The promise of Big Data looms large as banking institutions worldwide launch major customer centricity initiatives.  Today one has the means to capture and analyze much bigger quantities of data than ever before, and to make meaningful connections between different types of it.  One can analyze data in-stream for real-time decisions. One can distribute analytic tasks in a massively parallel manner across many processor nodes, then algorithmically assemble their outputs into a single result.

But is any of that helpful for achieving customer centricity?

It’s most helpful when one can systematically extract the most valuable analytic insights — causal relationships — from Big Data. These insights enable companies to understand individual customer behavior and sensitivities, anticipate needs, and predict likely responses to offers and treatments.  In some situations, one must find and act on such insights as data is streaming in. In others, one can use out-of-stream methods to dive deeply for them.

Big Data computing infrastructures are making it practical to employ automated machine learning algorithms for this purpose—but human expert oversight is essential to ensure results make business sense and are useful in operations. And, ultimately, whether any of these insights impact customer centricity depends on how quickly we can pump them into operations so that they inform every decision and every customer interaction.

These essentials for turning Big Data into an enabler for customer centricity are fundamental to what FICO calls “next-generation analytic learning.” At its core, next-generation learning elevates test-and-learn methods to a new level of efficacy.  It’s a systematic, highly efficient way of continuously advancing what we know about our customers and improving how we use those insights to interact with them.

Source:  FICO Banking Analytics Blog