Deduplicating Messy Data
Duplicate records creep into almost every system — the same customer entered twice, a contact imported from two lists, slight spelling differences treated as separate people. Left unchecked, duplicates inflate your numbers and frustrate your team.
This article explains how we find and merge duplicates safely.
Why Duplicates Are Tricky
'Jon Smith' and 'John Smith' at the same address are almost certainly the same person, but a computer needs rules to decide. Names, emails, phone numbers and addresses rarely match exactly, so we look for strong similarity, not just equality.
Our Approach
- Standardise fields first — trim spaces, fix casing, normalise formats.
- Score record pairs on how similar their key fields are.
- Auto-merge clear matches and flag the borderline ones for human review.
- Keep a record of what was merged so it can be undone.
Preventing Future Duplicates
Cleaning up is only half the job. We add checks at the point of entry — such as warning when a similar record already exists — so the problem does not simply return.
If you need a hand with any of this, your Progressive Robot delivery team is ready to help. Raise a ticket from the Support area of your client portal or speak to your account manager and we will guide you through the next steps.