Turning raw data into decisions people actually act on.

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Marketing Ad Campaign

This project analyzes two years of digital ad campaign data across Google Ads and Facebook Ads to evaluate spend efficiency by subchannel, device, and audience. Using Excel and Power Query to clean and pivot the raw data, I found that Brand campaigns delivered the lowest cost-per-click despite receiving less budget than Generic campaigns, which had the highest CPC. This suggests that the ad spend mix could be reallocated toward better-performing channels for a stronger return.

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Superstore Sales Data

A retail sales analysis project built on the Kaggle Superstore dataset, covering four years of US sales data. I cleaned and validated the raw data in MySQL, fixing a date formatting bug that was throwing off chronological analysis, then wrote SQL queries to dig into profitability by category, region, and individual product. The numbers told a more complicated story than the top-line sales figures suggested: Furniture ran a profit margin under 3% despite strong revenue, the Central region had the second highest sales total but the weakest margin by a wide gap, and a few best-selling products, including a high end videoconferencing unit, were actually losing money on every sale.

I turned these findings into a two page Power BI dashboard, with KPI cards, category and regional breakdowns, a sales trend view, and a dedicated product performance page highlighting top sellers and their profitability, with filters to slice the data by region, state, category, and sub-category.

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Project Portfolio Report

Built a Power BI report that tracks budget versus forecast for a 30-project IT portfolio, giving leadership a clear view of where spending is on track and where it's not. I set up a star schema in Power BI, connecting a project dimension table to separate budget and forecast fact tables through Power Query, then wrote DAX measures to calculate variance in both dollars and percentage. The report has three pages: a portfolio overview with KPI cards, a project performance page ranking projects by variance, and a page comparing internal labor costs against external contractor costs. The dashboard surfaces two projects trending over budget out of thirty, the kind of finding that would normally kick off a real conversation with a project manager about scope or vendor costs.