The Internet of Things (IoT) promises to change everything by enabling “smart” environments (homes, cities, hospitals, schools, stores, etc.) and smart products (cars, trucks, airplanes, trains, wind turbines, lawnmowers, etc.). I recently wrote about the importance of moving beyond “connected” to “smart” in a blog titled “Internet of Things: Connected Does Not Equal Smart”. The article discusses the importance of moving beyond just collecting the data, to transitioning to leveraging this new wealth of IoT data to improve the decisions that these smart environments and products need to make: to help these environments and products to self-monitor, self-diagnose and eventually, self-direct.
But one of the key concepts in enabling this transition from connected to smart is the ability to perform “analytics at the edge.” Shawn Rogers, Chief Research Officer at Dell Statistica, had the following quote in an article in Information Management titled “Will the Citizen Data Scientist Inherit the World?”:
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“Organizations are fast coming to the realization that IoT implementations are only going to become more vast and more pervasive, and that as that happens, the traditional analytic model of pulling all data in to a centralized source such as a data warehouse or analytic sandbox is going to make less and less sense.
So, most of the conversations I’m having around IoT analytics today revolve around looking at how companies can flip that model on its head and figure out ways to push the analytics out to the edge. If you can run analytics at the edge, you not only can eliminate the time, bandwidth and expense required to transport the data, but you make it possible to take immediate action in response to the insight. You speed up and simplify the analytic process in a way that’s never been done before.”
So I asked Shawn and his boss John Thompson, General Manager of Advanced Analytics at Dell, to help me understand what exactly they mean by “analytics at the edge.” It really boils down to these questions: