(Note: this presentation recording is joined in progress at slide 4). Data is abundant and growing exponentially. More and more water quality data are being collected as new multi-parameter analyzers are developed and the number of required laboratory analyses increases. Combine that with customer use data and a more informed public and you get data overload. The question facing the industry is how to use this data. Using data integration and data analytics to calculate or estimate energy use down to the asset level could improve the understanding of how much energy is being used and how it relates to the energy bill. Using real-time status and other sensor data on assets, data analytics can be used to estimate the energy use without installing many expensive energy meters. With this data, Key Performance Indicator (KPI) such as energy and cost calculations that show asset use and efficiency (kWh/1000 gallons or $/1000 gallons) can be calculated. These analytics could provide decision support about how to balance energy consumption and production with real-time energy market prices. For instance, a wastewater plant with a CHP could balance when to buy electricity from the grid, when to produce energy for the plant or to the grid, when to store gas, and when to buy gas from the grid. This presentation will discuss the implementation of a pilot project around energy management, their successes and failures, show the graphical interface, and discuss the benefits to plant operations. This presentation will benefit utilities that have lots of “data” but have not developed a program or approach to use that data to address issues management.