Empty value rules define how blank fields behave during deduplication.
This matters because many real datasets have missing emails, phone numbers, domains, addresses, or CRM fields.
Why blanks are tricky
Imagine two contacts:
| First name | Last name | Company domain | |---|---|---| | Ana | Lopez | example.com | | Ana | Lopez | |
Should they match?
The answer depends on the workflow. If company domain is required, they should not match. If the name fields are enough for a review queue, they might match.
Common empty value rules
Common rules include:
- Match when both values are empty
- Require a value before matching
- Allow a match when one side is empty
Each rule has a different risk profile.
⚠️ Blank fields can create false matches
When you match on weak fields, require values in stronger fields such as email, domain, or LinkedIn URL.
When to require non-empty values
Require non-empty values when a field is central to the match:
- Email for contact deduplication
- Domain for company deduplication
- Product SKU for catalog deduplication
- LinkedIn URL for profile deduplication
Allow empty values only when other columns provide enough evidence.
Datablist workflow
Datablist exposes empty value rules in advanced duplicate settings.
Use them with multi-column deduplication, smart matching, and distance matching to control how strict the duplicate check should be.
For practical examples, read the data matching guide and the CRM cleanup guide.