Fixing Messy Text Data

Text inconsistencies are the most common data quality problem. Names, addresses, categories, and free-text fields are where data gets messy fastest. AI is particularly good at fixing these because it understands language and context.

Standardizing Names

People's names and company names are notoriously inconsistent in datasets. The same person might appear as multiple entries:

Standardizing Categories

When categories are entered manually instead of selected from a dropdown, variations creep in quickly:

Standardizing Addresses and Locations

Address data is especially messy because there are so many valid ways to write the same location:

Cleaning Free-Text Fields

Free-text fields like notes, descriptions, or comments often contain useful information buried in inconsistent formatting:

Fixing Capitalization

Inconsistent capitalization makes data look unprofessional and causes grouping problems:

Removing Extra Whitespace

Invisible spaces cause silent matching failures—two values look identical but don't match:

Merging Variations

After identifying variations, ask AI to create the actual cleaned data:

Getting the Results Back Into Your Spreadsheet

After AI cleans your data, you need to get it back into your spreadsheet:

1. Ask for CSV format: Tell AI to output the cleaned data as CSV, then copy-paste into a new sheet
2. Ask for formulas: Request Excel or Google Sheets formulas that apply the same fixes
3. Ask for a mapping table: Create a reference table you can use with VLOOKUP or XLOOKUP

Key Takeaway

Messy text is the most common and most frustrating data problem. AI understands language context—it knows "NY" and "New York" are the same—which makes it far better than manual find-and-replace for standardizing text data.