Prescriptive analytics? My Twitter spat…

So at the Gartner BI Summit I got myself into a Twitter spat with the conference chair over the term “Prescriptive Analytics”.

Gartner have decide that the world of advanced analytics is split into four elements: Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, and Prescriptive Analytics.  Two of those categories will be very familiar – there are clear technical and conceptual differences between these two types (perhaps most succinctly identified in the old neural network terms unsupervised and supervised).


Diagnostic and Prescriptive Analytics are a bit different though, and I’m struggling to see what they mean that is significantly different from Descriptive or Predictive.

Gartner have an image that tries to take this further:



Image (c) Gartner

So here are my issues.

1) Descriptive vs Diagnostic

I’m not convinced that there is a real difference here. I don’t buy the idea that Descriptive analysis wouldn’t answer the question “Why did it happen?” or that Diagnostic analysis wouldn’t ask the question “What happened?”.  In fact (of course) you also typically use techniques from predictive analysis to help you with both of these – Cox Proportional Hazard Modelling would be one approach that springs to mind.  Technically it’s a two target regression approach, but it’s used to understand as much as to predict.

2) Predictive vs Prescriptive

The apparent difference here is twofold: firstly Predictive doesn’t lead directly to action, but Prescriptive does.  This simply doesn’t hold water.  Predictive analysis can lead directly to action.  Many predictive algorithms are embedded in systems to do exactly that. And if you contend that even that involves some human intervention, then the same is absolutely true of Prescriptive analytics – someone has to create the logic that integrates the analysis into the business process.

3) Prescriptive involves techniques that are different than Predictive

The suggestion is that meta techniques such as constraint based optimisation and ensemble methods qualitatively different and stand alone as a special category.  I don’t agree.  They don’t stand alone.  You can do predictive analytics without descriptive, and descriptive without predictive. You can’t do ‘prescriptive’ analytics without predictive.  It doesn’t stand on its own.  I’d also argue that these are techniques that have always been used with predictive models: sometimes internally within the algorithms, sometimes by hand, and sometimes by software.

4) Only prescriptive analytics leads directly to decision and action

Without human intervention. This also just isn’t true. I dare anyone to build prescriptive analytics without involving people to build the business logic, validate the activities, or just oversee the process. Yet this is the claim. Data mining is a fundamentally human, business focused activity. Think otherwise and you’re in for a big fall.  And, yet again, productionising predictive models has a long tradition – this is nothing new.

But the final defence of Prescriptive Analytics is that it is a term that has been adopted by users.  Unfortunately this doesn’t seem to be the case. Gartner use it, but they need to sell new ideas. SAS and IBM also use it, but they are desperate to differentiate themselves from R. A few other organisations do use it, but when pressed will admit they use it because “Gartner use it and we wanted to jump on their bandwagon”. But I could be wrong, so I looked at Google.

Predictive analytics: 904,000 results

Prescriptive analytics: 36,000 results

Take out SAS/IBM: 17,500 results