Internet of Things (IoT)

The Internet of Things is the concept of everyday objects – from industrial machines to wearable devices – using built-in sensors to gather data and take action on that data across a network. So it’s a building that uses sensors to automatically adjust heating and lighting. Or production equipment alerting maintenance personnel to an impending failure. Simply put, the Internet of Things is the future of technology that can make our lives more efficient.

• History
•      
• Importance
  
•      
• Who Uses It
•      
• How It Works
  

History of the Internet of Things

We’ve been fascinated with gadgets that function on a grander scale for decades (think spy movie-type stuff) – but it’s only been in the past several years that we’ve seen the IoT’s true potential. The concept evolved as wireless Internet became more pervasive, embedded sensors grew in sophistication and people began understanding that technology could be a personal tool as well as a professional one.

The term “Internet of Things” was coined in the late 1990s by entrepreneur Kevin Ashton. Ashton, who’s one of the founders of the Auto-ID Center at MIT, was part of a team that discovered how to link objects to the Internet through an RFID tag. He said he first used the phrase “Internet of Things” in a presentation he made in 1999 – and the term has stuck around ever since.

To make the Internet of Things useful, we need an Analytics of Things. This will mean new data management and integration approaches, and new ways to analyze streaming data continuously.

Thomas H. Davenport
 President's Distinguished Professor, Babson College
Co-founder and Director of Research, International Institute for Analytics
Author of Competing on Analytics and Big Data at Work

Who's using it?

The IoT is more than just a convenience for consumers. It offers new sources of data and business operating models that can boost productivity in a variety of industries.

How It Works

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.