Beyond Profit: Why Volunteer Data Scientists Must Use Algorithms for Social Good

By Ferdinand Ekechukwu

Data science has become one of the most powerful forces shaping the modern world. From targeted advertising to predictive policing, algorithms increasingly influence how we live, work, and interact. Yet for too long, these tools have been used primarily in service of profit. It is time we shift that narrative. Volunteer data science is more than a growing trend. It is a necessary movement that places people, not profits, at the center of technological progress.

As someone working in the data space, I have seen how algorithms can transform systems and drive efficiency. I have also seen how those same systems fail communities that lack visibility in the data. In under-resourced environments, the absence of reliable, real-time data can lead to poor planning, wasted resources, and deeply flawed decisions. These gaps are not just technical. They are structural. And volunteer data scientists are stepping in to fill them.

Across Africa and beyond, volunteers are using their time and skills to solve real-world challenges. They are helping small clinics build patient tracking tools. They are working with NGOs to create dashboards that monitor access to clean water or food security. They are building algorithms that support crisis response or improve maternal health outcomes in underserved regions. These projects are often done without funding, media attention, or institutional backing. But their impact is undeniable.

This work is powerful not just because of the technology involved, but because of the values behind it. Volunteer data projects are driven by trust, proximity, and relevance. They are not imposed from outside but co-created with the people they aim to serve. They succeed because they start with listening, adapt to local needs, and prioritise long-term usability over quick wins.

Collaboration is essential. In the most effective projects I have seen, data scientists work alongside teachers, health workers, linguists, and community organisers. Each brings a different perspective.

Together, they produce solutions that are both technically sound and socially informed. This kind of interdisciplinary work leads to algorithms that are not only accurate, but appropriate.

Still, the movement needs support to scale. Universities should recognise volunteer data science as legitimate, hands-on experience, offering course credit, guidance, and pathways into socially impactful careers.

Governments and nonprofits should develop frameworks that allow volunteers to engage ethically and transparently with public data systems. We must also create more spaces for this work to be celebrated, funded, and sustained without compromising its grassroots roots.

Volunteer data science is not about doing more with less. It is about doing what is right with what we have. It is a form of activism powered by algorithms. It challenges us to rethink who builds with data and why. And it proves that when technology is rooted in empathy, it can be a force for dignity, justice, and shared progress.

If we want a data future that serves everyone, then we must support those already building it, collaboratively, quietly, and with unwavering purpose.