Have you ever opened a folder full of spreadsheets and felt your stomach drop?
Twelve files staring back at you. Some are .csv. Some are .xlsx. They are exports from three different systems that were never meant to talk to each other.
And your boss just asked: "Can you merge these into one clean database by Friday?"
The Manual Tax
You open the first file. Phone numbers look like 5551234567. The second file has +1-555-123-4567 x100. The third has (555) 123.4567.
Dates are even worse. Is 01/12/2024 January 12th or December 1st? Who knows.
Thousands of duplicate emails buried in there. Blank rows. Misspelled headers. Random extra spaces that break every formula you try.
Your internal team estimates three to four weeks. Manual clicking. Endless VLOOKUP formulas. Copy-pasting until your wrist hurts.
It is the kind of work that makes you question your career choices at 11 PM on a Saturday.
The Solution
In my 15 years in retail operations, I got tired of saying it was impossible.
So I built Data Cleaner. A Python tool that treats messy data like a puzzle instead of a prison sentence. It merges every file, removes duplicates, standardises dates and phone numbers, strips blank rows, and exports one clean database.
That impossible month of work becomes 213 seconds of execution.
🎬 Watch the demo: YouTube
💻 Full project and configuration: Data Cleaner
If you are dealing with messy data that eats up your time every month, automation might be simpler than you think. Sometimes it just takes seeing someone else do it first.