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How do I clean messy Excel data?

How to Clean Messy Excel Data (Step by Step)

Messy Excel data usually means the same few problems: extra spaces, numbers stored as text, duplicate rows, inconsistent spellings, and blanks where values should be. Each one quietly breaks lookups, pivots and totals. The fix is a repeatable order of operations, not random edits.

Plumb is built on a simple idea: the data your business runs on should be trustworthy before anyone draws conclusions from it. Cleaning, checking and reshaping data should not need macros, scripts or a weekend. Plumb keeps your files private, shows every change it proposes, and lets you approve fixes instead of hunting for problems by hand.

How Plumb helps: AI data cleaning

Plumb's AI data cleaning scans a sheet, lists every issue it finds (whitespace, mixed types, duplicates, inconsistent categories, empty cells), and proposes a fix for each. You review the changes side by side and apply them in one click, with the original file left untouched.

How It Works

Whether you clean by hand or with Plumb, work in this order so later fixes do not undo earlier ones.

  1. Make a copy of the original sheet. Never clean the only copy of your data.
  2. Trim spaces: use =TRIM(A2) (and =CLEAN(A2) for hidden characters) in a helper column, then paste as values.
  3. Fix data types: convert numbers stored as text with Data > Text to Columns or =VALUE(), and make dates real dates.
  4. Standardize categories: unify spellings like 'NY', 'New York' and 'new york' with Find & Replace or a mapping table.
  5. Remove duplicates: Data > Remove Duplicates, choosing the columns that define a unique record.
  6. Handle blanks: decide per column whether to fill, flag or drop missing values.
  7. In Plumb: upload the file, review the detected issues, accept or edit each suggested fix, and export the clean version.

Where This Helps

CRM exports

Clean contact lists with stray spaces, duplicate emails and mixed-case company names before importing them anywhere.

Finance sheets

Turn text-formatted amounts and inconsistent dates into numbers and dates that SUM and pivot correctly.

Survey results

Standardize free-text answers like 'Yes', 'yes ' and 'Y' into one category before you count them.

early access

Try Plumb in early access

Plumb is in early access. Join the waitlist on tryplumb.pro to get an invite. Early access covers Excel (.xlsx), CSV and JSON files, and we are adding more sources as people ask for them. Your files are processed privately and are never used to train models.

Clean once, trust it everywhere

Now you have a repeatable way to do it, by hand or with Plumb. Join the waitlist and tell us what your data looks like; we read every reply.

Join the waitlist