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Data Governance

Data cleansing

In a nutshell

Correcting, completing, standardising or removing wrong, incomplete or duplicate data so that it is fit for use.

Data cleansing fixes what profiling has found: it standardises formats, fills gaps from reliable sources, merges duplicates and corrects impossible values.

Cleansing without changing the source is a sticking plaster: errors come back with the next load. So every cleanse should end with a question: which rule or control at the source stops it happening again?

Before cleansing data that will be used to train AI, it is worth documenting what was changed and why.

Read more: The data cleansing process step by step.

Reference: DAMA-DMBOK

Related terms

Data profilingUniqueness (data quality)Data quality rule
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← Back to the glossaryUpdated: October 2026 · Informational content, not legal advice.