“Operations Research? Management Science? Analytics? What’s in a brand name? How has the emerging field of Analytics impacted the Operations Research Profession? Is Analytics part of OR or the other way around? Is it good, bad, relevant, a nuisance or an opportunity for the OR profession? Is OR just Prescriptive or is it something more? In this panel discussion, we will explore these topics in a session with some of the leading thinkers in both OR and Analytics. Be sure to attend to have your questions answered on these highly complementary and valuable fields.”
Glenn Wegryn organized the session yesterday afternoon that the abstract above describes, and the panel he assembled was slated to include Jeff Camm from Wake Forest University, David Dittman from Proctor & Gamble, Don Kleinmuntz from Notre Dame, Jack Levis from UPS, author and futurist Thornton May, Anne Robinson from Verizon Wireless, and Robert Rose of Optimal Decisions. This group includes two past presidents of INFORMS and others who have been involved in the profession for years. I had hoped to attend, but my original flight was canceled. My rebooked flight didn’t get me in until long after the last seat was empty. I’ll just have to get a recap later, but here’s what I’ve been thinking about this question.
Interestingly, the American Statistical Association (ASA) is host to what strikes me as a very similar debate. On October 1 the ASA released a statement on the Role of Statistics in Data Science. There’s a whole wing of data science practitioners who are downright hostile to statisticians. This camp makes assertions like “sampling is obsolete,” due to computational advances for processing big data. The fringes of this crowd even extend the obsolescence to statisticians themselves. INFORMS member Randy Bartlett, who holds both the CAP and the PSTAT certification from ASA, has written about this statistics denial in an excellent series of blog posts he publishes on his LinkedIn page. In the face of such direct attacks it is no wonder the ASA felt a need to take a stance.
While I haven’t seen a similar assault on OR, there are those in the OR/MS community who see terms like analytics as a threat to the survival of operations research. An interesting study was conducted some years ago when INFORMS was evaluating an organizational shift to address the analytics community. Among those surveyed, some saw OR and analytics as unrelated, some saw them as the same, some saw analytics as a subset of OR, and some saw OR as a subset of analytics. No one view dominated, which, among other things, suggests that there is no agreement on the definition of analytics. As INFORMS has moved forward with its embrace of the term analytics there has been a vocal minority who have not agreed with this direction.
Trying to define analytics, data science, machine learning, and even operations research feels like chasing greased watermelons in a swimming pool (look it up) – very hard to get your hands around. But the watermelons are real, and I don’t think they are going away any time soon. They are distinct and different melons, but related. Analytics is used increasingly synonymously with data science, which is derived in large part from statistics, which also is a foundation for machine learning, which increasingly borrows from optimization techniques, which I’d argue are part of analytics.
We can parse the terms all day, but instead we should spend our energy on the opportunity at hand and the difference we can make. Some of these buzzy terms get people’s attention and provide an incredible opportunity to use mathematically-based methods to make a significant impact. I invited Jack Levis to give a keynote at an analytics conference SAS hosted last week about ORION, the OR project he leads at UPS and arguably the largest OR implementation in the world. While few there would have understood in any detail the operations research methods employed, all were amazed at the impact his team has had on saving miles, time, and money for UPS. The important academic work being done at this conference contributes to the research advances that lay the foundation for this kind of success at UPS. People here know it as operations research. Most attendees at the conference last week probably thought of it as analytics. Does it really matter what we call it, if those attendees value what was done and want to share the story? If it leads to the expansion of OR I don’t care if it is called analytics. OR/MS has a chance to have not only a bigger slice of the pie but a bigger slice of a bigger pie if analytics is embraced as part of this community. It’s our opportunity to squander.