This session is intended to assist analysts in generating the best variables, such as monthly amount paid, daily number of received customer service calls, weekly worked hours on a project, or annual number total sales for a specific product, by using simple arithmetic operators (square root, log, loglog, exp, and rcp). During a statistical data modeling process, analysts are often confronted with the task of computing derived variables using the existing variables. The advantage of this methodology is that the new variables might be more significant than the original ones. This paper provides a new way to compute all the possible variables using a set of math transformations. The code includes many SAS^® features that are very useful tools for SAS programmers to incorporate in their future code such as %SYSFUNC, SQL, %INCLUDE, CALL SYMPUT, %MACRO, SORT, CONTENTS, MERGE, MACRO _NULL_, as well as %DO &%TO & and many more

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During the cementing and pumps-off phase of oil drilling, drilling operations need to know, in real time, about any loss of hydrostatic or mechanical well integrity. This phase involves not only big data, but also high-velocity data. Today's state-of-the-art drilling rigs have tens of thousands of sensors. These sensors and their data output must be correlated and analyzed in real time. This paper shows you how to leverage SAS^® Asset Performance Analytics and SAS^® Enterprise Miner™ to build a model for drilling and well control anomalies, fingerprint key well control measures of the transienct fluid properties, and how to operationalize these analytics on the drilling assets with SAS^® event stream processing. We cover the implementation and results from the Deepwater Horizon case study, demonstrating how SAS analytics enables the rapid differentiation between safe and unsafe modes of operation.

A group tasked with testing SAS^® software from the customer perspective has gathered a number of helpful hints for SAS^® 9.4 that will smooth the transition to its new features and products. These hints will help with the 'huh?' moments that crop up when you are getting oriented and will provide short, straightforward answers. We also share insights about changes in your order contents. Gleaned from extensive multi-tier deployments, SAS^® Customer Experience Testing shares insiders' practical tips to ensure that you are ready to begin your transition to SAS 9.4. The target audience for this paper is primarily system administrators who will be installing, configuring, or administering the SAS 9.4 environment. (This paper is an updated version of the paper presented at SAS Global Forum 2014 and includes new features and software changes since the original paper was delivered, plus any relevant content that still applies. This paper includes information specific to SAS 9.4 and SAS 9.4 maintenance releases.)

Banks can create a competitive advantage in their business by using business intelligence (BI) and by building models. In the credit domain, the best practice is to build risk-sensitive models (Probability of Default, Exposure at Default, Loss-given Default, Unexpected Loss, Concentration Risk, and so on) and implement them in decision-making, credit granting, and credit risk management. There are models and tools on the next level built on these models and that are used to help in achieving business targets, risk-sensitive pricing, capital planning, optimizing of ROE/RAROC, managing the credit portfolio, setting the level of provisions, and so on. It works remarkably well as long as the models work. However, over time, models deteriorate and their predictive power can drop dramatically. Since the global financial crisis in 2008, we have faced a tsunami of regulation and accelerated frequency of changes in the business environment, which cause models to deteriorate faster than ever before. As a result, heavy reliance on models in decision-making (some decisions are automated following the model's results--without human intervention) might result in a huge error that can have dramatic consequences for the bank's performance. In my presentation, I share our experience in reducing model risk and establishing corporate governance of models with the following SAS^® tools: model monitoring, SAS^® Model Manager, dashboards, and SAS^® Visual Analytics.

SAS^® Model Manager provides an easy way to deploy analytical models into various relational databases or into Hadoop using either scoring functions or the SAS^® Embedded Process publish methods. This paper gives a brief introduction of both the SAS Model Manager publishing functionality and the SAS^® Scoring Accelerator. It describes the major differences between using scoring functions and the SAS Embedded Process publish methods to publish a model. The paper also explains how to perform in-database processing of a published model by using SAS applications as well as SQL code outside of SAS. In addition to Hadoop, SAS also supports these databases: Teradata, Oracle, Netezza, DB2, and SAP HANA. Examples are provided for publishing a model to a Teradata database and to Hadoop. After reading this paper, you should feel comfortable using a published model in your business environment.

Is your company using or considering using SAP Business Warehouse (BW) powered by SAP HANA? SAS^® provides various levels of integration with SAP BW in an SAP HANA environment. This integration enables you to not only access SAP BW components from SAS, but to also push portions of SAS analysis directly into SAP HANA, accelerating predictive modeling and data mining operations. This paper explains the SAS toolset for different integration scenarios, highlights the newest technologies contributing to integration, and walks you through examples of using SAS with SAP BW on SAP HANA. The paper is targeted at SAS and SAP developers and architects interested in building a productive analytical environment with the help of the latest SAS and SAP collaborative advancements.

In 2014, for the first time, mid-market banks (consisting of banks and bank holding companies with $10-$50 billion in consolidated assets) were required to submit Capital Stress Tests to the federal regulators under the Dodd-Frank Act Stress Testing (DFAST). This is a process large banks have been going through since 2011. However, mid-market banks are not positioned to commit as many resources to their annual stress tests as their largest peers. Limited human and technical resources, incomplete or non-existent detailed historical data, lack of enterprise-wide cross-functional analytics teams, and limited exposure to rigorous model validations are all challenges mid-market banks face. While there are fewer deliverables required from the DFAST banks, the scrutiny the regulators are placing on the analytical modes is just as high as their expectations for Comprehensive Capital Analysis and Review (CCAR) banks. This session discusses the differences in how DFAST and CCAR banks execute their stress tests, the challenges facing DFAST banks, and potential ways DFAST banks can leverage the analytics behind this exercise.

Managing and organizing external files and directories play an important part in our data analysis and business analytics work. A good file management system can streamline project management and file organizations and significantly improve work efficiency . Therefore, under many circumstances, it is necessary to automate and standardize the file management processes through SAS^® programming. Compared with managing SAS files via PROC DATASETS, managing external files is a much more challenging task, which requires advanced programming skills. This paper presents and discusses various methods and approaches to managing external files with SAS programming. The illustrated methods and skills can have important applications in a wide variety of analytic work fields.