Two teams can use the same data and model and still see large differences in performance. The gap almost always comes from the features.

Raw data by itself isn’t very useful to a model. It needs to be shaped into signals that capture real patterns. A latitude and longitude don’t say much on their own, but something like “average traffic speed on this road during peak hours” does.

Feature engineering is how you create those signals. It’s often the highest-leverage part of building an ML system, and the biggest driver of performance gains.