Predictive Analytics: Failure to Launch

How to Get Analytic Modeling Off the Ground and Into Orbit

The vast majority of BI professionals and big data enthusiasts are excited about the prospects of predictive analytics and data mining, but are fully mystified about where to begin or even how to prepare. Of those who did initiate a modeling initiative, an industry survey of predictive modeling practitioners reported that 51% of data mining projects either never left the ground, did not realize value, or the results were not measurable.

Thanks to modern analytic software, most who attempted an implementation did end up building technically accurate predictive models — that answered the wrong questions, were misapplied or never adopted. This is precisely like placing a perfectly good rocket upside down on the launch pad.

So, how does one approach an intangible, cryptic, seemingly immeasurable technology? Beyond the inherent up-front risks of engaging in what is perceived as a discovery process, just identifying a starting point can be intimidating and mystifying. Despite its elusive nature, data mining success stories express significant impact in mainstream publications more frequently. Your competitors are discovering that success in this field does not lead with data and software.

For any organization with annual revenues greater than $50 million, establishing an analytic practice that thrives on validated impact is not a matter of whether, but when. Data mining and predictive analytics are now in great demand for transforming big data investments into actionable information assets. It’s imperative that leaders shift their decision-making from gut-feel to data-driven and start advancing their analytic maturity.

Attend this free vendor-neutral webinar to learn how to get started with data mining and overcome limitations that cause most data mining projects to fall short of their potential.