Why Data Needs a Clean Up

Data collected from real-world sources is seldom neat or ready to use. It usually comes messy, with missing pieces, mixed-up labels, weird formats, and duplicates. It’s like trying to read a book with pages out of order and some words smudged—we need to clean it up before it makes sense.

Cleaning data isn’t just about correcting errors. It’s about transforming raw, unreliable data into a trustworthy foundation that can drive meaningful insights and decisions.