In a recent thought-provoking TDWI article, David Champagne informed readers of The Rise of Data Science: a discipline of emulating the scientific method when analyzing data, in a conscious and laudable effort to ensure objectivity and avoid poor analytical practices.

As I had just recently blogged on the Texas Sharpshooter Fallacy, a type of flawed analytical logic business intelligence users might fall into, David Champagne’s article caught my attention.

 

From David Champagne’s article:

Back in the “good old days,” data was the stuff generated by scientific experiments. Remember the scientific method? First you ask a question, then you construct a hypothesis, and you design an experiment. You run your experiment, collect and analyze the data, and draw conclusions. Finally, you communicate your results and let other people throw rocks at them.

Nowadays, thanks largely to all of the newer tools and techniques available for handling ever-larger sets of data, we often start with the data, build models around the data, run the models, and see what happens.  This is less like science and more like panning for gold…Perhaps the term “data scientist” reflects a desire to see data analysis return to its scientific roots…

Barry Devlin, in his business-focused commentary on David Champagne’s article, noted the worlds of science and business have rather different goals and visions, which I interpreted as data science might offer limited benefit to business managers.  But perhaps the best practices of data scientists have a lot more in common with those of business managers after all, in light of some commentary I came across on effective business decision-making.  That commentary gave high praise to the manager who utilizes the scientific method in the decision-making process. The author was not a technologist, but rather: Peter Drucker, the father of modern business management.

Revisiting Peter Drucker’s writings on effective decision-making process will show surprising similarities to the best practices of data science, and yield beneficial insights for business managers seeking to make more effective, data-informed decisions.

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