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Datauntangler

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

Address standardization before the file goes to a mail house

Address standardization means writing every address the same way, so that systems downstream can match and validate them. Most of the damage is in three fields: the state written four ways, the postcode missing its leading zero, and the country as a code in some rows and a name in others.

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 address standardization

The three fields that break batches

State

California, Calif., CA and california are one state. Datauntangler proposes the two letter USPS code for every variant it recognises.

Postcode

A five digit ZIP that arrives as four digits lost its leading zero in a spreadsheet. The column width tells you how many are missing.

Country

US, USA, U.S.A. and United States of America all become United States, so grouping and shipping rules work.

What this does and does not do

This standardizes the form of an address. It makes the values consistent, restores what a spreadsheet dropped, and removes the variants that break exact matching.

It does not verify that an address exists or is deliverable. That is a different job, done against a postal authority database, and no tool that runs entirely in your browser can honestly claim to do it. Standardizing first makes that verification step cheaper and more accurate, because fewer records fail on formatting alone.

Do it before you pay per record

Address validation services and mail houses charge per record, and a record that fails on a missing zero costs the same as one that succeeds. Cleaning the obvious problems first reduces the failures you pay twice for. Address form is one column of a wider data hygiene routine, the file around it usually needs Excel data cleaning as well, and the data cleaning tool proposes both sets of changes together.

03 What the mail house sends back

Three things a shipping file is rejected for

Validation and mailing are priced per record, so a file that is not standardized first is paid for twice.

Data hygiene

The variants count as different addresses

Calif., CA and California are three values to anything downstream, so one household is handled as three records and mailed three times.

The postcode lost a digit on the way

A spreadsheet read 02134 as a number and stored 2134. The validator rejects it as malformed, and the row comes back undeliverable.

Country is spelled four ways in one column

USA, U.S.A., United States and US split a single market into four, and the carrier rules that apply to it stop matching.

What standardizing first changes

One spelling per state, leading zeros restored, one country name, and a file that arrives at the paid service already in the shape it expects.

04 Before you run it

Questions about address standardization

Does it handle addresses outside the United States?
Country names are normalized for the common variants, and whitespace, casing and encoding fixes apply to any address. State code standardization is US specific.
Will it change a postcode that is genuinely four digits?
It proposes a change based on the width the rest of the column agrees on, and you approve or reject each one. Nothing is padded silently.
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