Think about a messy spreadsheet you've used before. Maybe it was for tracking sales, and for every sale, you entered the customer's full name, address, and phone number. If a customer bought ten things, you'd type their address ten times. It works, but it's inefficient and risky.

What if that customer moves? You'd have to find every single entry for them and update the address. Miss one, and your data is inconsistent. This is the kind of problem database normalization solves.

In simple terms, normalization is a formal process for organizing data into tables to minimize repetition. By reducing this redundancy, we also protect the data's integrity, ensuring it remains reliable and consistent. This process helps us avoid troublesome issues called data anomalies.

When a database isn't normalized, it's vulnerable to three types of problems: update, insertion, and deletion anomalies. Let's look at an example table for a small online store. Notice the repeated customer information.