I took an intentionally messy contact dataset and cleaned it up from scratch just to show what i can actually do with raw, unorganized data.
This dataset had all the usual problems you'd find in real client work; duplicate contacts, phone numbers with random dashes, dates written five different ways, names in all lowercase, and city names with inconsistent capitalization.
Here's what i did to fix it:
.Tracked down and removed all duplicate rows
.Reformatted every phone number into a clean, consistent structure
.Converted all dates to one standard format (YYY-MM-DD)
.Fixed name casing and spacing errors throughout
.Standardized all city names so the data is uniform
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The goal was to build a simple but functional CRM system to help a small business track clients, follow-ups, services, and payment status all in one place.
I built it entirely in Google Sheets, organizing client contact details, assigning status labels (Lead, Active, Closed), tracking last contact and next follow-up dates, logging services offered, and color-coding payment status for quick visibility.
The result is a clean, easy-to-use tracker that keeps client management organized and follow-ups from slipping through the cracks.