Predictive Analytics & Data Mining: Five Key Factors for Predictive Analytics Opportunity Identification
The vast majority of BI professionals are excited about the prospects of data mining and predictive analytics, but are mystified about where to begin or even how to prepare. Of those who did initiate a modeling initiative, a recent industry survey of predictive analytics practitioners reports that 51% of data mining projects either never completed projects, did not realize value, or the ultimate results were not measurable.
In most cases, those who attempted an implementation ended up building valid models that were not actionable, or answered the wrong questions. They essentially selected projects that were not well suited to the technology of predictive analytics.
So, how does one approach an intangible, cryptic and seemingly immeasurable technology? Beyond the inherent up-front risks of engaging in any new technology, just identifying a starting point can be intimidating and mystifying.
Despite its elusive nature, predictive analytics has surpassed the flash-in-the pan “magic wand” hype with widespread and sustained success stories highlighted frequently in mainstream publications and recurring case studies of improved operational efficiencies, enhanced business intelligence and residual payback.
For any organization with annual revenues more than $50 million, employing predictive analytics technology is not a matter of whether, but when. Attend this free webinar to learn how to get started with predictive analytics and overcome both strategic and tactical limitations that cause predictive analytics projects to fall short of their potential.
This webinar is intended for stakeholders, functional managers and business practitioners in business, industry, government and academia, who have made substantial investments in data collection, storage, retrieval, visualization and basic analysis but may not have the technical or strategic experience necessary to chart an effective roadmap to uncover the valuable predictive insights hidden within their existing data. No prior knowledge is required. The webinar will cover:
- How and where to get started
- Why failure to implement is so common, and why pitfalls are so avoidable
- Case studies that reveal the rewards of proper opportunity selection
- Why establishing an internal predictive modeling practice is within your reach
- Resources and direction on how to move forward with confidence
Tony Rathburn, Paul Kautza