This content originally appeared on DEV Community and was authored by Sujeet Patel
When I moved from traditional on-prem MDM to the cloud version of Informatica MDM on IDMC, one of the first surprises came during something I thought I already understood: match & merge logic.
I had already spent years working with match rules, comparators, survivorship, trust — so I assumed this would be a straightforward migration.
I was wrong.
What I quickly realized was that even though the concepts were the same, the way the Cloud MDM engine handles match/merge is different enough that we had to rethink a lot of things from scratch.
Here’s what I learned, and what you should be aware of if you're making the shift.
**🧠 Rule Design Has Moved to UI, But Complexity Remains
**In on-premise projects, we often had fine-grained control using XML configs and custom user exits. With IDMC:
You design match rules directly in a no-code visual interface
You configure comparators and thresholds using dropdowns, not XML
Survivorship rules are set at the Business Entity level with weights and conditions
Sounds simple — but here’s the trap: Less code doesn’t mean less logic.
If you don’t deeply understand how fuzzy match behaves in real datasets, your merge behavior will start giving unpredictable golden records.
**⚠️ The Things That Caught Us Off Guard
**In one implementation, our phone and email fields were matched too loosely. The system started merging distinct customers just because their landline numbers were similar.
That’s when we realized:
Default thresholds in cloud match rules aren’t always ideal
You must test rules on high-volume, real-world data before approving
Merge preview is your best friend — but it doesn’t always expose corner cases
**🔍 What Changed From On-Prem Logic
**Some differences that matter:
Blocking Strategy: The way IDMC clusters potential matches is different. You’ll see more groups unless your blocking fields are tight.
Merge Survivorship: You can't override via custom code — only trust weights and rule priority matter.
Audit Logging: Merge actions are captured differently — check your job logs, not just data view.
**📘 Suggestion for Teams New to Cloud MDM
**Before you finalize any match rule in Cloud:
Run at least 2 rounds of test loads with variation data
Use synthetic data to force edge cases (e.g., 2 customers with same name, different address)
Don’t carry over your old thresholds — design from scratch
Spend extra time on merge preview before go-live
And always document your trust rules with business logic behind them — not just field weights
**🧩 My suggestion:
**If you're new to this, try configuring a simple fuzzy match on name + email in a sandbox tenant — observe how blocking and merge behave across 100 records. You’ll catch more insights than reading 10 pages of documentation.
**🔗 Want to Learn This With Real Data?
**If you're exploring Customer 360, match rule design, and IDMC architecture in practice — here's a real-use case training that walks through it, start to finish:
👉 Informatica MDM Cloud SaaS Training – Customer 360 Logic Explained
Cloud makes it easier to configure — but harder to guess what’s happening underneath.
The only way to master match & merge in Cloud MDM is to test, tweak, and observe.
This content originally appeared on DEV Community and was authored by Sujeet Patel
Sujeet Patel | Sciencx (2025-07-19T08:48:00+00:00) Why Match & Merge Behaves Differently in Cloud MDM (And What Most People Miss). Retrieved from https://www.scien.cx/2025/07/19/why-match-merge-behaves-differently-in-cloud-mdm-and-what-most-people-miss/
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