Back to the Future of Big-Data Analytics

Mike Bacidore is the editor in chief for Control Design magazine. He is an award-winning columnist, earning a Gold Regional Award and a Silver National Award from the American Society of Business Publication Editors. He may be reached at 630-467-1300 ext. 444 or [email protected] or check out his Google+ profile.In 2015, a teenager will be able to make a quick escape from trouble on his very own hoverboard, a wheel-less skateboard that flies three inches off the ground. Of course, that is the fictional world where Marty McFly finds himself in the 1980s movie franchise, "Back to the Future." But, as Kate Johnson, GE's vice president and commercial officer for sales and marketing points out, "There's a hoverboard that works now. The future is here. Marty McFly's futuristic world of 2015 is just months away."

Johnson capped off a day of presentations on the Industrial Internet and big-data analytics at the GE Intelligent Platforms 2014 User Summit in Orlando, Florida. "There's a massive transformation that the Industrial Internet is having on our company and our customers. We're talking about leveraging big-data analytics. The future that we see is one with no unplanned downtime. That's the holy grail. That future is right on the horizon. It's going to go lightning fast," she predicted.

When the consumer Internet exploded and a billion people came online, everything changed, and the companies that leaned into it became mighty, said Johnson, citing the likes of Amazon and Google. "When 50 billion machines come online, everything's going to change again, and this time it's going to be faster because we already know how to do this," she explained. "The pace of innovation is changing. It's knowledge at the speed of thought. GE wants to lead and be on the mighty side. We've had to change some big things to take advantage of this opportunity."

"When 50 billion machines come online, everything's going to change again." GE's Kate Johnson on the coming transformation of the industrial landscape.

GE's internal transformation encompasses a focus on customer outcomes, as well as a push to leverage big-data analytics and a new style of customer engagement, explained Johnson. "What does it mean to leverage big-data analytics?" she asked. "To leverage it across our company, we had to build a platform, Predix, to drive the pace of innovation ever faster. Just like the open-source world of technology, the pace of innovation is taking off. Big-data analytic software development will go through the roof because of the Predix software development platform. We're borrowing best practices from Proficy, and vice versa. Over time, you'll see the unification of Proficy and Predix." And customer engagement will now entail making hardware, software and services available to customers, she explained, eliminating the borders between those three groups from a customer perspective, despite each group having its own P&L.

"Fifty billion machines will be connected on the Internet by 2020," said Johnson. "At GE, we have 10 million sensors collecting 50 million data elements, and we have $20 billion of potential annual customer savings in our line of sight. For example, one of our oil-and-gas customers is off the coast of Scotland with $6 billion in assets under management.

Another customer, e.on, was able to realize a 4% increase in power output across 283 wind turbines, thanks to analytics. "We were able to put together hardware, software and service to provide a platform," said Johnson. "That 4% increase is equivalent to 20 new turbines."

All of these customers want a world where they can understand their operations completely and want them to be thrillingly predictable, said Johnson.
To get there, Johnson recommends a three-step plan.

1. Know where you stand.
2. Set a goal and make a plan.
3. Pick the right partners.

Asset performance management (APM) has a major impact on enterprise value, and it can be tracked with an adoption curve called the Industrial Internet Maturity Model, which assesses the capabilities that customers have to manage assets. The first step on the curve is connecting to machines. Once connected, the machine conditions can be monitored. The data that's collected via monitoring then can be analyzed. That analysis enables predictability. And then, once you're on the final point of the curve, you are optimizing assets to eliminate unplanned downtime—the holy grail.