Although the statistical foundations of predictive analytics have large overlaps, the business objectives, data availability, and regulations are different across the property and casualty insurance, life insurance, banking, pharmaceutical, and genetics industries. A common process in property and casualty insurance companies with large data sets is introduced, including data acquisition, data preparation, variable creation, variable selection, model building (also known as fitting), model validation, and model testing. Variable selection and model validation stages are described in more detail. Some successful models in the insurance companies are introduced. Base SAS^®, SAS^® Enterprise Guide^®, and SAS^® Enterprise Miner™ are presented as the main tools for this process.

In any organization where people work with data, it is extremely unlikely that there will be only one way of doing things. Things like divisional silos and differences in education, training, and subject matter often result in a diversity of tools and levels of expertise. One situation that frequently arises is that of 'code versus click.' Let's call it the difference between code-based, 'power user' data tools and simpler, purely graphic point-and-click tools such as Microsoft Excel. Even though the work itself might be quite similar, differences in analysis tools often mean differences in vocabulary and experience, and it can be difficult to convert users of one tool to another. This discussion will highlight the potential challenges of SAS^® adoption in an Excel-based workplace and propose strategies to gain new SAS advocates in your organization.

Project management is a hot topic across many industries, and there are multiple commercial software applications for managing projects available. The reality, however, is that the majority of project management software is not applicable for daily usage. SAS^® has a solution for this issue that can be used for managing projects graphically in real time. This paper introduces a new paradigm for project management using the SAS^® Graph Template Language (GTL). SAS clients, in real time, can use GTL to visualize resource assignments, task plans, delivery tracking, and project status across multiple project levels for more efficient project management.

This paper builds on the knowledge gained in the paper 'Introduction to ODS Graphics.' The capabilities in ODS Graphics grow with every release as both new paradigms and smaller tweaks are introduced. After talking with the ODS developers, I have chosen a selection of the many wonderful capabilities to highlight here. This paper provides the reader with more tools for his or her tool belt. Visualization of data is an important part of telling the story seen in the data. And while the standards and defaults in ODS Graphics are very well done, sometimes the user has specific nuances for characters in the story or additional plot lines they want to incorporate. Almost any possibility, from drama to comedy to mystery, is available in ODS Graphics if you know how. We explore tables, annotation and changing attributes, as well as the block plot. Any user of Base SAS^® on any platform will find great value from the SAS^® ODS Graphics procedures. Some experience with these procedures is assumed, but not required.

SAS^® users are always surprised to discover their programs contain bugs (or errors). In fact, when asked, users will emphatically stand by their programs and logic by saying they are error free. But, the vast number of experiences, along with the realities of writing code, say otherwise. Errors in program code can appear anywhere, whether accidentally introduced by developers or programmers, when writing code. No matter where an error occurs, the overriding sentiment among most users is that debugging SAS programs can be a daunting and humbling task. This presentation explores the world of SAS errors, providing essential information about the various error types. Attendees learn how errors are created, their symptoms, identification techniques, and how to apply effective techniques to better understand, repair, and enable program code to work as intended.