The parallel between the Industrial and Data Science Revolution, and how their dynamic stands to sha

Updated: Apr 28

Written by Craig Gittelman, VP Sales & Customer Success at Systech

59 years ago, Thomas S Kuhn’s visionary book, The Structure of Scientific Revolutions, transcended steadfast conceptions about scientific thought and its progression in academia and society. In it, he identifies the concept of a paradigm shift occurring at the point “normal science” can no longer dismiss anomalies accepted in the current paradigm.[1] While Kuhn’s work can speak to the distinct set of concepts and patterns from a historical perspective, it made me wonder if a book written 59 years ago has anything to offer in modern times, specifically in the realm of data science.

Will Data Science live up to Kuhn’s definition of a Scientific Revolution or is it an advancement that simply continues the progression of current paradigms? Put more plainly, is it just a shift in technology that improves outcomes in and our application of data analytics, or are we underestimating the paradigm shift to something grander? Kuhn identified the concept of “Normal Science” as predicated on the assumption that the scientific community “knows what the world is like.” Normal science emerges when in any given scientific field, disparate ideas and competing positions are mollified into a common set of beliefs and accepted principles. The field becomes replete in its academic process and application becoming what Kuhn defines as a “paradigm.”

Research practices within the new paradigm typically embed prevailing thought and aim to build on it rather than diverge from it and Kuhn likened the ongoing research process to a “…strenuous and devoted attempt to force nature into conceptual boxes.”[2] Kuhn’s perspective on “Scientific Revolution” was both controversial and groundbreaking. He challenged the idea of accretion, that scientific knowledge and discovery was a universally accumulative process in that new progress always built on the conventions that came before it. Instead, Kuhn argued that “Scientific Revolution occurs at the point the paradigm’s community can no longer evade the anomalies that subvert existing tradition and then begins the extraordinary investigations that lead the profession at least to a new set of commitments, a new basis for the practice of science.”[3] It is this “crisis of emergence” of these unresolvable anomalies that cause a “paradigm shift,” a move to a new school of thought and a new approach, in lieu of the existing paradigm.

Consider the current data analytics paradigm. The digital revolution largely began in the latter half of the 20th century with the proliferation of computers and digital record keeping. More modern data analytics followed suit in the late 1960s as computers became more widely leveraged as decision support systems. The term “Business Intelligence” was first used in 1965. Howard Dresner at Gartner described the term as “making better business decisions through searching, gathering, and analyzing the accumulated data saved by an organization.” Over the next several decades, database approaches evolved, visualization techniques soared, and by the 1990s, data mining began.[4]

But it is in the last 20 years that data content breadth and depth has exploded exponentially, and cloud technologies have emerged to enable massive compute and storage capabilities that have enabled the explosion of data science, the application of sophisticated math to data to achieve more advanced predictive and prescriptive capabilities.

In our experience, companies and individuals in any given functional business paradigm are generally seeking to leverage analytics to improve and enhance existing modes of operation. Perhaps to make the supply chain more efficient. To create better lift in our marketing campaigns. Or even procure raw materials more cost effectively. But is data science just the latest progression within the paradigm to improve these outcomes, or is it more? Would data science fit Kuhns definition of a true scientific revolution? And if so, what of it?

I remember the first data science conference of my career, where a University of Texas Mathematics professor made a statement that expanded my way of thinking. He asserted that in human history most problems have been solved with relatively “simple” math. While superficially obvious, it shocked me. Whether the lack of more sophisticated application of mathematics is due to the absence of theory, the lack of enabling technology, or simple lack of vision for practical application, the fact is that the world basically operates on a framework of simple mathematics. But as he spoke, it was clear that by contrast, the disruptive ideas and capabilities coming down the pipeline are, I might suggest, ‘revolutionary.’

Data science is an acknowledged game changer within the analytics community, changing tactics, architectures, applications, and staffing approaches. It is also a game changer in its ability to create even more tangible insights and outcomes than ever before. Does data science’s tangible value as a revolution rest in its ability to enable greater paradigm shifts in human society? Is the promise of data science not only about making your supply chain simply more efficient, but as an enabler to disrupt and redefine supply chain management practices? Will data science simply improve campaign lift or change the paradigm on how you understand and engage your customers as a process? In essence, will it simply build upon what already exists, or reimagine the world we live in entirely?

Consider that in his “materialist conception of history” Karl Marx held that all of human history is largely a story of the means of production and of how humans within their societies interact with it. A societies superstructure builds around it and class struggles emanate from it.[5] While I would dispute the cause-and-effect most Marxist’s attribute to these class struggles, the framework of the means of production as a foundational element of all human society is largely true.

Data Science represents the greatest single impact and fundamental shift in the means of production since the Industrial Revolution. Imagine life back then. Knowing what you know now, what would you do if you could go back and saw the emergence of the steam engine or assembly line coming into play? Now fast forward.

We are in the midst of a new revolution, transforming business and by effect, human culture and society. What challenges does this create in our organizations? How do we participate as leaders, or at the very least as close followers of this disruption? How will we ready our systems, groom our people, and re-invent our processes?


The Systech Solutions, Inc. Blog Series is designed to showcase ongoing innovations in the data and analytics space. If you have any suggestions for an upcoming article, or would like to volunteer to be interviewed, please contact Olivia Klayman at

[1] Thomas S. Kuhn, The Structure of Scientific Revolutions, (Chicago: University of Chicago Press, Fourth Edition, 2012), chapters vii — ix

[2] Kuhn, The Structure of Scientific Revolutions, Chapters i-v

[3] Kuhn, The Structure of Scientific Revolutions, P. 6

[4] Keith Dl Foote, “A Brief History of Analytics”, DataVersity, 2018, Accessed on March 17th, 2021:,Data%20Analytics%20has%20evolved%2C%20significantly

[5] Eric Hobsbawm, “Marx and History,” The New Left Review, 1/143 (Jan/Feb 1984), 40

4 views0 comments