Hi Trailblazers,
I’m working on a Data 360 implementation where customer data is coming from multiple source systems.
The records are being ingested correctly, but some profiles that should represent the same customer are not getting unified as expected.
In this situation, what do you normally check first — match rules, reconciliation rules, source data quality, normalization, or identity resolution configuration?
Also, what are the common mistakes you’ve seen in real projects that prevent profiles from being unified correctly?
Would appreciate insights from anyone who has handled Identity Resolution issues in production.
I usually check in this order:
- Check the data mapping first
- Map each source to the Individual DMO.
- Map email, phone, and address to the Contact Point Email, Contact Point Phone, and Contact Point Address DMOs. Matching on email or phone does not work if they are only on the Individual.
- Check that the contact points are linked to the correct Individual. If this link is wrong, profiles will not match even when the values are the same.
- Source data quality: Compare two records that should match. Phone formats, extra spaces, typos in the email, and dummy values are the most common reasons for no match.
- Ruleset run: Changes to the match rules do not apply by themselves. Re-run the ruleset and check the job summary.
- Match rules: Conditions inside one rule work as AND, and separate rules work as OR. Avoid matching on name alone. Combine fuzzy name with email, phone, or address.
- Reconciliation rules: These only decide which value shows on the unified profile. They do not decide who matches, so check them last.
To confirm, use Profile Explorer or the Unified Link Individual DMO to see which source records were linked.
The most common mistakes are missing contact point mapping and forgetting to re-run the rulese
Hope this helps. If you share which match rules you are using, I can help look at them.
- Check the data mapping first
