MAR 1, 2011 9:21am ET

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Science of Business vs. Evidence-Based Management

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I've written a lot over the past couple of years on the science of business and evidence-based management. In my "Science of Business Manifesto" blogs a few months back, I used EBM as a synonym for the SOB, drawing on the connotations of both to the conduct of business by the rigorous formulation, measurement, testing and evaluation of alternative courses of action. But a recent interview/article in the MIT Sloan Management Review, “Matchmaking With Math: How Analytics Beats Intuition to Win Customers,” is making me question whether there might be a nuanced difference between the two that's important for BI.

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Comments (12)
This example supports my hypothesis that customers are a commodity and we are now in the Algorithm Age.

Customers are no longer important to organizations but their data is. Customer data is a commodity used to feed the algorithms that run the business.

I suggest that based on this, a follow on book to Jared Lanier's "You Are Not a Gadget" should be "You Are a Commodity".

Posted by Richard O | Wednesday, March 02 2011 at 12:51PM ET
Proof of the pudding is in the eating; there are so many holy cows of conventional wisdom that have gone on unchallenged because they were "self evident" and accepted as truth. Here the evidence speaks. This might become a future conventional wisdom if it can be repeated again in other situations and scenarios. Not that hypothesizing is wrong or does not work. Depends on situations and it might work in some and not in others. Social relationships are open systems. In open systems there is no black or white. You attempt something and it might work out the way you expected or some other dynamic might modify it. That is the lay of the land. Ask any family therapist. But in this case you have hard figures as feedback and larger amounts of data to do predictions and successful ones.
Posted by Muthukumar V | Wednesday, March 02 2011 at 1:07PM ET
I'm confused on how what the Hurst describes they were originally doing is actually an example of the top-down hypotheses-driven approach. They were operating based on conventional wisdom and were not actually doing step 1 of the algorithm for science.
Posted by James W | Wednesday, March 02 2011 at 1:44PM ET
This example should encourage many organizations to try similar exploratory analytics. In some cases such hypothesis-free explorations could make the world a better place, and not just profit a business.
Posted by Alan M | Wednesday, March 02 2011 at 1:52PM ET
As the nation's healthcare debate continues a paralell is drawn to the science and practice of medicine. Random controlled trials continue to confirm or dispute hypothesis and knowledge is gained. Today, the availability of healthcare data offers a breadth and depth of information far beyond the limits of RCTs. Comparative effectiveness research makes use of patient and transactional data to synthesize what works and what doesn't in real world care delivery settings. This bottoms up evidence of effectiveness approach has the potential to accelerate knowledge gain and transform healthcare practices faster than traditional methods.
Posted by BOB H | Wednesday, March 02 2011 at 2:35PM ET
Regardless of the merits of evidence based management, I can't get past the example in the Hurst article. If I find a $10.95 charge on my credit card for a service I did not request, i would be livid and no amount of smooth talk from a CSR would get me to change my mind. This example of using Science of Business (SOB sounds right in this context) techniques for unethical ends explains a lot about what happened to our economy in 2008.
Posted by Clay G | Wednesday, March 02 2011 at 4:48PM ET
Clay --

The customers agreed to the $10.95/month Hurst insurance premium initially. But seeing that cost on the credit card bill every month caused them to drop it.

Posted by steve m | Wednesday, March 02 2011 at 5:02PM ET
Great article and example on how to use data to improve business results, but I agree with the commenter above that it draws a false distinction. The point is to use understanding of the data to improve hypotheses, not to abandon the scientific method. The article doesn't describe them, but might some of the silicon-derived ideas bombed out in practice as well?
Posted by Bob L | Thursday, March 03 2011 at 6:36AM ET
Great article and example on how to use data to improve business results, but I agree with the commenter above that it draws a false distinction. The point is to use understanding of the data to improve hypotheses, not to abandon the scientific method. The article doesn't describe them, but might some of the silicon-derived ideas bombed out in practice as well?
Posted by Bob L | Thursday, March 03 2011 at 6:36AM ET
...We don't necessarily know the reason why. Nor do we need to ... And therein lies the difference. In our system there isn't a lot of science behind why these differences exist...

I understand, from a performance standpoint, that improvement is improvement and why care why, but; how can you share a best practice and broaden improvement in an organization if you haven't "learned" what that best practice is? Without learning - improvement is random. At some point you have to try & identify "What was Different" & "Can you transfer or replicate that difference to broaden improvement elsewhere".

Getting beyond the paradigms to improve a process is great but eventually you'd better learn what is different.

Posted by Tim H | Thursday, March 03 2011 at 9:01AM ET
I think the article hit it right on. SOB is top down. EBM is bottom up. There is likely an interplay between them. SOB gives you a framework within which to create tests for hypotheses. EBM provides the ability to generate new data-driven hypotheses while also measuring planned test results.
Posted by David R | Thursday, March 03 2011 at 9:17AM ET
There is more than one approach to doing science. The initial work done at Assurant has much in common with observational studies; finding what is really going on, independent of any particular hypothesis about why or how reality is what it is. Many practical advances can be made based on observation, even without an underlying theory.
Posted by Julian S | Monday, February 17 2014 at 2:28PM ET
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