Organizations on today’s super information highway rely on statistical analytical models, which play an increasingly important role in guiding strategic and tactical decision making, such as estimating risk, predicting customer behavior, and analyzing business strategies. However, the current statistical approach yields an ever increasing number of models, causing a logistic “overload” on operational systems. This challenge is abound across virtually all industries, such as financial services, retail, and telecommunications. The purpose of this article is to present a new nonstatistical machine learning analytical model – the GenIQ Model
© – that provides the crucial function of guiding strategic and tactical decision making with an added boost of predictive power via genetic data mining
. Moreover, GenIQ produces fewer models, moderating the otherwise overload on an operational system. I illustrate the GenIQ paradigm for easy to develop and deploy analytical models, well suited for the data onslaught facing organizations today.