Say “cognitive analytics” and you typically get two reactions. For some, it’s a new consulting buzzword that makes a simple thing sound complicated. For others, the feeling is, “Maybe companies somewhere are doing it, but we’re not and we’re never going to.”
The first may be true. The second is likely not. Cognitive analytics are coming your way and there’s no doubt about it.
Business professionals generally refer to cognitive analytics when talking about various uses of big data for business intelligence. The general concept here is that enterprises collect and aggregate large amounts of data from diverse sources. Specific software programs analyze these in depth to provide specific results and metrics that help the business get a better view of its own internal processes, how the market perceives its products and services, customer preferences, how customer loyalty is generated or other key questions where the insights are used to provide the business with a competitive edge.
In many cases, the underlying data is not really new. What’s new is the way in which it is combined and analyzed. Here are two case studies in cognitive analytics that illustrate the point, from my world of enterprise level automation and high speed business process testing.
Case 1 - Business Process Analytics: Where to optimize. In this example, we’re layering together two of the most vital data sets in an enterprise. The first is a description of every business process in the enterprise, and all their variations. The second is actual transaction information corresponding to each one of those processes.
Both data sets are big. A few years ago, assembling either one of them would have been viewed as virtually impossible. Today, actual business processes can be efficiently captured at the enterprise level with software for automated business process discovery. Transaction detail for nearly all processes are available as well from the enterprise apps that support them. After all, nearly every business process is software-tracked today.