I was invited to speak on a panel at the MassTLC VoT conference in June. The topic of the panel was “Analyzing data to get actionable intelligence” which was a perfect opportunity to discuss the applicability of Machine Learning to the analysis of the data deluge that the Internet of Things will likely bring.
It was a pleasure to meet the other members of the panel, and the discussion was lively and informative. A video has been promised but hasn’t surfaced yet. I will post it when it is available.
I did come across an article that touched on the panel and includes the following quote:
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Still, similar to the initial buzz around big data, IoT discussions evoke excitement about the wonderful possibilities: new business models! Competitive advantage! Deeper insights! And they often leave out what’s practical, as Poul Petersen, chief infrastructure officer for Corvallis, Oregon-based BigML Inc., noted during a panel discussion on data analytics. Attaching sensors to every grapevine, cargo ship, train car or transformer, “that’s not too hard,” he said. But “how on earth do you get to that last step?”
By last step, Petersen means how you mine sensor data to find what he called those “aha moments,” or correlations between two seemingly unrelated data points. Sensor data is “big in terms of complexity,” giving businesses millions of data points to dig through. “You don’t know at the outset if two things are related,” he said. “Or you may just get it wrong.” It should be noted that BigML is in the business of helping companies make the leap into advanced analytics to find those correlations, but still, Petersen’s perspective was more than an on-message advertisement. As CIOs know from forays into other types of big data, rich insights don’t just fall out of the data—not even for a data scientist.
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