Chris Couper, is an IBM Distinguished Engineer with over thirty years of industry and IT experience. He currently works on IBM’s smarter planet opportunities related to risk analytics and mitigation. Chris earned a bachelor’s and a master’s degrees in physics from the University of California, Davis.
Chris has deep practical utility experience at many of North Americas leading utilities, such as Progress Energy, Xcel Energy, and PG&E. He has 30 years of experience as an IBM systems engineer and certified IT architect, development and product management experience (10 years of services and consulting), public safety acumen (25 years in the fire services with 10 years as a chief officer), experience as the chief technology officer for IBM’s wireless and pervasive EBO, prior vice president position of the Emergency Interoperability Consortium (EIC), prior board member of the Center for Telecom
Chris designed a smarter planet solution to collect real-time information from sensors and control devices in all industries, to process the information in real-time and efficiently distribute the improved data streams to consuming applications and systems. Chris has been collaborating with IBM’s research division in China to create the advanced analytics and IBM’s SWG who is building the appliance the analytics will be embedded within. Chris’s ability to understand every part of the solution ecosystem makes him ideally suited to work with each organization he engages with. Chris is currently designing a solution for transmission oscillation management and has extensive experience in distribution control centers and management. Chris is also an expert in utility emergency operations and EOCs’.
The lecture will discuss the application of big data analytics to managing operational risk in the electric grid. The characteristics and sources of big data will be described. We will then look at the types of problems most appropriate for using big data analytics. A brief discussion of the business an architectural framework for big data operational risk analytics will follow. We will then describe a working scenario where big data analytics is being applied to solve potential grid operational problems. Lastly we will cover the issues and challenges facing the industry today to enable big data analytical solutions to be successful.