Presenting Predictive Analytics
The nature of forecasting the future makes presenting predictive analytics unique and challenging. There is no flashy server or dashboard that will make presenting analytics any easier. There is only a model that tells a story about the future of users’ business, customers, non-customers and competitors. While models are very valuable they are not your typical business intelligence artifacts. To produce a meaningful return on investment you need to translate the details of the story into results that can be applied to a specific business question.
While you can not replace sound scientific and statistical methodology, CAN has found that users don’t care about a model’s Durbin-Watson, standard error, or R², or they are not familiar enough to properly understand the statistical nuances. The key to proving that a model works and getting political support required for implementation is to ask the experts if the story the model tells reflects reality. It is also valuable to prove that a model works by letting the prove itself over time.
It is important to note that predictive models typically do not provide solutions to business questions, but instead often offer incomplete answers and important insights. When presenting predictive analytics your audiences expectations should be set on becoming less wrong, instead of finding the perfect solution. CAN finds that users appreciate our philosophy of Less Wrong. While it seems counter intuitive, our lack of hubris builds confidence in our models and sets realistic expectations. The basic principle behind, Less Wrong is that in business winners are not right, they are simply less wrong. There are no perfect answers in complex sciences, such as data science and predictive analytics, only less wrong answers. The goal is to reduce the uncertainty of making the wrong decisions, not thinking uncertainty can be eliminated.
In conclusion when presenting predictive analytics don’t be afraid to kill your darlings. If you can not justify an element of your presentation get rid of it. This will help you focus your presentation, your audience will listen and the results of your hard work building predictive model will get implemented.
