Flaws in the Data
Apr 03, 2017
By Katherine Heires, Risk Management
To illustrate the impact of data-driven decision-making, Douglas Mirsky, director of advisory services at the International Institute for Analytics (IIA), used the example of football helmets: For years, sports organizations went with their gut feeling and believed that the hard, crumple-free design of football helmets would keep players safe from serious head injury. More recent data-driven analysis, however, has shown that is not the case. In fact, the design may have contributed to the number of concussions by creating a false sense of safety, Mirsky said.
Indeed, in the aftermath of lawsuits by injured players, we now know that the perception of safety that comes with the use of hard, seemingly impenetrable helmets has encouraged athletes to become far more aggressive and increased the impact force on the field. New data-driven models are being deployed to help create helmets to specifically mitigate the risk of concussions. These have softer outsides that absorb the impact rather than transfer it to the brain.
But data-driven decision-making is not just about the avoidance of risk. If managed correctly, experts say, DDDM can also help businesses gain a competitive edge, better understand customer groups, identify new product categories, increase company revenue and improve efficiency.