AS THE INTERNET of Things (IoT) continues its run as one of the most popular technology buzzwords of the year, the discussion has turned from what it is, to how to drive value from it, to the tactical: how to make it work.
IoT will produce a treasure trove of big data – data that can help cities predict accidents and crimes, give doctors real-time insight into information from pacemakers or biochips, enable optimized productivity across industries through predictive maintenance on equipment and machinery, create truly smart homes with connected appliances and provide critical communication between self-driving cars. The possibilities that IoT brings to the table are endless.
As the rapid expansion of devices and sensors connected to the Internet of Things continues, the sheer volume of data being created by them will increase to a mind-boggling level. This data will hold extremely valuable insight into what’s working well or what’s not – pointing out conflicts that arise and providing high-value insight into new business risks and opportunities as correlations and associations are made.
It sounds great. However, the big problem will be finding ways to analyze the deluge of performance data and information that all these devices create. If you’ve ever tried to find insight in terabytes of machine data, you know how hard this can be. It’s simply impossible for humans to review and understand all of this data – and doing so with traditional methods, even if you cut down the sample size, simply takes too much time.
We need to improve the speed and accuracy of big data analysis in order for IoT to live up to its promise. If we don’t, the consequences could be disastrous and could range from the annoying – like home appliances that don’t work together as advertised – to the life-threatening – pacemakers malfunctioning or hundred car pileups.
The only way to keep up with this IoT-generated data and gain the hidden insight it holds is with machine learning.