Now that everyone is thinking about IoT and the phenomenal amount of data that will stream past us and presumably need to be stored we need to break out a vocabulary well beyond our comfort zone of mere terabytes (about the size of a good hard drive on your desk).
In this article Beyond Just “Big” Data author Paul McFedries argues for nomenclature even beyond Geopbytes (and I'd never heard of that one). There is a presumption though that all that IoT data actually needs to be stored which is misleading. We may want to store some big chunks of it but increasingly our tools are allowing for 'in stream analytics' and for filtering the stream to identify only the packets we're interested in. I don't know that we'll ever need to store Geopbytes but you'll enjoy his argument. Use the link Beyond Just “Big” Data.
Here's the beginning of his thoughts:
When Gartner released its annual Hype Cycle for Emerging Technologies for 2014, it was interesting to note that big data was now located on the downslope from the “Peak of Inflated Expectations,” while the Internet of Things (often shortened to IoT) was right at the peak, and data science was on the upslope. This felt intuitively right. First, although big data—those massive amounts of information that require special techniques to store, search, and analyze—remains a thriving and much-discussed area, it’s no longer the new kid on the data block. Second, everyone expects that the data sets generated by the Internet of Things will be even more impressive than today’s big-data collections. And third, collecting data is one significant challenge, but analyzing and extracting knowledge from it is quite another, and the purview of data science.