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Clean data

Clean messy CSV and Excel data before you import it.

Find common formatting and data-quality problems. Choose the cleanup your file needs, preview proposed changes and prepare a clean CSV for your next CRM or business-system import.

Start cleaning dataSee how RowDesk works
  • CSV and XLSX input
  • Your choice of changes
  • Browser-local processing
Preview of cleaning operations and sample data. This illustration does not process files.
RowDesk
customer-export.csv12,487 rows · 24 columns · 2.8 MB
Your data stays on this deviceProcessed locally in your browser. Local mode.
Features

Clean your data

Apply common data cleaning operations. Preview changes before applying.

Safe fixes (2)

These operations are low risk and usually safe to apply.

Trim surrounding whitespace

Remove leading and trailing whitespace from text values.

Remove completely blank rows

Delete rows where all cells are empty or contain only whitespace.

Review recommended (1)

These operations can modify data. Review changes before applying.

Normalize email casing

Convert email addresses to lowercase.

1 column selected

Other options

Additional cleaning operations.

Standardize text casing

e.g. Title Case

Replace common variations

e.g. Yes/No, True/False, 1/0

Clean null placeholders

e.g. NULL, N/A, - with empty values

Data preview (sample of 100 rows)

This shows a preview of your data. Use the checkboxes to select columns for cleaning operations.

Six illustrative customer records, with email values highlighted for review
Selection#NameEmailCompanyPhoneJob TitleLocation
1John SmithJOHN@ACME.COMAcme Inc.+1 555 0100DirectorNew York, NY
2Sarah Johnsonsarah@globalco.comGlobal Co+1 555 0101Marketing ManagerChicago, IL
3Michael Chenmichael@nextgen.coNextGen+1 555 0102Sales RepSan Francisco, CA
4Emily DavisEMILY@brightpath.comBrightPath+1 555 0103Customer SuccessAustin, TX
5David Wilsondavid@momentum.comMomentum+1 555 0104ConsultantMiami, FL
6Lisa Brownlisa@acme.comAcme Inc.+1 555 0105Project ManagerSeattle, WA
Choose your cleanup

Fix the formatting.
Keep the meaning.

CSV data cleanup is not one blanket operation. Start with common issues, then decide which changes are appropriate for your file.

Safe fixesLow-risk operations that usually preserve the meaning of populated values. Still check their scope and impact.

Review recommendedChanges that can alter values. Confirm the selected columns, examples and intended result before applying.

Safe fix

Trim surrounding whitespace

Remove spaces before and after a value while preserving internal spaces by default. Useful for exported email addresses, company names and other text fields.

Before
" Acme Inc. "
After
"Acme Inc."
Safe fix

Remove completely blank rows

Remove rows only when every field is empty or contains whitespace. A record with one missing field is not a completely blank row.

Before
All fields blank
After
Row removed
Review recommended

Clean configured null placeholders

Replace tokens you have chosen, such as N/A or null, with a blank value. Check the context first: a token may have a real meaning in a particular column.

Before
"N/A" (configured)
After
Blank value
Review recommended

Standardize text casing

Choose lowercase, UPPERCASE or Proper Case for selected columns. Review names, abbreviations and case-sensitive identifiers before changing them.

Before
"LONDON"
After
"London"
Review recommended

Normalize email casing

Apply lowercase to the email column you select. This normalizes formatting; it does not verify that an address exists or can receive messages.

Before
"JORDAN@EXAMPLE.COM"
After
"jordan@example.com"
Review recommended

Standardize selected values

Define which variations should become one consistent value in a chosen column. You choose the replacement, rather than assuming similar-looking values mean the same thing.

Before
"YES" / "Yes"
After
"Yes" (chosen value)

Duplicate removal is a separate Dedupe step. Cleaning values does not decide which records represent the same contact or company.

Preview before applying

Know the impact.
Then confirm the change.

Preview proposed cleanup before it changes your working dataset. See affected rows, before-and-after values, proposed removals and an operation summary. Nothing in the proposed cleanup is applied until you confirm.

Preview changesIllustrative 6-row dataset
Rows affected
4
Rows with value changes
3
Values changed
4
Row removed
1
Changed values 4 values across 3 rows
Proposed changes in sample rows 1, 2 and 5; quotation marks show surrounding spaces.
RowColumnBeforeAfterOperation
1Email" JORDAN@EXAMPLE.COM "jordan@example.comTrim + lowercase
1Company" Acme Inc. "Acme Inc.Trim whitespace
2EmailSAM@EXAMPLE.COMsam@example.comLowercase
5Company" Northwind "NorthwindTrim whitespace
Removed rows 1 completely blank row
Row 3

All fields are empty. This row is proposed for removal by Remove completely blank rows, not by a duplicate-removal rule.

Example proposal, not applied. A row can contain more than one changed value.

Column-specific cleanup

The right rule.
On the right columns.

Data normalization should follow what a column means. Lowercasing email addresses is a different decision from changing product codes, customer names or account IDs.

Choose columns for casing and selected-value standardization. Apply email cleanup to Email, define a consistent set of values for Status, and leave columns that do not need that operation alone.

Identifiers are preserved as text. Leading zeros and long numeric-looking values should not be lost simply because you opened the file to clean it.

Different columns, deliberate rules
Email

Trim surrounding spaces + lowercase

JORDAN@EXAMPLE.COMjordan@example.com
Status

Replace a configured variation

YESYes
Customer ID

No casing or replacement operation selected

001042001042

Illustrative configuration. Select the columns and values appropriate to your own file.

Review and undo

A cleanup step.
Not a point of no return.

Applied cleaning operations become readable steps in RowDesk Changes. Inspect what changed, review removed rows and undo the most recent transformation while your session is active. Redo is available in that same session.

Explore change review and active-session undo

  • Inspect the transformation

    Review the operation, its selected columns and its effect on the working dataset.

  • Undo within your session

    Return to the previous state while the active workspace remains open. Session undo is not a permanent version archive.

  • Keep the original file

    The source CSV or XLSX file is never overwritten. Cleanup takes place on the working dataset.

Prepare for the next system

Make the next import
easier to prepare.

Consistent values and a tidy file structure make it easier to prepare clean data for CRM import. RowDesk handles file preparation; you review the result against your destination's requirements.

  • Start with usable values

    Remove accidental outer spaces and standardize selected text values before they become extra variants in the next system.

  • Keep missing data visible

    Turn chosen null placeholders into genuine blanks, without pretending cleanup can supply missing business information.

  • Export a separate CSV

    Clean CSV data or import a tabular Excel worksheet, then export the prepared dataset as CSV. RowDesk does not edit workbook formulas or formatting.

A clean file is not a guarantee of import acceptance. Check required fields, accepted values and field mappings in your destination before importing.

Processed locally in your browser. In local-processing mode, your raw dataset is not uploaded to RowDesk servers.

About local processing
Keep preparing your data

Start with messy data.
Leave with a cleaner file.

Choose your cleanup, preview the impact and export a separate CSV.

Start cleaning dataExplore all RowDesk features