“The essence of the independent mind lies not in what it thinks, but in how it thinks.”― Christopher Hitchens, Letters to a Young Contrarian
This quote touches on a subtle yet critical distinction between thinking (the how) and learning (the what). While learning is acquisition of a new piece of knowledge, thinking is the art of utilizing that knowledge to engage the mind in a novel way that results in insight.
In applying this fine distinction to the current processes and practices in the field of data science, I realized that there was a gap. Contrary to what the numerous Venn Diagrams flooding the internet and myriad of job titles (data scientist, data analyst, data engineer, BI developer, machine learning engineer, big data developer) will have you believe, data science like any other field of knowledge is one-dimensional in and of itself.
In capturing, maintaining, processing, analyzing and visualizing data, we are still limited to the realm of learning. To go from knowledge to insight, we need to think!
One might argue that business intelligence and data-driven decision making is where the thinking occurs. Yes, sometimes…may be. For the most part, what is happening is reduction and filtering of the data in an attempt to find the “right answer” or the “right model”. Organizations are reducing the data and/or reducing the problem, until one somehow fits the other. This reduction is evident in pyramid models like the Data Science Hierarchy of Needs, Data Information Knowledge and Wisdom Hierarchy .
In the hopes of being data driven, most organizations are putting on data goggles, and edging towards a rather one-sided view of complex challenges . To clarify, there is nothing inherently wrong with taking the data point-of-view, as long as it is not the only point of view being considered.
Most thought-provoking in our thought-provoking time is that we are still not thinking.Martin Heidegger, What is called thinking?
This quote from Martin Heidegger’s 1936 lectures, is eerily apt today. In a world where data and information are ubiquitous, there is no shortage of content to think about. What is lacking is the pause to comprehensively engage our minds with this content. To think better one must try on a variety of theories and mental models and develop a deeper understanding of the knowledge derived from data.
Here is an excerpt from Charlie Munger’s 1994 address to the USC Business School:
Well, the first rule is that you can’t really know anything if you just remember isolated facts and try and bang ’em back. If the facts don’t hang together on a latticework of theory, you don’t have them in a usable form. You’ve got to have models in your head. And you’ve got to array your experience—both vicarious and direct—on this latticework of models… And the models have to come from multiple disciplines—because all the wisdom of the world is not to be found in one little academic department.Charlie Munger
This is why the next “Sexiest Job of the 21st century” has to be that of a Data Thinker.
Data thinkers’ toolbox is full of mental models that span the field of mathematics, physics, psychology, cognition, economics, engineering, biology, business, philosophy, innovation, strategic management, organization development and others. They keep all the stakeholders oriented in the same direction. They bring together knowledge from technical and non-technical experts, and think about the challenge at hand in a multidimensional framework. They are data literacy champions.
Above all, a data thinker has an innate sense of curiosity and is willing to question the status quo.