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How analytics can prevent rather than explain tragedy

http://www.cio.com/article/3085565/analytics/how-analytics-can-prevent-rather-than-explain-tragedy.html

The political response to yet other mass shooting, this time in Orlando, showcases not only a problem in politics but also a core problem associated with how we make decisions. We are all plagued by a human condition called Confirmation Bias, which means we tend to see things in a framework of positions we have already taken and not focus on the actual causes. In this instance, one politician doubled down on blocking Muslims from entering the country, while the other focused on gun control talking points. Meanwhile, the reason why all those people died was more about the inability to identify what should have been a clear coming threat.

We had all the elements of disgruntled employee, history of domestic violence, high intolerance and behavior consistent with a coming attack (purchase of multiple weapons and mass ammunition). This has all been reported after the fact, largely using pre-existing data suggesting that if we want to actually prevent this in the future we need to connect the data elements (which apparently no one is currently doing) and provide an effective early warning system. However, this is the one thing that it appears no one running for president is discussing because they jumped to solutions without looking at the causes first.

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This is how many of us approach most decisions. We make the decision first then force fit the data to back the decision up. But if we approached the problem the other way around we’d have a better chance of being right as opposed to being able to successfully argue we are right.

This is a core problem as we move to analytics as decision support.

There are situations where analytics could actually accelerate bad decisions. Years ago I attended a class that stuck with me. This was back when we did a lot more market and business analysis as practice than I think we do today. The instructor drew a chart on the wall, a typical x/y chart. Horizontally was speed with faster to the right, and vertically was direction with the correct direction on the top and the wrong direction on the bottom.

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