Statistics, Statisticians, and the Internet of Things
Within the overall rubric of big data, one emerging subset holds particular promise, peril, and attraction. Machine-generated traffic from sensors, data logs, and the like, transmitted using Internet practices and principles, is being referred to as the “Internet of Things” (IoT). Understanding, handing, and analyzing this type of data will stretch existing tools and techniques, thus providing a proving ground for other disciplines to adopt and adapt new methods and concepts. In particular, new tools will be needed to analyze data in motion rather than data at rest, and there are consequences of having constant or near-constant readings from the ground-truth phenomenon as opposed to numbers at a remove from their origin. Both machine learning and traditional statistical approaches will coevolve rapidly given the economic forces, national security implications, and wide public benefit of this new area of investigation. At the same time, data practitioners will be exposed to the possibility of privacy breaches, accidents causing bodily harm, and other concrete consequences of getting things wrong in theory and/or practice. We contend that the physical instantiation of data practice in the IoT means that statisticians and other practitioners may well be seeing the origins of a post-big data era insofar as the traditional abstractions of numbers from ground truth are attenuated and in some cases erased entirely.
KeywordsMachine traffic Internet of Things Sensors Machine learning Statistical approaches to big data
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