IoT | Internet of Things

by | Jul 28, 2017 | Data Analytics, Forcasting, Modeling | 0 comments

Are You Paying Attention to IoT?

The Corporate World Needs to Keep Pace With Innovative Analytics

In case you missed it, analytics exploded in 2015.

From Big Data to IoT, we have been thrust into the midst of a tech revolution – one that happened to pick up speed quite quickly.  So it doesn’t come as much of a surprise that there are still many people out there who would scratch their heads if asked to define data technology, let alone the complex web known as IoT.

What is IoT ?

The term IoT is short for Internet of Things.  You may be asking yourself – “what exactly does that mean?”

IoT can best described as the ever-growing network of devices that use an IP address to connect to the Internet.  It also refers to the communication that occurs between these devices, in addition to other Internet-enabled systems.

Now, this goes far beyond the traditional devices we know so well, like our computers, tablets or smartphones.  What we are talking about is the embedded technology in certain devices that is used on a daily basis to interact and communicate with the surrounding environment, all through the World Wide Web.

This covers a broad spectrum of modern technology.  We’re talking about cars, security systems, electronic appliances, alarm clocks, vending machines, speaker systems and so much more.

With this type of technology, the goal should be the insight gained.  However, it appears that more and more companies are missing the mark when it comes to honing in on IoT strategies.

Missing The Mark

It is first important to point out that, over the course of the last decade, we have produced A LOT of data.  So, that means that we need ways to harness analytics for a greater benefit.  This is has naturally become a common concern at the corporate level, as companies look to keep pace with technology and optimize cost efficiency.

However, despite the many innovations in predictive analytics and other data practices, there are some people using a very shortsighted approach to IoT.  It is not uncommon today to see people using IoT as a way to gather a wealth of sensor data, or the analytics generated through wired or wireless sensors.

Quite frankly, that’s dated thinking.  In addition to being behind on the times by a good decade or so, collecting data with a quantity-first attitude is also increasingly problematic.

The fact is that this methodology does not yield actionable data.  Instead, it creates a glut of virtually un-actionable data.  And we are talking a big glut – more than 99 percent.   It is, quite simply, a quality vs. quantity argument.

It’s important to note that sensor analytics are primarily used to detect anomalies.  We see these at work in our daily lives – such data is used to notify nurses in intensive care of patient blood pressure drops, or to prevent equipment failures in manufacturing facilities.

The goal here is for continuous, constant data collection and storage, but not constant interpretation.  Typically, data sets of this size are reviewed and interpreted periodically, rather than as the data instances occur.  However, we need to be more sophisticated in our thinking.

The Insight-First Approach

While a learning curve is expected with any emerging technologies or trends, there have been some organizations that have demonstrated an ability to dig in and explore the valuable analytics offered through IoT technologies.

For example, sports organizations are increasingly looking to IoT to optimize elements like player safety and in-game strategy, in addition to broadening injury prevention practices.

The National Football League (NFL) is one such organization.   In recent years, the game has faced increased scrutiny over the long-term safety and well-being of its players after repeated significant injury, specifically concussions and other head trauma.  So, it was only natural that they would turn to the power of IoT to gain valuable insight into player safety.

One NFL team even used small computers on the backs of every player as they ran drills during practice.  This technology was able to measure heart rate, breathing, and temperature vertical movement – virtually everything that the player did.  This actionable data provided team officials the ability to evaluate their overall injury prevention and treatment strategy.

So the real question is, if the NFL can successfully do it, why can’t the corporate world?

As the world of data continues to evolve, look for more organizations to adapt an insights-first approach, versus a quantitative approach to their analytics strategies.  After all, when it comes down to it, quality is a pillar of any good business plan.

What is your perspective on the ways we can better use IoT to gain valuable insights into the tremendous data we produce? Follow @ScienceProfits on Twitter and let’s keep the conversation going.

About the Author

George Danner is president of Business Laboratory, LLC, an award-winning firm that uses scientific techniques and methods to improve organizational performance.   With more than 30 years of experience in corporate strategy, George keeps his finger on the pulse of the latest trends global data realm.  He recently authored a book on Big Data business strategies, Profit From Science, which debuted as the #1 Bestseller in Business Mathematics on Amazon.  To learn more about George and Business Laboratory, visit