Jeffrey S. Katz / Head of Grid Technology,IBM
website
jskatz@us.ibm.com
Title: Between Big Data and Analytics-What to Do and What Not to Do?
Date: November 26, 2018
Bio: Jeffrey S. Katz is a Senior Member of the Institute of Electrical and Electronics Engineers. He is a co-chair of the Industrial Internet Consortium’s Energy Task Group, and he was a co-chair of the IEEE 2030 Standard on Smart Grid Interoperability Guidelines, IT Task Force. He is on the Advisory Board of the Advanced Energy Research and Technology Center. He was on the 'Networked Grid 100 of the Smart Grid in 2012' list from Green Tech Media. He was appointed to the IEEE Standards Association Standards Board for 2014. He co-chaired the first IEEE Power and Energy Society workshop on Big Data in Utilities in September 2017, and the first PES workshop on Utility Cybersecurity in December 2017. He is an author on seven patents, in tele-medicine, robotics and computer vision, intelligent electric power distribution, energy aware cloud computing, with others pending. He earned a Commercial General Radiotelephone license from the U.S. Federal Communications Commission.
Abstract: There have been many analytics projects started since utilities began to build upon their troves of Big Data. We will cover some essential elements of the data origination side and the data processing side. Common problems in getting started will be explored. Some non-technical problems that have been noticed will be mentioned. Using unstructured (non-numerical data) and its fusion with structured data, will be covered. Items to be mentioned include the path from sensor to analytic output, what can go wrong in between, why analytics teams need instrumentation engineers and data scientists, the importance of visualization, cognitive analytics, suggestions for starting points, the advantage of data visualization and freedom for the data. The practice of pre-determined analytics versus engineer friendly exploration will be discussed, along with security and some business considerations. System integration with existing major applications, data lakes versus data swamps, and use of standards will be brought up.
Learning Materials: Talk Flyer Talk Slides
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