Why Do We Clean Data?

Most datasets collected during a study contain “dirty” data that can lead to disappointing or misleading conclusions if utilized. As a result, scientists must ensure that data is well-formatted and free of irrelevant information before using it. Issues that develop as a result of data sparsity and formatting discrepancies are eliminated during data cleaning. Cleaning is done in data analysis not only to make the dataset more appealing to analysts but also to address and prevent problems that may result from "dirty" data.

Data cleansing is critical for businesses since marketing effectiveness and revenue can be affected without it. While the data concerns may not be totally resolved, reducing them to a bare minimum has a substantial impact on efficiency.