I enjoy maturity and evolution models of all kinds, especially for business. There is a stages of maturity model for information technologies including as an example a four stage one from the MIT Sloan Center for Information Systems Research. What I like about stages of maturity models is they provide confidence that regardless what stage an organization is at – low or high – there is a next stage further up that can be attained in an evolutionary way.
In biology there is an evolution of humans that has in earlier stage, Australopithecus; then Homo erectus; then Neanderthals; and our current stage Homo sapiens. Examples of important changes are brain size, hand grip, and a larynx for speaking.
Homo Analysticus – the primitive analysts
Just to have some fun I will take the position that some statisticians, analysts, and Data Scientists are primitive Homo Analysticus. Just as with humankind there are overlap periods where primitive statisticians co-exist with more sophisticated ones with more capabilities and skills. This implies the primitive ones have evolutionary steps in their future. A stereotype of a Data Scientist is they are geeks with pen pocket protectors who rarely stray from their cubicle. These are the Homo Analysticus. In the evolutionary ladder in the future they can become decision makers and executives. They can add value beyond just analyzing data to assisting their organization to gain insights and make better decisions.
I am obviously not suggesting that many analysts are prehistoric humans with beards wielding clubs and appear like the Flintstone cartoon characters wearing animal clothing (although fashionable clothes may not be in their wardrobes). I am suggesting that some analysts have yet to evolve to fulfilling their potential to be truly creative and imaginative.
For example, when examining a population of event data, don’t just calculate an average. Ever hear the joke about an “average”? My feet are in the hot oven and my head is in the refrigerator, but on average I feel OK. A basic step is to calculate a median and beyond that to investigate the data distribution which in many cases does not follow the bell-shaped “standard normal distribution” where random variables cluster symmetrically on each side of a peak mean.
How might a Data Scientist’s brain work?
I mentioned the brain as an important change in this evolutionary ladder. There has been excellent research about the brain by Daniel Kahneman, recipient of the 2002 Nobel Prize in Economic Sciences for his seminal work in psychology, which challenged the rational model of judgment and decision making.
In his book, Thinking, Fast and Slow, Kahneman explains the two systems that drive the way we think. System 1 thinking is fast, intuitive, and emotional. System 2 thinking is slower, more deliberative, and more logical. System 1 is largely unconscious and it makes snap judgments based upon our memory of similar events and our emotions. System 2 is painfully slow, and is the process by which we consciously check facts and think carefully and rationally.
A problem Kahneman points out is that System 2 thinking (slow) is easily distracted and hard to engage and that System 1 thinking (fast) is wrong as often as it is right. System 1 thinking is easily swayed by our emotions. An example he cites include the analysis that professional golfers are more accurate when putting for par than they are for birdie regardless of the distance. Another example of a controlled experiment observes that people buy more cans of soup in a grocery store when there is a sign on the display that says "Limit 12 per customer." People miss the opportunity to analyze.
What caught my attention is that System 2 thinking, which is deliberate and logical, is easily distracted. In our busy day there is little time for solitude and deep thinking. An analysts’ day may be consumed with tasks crunching numbers to meet a deadline. There is little time to consider the quality and validity of the data they are relying on. You have heard the phrase “garbage-in, garbage out.” They may also subconsciously have a bias to support a pre-conception that their managers and users already believe in.
Why would analysts who appear to be genetically born to seek precision, accuracy and detail rely on creating and worse yet using flawed information or having a bias? My belief is System 1 thinking – quickly accepting that their analyzed information is perfectly correct – is distracting the analysts from the deeper understanding of what they are doing. Higher forms of the analyst species possess more Systems 2 thinking by being deliberate and logical. These higher level analyst species first carefully frame a problem or opportunity to test before diving into the data.
The evolving analyst species
Gaining insights from data gets to the heart of what differentiates the advanced analytics species from the primitive ones – the Homo Analysticus. It is not about having bigger brains. The advanced analysts have a mission. They want others to see things that have not been seen before. They want to reveal clues, in many cases unarguably supported with facts that can solve problems and surface unknown opportunities. They want to help their colleagues make better decisions.
What motivates statisticians, analysts, and Data Scientists? The primitive Homo Analysticus have basic needs not too dissimilar from food, warmth, and shelter. They want to earn a living by solving problems. The author Daniel Pink’s book Drive stimulated me to think that the advanced analyst species has greater motivational elements. They want autonomy to be self-directed to explore and investigate. They seek mastery of their craft which can be painful like exercise. They want purpose to pursue causes that are larger than themselves. Higher forms of the analyst species possess these special traits.
What kind of analysts in your organization are producing studies and reports for users to gain insights and make better decisions? Are they Homo Analysticus? How far along the evolutionary continuum are they?
(About the author: Gary Cokins is the founder of Analytics-Based Performance Management LLC, an advisory firm. He is an internationally recognized expert, speaker and author in advanced cost management and performance improvement systems; previously a principal consultant with SAS. You can contact him at email@example.com. For more of Cokins' unique look at the world, visit his website at www.garycokins.com)
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