Simply having predictive models that suggest what might be done won’t be enough to stay ahead of the competition, Bill Frank states in his article “Let algorithms decide – and act – for your company”, part of the Harvard Business Review “Predictive Analytics in Practice”.
“Instead, smart organizations are driving analytics to an even deeper level within business processes—to make real-time operational decisions, on a daily basis. These operational analytics are embedded, prescriptive, automated, and run at scale to directly drive business decisions. They not only predict what the next best action is, but also cause the action to happen without human intervention”, he continues, besides emphasizing that the same evolution the manufacturing went through during the industrial revolution is actually happening in analytics.
If predictive analytics have been a pretty cost-intensive customized endeavor in the past, operational analytics recognize by now the need to deploy predictive analytics more broadly, but at a different price point. By creating an automated process that builds a reasonable model for hundreds or thousands of products or offers, rather than just the most common ones, predictive analytics can impact the business more deeply.
“Operational analytics are already part of our lives today, whether we realize it or not. Banks run automated algorithms to identify potential fraud, websites customize content in real time, and airlines automatically determine how to re-route passengers when weather delays strike while taking into account myriad factors and constraints. All of these analytics happen rapidly and without human intervention. Of course, the analytics processes had to be designed, developed, tested, and deployed by people. But, once they are turned on, the algorithms take control and drive actions”, Bill Frank explains, adding that “the power and impact of embedded, automated, operational analytics is only starting to be realized, as are the challenges that organizations will face as they evolve and implement such processes”.
“Just as it is still necessary to design, prototype, and test a new product before an assembly line can produce the item at scale, so it is still necessary to design, prototype, and test an analytics process before it can be made operational”, he clarifies. Although many most probably won’t be comfortable at first “with the prospect of turning over daily decisions to a bunch of algorithms”, it makes sense doing it. The fact is that predictive analytics applied in batch to only high value problems will no longer suffice to stay ahead of the competition, rather it is necessary to evolve to operational analytics processes that are embedded, automated, and prescriptive.