Cleaning and Formatting Messy Data

Real-world data is rarely clean. AI can help you identify problems and fix them without writing complex formulas or code.

Common Data Problems

1. Inconsistent Formats

Dates written as "01/15/2024", "Jan 15, 2024", and "2024-01-15" in the same column.

2. Duplicate Entries

The same transaction appearing multiple times.

3. Missing Values

Blank cells where there should be data.

4. Typos and Inconsistencies

"New York", "new york", "NY", "N.Y." all meaning the same thing.

5. Wrong Data Types

Numbers stored as text, or text in number columns.

Identifying Data Problems

Start by asking AI to audit your data quality.

Standardizing Text Data

Fixing Date Formats

Handling Missing Values

Finding and Removing Duplicates

Correcting Obvious Errors

Restructuring Data

Sometimes the data structure itself needs to change.

Creating a Clean Dataset

After identifying issues, ask AI to create the clean version.

Key Takeaway

Clean data leads to accurate analysis. Use AI to audit your data quality first, identify specific problems, and then systematically fix them—all without writing complex code.