In operations, simulation and control of power systems, the presence of real-time data relating to system states can yield precise forecasts and can enable robust active control. In this research we are developing efficient and robust methods to produce "data enhanced" reduced order models and filters for large-scale power systems. The application that this paper focuses on is the creation of new data-driven tools for electric power system operation and control. The applications systems include traditional SCADA systems as well as emerging PMU data concentrators. A central challenge is to provide near real-time condition assessment for "extreme events," as well as long-term assessment of the deterioration of the electrical power grid. In order to provide effective guidance for power system control, we are also developing visualization methods for integrating multiple data sets. These visualization methods provide an up-to-date view of the system state, and guide operator-initiated power system control.