This article is part of a series of articles written by MBA students and graduates from the University of New Hampshire Peter T. Paul College of Business and Economics. Josh Hutchins received his B.S. in Business Administration from the University of New Hampshire in 2005. He is currently pursuing his MBA at the Peter T. Paul School at the University of
Concurrent with the extraordinary rise of the Internet of Things (IoT), predictive analytics are gaining in popularity. With an increasing number of companies learning to master the precursors to developing predictive models — namely, connecting, monitoring, and analyzing — we can safely assume the art of gleaning business intelligence from foresight will continue to grow rapidly.
A distribution center struggling with a high number of forklift truck impacts found a way to leverage big data to address a nagging, costly warehouse issue. The company had installed a telematics solution on its forklift trucks, but could not determine the cause of the impacts. The time and location of impacts were known, as
“[Companies] don’t know how to manage it, analyze it in ways that enhance their understanding, and then make changes in response to new insights… they don’t magically develop those competencies just because they’ve invested in high-end analytics tools.” –You May Not Need Big Data After All” Harvard Business Review, December 2013 Since the concept of
Big data is big. It is revolutionary. It is transformative. But what the heck is it? MIT’s Technology Review does a great job of outlining the hype and the confusion around big data: “There is unanimous agreement that big data is revolutionizing commerce in the 21st century. When it comes to business, big data offers
