The Next Big Thing
Posts about next generation technologies and their effect on business.

Hype fatigue

Recently there has been quite a bit of press about over-hyped technologies. Gartner came out with a list of their top 10 back in August. They also included a discussion of frameworks for these technologies:

  • IoT and operational technologies
  • Mobile Infrastructure
  • Enterprise Mobility Management
  • Analytics
  • Big Data
  • Social
  • Cloud

I actually think a few of those overlap but it’s their article. I was also surprised that security didn’t make the list but maybe they view that security needs to permeate the whole environment. Dr. Dobbs also came out with their own post of Overhyped things. Not a new topic, since I did a post on over-hype back in 2007.

 

Now that we’re into the last quarter of the year, we’re going to start seeing more game-changing technology trend articles. Even the IEEE has their own Top Tech Trends for 2014 article.

 

One thing that concerns me is that so many of these trends are just reworked analysis of those same article from the past. Are there no new trends? Or are we just tired of change and it is easier to just repackage stuff. I’ll have to give this some thought before I do my annual trends to look out for in December for 2015 post.

 

I also wonder if we shouldn’t look at the entire life cycle, not just the hype cycle -- even though there never seems to be extinction in this business.

 

entire lifecycle.png

The multi-dimensional value of IoT

dimensions.pngThe value and inevitable nature of the Internet of Things can be hard to quantify.

 

It has value in the vertical dimension based on what it can do for a particular industry. For example being able to understand the materials on hand, the machine capability and performance and the product location all can fit together to provide much greater insight. This is one of the reasons the manufacturing industry was an early adopter of IoT techniques.

 

From a breadth perspective, we’re seeing more devices with connectivity as well as more wearables and other ways to communicate. I can easily see a day where my oven reminds me of a meals status much more effectively than the kitchen timer. Or even the act of entering the garage can get dinner started because that’s what would be next on my agenda. Essentially it leads to a much broader range of devices working in collaboration to meet my needs.

 

In a depth sense, various devices that are doing their own thing, for their own reasons can provide a much greater contextual depth of understanding that any single view could provide. This is where the contextual understanding that is derived from multiple pieces of information comes into play.

 

I am sure there are more dimensions beyond these three… What are they for you?

 

Will the Internet of Things turn all CEOs into CIOs?

 

CEO.pngSince the CIO's role is focused on generating business value out of information securely and reliably, and now an ever increasing percentage of our enterprise environment will be collaborating in that goal – the CEO’s dependence and need to manage the use of information will increase.

 

IoT means sensitive information, can be derived leading to information about enterprise operation details and personal data crossing from secure networks to devices and third party services. The risk and the benefit are far different than what traditional CIOs have had to address. The CEO will need to understand (at least at some level) the rapidly changing world of security and information consumption and the implications of IoT – even if it is just to make sure that the delegated business and IT responsibilities are being addressed effectively.

 

Some view that IoT hype has peaked. If that is the case, it would only be because organizations have internalized the change, but I doubt that. I think we have a long way to go before the possibilities are even well understood, let alone embraced and incorporated to generate value outside the initial deployment silos.

Leaders need to ask two questions:

  • So what? – find out the perspective of business value for the effort
  • Is that all? – see if the teams are thinking broadly enough about where and how the information can be used. There seems to be a great deal of potential being left on the table.

 

IoT model update from the one I used 4 years back...

Back about four years ago, I used a model to think about machine-to-machine (M2) from a holistic perspective. Today, this would be viewed more through an Internet of Things (IoT) lens. In talking with some others last week, it seemed that the simple progressing from sensors all the way through action is still valid but may need to be expanded a bit.

Internet of things model.png

 

In really starts with the ‘thing’ that has been tagged (and its sensors and controllers). There is also a supporting device management layer that adds security, power management and other fleet management features. I didn't really show that the first time.

 

Data collection continues to have integration capabilities but the analytics layer needs to add more context and pattern recognition than just traditional analytics. There is an automation layer that rides on top that performs a number of the action oriented features.

 

I didn’t really think about the management layer that is inherent in the approach, even though some functions may only be useful for a subset of the environment. A pluggable set of standards is needed to minimize the complexity.

 

The Internet of Things will require a significant degree of autonomous control. It can’t be as needy as the tools we’re using today – crying out for our attention all the time.

 

Where did the IoT come from?

I was talking with some folks about the Internet of Things the other day and they showed me some analysis that made it look like it was relatively recent.

 

where did the IoT come from.jpg

 

My view is that its foundations go back a long way. I worked on (SCADA) Supervisory Control and Data Acquisition systems back in the 80s, which were gathering data off the factory floor, analyzing it and performing predictive analytics, even way back then.


In the 70s, passive RFID came into being and one of the first places it was used was tracking cows for the department of agriculture to ensure they were given the right dosage of medicine and hormones – since cows could talk for themselves.

 

In the late 70s and early 80s barcodes become widely used to identify objects, allowing greater tracking of manufacturing lines as well as consumers in stores.

 

In the 90s, higher speed and greater range allowed for toll tags to be placed on cars, allowing for greater ease of identification but still very little use of sensors to collect additional information.

 

At the turn of the century, the military and Walmart required the use of RFID to track products and that caused significant increase in their adoption. About the same time, low powered sensing capabilities were developed since RFID only provided identification and the scanner provided location, people began to look at other information that could be collected like temperature, humidity as well as ways to gather information remotely like smart metering in the utilities space (although even that started much earlier).

 

Most technology adoption follows an S curve for investment and value generation. We’re just now entering the steep part of the S curve where the real business models and excitement is generated. It is not really all that new it is just that the capabilities have caught up with demand and that is making us think about everything differently (and proactively).

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About the Author(s)
  • Steve Simske is an HP Fellow and Director in the Printing and Content Delivery Lab in Hewlett-Packard Labs, and is the Director and Chief Technologist for the HP Labs Security Printing and Imaging program.
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