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.
This is a common challenge when customer data comes from multiple systems.
I would normally start by checking the match rules and source data quality.
A few things I would suggest:
- Match Rules: Check whether the rules are too strict. Sometimes small differences in names, emails, or phone numbers prevent matching.
- Data Quality: Look for missing values, duplicate records, extra spaces, and inconsistent formats.
- Normalization: Try normalized email and phone matching where applicable.
- Data Mapping: Make sure Individual, Contact Point Email, and Contact Point Phone are mapped correctly, including their relationships.
- Ruleset Execution: Check whether the Identity Resolution ruleset completed successfully and review the processing results.
- Reconciliation Rules: If records are unified but the expected field values aren't showing, check which source is given priority.
Simple Example:
One system has "Santhosh Kumar" and another has "Santosh Kumar". An exact name match may fail, but fuzzy matching could help identify them as the same person.
My suggestion is to take 2–3 customer records that should match, compare their source values, and test the matching rules step by step.
One important point: Match rules decide which records get unified, while reconciliation rules decide which field values appear in the unified profile.
Hope this helps!
Thank you,
Venkat Yadav.
