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The RowDesk workspace

Clean, deduplicate and prepare your data with confidence.

Turn CSV and Excel exports into data you can confidently import. Scan for issues, clean inconsistent values, compare records and review your changes in one browser-based workspace.

Start freeSee how it works
  • CSV and XLSX input
  • Preview before applying
  • Browser-local processing

One file. A clear path forward.

Start with a CSV or an Excel worksheet. Use the steps your file needs, then export for your next system.

  1. 01

    Scan

    Understand the file before making a change.

  2. 02

    Clean

    Standardize values and prepare your columns.

  3. 03

    Dedupe

    Review matching records and choose what stays.

  4. 04

    Compare

    Separate new records from those you already have.

  5. 05

    Review changes

    Inspect changed values and removed rows.

  6. 06

    Export

    Download a separate, clean CSV file.

Start with the Dataset Overview

See row and column counts, blank values, duplicate candidates and formatting issues. Safe fixes, Review recommended and Warnings help you decide what needs attention. Mixed date formats are flagged for review, not silently converted.

Explore the dataset scan
01 / Clean data

Consistent values.
A cleaner starting point.

Clean CSV files and Excel worksheets with deliberate, column-level operations. Preview the impact of important changes, standardize inconsistent values and keep identifiers as text.

  • Clean the values that need it

    Trim surrounding whitespace, normalize text casing and turn configured null placeholders into blank values.

  • Prepare the structure

    Remove completely blank rows. Rename, remove and reorder columns to prepare the fields your import needs.

  • Keep identifiers intact

    Preserve leading zeros and long identifiers as text. Review affected rows and before-and-after examples before applying important changes.

Explore CSV and Excel data cleaning
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
02 / Deduplicate

Keep the right record.
Know why it stays.

Deduplicate CSV data using the fields that identify a record in your workflow. Review each group and its proposed removals before changing the working dataset.

  • Choose the matching rule

    Match on one or more columns using exact values, or normalize capitalization and surrounding spaces. Blank keys do not match by default.

  • Make the keep rule explicit

    Keep First, Keep Last or Keep most complete. Completeness counts populated fields; it does not guess which information is correct.

  • Review the proposed removals

    See duplicate groups, the selected record and the reason it was selected. The operation can be undone during the active session.

Explore duplicate detection
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
03 / Compare files

Find what is new.
Leave repeats behind.

Compare CSV files before your next import. Use an incoming lead list as File A and an existing CRM export as File B, then keep only records that are new to your existing list.

  • Map your business keys

    Map Email in File A to Email Address in File B, even when the column names differ. Use selected fields and conservative matching options.

  • Understand every result

    Review new records, matched records and records only in File B, with an explanation of the mapped values used to match.

  • Prepare an intentional import

    Keep only new records from File A for your output. Both files are processed locally in local-processing mode.

Explore two-file comparison
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.

04 / Review changes

The result matters.
So does what changed.

Review data changes before you prepare an import. Important operations show affected-row counts and before-and-after examples. Applied steps stay visible in the session history, so you can inspect the result and undo the most recent step.

Explore change review and session undo
A change you can inspectIllustrative example
FieldBeforeAfter
EmailJORDAN@EXAMPLE.COMjordan@example.com
Company" Acme Inc. "Acme Inc.
Customer ID001042001042 Preserved

Selected operations: normalize email casing and trim surrounding whitespace.

  • Changed values

    See the effect of your cleaning operations rather than relying on the final file alone.

  • Removed rows

    Inspect records removed by blank-row cleanup, deduplication or comparison.

  • Active-session undo

    Undo and redo session steps while the workspace remains open. Your source file is unchanged.

05 / Export

A clean file.
A clear handoff.

Prepare data for CRM import, then download the current working dataset as a separate UTF-8 CSV. RowDesk prepares the file; you choose when and where to import it.

XLSX files are imported as tabular data, with a sheet selector for multi-sheet workbooks. The prepared output is CSV, not an edited Excel workbook.

Explore CSV and change exports
Clean CSV
Your current output dataset, with the selected columns, final row count and consistent quoting.
Changed rows
A separate export of rows whose values changed, for follow-up review.
Removed rows
Records excluded from the output, kept separate from the clean dataset.
Audit summary
A readable account of operations, key parameters, counts and the final result.
Advanced exports
Changed-row and removed-row exports, plus the audit summary, are Pro features.

Your file stays in your workflow.

In local-processing mode, the raw dataset is processed in your browser and is not uploaded to RowDesk servers. Changes apply to a working dataset; the original source file is never overwritten.

About local processing

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.