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Datauntangler

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

Data hygiene that survives contact with a real list

Data hygiene is the routine work of keeping records accurate, consistent and free of duplicates. In practice it is four jobs: remove the repeats, standardize the formats, repair the damaged values, and be able to show what you changed.

In-browser your file never leaves your computer

Every row proposed to you before it is applied

CSV log of what you approved and rejected

Clean a file
01 Try it on this page

Load a messy sample and watch it work

The same engine described below, running on your own machine. Nothing is uploaded and nothing is applied until you approve it.

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.

02 About data hygiene

What a dirty list costs before anyone notices

A duplicate contact is not one wasted email. It is a second touch that reads as carelessness, a double count in the pipeline, a lead score split across two records, and an unsubscribe that only applies to one of them.

Format problems are quieter and worse. A date column with three formats in it will sort wrongly and nobody will question the chart. A postcode that lost its leading zero fails address validation in a batch you already paid for.

A hygiene pass that takes minutes

Dedupe on more than email

People sign up twice with two addresses. Match on name and company as well.

Normalize before you compare

Trim, fold case, strip invisible characters. Half the duplicates disappear at this step.

Fix dates to one format

ISO 8601 sorts correctly everywhere and removes the day-first or month-first argument for good.

Repair encoding damage

Names with accents are the first thing a bad export breaks, and the people whose names they are do notice.

Keep the evidence

Export the change log with the cleaned file so the next person knows what was done.

Make it a habit, not a rescue

The cheapest time to clean a list is before it is used, not after a campaign goes out. Cleaning on a schedule, or at the moment of export, keeps the problem small enough to review by eye.

Saved cleaning recipes and scheduled runs are part of the paid plans, so the same rules apply every time rather than being reinvented by whoever has the file. On a contact list the two rules that carry most of the benefit are data deduplication and address standardization, both run on the export rather than inside the CRM.

03 The rescue clean

Why the list is only ever cleaned in an emergency

Hygiene that happens once, under deadline, is not hygiene. It is a rescue, and the same rescue is needed again next quarter.

Data deduplication tools

It happens the week of the campaign

The list is cleaned under time pressure by whoever is free, which is the worst moment to be making judgement calls about who is who.

Nothing changed upstream

The next export carries the same duplicates, the same three date formats and the same broken characters, because the source that produced them was never touched.

The rejected rows are gone

A destructive clean keeps no record, so the same rows get found, argued about and deleted again the next time around.

What a habit looks like instead

A pass that takes minutes, runs on every export as it arrives, and leaves a log you can put next to last month's.

04 Before you run it

Questions about data hygiene

How often should a list be cleaned?
Before any send or import, and on a schedule for lists that keep growing. Contact data decays continuously as people change roles and companies.
Does cleaning remove people who unsubscribed?
No. Suppression is a separate job that belongs in your sending platform. This tool works on the shape and consistency of the data, not on consent.
05 What it costs

Plans for when the file is bigger than the demo

Yearly billing is two months free. There is no free plan, and no card is needed to use the browser demo above.

Yearly billing is two months free.

Switch to yearly and two months are free.

Analyst

One ops person or analyst with files to clean.

$49$59/mo

Billed $590 a year Billed monthly

  • 50,000 rows a month
  • Files up to 25MB
  • 1 seat
  • Fuzzy matching and entity resolution
  • Reviewable diff and audit log export
  • 3 saved cleaning recipes
  • Email support

Team

Recommended

A RevOps or marketing ops team sharing the work.

$166$199/mo

Billed $1,990 a year Billed monthly

  • 250,000 rows a month
  • Files up to 100MB
  • 5 seats
  • Everything in Analyst
  • Unlimited saved recipes
  • Scheduled recurring cleans
  • CRM connectors (Salesforce, HubSpot)
  • Priority email support

Business

A data team cleaning for several departments.

$499$599/mo

Billed $5,990 a year Billed monthly

  • 1,500,000 rows a month
  • Files up to 500MB
  • 20 seats
  • Everything in Team
  • API access
  • Roles and permissions
  • Priority email support

Enterprise

A head of data who needs it defensible across the company.

Talk to sales

Custom terms, invoicing and PO

  • Unlimited rows and custom file sizes
  • Unlimited seats
  • Everything in Business
  • SSO (SAML / Okta)
  • Custom data retention
  • DPA on request
  • Named support contact
  • Invoicing and PO
  • Named onboarding

A row is one data line in a file you process, not counting the header. The browser demo is free to use and is not a plan: it reads CSV and TSV files up to 5MB on your own machine.

Related pages

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