Sensor data in its raw form is not enough to monitor and make decisions on complex processes or systems. Equations from physics, thermodynamics, and other disciplines are used to make sensor data actionable. If you want to go further and make comparisons between equipment or site performance, normalization and event identification are a must.
In this talk, you will get an introductory level understanding of
• how you can elevate raw sensor data to be more actionable with PI System as well as
• how PI System integrates this data with business intelligence tools and popular advanced analytics platforms, enabling you to find correlations and apply machine learning algorithms
Landry Khounlavong began his journey with OSIsoft in 2010 as a Technical Support Engineer. He then continued his passion for helping customers a few years later as a Product Support Strategist. Today, he is Product Marketing Manager for PI Integrator for Business Analytics and PI Integrator for Microsoft Azure, and has helped early adopters jumpstart their journey on applying machine learning algorithms to PI System data. He holds a BS in Chemical Engineering from the University of Florida and a PhD in Chemical Engineering from The University of Texas at Austin.