"There is an apparent contradiction between the buoyant job market for big data practitioners and Gartner’s judgment that, on the perception scale, big data has moved from high expectations to what Gartner calls the “trough of disillusionment.” But, in fact, it fits a familiar pattern of technology absorption and use. Significant new technologies always take time to move into the mainstream as people and organizations learn to exploit them. It takes years. The classic study of the phenomenon, “The Dynamo and the Computer: An Historical Perspective on the Modern Productivity Paradox,” by Paul David, an economic historian at Stanford University, was published in 1990. In it, Mr. David noted, the electric motor was introduced in the early 1880s, but its real payoff in productivity was not evident until the 1920s. It took that long for businesses to reorganize work around the industrial production line, the efficiency breakthrough of its day, made possible by the electric motor. Similarly, it took a while for personal computers and the Internet to deliver big gains. And so too for big data, which harnesses computing, modern digital data and the software tools of artificial intelligence. A report this week from Forrester Research described the challenge ahead. “Businesses are drowning in data but starving for insights,” the report began. “Worse, they have no systematic way to turn data into action.”"
Issues and developments related to IP, AI, and OM, examined in the IP and tech ethics graduate courses I teach at the University of Pittsburgh School of Computing and Information. My Bloomsbury book "Ethics, Information, and Technology", coming in Summer 2025, includes major chapters on IP, AI, OM, and other emerging technologies (IoT, drones, robots, autonomous vehicles, VR/AR). Kip Currier, PhD, JD
Showing posts with label hype. Show all posts
Showing posts with label hype. Show all posts
Wednesday, April 29, 2015
Less Noise but More Money in Data Science; New York Times, 4/28/15
Steve Lohr, New York Times; Less Noise but More Money in Data Science:
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