The Dashboard Delusion: Gbolahan Adebayo on the Invisible Craft of Data Intelligence

In the high-stakes environment of fintech and global business intelligence, the difference between a successful enterprise and a failing one often comes down to a street-level problem: guessing. Gbolahan Adebayo, a Senior Data Analyst at Sanlam FinTech in Johannesburg, uses a pragmatic analogy of a roadside jollof rice and puff-puff vendor to expose the core inefficiency of modern markets. Whether it’s a street cook wasting inventory or a multinational misallocating capital, the root cause is the same—operating without a 'board' that tells you plainly when the rush is coming.

Adebayo, who was recognized in 2025 and 2026 as one of only 46 Tableau Visionaries globally, argues that the industry has been seduced by the 'pretty dashboard.' In reality, the visual element represents only the final 20% of the process. The real work is an unglamorous combination of 'detective work' and the translation of basic business questions. Adebayo’s track record, spanning infrastructure builds for MAGNiTT, Aspire, and Subway across markets including Nigeria, Singapore, and the United States, suggests that the primary hurdle isn't a lack of data, but the presence of 'poisoned' information.

Technicians often face the 'messy reality' of conflicting systems and data entry errors from months prior that quietly sabotage current reports. Adebayo notes that a significant portion of his day-to-day involves asking 'annoyingly basic' questions to stakeholders—specifically, what action would actually be taken if a specific number moved. This leads to a critical industry insight: half the time, a complex dashboard is unnecessary. Efficiency often lies in delivering a single, accurate number via email rather than a cluttered visual suite.

This discipline of 'saying no' and deciding what deserves to be shown is the invisible craft that separates leaders from mere tool-users. For aspiring professionals, Adebayo points to SQL as the actual 'workhorse' of the industry, supplemented by AI tools as tutors to accelerate the understanding of complex queries. The goal is to move past the 'visual' and toward actionable intelligence.

In the Kenyan context, this shift from guesswork to data-backed decision-making is critical for the informal and service-based sectors. Platforms like SErraND | Plug Wa Kazi, which connect users to local service providers, operate in the same high-demand, high-variability environment as the jollof vendor. For a 'Plug Wa Kazi' to be effective, the underlying system must do what Adebayo describes: quietly remember the patterns of demand so that providers aren't just 'guessing' where the next job is. When data is used to coordinate service delivery, it eliminates the waste of time and money that plagues the unmanaged marketplace.

Ultimately, the value of data analysis is not found in the aesthetics of a Tableau public board, but in the elimination of the 'guess.' As Adebayo’s experience across South Africa and the UAE proves, the most sophisticated systems are those that allow a decision-maker to look at a board and know exactly how much to 'cook' for the day ahead.