Review changes

See exactly what changed before you export.

Keep your cleaning and transformation steps visible. Review changed values, removed rows and applied operations before downloading the final CSV prepared from your CSV or Excel data.

Start with your data See how it works

Review the impact
Original file unchanged
Browser-local processing

RowDesk workspaceSample data

customer-export.csv1,248 rows · 6 columns

Local mode

Changes

Review your working dataset before export. Illustrative session, not a live file.

Original rows: 1,248
Current rows: 1,200
Rows removed: 48
Values changed: 5
Transformations: 6

Five changed values across four retained rows. Quotation marks show surrounding spaces.
Source row	Column	Before	After	Operation
12	Company	" Acme Inc. "	Acme Inc.	Trim whitespace
34	Company	" Northwind "	Northwind	Trim whitespace
12	Email	JORDAN@EXAMPLE.COM	jordan@example.com	Email casing
57	Email	SAM@EXAMPLE.COM	sam@example.com	Email casing
89	Email	LEE@EXAMPLE.COM	lee@example.com	Email casing

5 changed values across 4 retained rows. Customer IDs remain unchanged.

Active-session history1,200 rows ready for review

Original vs current

Understand the result.
Not just the row count.

Compare original and cleaned data at a glance. Original rows, current rows, removals, changed values and applied transformations describe different parts of the cleanup. A value change does not necessarily remove a row.

Account for every removal

The sample session starts with 1,248 rows and removes 48, leaving 1,200. Blank-row cleanup, deduplication and comparison each contribute to that difference.

Separate values from rows

Five values changed across four retained rows in the example. One row can contain multiple changed cells. Renaming a column changes its header, not its cell values.

Count the applied steps

Six transformations describe how the example reached its current state. Review the operation summaries as well as the final totals before exporting.

Transformation history

A visible sequence.
Not an opaque result.

Track data cleaning changes through readable session steps. Each applied transformation records the operation and its impact, so you can connect the current dataset to the choices that produced it.

Cleaning: Review the selected columns and affected values, such as trimming Company or lowercasing Email.
Duplicate removal: Check the matching keys, keep rule and number of rows removed from the working dataset.
Comparison and filtering: See the impact of keeping only new records from File A after comparing against File B.
Column changes: Follow renames, removals and reordering that shape the output, separately from changes to cell values.

This is a data cleaning audit trail for the active session, not a permanently stored cross-session history or a collaborative version-control system.

Changed values

Before and after.
With the reason attached.

Review CSV changes in context: the source row, column, previous value, current value and operation responsible. Check the actual difference instead of assuming a successful operation produced the intended result.

Source row 12 / Email

Before: JORDAN@EXAMPLE.COM
After: jordan@example.com

Operation: Normalize email casing

Only the casing changed. This does not verify that the mailbox exists. The same row also has a separate Company whitespace change in the example above.

Removed rows

What left the dataset?
Know why it left.

Review removed rows as well as the records left behind. The reason and originating operation help explain why a row is absent from the prepared output.

Duplicate

A record was excluded by the configured matching and keep rules. Check the retained record before accepting the removal.

Blank Row

Every field in the row was blank. This is different from a record with only one missing value.

Matched Existing Record

The row from File A matched the reference file and was excluded by Keep only new records.

User Exclusion

Where a row is explicitly excluded by your choice, distinguish that decision from a rule-based removal. This reason is not present in the illustrated session.

Preview before applying

Check the proposal.
Then check the result.

Preview Changes and Changes serve different moments. Preview important cleanup or removal operations before confirming them. After applying, use Changes to inspect the resulting values, removed rows and transformation summary.

Before: proposed impact

Review affected-row counts, before-and-after examples and proposed removals. Nothing in that proposal is applied until you confirm it.

After: applied transformation

Verify the actual working dataset and the new session step. A preview is not a saved transformation; the applied operation is what appears in the history.

Undo and reset

Room to reconsider.
Your source stays intact.

Keep control while the workspace session is active. Review, undo or start over without overwriting the file you loaded.

Undo the most recent step

Undo and redo transformations during the active session. To reach an earlier state, undo later steps first. This is not a promise of recovery after closing the session.

Reset the working dataset

Return the working dataset to the originally loaded data when you need to start again. Reset clears the applied session changes; review or export anything you need to retain beforehand.

Preserve the original file

Cleaning, filtering and resetting operate on the browser workspace. Your source CSV or XLSX file is never modified. A prepared export is a separate file.

Export with confidence

Verify the data.
Then prepare the handoff.

Check the final row count, changed values and removed records before CRM or business-system import. Download the prepared dataset as CSV and use your destination system's own import workflow.

CSV and XLSX are supported inputs; the prepared output is CSV, not an edited Excel workbook. Separate changed-row, removed-row and audit-summary exports are Pro features. Reviewing the data helps you verify it, but does not guarantee acceptance by the destination.

Clean data

Review whitespace, casing and selected-value transformations before preparing your output.

Explore reviewable data cleanup

Deduplicate

Check matching groups, the retained record and the rows proposed for removal.

Explore duplicate record review

Compare files

See which records are new or matched before applying a filter to your incoming list.

Explore two-file comparison

Export

Download a clean CSV, with separate change and removal exports where available.

Explore CSV and change exports

Browser-local processing. In local-processing mode, raw dataset contents stay in your browser and are not uploaded to RowDesk servers.

About local processing

Know what changed.
Know what you're exporting.

Review the work between the original file and the final CSV.

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