Internet Of Things: What's Holding Us Back - InformationWeek

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5/5/2014
09:06 AM
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Internet Of Things: What's Holding Us Back

The likes of Union Pacific, GE Power & Water, and ConocoPhillips are turning IoT hype into reality, but they want to do more. Here's what's still getting in the way.

GE is using isn't as timely, complete, or accurate as it wants (timeliness is the biggest challenge). So Fowler has taken two main steps to improve data quality.

One, a year ago he assembled a data science team that includes a data quality group, which is looking for ways to automate areas where employees collect critical data manually. Two, he created a data quality portal that shows the top 10 problems the group is trying to improve. The unit's CEO sees a report monthly.

As CIO, Fowler thinks he should "own every piece of data in our environment" but that business unit leaders must see and understand the data-collection problems that cause bad data, so they can have a hand in fixing those quality problems.

Some data just isn't available. While we hear a lot about early implementations of "smart meters" or Nest-type home automation thermostats that provide real-time insight into power use, there still isn't a lot of energy demand data coming into power generators. This winter was brutally cold, and some power plants saw surges -- and resulting outages -- unlike any they've seen before. If the plants had had accurate data on that rising demand, they could have run at higher capacity. "That linkage is one of the next big areas to look at," Fowler says.

Networks aren't ubiquitous.
Cellular networks cover a lot more ground than they did even a few years ago, and 4G networks are expanding, but once you move beyond big metropolitan areas, you still can't count on cellular.

ConocoPhillips spread its own radio towers across parts of Texas to transmit sensor data in order to optimize gas and oil well production. But newer techniques and sensors could generate 100 times more data than those wells gather and transmit today -- more than the network can handle for real-time analysis. "Wired pipe," for example, lets a driller put fiber optic cable miles down the well to collect sound, pressure, and seismic data in a constant stream. "Then you're talking about gigabytes of data flowing off this all the time," says Richard Barclay, ConocoPhillips' manager of infrastructure and operations.

Does the company need to pipe all that data back to some command center for analysis? If so, how quickly? Or can it crunch some of that data at the wellhead to guide urgent decisions about drill pressure and speed, and send less-urgent data back for historical analysis? Barclay says his team is examining what amount of data, measured at which intervals, people really need to make a decision.

The future Internet of Things model often will combine on-machine processing for urgent needs and batch-data uploads for less timely analysis. Bill Ruh, VP of GE Software, describes this as "real-time, big data processing at the machine. We don't have anything like that today."

For companies tying the Internet of Things into their products, the concern is less that there isn't a network available. Instead, the worry is that the network becomes key to the customer experience, yet it's something the product maker doesn't control. "You're dealing with almost a massive outsourcing of your brand, in terms of the eyes of the customer," says Alex Brisbourne, president of Kore, a company that provides wireless machine-to-machine connectivity.

Integration is tougher than analysis.
Connected devices and machine-to-machine communication no doubt generate a lot of data. But analyzing that data to get useful insight is not, surprisingly, among the top barriers to the Internet of Things. "Analytics is the least of our problems," says Union Pacific's Tennison.

Data analysis does take expertise -- data-savvy people who understand the business problems their companies are trying to solve and the opportunities they're trying to seize. Companies often must change their business processes to let employees respond to and use the insights the data analyses present.

But the hard part isn't crunching the data; it's connecting all the systems needed to paint a complete data picture, says Richard Soley, executive director of the Industrial Internet Consortium. A group of big-name companies -- led by AT&T, Cisco, GE, IBM, and Intel -- created the Industrial Internet Consortium to spur the kind of integration, authentication, and security

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Chris Murphy is editor of InformationWeek and co-chair of the InformationWeek Conference. He has been covering technology leadership and CIO strategy issues for InformationWeek since 1999. Before that, he was editor of the Budapest Business Journal, a business newspaper in ... View Full Bio

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Laurianne
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Laurianne,
User Rank: Author
5/5/2014 | 10:57:28 AM
IoT software vs. hardware
Tennison's story about asking analytics software vendors to solve his sensor hardware problem is striking. It makes me wonder if we are much further along on the software side than the hardware side regarding IoT.

Also his problem is specialized to the railroad industry -- and there will be examples that need to be customized for every vertical. How will sensor manufacturers achieve affordable scale? Lots of interesting food for thought here.
ChrisMurphy
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ChrisMurphy,
User Rank: Author
5/5/2014 | 11:34:27 AM
Re: IoT software vs. hardware
The lack of deep concern about analytical capabilities was one of the biggest surprises to me from the reporting in this article. If others have had a different experience, glad to hear about it.

In terms of sensors, we'll see about how much industry-specific adaptation is needed. Bill Ruh of GE noted that in the mechanical world, measuring vibration is something of a universal need -- it's rarely a good thing with machines. However, not all the innovation in sensors will be hardware driven -- virtual sensors, or software-based sensors that combine inputs from multiple sensors, will emerge as companies needs get more sophisticated.   

 
mnamboodiri
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mnamboodiri,
User Rank: Apprentice
5/5/2014 | 1:49:29 PM
IoT with more capable platforms
Chris - interesting article. I think the way we will be able to address some of this complexity and integration challenges is with the platform (or middleware) taking up a larger burden. If we continue to custom build each solution (as the article describes we do today) with massive integration efforts, security, burdened devices and heavy apps that have to embed connectivity, networking, data massaging, QoS etc, it will be a long time before we get to 50 billion useful connected devices! 

I believe some of the burden will be delegated to platforms that can then enable more focused devices and "thin" apps - while providing the networking, security, contextual intelligencem modularity, data flow/access and APIs to build solutions faster. 

-Manu
ChrisMurphy
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ChrisMurphy,
User Rank: Author
5/5/2014 | 2:17:46 PM
Re: IoT with more capable platforms
Manu, thanks for the perspective. It does seem like there's a role for a management platform in here somewhere that is still evolving today. 
Drew Conry-Murray
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Drew Conry-Murray,
User Rank: Ninja
5/5/2014 | 3:51:42 PM
Re: IoT software vs. hardware
I'm surprised about the lack of concern about analytical capabilities too. The security industry has been wrestling for years about how to identify actionable information from massive quantities of data. Maybe with IoT you're dealing with a small subset of known data types (pressure, temperature, vibration, etc), which makes it less about needles and haystacks.
Todder
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Todder,
User Rank: Moderator
5/5/2014 | 4:34:00 PM
Re: IoT software vs. hardware
The railway and oil industry requirements are the same for the most part (like any other industry, resource-based or other that are remote from urbanania).

Remote locations, little WiFi, power in short supply, and the environmental challanges. Solar powered units would resolve some of this potentially but you also need some fail safes built in to any IoT application. Like when the power to traffic lights goes off at an intersection, you can get a Bobby to manually direct traffic. Any implementation requires an audit trail of real-time testing that it is working and a backup (tested to fall over to when it isn't).
Thomas Claburn
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Thomas Claburn,
User Rank: Author
5/5/2014 | 4:51:32 PM
Re: IoT software vs. hardware
I find it interesting that the IoT is hobbled by the same thing that limits smartphones: power. We need a breakthrough in power storage and generation that improves current technology by an order of magnitude, something along the lines of Iron Man's arc reactor.
ChrisMurphy
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ChrisMurphy,
User Rank: Author
5/5/2014 | 4:57:11 PM
Re: IoT software vs. hardware
The power limitation is definitely true, Tom. One difference from smartphones is that industrial uses often involve relatively low power demands -- sending small bits of data back, but needing to stay powered over many months because replacing/recharging is difficult.  
Laurianne
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Laurianne,
User Rank: Author
5/5/2014 | 5:03:32 PM
Re: IoT software vs. hardware
We need a bandwidth breakthrough, also. Bonus points for connecting Iron Man to this debate.
SteveJ447
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SteveJ447,
User Rank: Apprentice
5/5/2014 | 5:20:41 PM
Delivering the data
Interesting that the focus is mostly on the edge (e.g. sensors) and the centre (e.g. cloud-based analytics) without much discussion of the data-sharing infrastructure needed to support any end-to-end system.

Whether the system designer leans-toward sensor-based (edge) computation or, alternatively, 'thin' apps connected via a cloud service, it is inevitable that both will be required in any business-critical system, and furthermore, device-to-device data-sharing (e.g. for local real-time control) will often be required too (e.g. when the latencies to/from the cloud are too long). So a lot more attention needs to be given to the real-time data-sharing platforms that will be needed to underpin and enable these IoT systems. Many system designers will recognize this as the 'elephant in the room' since commentators tend to focus on: 1/ smart sensors and 2/ big-data analytics, while assuming some kind of wireless connectively alone is sufficient for real-time data-sharing. Fortunately the technology exists (e.g. the OMG's DDS specification) and is standardized and proven. Hopefully we'll start to see a lot more discussion of the system infrastructure...rather than just the data sources and sinks that it connects. Ubiquitous, real-time, secure data availability won't just happen.              
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