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Internet of Things Analytics

Internet of Things Analytics

Internet of Things Analytics, or IoTA for short, is the term we use to refer to the measurement and transformation into business intelligence of the Internet of Things. (It also happens to be what we at Tellient call our embedded device agent, but more on that in another post.) So what exactly is it and how does it work?

You are familiar with web analytics already: You put a few lines of code at the bottom of your web page and Google Analytics (or your preferred platform) tells you about your visitors so that you can make better business decisions. With web analytics, you can make data-driven decisions about your website that make you more competitive.

Web analytics vs. Internet of Things Analytics

With web analytics, you generally have a single website or app and a bunch of people visit it and do stuff when they are there. Because a single web site is a natural bottle neck visited by multiple users, a single point of measurement is all that is needed. Also, because the web is basically comprised of a bunch of pages, it is conceptually simple to measure things like where someone clicks or how long they stay on a page. It is fairly simple to deploy web analytics, and the value you get is pretty substantial, so you can be pretty sure that the servers crunching all your data and turning them into happy graphs are highly sophisticated. All of those great web analytics providers only make it look easy.

Internet of Things Analytics differs from web analytics in a few fundamental ways. First of all, while the definition of “the internet of things” varies widely, it generally refers to internet-connected devices that are not computers nor cell phones: Think appliances or consumer electronics, or in more industrial applications, sensors in turbines or smart water meters. In any case, millions of internet-connected coffee makers, TVs, dog collars and house-cleaning robots are not websites. Collecting data, interactions and context on millions of geographically distributed, intermittently-connected and resource-constrained devices is hard to do because there isn’t a single collection point. Instead, Tellient collects data about every individual device and combines those data streams on our server to provide a global view of product usage.


Electronic devices and appliances have historically been black boxes. You won’t find Windows or MacOS running a toaster. A coffee maker has a few buttons on it and it is supposed to do one thing – make coffee – and they do that very well using a microcontroller with a very slim RTOS that runs everything. It isn’t pejorative to refer to these kinds of devices as “dumb,” it’s just that they aren’t expected to be particularly smart, in the same way you old mobile phone only seems dumb compared to your new “smart” phone.

A newer, technologically-advanced coffee maker is network-connected and able to allow a user to control it from their mobile phone, which is great added value for consumers, and compared to older coffee makers, it is “smart.” But without an onboard analytics agent, a manufacturer of next-generation internet-connected coffee makers will have no way of knowing what their smart coffee makers are doing even though they could. That is where Tellient comes in: We put the technology in connected devices that makes them smarter for the consumers that use them and the companies that make them because even without a “smart” operating system, they can phone home and report on their actions and interactions so that decisions can be made and other actions can be taken.

How About A Concrete Example?

A maker of connected coffee makers can know how their owners use them. Their R&D department may be very interested to know that most of their users make way more coffee than they had originally thought, which could tip the scales on the decision to use a more durable case material in the next generation. The marketing department might use that information to push discount promotions to registered owners for bulk orders on consumables such as filters. The customer service department might be able to respond better to customers who reach the projected 20,000-hour lifespan ahead of the expected timeframe to offer them a deal on a future purchase. The point is that without the information, no action can be taken, and getting the information as soon as possible means taking action as soon as possible, and the way to do that is to discreetly report useful information in real time and let the entire organization benefit from it.

But why stop there? Why not give the consumer their own analytics, a sort of FitBit (love that company) dashboard for their coffee maker? They can see how much coffee they consume, the financial benefit of operating their coffee maker vs. buying their lattes at Starbucks and maybe also feed that nutritional information into their fitness plan on another platform. Providing unit-level analytics out of the box is a consumer benefit that extends the value of the product itself.

But why stop there? By integrating other sources of data and analytics, the same manufacturer of coffee makers can go beyond just knowing what their devices are doing to knowing what they are doing in context. By knowing that a thousand coffee makers in a very narrow geographic area in southern California all broke at the exact same time which happened to be at the exact same moment there was a magnitude 7.5 earthquake, a customer service opportunity pops up. If the coffee makers literally jumped off the counter to their deaths because of the earthquake, the manufacturer does not have a warranty liability because the product didn’t break due to a defect. At the same time, having that information also means that the manufacturer has a great customer service opportunity by proactively providing their customers with accidentally broken coffee makers with a discount coupon on a future purchase.

But why stop there? Connected devices needn’t be connected to each other directly to form a beneficial network; they can be virtually connected through the data they generate. For instance, a maker of connected sprinkler systems can use the collective intelligence of their users combined with real-time weather information to create hyper-localized device profiles. Consumers could then choose to set their sprinkler systems to a “genius” mode driven by collective analytics to better manage their water usage for their area, potentially saving millions of gallons per year and saving individual users real dollars.

Intuition That's More Than Data

Tellient does a lot more than reporting on button pushes and on/off cycles. Yes, we provide real-time product intelligence on devices that otherwise wouldn’t be capable of providing it on their own, but our mission is to go far beyond that and provide a sphere of actionable intelligence around a massive network of devices and data. Simply put, our vision is to use Tellient to be the intuition of the Internet of Things to make the entire ecosystem – including manufacturers, consumers and even other devices – smarter and better for everyone.

Download the Free Whitepaper: The Network Operator's Guide to Monetizing IoT Data


About The Author

Shawn Conahan

Shawn Conahan is the founder of Tellient. His mission is to make smart things smarter. (Just ask his modded Roomba named Robbie with adaptive mapping and navigation.) Shawn also loves infographics, and his all-time favorite is the Carte figurative des pertes successives en hommes de l'Armée Française dans la campagne de Russie 1812-1813 on his office wall.