Advanced Analytics is surging. Now more than ever before, companies are delving into their data, trying to put it to use to minimize customer churn, analyze financial risk, and improve the customer experience. Many companies have already invested in descriptive analytics, but in order to truly gain a competitive edge, they must take the next step to advanced analytics for predictive, streaming, and prescriptive insights.
Despite the big data analytics hype, many organizations are stuck at the starting line, delaying projects or failing to launch all together. For those who have set their big data analytics strategies in motion, many fail, and mostly for the same reasons.
Join guest speaker, Forrester analyst Mike Gualtieri and Skytree CTO and co-founder, Alexander Gray, Ph.D., to understand the challenges, common mistakes and best practices for big data porjects including:
Mike's research focuses on software technology, platforms, and practices that enable technology professionals to deliver prescient customer experiences and breakthrough operational efficiency. His key technology and platform coverage areas are big data strategy, Hadoop, advanced analytics, machine learning, data science practices, predictive apps design, and emerging technologies that make software faster and smarter. Mike has more than 25 years' experience in the industry helping firms design and develop mission-critical applications in eCommerce, insurance, banking, travel/hospitality, manufacturing, healthcare, and scientific research for organizations including NASA, eBay, Bank of America, Liberty Mutual, Nielsen, EMC, and others.
Dr. Gray is CTO at Skytree and Associate Professor in the College of Computing at Georgia Tech. His work has focused on algorithmic techniques for making machine learning tractable on massive datasets. He began working with large-scale scientific data in 1993 at NASA’s Jet Propulsion Laboratory in its Machine Learning Systems Group. He recently served on the National Academy of Sciences Committee on the Analysis of Massive Data, as a Kavli Scholar, and a Berkeley Simons Fellow, and is a frequent advisor and speaker on the topic of machine learning on big data in academia, science, and industry.