The CleanSmartLabs Blog

Nobody wakes up excited to clean data.

But here you are. Maybe a spreadsheet just broke your pivot table. Maybe you're staring at 12,000 rows wondering how many are duplicates. Maybe someone just asked for "clean customer data by Friday" like that's a simple request.


We get it. Data cleaning is the work that has to happen before the work that actually matters. It's unglamorous, time-consuming, and weirdly satisfying when you finally get it right.


This blog is about making that process less painful. We write about the stuff that actually causes problems—duplicate records, formatting disasters, missing values, outliers that wreck your averages—and how to fix them. Sometimes we'll get technical. Sometimes we'll just commiserate.


No thought leadership fluff. No "data is the new oil" nonsense. Just practical advice for people who have real datasets and real deadlines.

Abstract illustration of data transformation, with fragmented elements flowing toward a glowing cube on a platform.
By William Flaiz December 12, 2025
The cost of bad data is wasted spend, missed deals, and broken trust. Learn how to quantify it, stop duplicates, standardize, and build a lasting fix.
By William Flaiz December 9, 2025
You've got a dataset. You've got a deadline. You've got a boss who wants insights by Thursday. The temptation is to skip straight to the analysis. Don't. Dirty data doesn't announce itself. It hides in plain sight until your quarterly report shows revenue doubled (it didn't) or your email campaign goes out to 4,000 contacts who are actually the same 900 people entered multiple ways. I've seen both happen. The revenue one was worse. Here's what to check before you trust any dataset enough to make decisions from it.