Rotation is an important step in PCA because it maximizes component separation within the variance. The interpretation of components becomes easier as a result of this.
The goal of PCA is to choose a small number of components that can explain the most variance in a dataset. The initial coordinates of the points are altered when rotation is done. The relative positions of the components, however, remain unchanged.
We’ll need more extended components to represent the variation if the components aren’t rotated.