Way back in 2012, the whole industrial world lost its collective mind about Big Data.
With a couple of well-placed sensors and a hard drive big enough, suddenly manufacturers could track every process imaginable—every spin of every spindle, every rotation of every rotor. Every single process every single machine was performing around the world could be logged, recorded, and monitored in real time.
It was a revelation; the possibilities seemed endless.
We all dove in—manufacturers invested and IT implemented as we tech reporters rolled out endless click bait headlines touting this amazing new Big Data Revolution.
But that excitement was short-lived. Data, we all found, is stupid. It’s meaningless.
And I say “we” because this bubble affected every industry. Here, Big Data allows us to track every visitor to NewEquipment.com; we can follow every ebb and flow of traffic on every article. I can tell what’s popular, what is working, and what falls flat.
In the same way, you out there can tell at a glance what every piece of equipment is doing at this very moment. All of us can probably check our data on our smartphones at 3 am if we really want to.
But this information doesn’t help us do our jobs. It doesn’t tell me how to write better articles and it doesn’t tell you how to improve efficiency.
All Big Data does is tell us what is happening. And that’s important, don’t get me wrong. But if we’re going to make any progress, we need more than “what.” We need “why.”
This is why I am excited to feature GE’s Predix platform on this month’s cover, with John Hitch’s article, “GE’s ‘Brilliant’ Strategy Is Ready for Takeoff.”
GE, arguably, started us on this Big Data path with its 2012 report on the Industrial Internet. This work—legitimately ground-breaking at the time—gave us our first figures of the potential value of Big Data: a mind-melting estimate of $32.3 trillion. Who could resist?
It seems perfect and fitting that, four years later, GE is following through with that early work with a system designed to capitalize on that data to bring us all what we really want: actionable intelligence.
Big Data was the base—the first building block of the Internet of Things and the whole digital puzzle manufacturing has become—but to make it work, we need brilliant software breaking it all down, telling us not what is happening, but why.
Sure, the motor is vibrating, but what is wrong? The engine is burning too much gas, but what needs repaired? This article is spiking, but what is pulling the traffic? And, more importantly: what do we do next?
This is the real future of the digital industrial revolution: how do we take all of this data and make something useful out of it? How do we turn dumb data into smart answers?
- Travis Hessman
New Equipment Digest