Data Science (DS) algorithms interpret outcomes of empirical experiments with random influences. Often, such algorithms are cascaded to long processing pipelines especially in biomedical applications. The validation of such pipelines poses an open question since data compression of the input should preserve as much information as possible to distinguish between possible outputs. Starting with a minimum description length argument for model selection Joachim Buhmann motivates a localization criterion as a lower bound that achieves information theoretical optimality for sufficient statistics. Uncertainty in the input causes a rate distortion tradeoff in the output when the DS algorithm is adapted by learning. Joachim Buhmann presents design choices for algorithm selection and sketches a theory of validation.