Understanding the convergence of IoT and data analytics
As the number of internet connected devices continue to explode, organisations need to understand the convergence of IoT and data analytics
The number of IoT devices is expanding at a significant rate. All these devices are capturing and relaying different data sets, which are giving both private and public organisations insights that would have previously been unknown.
Utilising all this IoT data, taking advantage of it, requires an effective analytics strategy that responds in real-time.
“IoT is all about near-real time measurements and telemetry enabled by data analytics to drive the desired business outcome,” explains Nico Fischbach, CTO at Forcepoint.
Below, four experts explore how organisations can take advantage of the data made available by the IoT.
Peter Ruffley, CEO at Zizo, says that there are many practical processes in which a company can establish whether they can take advantage of the data made available by IoT.
“One way that organisations can check whether they are capable of moving into IoT and data analytics is to look for third-party data which may enhance the data they already have.
“Once sourced, they then need to see whether they can easily combine this data with the originally stored data and put this into an analytics platform to test the capability.”
After finding the right partner, organisations can begin to see whether there is an actual business benefit coming from it.
In order to do this, Ruffley explains that organisation’s don’t need to invest heavily. “In fact, this can be done on generic platforms such as Google, where the original investment is very low. Just by doing a simple practical exercise like this, companies can see how ready they are to take advantage of IoT and data analytics, and whether there is business benefit in doing so,” he says.
Matt Watts, director and CTO at NetApp, says that the “focal point of IoT’s convergence with data analytics is in successfully managing data.”
He points to 5G’s roll out as being an enabler for an “unbridled level of data flow and a huge uptick in new edge computing capabilities. This means that the challenge of organising data and making it valuable has never been greater.
Using an example of a well-known car company, Watts explains: “[they] built up about 14 petabytes of storage over several years, but in just the space of a few months, its newly deployed driverless car program created an additional 4.5 petabytes. It’s the breadth of potential deployment mediums, interoperability and data formatting hurdles that makes analytics a real headache. The task of seamlessly connecting the edge to core to cloud in an actionable and accessible way is paramount due to the nature of distributed compute.
