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IoT Has Changed the Big Data Business Model

Tom_GilleyIn this special guest feature, Tom Gilley of wot.io talks about the role that the Internet of Things plays in the big data market. Tom Gilley is CTO of wot.io, an IoT data service exchange™ for connected devices and data that lets current and future players in the IoT ecosystem – app creators, system integrators, analytics providers, and hardware companies – bring together applications and devices on any standard or connectivity, instantly and intuitively. Tom is a passionate technologist for mobile technologies, digital media, Internet of Things (IoT), and social computing. Before founding wot.io, he sold his on-demand Web media company to Vignette and acted as CTO throughout the transaction and through the company’s ultimate acquisition by OpenText. He also served at Apple Computer in the Advance Technology Group and Portable Products Group.

The Internet of Things (IoT) is one of the most talked about technologies of 2015. It currently sits atop Gartner’s Hype Cycle for Emerging Technologies, narrowly beating out Big Data, which rests in the “trough of disillusionment.” And yet, with these two emerging technologies so closely intertwined, it’s not completely clear the role that IoT plays in the big data market.

So what is IoT? Its a marketing term that encompasses any device with network connectivity that allows for communication and data transfer to a networked application on the Internet. The descriptive terms would be “connected devices” that can communicate over the Internet to “data services.”

A simple example of IoT devices is a connected thermostat, which allows a user to easily control the heater and air conditioning unit. The thermostat, in this use case, is connected to the network through a gateway to the Internet, allowing the user to control the connected device locally or remotely by way of browser or mobile application. The thermostat data can be shared with various applications to add new abilities, such as triggering specific temperatures, and letting the user know the home is getting too cold or hot.

A single thermostat would not produce large amount of data for a big data use case, so let’s say that you own office buildings or apartment buildings with many connected thermostats, and you have permission to collect data from the thermostats throughout the building. This is where we begin to see big data opportunities from the connected devices.

Big Data at Rest and in Motion

To master IoT data we can look at big data as analytics on data from connected devices that is stored (data at rest) or analytics on connected device data as it is streaming (data in motion).

The breadth and volume of data generated by IoT devices creates the obvious demand for data storage services, such as Hadoop, MongoDB, ScaleDB, to name just a few. Analyzing the collective thermostats from a building would allow discovery of heating and cooling issue patterns that offer the opportunity to improve the efficiency of specific areas of the building.

We can also analyze data that’s in motion from the thermostat example above. Rather than just analyzing the data at rest with the right data service, we can process the data in motion with data processing services like SQLStream. A simple example would involve analyzing the connected thermostats for temperature changes and sending an instant alert upon reaching a too hot or too cold temperature threshold event.

Explore Big IoT Data

As organizations look to use IoT data, it’s critical that they create a data environment that enables them to use data services to explore data at rest and data in motion interactively. You’ll be surprised at how many patterns you can find within the data through exploration, rather than simply using it with a specific end goal in mind. Business users often have preconceived notions of the data, but fail to realize the new patterns they could unearth by simply exploring and playing with the data. People in different roles in a company may also expose new dimensions of the data by seeking different insights that apply to their position.

It’s easy to underestimate your data. And even more so – not think of your IoT data as valuable. However, if you examine your data, you never know the sort of insights you could discover.

 

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