What is Data Cleaning?
Data cleaning is finding and fixing errors in data, such as duplicates, wrong formats, typos and missing values, before analysing it.
Data Cleaning explained
Typical steps are trimming stray spaces, standardising case and spelling, converting text to proper dates and numbers, removing or merging duplicates and deciding how to treat missing values.
Cleaning rules should be written down and repeatable, ideally in the pipeline, so the same fix is applied every time and reports do not change unexpectedly.
Example
Converting 'london ', 'London' and 'LONDON' to one value, and turning '31/12/2026' into a real date, are cleaning steps.