By Gareth Noyes
Analytics has become a major buzzword these days, whether in the realm of connected devices, the Internet of Things, web analytics or big data business analytics. In the context of the Internet of Things, I thought I’d share some observations on different analytics paradigms and use cases.
One common analytics model is what could be termed “store-and-analyze-later,” where massive amounts of data are streamed up to cloud servers and Hadoop clusters for later analysis. The problem with this approach, especially given the ever increasing amounts of data, is that it doesn’t scale, the amount of data quickly overwhelms our ability to make sense of it. Compare that to a model where intelligence (gained from analytics) is tailored to the use case and system topology, leading to “intelligence where and when you need it”– the notion of multi-tiered intelligence where edge devices have a configurable amount of autonomy and decision making authority, and no longer act as “dumb data generators.”