Everyone’s talking of the Internet of Things (IoT) and the impact it’s started to make even on day to day lives. Yet, more than anything, it’s also becoming increasingly clear that analysis of the IoT data will be the differentiator between those who simply collect data and those who “use” it to drive their businesses.
Enterprises that will use IoT analysis will see themselves implementing faster customer services than their rivals, as well as add new amounts of additional yields.
That said, it’s clear that the IoT analytics will require a well-thought out strategy on part of the Enterprise. Unlike the other, modern-day streams of data analytics, this branch is slightly more complicated. The primary reason – the humungous amounts of streaming data that is/will be generated and analysed, in real time.
How does one collect this data? In fact, is it necessary to collect all the data? These and many other questions need to be answered by an Enterprise’s decision-makers to tackle the complexities of the IoT data.
Traditional data that is used in a B2B or B2C operation, and its analysis, requires the collection of raw data, locating it in a data hub, scrubbing it and then handing it over to the analysts to draw predictive or other forms of analytic models.
But that same structure cannot be applied for IoT data because the huge volumes that will pour out in real time means centralizing it will be almost impossible. Imagine you running a national cold storage company with your own in-house fleet of trucks, mini-vans and warehouses.