We’ve all been in meetings in which someone says something like, “We’re in the midst of a revolution here, we’ve got to start applying this machine learning stuff!” Or this IoT stuff, or that new collaborative technology, or . . . But then someone else reminds everyone that it’s not really about the technology, it’s about the people. And then another person reminds the group that without proper processes the value of technology and people are never fully achieved. And then the group’s discussion continues to go around in circles and decisive actions are deferred until the next meeting, at which time the group goes around in circles some more, and the merry-go-round continues.
While high-level heuristics or rules of thumb such as, “we need to be a machine learning-based company” or “it’s really always about the people” have their place as general motivators and reminders, they are simply too coarse-grained by themselves to serve as the basis for good decision making. What’s needed is a way to understand exactly what will drive value—and what won’t.
And there is only one way to do that: by finding the limiting constraints in data-to-learning-to-action processes. Identifying a limiting constraint is critical because investing in additional capabilities anywhere except the limiting constraint simply won’t make a difference—in fact, it is wasted investment.
But how do we find the limiting constraint on value? Not by merely tossing high-level management shibboleths back and forth! Rather, we need to apply a systematic method that ensures that we get to exactly the right answer. And the reality is that the right answer lies at only one place, the limiting constraint of a data-to-learning-to-action process.
The important trick to finding the limiting constraint is to systematically work backwards from the decision along the data-to-learning-to-action process. Proceeding step-wise backward along a flow of actionable learning of a data-to-learning action process, we determine whether the limiting constraint on actionable learning is internal to an element of the data-to-learning-to-action process or whether the limiting constraint is due to deficiencies in the inputs flowing from elements upstream of the element currently being analyzed (for example, the Process and Collaborate element). If it is the latter, we move upstream one element (for example, to the Predictive Analytics element) and ask the same questions. We are guaranteed to eventually find the limiting constraint by applying this step-wise process.
Because we have applied a process that guarantees that we have found exactly the right problem to address, we can then look for and apply exactly the right solutions to resolve the issue. Sometimes the answer will be mostly a technology-based solution opportunity—the rapid advances in technologies across multiple fronts make it increasingly likely there will be something available that can improve a limiting constraint. But sometimes it is a people-based solution that is needed. And other times an enhanced process is required. Regardless, we will know for sure because we have a provable way of determining it, and one that can be demonstrated to others.
So, get off the merry-go-round of reliance on high-level assertions and rules of thumb. The Optimizing Data-to-Learning-to-Action method provides the provable answers needed to make the best decisions and get the absolute most out of your investments.
Want to know more? Please contact us—we’ll be happy to discuss!
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