Editor's Note: In this blog, we curate relevant and  remarkable content for data and analytics community. Here's an interesting piece written by Andy Pulkstenis.

In my 20-year applied analytics career, I’ve been fortunate to witness to the evolving landscape of business analytics. One notable shift was when companies finally discovered the power of predictive modeling. Initially a tough sell in a world then-dominated by tradition, experience, & classic MBA methodology, it’s now difficult to imagine any company a serious contender if they don’t include predictive modeling in their analytic arsenal. Today when you examine most market leaders, statistical modeling is as firmly entrenched in the corporate culture as Microsoft Windows, SAS, khakis, and snarky Dilbert cartoons. Predictive analytics finally made it, but its cousin experimental design (i.e. statistical testing, or MVT, or DOE, or A/B testing, or test-and-learn, etc.) remains largely on the outside looking in.

Despite the potential to radically transform currently-held anecdotal beliefs about a business, unlock new or deeper insights into drivers of customer behavior, and truly optimize strategy delivery, the applied analytics community has been very slow to embrace statistical testing in the business world, even in the midst of a growing number of success stories. I can say with confidence that my conference presentations on business experimentation are consistently the best talks on testing at a given event – unfortunately because I’m typically the only speaker there talking about the topic!

I suspect this slow adoption rate is due to being blind to the power of testing, misplaced fears around complexity of implementation (in reality the degree of difficulty is on par with building and implementing predictive models), and a scarcity of skilled corporate practitioners (outside of manufacturing, agriculture, and biostatistics, that is). We ignore this valuable tool at our own peril.

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This article entitled "Don’t ignore the next great analytic competitive advantage" was originally posted on SAS Learning Post. To read full story, click here.