This content was created by Chase B Barrows.

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Learn Business: Big Data in Business, Industry, and Education

Phases of Analytics

One of GE’s strong suit in their Data Analytics tool bag is their ability to not only predict what might happen in the near or far future through pattern recognition but to also use these patterns to prescribe different decisions. The idea is that a digital model of devices and a model of the system in which those devices exist allows seeing how decisions impact everything else. Classic predictive analytics is sort of reactionary. It may allow for patterns to be spotted that requires a reactive decision. With enough data, like GE has, outcome from future decision can start to be accurately modeled. By Modaniel - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=36478653

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Big Data and Analytics
The topic I chose to research was Big Data and the accompanying Analytics that inherently come with collecting Big Data. Specifically I focused on the use of Big Data in the Industrial context. Today there is what is known as the Internet of Things, or IoT. This is the ever growing network of internet connected devices that both produce and consume data. Things like your phone and smart watches connect you to the internet. The modern car receives and produces massive amounts of Data. Even refrigerators, thermostats, and doorbells create data. All this data can be collected and sifted through in order to find patterns that in turn create more efficient systems, and modern Industry is no different.
GE is at the forefront of what is known as the Industrial Internet of Things,or IIoT. Like a car produces data, so do wind turbines, jet engines, and natural gas power plants. Because of advancement in the price of computing power, speed and infrastructure of data communication, and software compatibility and advancement, this data can now be collected, analysed, and used to make data driven decisions to increase efficiency of production and function. Moore'slaw has produced computer chips twice as fast for the same price every 18 months for the last 50 years. This exponential growth is just one peaice of the puzzle that has allowed for such in depth data collection. An example how GE has leveraged new technologies and abilities is by creating created ‘smart trains’. Simply by adding an internet enabled device to locomotive engines that track their operations patterns can begin to be discerned. By understanding in more detail the relationship between fuel use, acceleration, and terrain gradients, the fuel efficiency can be greatly improved. Using relatively cheap devices to report data back to GEs data centers allows for efficiency gains and savings for all of its customers operating their train engines.
Increases in efficiency like the locomotive can be less than 1% in relative efficiency but can lead to billions of dollars saved in the long run. This sort of savings can be applied to many industries and means that if a business isn't leveraging Big Data yet, it soon will be.