SQL analytics · dashboard building · data cleanup 📧 [email protected] · 💼 Omonuwa Dave Osamwonyi · 📍 Italy
I help hospitality and ecommerce businesses turn raw sales data into clear, actionable dashboards — so you know what’s working, what’s dead weight, and what to do next month.
What I offer:
The question: How is Ahmedabad’s ecommerce performance trending month over month, and where’s the opportunity?
What I did: Built a full Tableau dashboard tracking revenue, category and product mix, and month-over-month change — with a written insight layer flagging a Home Appliances sales drop worth investigating.
Tools: SQL (data exploration) → Tableau (dashboard build)
Key insight delivered: “Home Appliances sales fell by roughly half in May while every other category more than doubled — worth investigating whether this is a stock/availability issue, reduced marketing push, or seasonal demand drop.”
The question: Can messy, inconsistent transaction data be cleaned and trusted for reporting?
What I did: Started with 10,000 rows, with missing/invalid data ranging from 4.6% (Date) to 39.6% (Location). Recovered Quantity, Price, and Total Spent mathematically using their relationship to each other. Relabeled Payment Method, Location, and Date as "Not Recorded" where no calculation was possible. Result: under 5% missing across all numeric columns, with remaining gaps limited to rows missing multiple fields at once — left unresolved rather than guessed.
Tools: SQL (CASE WHEN, COALESCE, CTEs) → Tableau