According to a recent report published by Gartner, by 2020 more than 26 billion devices (excluding smartphones, tablets and PCs) will have some sort of IoT functionality built into them. 26 billion – more than thrice of world’s population! Just think about it. Even if half of these figures come to life, it’ll still be huge. Pretty much everything that we’ll be using will have internet connectivity and other smart tools built into it.

But smart doesn’t become smart by itself. It becomes smart when it learns using data smartly. Usage of IoT can provide your enterprise an endless stream of data. But what will you do with it? Before you implement IoT in your products, you should have answer to this question.

Now, a number of things can be done with it. But arguably the smartest thing that you can do is ‘using it to predict the future’. Yes, IoT can help your company in predicting IOT future, and that too in a much more accurate manner than you ever could. All you need to do is combining it with Predictive Analytics.

IoT and Predictive Analytics: Possibilities and Challenges

The marriage of IoT and predictive analytics seems like a match made in heaven. If data collected through IoT based devices is combined with right type of predictive analytics systems, we can tap into a whole new world of possibilities. A world where pretty much everything can be predicted before taking place.

But of course, doing that is easier said than done. The biggest challenge that companies face while making such systems is combining data that comes from different sources in different forms. While simple systems are possible where a single type or source of data can do the trick, the most effective and magical systems can only be engineered by combining data of different types and sources. For example:

• IoT data with other real-time data: NCR Corporation, a leading manufacturer of ATMs, has done projects where it combined performance data about its ATMs with real-time weather data to determine if humidity, heat or any other environmental factors were hampering the performance of its machines.
• IoT data with historical data: A TV company uses streaming data collected from set-top boxes to determine who’s watching what. That data is then combined with historical data to build predictive models that can tell in advance about channels that any particular user will switch to during the breaks. This helps the company in determining channels most of its viewers can switch to during any break, so it can automatically scale its content delivery network to handle the sudden shift of load on to any particular channel.

These are just a couple of examples. Possibilities, as you may expect by now, are endless. The only barrier is that task of integrating data that comes from different sources sound a bit difficult as of now. It requires serious investment of time and money into developing systems that can combine data and make sense out of it. Things are indeed improving and advancing pretty quickly as better IoT companies are emerging in the market and actively researching in the field to address the upcoming needs.

AuroSys Solutions is a provider of end-to-end product engineering and continuously working towards innovation in the field of IoT. AuroSys helps enterprises reduce costs and maximize revenues with help of embedded engineering, connectivity solutions, data analytics, mobility and cloud solutions.

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