Quantifying the Value
In the last chapter, we reviewed some examples of working backward from decisions to understand the value of addressing bottlenecks on actionable learning. For instance, for the pricing example, we needed to understand how much more profit would be expected to be attained if we could better estimate competitors’ bids. And, more particularly, we wanted to know how much more profit would be expected for various levels of accuracy in predicting competitors’ bids. With that information in hand, we could then work backward in our straightforward way to identify the constraints that contributed to the current state of less-than-perfect-predictability of competitors’ bids and determine what it would be worth to resolve those constraints, starting with the constraint that was identified to be the current most-limiting factor in the data-to-learning-to-action chain.