What is Data Cleansing? Process, Techniques & Best Practices

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

Data Cleansing Techniques and Applications

Technique	Problem Addressed	Approach
Deduplication	Duplicate records	Matching algorithms; survivorship rules; merge/purge
Standardization	Inconsistent formats and values	Format conversion; code mapping; value normalisation
Validation	Invalid values; rule violations	Business rule checks; reference data lookup; range checks
Enrichment	Missing values	Third-party data; internal lookup; derived values
Correction	Known errors	Manual correction; automated rules; exception handling

CDMP Exam Relevance

Data cleansing is tested in the Data Quality knowledge area (11% of the CDMP exam). Key exam topics include: the definition and purpose of data cleansing, the common cleansing techniques and what each addresses, the difference between data cleansing and data quality management, and the role of data profiling in identifying cleansing requirements. Data cleansing is also relevant to Data Integration questions, as cleansing source data is a critical step in ETL processes and data migration projects.

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