The vast majority of data scientists and AI enthusiasts are excited about the prospects of predictive analytics and machine learning, 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 predictive analytics 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, analytics success stories convey significant impact in mainstream publications more frequently. Your competitors are discovering that success in this field does not start with data and software.
For any organization with annual revenues greater than $50 million, establishing an analytic operation that thrives on validated impact is not a matter of whether, but when. Predictive analytics and machine learning are now in great demand for transforming AI 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 capability.
Attend this free vendor-neutral webinar to learn how to get started with predictive analytics and overcome limitations that cause most machine learning projects to fall short of their potential.
Why failure to implement is so common, and why pitfalls are so avoidable
Case studies that reveal the rewards of proper design and implementation
Why establishing an internal predictive modeling practice is within your reach
Tips, tricks and techniques for data preparation and method selection
Live participant polls to share sentiments and Q&A with the experts
AI INVESTIGATORS, CIOs, CKOs, CTOs, Stakeholders, Functional Officers, Technical Directors and Project Managers
LINE-OF-BUSINESS EXECUTIVES AND FUNCTIONAL MANAGERS: Risk Managers, Customer Relationship Managers, Business Forecasters, Inventory Flow Analysts, Financial Forecasters, Direct Marketing Analysts, Medical Diagnostic Analysts, eCommerce Company Executives
DATA SCIENTISTS: Who recognize the importance of complementing their tactical proficiency with a strategic planning and design approach to advanced analytics
TECHNOLOGY PLANNERS: Who survey emerging technologies in order to prioritize corporate investment
THE ANALYTICALLY CURIOUS: Who desire additional perspectives on the topic
CONSULTANTS: Whose competitive environment is intensifying and whose success requires competency with data science and related emerging information technologies