Data assimilation is the process by which PDE-based models use measurements to produce an optimal representation of the state of the system. Different measurements bring different contributions to reducing uncertainty in the inference results. Quantifying the impact of observations is important for data pruning and for the configuration of sensor networks. This talk discusses several adjoint-based methodologies for formal observation impact assessment and for optimal design of sensor networks.