One project that really changed my perspective was building a customer churn prediction model. At first, I thought the hardest part would be choosing the right machine learning algorithm. It turned out that data quality, feature engineering, and understanding the business context had a much bigger impact on the final results.

Spending time with domain experts, cleaning inconsistent data, and creating meaningful features improved the model far more than endlessly tuning hyperparameters. Since then, I’ve started every analytics project by asking “Do I really understand the data and the business problem?” before thinking about the model itself.

That experience completely changed how I approach analytics, success is usually driven more by the foundation than by the algorithm.