By Epiphanus Obia
Many organisations today are collecting more data than ever before. Product teams have dashboards, customer analytics, funnel reports, performance metrics, user feedback, support tickets, transaction records, and market research. Yet the presence of data does not automatically lead to better decisions.
This is one of the quiet challenges facing digital businesses. Companies often assume that once data exists, insight will follow. But in practice, many teams still struggle to decide what to build, what to improve, what to stop, and which customer problem deserves priority. The issue is no longer simply access to data. The deeper issue is the ability to convert data into judgement. This is changing the role of the modern product manager.
The product manager is no longer only the person who writes requirements, manages stakeholders, and ensures that teams ship features. In more mature digital environments, product managers are expected to understand customer behaviour, interpret analytics, evaluate business impact, work with technical teams, and make difficult trade-offs between user needs, operational constraints, and commercial goals.
This shift is especially visible in sectors such as fintech, lending, education technology, and quick products. In these spaces, product decisions can affect how quickly a user accesses credit, how efficiently a team processes applications, how easily students use learning platforms, or how confidently businesses adopt new digital tools. A small product decision can influence onboarding, retention, conversion, service speed, and user trust.
The challenge is that dashboards can show what is happening, but they do not always explain why it is happening or what should be done next. A drop in usage may reflect poor onboarding, weak product-market fit, pricing friction, low trust, technical issues, or a mismatch between user expectations and product experience. A product manager must be able to investigate the signal behind the number.
This is where product judgement becomes important. Good product management requires more than monitoring metrics. It requires asking better questions: What behaviour are we trying to change? What user problem are we solving? Which part of the journey is creating friction? Is this a technical issue, a communication issue, a trust issue, or a product design issue? What evidence do we have? What experiment can we run? What should we not build?
Professionals such as Oluwasegun Babatunde Esho, also known as Kelvin Esho, reflect this broader evolution in product management. His work sits across product strategy, business analysis, analytics, fintech, education technology, API management, Agile delivery, and stakeholder coordination. That kind of cross-functional background is becoming increasingly relevant because product teams now need people who can move between business goals, user needs, technical requirements, and performance data.
In fintech, for example, product decisions often have direct operational consequences. Esho has contributed to digital product optimisation work that improved loan processing turnaround time by 20%, showing how product changes can improve both internal efficiency and user access. He also supported the building and launch of the Zedvance App, a digital personal banking product created to improve access to loans and deposits.
His experience also extends to SME-focused product work, where improved retention by 25% points to a key product reality: growth is not only about acquiring users. It is also about keeping them engaged, solving their recurring problems, and making the product valuable enough for continued use.
In education technology, Esho delivered a web-based Learning Management System for a UK school, improving the learning experience and administrative efficiency. This shows how product management principles apply beyond commercial apps. Whether the product is used by students, teachers, borrowers, SMEs, or internal teams, the core discipline remains the same: understand the user, define the problem clearly, build around real needs, and measure whether the solution is working.
The next stage of product management will be shaped by professionals who can combine data literacy with decision-making discipline. It will not be enough to know how to read dashboards. Product managers will need to understand where data comes from, what it does not show, how to test assumptions, and how to connect insight to product action.
This matters even more as AI becomes part of product workflows in the future. AI can help teams analyse information, generate options, automate processes, and accelerate delivery. But it does not remove the need for human judgement. Product managers will still need to decide which problems matter, which solutions are responsible, and how to balance speed with reliability, usability, and trust.
The strongest product teams will not be the ones with the most dashboards. They will be the ones that know how to turn information into better decisions. In that environment, the product manager’s value will increasingly come from the ability to connect data, context, user behaviour, and business outcomes into clear product direction.
The future of product management is not simply data-driven. It is decision-driven.