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Datauntangler

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

Data deduplication tools that match more than an exact value

Deduplication removes records that describe the same thing twice. The reason most files stay full of duplicates is that the usual tools compare values exactly, and real duplicates almost never match exactly. This one compares them the way a person would.

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 deduplication tools

Three passes, in order of certainty

Each cluster reports which pass caught it, so you can trust a same-email cluster quickly and spend your attention on the close matches.

Same email address

After trimming and folding case. The strongest signal there is, and the one exact tools already find.

Same normalized identity

Name and company with accents folded, punctuation dropped and legal suffixes removed.

Close match

Edit distance and shared tokens above a calibrated threshold, inside a blocking key so it stays fast on large files.

Nothing is deleted

When you approve a merge, the rows are left out of the cleaned file you download. Your original file is untouched, and every merged row is written into the audit log with the row it merged into. If you decide later that a merge was wrong, the record of it is right there.

Where duplicates come from

They come from forms without validation, from imports run twice, from a sales team that adds an account rather than searching for it, and from joining two exports that disagree about spelling. None of those are fixed by deduplicating once.

Cleaning at the point of export, every time, keeps the number small enough that reviewing the list stays a five minute job. The scoring behind the close matches is fuzzy matching, collapsing a cluster to one record is entity resolution, and the CRM specific version of this page is Salesforce deduplication.

03 After the last dedupe run

Why the duplicates survived the pass you already ran

Most files have been deduplicated at least once. The rows that are still there are the ones the last run could not see.

Fuzzy matching

Only one column was compared

Dedupe on email alone and every record entered without one, or with a personal address the second time, stays exactly where it was.

The comparison ran before the normalizing

Uppercase, trailing spaces, accents and a full stop after Corp are enough to make one company look like two to an exact match.

The emptier copy was the one kept

Deleting on sight keeps whichever row came first in the file, and that is often the one somebody typed in a hurry.

What three passes catch instead

Exact email, then normalized identity, then a scored close match, each one shown to you as a cluster with the reason those rows grouped.

04 Before you run it

Questions about data deduplication tools

Which row is kept when rows merge?
The most complete one, measured by how many fields are filled. It is shown as Keep in the cluster, and the others are shown as merging into it.
Can I dedupe on a column you did not detect?
The demo matches on the name, company and email columns it detects. Custom match keys are part of the paid plans.
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