3 Trends Driving Big Data Breakthroughs: A CIO's View - InformationWeek

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3 Trends Driving Big Data Breakthroughs: A CIO's View

GE Water & Power CIO says data blending, scalable storage and processing, and a maturing Internet of things are shaping big data progress.

GE Water & Power has been monitoring its industrial turbines and predicting maintenance and part-replacement needs for nearly 10 years. But three changes in recent years have really enabled this $28 billion unit of GE to up its game, according to CIO Jim Fowler.

The first trend has been the emergence of critical masses of data paired with an ability to blend big data sets, said Fowler in a keynote at last week's InformationWeek Conference in Las Vegas. "Not only do we now have 100 million hours of operating-data and maintenance-and-part-swap-data across 1,700 turbines, but we can now marry that with external data such as weather information and process against it in one place," Fowler said.

[Want more on GE's use of big data? Listen to our radio chat: The Internet of Important Things: GE Power & Water CIO Jim Fowler.]

By marrying external data with the terabyte of data per day spinning off of each of its sensor-equipped turbines, Fowler said GE is helping customers eke out a seemingly small 1% improvement in output that will translate to $2 to $5 million in savings per turbine, per year. That will net $66 billion in savings over the next 15 years across all 1,700-plus turbines that GE customers have in operation.

The second trend changing the game in the use of big data is new platforms such as Hadoop and NoSQL databases, Fowler said. "We've seen the cartel of database vendors broken up, and some great new entrants give us new capabilities that we've never had before at a cost that we've never seen," he said. Fowler specifically mentioned MongoDB, Pivotal, and open-source data-integration vendor Talend as vendors that the company is either working with or watching. In 2013, GE made a $105 million investment in cloud-computing, app-development, database, analytics, and Hadoop-distribution vendor Pivotal.

GE CIO Jim Fowler (right) shares the big data trends he sees with InformationWeek editor Chris Murphy at last week's InformationWeek Conference in Las Vegas.
GE CIO Jim Fowler (right) shares the big data trends he sees with InformationWeek editor Chris Murphy at last week's InformationWeek Conference in Las Vegas.

The third trend unlocking big data, said Fowler, is the emergence of the "industrial Internet" -- GE's term for the Internet of things -- and big data platforms. The challenge, he said, is creating open platforms for ingesting and sharing high-scale data, and building applications while also ensuring security. To that end, he said GE is working with AT&T and others on standards for communications and security protocols.

"The turbine control systems that we built 30 years ago never envisioned the network connectivity that we have today," Fowler explained. "We're building a control room of the future that is certificate based, with end-to-end encryption, making sure we're authenticating person-to-machine but also machine-to-machine communications."

The danger is the threat of hackers taking big industrial equipment or a power plant offline. That's one reason operational systems such as power plants aren't connected directly to the Internet. Rather, data used for performance tuning is sent out on dedicated, secure pipes.

One next wave in power plant tuning will be blending historical data and information such as weather predictions to better anticipate demand. During this year's harsh winter weather, for example, several power plants in the Northeast weren't prepared for demand spikes and ended up going offline.

"If we can predict demand from the grid and match it with plant operations, we can prevent those outages," he said.

What do Uber, Bank of America, and Walgreens have to do with your mobile app strategy? Find out in the new Maximizing Mobility issue of InformationWeek Tech Digest.

Doug Henschen is Executive Editor of InformationWeek, where he covers the intersection of enterprise applications with information management, business intelligence, big data and analytics. He previously served as editor in chief of Intelligent Enterprise, editor in chief of ... View Full Bio

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User Rank: Ninja
4/10/2014 | 11:41:57 AM
Re: Supply-side versus demand-side data
It's nice to see efficincies happening at the supplier level, in this case with the wind turbines themselves.  This type of advancement should not just lead to better designed systems that can adapt more fluidly to changes in the environment, but should hopefully translate to reduced costs to end users.
User Rank: Author
4/7/2014 | 9:59:03 PM
GE's insight
One of Jim Fowler's other points that showed a lot of  insight from the beginning, was the notiion that having this data was a way to help customers look at their total cost of operations and avoid potential downtime. In other words, GE wasn't competing on providing better software or data analytics, but against the risk of fees, losss of revenue, etc that can come when an electric system is down.  In the process, GE got pretty good at engineering these devices to communicate how they were peforming.  It's certainlly paying off for them.

User Rank: Ninja
4/7/2014 | 2:47:42 PM
industrial Internet
People have been talking about the smart grid for quite some time now. It's great to see it now beginning to emerge from its cocoon, nurtured by big data progress in one of its key components, power generation. Almost makes me ashamed to say that yes, my electrical use is still being monitored by a mechanical meter.
User Rank: Author
4/7/2014 | 2:25:56 PM
Re: Supply-side versus demand-side data
This is an interesting example of what Bryson Koehler, CIO of The Weather Company, referenced at the conference with regard to weather-related big data affecting so many diverse businesses. 
D. Henschen
D. Henschen,
User Rank: Author
4/7/2014 | 2:02:08 PM
Supply-side versus demand-side data
At this point GE is premarily making use of supply-side data such as sensor info from its own turbines. The idea is to use automated software to match the performance of "the best operator on the best day" instead accepting average performance, said Fowler. With data from the demand side -- meaning real-time energy consumption on the grid -- operators will know when to spin up extra capacity and when it can take equipment offline without penalizing efficiency. Smart meters will give utilities and utility customers better insight into the demand patterns, and both sides kind find pricing plans and operational practices aimed at evening out the demand. We're only just beginning to see the benefits at multiple levels.
User Rank: Ninja
4/7/2014 | 12:51:56 PM
I think that this new paradigm of systems, primarily of the industrial sort, adapting to data. This is something that I think we all have expected to arrive, yet it has taken time for processes and infrastructure to be put in place for this. 

A great example of the amount of time it takes to change industrial systems is smart grid technology. The majority of households are still being metered with the use of mechanical devices. The problem has been deployment on a large scale of new devices that have been tested for a mass market. But it would seem that in 2014 we would have already moved beyond mechanical meters! Not so, at least not yet. 
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