What is Data Profiling? Process, Tools & Why It Matters for Data Quality

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A professional-level summary covering key definitions, frameworks, and exam-relevant points.

Data Profiling Types and Outputs

Profiling Type	Analyses	Key Outputs	Issues Revealed
Column profiling	Individual columns	Null rate, cardinality, value distribution, min/max	Nulls, out-of-range values, format issues
Cross-column profiling	Column relationships within a table	Functional dependencies, business rule compliance	Rule violations, unexpected correlations
Cross-table profiling	Relationships between tables	Referential integrity, record matching rates	Orphaned records, integration inconsistencies

CDMP Exam Relevance

Data profiling is a key concept in the Data Quality knowledge area (11% of the CDMP exam). Key exam topics include: the definition and purpose of data profiling, the three types of profiling and what each reveals, the role of profiling in data quality improvement initiatives, and the relationship between profiling and data quality dimensions (completeness, accuracy, consistency, etc.). Data profiling is also relevant to Data Integration questions, as profiling source data is a critical step before any integration or migration project.

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