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Debugging SAS® Code Jeff Simpson (SAS) Ever encounter a SAS error message that didn’t seem to make sense? Have you gotten lost trying to fix every error when there doesn’t appear to be anything wrong with your code? This presentation follow a step-by-step process to list common error messages and give explanations for finding what generated them. Jeff Simpson, Senior Systems Engineer, SAS Customer Loyalty Team Jeff’s 24 years of tenure at SAS includes 11 years as a Systems Engineer in SAS in SAS Customer Loyalty and Inside Sales Divisions, 9 years in SAS’ Research and Development and 4 years in SAS’ Technical Support Division. A Day in the Life of Data: Part 4 Graphics and Reporting Sanjay Matange (SAS) Although we often work in our own department with little contact with others, whether they’re on the next floor or halfway around the world, everyone has an impact on each other. In this four-part series, each paper examines one of the four major aspects of SAS® Data Management: initial data input; data manipulation; data and program management; and graphics and reporting. Each paper teaches some fundamental skills and shows how each step adds value to the data. Sanjay Matange is R & D Director in the Data Visualization Division at SAS, responsible for the development and support of ODS Graphics. This includes the Graph Template Language (GTL), Statistical Graphics (SG) procedures, ODS Graphics Designer and other related graphics applications. Sanjay has been with SAS for over 20 years and is author of two SAS Press books. Using SAS/STAT®: A Gentle Introduction to Some Frequently Used Tools T. Winand (SAS) Frequently, business interventions are evaluated by comparing two groups with respect to some outcome measure(s). For example, we might want to compare customers who received a marketing campaign with customers who did not receive the campaign, with respect to whether they bought additional products or services, quantity of a product purchased, or other measures. We might need to compare two, or more than two, groups of customers with outcome measure(s) might be dichotomous (e.g., product buy-up vs. no buy-up) or continuous (e.g., quantity of a product purchased) with a variety of distributions. SAS/STAT provides several easy-to-use tools for such analytic situations, including PROC FREQ (chisquare tests, Fisher’s exact test), PROC TTEST and PROC NPAR1WAY. This presentation will cover some of the tests most frequently used in the types of analytic situations outlined above. We will cover basic guidelines for using different tests and provide examples. This presentation is intended as an introduction for SAS users with a minimal statistics background. T Winand is a computer software professional and analyst with 24 years’ experience in program design and development, data management, statistical analysis and reporting. T has fifteen years of programming, statistical analysis and modeling experience with SAS – fourteen of those years as a Systems Engineer, Senior Systems Engineer and Technical Account Manager at SAS. For the majority of his time at SAS, T has focused his efforts on helping organizations achieve value and growth through the effective use of analytics: statistical analysis, descriptive and predictive data mining, text mining, forecasting, and model management. While at SAS, T has worked with leading organizations across many different industries but has spent seven years working exclusively with Health and Life Sciences organizations. In addition, T has complementary expertise with SAS capabilities that support the complete analytical lifecycle: data integration & management, data exploration, operationalization, and business intelligence. Finally, in recent years, T has helped several large organizations modernize their SAS architectures to achieve greater speed, flexibility and scale, to improve processes and utilization of resources, and, ultimately, to improve value. T graduated from Cornell University and has an MBA from Kellogg, Northwestern University. Best Practices: PUT More Errors and Warnings in My Log, Please! Kirk Paul Lafler Demystifying PROC SQL Join Algorithms Kirk Paul Lafler Essentials of PDV: Directing the Aim to Understanding the DATA Step! Arthur Li (PharmaSUG) Big Data, Fast Processing Speeds Gary Ciampa (SAS) As data sets continue to grow, it is important for programs to be written very efficiently to make sure no time is wasted processing data. This paper covers various techniques to speed up data processing time for very large data sets or databases, including PROC SQL, DATA step, indexes and SAS® macros. Some of these procedures may result in just a slight speed increase, but when you process 500 million records per day, even a 10 percent increase is very good. The paper includes actual time comparisons to demonstrate the speed increases using the new techniques. Gary Ciampa is currently employed with SAS Institute Inc, Cary, NC and joined SAS in 1995. He has served in various development roles and capacities throughout his SAS career to include development and product manager for the SAS/C and SAS/C++ Compiler suite of products, development manager for SAS on z/OS and UNIX environments, and development manager for a series of Enterprise Systems Management products. Currently, Gary serves as a Software Manager in the Retail Practice with the Analytic Solutions OnDemand organization. Prior to joining SAS, Gary worked for IBM in Research Triangle Park, NC for 13 year, in the TCP/IP development organization. He also worked at IBM facilities in Manassas, VA and Burlington, VT. Mr. Ciampa is a retired officer with the NC Air National Guard and served in roles as a Combat Communications Officer, Air Liaison Officer, F-4D Weapons Systems Officer and service included overseas deployments to the Middle East during Operation Enduring Freedom. Mr. Ciampa holds an Associate Degree in Electronic Engineering and a Bachelor of Science Degree in Business Management from Liberty University. Gary lives in Fuquay Varina, NC with his wife Laura, he has two adult children who are serving in the Armed Forces. His hobbies include cycling and aviation where he enjoys a commercial and instrument pilot rating. SAS Solutions OnDemand: Your Partner for SAS Advanced Analytic Computing The Information Technology (IT) demands for providing a secure, robust and agile infrastructure for business is becoming increasingly more complex. IT organizations are being tasked to provide faster delivery of solutions, greater capacity to respond to business requirements and technical competencies for sophisticated analytic solutions. Business requirements are increasing in scope due to faster time to market, higher ROI margins and regulatory compliance. The SAS Solution OnDemand organization provides a rich set of Software-as-a-Service (SaaS) and Enterprise Hosting strategies to help both Business and IT functions meet the challenges for implementing and sustaining an analytic computing infrastructure. For the past 10 years, SAS Solutions OnDemand has established a track record of providing organizations with state-of-the-art outsourced applications and the subject matter experts to manage these sophisticated applications. This session will review the Solutions OnDemand organization, offerings, infrastructure, and governance capabilities of SAS Solutions OnDemand. SAS software is delivered to our customers through SaaS infrastructure for fast, low-risk, time to value business requirements. Or, for customized off-premise alternatives SAS Solutions OnDemand provide cost-effective, full-service advanced analytic hosting.