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I analysed two years of operating expenditure for a company with

7 departments and 10 expense categories, and wrote the findings

up as a 5-page report.

I started from a raw expense ledger with real problems in it:

118 exact duplicate rows, 132 transactions still marked Pending,

department names written 23 different ways (IT / I.T. / Mktg / HR),

amounts stored as text with the currency inside the cell, credit

notes recorded in brackets, and dates stored as text.

What I did:

- Cleaned the ledger in Power Query and documented every single

correction on a Cleaning Log sheet, with a reconciliation from

3,037 raw rows down to 2,787 usable transactions.

- Built a Power Pivot data model: two fact tables joined to shared

Department, Category and Year dimensions.

- Wrote 8 DAX measures for budget, actual, variance and variance %.

- Built 6 PivotTables and a dashboard with 4 KPI cards, 4 charts

and a year slicer that drives all of them.

- Wrote an analytical report covering methodology, the variance

breakdown, and 5 recommendations tied to specific numbers.

Key findings:

- FY2025 came in 6.1% over budget, after FY2024 closed 3.7% under.

The budget grew 5.0% year on year but actual spend grew 15.7%.

- 86% of the overrun sat in controllable spend, which ran 13.4%

over. Fixed commitments were only 1.4% over.

- Marketing (+20.4%) and IT (+16.3%) drove JOD 567,379 of adverse

variance, while HR and Operations left JOD 168,183 unspent — so

the problem is allocation as much as control.

Tools: Excel, Power Query, Power Pivot, DAX, PivotTables, Word

Note: this is a project I built on a fictional dataset to

demonstrate my method.

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