UPDATED 07:29 EDT / NOVEMBER 14 2013

Causation vs. Correlation in BI : Nate Silver + others chime in

Nate Silver, Big Data, Tableau Customer ConferenceIn traditional decision making, the metrics that impact the bottom line will almost always take precedence over the underlying drivers of change. The reasons behind a purchase decision fade into insignificance when you can deduce what a customer will buy next by other means – at least, that’s the theory. With the emergence of Big Data, it’s becoming more and more apparent that correlation does not imply causation, nor can the former substitute the latter. Analytics and other emerging technologies are changing the competitive landscape in vertical markets, and knowing What but not Why is simply no longer enough to maintain an edge.

Appearing on theCUBE at the recently concluded Tableau Customer Conference 2013, Jim Wahl, the senior director of product management at biometrics specialist Aware, pointed out that correlations serve as a stepping stone to identifying the root cause of a business problem. But many organizations don’t make the jump.

“Well, causation is always better than just a correlation, but it’s also much, much more difficult to find,” Wahl said. “So I think nine times of ten, the correlation is enough to better understand the problem, and maybe that leads to a subsequent experiment that is trying to find the causation.”

Speaking at the same event, celebrity statistician Nate Silver reiterated that causation is the key to sustainable economic growth.

“If it’s true that merely finding a lot of correlations and very high-volume data sets will improve productivity, then how come we’ve had such slow economic growth over the last ten years? Where is the tangible increase in patent growth, or different measures of progress?” he asked.

Check out the full interview with the statistician who correctly predicted the 2012 presidential election results by clicking on the video below:

 

Jon Schwabish, a Principal Analyst at the Congressional Budget Office, expanded on the economic importance of distinguishing between correlation and causation, giving us a glance into his work.

“I mentioned I do some work on food stamps. When we think about food stamps and the economy and the unemployment rate [and] how many people are working, that’s sort of a correlation. But then you look how a policy might affect certain policies and that’s a causation. We have tease out how those things differ and how they may change over time,” Schwabish told theCUBE hosts Dave Vellante and Jeff Kelly.


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