How to Clean Messy Data in Google Sheets with AI (2026)

If you are searching for google sheets ai data cleaning, you usually want one thing: turn inconsistent rows into a table you can trust for reporting, formulas, and charts.

This guide uses Sheet Agent in Gemini AI for Sheets and focuses on repeatable cleanup steps (not one-off fixes).

When this workflow is the right fit

• Your sheet has mixed date formats, inconsistent text casing, or extra spaces.
• You need to normalize categories before running pivot tables or charts.
• You want to keep a clear audit trail of what was changed and why.

Step 1) Define a clear cleanup spec before prompting

Do this first. AI cleanup works best when the target format is explicit.

• Date standard: decide one format (for example YYYY-MM-DD).
• Category standard: define canonical labels (for example IT Services instead of mixed variants).
• Text standard: choose casing rules (Title Case, UPPERCASE, etc.).
• Duplicate policy: define key columns used for dedupe.

If duplicate removal is your main problem, use this dedicated guide: How to Remove Duplicates in Google Sheets with AI.

Step 2) Open Sheet Agent and scope the exact tabs

1. Open Extensions → AI for Sheets → Sheet Agent.
2. Click the Sheet selector (+) and choose only relevant tabs.
3. Keep the request scoped to one cleanup job per prompt.

Sheet Agent supports tab selection, so you can avoid cleaning the wrong worksheet by accident.

Step 3) Run cleanup prompts in priority order

Start from structural consistency, then move to business labels.

Prompt A: normalize formatting

Clean this table for analysis:
- Trim leading/trailing spaces in all text columns
- Standardize dates to YYYY-MM-DD
- Convert amount columns to numbers
- Keep original row order

Prompt B: normalize categories

Standardize category values using these canonical labels:
- IT Services
- Marketing
- Operations
Map close variants to these labels and leave unknown values unchanged.

Prompt C: add QA helper columns

Add two helper columns:
1) Data Quality Flag (OK / Review)
2) Cleanup Notes (short reason when Review)
Mark rows with missing required fields as Review.

If the request needs formula help, use AI formula generator workflow to validate edge cases before final reporting.

Step 4) Validate before reporting or charting

• Type check: date/number columns must be real typed values, not plain text.
• Null check: required fields should have no unexpected blanks.
• Category check: same concept should not appear under multiple labels.
• Sampling check: manually inspect 20–50 rows before publishing dashboards.

After cleanup is stable, continue with chart generation or multi-table analysis.

FAQ

Should I ask for all cleanup changes in one huge prompt?

Better to run short, scoped prompts in sequence. It improves reviewability and rollback safety.

Can I clean multiple tabs in one run?

Yes. Use the Sheet selector to choose target tabs first, then provide a precise cleanup spec.

How do I reduce cleanup mistakes?

Define canonical labels first, ask for helper QA columns, and sample-check rows before downstream analysis.