Open Thoughts on Analytics
JUL 2, 2013 11:04am ET

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Business Analytics: Why Not Experiment?


I recently came across a couple of good articles on business experimentation from my meanderings of more than thirty LinkedIn group discussions. Randomized experiments or related quasi-experimental designs for measurement are now part and parcel of evidence-based business. Indeed, whether you refer to the focus as data-driven, super-crunching or big data analytics, central are large data sets, statistics/machine learning algorithms and sophisticated designs.

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Comments (1)
I'm happy to see that there's a counterbalance to the 6-sigmatists. Six Sigma has its place, but like too many ideas it's been latched onto as a panacea outside its area of applicability.

Modern business management, particularly the American model, is based on an industrial production paradigm wherein establishing rigorous error-free production processes is a first principle. It's also an inappropriate model for dynamic environments where constant adaptation is necessary for survival.

It was particularly galling to see the pernicious invasion of six sigma into BI, with its attendant emphasis on establishing the IT frameworks, policies, processes, and management controls prior to actually using data to shed light on important areas. The six sigma approach presupposes that all of the questions one could ask of data should be identified up front before the answer-anything universal analytical engine would be built, after which it would be just like any other industrial process ingesting data inputs and producing answer outputs. This model was flawed, but it resonated with a generation of managers who succeeded because of their mastery of the industrial management canon.

Posted by Chris G | Monday, July 08 2013 at 1:20PM ET
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