Turning business data into clearer decisions.

Transport service analysis

From transaction data to service-level performance insight.

Built a six-page synthetic-data case study covering network demand, fare revenue, recurring services, booking behaviour, timetable effects, origin-destination flows, and a weekday-departure heatmap.

• Analysed 484,230 transaction records at the correct grain, using passenger totals and distinct journey counts to measure demand per service.
• Used CTEs, window shares, and dense ranking to separate high-demand departures from high-value revenue services.
• Published the documented SQL, synthetic dataset, Power BI dashboard, and final report screenshots in a reproducible case study.

Transport service analysis

What changed after a modelled taxi-service launch?

Built a synthetic-data case study that uses SQL and Power BI to assess service mix, route movement, cancellation risk and acquisition-source quality around a modelled Local Taxi launch.

• Used four equal 21-day comparison windows and daily normalisation to avoid treating a pre-launch peak as baseline performance.
• Found Airport Transfer revenue per day 18.1% below the pooled baseline, with Airport-to-West-Cork journeys driving most of the decline.
• Separated cancellation frequency from booking-value impact, then prioritised Meta Ads and Taxi Google Ads as diagnostic follow-up areas.

Coffee shop sales analysis

From SQL exploration to a published Power BI report.

Built an end-to-end analysis of roughly 149,000 transaction records across three New York coffee shop locations, starting with SQL EDA and finishing with a three-page interactive Power BI report.

• Validated the data grain and replaced an unsupported AOV assumption with Average Transaction Record Value.
• Used CTEs, window functions, ranking, and DAX filter context to analyse stores, products, trends, and holidays.
• Published the report and documented the full analytical process, including mistakes, limitations, findings, and recommendations.

Olist e-commerce analysis

From transactions to commercial questions.

Built a clean analysis layer and Tableau dashboards to explore GMV, orders, customer growth, repeat purchase share, and category and product performance.

• Prepared raw transactional data for consistent reporting.
• Created executive and commercial dashboard views in Tableau.
• Explored customer acquisition, retention, and product performance.

Confidential internal project

Operational data & sales automation.

Mapped a manual operational reporting process and built a scheduled Google Sheets automation that ingests sales data, validates operational updates, and supports reliable route-planning panels for an event transport operation.

Designed the workflow around a structured ledger, controlled event inputs, cancellation checks, and safe rebuilds—improving the consistency and recoverability of planning information.

Impact: Reduced a recurring manual update workflow by approximately 80%, from around 9 hours to under 2 hours per month, with greater operational benefit during high-volume periods.

Operational details, figures, and system screenshots are intentionally anonymised.

Confidential internal project

Sales & customer performance analytics.

Built a structured Excel reporting model to turn several years of sales data into a clear view of demand, seasonality, customer behaviour, and event performance.

Prepared and transformed the data with Power Query, then created KPI views, charts, PivotTables, and interactive filters for practical exploration of the 2023–2025 reporting period.

Business value: Made it easier to compare event performance, identify busy and quiet periods, and distinguish new-customer acquisition from repeat-customer activity.

Commercial data and dashboard visuals are intentionally anonymised.