Several researchers have shown that uncertainties during model calibration affect model predictions while others have examined the impact of uncertainty on model decisions. This paper discusses linking the impact of calibration uncertainties to model decisions and examining the implications of the uncertainties on future data collection efforts. To complete the analysis, model parameter uncertainty is evaluated using a first order second moment (FOSM) analysis of uncertainty that is also known as D-optimality. These uncertainties are propagated to model prediction uncertainties through a second FOSM for a set of predicted demand conditions. Finally, prediction uncertainties are embedded in alternative optimization problems to assess the effect of the uncertainties on model based decisions. If uncertainty levels are large, the monetary benefits of reducing uncertainties from additional data collection can be addressed directly by examining the change in the optimization problem objective function if additional data is available to reduce the parameter and model prediction uncertainties. This paper presents an application to a small network. An application to a realistic system is being completed. A comparison of the quality of calibration needed for a model intended to be used for design of a system expansion with that desired for optimizing pump operations will also be addressed for the same network layout.