Should you trust the numbers?

Recently NBC reported that the that the offensive coordinator of the Tampa Bay Buccaneers, Dirk Koetter said, “I dont need a bunch of numbers, … I trust my eyes”. Many individuals in organizations feel the same way. Sometimes they don’t trust the numbers. Why should they, as Mark Twain said, there are “Lies, damn lies, and statistics.” Any information obtained, will have a use, whether you use it properly is a completely different question. Sun Tzu said, “Know thy enemy and know thyself and you need not fear the result of a hundred battles”.

Ultimately, the key to success is studying and learning about yourself and your opponents. Your eyes can deceive you because, while your eyes can see what is happening your brain interprets the images received, and bias can set in. Countless times in history, events are clearly seen, however, people and leaders fail to act because of cognitive paralysis, cognitive bias,  or cognitive distortion.

“Know thy enemy and know thyself and you need not fear the result of a hundred battles”.

Data and analytics provides an unbiased approach, so long as the analyst conducts the analysis in an unbiased manner. You cannot discount the success of professional sport teams that use data to their advantage. The Oakland Athletics, shown in the movie “Moneyball”, are probably the most famous at using analytics and data to change their way of thinking and thus removing bias. Many critics argue that while they have been successful in having a lower payroll, it has not translated into World Series Championships. However, the Boston Red Sox, under General Manager Theo Epstein, utilized the data to obtain a roster that would ultimately defeat the New York Yankees in what might be considered one of the most memorable comebacks in baseball history, and culminate their cinderella run defeating the St Louis Cardinals in 2004, ending their 86 year drought. Many other teams in baseball such as the Arizona Diamondbacks, New York Mets, and St Louis Cardinals all have very deep statistical departments, as do many other teams in different sports.

What shouldn’t be a surprise is that the analytics by themselves cannot win championships. Everything is a process and a give-and-take between the intuition and the mathematics (See the Yin Yang of Analytics). There are gut feels based on environment and changes in the landscape that a leader must take into account; however a good leader will never underestimate an opponent and would never want to overlook information. Sun Tzu again sais that “it is only the enlightened ruler and the wise general who will use the highest intelligence of the army for purposes of spying and thereby they achieve great results.” In this case, the spies are the virtual spies, or the data that yields the intelligence unknown previous to the leader, and in many cases unknown to the opponent.

Consider always that information is useful, and the analysis and analytical process is a fundamental part of sports teams, and every organization. Without having a quality set of analytics, you will be at a disadvantage and thus increase your chances of losing; with good analytics you set the stage for victory and thus “not fear the result of a hundred battles”.


Analytics: Ph.D. Not required!

Analytical talent has become the hottest acquisition for companies, and many organizations are scouring for the best talent to bring on board. A question commonly asked is, “Should we hire Ph.D in Statistics or some related field”. The simple answer is, a Ph.D ISN’T required. What is needed for an analyst is a good math background, with some expertise or interest in experimental design.

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3 Big Reasons Why A/B Tests Fail

A/B tests are more popular in today’s environment and can provide a great value to an organization. A/B tests are conducted when one is trying to understand the effect of a change to a system or environment. This type of testing has been done for many decades, but is known in academia and research organizations as experimental designs. Therefore, good A/B tests should follow sound methodological research practices, while remaining cognizant of practical limitations, such as sample size and construction and lack of a controlled environment, such as a laboratory. There are three reasons why many of these AB tests fail to yield expected results. Continue reading 3 Big Reasons Why A/B Tests Fail

The Analytics Organization – Part 1

Having been a part of large organizations, I have seen first hand the effect of both good and bad structures in Information Technology and Analytics. In analytics organizations, there are many different skill sets and the number of people calling themselves data analysts and data scientists it becomes increasingly difficult to determine how the analytics organization should be constructed. Continue reading The Analytics Organization – Part 1