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Tuesday, August 09, 2005

Benchmarking and Biased Data

After reading several books and articles on successful leaders and entrepreneurs, it seems that the world agrees that all great leaders and entrepreneurs inevitably have two characteristics common in them: perseverance and ability to persuade others. Great leaders continue to persist despite initial failures and during testing times, they have the ability to persuade others in believing that what they are doing is right. So the implicit (and sometimes explicit) conclusion is drawn that for anybody do become a successful leader, presence of these two traits is a must. What could make more sense? What could be more dangerous?
If we apply not-so-uncommon common sense, we’ll see that these two selfsame traits are present in all those leaders also who led their men to disastrous ends. One need to have great persistence to follow the same path even after meeting failure after failure and one also need to have great persuasion skills to convince investors to pour their money through drainage.
Such notions of formula-of-success are so prevalent in the corporate world because of mutual dependency of following two statements: One, managers learn by example and two, success feeds itself.
Breeding managers are taught through case studies, putting them into simulated environment. Corporate managers are told to adopt best practices and realign their process to achieve benchmarks of operational efficiency. Throughout his lifetime a manager is supposed to learn from what other are doing. There is always a sword of benchmarking hanging over his/her head. But how many times, a manager is exposed to the flip side of a best practice? How many managers seek to find why a particular company doomed even after adopting CRM, TQM, Six Sigma or any other best-of-the-world concepts?
The second statement merely postulates a fact that over a period of time, only successful companies remain in the game and failures are erased. Erased not only from the game, but from the minds of the people also, unless they have created havoc by their failure like Enron. So in a mature industry like steel or cement, when a manager looks out of his window he sees only successful companies. And he is made to believe that these companies are present here because what they did was a best practice. He simply doesn’t have the data to see and check whether a failed company also adopted a particular best practice or course of action. He is actually seeing what statisticians call “a biased sample”. In contrast, a newly born industry (internet based business model) is plentiful of failures. Every day smaller companies are either being engulfed by big sharks or being deserted by investors. Here the data is actually available to test the hypothesis of adopting a best practice. Here the sample will more accurately reflect the population.
The word of caution for anyone who wants to adopt a particular business model, best practice or management concept is that don’t fall into the trap of biased or skewed data. Always look for the other side also. Try to collect as much as data on failures also. Tools are available to correct such anomalies in a given data. In fact two people have won noble prize by working on this. Still who don’t want to pay heed to this may read the anecdote of Abraham Wald.
[During World War II, Royal Air Force was suffering major causalities. Top brass called upon statisticians and engineers to suggest which parts of airplanes should be reinforced so that there are lesser events of crashes. Extensive data was collected from planes returning with a hit. Certain parts/areas were showing more vulnerability than others. People unanimously decided that those area/parts should be reinforced. However Mr. Abraham Wald, the project manager, had a different opinion. He told to reinforce parts showing less vulnerability. And he made a right decision. Can you guess the logic?]

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