The Internet of Things Becomes the Future of Things

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Last week I attended the Oracle Open World conference in San Francisco. During the opening keynote, Oracle CEO Mark Hurd talked about how Oracle is positioning to be a core enabler for the connected world of today – and tomorrow. Mark dropped a nice little tweetable prediction that more data will be created in the next four years than in the history of the planet. It’s true, data is growing at staggering volumes. Some studies suggest that more data was created in the year 2007 than within published documents over the last 40,000 years of humanity.

So what is this concept of the internet of things and what type of impact is it having on the world? The term refers to the explosive growth of connected devices and gadgets that are capable of collecting and creating data (also called Machine-to-Machine or M2M). According to a study from Cisco, over 8.7 billion devices were connected to the internet in 2012, and that number is expected to grow exponentially by 2020. That means managing the internet of things could be a trillion dollar industry for companies like Oracle.



The term was coined by Kevin Ashton, cofounder and executive director of the Auto-ID Center at MIT who first mentioned the Internet of Things in a presentation he made to Procter & Gamble. Ashton said:

“Today computers — and, therefore, the Internet — are almost wholly dependent on human beings for information. Nearly all of the roughly 50 petabytes (a petabyte is 1,024terabytes) of data available on the Internet were first captured and created by human beings by typing, pressing a record button, taking a digital picture or scanning a bar code.

The problem is, people have limited time, attention and accuracy — all of which means they are not very good at capturing data about things in the real world. If we had computers that knew everything there was to know about things — using data they gathered without any help from us — we would be able to track and count everything and greatly reduce waste, loss and cost. We would know when things needed replacing, repairing or recalling and whether they were fresh or past their best.”

Examples of the internet of things.

Sensor technology is at the forefront of machine to machine learning. Manufacturers are imbedding sensors in things like farm equipment, airplane engines, and vehicles to monitor use, maintenance, and defects. It turns out the Oracle TEMA USA boat contained over 300 sensors that collected information on everything from the strain on the mast, wind conditions, and every tiny change made by the trimmers who adjusted the sail wing to maximize wind conditions.



  • In the manufacturing sector, farm equipment manufacturer John Deer is imbedding sensor sin farm equipment to monitor usage, maintenance, and engine conditions. Some equipment is so smart it’s GPS enable and can operate without human intervention.
  • In the utility sector, smart technology links electricity, water, and gas meters for increased visibility on spikes in demand, outages, and supply. FedEx recently released a new tracking device and web service called SenseAware which keeps tabs on temperature, location, and even when a package is opened.
  • In the transportation and logistics sector, sensor technology is being used to link pallets and packages to communicate their position in real-time helping create greater efficiencies in transportation and reduced overhead costs.

What does this mean for businesses?

While all this “internet of things” is exciting to talk about, it’s also still an emerging concept. We continue to have a problem looking for a solution. The problem is there is no end to the data we can collect, but we are very limited in how we can use it. What do we do with all this data? At some point analysis needs context and that context will likely come from experienced human intervention. That remains the biggest hurdle to overcome. It’s one thing to collect the data, it’s quite another to ascertain meaningful insights from overwhelming massive volumes of structured and unstructured information. By the way, all this talk about big data is really only compelling when large volumes of unstructured information can be used to glean insights. It’s analyzing unstructured data that makes big data analytics really exciting.



According to Gleanster, big data and big data analytics solutions are in the early adopter stages of growth; even among the largest and most well-funded IT departments. For business leaders, job security will come from knowing what to do with massive volumes of information, not finding new ways to collect the data. We are certainly entering the age of the internet of things, but the real benefits come from the ability to extract insights, define new business rules, and take action off the data, regardless of whether this data comes from humans or machines.

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