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Beyond Things: The Internet of Everything Takes Connections to the Power of Four

- October 8, 2013 - 25 Comments

Over the last year, I (and many of my colleagues) have spent a lot of time talking about the Internet of Everything (IoE) and how it’s transforming our world. I thought, however, it would be good to pause in this blog and clarify what we mean by the “Internet of Everything” in just a little more detail. I’ve mentioned in the past that IoE consists of four “pillars”: people, process, data and things, but let’s take a closer look.

Many people are familiar with the concept of the Internet of Things (IoT). Not only does it have its own Wikipedia article, but last month the Internet of Things was added to the Oxford dictionary, which defines it as “a proposed development of the Internet in which everyday objects have network connectivity, allowing them to send and receive data.” So it’s not surprising that people might be confused when we start talking about the Internet of Everything. What’s the difference? Is IoE simply a rebranding of IoT?

The fact is, the Internet of Things is just one of four dimensions — people, process, data, and things — we talk about in the Internet of Everything. If we take a closer look at each of these dimensions, and how they work together, we’ll begin to see the transformative value of IoE.

IoEprimer1People: The ways we connect to the Internet have changed in the last three decades — from dumb terminals to desktop computers and a variety of mobile devices, including laptops, smartphones, and tablets. But that’s nothing compared to the wave of transformation we are now entering. Google Glass and smart watches are just the beginning of an array of wearable technologies that will radically change the ways we consume and share information. We already have self-monitoring devices such as Fitbit and the Nike FuelBand that enable us to track exercise, monitor heart rates, and even monitor the quality of our sleep. In the next few years, these capabilities will grow profoundly. We’ll be able to swallow a pill that can monitor our digestive tract and intelligently send relevant information to our doctors at the right time and in the context of what we’re doing. Expectant mothers will wear “smart tattoos” to monitor the health and activity of their babies, and send the doctor an early alert when labor begins. We’ve only begun to scratch the surface of how wearable technology will transform our lives. In fact, look for a blog in the near future where I will go deeper into wearable technology of the present and future.

Process: Connecting processes is not something most people think about, but already the Internet has revolutionized the ways businesses manage their supply chains, and the ways consumers shop—to name just two. As we continue to “instrument” our world, we’ll have visibility into processes we could never see before, providing opportunities to make these processes faster, simpler, and more efficient. For example, Cisco is working with major retailers to use a fusion of sensors, video, and analytics to improve both store productivity and customer experience. Cameras and sensors in the parking lot can count arriving cars and the number of people coming into the store; combined with sensors on shopping carts and an analysis of store traffic patterns, the system predicts back-ups or slow times at the front registers and automatically adjusts staffing. Customers are happy to avoid long checkout lines, and the store can optimize employee productivity by having neither too many nor too few cashiers available.

The Internet of Everything is changing how people and things connect, how we collect and harness data, and how they all work together to enable intelligent processes.

Data: The world is awash in data. By the end of 2013, we will create more new information every 10 minutes that we did in all of human history up to 2008—most of it rich media. Not only is the volume of data increasing exponentially, the data itself is becoming richer. For example, we are beginning to move from HD video (720P / 1080P) to even richer video (4K displays). 4K displays have now dropped below the $1,000 price point, making them more accessible than ever before. Camera sensors are getting denser, too. Nokia for example, introduced its Lumia 1020 phone with a 41-megapixel camera. Granted, these advances are not yet mainstream, but they are coming fast, and this growth in content creation and consumption means that the data tsunami will quicken its pace.

And it’s not just rich media creating all this data. New types of devices that never existed before are starting to create even more data—for example, sensors on food to alert you before it spoils. Big Data analytics are helping us make sense of this avalanche of information—identifying and combining relevant data points in ways that reveal new insights and enable better decision making.

IoEprimer2And now, back to Things: Today, less than 1 percent of the things in the world—or about 10 billion things—are connected to the Internet. That number will grow to more than 50 billion in the next decade—enough for at least six connected devices for every man, woman, and child on earth. But the real growth will not be in the things we expect to be connected, such as computers, phones, and tablets; the exciting part of this growth is in the unexpected things that are just beginning to be connected. Connected cars will lead the way to self-driving cars. Connected water delivery systems will pinpoint leaky pipes and shut off faucets when not in use—reducing the 30 percent of water that is lost due to leaks and waste. Smart buildings will manage themselves better, contributing to smarter, more efficient cities. Even cows and the fields they graze in will be connected to help farmers and ranchers know when to irrigate, when to fertilize, and when to move the herd to a different field, all contributing to improving yield and reducing waste.

The Internet of Everything is built on the connections among people, processes, data, and things—but it is not about these four dimensions in isolation. Each amplifies the capabilities of the other three. It is in the intersection of all of these elements that the true power of IoE is realized.


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  1. There is nothing bad in being literal. Take, for example the following (wrong) statement: "By the end of 2013, we will create more new information every 10 minutes that we did in all of human history up to 2008—most of it rich media." First, data is not necessarily information. Does a 4k TV show contain 4 times as much information (or new information) as the same in 1080p HD? I doubt. Yes, rich media is nice, but not for information - in most cases for comfort and entertainment. Then, it gets even more tricky when you add the dimension of "what makes sense" to distinguish data from information, because it depends on context... In the end it is important to define you terms. Like Dave did in his blog. While I am at it: Don't miss the point that we can do a lot of smart and smarter things (no pun intended) when more "things" are connected, but we need to watch the cost as well. How much waste is produced to build and run all the new devices and connections? Anyone done the TCO?

  2. Of course much of the confusion is likely due to the common use of the word "Thing" in both terms. Taken literally, you would start with a thing, then a few things, then many things, utlimately ending up at Everything. So, in this context People, Process and Data are all Things. Perhaps it would be more clear to use the term "Objects" instead of "Things" when differentiating People, Process and Data. Or, perhaps I just need to stop being so literal.

    • Yup, it's the literal thing, Chris. Please stop. :-)

  3. Good Article. Predicting required number of cashiers? Who needs them really? Self checkout is already growing and I expect in future there will be smart carts, which can automatically checkout a customer once they step out of door.

  4. There's a lot of discussion and confusion about IoT vs IoE. I can say that for the four components of IoE, IoT is the new kid on the block and the other three were already assumed to exist. Labelling it IoE is (to many of us) just IoT+what-was-already-there.

  5. Great article. It makes you think about what could be. The internet of everything. I love the idea, but as many commenters noted, there are many problems to sort out. I hope we don't let that deter us from pursuing the dream. As far as privacy, I see the future where I own my personal information and it stays with me. If anyone wants to view it or use it, they have to come and ask me. They cannot store my name, credit card, address etc. They link up to my personal data locker to check this data - but they can't copy or store it. Let's make it happen...

  6. Interesting thread, Dave. Before we get to the future state for IoE, the industry needs to sort out privacy and data concerns. That's the big hurdle. The rest is just bits and bytes.

    • Rob - very much agree, security and privacy need to be a top consideration. Much work remains to be done in these areas.

  7. I don't want any unnecessary external dependencies in my life. I won't be happy if I can't get a drink of water because the internet connection is down.

    • Agreed, but one might want to know if the water is safe to drink, has been contaminated, or is simply being lost in transit to reduce cost. It should not be technology for technology's sake - there must be a net benefit.

    • Neither would I. My mechanical engineering background makes me feel comfortable that your situation wouldn't happen. I look at the PG&E gas fire in San Bruno as a good example of how IoE can help. People had to physically go to a flowmeter to turn off the flow of gas, which led to a 45 minute delay. If that flowmeter were connected to an actuator, we could turn it off manually. By using secure, encrypted virtual network overlays, we can guarantee that no one can abuse the connection. Of course, I'm a big fan of having a manual override.

  8. I think you need to expand on the "People" portion more. Because everything you wrote in that section covered things - Google Glass, smart watches, fitbit and Nike Fuelband, medical diagnostic "pill", smart tattoo, and wearable technology. Those are all things ...

    • I see the same for the other two pillars(process, data) as well, it's all using "things' to collect data, maybe IoE is trying to put another layer on top the raw data to categorize it for better handling?

    • I agree with the challenge to the "People" aspect as I believe the greater benefit will be the ability for us as individuals is to ask "better" questions of ourselves. The "things" will yield great data but it will be our ability to extract meaningful actions from that data and challenge ourselves. i.e. Whether it's a tatoo, pill, band, or other item. It will be us asking "When is it best for ME to exercise? What times are best for ME to sleep? When am I in a peak state for work? The quality of our questions will correlate directly to our results as employees, spouses, children, volunteers, and humans.

    • Thanks for the comment--you make a good point! As a matter of fact, people have always used "things" to communicate across time and distance--beginning with cave drawings and drums, and progressing to ink on paper, telegraph, radio, and today, computers, tablets and smartphones. When I talk about "things" in IoE, I mean objects that connect on their own behalf--factory robots, streetlight sensors, or weather balloons that gather and communicate data to other things, data warehouses, or processes. People aren't involved. When I talk about the "people" aspect of IoE, a living human being is at the center, even though he or she may use "things" to make that connection. So, you're right--Google Glass and smart tattoos are things, but they are the means by which people connect to other people, as well as to data and things.

      • I think your 4 IoE pillars of people, processes, data, and things is a useful explanation. For those who need these terms further disambiguated, you might define IoE to be the Internet of *people*-based I/O technologies, personal, business, and industrial *processes*, simple and processed *data*, and autonomous non-people-based *things*.

      • Thanks for the clarification, Dave. That makes more sense and makes the distinction more clear.

    • The way I've been looking at people lately is that we decide what's important. Sure, algorithms and 'user experience' and incentives affect people, but we are the ones who decide what has value. The internet of everything is about cutting out the bloatware that's accumulated in the last century through organizational structures and connecting the interested with the interesting.

  9. Interesting and thought-provoking post. My concern is that we not confuse change with progress. Ray Kurzweil has predicted that in the next decade we will see as much change as occurred during the entire 20th century. While some--perhaps even most--of the change may be for the better, we need to renew the social contract to insure that the citizenry actively participates in the adoption of change or we'll end up with either a police state or a revolution.

    • Genie is already out of the bottle with that one =) Just hope that machines are doing the work... and the government is not the issue, as much as the private sector is interfering with human decision making to sell us stuff... but that train also has left the station, so prepare for exciting times!

  10. Good Article David ! The connections and mashups between all these different pillars is what takes IoE from x to x^4 ! IMO We need to make sure this done in the network not over the top on servers. Think about how many devices we can uniquely find on the net today, if we waited for servers to make these connections it would never have scaled in to the network we know and love today.

  11. Dear Mr. Evans, Great post filled with dense knowledge. This is particularly significant for developing countries where, a lot of usable produce is wasted due to poor efficiency utilization and thefts. In India I can see just not reforming agriculture but also schools and many other sectors. Kind regards, Gaurav Varma

  12. Thank you for this great article - it answered that big question about whether IoE was just a re-branding of IoT.

  13. All is very intriguing. As a PhD Statistician I saw the interest in data wane as the focus moved to IT due to --wrongly-- believing IT firms could lead data analysis, as if data analysis were a less important or difficult task than systems design. That marketing mistake generated a steady demand for statistical services to fix unlimited data analysis/data understanding errors of "statisticians" within IT firms. Now "big data" and "data of everything" suggest increased understanding of the need for trained statisticians to compile and simplify volumes of data. Google zoomed forward partly due to their understanding of the need for statisticians rather than believing IT firms could turn systems folks into data analysts. As it becomes easier to collect voluminous data it becomes more important --and difficult-- to make meaningful use of it all.

    • Thank you Rebecca for your post. let me start by saying that I am not an expert on Big Data. There is no argument here regarding the critical role for statisticians in the world of Big Data. However, aside from the need for aggregating, testing and cleaning big data, I see a big piece of the puzzle missing related to the acqusition of any useful insights big data is to provide. Big data will need big ideas, deep ideas that hypothesize how all the big data fits together. Big data does have the potential to produce knowledge that can transform what we know about people and processes. How will we know if the conclusions based on big data analysis are not spurious or incorrect? It is quite possible to identify relationships that make sense from a numerical point of view that make no sense from a conceptual point of view. Before big data can tell us anything, we need the big ideas that piece together the element of the data in a way that provides some useful information. The question we need to ask is this: What is it that we really want to learn from our particular big data?