The notion has arisen that analytics is an emerging field that represents a convergence of the quantitative decision sciences. For example, in a 2015 paper in Interfaces, the authors state: “…the emerging definition of analytics as a field of expertise that subsumes OR.”. If true, such a convergence would be a surprising and remarkable development, as it would represent a dramatic reversal of the trend in human history toward specialization. It is worthwhile therefore, to examine this idea, and consider its logical consequences.

A convergence of the decision sciences into a single field would imply that this new field would contain all the knowledge and methods currently included in the decision sciences, and would lead to one of four possibilities being true.

**A new unifying theory is developed. **Currently,** **hundreds of theoretical physicists are working on the development of string theory, which they hope will mathematically unify quantum mechanics, particle physics, and gravity. Unlike string theory, there is no history of an analytics theory going back to the 1960’s, no founders of an analytics theory, no seminal papers introducing an analytics theory, and no conferences where an analytics theory is discussed.

In fact, in a 2014 paper in the European Journal of Operational Research, the authors found only 15 articles in theory oriented journals that were listed in the International Abstracts in Operations Research database with the term analytics in the title or abstract. Moreover, the types of analysis that analytics thought leaders offer as examples of analytics, such as advanced statistical analysis, econometrics, and optimization, are actually examples of existing methods from existing disciplines. (See for example, ‘Competing on Analytics’.) So, while string theory may provide a unifying ‘theory of everything’ for physics, there is no evidence that a unifying theory of the decision sciences exists, or any reason to believe that one could be developed.

**Analytics ****practitioners must master all the knowledge and methods of the decision sciences.** Without a new simplifying mathematical theory, mastering the knowledge and methods of all the decision sciences would require five or six Ph.D.s and 80 to 100 years of experience. Since the human life span is insufficient to accomplish this, we can reject this possibility.

**Analytics ****practitioners produce simplistic or superficial work.** Lacking a new simplifying mathematical theory, or a sufficient life span, analytics generalists would be unable to perform at a level comparable to current experts in the decision sciences. I will assume that those who believe that analytics is an emerging field, those who would employ analytics practitioners, and everyone else, will view this outcome as a highly negative development, and therefore, will not accept it.

**Analytics is practiced by individuals specializing in different areas. **Since the other possibilities are impossible or undesirable, we must conclude that specialization is necessary.

Allow me to list these specialties for you: statistics, computer science, operations research, industrial engineering, economics…

John ZaleskiHaving been employed in the decision sciences as well as hard sciences for 30 years, I actually prefer the hard sciences as a basis for my analytics practice. In my observation it is through an understanding of the hard sciences that the type of modeling I do and have done has been revealed as most important to rendering accurate models. This is not to say that an understanding of OR, probability & statistics, etc., is not essential. It is simply that physical matter, when modeled highly accurately, does not follow a random process. I have found, again–in my experience, that analytics and operations research has been essential to representing the atomic level physical models at a more macroscopic, system level. This is particularly important for strategic decision making.