Fracture-driven interactions in horizontal wells are receiving considerable attention due to the costly negative effects on the full field development of unconventional reservoirs. Operators have tested various methods to minimize these interactions. Active well defense (AWD) describes the process of pumping into existing wells while completing new wells in a unit. Detailed evaluations of active well defense projects are cumbersome due to the extremely large volume of data generated from multiple sources, further complicated by the fact that the data are often stored in variable formats. This paper demonstrates that near real-time evaluation of an active well defense project is possible. Minimization of fracture-driven interactions has been accomplished by a two-fold approach: optimization of the completion design for the new wells and improvements to the active defense process. Building upon previous successful projects (Bommer et al. 2017; Bommer and Bayne 2018), this case study is based upon a new 16-well data set. The workflow developed allows near real-time optimization of the defense process. An improved understanding of the impact of design and execution parameters (rates, pressures, fluids, diverters) on fracture-driven interactions is possible using only the time-series data traditionally gathered during fracturing and well defense operations. Increasing the number of fracture initiation points along the lateral by maximizing perforation cluster efficiency is the first step toward minimizing fracture-driven interactions. A common tool is the use of dynamic diversion. Operators apply various diversion techniques in multi-stage fracturing to increase cluster efficiency. The ability to assess diverter performance in time-series data is valuable when optimizing fracture operations. Active well defense is the second step in minimizing fracture-driven interactions (FDI). In this case study legacy wells are defended by pumping treated water into the legacy wells while completing new wells in the unit. FDIs are monitored with high resolution gauges while the pump-in rates into the legacy wells are dynamically adjusted based upon the pressure responses. Post-project evaluations involve multiple time-series data streams containing an extremely large amount of data. The raw data (.CSV files) are collected and analyzed using a cloud-based application optimized for time-series frac data. Combining the frac treatment data from the new well with the legacy well defense data and applying advanced analytics techniques, it is possible to identify trends and quantify the effectiveness of the active well defense process. Finally, well records and historical production data are combined with the treatment data to demonstrate the overall economic benefit of the process.