How to Clean Spreadsheet Data with AI
Turn messy, inconsistent data into clean, analysis-ready datasets in minutes instead of hours.
Dirty data is one of the biggest productivity killers for anyone working with spreadsheets. Inconsistent formatting, duplicate entries, missing values, and typos can derail your analysis and lead to costly mistakes. Traditionally, cleaning data requires tedious manual work or complex formulas. With ModelMonkey's conversational AI assistant, you can clean thousands of rows using simple, natural language commands - just describe what you need and approve the changes.
What You'll Need
- A Google Sheets account
- ModelMonkey add-on installed
- A spreadsheet with data that needs cleaning
Step-by-Step Guide
Identify Your Data Quality Issues
Before cleaning, understand what problems exist in your data.
- Open your spreadsheet and scan for common issues: duplicates, inconsistent capitalization, extra spaces, mixed date formats, typos, missing values
- Make a list of the specific cleaning tasks you need to perform
- Create a backup copy of your data (File > Make a copy) before making changes
- Open ModelMonkey by going to Extensions > ModelMonkey > Open
Pro Tip
Having a clear list of issues helps you give specific instructions to the AI assistant.
Remove Duplicate Rows
Eliminate duplicate entries while preserving unique records.
- In the ModelMonkey chat interface, type: "Remove duplicate rows based on [column name], keeping the first occurrence"
- If you want to identify duplicates before removing them, ask: "Highlight duplicate rows in yellow"
- ModelMonkey will propose the changes and show you what will be affected
- Review the preview and click "Approve" when you're ready
- The AI will execute the deletion or highlighting
Pro Tip
If some duplicates are legitimate (e.g., recurring transactions), specify which columns make a row unique: "Remove duplicates based on Customer Name AND Date."
Standardize Text Formatting
Fix inconsistent capitalization, spacing, and text formatting.
- In the chat, type: "Standardize all company names to proper case and remove extra spaces"
- For addresses: "Standardize all state abbreviations to uppercase (CA, NY, TX)"
- For names: "Convert all names in column A to title case"
- ModelMonkey will propose formulas or direct edits to standardize the formatting
- Review and approve the changes to apply them across your data
Pro Tip
Be specific about which columns need formatting. You can say "Apply proper case to columns B, C, and D" to target multiple columns at once.
Fix Date and Number Formatting
Standardize dates and numbers to a consistent format.
- For mixed date formats, ask: "Convert all dates in column B to YYYY-MM-DD format"
- For currency: "Format all amounts in column E as currency with 2 decimal places"
- For percentages: "Convert values in column F to percentages"
- ModelMonkey will detect the various formats and propose the conversion
- Review the preview to ensure the formatting looks correct, then approve
Pro Tip
If dates are stored as text, ask ModelMonkey to "Convert text dates to actual date values" first, then apply your desired formatting.
Handle Missing Values
Deal with blank cells and missing data intelligently.
- Identify missing values by asking: "Highlight all blank cells in columns A through E in light red"
- Fill with defaults: "Replace blank cells in column C with 'Not Specified'"
- Fill forward: "Fill blank cells in column D with the value from the cell above"
- Remove incomplete rows: "Delete rows where any of columns A, B, or C are blank"
- Review each proposed change and approve when satisfied
Pro Tip
Be thoughtful about how you handle missing data. Sometimes a blank truly means "zero" or "not applicable," other times it indicates incomplete data that should be removed.
Correct Typos and Inconsistencies
Use AI to detect and fix common misspellings and variations.
- Ask ModelMonkey: "Find variations of company names like 'Google', 'Google Inc', 'Google LLC' and standardize them to 'Google'"
- For categories: "Look for similar category names that might be duplicates and suggest consolidations"
- The AI will analyze your data and propose specific replacements
- Review the suggestions carefully - the AI will show you what it found
- Approve the changes you want to apply
Pro Tip
You can iterate - after the AI makes suggestions, you can refine by saying "Also standardize 'Acme Corp' and 'Acme Corporation' to 'Acme'".
Validate and Verify Your Clean Data
Ensure your data cleaning didn't introduce new problems.
- Ask ModelMonkey: "Create a summary showing total rows, number of blank cells by column, and count of unique values in each column"
- The AI will generate formulas to calculate these statistics
- Spot-check a random sample of cleaned rows to verify accuracy
- Compare the row count before and after to ensure you didn't accidentally delete good data
- Ask for a new sheet: "Create a Cleaning Log documenting the transformations I've applied today"
Pro Tip
Keep your original data in a separate tab. If something goes wrong, you can always refer back to it or re-run the cleaning with different instructions.
