Summary: Is the addition of “Prescriptive” analytics to our nomenclature really worthwhile or are we just confusing our customers?
I admit to being annoyed when this or that industry wag tries to coin a new term to describe some portion of the discipline we are already practicing. Some of these folks I think are well intentioned and believe they have seen an area sufficiently unique to deserve separate description. Some I believe are motivated more by the ability to sell more reports if there are now two disciplines to be ranked and rated instead of just one.
It is with this admitted mindset that I’ve come to the conversation about whether ‘prescriptive’ analytics is a real thing or is it just an aspect of what we’ve been doing all along.
My concern has been that if we make our clients believe that this is some new form of analytics requiring new techniques and new tools or analytic platforms we will have made their perceptions less clear and our own lives that much more difficult. Believe me, with only about 1/3rd of companies having yet adopted predictive analytics the last thing we need is introducing ‘the next big thing’ into that conversation unless there is some real value to be had.
So with this in mind I decided to focus on this issue over the last few weeks and see if I could convince myself one way or the other. Could I find real world use cases where the distinction between prescriptive and predictive was important and easy to see?
The most common definition for this difference is that predictive analytics describes what will happen while prescriptive analytics describes what should happen. Perhaps a better way to say this was offered by TDWI senior researcher Fern Halper when she says prescriptive is about improving the outcome.
The graphic that she offers shows the ‘fuzzy transition’. It should probably be bigger.
The Argument Against
I would argue that most of what has been broken off as ‘prescriptive’ is actually what we’ve been doing all along. Let me offer two examples.
If I am a bank or insurance company and have say six major service offerings then one of the things I would like to know is if customer John calls my service center, then during that conversation what is the next most likely product my CSR should offer to John after his question is answered. This is a very common application of predictive modeling and a simple scoring model will reveal that John is most likely to buy X if he already has Y. I use this score to display on the CSR’s screen that X should be offered at the end of the call. This described both what will happen (the probability of a cross sell of X) and simply displaying this information to the CSR defined what should be done and improved the outcome. No additional secret sauce required.
If I am a medical provider trying to decide what portion of say the pre-diabetic population that I serve should receive a specific disease management program and which should not, I can use predictive modeling to determine the likelihood that each member will respond positively to the program. (See my earlier article on ‘Optimizing Disease Management Using Predictive Modeling’). The disease management program has a cost but it is presumably lower than the cost of treating the member if he converts to fully diabetic. Knowing that not all members will respond as well as others to the disease management program I can build a predictive model and from that prepare a simple lift analysis that will target my cost breakeven point. We’ve been using lift analysis from predictive models for more than 20 years. It describes what will happen. It also describes what should happen, that is offer the disease management program to the portion of the population that is most likely to respond which best allocates my scarce health care resources. Anything new here? No.
The Argument For
I did find several use cases where prescriptive analytics required the use of additional techniques to move from what will happen to suggest or automatically initiate a subsequent action that could produce an optimal result (what should happen). Curiously, they all borrow techniques from Operations Research, a field far more mature than predictive analytics so not new in that regard. Specifically these examples all rely on mathematical techniques of optimization in the face of specific constraints (cost, labor, time, or similar).
It makes perfect sense that the learning from the predictive analytics should be tailored to deliver the best possible result in the face of real world situations. Here are a few of the examples I found.
Are Prescriptive Analytics Different in Big Data?
I did discover one application of what is described as prescriptive analytics specifically relating to big data, in this case the analysis of unstructured text data.
More than one big data analytic platform with a healthcare focus offers a type of text analysis in which they aggregate the text descriptions of different steps in the treatment of specific medical conditions. Take for example back problems leading up to surgical or non-surgical resolution of the problem. Through clever analysis relying more on visualization than numerical quantification this helps medical professionals identify the optimum series of procedures leading to the best outcomes for different subsets of this population.
This meets the definition of prescriptive analytics (improves the outcome) but to my eye looks more like visualization in big data than something uniquely different from highly quantified predictive analytics.
Wrapping It Up
OK so my little adventure in self-education did indeed find some examples of prescriptive analytics identifying improved outcomes. And these examples did involve analytic steps beyond what some of predictive analytics usually encompasses. However, the use of optimization techniques on predictive models is not new. Those of us who come from operations research or a statistics background have been applying these techniques all along.
On the customer side I’d say the impact is mixed. By adding complexity through the ‘invention’ of another type or dimension of analytics we’re certainly making it more difficult, not easier for them to understand. And if you’re dealing with customers who are low on the ladder of adoption I’d say, watch out, too many choices is the mother of indecision.
However, I must admit that the definition of prescriptive analytics with its immediate call to action – tell me specifically what to do to make it better – can be less theoretical than explaining the whys of predictive analytics and even may be the compelling message that we need.
October 23, 2014
Bill Vorhies, President & Chief Data Scientist – Data-Magnum - © 2014, all rights reserved.
About the author: Bill Vorhies is President & Chief Data Scientist of Data-Magnum and has practiced as a data scientist and commercial predictive modeler since 2001. He can be reached at:
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