RowDesk
Start free
For CRM and business data

Messy data in.Clean, import-readydata out.

RowDesk is a browser-based data workspace that helps business teams clean, deduplicate, compare and prepare CSV and Excel data before importing into CRMs and other business systems.

Start freeSee how it works
  • Runs in your browserLocal data processing
  • Preview before applyingSee exactly what will change
  • No account requiredStart with a local file
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
The problem

Getting data ready for import
shouldn't take this much work.

Messy exports, duplicate records and inconsistent formatting waste time
and often lead to import failures in your CRM or business systems.

Duplicate records

Repeated contacts, companies or deals create conflicts and unreliable data in your CRM.

Common examples:

Repeated contact emails, duplicate companies, repeated deals.

Inconsistent formatting

Different date formats, casing and whitespace turn simple imports into manual cleanup.

Common examples:

01/02/25 vs 2025-01-02, JOHN vs John, extra spaces, missing or null values.

Import failures

Blank rows, malformed values and invalid identifiers can cause failed or unreliable imports.

Common examples:

Missing required fields, invalid email addresses, wrong data types.

No visibility into changes

Spreadsheet cleanup makes it difficult to know exactly what was modified before importing.

Common examples:

Hard to track what was updated, no audit trail, risk of accidental changes.

Before RowDesk changes anything,
it shows you exactly what needs attention.

  • Runs in your browser

    Your raw dataset stays on your device in local mode.

  • Preview before applying

    See exactly what will change before you make any updates.

  • Undo during your session

    Review and reverse changes within your active session.

  • Built for CRM imports

    Prepare clean, import-ready data for your CRM or business system.

See it in action

See what's wrong before
you change anything.

RowDesk scans your dataset and highlights duplicates, formatting issues,
blank rows, risky values and other problems so you can review and fix with confidence.

Find duplicates

Identify and review duplicate contacts, companies or deals.

Detect formatting issues

Catch date, number and text format problems.

Spot empty values

Find blank rows, missing fields and incomplete data.

Illustrative scan of customers-2025.csv. Review recommended actions and highlighted sample data before applying changes.
RowDesk
customers-2025.csv12,487 rows · 24 columns · 2.8 MB

Dataset Overview

Scan complete. 1,008 issues found. Review recommended fixes below.

648Safe fixes
312Review recommended
48Warnings
872Unique rows affected
Data previewIssues by column 24All issues 1,008
Sample of eight rows. Highlights identify duplicate candidates, formatting issues, missing emails and email syntax warnings.
Selection#NameEmailCompanyPhoneJob TitleLocationIssues
1John Smithjohn@acme.comAcme Inc.+1 555 0100DirectorNew York, NY2
2Sarah Johnsonsarah@acme.comAcme Inc.+1 555 0100Marketing ManagerNew York, NY1
3Sarah Johnsonsarah@acme.comAcme Inc.+1 555 0100Marketing ManagerNew York, NY3
4Michael Chenmichael@nextgen.coNextGen+1 555 0102Sales RepSan Francisco, CA1
5Emily DavisEmily@brightpath.comBrightPath+1 555 0103Customer SuccessAustin, TX2
6David Wilsondavid@momentum.comMomentum+1 555 0104ConsultantBoston, MA1
7Lisa BrownMissing emailAcme Inc.+1 555 0105AnalystSeattle, WA1
8James Millerjames@acmeAcme Inc.+1 555 0106ConsultantChicago, IL2

No changes applied. Your original file is unchanged.

Identify risky values

Flag email syntax, ambiguous dates and identifier risks.

Preview before applying

See exactly what will change and approve only what you want.

Keep control

Review and undo changes in this session.

  • Runs in your browser

    Your raw dataset stays on your device in local mode.

  • Preview before applying

    See exactly what will change before you make any updates.

  • Undo during your session

    Review and reverse changes within your active session.

  • Built for CRM imports

    Prepare clean, import-ready data for your CRM or business system.

How it works

From messy files to import-ready data in a few clear steps

RowDesk cleans, deduplicates, compares, and prepares your CSV or Excel files
so they're ready to import into your CRM or business system.

  1. Load

    Load data from CSV or Excel files.

  2. Scan

    Scan for structure, content, and potential issues.

  3. Clean

    Clean columns and standardize data formats.

  4. Dedupe

    Find and remove duplicate records with confidence.

  5. Compare

    Compare two files to find new and matched rows.

  6. Review changes

    Review changes and undo in your current session.

  7. Export

    Export a clean, import-ready CSV for your CRM.

Dedupe

Clean duplicate records with confidence.

RowDesk groups duplicate contacts, companies, or records, recommends which row to keep, shows why, and lets you review before removing duplicates.

  • Review duplicate groups before anything is removed

    See and verify every group of matching records.

  • Keep the most complete record automatically

    RowDesk recommends the row with the most populated fields within each matching group.

  • See exactly why a record was retained

    Compare populated fields and see the reason for each recommendation.

  • Undo duplicate cleanup from Changes

    Review and reverse removals during your active session.

Start freeSee how dedupe works

Review before removal. Undo during your session.

Find true duplicates

Exact or normalized field matches.

Keep most complete

Recommends the row with the most populated fields.

Example duplicate review, not a live dataset. Normalized Email and Company matches form 298 groups containing 894 rows. Keeping one row per group proposes 596 removals. No changes have been applied.
RowDesk

Duplicates

Find and remove duplicate records from your dataset.

customer-export.csv12,482 rows 5 columns
298duplicate groups
894duplicate rows
596rows proposed for removal

Match on these fields

EmailCompanyAdd field

Matching options

Ignore capitalization

Trim surrounding spaces

Blank keys do not match.

Keep which row?

Keep most complete (recommended)

Keeps the row with the most populated fields.
You can change this before applying.

Duplicate groups (298)

Review each group and confirm which row to keep. 596 rows are proposed for removal.

Group 13 recordsWhy this group?
These records match after trimming and ignoring case in Email and Company. Completeness counts the five displayed business fields.
SelectionRowNameEmailCompanyPhoneLocationCompletenessAction
108Sarah Johnsonsarah@acme.comAcme Inc.(555) 010-2200Boston, MAKeep (recommended)
245Sarah J. JohnsonSARAH@ACME.COMAcme Inc.EmptyEmptyRemove
617Emptysarah@acme.comACME INC.EmptyEmptyRemove

Keeping Row 108 because it contains 5 populated fields compared with 3 and 2 in the other records.

Group 24 records
Group 32 records
Remove 596 duplicates

Review every duplicate group

Verify before removing anything.

Undo later from Changes

Undo removals during this session.

Reviewable changes

See what will change.
Then make the call.

From duplicate removal to your final export, stay in control of the changes you make.

  • Preview before applying

    See affected rows and before-and-after examples for important changes.

  • Review what changed

    Inspect changed and removed rows before preparing your export.

  • Undo during your session

    Reverse transformations while your active session is open.

  • Keep your original file

    Work on a separate dataset. Your source file is never overwritten.

Compare files

Find what's new before you import.

Compare an incoming list with an existing export. See which records are new, matched, or only in the other file, so you can review before importing.

  • Compare two files

    Spot new, matched, and only-in-B records.

  • Match by your key fields

    Map columns like Email to Email Address.

  • Conservative matching options

    Use exact matching or normalize case and spaces.

  • Review before you import

    See why records matched, then keep only new ones.

Start freeSee compare workflow

Compare two files

Load File A and File B to see what's new, matched, or unique.

Match by mapped columns

Map your key fields, like Email to Email Address, between files.

Interactive illustration with five sample contacts, not a live dataset. File A is the incoming list; File B is an existing export. Each email key is unique within each illustrative file. Filters and selection affect only this preview.
RowDesk

Compare files

Find new and matched records before you import.

File Aincoming-customers.csv12,482 records
File Bcrm-export.csv14,021 records

Match by column

Email (File A)Email Address (File B)

Match options

Ignore capitalization

Trim leading and trailing spaces

1,450New recordsIn File A, not in File B
11,032Matched recordsPresent in both files
2,989Only in BIn File B, not in File A
Sample results using normalized Email to Email Address matching.
SelectionStatusNameEmail (File A)Email Address (File B)CompanySource
New (in A)Sarah Chensarah.chen@acme.co-Acme CoFile A
MatchedMichael TorresM.TORRES@GLOBAL.IOm.torres@global.ioGlobal IncBoth
MatchedPriya Patelpriya@vertex.compriya@vertex.comVertexBoth
New (in A)Daniel Kimdaniel@raftlabs.com-Raft LabsFile A
Only in BEmma Wilson-emma@brightpath.comBrightPathFile B
Why did these records match?

Email in File A is mapped to Email Address in File B. Leading and trailing spaces are trimmed, then values are compared without case differences. For example, M.TORRES@GLOBAL.IO matches m.torres@global.io. Names are not used; no fuzzy matching is applied.

5 sample rows shown; 15,471 all results total.

Review results

See matched and new records before changing your output.

Keep only new records

Continue with File A records not already present in File B.

Your data, your control

Your data stays under your control.

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

  • Processed locally in your browser

    In local-processing mode, RowDesk works with your raw dataset on your device.

  • No raw dataset uploads

    Your raw dataset is not uploaded to RowDesk servers in local-processing mode.

  • Preview before applying

    Review important changes before applying them to your working dataset.

  • Your original file stays unchanged

    Export a separate CSV when you are ready. Your original source file is never overwritten.

Raw data stays on your device

Processed locally in your browser in local mode.

Preview important changes

Review what will change before applying it.

No raw dataset uploads

No raw dataset is sent to RowDesk servers in local mode.

Original file preserved

Export a separate CSV. Your source stays unchanged.

CSV / XLSXYour file
Your browser

Processing in your browser

Your raw dataset stays on your device in local-processing mode.

  1. Read file
  2. Scan data
  3. Review
  4. Export CSV
CSVYour export
Illustrative local-processing workflow. Open a CSV or Excel file in RowDesk, process and review it in your browser, then export a separate CSV. The original file is not overwritten.

Built with privacy in mind

Prepare customer, candidate and business data locally. Review changes and export a separate file when you are ready.

  • Browser-local processing
  • Preview changes
  • Original file preserved

Use cases

Built for teams that work with data every day.

For sales, marketing, operations and finance. Prepare clean, import-ready data for your next business workflow.

  • Reduce repetitive manual cleanup
  • Review issues before importing
  • Standardize your business data
  • Prepare files for business systems
Explore all use cases
Clean leads before import

Sales & Marketing

Clean lead lists and compare new prospects against existing CRM exports before importing.

RevOps and Marketing Ops can map email columns, review matches and keep only new records.

Review data before import

Operations

Standardize recurring spreadsheets and review inconsistent values before preparing your next import.

CRM admins can scan customer exports, preview formatting fixes and review duplicate groups.

Standardize supplier data

Finance & Admin

Clean customer and supplier lists. Review duplicates and prepare consistent records for import.

Normalize casing and whitespace, check missing values and export a separate cleaned CSV.

Clean candidate data

Recruiting

Review duplicate candidates and formatting issues before preparing a CSV for your ATS import.

Match on selected fields, review which record to keep and check important changes before applying.

Prepare product or order data

eCommerce

Clean product, order or customer spreadsheets before importing them into your business platform.

Trim whitespace, review blank values and compare an incoming file with an existing export.

Prepare CSV or Excel data

Any Team with Data

Prepare CSV and Excel files for business imports, from internal teams to agencies and consultants.

Review client files in local-processing mode, inspect changes and export a separate CSV.

Popular destinations

Prepare clean files for the tools your team already uses.

Transform, clean and format your data into ready-to-import CSV files
for the systems your team relies on.

  • HubSpot

    Prepare contacts, companies and deals.

  • Salesforce

    Format accounts, leads, opportunities and custom objects.

  • Pipedrive

    Prepare people, organizations, deals and activities.

  • Microsoft Dynamics

    Structure records for accounts, contacts and opportunities.

  • Zoho

    Prepare leads, contacts, deals and customer data.

  • Google Sheets

    Create clean datasets for spreadsheets and workflows.

Free CSV tools

One file.
One focused task.

Clean, check, deduplicate or compare a CSV in your browser. No account needed.

Explore all free tools

FAQ

Got questions?
We've got answers.

A few things to know about using RowDesk to prepare your data for business imports.

Does my data leave my device?

In local-processing mode, your raw dataset is processed in your browser and is not uploaded to RowDesk servers. This refers to file data; account and billing services are separate.

What file types does RowDesk support?

RowDesk supports CSV and Excel .xlsx files as tabular data. For a workbook with multiple sheets, you choose the sheet to work with. The prepared output is a CSV file.

Can I preview changes before applying them?

Yes. Important transformations show affected-row counts and before-and-after examples, so you can review their impact before applying them.

Can I undo changes?

You can undo and redo transformations during your active session. RowDesk works on an output dataset and never overwrites your original source file.

How does duplicate detection work?

Choose the columns to match, then use exact matching or normalized matching that trims surrounding spaces and ignores capitalization. Review duplicate groups and select which row to keep. Fuzzy matching is not part of the initial release.

Do I need an account to get started?

The core workflow lets you open a file, inspect it and perform basic cleanup without an account. Account requirements for paid features will be detailed alongside the final plans.

Is there a file-size limit?

Practical limits depend on your device, browser and file format. Tested file-size ranges will be published after benchmarking; we do not promise unlimited file sizes.

Can RowDesk prepare files for CRM imports?

Yes. RowDesk helps you clean and review CSV and Excel data, then export a CSV for import into your CRM or business system. You still need to check the destination's required fields and import rules. This does not require a direct CRM integration.

Get started

Turn messy data into
import-ready data today.

Review your changes. Export a clean CSV.
Make your next import a confident one.

Try RowDesk freeSee how it works
  • Browser-local processing
  • Preview before applying
  • CSV / XLSX import
Illustrative duplicate review: six records in three groups, with three records selected to keep before exporting a separate CSV.

Duplicates (3 groups)

Keep most complete. Review before removal.

Three illustrative duplicate groups matched by normalized email
Selected to keepNameEmailCompanyReview
John Smithjohn@acme.comAcme Inc.Keep
Jon SmithJOHN@ACME.COMAcme Inc.Duplicate
Sarah Johnsonsarah@nextgen.coNextGenKeep
Sarah JohnsonSARAH@NEXTGEN.CONot providedDuplicate
Michael Chenm.chen@vertex.coVertex Ltd.Keep
Michael ChenM.CHEN@VERTEX.CONot providedDuplicate
3 groups · 3 records to keepReview selection
Ready for your next step

Reviewed data.
A separate CSV export.