Putting Industrial Data to Work with GE ...

Industrial equipment lasts for decades, and the data produced by these machines are invaluable to the organizations that rely on them. So what happens when industries that rely on industrial equipment, with on-location workers, and limited digital reliance face a pandemic and limited to no travel. 

“I had a CEO of one of the big utilities [companies] telling me, he taught, given the last discussions he had with his leaders, that it would take five to six years to do digital transformation. When COVID started last year, they did a lot of what they thought would take five years in five weeks. It worked, so now the question is can you continue doing it?”

Colin Parris is the SVP and CTO of GE Digital, a billion dollar software division dedicated to creating better outcomes using the immense data produced by industrial machines.At GE, Coliin leads software, systems, and analytics teams to push the boundaries of how data can power industrial digital transformation. On this episode of IT Visionaries, Colin explains one of the ways GE Digital is transforming industries with their digital twins program. He explains exactly how digital twins is helping factories effectively predict the lifetime of machines, maintenance schedules, and predictive optimum yields all with an eye towards safety to ensure their plants stay up and running. Colin also touches on the importance of gathering data at the edge and the impact that kind of computing will have on efficiency.

Main Takeaways

• Measuring Success and Proving Value: Measuring success or measuring value from data and analytics is a difficult proposition. A best practice for measuring value is to set a baseline and make sure that you pick the right tool to advance your use case. Then you can measure that baseline to provide the value of those investments to gain the credibility to advance your analytics.
• It’s a Balancing Act: When you are measuring your data and setting baselines, you must be using both data at the edge and cloud-based data to predict the life expectancy of a system. When you manage data locally, you are consistently setting benchmarks with your local products, and then by sending that data to the cloud, you’re allowing your systems in other areas to use that data to then prevent potential problems.
• Industrial Problems: The two biggest issues currently facing the industrial industry right now are decarbonization, reducing a factory’s carbon footprint and cyber security, protecting that software that manages and runs their machines. The software that runs inside machines now must not only be able to predict when a machine might need maintenance, but that software must make sure the machine is not only safe and secure, but keeps it running efficiently in order to help it’s sustainability efforts.

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