Introduction
The internet in the 1990s could connect 1 billion users through shaky dial-up networks. The mobile wave of the 2000s made it possible for over 2 billion users to find information, keep in touch with friends around the world, and watch videos.
And now, the Internet of Things has the potential to connect 10 times as many (28 billion) gadgets to the internet–from cars to bracelets–by 2020.
In the last two decades, more than six billion devices are online. That’s enough to fill 57.5 billion 32 GB iPads per day (source Gartner). All this data is bound to significantly impact many business processes over the next few years. Thus, the concept of IoT Analytics (Data Science for IoT) is expected to drive the business models for IoT.
Now just imagine these many smart devices and sensors connected to the web and just visualize the phenomenal amount of data flowing through them. Cisco, which is well known for its technology predictions estimates the total volume of data generated by IoT will reach 600 ZB (1 ZB = 1 trillion gigabytes) per year by 2020.
The Internet of Things
IOT stands for “Internet of Things” and is a recent buzzword heard in the technology landscape as it is a trending topic. Technology has advanced rapidly in the past few years, and micro-controllers are now easily connected to the internet. In other words, linking any object in the real world to the web is easier than ever.
Think of things like a plant that can tweet when it needs watering, a collar for your cat that tracks their location, socks that people with Alzheimer’s wear that alert their family when they’re walking, or anything else you can imagine and IoT the next big thing, bigger than one anticipates!
Challenges with Big Data
The most obvious challenge with data coming in from so many devices is how an organization will handle and store the data. One can host the infrastructure on-premise, which brings on a particular set of skills needed to put this information in the cloud, which brings on a different skillset requirement.
Data Scientists & Machine Learning
With so much data, how does one review, analyze, and gain insight from it? The answer to that question is the very reason that the field of data science and the concept of machine learning is currently growing very rapidly.
Data scientists are skilled at mining and analyzing data in various forms. Data scientists are to the point of being able to accurately predict analytics, behaviors, and statistical trends. Once an organization gets close to predicting outcomes, they’re able to run more efficiently, which helps the bottom line in the end.
Whether insights come from humans or machine learning, the ultimate goal is to glean insight from all the data points and make educated business decisions.
Platforms & Tooling Options for Big Data
When it comes to software, tooling, and platforms for handling “big data” there are several options. As imagined, one’s choice of platform may be dictated by their existing in-house technologies and skillsets. Regardless of the technology stack, full arrays of solutions are available.
Some solutions offer dynamic scaling to store significant amounts of data while others are more focused on processing the data. In the world of Big Data, software called “Hadoop” is very popular. Hadoop is an open source programming framework that simplifies processing large sets of data. Many solutions on the market are built on top of or integrate with Apache’s Hadoop software.
Setting up a Big Data Analytics platform in organizations
Once organizations have a protected and efficient system to store IoT-related data, they need to be able to evaluate it. Extracting and managing value from IoT is a big task that companies are facing.
An excellent analytics platform should be designed according to three different parameters: right-size infrastructure, performance, and future growth. To improve performance, a single-tenant physical server dedicated to the only single customer is the best fit. To ensure future development growth and the right size of infrastructure, the hybrid approach is the way to go.
Hybrid deployments consist of platforms like the cloud, managed hosting, collocation, and dedicated hosting. This deployment combines the best features from various platforms into a single, optimal environment.
Big Data in Action
One might ask why “Big Data” matters or where can we see examples of big data in use? Big data is being used all around us and as the IOT field continues to grow, we’ll only see it become more relevant in –
