Data cleansing (or data cleaning) is the process of detecting and correcting or removing inaccurate, incomplete, or duplicate records within a dataset to improve its quality and reliability.
Typical steps include removing duplicate records, standardizing formats (like currencies or dates), filling or flagging missing values, and validating fields against expected patterns.
Raw scraped data often contains inconsistencies from source-site formatting differences. Cleansing is what turns that raw output into a reliable dataset a customer’s systems can trust and use directly.
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