Leading organizations around the world are increasingly launching digital transformation initiatives in reaction to today’s amazing advances of information technologies that are occurring across multiple fronts—AI/machine learning, internet of things, new ways of collaborating, etc. All exciting possibilities, but if everything is important then nothing is important; so how to prioritize the opportunities? Fortunately, the optimizing data-to-learning-to-action approach and method is uniquely suited for that.
With the optimizing data-to-learning-to-action method, we focus on key decisions that are aligned with an organization’s important value drivers. Why decisions? Because decision effectiveness correlates almost perfectly with financial performance. Make better decisions, and business performance is better. The data suggests that, but it’s also just plain common sense!
Every decision has an associated data-to-learning-to-action process, and the process typically includes the standard elements in the diagram above: data acquisition, data filtering, information management, search and discovery, predictive analytics, process and collaborate, all leading up to, decide and act. Each of these elements can be a mix of people and systems, and learning flows forward along this chain of elements, ultimately informing the decision. The essence of learning is the ability to better predict through the reduction of uncertainty, and better predicting means better decisions.
So, we want to optimize that learning flow so that the decisions are as effective as possible. How do we do that? Well, constraint theory tells us that there will necessarily be a limiting constraint of the learning flow, and we need to identify the limiting constraint. We do that by systematically working backward from the decision, applying analysis and conducting structured interviews of the participants of each of the elements as we work backward along the data-to-learning-to-action chain.
Identifying the limiting constraint is critical, because another fundamental law of the theory of constraints is that investing in additional capacity anywhere except the limiting constraint is a wasted investment. This fact certainly has implications for digital transformation. The good news is that today’s rapidly evolving technologies offer far more possibilities than ever before to address limiting constraints and thereby improve our flows of learning. That makes our decisions more effective, which, in turns, drives improved financial performance. But the bad news is that if the limiting constraint isn’t identified and addressed first, there are many more ways to misfire by investing in the wrong places!
Once the limiting constraint is determined, then it’s time for people, process, and/or technology-based solutions to be evaluated to address the constraint. Because resolving the limiting constraint delivers enhanced predictability (i.e., reduced uncertainty) that can potentially change the decisions from what they would otherwise be, we have techniques from the field of decision analysis that we can employ to calculate in rigorous financial terms the value of resolving the limiting constraint.
After the limiting constraint is addressed and resolved, there will necessarily be another limiting constraint, and we can again evaluate people, process, and/or technology-based solution opportunities that target that specific constraint. The process continues until there are no longer any current solutions that have an expected value greater than the cost of the solutions for the given data-to-learning-to-action process.
The optimizing data-to-learning-to-action method is most fundamentally a way to prioritize. And it is really the way to prioritize that is aligned with value. As organizations struggle with prioritizing the complex opportunities associated with digital transformation, optimizing data-to-learning-to-action uniquely provides the way to continuously keep the focus on quantifiable value.
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