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Datauntangler

No card required. The browser demo is free to use.

Data cleansing process in four steps

The whole job is four steps, and you make the decision in the third one. Nothing is written to your data before you approve it, and your original file is never modified.

4 steps, messy file to clean one

Step 3 is yours, nothing applies without it

2 files out, data and change log

01 Step one, the file arrives

Drop the file

A CSV or TSV export from your CRM, your billing system, an event platform or a spreadsheet. It is read by JavaScript in your browser, on your machine.

The delimiter is detected

Comma, semicolon or tab, decided by which one parses into a consistent number of columns.

Quoting is handled properly

Commas and line breaks inside quoted fields stay where they belong instead of splitting the row.

Headers become column names

Duplicate or blank header cells are given stable names so nothing collides silently.

Nothing is uploaded

There is no request to a server, so there is no data processing agreement to sign before you can try it.

02 Step two, the pass runs

It finds the problems

First it works out what each column holds, then it runs only the rules that apply to that kind of column. A date rule never fires on a company name.

Watch it run on a sample
Detected column Rules that run on it
EmailWhitespace, casing, invalid address flagging, duplicate identity key
DateFormat detection across the column, then ISO 8601 rewriting
CurrencySymbol and separator removal, negative forms, decimal places
Postcode or IDLeading zero restoration, whitespace, casing
State or countryVariant standardization, so Calif. and California agree
Name or companyEncoding repair, casing, legal suffix handling, fuzzy match keys

Column detection is a proposal too. If it reads a column wrongly, rejecting its changes costs one click and nothing is applied.

03 Step three, the one that matters

You approve each change

Every proposal is one line: the original value, the proposed value, the rule that fired and how confident the engine is. Approve it, reject it, or take the lot in one click.

Row by row or in bulk

Work through a handful of columns by hand, or approve everything and reject the two you disagree with.

Duplicates are clusters

A matched group shows every row and which one it suggests keeping, with the reason they matched.

Nothing happens by default

An unreviewed proposal is not applied. The tool waits rather than guessing on your behalf.

04 Step four, you take the result

Download the clean file and the audit log

Two files leave the browser. Neither of them overwrites what you started with.

File one

The cleaned CSV

Your original columns and rows, with only the changes you approved applied and the merges you accepted collapsed.

File two

The change log

One line per proposal: row, column, original value, proposed value, the rule and your decision. It is a CSV, so it opens anywhere.

Why the second file is the point

When someone asks why a number moved, a list of approved changes ends the conversation in a minute. Without it, the honest answer is that a tool cleaned the data, which is not an answer anyone accepts. This is what separates the process from removing duplicates in Excel and from a script nobody kept.

05 All four steps, live

Do the whole process on this page

Load a messy sample or drop your own CSV.

1. Load a file

Your file is read on your own machine. Nothing is uploaded, so nothing can leak.

Or start from a messy sample

Status

Loading the sample file.

File: CRM contact export

Rows

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Columns

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Changes

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Duplicate rows

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Date formats

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Blank emails

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Load a file to see the diff

Drop a CSV or pick one of the messy samples. Every proposed change appears here as a line you approve or reject.

Clean the file, and be able to show what you changed

Try the demo on your own CSV first. It runs in your browser, it costs nothing, and no card is required.

No card required. Your file never leaves your computer.

Clean a file