Shifting from Reactive to Predictive in Energy: Driving Valuable Outcomes in a New Digital Landscape, Part 1

Most digital projects today rely heavily on disparate data generated from censored, physical objects or “things” – as used in the common term, Industrial Internet of Things (IIoT). To put some numbers on this, Gartner, Inc. forecasted that 8.4 billion connected things would be in use, worldwide by 2017, up 31 percent from 2016, and will reach 20.4 billion by 2020. The number of individual sensors could top one trillion as of 2020 according to estimates from several companies and industry groups. As the number of installed, object-level sensors increase, vast quantities of new data are sent, stored and must be analyzed to build predictive models and prevent break down on equipment; this is accomplished by developing and training predictive algorithms in the form of neural networks and implementing those algorithms within the appropriate technology-based solution frameworks.

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