Most people learn CDQ by watching UI clicks and memorizing tool names — and that’s exactly why their projects fail. This infographic breaks down how CDQ actually works in real enterprise pipelines. From profiling and rule spec to dictionary, cleanse logic, and address verification — this is what goes into a proper Informatica Cloud Data Quality setup. No theory, no fluff. Just what’s used in real mappings. If you're stuck in low-value tasks and want to actually understand how CDQ fits into modern cloud data pipelines — this is where to start.

1. FROM DIRTY DATA TO
TRUSTED PIPELINES
DATA PROFILING
DICTIONARY
STANDARDIZATION
ADDRESS VERIFICATION
RULE SPECIFICATION
CLEANSE
TRANSFORMATION
Check nulls, patterns, duplicates, outliers
— the stuff that breaks your pipelines
later.
Standardize incoming values — like
mapping "U.S.", "USA", "America" to a
single valid term.
Standardize, enrich, and geocode
addresses so they’re not just "valid" —
they’re business-ready.
Define logic upfront — what's valid, what
needs fixing, and catch failures before
production.|
Fix messy casing, remove blanks, junk
characters — catch it before it damages
reports.
CORE COMPONENTS
OF REAL
INFORMATICA CDQ
PROJECTS
READY TO LEARN HOW THIS WORKS IN A REAL PROJECT?
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