Confidence in Decision Equity Comparisons

The ability to confidently compare alternate investment options, such as the two cited in the previous case study, is contingent on other important considerations. From a process perspective, two salient inputs that inspire confidence are the development of a comprehensive flowprint and the availability of reliable and valid data to breathe life into the flowprint. The inability to meet either of these two criteria can cast serious doubts on the quality of decision support provided by the decision equity approach. It is therefore critical that managers invest enough time in the up-front design phase toward developing a relevant flowprint, and gathering the required data. Managerial inputs on investments required also play an important role in the comparison of the decision equity metrics associated with the alternate investment decisions. These resources could relate to the monetary investments, time constraints, as well as other inputs. For example, if the cash outlay for hiring more tellers were $60 million versus the estimated $20 million in the case study discussed previously, the chosen option would be very different. Similarly, if management was interested in a 1-year versus a 3-year time frame, the chosen action plan could have been different. Last, but not the least, nonmathematical strategic considerations may often override the considerations highlighted by the estimation of the decision equity metric. For example, if the equity estimates recommend acting on a certain performance domain, but organizational legacy forbids managers to make investments in that area, then management might select an alternate option despite lower equity. Overall, the computation of decision equity relative to the status quo is conceptually closest to the definition of the construct. Therefore, wherever possible, we try to embrace this approach to estimate its value. However, more often than not, we run into either composite decisions or management’s desire to test multiple but a limited number of options in one shot. In such cases, we use data analysis tools to parse out the decision equity associated with each of the individual choices. Management then makes an informed call regarding the decisions to continue with and those that need to be dropped. We rarely see a bake-off among many independent decisions and rank ordering them based on their respective equities. However, the equity estimation serves as a powerful tool to compare noncomparable options on a common ultimate metric, and in conjunction with other strategic considerations, leads to well-informed, data-driven choices. When done right, the decision equity–driven approach is a vastly superior alternative to intuition-based decision making.