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BEFORE: Client had messy Excel inventory with 500 rows, duplicates (A12 / a12), blanks, wrong prices like "$ 2.5" and "N/A", and different warehouse names (KER / ker / Kericho).
AFTER: I did:
Removed 12 duplicate SKUs
Standardized item names to Title Case
Fixed QTY and Prices to 2 decimals
Unified Warehouse to Kericho
Made sheet ready for pivot dashboard
Result: Clean, ready-to-use inventory in 2 days using Excel.
Tools: Excel, Remove Duplicates, Text to Columns, TRIM, PROPER
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Excel PivotTable — Sales Summary
Client had raw sales data (14 rows across UK/USA, split by quarter) and needed it turned into something actually usable. I cleaned it up and built a PivotTable that breaks total sales down by rep and country, with grand totals for both.
What I did:
Structured the raw data into a proper table (consistent headers, currency formatting)
Built a PivotTable summarizing Sum of Sales by Last Name and Country
Used SUMIFS so the totals update automatically if the source data changes — nothing hardcoded
Checked every formula for errors before delivery
Delivered as a working .xlsx file, ready to plug into a bigger report or dashboard.
If you've got messy sales/ops data sitting in a spreadsheet and need it turned into something you can actually read at a glance, this is exactly the kind of thing I can help with.
I worked on an Excel data organisation and reporting project involving a large retail sales dataset with inconsistent formatting, duplicate records and information across multiple worksheets.
I cleaned and standardised the data, used VLOOKUP/XLOOKUP and INDEX/MATCH to connect information, and created PivotTables to analyse sales by category and region.
I then structured the workbook into clear, easy-to-navigate sections and created a summary dashboard with key metrics and visual insights, making the data much easier to review, analyse and use for reporting.
Cleaned and validated a synthetic operational dataset containing duplicate records, missing values, invalid entries, and inconsistent formatting. Documented each issue, standardized the data, removed duplicates, and created a data-quality summary to demonstrate a practical end-to-end cleaning and validation workflow.
