Oliseataka Chiedu is a data and analytics leader with over 11 years of experience driving data engineering, business intelligence, and predictive analytics across high-growth organizations. She currently leads data architecture and engineering at Flutterwave, building scalable data platforms that support strategic decisions across the business. In this interview, Chiedu talks about the importance of data intelligence for Africa and why Africa must move from being a consumer to being a producer of data-driven insights. excerpt.
Africa is experiencing rapid digital transformation. From your perspective, what role will data and analytics play in shaping Africa’s economic future?
Data and analysis will determine whether Africa’s digital transformation creates widespread prosperity or simply digitizes existing inequalities. Data-driven decisions are being made in everything from finance to healthcare to agriculture to energy. Countries and businesses that can generate, trust, and act on their own data are 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’s strategic infrastructure. The future of Africa’s economies depends on moving from being a consumer of insights generated elsewhere to a producer of information based on African realities.
You lead data architecture and engineering at one of Africa’s largest fintech companies. What does it take to build a data infrastructure that is scalable, secure, and reliable at that level?
At scale, data infrastructure is more about discipline than tools. You need clear data ownership, strong governance, and systems designed for reliability as well as speed. Trust is non-negotiable, especially in fintech. Customers may never see their data systems, but they will experience the consequences of failure.
Building at that level means designing for volatility, such as fluctuations in trading volumes, regulatory changes, and infrastructure constraints. It also means investing early in automation, security by design, and observability to help teams move quickly without disrupting what matters most.
Many organizations collect data but struggle to use it effectively. What are the biggest barriers to data maturity for businesses in Africa?
The biggest barrier isn’t data availability. It’s an adjustment. Many organizations collect data without clear business questions in mind, or treat data as a side feature rather than a core asset.
There is also a skills conversion gap. While technical teams can make sense of data, business leaders don’t always have the ability to translate insights into decisions. Finally, inconsistent infrastructure and poor data quality erode trust, forcing teams to rely on instinct rather than evidence.
True data maturity occurs when data, leadership, and decision-making are purposefully connected.
Talent remains one of the biggest gaps on the continent. What does Africa need to change to build a strong pipeline of data professionals?
There is no shortage of talent in Africa. Path is missing. We train people, but we don’t always create an environment where those skills can be applied, rewarded, and retained locally.
Building a strong pipeline will require closer collaboration between industry, universities and policy makers, as well as increasing entry-level and mid-career opportunities for people to grow without leaving the continent. It also means recognizing that mentoring and coaching are just as important as technical instruction.
Sustainment is just as important as training, and it requires intentional investment in local ecosystems.
You’ve championed women in data and moderated high-profile conversations about AI. How can we ensure that women and underrepresented groups are not left behind in the AI revolution?
Inclusion cannot be an afterthought. Women and underrepresented groups need access to education, tools, guidance, and real decision-making roles at an early stage.
AI systems reflect the people who build them. If women are not included in data collection, model design, and governance, their realities will not be reflected in outcomes. Practical steps include mentorship as well as sponsorship. Funding women-led ventures. Ensuring representation in leadership and policy discussions.
Fairness in AI is not just a question of fairness, but directly impacts the quality and relevance of the technology itself.
As AI adoption increases across Africa, so too do conversations around governance, ethics, and regulation. What should policymakers and business leaders prioritize?
The priority is not just policy, but capability. Many African countries have strong ethical principles on paper, but limited ability to enforce them.
Policymakers need to invest in regulatory technical expertise and regional collaboration, and business leaders need to embed ethics into product design rather than treating compliance as a checkbox. Data privacy, transparency, and accountability should be seen as enablers of trust, not obstacles to innovation.
Good governance allows AI to scale responsibly.
What practical steps can startups and small businesses without big budgets take to build a data-driven culture?
Start with clarity, not complexity. Define a small number of metrics that truly reflect the performance of your business and build your decisions around them.
Use simple, reliable tools, focus on data quality early on, and encourage your team to ask questions before building dashboards. Most importantly, leaders need to model data-driven behavior, and when leaders use data consistently, their teams will follow suit.
You don’t need an advanced stack to build a data culture. It takes discipline and intention.
You’ve overseen major transformations from data warehouses to automation frameworks. What lessons have you learned about driving organizational change in a technology environment?
Change management is always the most difficult part because technology changes faster than people. Successful change requires clear communication, gradual wins, and deep respect for the people involved in the work.
One important lesson is that resistance often indicates uncertainty rather than opposition. Make your transformation sustainable by engaging stakeholders early, demonstrating value quickly, and investing in enablement.
Ultimately, technology succeeds when people trust it and see themselves in the future it creates.
What opportunities do you see for homegrown innovation in the African data ecosystem in the next five years?
Africa has a unique opportunity to build data solutions that address conditions not fully understood by global platforms, including informal economies, multilingual societies, low-infrastructure settings, and youth-driven markets.
We will see growth in local AI models, financial infrastructure, health and climate analytics, and cross-border data platforms. The most successful innovations will be those built with African users, not just for them.
This is where Africa can move from adoption to leadership.
Finally, what is the personal mission that drives your work in data leadership and what legacy do you hope to leave in Africa’s technology sector?
My mission is to help transform Africa from a source of raw data to a creator of intelligence and value. I care deeply about building the systems, teams, and policies that enable Africa’s talent, especially women, to grow and achieve leadership.
The legacy I hope to leave behind is one where data and AI are tools of inclusion rather than extraction, and where the next generation of African women and young professionals do not have to leave the continent to do world-class work. If they can build, decide, and lead with confidence from here and see themselves reflected in the leaders who came before them, then I will have done meaningful work.
