Why Data Cleaning Matters

This lesson explains why data cleaning is a core step in any analytics workflow. Learners see how messy data can distort results, create misleading charts, and lead to poor decisions.

The lesson introduces common data quality problems such as missing values, inconsistent formats, duplicates, and invalid entries, and shows how these issues affect reliability before analysis begins.

By the end, learners will understand the business cost of dirty data, recognize early warning signs of low data quality, and know why preparation must happen before modeling, reporting, or visualization.