We present an axiomatic viewpoint on quantifying the predictive benefit of side information, which we define as the reduction in optimal estimation risk when side information becomes available to the predictor. Under mild regularity conditions, we show that if a measurement of predictive benefit satisfies the data processing inequality, it is uniquely characterized by the optimal estimation risk under logarithmic loss. Connections to various causality measures will also be discussed.