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Identify Biases Before Trusting Analytic Outcomes

Posted by Gary Corrigan

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Jul 2, 2014 11:30:36 AM

Back in 2012, The Memphis Daily News revealed some noteworthy results of a McKinsey Quarterly survey of 2,207 executives. In this survey, only 28% of participants stated that the quality of strategic decisions was generally good, and 60% thought that bad decisions were about as frequent as good ones. Think about that last stat point for a second. If those bad decisions translate into equally bad outcomes, there’s no telling how many failed projects, failed hires, and failed experiments have occurred, to name a few failures. So what gives?

Believe it or not, there are plenty of biases that get in the way of would-be objective data analysis, and those biases largely impair decision-making. It’s especially a troublesome prospect for business leaders who count on well-founded information.

Along those lines, Nate Silver pointed out, and ReadWrite expanded upon, the notion that the more data we have, the less likely we are to agree. Biases can rise even taller because there is more data available to support varying points. The data itself may be neutral. But when data is manipulated to support a bias, neutrality flies out the window.

Silver’s solution to dealing with biases is for business leaders to take a proactive approach. They have to be ready with questions and actively look out for biases—even if that means looking closely at not only the numbers and graphics, but the persons presenting those numbers and graphics.

The Daily News discussed specific biases that can affect strategic decision-making:

  • Belief Bias—in this instance, decision-makers may lean too heavily on their past experiences and close off the acceptance of new evidence or a new school of thought
  • Hindsight Bias—this is tendency to review past events as inevitable, thus, leading to less analysis of past failures
  • Anchoring—this is the idea that being first trumps everything else; an initial piece of information is leveraged to make subsequent judgments
Ultimately, for decision-makers, it’s important to recognize these biases and really attack analytics with a healthy curiosity. On top of looking closely at who is presenting what information, further market research is needed. By gathering all of the key facts, you’ll be able to arrive at the most objective and well-thought out conclusion.

QueBIT can be your guide to gain the most value from your analytics and predictive modeling. Contact our team to learn more.

   

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