I don't just produce dashboards. I produce arguments — backed by original data,
structured as research, and written to reach the people who need to act on them.
Most analysts can visualise data. I can collect it, interrogate it, and tell you what it means —
in a way that non-technical audiences understand and respond to.
My startup research exposed that CleanTech and HealthTech achieve 90% and 81.5% survival rates on a fraction of Fintech's capital — directly contradicting the dominant market narrative. I don't confirm what people already believe. I find what the data actually says.
From scraping raw data in Python, thorough cleaning and modelling in SQL and Excel, to building interactive Power BI and Tableau dashboards — I execute the complete data analyst workflow end to end. No handoffs. No gaps.
My findings aren't buried in appendices. They are written to reach founders, investors, and policy makers. I turn statistical patterns into clear business arguments. That is a skill most technical analysts don't have — and most writers can't replicate.
"The business model beats the sector — every time."
Collected and cross-validated $4.17B in startup funding data across 293 companies, 528 deals, 8 sectors and 72 subsectors — resolving naming inconsistencies, currency discrepancies and unverified claims from TechCabal, Techpoint Africa, Crunchbase and LinkedIn. Built entirely on self-collected, independently verified data.
"CleanTech and HealthTech delivered 90% and 81.5% survival rates on a fraction of Fintech's capital — directly challenging the dominant market narrative that Fintech is Nigeria's safest investment sector."
"The crisis is not Nigerian. It is Lagos and Abuja."
Scraped 10,030 live rental listings from Nigeria Property Centre and Jiji.ng across six cities using Python. Applied the globally recognised 30% housing cost standard against NBS NLSS income quintile data to map housing supply against actual household earning power. Developed an original four-tier city classification framework from the data.
"In Lagos and Abuja, 89% of listings are Luxury or Ultra-Luxury. In Benin City, 91% are Affordable or Mid-Range. Same country. Same year. Same platform. Remove Lagos and Abuja and Nigeria's housing market looks fundamentally different — Ultra-Luxury collapses from 42% to 7.7%."
"From research to a tool anyone can actually use."
Built directly on top of the housing research dataset — an interactive web app where users enter their income and city to receive neighbourhood-level rent affordability recommendations. Powered by 10,030 real scraped listings and the same 30% housing cost standard used in the original study. Built and deployed using Bolt.new.
"Most analysts stop at the insight. This goes one step further — turning the data into a decision-making tool that real renters across six Nigerian cities can use right now."
"From legitimacy debate to preference debate."
Collected and manually coded 493 data points from 500+ tweets across 14 organic X (Twitter) threads between December 2025 and June 2026 — including a viral 311K-view "what I ordered vs what I got" thread. Coded each data point for sentiment, consumer archetype, platform preference and proximity perception across 457 unique users, then tested eight common beliefs about Temu against the data.
"Temu has not replaced Jumia — but it has fundamentally changed the conversation. The dominant question went from 'Is Temu even real?' to 'Which platform wins?' Nigerian consumers are rationally segmenting their spend: Temu owns low-risk, high-variety discretionary purchases, while Jumia holds high-risk capital items like phones and laptops through Pay-on-Delivery trust."
I came to data analytics from five years of professional writing and market research. That path is unusual — and it is the source of whatever makes my work distinctive.
Writers and analysts share one discipline: they both have to decide what the most important thing is, and say it clearly. The difference is that analysts can prove it. I learned to do both.
My three published studies and the live product built on top of them were all self-initiated — no client brief, no provided dataset, no guided tutorial. I identified the questions, collected the data, built the methodology, published the findings, and then turned them into a working app. That is the kind of analyst I am: self-directed, rigorous, and focused on insight that reaches people who can act on it.
Available remotely. Open to data analyst, market intelligence, research analyst, and business intelligence roles.