Almost every physical object can be connected to the internet.
We are racing forward to an era of hyper-connectivity. Objects now have the ability to communicate with each other, all using DATA!
The Internet of Things is happening now!
The Internet of Things (IoT) is a term that has been thrown around for a few years now, but many of us may still not fully understand what it means.
The IoT refers to a network of physical objects or “things” that have the capabilities to both collect and exchange any amount of data. They do this through a variety of software, electronics, sensors, and network connectivity.
So many devices that we own are connected to this “Internet of Things”, and we are using this network of data everyday to make our lives easier. This includes everything from cell phones, coffee makers, washing machines, headphones, wearable devices and almost anything else you can think of. If it has an on and off switch, then chances are good that it is connected to the IoT.
Now that more and more objects are connected to the IoT, those objects can all communicate to optimize day-to-day practices, create new opportunities, and save us money!
This is a great video looking at 7 bind-blowing facts of the Internet of Things.
“In 2008, there were already more objects connected to the internet than people.”
“The ATM was one of the first objects of the Internet of Things, dating back to 1974.”
“This year we will have 4.9 Billion connected things.”
“By 2020, we will have 6.1 Billion smartphone users.”
“The global market for wearable devices has grown 223% in 2015.”
“By 2020, a quarter of a billion vehicles will be connected to the internet.”
“All of these devices collect and transmit data, contributing to our Big Data world!”
How Your Sensor Data and The Internet of Things Can Save You A Lot Of Money
The Internet of Things, Industrial Internet, Internet of Everything, no matter how you name it, the upcoming connected world will change everything and create massive amounts of sensor data.
For manufacturers, the Internet of Things (IoT) will mean using sensor data to optimize manufacturing processes and improving products.
90% of all the world’s data has been created within the last two years.
25% of organizations now have a data scientist on staff.
According to CIO, $3.2 billion was spent by companies on big data in 2010; it is predicted companies will spend $16.9 billion on big data by 2015
$300 billion could be saved if big data was used effectively the US healthcare sector; thereby reducing expenditure by 8%
Progressive Casualty Insurance Company, uses Big Data as part of its “pay as you drive” program, offers drivers the chance to lower their insurance premiums based on real-time analysis of their driving habits.
328 million things connect to the internet each Month!
91% of marketing leaders believe successful brands use customer data to drive business decisions.
89% of companies believe that if they do not develop and adopt a big data analytics strategy, that they could lose both market share and momentum.
Why? Because big data tells you things about the world, and about your business that you didn’t know before. And as more and more of your competitors adopt data strategies, they will learn more and more about your industry, putting you at a disadvantage. It is quickly becoming the “new thing” in business.
Luckily, it is quickly becoming easier and easier to adopt a big data strategy.
Step 1: What is Big Data?
The first step is to learn what Big Datareally is. Understand it first, in order to appreciate its value to your company.
Step 2: Get Comfortable With the Idea of Using Big Data
The definition may scare you, because it is a little mind-boggling and difficult to comprehend. However, it is not as big and scary as it seems. It is also not as costly as it seems. The Internet of Things (IoT) movement is making collection much easier, and the cloud is making storage much easier. Even analytics are becoming easier for companies of all sizes. If you don’t have enough money to spend on hiring a whole team of data analysts and data scientists, don’t sweat! There are firms out there that specialize in this that can analyze your data for a much lower cost than hiring a team.
Step 3: Ask Yourself These Questions to Develop a “Big Data Plan”
A good first question to ask yourself is, “what do we have?” Then identify what kind of data you think you are missing that you may need. This will help you identify what kind of data to collect. Many companies think that simply collecting a lot of data is a “data strategy.” However, data is only useful if it is relevant to your company. Furthermore, collecting a lot of data can make it more difficult for a data analyst to extract information and insight from that data. Having a lot of data makes the analyzing process more difficult, as they have to sift through all of the irrelevant information (and initially decide what information is irrelevant in the first place).
Next ask, “What assumptions do I have about my company?” This is a great question to ask, as it is a great starting point for companies that don’t have a big data analytics plan already in place. At this point, much of what you know (or rather, think you know) about your company are based on assumptions. You may have reports that tell you things, but this data can be manipulated and often comes from many, disparate sources. Because of this, you may not be getting accurate information.
Therefore, a good starting point is to test your hypothesis about your assumptions of your company. This also makes it easier for a data scientist to determine where to start…and you never know, along the way, he or she may find something else that will be useful.
Doesn’t it frustrate you when you buy an airline ticket to go home for the holidays, only to find out that your younger brother, King of procrastination bought his plane ticket a day before your flight departed (4 months after you), and he ended up paying less?
It is this very reason that Farecast was created in 2003. They understood that because of the internet, fixed pricing was a thing of the past; but now prices are so volatile that there’s almost no way to decide when it will be cheapest. Farecast solved this problem, establishing the motto “Know When to Buy.”
So how much data needs to be mined in order to predict when fares will be cheapest? According to the interview in this video, hundreds of billions of price observations that have been gathered over more than 8 years. This data is crunched to find patterns; both short and long term. Should I buy on Monday? Should I buy in the Spring?
These data predictions are about 70-80% accurate (this is more accurate than the weatherman!). This is the case because they gather more than just historical data. They gather unstructured data on rumors, gossip, blogs, PR announcements and more to come up with more accurate predictions.
This same principle can be taken into pretty much any industry, just as big data analytics can help businesses in just about any industry.
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