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Spot likely duplicate records without deleting a person’s information by mistake.

Illustrative idea, not a published runnable template. Grunt is prelaunch; live service is paused and live provider connections and outcomes remain unverified. The steps, providers, and outcomes below describe a concept.

For a data-cleaning exercise or an authorized import, identify records that share an email address and prepare a proposed unique list with counts for review. A shared or changed address does not always mean two records belong to the same person, and keeping only the first match may discard useful details. Preserve the original dataset, normalize matching rules carefully, inspect conflicts, and require a data owner to approve any merge or deletion.

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Actual availability depends on published steps, supported connections, permissions, and provider approval.

Illustrative workflow and outcome
A data owner requests a duplicate reviewA permitted dataset with a documented matching purpose
“Highlight likely duplicate records while preserving the original information and uncertain matches.”
Work through the request.

Clear tasks, with review where it matters.

  1. Confirm data ownership and matching purpose
  2. Review how email matches are defined
  3. Compare likely duplicates and conflicting fields
See all 5 tasks
A proposed unique-record list with duplicate counts, source references, and approval status.

What this workflow could do

Helps teams identify possible duplicate records by comparing email fields.

Review points proposed in this idea

Matching values can belong to different records, and discarding all but one may lose important history. Keep an unchanged source copy, review conflicting fields and shared addresses, protect contact details, and have an authorized owner approve merges or removals.

How the idea could work

Select an illustrative task to explore its proposed role. No workflow runs here.

Explore the sequenceStep 1 of 5

  1. Check that the dataset may be cleaned for the intended use.

Step 1 of 5

Confirm data ownership and matching purpose

Check that the dataset may be cleaned for the intended use.

Proposed setup considerations

These are planning notes for the concept, not instructions for a currently available template.

  1. 01

    Keep an unchanged source copy

    Make every proposed change reversible and traceable.

  2. 02

    Document matching and conflict rules

    Define how shared, blank, or outdated addresses are handled.

  3. 03

    Limit access to contact information

    Only authorized reviewers should see personal data in the dataset.

  4. 04

    Require approval before changing records

    Assign a data owner to review and confirm merges or removals.

Read the prelaunch status

Published templates and verified customer availability remain pending.

A little help with the whole task

A little less busywork.
A little more room for you.

Explore the sample experience while launch details are being finalized.

View pricing status Explore the simulation