SAS Global Forum 2013, San Francisco, California, April 28 - May 1, 2013

Billy Beane
General Manager of the Oakland Athletics

Moneyball: The Art of Winning an Unfair Game
Billy Beane's strategic use of analytics in pinpointing undervalued players for the Oakland Athletics inspired the best-selling book Moneyball: The Art of Winning an Unfair Game, now a major motion picture.

As General Manager of the A's, Beane shattered traditional baseball beliefs that big payrolls translate into big wins. His unorthodox use of statistical methodology led the A's -- one of the worst teams in baseball with one of the lowest payrolls -- to three American League West division titles.

Striking unlikely parallels between baseball and business, Beane shares his innovative take on management, leadership and using undervalued assets to create and sustain a competitive advantage. View ESPN's short YouTube clip: Moneyball – New Way of Thinking.

Tim Patrick
Director of Enterprise Applications, United States Olympic Committee

London 2012 Olympics: Real-Time Data and Analysis
For the first time in history, the National Organizing Committee for an Olympic games provided a high-quality XML-based real-time data feed for all activities related to Olympic competitions to each participating country. The consumption of this data provided an unprecedented opportunity to observe in detail how the games were unfolding from the level of country, to the level of individual athletes, and in some cases at cross-sections of the competition as it unfolded. This talk will show how London 2012 Olympic data was used to visualize and analyze events during the London 2012 Games and how it is likely to change the approach to Olympic Sport Analysis going forward.

Roger Craig
Jeopardy! Tournament of Champions winner & CEO and cofounder of Cotinga LLC

Predictive Analytics For the Win: Data Science Meets the Quiz Show Jeopardy!
This keynote will provide a first-hand account of how predictive analytics techniques were applied to the problem of preparing for Jeopardy!. Data analysis of both the problem domain, Jeopardy!, and sampling of human learners to pinpoint strengths and weaknesses were used to develop a plan for preparing for the show. This analysis and plan allowed Craig to set many Jeopardy! records, including the highest one-day winning total ever and the fourth-highest overall total ever. Comparisons to the contemporaneous IBM's Watson project will also be presented, chiefly the difference between a 100% AI solution and a solution which involves augmenting a human player with insights gleaned from predictive analytics and machine learning techniques

Timothy D. Rey
The Dow Chemical Company

Using Data Mining in Forecasting Problems
In today's ever-changing economic environment, the savvy business decision maker has ample opportunity to use the numerous sources of time series data now readily available. This time series data can be used for business gain if the data is converted to information and then knowledge. Data mining processes, methods and technology oriented to transactional-type data (data not having a time series framework) have grown immensely in the last quarter-century. There is significant value in the interdisciplinary notion of using data mining for forecasting to solve time series problems. Attendees will learn how to get the most value out of the myriad time series data available by using data mining techniques specifically oriented to data collected over time; methodologies and examples will also be presented.

Matthew J.Becker
Sr. Director, Global Head of Statistical Programming
inVentiv Health Clinical

To Infinity and Beyond: Current and Future State of Big Data and Analytics in Life Sciences
Our biggest asset is our data.” We have all heard some form of this phrase in the life sciences industry. While we may agree with the sentiment, it still raises many questions: Are we tapping into the data as we should? Are we pulling the multiple sources of data together so that all parameters are analyzed? Do all end users truly understand how to use analytics? In this keynote, we will look at the current state of big data in the life sciences industry and offer a glimpse into the future of big data and analytics.

Dr. J. Michael Hardin
Dean and Professor of Statistics
Culverhouse College of of Commerce and Business Administration
The University of Alabama

What is Business Analytics?
Analytics has become the hot, “sexy” job of the new century. The demand for individuals with skills and expertise in the field are in great demand, as evidenced by feature articles appearing in such publications as The New York Times, The Harvard Business Review and The Wall Street Journal. However, the area has not always been so well received, especially within some academic areas. And, even today there still remains confusion and disagreement over the implementation and interpretation of results obtained from the analytic process.

This presentation will examine the history, development and, particularly, the philosophy underlying the analytic process. Theories to understanding and interpreting the analysis process and the associated results will be explained. Additionally, suggestions on presenting and explaining results within a consistent philosophy will be offered. The presentation will conclude with thoughts and speculations on the future of analytics.

David A. Dickey
William Neal Reynolds Distinguished Professor
Department of Statistics
North Carolina State University

Finding the Gold in Your Data: An Overview of Data Mining
The term “data mining” appears often in analytic literature – and even in popular literature. But what is it exactly and what does SAS provide in terms of data mining capabilities? The quick answer is that data mining is a collection of tools designed to discover useful structure in large data sets. With an emphasis on examples, this talk gives an overview of methods available in SAS® Enterprise MinerTM and should be accessible to a general audience. Topics include predictive modeling, decision trees, association analysis, incorporation of profits and neural networks. Attendees will see that some of the basic ideas underlying these techniques are closely related to standard statistical techniques that have been around for some time. However, many techniques now have new, more appealing names than their statistical ancestors and have become more automated, thus more user friendly.