A popular pair of PowerPoint slides used by business conference speakers first displays a distant car behind in the rearview mirror of an automobile followed by a second PowerPoint of the same auto’s windshield view with a big oncoming truck directly in your lane – beep-beep! I am conflicted with this not so veiled message. I passionately embrace the increasing emphasis on forecasting outcomes and its value to narrow the uncertainty of the future. But this implies knowing and understanding the past is of much less informational value. There is substantial value in both views – the past and the future.
I agree with the symbolism that the rear view mirror implies that events have already happened, so they are already behind you and can not be affected. But this message to only look forward is distorting. I personally like having rearview mirrors, and when driving I glance at them often. I want to see what types of vehicles are behind me and what rate they may be speeding up on me.
Why does understanding the past have importance too? There is much to be gained from analyzing trends and drawing inferences from the past. A trend starts back in time but it ends with last moment you checked. Collectively that information is nearly real-time. An inference is a conjecture that allows you to deduce what is going on, and in many cases our wonderful human brain can instantly draw conclusions about what it all means to respond with a next action. If I am driving in the fast lane, the passing lane, of the German autobahn, and I see a BMW approaching me; then I should shift to the slow lane (where you are foolish to not mainly drive in anyway).
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In some cases, such as with profit and cost analysis of products and customers, the historical absolute data is less important than the relative relationships. The relationships are essential for modeling the future.
In some cases your inference and subsequent alternative actions needs to be validated. This is where the symbolism of the windshield comes in. The ability to project what-if scenarios is powerful because then you can select the best alternative – strive for optimization. As the emphasis shifts from traditional control to better planning, there is increasingly more being written and discussed about predictive analytics and risk management. Professor Tom Davenport’s book, Competing on Analytics, is an excellent source to learn more. However, there are many flavors of analytics, including segmentation analysis and complex pattern recognition, so do not equate Davenport’s message to be only predictive analytics are important. There is rich information from understanding history too.
