On Tuesday, GE announced a platform and tools to handle and analyze the massive amounts of data created by industrial sensors, including adding a public cloud provider to its platform infrastructure. On Thursday, GE introduced a set of monitoring and analysis tools to run on top of that platform.
GE released a set of tools and services called Predictivity, and introduced Proficy Historian HD, which will let its industrial customers move their data into Hadoop clusters. GE will work with Amazon Web Services and cloud application provider Pivotal, and through Taleris, its joint venture with Accenture, which will now expand globally. The aim is to make it easier for industrial customers to bring their data into the cloud. The platform infrastructure will help customers manage data more effectively, GE hopes.
It intends for its platform to be "agnostic," said Brian Courtney, general manager of GE Intelligent Platforms' Industrial Data Intelligence Software unit. That's why GE is working with Pivotal, which makes cloud applications, to make it easier to port and analyze data uploaded into Hadoop clusters. GE invested $105 million in Pivotal in April.
For now, such services are limited to GE customers, meaning they can have an Internet of GE Things. But this will change over time.
Thursday's announcement of GE's Proficy Monitoring & Analysis software being available as a suite of tools will help them run on top of the data platform. Four of the tools have been available for a while: Historian, Historian Analysis, SmartSignal and CSense. The two new tools are Historian HD and Proficy Knowledge Center, a visualization tool.
Courtney said that the aim was to make industrial management less reactive and more predictive, using analytics. Predictive analytics is a major component of GE's Internet of Things strategy.
Pricing is based on the asset value of what's being monitored. GE expects to see demand for data management services from the large number of companies that cannot afford data scientists and related personnel, but want to do a better job of managing their data.
The numbers at stake are large. Courtney said Historian can handle up to 3 million sensor data tags per second, or 5 terabytes of data a day for GE, which monitors industrial data for a number of its customers. And 5 terabytes is in the dawn of the Industrial Internet. Such large volumes can quickly drive costs up on storage-area networks. Moving data to Hadoop clusters in the cloud will make it as much as 85% cheaper to manage such data.
Even a company with data scientists and statisticians and process control engineers can struggle to deal with large amounts of sensor data. Courtney said GE itself runs into problems. He noted that a turbine it was monitoring started and "we saw a sequence we weren't familiar with. We wanted to say, have we seen it before?' "
The Historian database has almost 40 terabytes of data in it. So GE wrote a query to let it see the first five minutes of every startup sequence for a turbine contained in the database. The result? "It crashed the client," Courtney said.
So the data was pushed into a Hadoop cluster running on 10 computers. The data was processed and showed that the sequence had been seen 12 times.
Here's another number: only about $20 billion was spent in 2012 on Internet technology aimed at industrial machines. That's projected to vault to $514 billion in 2020, according to Wikibon, an open source community providing technology insight.
Jeff Kelly, Wikibon' s lead big data analyst, wrote that GE's status as the world's largest manufacturer of industrial equipment meant it was "well positioned" to bring together the platform and tools needed to take advantage of an industrial Internet.
But, he wrote, "IBM has a five-year head start, having launched its version of an Industrial Internet line of business with its Smarter Planet initiative in 2008." Kelly said he expects the two companies to fight for leadership in the market for years to come.
If there' s really a $514 billion market ahead, others will come fighting for it, too.
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