UNIX and Linux SAS^® administrators, have you ever been greeted by one of these statements as you walk into the office before you have gotten your first cup of coffee? Power outage! SAS servers are down. I cannot access my reports. Have you frantically tried to restart the SAS servers to avoid loss of productivity and missed one of the steps in the process, causing further delays while other work continues to pile up? If you have had this experience, you understand the benefit to be gained from a utility that automates the management of these multi-tiered deployments. Until recently, there was no method for automatically starting and stopping multi-tiered services in an orchestrated fashion. Instead, you had to use time-consuming manual procedures to manage SAS services. These procedures were also prone to human error, which could result in corrupted services and additional time lost, debugging and resolving issues injected by this process. To address this challenge, SAS Technical Support created the SAS Local Services Management (SAS_lsm) utility, which provides automated, orderly management of your SAS^® multi-tiered deployments. The intent of this paper is to demonstrate the deployment and usage of the SAS_lsm utility. Now, go grab a coffee, and let's see how SAS_lsm can make life less chaotic.

The Consumer Financial Protection Bureau (CFPB) collects tens of thousands of complaints against companies each year, many of which result in the companies in question taking action, including making payouts to the individuals who filed the complaints. Given the volume of the complaints, how can an overseeing organization quantitatively assess the data for various trends, including the areas of greatest concern for consumers? In this presentation, we propose a repeatable model of text analytics techniques to the publicly available CFPB data. Specifically, we use SAS^® Contextual Analysis to explore sentiment, and machine learning techniques to model the natural language available in each free-form complaint against a disposition code for the complaint, primarily focusing on whether a company paid out money. This process generates a taxonomy in an automated manner. We also explore methods to structure and visualize the results, showcasing how areas of concern are made available to analysts using SAS^® Visual Analytics and SAS^® Visual Statistics. Finally, we discuss the applications of this methodology for overseeing government agencies and financial institutions alike.

Digital transformation and analytics for incumbents isn't a question of choice or strategy. It's a question of business survival. Go analytics!

Investors usually trade stocks or exchange-traded funds (ETFs) based on a methodology, such as a theory, a model, or a specific chart pattern. There are more than 10,000 securities listed on the US stock market. Picking the right one based on a methodology from so many candidates is usually a big challenge. This paper presents the methodology based on the CANSLIM1 theorem and momentum trading (MT) theorem. We often hear of the cup and handle shape (C&H), double bottoms and multiple bottoms (MB), support and resistance lines (SRL), market direction (MD), fundamental analyses (FA), and technical analyses (TA). Those are all covered in CANSLIM theorem. MT is a trading theorem based on stock moving direction or momentum. Both theorems are easy to learn but difficult to apply without an appropriate tool. The brokers' application system usually cannot provide such filtering due to its complexity. For example, for C&H, where is the handle located? For the MB, where is the last bottom you should trade at? Now, the challenging task can be fulfilled through SAS^®. This paper presents the methods on how to apply the logic and graphically present them though SAS. All SAS users, especially those who work directly on capital market business, can benefit from reading this document to achieve their investment goals. Much of the programming logic can also be adopted in SAS finance packages for clients.

In today's instant information society, we want to know the most up-to-date information about everything, including what is happening with our favorite sports teams. In this paper, we explore some of the readily available sources of live sports data, and look at how SAS^® technologies, including SAS^® Event Stream Processing and SAS^® Visual Analytics, can be used to collect, store, process, and analyze the streamed data. A bibliography of sports data websites that were used in this paper is included, with emphasis on the free sources.

After you know the basics of SAS^® Visual Analytics, you realize that there are some situations that require unique strategies. Sometimes tables are not structured correctly or become too large for the environment. Maybe creating the right custom calculation for a dashboard can be confusing. Geospatial data is hard to work with if you haven't ever used it before. We studied hundreds of SAS^® Communities posts for the most common questions. These solutions (and a few extras) were extracted from the newly released book titled 'An Introduction to SAS^® Visual Analytics: How to Explore Numbers, Design Reports, and Gain Insight into Your Data'.

What will your customer do next? Customers behave differently; they are not all average. Segmenting your customers into different groups enables you to build more powerful and meaningful predictive models. You can use SAS^® Visual Analytics to instantaneously visualize and build your segments identified by a decision tree or cluster analysis with respect to customer attributes. Then, you can save the cluster/segment membership, and use that as a separate predictor or as a group variable for building stratified predictive models. Dividing your customer population into segments is useful because what drives one group of people to exhibit a behavior can be quite different from what drives another group. By analyzing the segments separately, you are able to reduce the overall error variance or noise in the models. As a result, you improve the overall performance of the predictive models. This paper covers the building and use of segmentation in predictive models and demonstrates how SAS Visual Analytics, with its point-and-click functionality and in-memory capability, can be used for an easy and comprehensive understanding of your customers, as well as predicting what they are likely to do next.