Having a bunch of sensors and tiny computers working in tandem, while being connected to the internet may seem like the premise of a sci-fi movie but nowadays it is a reality. Maybe not on a larger scale but it’s getting there. Since this whole idea may not sound so appealing to the average citizen, somone came up with the term Internet of Things (IoT) to describe it. “What does all this have to do with data science?” you may ask. “Everything,” I would reply.
Interestingly, the IoT movement is not about the sensors, nor about the connectivity of these sensors to a global network via some high-tech computers the size of your phone. It is all about the data that is collected and then aggregated on the cloud, via the Internet. People have been using sensors for decades and today most cars have several of them built in their information infrastructure that is managed by their built-in computers. Yet, only recently have a series of such sensors been made widely accessible physically (due to their low cost) and informationally (due to the Internet and the cheap distributed computing infrastructure that tiny computers allow).
Naturally, there is no better way to obtain accurate data than with a sensor. A patient may tell his doctor that he feel that he has a fever, but if the doctor is to do her job, she won’t take his word for it. After listening to him explain the symptoms he’s experiencing, she will take his temperature with a thermometer. Although it may not look it, this simple medical device is nothing more than a specialized sensor for obtaining temperature data. So, sensors are not some abstract piece of tech that some engineers use. They are everywhere and have a variety of uses, many of which are critical.
The data streams that flow from the abundance of sensors in an IoT framework are therefore very rich in information and insights. Without this data, a modern plane wouldn’t be able to cruise safely at the high altitudes that it is designed for. However, the data on its own is not enough to make anything interesting happen. It’s its processing that makes it useful and valuable. And with many modern data science systems, this task is made much easier.
However, before diving into IoT data and immerse ourselves in the brewing of insights from it, there are a few things we need to consider. These may not seem relevant and will probably be shunned by most conventional data scientists, people who care only about doing what they are told. Yet, a fox-like data scientist who sees things from various angles, exploring different possibilities and several aspects of the problem at hand, such a person would definitely consider the following:
Applying foxy data science is not just about finding clever and innovative ways of working the data and presenting it in visuals that are engaging and insightful. Foxy data science, in my experience, is also about seeing the bigger picture and asking some interesting, albeit sometimes hard, questions. This may seem obvious to conventional scientists but it is not so obvious to many people today who are engaged in data science so much that they lose sight of what it’s for. Some of us have had the privilege of working with talented and relatable managers who would share the bigger picture in a succinct and elegant way. However, most people just don’t bother, partly because they are too focused on the technological aspects of it. Even though this may have been relatively harmless in most cases so far, the IoT framework is a whole new animal and may require closer attention. Because once the djini is out of the bottle, it isn’t going to go back...
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy flair when it comes to technology, technique, and tests.