Whatever technique is chosen to compute principal components or common factors, the new components or factors may not have recognizable meaning. Correlations will be calculated between the new factors and the original input variables, which presumably have business meaning to the data analyst. But factor-variable correlations may not possess the subjective quality of simple structure. The idea behind simple structure is to express each component or factor in terms of fewer variables that are highly correlated with the factor (or vice versa), with the remaining variables largely uncorrelated with the factor. This makes it easier to understand the meaning of the components or factors in terms of the variables.
Factor rotations of various types are offered to allow the data analyst to attempt to find simple structure and hence meaning in the new components or factors. Orthogonal rotations maintain the independence of the components or factors while aligning them differently with the data to achieve a particular simple structure goal. Oblique rotations relax the requirement for factor independence while more aggressively seeking better data alignment. Teradata Warehouse Miner offers several options for both orthogonal and oblique rotations.