In this presentation, Terry will present a unique method for accessing and processing industrial data to optimize asset and process control using statistical modeling and visualization on the Edge. Specifically, he will evaluate how to securely stream data from a simulated wastewater treatment plant flow loop in order to model a control-valve failure condition called “stiction”. He will evaluate how to predict stiction (or not) using Linear Regression. In addition, Terry will evaluate the best practices of asset & process control using scored models and visualizations on the Edge. The key takeaways of this deep dive are: remote Data Science teams can, and should, leverage innovative cybersecurity tools to access industrial data in near real time via machine-to-cloud and machine-to-edge architectures for predictive & explanatory analytics; and industrial Data Science teams should contribute to process & asset control via predictive and explanatory analytics. Edge analytics should incorporate the best practices of statistical modeling and visualization development to define industrial operations procedures.