Sunday, 21 June 2015

Prescriptive analytics, the next big step?

Now that you have hooked all the data of your organisation to your KPI dashboard to monitor every day performance and are busy estimating forecasting models for order intake and customer satisfaction, you’re wondering what will be your next step in analytics. Should it be prescriptive analytics? It’s the most advanced, most promising variant of analytics, at least that’s what vendors of analytics software are saying, but it is also the most demanding.  
Reviewing the literature on analytics you deduct that the only way to be able to use prescriptive analytics is to gradually grow your analytics maturity from descriptive, diagnostic, and predictive to prescriptive analytics. The graph Tom Davenport uses in Competing on Analytics to position the different types of analytics cleary shows that. Gartner positions prescriptive analytics as an emerging technology in the hype cycle, comparable to autonomous vehicles and biochips, suggesting it is a new high tech kind of thing. Something that need to proof it's value still. You wonder if it's the right way to go.....
Gartner hype cycle august 2014
My experience is that analytics maturity is of less importance when it comes to the the kind and complexity of analytics used to solve a business problem. Analytics maturity is about the factors that determine the organisation readiness to adopt analytics in decision making throughout an organisation Davenport uses the DELTA (Data, Enterprise orientation, Leadership, Targets, and Analysts) metaphor to asses an organisations’ maturity. When you review Davenport’s DELTA model, you will see that the complexity of the analytics used is not a driving factor for maturity. the other way around also holds.
Gartners’ positioning of prescriptive analytics as a new technology is strange to me. Prescriptive analytics (or Operations Research as we used to call it) has been around for some time already, it originated from the research done by the British Army to beat the Nazi’s during the 2nd World War. At that time, analytics was essentially the application of common sense and the careful study of data to the messiness of war. With success, as the insights from the analysts let to the defeat of the German U-boat campaign. Since then Operations Research has been applied to all kinds of decision problems within big and small organisations, some of them could be called analytical competitors (organisations like Google and Amazon) many of them analytical impaired.  
In my 25 year career as an analytics professional I have come across many examples in which operations research (or prescriptive analytics) proved to be of immediate value, even though the organisation didn’t have sophisticated analytical skills. I have supported Mon and Pop 3PLs with route optimisation models to create routing schedules for their trucks. With the low margins they get, making the most out of their assets is crucial to them.  In healthcare, not really a sector in which analytics has gained a strong foothold, the use of shift optimisation and shift scheduling has let to better balanced schedules, reducing illness and stress, beneficial to both nurses and patients, lowering the cost of healthcare. Similar, benchmarking using optimisation modelling resulted in better insights in hospital performance and the identification of best practices. Governments also are not very analytical mature, than again using optimisation to construct routes for the de-icing of high ways and local roads reduced cost and improved road safety. I could go on with many more examples, but I guess you get the point, it is not your analytics maturity that determines whether you can use prescriptive analytics, but the problem you need to solve.
In summary, prescriptive analytics is not a concept in a hype stage, nor an approach with little use in every day decision making. The above examples proof that. It doesn’t require big budgets nor is it only available to you when you have mastered predictive or descriptive analytics. It is the problem you need to solve that determines the analytics technique you require. So what’s keeping you? Start optimising and start today!