Dr. Hillier,
Exposing students to the descriptive/predictive/prescriptive taxonomy is highly desirable since that terminology is very prevalent in industry, thanks largely to efforts of large consulting organizations. That said, you could present those terms and also explain, as you have done, why other terms may be more appropriate and why. You could also subsequently use the more appropriate terminology in the remainder of the textbook.
Terminology should be considered not only from the viewpoint of practitioners, but also from that of potential consumers of analytics. As mentioned, large consulting organizations have educated potential clients using descriptive/predictive/prescriptive. While these consumers have never really understood what Operations Research is, they at least think they know what analytics is in its various forms (thanks to Money Ball, etc.) and are spending freely in pursuit of analytics value. Your textbook can be influential in providing practitioners with a proper understanding of the nature of analytics, but it would be a tall order to override prevalent industry terminology not only among practitioners, but also among those who consume and pay the bills for analytics.
Original Message:
Sent: 12-02-2016 02:31
From: David Hunt
Subject: Can we improve the terminology of analytics?
Dr. Hillier,
I also appreciate your efforts to establish the best terminology to describe the different stages of analytics. The difficulty is selecting a single word to describe each of the stages. I have found the following to be useful when describing analytics (I would give the proper attribution, but have seen this in a few places and am not sure of the origin):
- Descriptive analytics - what has happened?
- Predictive analytics - what will happen?
- Prescriptive analytics - what should I do?
If given a vote, I would stay with descriptive and predictive, but switch from prescriptive to your recommendation of decision analytics. My concern with data analytics rather than descriptive analytics is that data analytics does not convey the meaning of "what has happened." I can analyze historical data, but I can also forecast data, optimize data, and make decisions using data.
I do like decision analytics because I think it conveys "what should I do" much clearer than prescriptive analytics. Also, as you may be aware, the term "Prescriptive Analytics," with both the "P" and the "A" capitalized, is a software product by Ayata and the name is trademarked.
Best Regards,
David
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David Hunt
Oliver Wyman
Princeton NJ
Original Message:
Sent: 11-30-2016 15:15
From: Tamás Terlaky
Subject: Can we improve the terminology of analytics?
Fred,
I appreciate your efforts to find the best possible terminology for "Analytics".
You are right, broadly used textbooks have profound impact of the subject area, and so the used terminology have to be chosen carefully.
I agree that the "descriptive/predictive/prescriptive analytics" classification is confusing, and while they are supported by some major players, their interpretation managers, and even engineers outside of the CS/OR area, might have negative impact.
I like the Phase (level) 1) Data Analytics, and Phase (level) 3) Decision analytics proposals, in my opinion these are excellent descriptive terms to focus on analyzing data and quantitative support for decisions.
However, with Phase (level) 2) Predictive/Forecast-based Analytics, my choice is Predictive Analytics. Not only "forecast" reminds me (as it reminds Marc) to weather forecast, but "forecast-based analytics" is a too cumbersome expression. Predict and Forecast are synonyms anyway,
Best Regards
Tamás
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Tamás Terlaky
Professor, Chair
Lehigh University
Bethlehem PA
Original Message:
Sent: 11-29-2016 21:27
From: Frederick Hillier
Subject: Can we improve the terminology of analytics?
My co-author and I currently are developing the 6th edition of our McGraw-Hill textbook,
Introduction to Management Science: A Modeling and Case Studies Approach with Spreadsheets, and when that is done, I will be developing the 11th edition of the Hillier-Liberman textbook,
Introduction to Operations Research. (My co-author for the former textbook is my son, Mark hillier, a faculty member in the University of Washington business school who keeps winning teaching awards for his management science and spreadsheet modeling courses.) I just have written a draft of a new section on
the relationship between analytics and management science for the new edition of the former textbook (and later will write a similar section for for the next edition of the latter textbook). This draft uses the traditional terminology for the three phases of the analytics approach (
descriptive analytics, predictive analytics, and prescriptive analytics). However, my reaction when doing this was that, while this terminology works quite well for us "Insiders," its usage has some significant drawbacks when we reach out to students, managers, etc. It seems to me that we could do much better by using "plain English" names for these phases of the analytics approach. I am writing to solicit feedback from others on this issue.
I briefly describe below my objections to the traditional terminology, after which I will present my suggestions for improved terminology.
Phase 1: Descriptive analytics. Put yourselves in the shoes of an "outsider" and try to decipher what this name for phase 1 means. Analytical techniques that have steps that are well described? Analytical techniques that provide guidelines for describing something well? Or what? More importantly, it conveys nothing about what actually is to be done. The whole focus revolves around dealing with data, perhaps even massive amount amounts of data. It involves applying data science through the techniques of information technology for data warehousing, data mining, data cleaning, etc., in order to transfer the data into insight. However, the name for phase 1 does not even hint at the central role of dealing with data.
Phase 2: Predictive analytics. This one isn't too bad. However, predictive isn't quite the right word. A dictionary definition of predict is "to state what one believes will happen," or "prophecy," as if one is just making an educated guess without any calculations about what will happen in the future. However, the actual activity involves forecasting, which involves performing calculations on historical data to obtain a statistical forecast of the future. Therefore, it would seem more appropriate to have forecasting in the name.
Phase 3: Prescriptive analytics. A dictionary definition of prescriptive is "making strict requirements or rules." Managers certainly would object to analytics professionals imposing strict requirements or rules. The focus instead is on using the techniques of analytics (i.e., the techniques of operations research in this case) to guide data-driven decision making. This ties in with the INFORMS definition of analytics as "the scientific process of transforming data into insight for making better decisions." Therefore, it would seem more appropriate to have decision in the name.
With all this in mind, I list below my suggestions for the names of the three phases of analytics:
Phase 1: Data analytics. This name already is being widely used and nicely conveys the focus of this phase.
Phase 2: Forecasting-based analytics. This name nicely conveys the focus of this phase.
Phase 3: Decision analytics. This name nicely conveys the focus of this phase.
For those of you who have been more heavily involved in the analytics movement than I have, I would greatly appreciate your reaction to the above. Should I go ahead with the above suggested names for the three phases of analytics? Or what is your advice? Textbooks can be quite influential and I want to make sure that I get it "right" in what I write about this important topic in the new editions of my textbooks. Thank you.
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Fred Hillier
Professor of Operations Research, Emeritus
Stanford University