What’s the most important driver of long-term financial performance? It’s the effectiveness of decisions. That’s what the data tells us, but it’s also just plain common sense. How can it not be the case that ultimately financial performance is a function of the effectiveness of an organization’s decisions? What else could it possibly be? It’s not necessarily quality or efficiency, it’s not digital transformation, it’s not even people. Those are subsidiary because they are examples of outcomes of decisions or the means for potentially making more effective decisions. When it comes to directly driving performance, it’s the overall effectiveness of an organization’s decisions that rules. This is so obvious when you really think about it, but it is so often overlooked or just taken for granted by organizations.
This primacy of decisions as the driver of financial performance is exactly why the optimizing data-to-learning-to-action method is so focused on decisions and their effectiveness. And not only the huge, one-off decisions, but the recurring, everyday decisions that are so core to sustainable performance. Every decision has a data-to-learning-to-action process that supports it. It is the effectiveness of that data-to-learning-to-action process that dictates the effectiveness of the decision, and the effectiveness of the decision in turn dictates its contribution to the organization’s financial performance. So, the effectiveness of data-to-learning-to-action processes clearly directly drives an organization’s financial performance. And the effectiveness of data-to-learning-to-action processes, like that of any other type of process, is directly governed by their limiting constraints.
This primacy of decisions and their associated data-to-learning-to-action processes is why the optimizing data-to-learning-to-action method is best considered as positioned right under overall business strategy (although it can and should inform and help shape strategy), but above other enterprise initiatives such as quality, cost reduction, digital transformation, etc. For example, you want to cut costs? Well, you’ll need a way to prioritize the cost cutting. And the only way to properly do that prioritization is to understand the limiting constraints of your data-to-learning-to-action processes so that you avoid reducing capabilities at the limiting constraints when you reduce costs. So, yes, reduce costs, but you must first carefully analyze your data-to-learning-to-action processes.
The same holds true, for example, with digital transformation. You need a way to prioritize your digital transformation investments. And the only way to do that properly is to first understand the limiting constraints of data-to-learning-to-action processes. In the old days we often spoke of “decision support systems” as a special category of enterprise IT applications. The reality is that today just about all systems that truly matter from a financial performance standpoint are decision support systems. That’s therefore why digital transformation really has to be all about improving the effectiveness of data-to-learning-to-action processes!
The limiting constraints of data-to-learning-to-action processes tell us where to focus. And the potential value of changing the associated decision if the limiting constrained can be de-constrained tells us how much to focus. Together, these techniques (and only these techniques!) guarantee that we make the best possible investments.
In summary, here is why the optimizing data-to-learning-to-action method is the only proper method for prioritizing investments associated with any type of initiative (or even reducing costs):
1. The primacy of the effectiveness of decisions in driving long-term financial performance mandates that the focus be on the effectiveness of data-to-learning-to-action processes.
2. The method provides the only provably correct means of determining exactly where and what to invest at any given point in time.
3. The method provides the only proper way to quantify the expected return on the investments.
So, if your organization is discussing, for example, where to place its information technology bets, enhancing its R&D results, or reducing costs, and you aren’t often hearing the words “decision” and “limiting constraint,” it is unfortunately pretty much guaranteed that value is going to be left on the table. The good news is that you now have the opportunity to do better!
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