Cleaning the mess How "dirty data" affects everything

In this episode, we explore why real-world data is almost never clean, and how issues like missing values, inconsistencies, outliers, and collection bias can quietly distort analytics, dashboards, and machine learning models. Through simple explanations and two real stories, we break down how “dirty data” happens and why data cleaning is one of the most important steps in any data project.