Why Africa Must Become a Producer of Data Intelligence – THISDAYLIVE

Africa is experiencing rapid digital transformation. From your vantage point, what role will data and analytics play in shaping Africa’s economic future?

Data and analytics will determine whether Africa’s digital transformation creates broad-based prosperity or simply digitizes existing inequalities. Across sectors finance, health, agriculture, energy decisions are becoming data-driven. The countries and companies that can generate, trust, and act on their own data will be better positioned to build resilient economies, attract investment, and design solutions that actually work in local contexts.

Importantly, data is no longer just an efficiency tool; it is strategic infrastructure. Africa’s economic future will depend on moving from being a consumer of insights generated elsewhere to being a producer of intelligence grounded in African realities.

You lead Data Architecture & Engineering at one of Africa’s biggest fintechs. What does it take to build scalable, secure, and reliable data infrastructure at that level?

At scale, data infrastructure is less about tools and more about discipline. You need clear data ownership, strong governance, and systems designed for reliability, not just speed. In fintech, especially, trust is non-negotiable—customers may never see your data systems, but they experience the consequences if they fail.

Building at that level means designing for volatility: fluctuating transaction volumes, regulatory change, and infrastructure constraints. It also means investing early in automation, security-by-design, and observability, so teams can move fast without breaking what matters most.

Many organisations collect data but struggle to use it effectively. What are the biggest barriers to data maturity among African businesses?

The biggest barrier is not data availability; it’s alignment. Many organisations collect data without a clear business question in mind, or treat data as a side function rather than a core asset.

There is also a skills translation gap technical teams may understand the data, but business leaders are not always equipped to turn insights into decisions. Finally, inconsistent infrastructure and poor data quality undermine trust, which makes teams fall back on instinct instead of evidence.

True data maturity happens when data, leadership, and decision-making are deliberately connected.

Talent remains one of the continent’s biggest gaps. What does Africa need to do differently to build a strong pipeline of data professionals?

Africa does not lack talent; it lacks pathways. We train people, but we do not always create environments where those skills can be applied, rewarded, and sustained locally.

Building a strong pipeline requires closer collaboration between industry, universities, and policymakers, as well as more entry-level and mid-career opportunities that allow people to grow without leaving the continent. It also means recognizing that mentorship and exposure matter just as much as technical instruction.

Retention is as important as training and that requires intentional investment in local ecosystems.

You’ve championed women in data and moderated high-profile conversations on AI. How can we ensure women and underrepresented groups are not left behind in the AI revolution?

Inclusion cannot be an afterthought. Women and underrepresented groups need access at the earliest stageseducation, tools, mentorship, and real decision-making roles.

AI systems reflect the people who build them. If women are absent from data collection, model design, and governance, their realities will be absent from the outputs. Practical steps include sponsorship, not just mentorship; funding women-led ventures; and ensuring representation in leadership and policy discussions.

Equity in AI is not only a fairness issue it directly affects the quality and relevance of the technology itself.

AI adoption is rising across Africa, but so are conversations about governance, ethics, and regulation. What should policymakers and business leaders prioritise?

The priority should be capacity, not just policy. Many African countries have strong ethical principles on paper, but limited ability to enforce them.

Policymakers need to invest in technical regulatory expertise and regional collaboration, while business leaders must embed ethics into product design rather than treating compliance as a checkbox. Data privacy, transparency, and accountability should be viewed as enablers of trust, not obstacles to innovation.

Good governance is what allows AI to scale responsibly.

For startups and SMEs that lack large budgets, what practical steps can they take to build data-driven cultures?

Start with clarity, not complexity. Define a small number of metrics that truly reflect business performance and build decision-making around them.

Use simple, reliable tools, focus on data quality early, and encourage teams to ask questions before building dashboards. Most importantly, leadership must model data-driven behavior, when leaders use data consistently, teams follow.

You don’t need a sophisticated stack to build a data culture; you need discipline and intent.

You’ve overseen major transformations—from data warehouses to automation frameworks. What lessons have you learned about driving organisational change intechnical environments?