Business intelligence (BI) practitioners continue to hear about the tremendous return and impact of data mining and predictive analytics applications through reinforcing case studies across industries. It’s no wonder so many organizations are striving to make their way down the BI development chain to arrive at a practice that offers self-validating prospective insight.
These organizations are anxious to uncover and leverage the highly valuable intelligence hidden within their existing operational data. Like any endeavor with rich rewards, there are often numerous risks, barriers and pitfalls that stand in the way. In predictive analytics, those barriers are not in the typical places that a seasoned BI practitioner would expect. Time and again, those new to data mining fall to rookie mistakes.
But don’t take our word for it. We will reference two recent industry surveys that reveal the majority of data mining practitioners focus on the wrong end of the problem. And, not surprisingly, a similar proportion fails to achieve positive results …or even know the difference. With a complex practice like predictive analytics, it stands to reason though, right? Predictive analytics isn’t exactly like maintaining a checkbook, is it? So, what do you think is the greatest challenge to succeeding in predictive analytics?
#1: Lack of a Solid Project Definition
#2: Over-Reliance on Software Solutions
#3: Focus on Methods, Tactics and Optimal Model Performance
#4: Gloss Over a Comprehensive Project Assessment
#5: Approach Predictive Analytics Like an Engineering Project