Analytics and the myth of the aha moment

I often hear people talk about analytics, especially advanced analytics like data mining or predictive analytic modeling, as though the value comes from “aha moments”. Sudden moments of clarity, defining moments, where the analytics deliver some piece of dramatic insight that enables a company to see some fantastic new market opportunity or fundamentally change the way it does something. This is a myth. Most companies that get value from analytics either do so without ever having an aha moment or they have both an aha moment and something more – the aha moment is not enough on its own.

I often hear people talk about analytics, especially advanced analytics like data mining or predictive analytic modeling, as though the value comes from “aha moments”. Sudden moments of clarity, defining moments, where the analytics deliver some piece of dramatic insight that enables a company to see some fantastic new market opportunity or fundamentally change the way it does something. This is a myth. Most companies that get value from analytics either do so without ever having an aha moment or they have both an aha moment and something more – the aha moment is not enough on its own.

Let’s take the first of these circumstances. Many of the companies I work with (as well as many that I read about) are getting tremendous value from analytics. But they are getting this value by applying analytics to improving decisions, operational decisions, that they make in huge numbers. They use analytics to better predict the fraud risk of a claim so they can pay fewer fraudulent claims. They use analytics to better predict credit risk so they can manage credit lines more effectively. They use analytics to predict which cross-sell offer will be most likely to succeed. There’s no great aha moment, no one thing that analytics teach them about their business. What there is, instead, is an industrialized approach to analytics that focuses on building models that will predict something useful about a single customer or single transaction. There is an implementation process to make sure these predictions are turned into useful actions, often by applying business rules, and there is a feedback or test-and-learn process to constantly evaluate how well the model and the actions based on it are working.

In the second kind of company there is an aha moment – analytics tell them that they have far more fraud than they expected or that a particular product is not as profitable for certain customers as they believed. But this aha moment is not actionable without understanding the operational decisions that must be made differently and without exactly the kind of process I just described to make sure those operational decisions are managed. What both kinds of companies have in common is a focus on operational decisions as a source of competitive advantage.

Now, obviously, I am being provocative. Some companies do, in fact, get value from analytics from a single aha moment. There are drug companies that find the group of patients that a drug will work for, insurance companies that find that one particular policy type is causing huge losses, medical professionals that find the one thing that predicts a negative outcome to a treatment and so on. Most do not. So when you think about analytics, don’t think about aha moments, think about the operational, transactional, micro-decisions that drive your front-line systems and think about how analytics could make each of those decisions a little better.