DATA QUALITY REVIEW · Workflow idea
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.
01 / The starting point
A permitted dataset with a documented matching purpose
“Highlight likely duplicate records while preserving the original information and uncertain matches.”
02 / The work
Helps teams identify possible duplicate records by comparing email fields.
Check that the dataset may be cleaned for the intended use.
Agree how case, whitespace, missing values, and changed addresses are handled.
Check more than one value before deciding records refer to the same person.
Keep a recoverable original and a clear list of records for review.
Do not delete or overwrite records based only on a matching email.
03 / The result
A proposed unique-record list with duplicate counts, source references, and approval status.
Reviews & approvals · Example outcome
A proposed unique-record list with duplicate counts, source references, and approval status.
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.
04 / Make it yours
A few choices shape the work around your team.
Make every proposed change reversible and traceable.
Define how shared, blank, or outdated addresses are handled.
Only authorized reviewers should see personal data in the dataset.
Assign a data owner to review and confirm merges or removals.