benefits in areas such as economic mobility, gender equity, and even public health and climate, so we are eager to see how the data practitioners and social impact organizations involved will address this challenge in a way that serves the public good in the Asia Pacific and beyond.” 

Today’s event included keynotes on topics such as data and AI driving inclusive growth, the power of collaboration among government and social impact leaders, and the unique challenges and opportunities of AI in social impact. Subject matter experts shared their perspectives through panel discussions on bridging the data talent demand-supply gap, data-driven decision-making in multistakeholder partnerships, and scaling innovation and resources. 

About data.org

data.org is accelerating the power of data and AI to solve some of the world’s biggest problems. By hosting innovation challenges to surface and scale groundbreaking ideas, and elevating use cases of the most effective tools and strategies, we are building the field of data for social impact. By 2032, we will train one million purpose-driven data practitioners, ensuring there is capacity to drive meaningful, equitable impact.

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  LinkedIn,  Instagram, and subscribe to its newsletter.  

About the Asian Institute of Digital Finance

The Asian Institute of Digital Finance (AIDF) is a university-level institute at the National University of Singapore (NUS), jointly founded by the Monetary Authority of Singapore (MAS), the National Research Foundation (NRF), and NUS. AIDF aims to be a thought leader, a FinTech knowledge hub, and an experimental site for developing digital financial technologies, as well as for nurturing current and future FinTech researchers and practitioners in Asia. For more information, please visit https://www.aidf.nus.edu.sg/.

About the Association of Pacific Rim Universities (APRU)

As a network of leading universities linking the Americas, Asia, and Australasia, the Association of Pacific Rim Universities (APRU) is the Voice of Knowledge and Innovation for the Asia-Pacific region. APRU brings together thought leaders, researchers, and policy-makers to exchange ideas and collaborate on practical solutions to the challenges of the 21st century. For more information, please visit https://www.apru.org/.

Media Contacts

data.org:
Emma Marty 
emma@data.org 

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That is the first line in the key findings section of “Workforce Wanted: Data Talent for Social Impact,” a first-of-its-kind report that data.org released in June of 2022 about a nascent sector that we are determined to build. Ever since we put those words to paper, that opportunity and the responsibility to realize it have been a guiding force for our organization. We don’t want to work on the edges. We want to be on the frontline of this work.

That is why data.org set an ambitious goal of training one million purpose-driven data practitioners by 2032. 

“It’s a huge goal, but as you can see, the need is huge,” said Priyank Hirani, the director of capacity building at data.org. “Aiming low is not going to cut it to get the nonprofit and public sector to be able to leverage data and AI as well as the private sector does now. Having this huge number signals our seriousness.”

With the need clearly outlined, Hirani and the rest of our team are focused on how to deliver across what we call cases, capacity, and commons. We are proving the case through a series of innovation challenges that are amplifying and scaling effective strategies for data and AI. We are transforming the commons by supporting the community and creating and driving the adoption of digital public goods. Our library of resources is always evolving, from our easy-to-use playbooks to step-by-step guides. And lastly, we are strengthening the capacity for individuals and organizations to advance on their data journey. 

“The march to one million is a collective goal for the organization underpinned by our platform and programs, with our work across cases, capacity, and commons in service of this goal,” said Hirani.

Hirani anchors data.org’s programmatic strategy to build capacity for individuals and organizations by guiding internal teams and supporting data.org’s network partners to design interventions at scale. Two of the primary channels through which data.org is building the field and enhancing a pipeline of digital workforce with an impact-first mindset are its flagship Capacity Accelerator Network (CAN) and its digital learning offerings. With accelerators based in the United States, India, and across  Africa, Latam, and Asia Pacific (APAC), data.org’s network of networks approach is driving data for social impact (DSI) in financial inclusion and the intersection of climate and health. data.org delivers this through training, curriculum development, and experiential learning opportunities with a key focus on inclusion, diversity, equity, and accessibility (IDEA). Our digital learning initiative is designed to work with large-scale partners to take local curricula and deliver them to the broadest audience possible. 

In both workstreams, finding the right partner is the most important first step.

“It’s about identifying partners who resonate with our vision and commitment towards this mission, and then building trust and co-designing programs to ensure quality outputs,” he said. “Once you have these foundational blocks in place, it triggers this idea of self-accountability. We are all working towards shared goals and mission with a learning mindset to create resources in service of the impact sector.”

In the case of digital learning, those early partners have been governments in Nigeria and India. Working with the Federal Ministry of Communications, Innovation, and Digital Economy and the National Data Protection Commission in Nigeria and the Capacity Building Commission and Karmayogi Bharat in India, we have launched courses that cover key topics like responsible data management, data governance, and global data regulatory frameworks. The modules in India are available to more than three million civil servants alone through the iGOT platform, presenting us with unprecedented reach and potential impact to support key decision-makers with crucial skills in handling data throughout its lifecycle. 

Most importantly, says Hirani, the approach allows us to meet learners where they are. 

“People learn in different ways. No one size fits all,” he said. “There are people who are more visual, they learn better through interactive elements, and then there are some who prefer reading or like a lot of additional references so they can do their own research. Our early impressions are that in order to cater to different needs of different people, having a modular approach and a suite of options to learn as well as several avenues and channels to learn from is most beneficial.”

Offerings may be self-paced and asynchronous in some cases, or cohort-based in others. But regardless of the delivery or where the learning is taking place in the world, we continue to emphasize leading with localism, learning by doing, and looking first to the needs of the community.

“While the core data science concepts remain the same, experiential learning and contextualization of content is where our capacity-building initiatives stand out. We weave relevant pedagogical and curricular elements into the training, including scenario-based activities, deep dives on country-specific data regulatory frameworks, and amplifying unusual voices. The localism lens is also applied through key questions like, ‘Who are the people involved in curating or creating that content,’ ‘Who are the people reviewing that content,’ and ‘How are we getting local experts involved at every stage?’ ‘How do we make the content relevant for the learner in that context?’”

Those are the questions Hirani continues to ask as we identify more prospective CAN partners and expand our curricular offerings through digital learning.

“We have an amazing, organically growing community of learners,” he said. “We are always looking for more partners who share our commitment to be able to leverage data and AI for social good, who also share a commitment to create open resources and if there are partners who have created resources and they want to make them accessible to a global community, we would love to talk to them.” 

These partners and programs are a powerful force multiplier, exponentially increasing the number of people trained and engaged. These new purpose-driven data practitioners are part of an emerging global workforce, bolstered by data.org’s march to one million.

To learn more about how to join or support data.org’s community of capacity leaders, visit www.data.org/initiatives. 

The post The March to One Million: Partnerships and Programming to Build a Data for Social Impact Workforce appeared first on data.org.

Tell us about your work with the Capacity Accelerator Network. What impact or outcome are you most excited or encouraged by?

I am thrilled to have the opportunity to work with students from the Capacity Accelerator Network for the third consecutive year. As part of the Data Science for Social Impact Summer Experience, I teach a data science “crash course” and mentor groups of CAN students on real-world projects. In 2022, I worked with a group of students to analyze traffic stop data for Business Professionals for the Public Interest who work to improve legislation around biased police practices. Last summer, my students and I analyzed geospatial data capturing where different pesticides are applied for Californians for Pesticide Reform. Through our work, students see how data science can be applied to do good in their community.

Working with CAN students has been incredibly fulfilling for me. Witnessing their growth as aspiring data scientists throughout the program is truly rewarding. They learn a lot of material at a very quick pace, not only through formal lessons but through the experiential learning involved in their research project. The program is meant to jumpstart interested students’ data science careers. This is really important since we need more data scientists with diverse backgrounds and experiences. I have personally seen and experienced how crucial it is to feel represented and heard in your field. That’s why I am most excited when I see an increase in their confidence in their abilities and their sense of belonging in this field as the program progresses.

What are some of the challenges of doing this work? Which were anticipated, and which unexpected?

Developing a curriculum that takes into consideration the differing levels of prior knowledge among students and aims to foster equal opportunities for learning has been one of the major challenges of this work. Data science is an interdisciplinary field, incorporating elements of statistics, computer science, and research methodology, and students often have varying levels of experience with these different aspects. For instance, some students are highly skilled in mathematics but have no experience in coding, while others are proficient coders but have never taken a statistics course. And some students may not have had the opportunity to be exposed to much of either. This challenge is also present in my work at both the University of Chicago and the City Colleges of Chicago. The introductory data science material must meet a variety of needs to ensure that students are well-prepared for the next stage, whether that be a research project or a course in advanced machine learning. 

How has your approach and work evolved based on what you have learned and observed from your colleagues across the CAN network?

My teaching approach has certainly developed through my involvement with CAN. I’ve learned a great deal from conversations with faculty members at Truman College, especially about how to effectively teach students from different educational backgrounds. Talking with educators like Kate Connor has been really valuable—it’s given me a sense of support and guidance as I navigate my role as a young professor.

There can be a disconnect between academia or government institutions and social impact organizations doing the work on the ground. How do you build trust and increase adoption?

This is a very important question, and it’s been a topic of intense discussion among members of the data science community. Drawing from my education and personal experiences, I believe it’s crucial to involve social impact organizations and the people they work with in every step of our projects. By keeping these organizations meaningfully informed and involved, we not only enhance transparency but also foster a sense of ownership, which is fundamental for building trust and increasing adoption. After all, these tools are being designed for use by these organizations, so it really makes sense to solicit their input for the design process. This collaborative approach also ensures that we are tackling the most pressing issues as identified by those directly impacted, leveraging their domain expertise to keep our work both relevant and enduring. 

During the CAN DSSI Summer Experience, we emphasize the importance of this collaboration, and students regularly engage with organization members through Zoom calls to present their progress. This model mirrors our approach at the University of Chicago in the DSI Clinic (an experiential learning opportunity for data science majors), where students routinely cite their interactions with clients as one of the most impactful parts of the experience.

The US CAN playbook Data Science for Social Impact in Higher Education: First Steps, provides educators with a range of ways to bring data for social impact to students. The playbook also includes ways to embrace social impact and ethics, elevate support and reduce barriers, and engage partners. How are these topics useful as you bring the UChicago DSI course to City Colleges of Chicago?

These topics are a key component of my curriculum planning for the data science sequence at City Colleges of Chicago. Part of the motivation for bringing these courses to the City Colleges is to make data science education more accessible to students. During the courses, we have built up a strong support network for students including lab sessions, office hours, and discussion boards. I also encourage peer support and community building by promoting an environment where students can collaborate, answer each other’s questions, and talk about common concerns or sources of confusion. In the classroom, I work to build a community where students feel comfortable asking questions, commenting on material, and connecting what they learn to their own experiences. We discuss ethics and social impact using case studies and topical data sets. For example, we analyze data on bias in policing and trends in college admissions. We also take time to explore datasets that are more lighthearted yet still relevant to students, such as Spotify listenership. Through these discussions, we aim to maintain an atmosphere of curiosity as well as mutual respect and support. This approach helps students engage with and learn from difficult social impact and ethical questions. I have found that this approach motivates students to question and explore data beyond what may be asked in an assignment.

The post 5 Minutes with Dr. Amanda R. Kube Jotte appeared first on data.org.

In data.org’s landmark report, Workforce Wanted: Data Talent for Social Impact, we explored this explosive growth in demand for purpose-driven data talent across nonprofits, research organizations, educational institutions, advocacy coalitions, and beyond. The field is growing rapidly, with new roles consistently emerging across sectors.

We are committed to helping to fill those roles with a pipeline of diverse, interdisciplinary data practitioners.

That was the inspiration behind our Capacity Accelerator Network, a global community committed to building the field. With accelerators based in the United States, India, Africa, and soon Singapore, CAN is driving data for social impact in financial inclusion and the intersection of climate and health through training, curriculum development, and experiential learning opportunities with a key focus on inclusion, diversity, equity, and accessibility (IDEA).

We also see a worldwide demand for people to fill roles in large distributed projects creating integrated and generalizable community-driven software, a need that has become more visible through our Epiverse collaborative. Over the last two years, researchers interested in data science tools for public health and epidemiology have been working together on the Epiverse-TRACE (Tools for Response, Analytics, and Control of Epidemics) research project.  From Bogota to the Gambia to Berlin, the teams work together to change how analytics are used in global infectious disease response.

Through our involvement with Epiverse-TRACE, the data.org team sees firsthand the need for skilled data science practitioners, and also has identified the need for social science, communication, and translation roles geared toward improving the interaction between developers, users, and other stakeholders. These are some of the roles we hope to continue to identify, design, and feature here as part of our commitment to field building.

Finally, we know we are in an environment with roles that are new, or being performed without formal recognition.

The social sector has become increasingly digitally mature, but digital public goods are often built without the unique challenges and opportunities of social impact in mind. As a global convener and thought leader in data and AI, we at data.org believe that we need to acknowledge and name a role in the social sector called the “data ecosystem designer” that is charged with creating the data ecosystem that allows digital public goods to thrive and scale in the social sector. These data ecosystem designers are akin to city planners, as we describe in a recent blog post. As we collaborate with social impact organizations globally, we will help support new and unrecognized roles and what they may accomplish to drive change in the future. 

For individuals in the workforce who are energized by the idea of being that force for change, we are excited to share the new data.org jobs board.

Our curated jobs board taps into our expansive network, connecting data professionals with organizations doing good work around the world. Because that is our primary focus: jobs that drive impact. From entry-level to senior positions, job seekers will find meaningful career opportunities, and social impact organizations will connect with the field’s most qualified and passionate candidates. We are approaching this resource in a way that is both targeted in its focus on quality DSI opportunities, yet also open-minded, recognizing that DSI jobs of the future can be in any sector and, quite often, will require interdisciplinary experiences and skills. As we explore the world and tackle challenges through the lens of data and AI, we must remain open to approaches we have not yet considered and roles we have not yet imagined.

This jobs board is not a catch-all for every posting with even a passing reference on data or AI. This is a carefully vetted and intentionally curated resource that will make it easier for top talent to find high-quality jobs that fuel purpose-driven careers. 

Purpose-driven careers like those profiled in our Pathways to Impact series. Jacqueline Chan, for example, is the senior director of data and evaluation at the United Way Bay Area, a regional nonprofit that brings together partners from the nonprofit, business, and government sectors to address poverty in the greater San Francisco area. Hers has been a long and winding road to get into DSI, but each stop along the way has moved her closer to what she now considers a calling. Or like Linda Kamau, the executive director of AkiraChix, a social impact organization that provides young women in Africa with skills to compete economically and bridge the gender gap in technology. Linda saw the gender barriers firsthand when she started her career as a software engineer, and she made it her mission to open opportunities for other women in technology.

To do good work, we need good people. Good people with the right skills and a shared commitment to and passion for social impact. If you’re one of those people, you know someone who is, or you have a job opportunity that you think belongs on the Jobs Board, check out the listings or send us a message.

Together, we can build a stronger, more diverse, and more collaborative field of data for social impact.

The post Uplifting Talent and Driving Demand: Building the Data for Social Impact Workforce Together appeared first on data.org.