Defining the Internet of Things isn’t easy. When it’s defined in terms of market size, some focus on the potential revenue (it’s in the trillions), while others focus on the number of potential “Things” (it’s in the billions). Some definitions focus on the exponential growth of sensors, excluding smartphones, tablets and desktop computers, while others only consider devices with an IP address.

There isn’t really a standard definition of IoT. Different organizations describe it in different ways. According to CISCO,

The IoT links objects to the Internet, enabling data and insights never available before.

And according to Gartner,

The network of physical objects that contain embedded technology to communicate and sense or interact with their internal states or the external environment.

Further, the Internet of Things Global Standards Initiative describes it this way:

A global infrastructure for the information society, enabling advanced services by interconnecting (physical and virtual) things based on existing and evolving interoperable information and communication technologies.

The IoT is the concept of everyday objects and how they’re connected. It includes everything from machines to wearable devices that use built-in sensors to gather data and take action on that data across a network. It can be a residence that uses sensors to automatically adjust heating and lighting. It can also be sensors in a factory that can alert workers of mechanical failure. We can sense and monitor if the coffee pot or oven is left on. It takes any device onto the network and gathers data based on human use.

From SAS: The Internet of Things and Why it Matters

In IoT discussions, it’s recognized from the onset that analytics technologies are critical for turning this tide of streaming source data into informative, aware and useful knowledge. But how do we analyze data as it streams nonstop from sensors and devices? How does the process differ from other analytical methods that are common today?

In traditional analysis, data is stored and then analyzed. However, with streaming data, the models and algorithms are stored and the data passes through them for analysis. This type of analysis makes it possible to identify and examine patterns of interest as data is being created – in real time.

So before the data is stored, in the cloud or in any high-performance repository, you process it automatically. Then, you use analytics to decipher the data, all while your devices continue to emit and receive data.

With advanced analytics techniques, data stream analytics can move beyond monitoring existing conditions and evaluating thresholds to predicting future scenarios and examining complex questions.

To assess the future using these data streams, you need high-performance technologies that identify patterns in your data as they occur. Once a pattern is recognized, metrics embedded into the data stream drive automatic adjustments in connected systems or initiate alerts for immediate actions and better decisions.

Essentially, this means you can move beyond monitoring conditions and thresholds to assessing likely future events and planning for countless what-if scenarios.

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