In an effort to increase transparency and accountability in the US health care system, the Obama administration mandated the Centers for Medicare & Medicaid Services (CMS) to make available data for use by researchers and interested parties from the general public. Among the more well-known uses of this data are analyses published by the Wall Street Journal showing that a large, and in some cases, shocking discrepancy between what hospitals potentially charge the uninsured and what they are paid by Medicare for the same procedure. Analyses such as these highlight both potential inequities in the US health care system and, more importantly, potential opportunities for its reform. However, while capturing the public imagination, analyses such as these are but one means to capitalize on the remarkable wealth of information this data provides. Specifically, data from the public distribution CMS data can help both researchers and the public better understand the burden specific conditions and medical treatments place on the US health care system. It was this simple, but important objective that motivated the present study. Our specific analyses focus on two of what we believe to be important questions. First, using the total number of hospital discharges as a proxy for incidence of a condition or treatment, which have the highest incidence rates nationally? Does their incidence remain stable, or is it increasing/decreasing? And, is there variability in these incidence rates across states? Second, as psychologists, we are necessarily interested in understanding the state of mental health care. To date, and to the best of our knowledge, there has been no study utilizing the public inpatient Medicare provider utilization and payment data set to explore the utilization of mental illness services funded by Medicare.

For me, it's all about avoiding manual effort and repetition. Whether your work involves data exploration, reporting, or analytics, you probably find yourself repeating steps and tasks with each new program, project, or analysis. That repetition adds time to the delivery of results and also contributes to a lack of standardization. This presentation focuses on productivity tips and tricks to help you create a standard and efficient environment for your SAS^® work so you can focus on the results and not the processes. Included are the following: setting up your programming environment (comment blocks, environment cleanup, easy movement between test and production, and modularization) sharing easily with your team (format libraries, macro libraries, and common code modules) managing files and results (date and time stamps for logs and output, project IDs, and titles and footnotes)

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Whether you have been programming in SAS^® for years, are new to it, or have dabbled with SAS^® Enterprise Guide^® before, this hands-on workshop sheds some light on the depth, breadth, and power of the Enterprise Guide environment. With all the demands on your time, you need powerful tools that are easy to learn and deliver end-to-end support for your data exploration, reporting, and analytics needs. Included are the following: data exploration tools formatting code--cleaning up after your coworkers enhanced programming environment (and how to calm it down) easily creating reports and graphics producing the output formats you need (XLS, PDF, RTF, HTML) workspace layout start-up processing notes to help your coworkers use your processes This workshop uses SAS Enterprise Guide 7.1, but most of the content is applicable to earlier versions.

Every day, we are bombarded by pundits pushing big data as the cure for all research woes and heralding the death of traditional quantitative surveys. We are told how big data, social media, and text analytics will make the Likert scale go the way of the dinosaur. This presentation makes the case for evolving our surveys and data sets to embrace new technologies and modes while still welcoming new advances in big data and social listening. Examples from the global automotive industry are discussed to demonstrate the pros and cons of different types of data in an automotive environment.

Business Intelligence users analyze business data in a variety of ways. Seventy percent of business data contains location information. For in-depth analysis, it is essential to combine location information with mapping. New analytical capabilities are added to SAS^® Visual Analytics, leveraging the new partnership with Esri, a leader in location intelligence and mapping. The new capabilities enable users to enhance the analytical insights from SAS Visual Analytics. This paper demonstrates and discusses the new partnership with Esri and the new capabilities added to SAS Visual Analytics.

SAS^® Visual Analytics Explorer puts the robust power of decision trees at your fingertips, enabling you to visualize and explore how data is structured. Decision trees help analysts better understand discrete relationships within data by visually showing how combinations of variables lead to a target indicator. This paper explores the practical use of decision trees in SAS Visual Analytics Explorer through an example of risk classification in the financial services industry. It explains various parameters and implications, explores ways the decision tree provides value, and provides alternative methods to help you the reality of imperfect data.

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As part of promoting a data-driven culture and data analytics modernization at its federal sector clientele, Northrop Grumman developed a framework for designing and implementing an in-house Data Analytics and Research Center (DAARC) using a SAS^® set of tools. This DAARC provides a complete set of SAS^® Enterprise BI (Business Intelligence) and SAS^® Data Management tools. The platform can be used for data research, evaluations, and analysis and reviews by federal agencies such as the Social Security Administration (SSA), the Center for Medicare and Medicaid Services (CMS), and others. DAARC architecture is based on a SAS data analytics platform with newer capabilities of data mining, forecasting, visual analytics, and data integration using SAS^® Business Intelligence. These capabilities enable developers, researchers, and analysts to explore big data sets with varied data sources, create predictive models, and perform advanced analytics including forecasting, anomaly detection, use of dashboards, and creating online reports. The DAARC framework that Northrop Grumman developed enables agencies to implement a self-sufficient 'analytics as a service' approach to meet their business goals by making informed and proactive data-driven decisions. This paper provides a detailed approach to how the DAARC framework was established in strong partnership with federal customers of Northrop Grumman. This paper also discusses the best practices that were adopted for implementing specific business use cases in order to save tax-payer dollars through many research-related analytical and statistical initiatives that continue to use this platform.

Pedal-to-the-metal analytics is the notion that analytics can be quickly and easily achieved using web technologies. SAS^® web technologies seamlessly integrate with each other through a web browser and with data via web APIs, enabling organizations to leapfrog traditional, manual analytic and data processes. Because of this integration (and the operational efficiencies obtained as a result), pedal-to-the-metal analytics dramatically accelerates the analytics lifecycle, which consists of these steps: 1) Data Preparation; 2) Exploration; 3) Modeling; 4) Scoring; and 5) Evaluating results. In this paper, data preparation is accomplished with SAS^® Studio custom tasks (reusable drag-and-drop visual components or interfaces for underlying SAS code). This paper shows users how to create and implement these for public health surveillance. With data preparation complete, explorations of the data can be performed using SAS^® Visual Analytics. Explorations provide insights for creating, testing, and comparing models in SAS^® Visual Statistics to predict or estimate risk. The model score code produced by SAS Visual Statistics can then be deployed from within SAS Visual Analytics for scoring. Furthermore, SAS Visual Analytics provides the necessary dashboard and reporting capabilities to evaluate modeling results. In conclusion, the approach presented in this paper provides both new and long-time SAS users with easy-to-follow guidance and a repeatable workflow to maximize the return on their SAS investments while gaining actionable insights on their data. So, fasten your seat belts and get ready for the ride!

Horizontal data sorting is a very useful SAS^® technique in advanced data analysis when you are using SAS programming. Two years ago (SAS^® Global Forum Paper 376-2013), we presented and illustrated various methods and approaches to perform horizontal data sorting, and we demonstrated its valuable application in strategic data reporting. However, this technique can also be used as a creative analytic method in advanced business analytics. This paper presents and discusses its innovative and insightful applications in product purchase sequence analyses such as product opening sequence analysis, product affinity analysis, next best offer analysis, time-span analysis, and so on. Compared to other analytic approaches, the horizontal data sorting technique has the distinct advantages of being straightforward, simple, and convenient to use. This technique also produces easy-to-interpret analytic results. Therefore, the technique can have a wide variety of applications in customer data analysis and business analytics fields.

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Our company Veikkaus is a state-owned gambling and lottery company in Finland that has a national legalized monopoly for gambling. All the profit we make goes back to Finnish society (for art, sports, science, and culture), and this is done by our government. In addition to the government's requirements of profit, the state (Finland) also requires us to handle the adverse social aspects of gaming, such as problem gambling. The challenge in our business is to balance between these two factors. For the purposes of problem gambling, we have used SAS^® tools to create a responsible gaming tool, called VasA, based on a logistic regression model. The name VasA is derived from the Finnish words for 'Responsible Customership.' The model identifies problem gamblers from our customer database using the data from identified gaming, money transfers, web behavior, and customer data. The variables that were used in the model are based on the theory behind the problem gambling. Our actions for problem gambling include, for example, different CRM and personalization of a customer's website in our web service. There were several companies who provided responsible gambling tools as such for us to buy, but we wanted to create our own for two reasons. Firstly, we wanted it to include our whole customer database, meaning all our customers and not just those customers who wanted to take part in it. These other tools normally include only customers who want to take part. The other reason was that we saved a ridiculous amount of money by doing it by ourselves compared to having to buy one. During this process, SAS played a big role, from gathering the data to the construction of the tool, and from modeling to creating the VasA variables, then on to the database, and finally to the analyses and reporting.

Create a model with SAS Contextual Analysis, deploy the model to your Hadoop cluster, run the model using map/reduce driven by SAS Code Accelerator not alongside, but in Hadoop. Abstract: Hadoop took the world by storm as a cost efficient software framework for storing data. Hadoop is more than that, it's an analytics platform. Analytics is more than just crunching numbers, it's also about gaining knowledge from your organization's unstructured data. SAS has the tools to do both. In this paper, we'll demonstrate how to access your unstructured data, automatically create a Text Analytics model, deploy that model in Hadoop and run SAS's Code Accelerator to give you insights into your big and unstructured data lying dormant in Hadoop. We'll show you how the insights gained can help you make better informed decisions for your organization.

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In today's Business Intelligence world, self-service, which allows an everyday knowledge worker to explore data and personalize business reports without being tech-savvy, is a prerequisite. The new release of SAS^® Visual Statistics introduces an HTML5-based, easy-to-use user interface that combines statistical modeling, business reporting, and mobile sharing into a one-stop self-service shop. The backbone analytic server of SAS Visual Statistics is also updated, allowing an end user to analyze data of various sizes in the cloud. The paper illustrates this new self-service modeling experience in SAS Visual Statistics using telecom churn data, including the steps of identifying distinct user subgroups using decision tree, building and tuning regression models, designing business reports for customer churn, and sharing the final modeling outcome on a mobile device.