In Episode 31 of '100 Days of Data,' titled 'Data Preparation for AI,' Jonas and Amy explore why up to 80% of AI work revolves around data cleaning, preprocessing, and feature engineering. They break down these critical steps in transforming raw, messy data into structured formats ready for modeling. From handling missing values and duplicates to encoding categories and scaling features, the hosts blend theory with vivid real-world examples—including use cases in automotive, retail, logistics, and healthcare. Listeners also learn how effective feature engineering combines creativity and domain knowledge to boost model performance. Whether you're building AI applications or advising businesses, this episode provides a clear roadmap to data preparation as the foundation of trustworthy, high-impact AI systems.