SID file, SAS^® Deployment Wizard, SAS^® Migration Utility, SAS^® Environment Manager, plan file. SAS^® can seem very mysterious to IT organizations used to working with other software solutions. The more IT knows and understands about SAS how it works, what its system requirements are, how to maintain it and back it up, and what its value is to the organization the better IT can support the SAS shop. This paper provides an introduction to the world of SAS and sheds light on some of the unique elements of maintaining a SAS environment.

SAS^® 9.4 has overhauled web authentication schemes, and the integration with enterprise security infrastructure is quite different from that of SAS^® 9.3. This paper examines advanced security features such as Secure Sockets Layer (SSL) configuration, single sign-on (SSO) support through Integrated Windows authentication (IWA), and third-party security packages like CA SiteMinder and IBM Tivoli Access Manager and WebSEAL. FIPS 140-2 compliance efforts that enforce the use of a stronger encryption algorithm for web communication and the SAS^® system itself are also described. The authentication support for mobile devices such as the iPad is different. The secure Wi-Fi connection from a mobile device to the IT internal resources, as well as how it can be safely integrated into the enterprise security configuration by using the same user repository as the SAS web applications, is explained. The configuration example is shown with SAS^® Visual Analytics 6.2.

SAS^® 9.4 and SAS^® Visual Analytics support a wide list of authentication protocols such as Integrated Windows authentication (IWA), client certificate, IBM WebSEAL, CA SiteMinder, and Security Assertion Markup Language (SAML) 2.0. However, advanced customers might want to use some of these protocols together and also have the flexibility to select which protocols to use. In this paper, we focus on a fallback authentication framework that supports IWA as the primary authentication method. When IWA fails, it uses the X509 client certificate as the secondary authentication method, and when the client certificate fails, it uses the form-based username/password as the last option. The paper first introduces the security architecture of SAS^® 9.4 and SAS Visual Analytics. It then reviews the three above-mentioned security protocols. Further, it introduces the detailed fallback authentication framework and discusses how to configure it. Finally, we discuss the use of this framework in the customer scenario from implementing the fallback authentication framework in a customer s SAS^® 9.4 and SAS Visual Analytics environment.

With a growing enterprise analytics environment that comprises global users and a variety of sensitive data sources, a system administrator is faced with the challenge of knowing who logs into the system, how often, and what applications and what data sources are being consumed. This information is necessary for auditing the consumers of data as well as for monitoring the growth of data sources for hardware expansion. With the use of SAS^® Audit, Performance and Measurement Package, along with some additional middle-tier logging and SAS^® code, information about the major consumers of the environment can be loaded into LASR tables and analyzed with SAS^® Visual Analytics reporting tools.

SAS^® Visual Analytics and the SAS^® LASR™ Analytic Server provide many capabilities to analyze data fast. Depending on your organization, data can be loaded as a self-service operation. Or, your data can be large and shared with many people. And, as data gets large, effectively loading it and keeping it updated become important. This presentation discusses the range of data scenarios from self-service spreadsheets to very large databases, from single-subject data to large star schema topologies, and from single-use data to continually updated data that requires high levels of resilience and monitoring. Fast and easy access to big data is important to empower your organization to make better business decisions. Understanding how to have a responsive and reliable data tier on which to make these decisions is within your reach.

Digital data has manifested into a classic BIG DATA challenge for marketers who want to push past the retroactive analysis limitations of traditional web analytics. The current groundswell of digital device adoption and variety of digital interactions grows larger year after year. The opportunity for 'digital intelligence' has arrived, as traditional web analytic techniques were not designed for the breadth of channels, devices, and pace that fuels consumer experiences. In parallel, today's landscape for data visualization, advanced analytics, and our ability to process very large amounts of multi-channel information is changing. The democratization of analytics for the masses is upon us, and marketers have the oppourtunity to take advantage of descriptive, predictive, and (most importantly) prescriptive data-driven insights. This presentation describes how organizations can use SAS^® products, specifically SAS^® Visual Analytics and SAS^® Adaptive Customer Experience, to overcome the limitations of web analytics, and support data-driven integrated marketing objectives.

The Affordable Care Act (ACA) contains provisions that have stimulated interest in analytics among health care providers, especially those provisions that address quality of outcomes. High Impact Technologies (HIT) has been addressing these issues since before passage of the ACA and has a Health Care Data Model recognized by Gartner and implemented at several health care providers. Recently, HIT acquired SAS^® Visual Analytics, and this paper reports our successful efforts to use SAS Visual Analytics for visually exploring Big Data for health care providers. Health care providers can suffer significant financial penalties for readmission rates above a certain threshold and other penalties related to quality of care. We have been able to use SAS Visual Analytics, coupled with our experience gained from implementing the HIT Healthcare Data Model at a number of Healthcare providers, to identify clinical measures that are significant predictors for readmission. As a result, we can help health care providers reduce the rate of 30-day readmissions.

If you have an existing SAS^® Business Intelligence environment and you want to add SAS^® Visual Analytics, you need to make some architectural choices. SAS Visual Analytics and SAS Business Intelligence can share certain components, such as a SAS^® Metadata Server and the SAS^® Web Infrastructure Platform. Sharing metadata eliminates the need to create and maintain duplicate information, and it enables your users to take advantage of functionality that can be shared between SAS Visual Analytics and SAS Business Intelligence. Sharing the SAS Web Infrastructure Platform enables SAS middle-tier applications such as SAS^® Visual Analytics Services and SAS^® Web Report Studio to communicate with each other. Intended for SAS architects and administrators, this paper explores supported architecture for SAS Visual Analytics and SAS Business Intelligence. The paper then identifies areas where the architecture can be shared as well as where resources should be kept separate. In addition, the paper offers recommendations and other considerations to keep in mind when you are managing shared resources.

SAS^® Visual Analytics Designer enables you to create reports with different layouts. There are several basic graph objects that you can include in these reports. What if you wanted to create a report that wasn't possible with one of the out-of-the-box graph objects? No worries! The new SAS^® Visual Analytics Graph Builder available from the SAS^® Visual Analytics home page lets you create a custom graph object using built-in sample data. You can then include these graph objects in SAS Visual Analytics Designer and generate reports using them. Come see how you can create custom graph objects such as stock plots, butterfly charts, and more. These custom objects can be easily shared with others for use in SAS Visual Analytics Designer.

A revolution is taking place in the U.S. at both the national and state level in the area of health care transparency. Large amounts of data on the health of communities, the quality of health care providers, and the cost of health care is being collected and is being made available by both levels of government to a variety of stakeholders. The surfacing of this data and the consumption of it by health care decision makers unfolds a new opportunity to view, explore, and analyze health care data in novel ways. Furthermore, this data provides the health care system an opportunity to advance the achievement of the Triple Aim. Data transparency will bring a sea change to the world of health care by necessitating new ways of communicating information to end users such as payers, providers, researchers, and consumers of health care. This paper examines the information needs of public health care payers such as Medicare and Medicaid, and discusses the convergence of health care and data visualization in creating consumable health insights that will aid in achieving cost containment, quality improvement, and increased accessibility for populations served. Moreover, using claims data and SAS^® Visual Analytics, it examines how data visualization can help identify the most critical insights necessary to managing population health. If health care payers can analyze large amounts of claims data effectively, they can improve service and care delivery to their recipients.

Euramax is a global manufacturer of precoated metals who relies on analytics and data visualization for its decision making. Euramax has deployed significant innovations in recent years. SAS^® Visual Analytics fits in the innovative culture of Euramax and its need for information-based decision making. During this presentation, Peter Wijers shares best practices of the implementation process and several application areas.

The evolution of the mobile landscape has created a shift in the workforce that now favors mobile devices over traditional desktops. Considering that today's workforce is not always in the office or at their desks, new opportunities have been created to deliver report content through innovative mobile experiences. SAS^® Mobile BI for both iOS and Android tablets compliments the SAS^® Visual Analytics offering by providing anytime, anywhere access to reports containing information that consumers need. This paper presents best practices and tips on how to optimize reports for mobile users, taking into consideration the constraints of limited screen real estate and connectivity, as well as answers a few frequently asked questions. Discover how SAS Mobile BI captures the power of mobile reporting to prepare for the vast growth that is predicted in the future.

Ebony and Ivory was a number one song by Paul McCartney and Stevie Wonder about making music together, proper integration, unity, and harmony on a deeper level. With SAS^® Visual Analytics, current Enterprise Business Intelligence (BI) customers can rest assured that their years of existing BI work and content can coexist until they can fully transition over to SAS Visual Analytics. This presentation covers 10 inter-operability integration points between SAS^® BI and SAS Visual Analytics.

Both recent banking and insurance risk regulations require effective aggregation of risks. To determine the total enterprise risk for a financial institution, all risks must be aggregated and analyzed. Typically, there are two approaches: bottom-up and top-down risk aggregation. In either approach, financial institutions face challenges due to various levels of risks with differences in metrics, data source, and availability. First, it is especially complex to aggregate risk. A common view of the dependence between all individual risks can be hard to achieve. Second, the underlying data sources can be updated at different times and can have different horizons. This in turn requires an incremental update of the overall risk view. Third, the risk needs to be analyzed across on-demand hierarchies. This paper presents SAS^® solutions to these challenges. To address the first challenge, we consider a mixed approach to specify copula dependence between individual risks and allow step-by-step specification with a minimal amount of information. Next, the solution leverages an event-driven architecture to update results on a continuous basis. Finally, the platform provides a self-service reporting and visualization environment for designing and deploying reports across any hierarchy and granularity on the fly. These capabilities enable institutions to create an accurate, timely, comprehensive, and adaptive risk-aggregation and reporting system.

The growing adoption of electronic systems for keeping medical records provides an opportunity for health care practitioners and biomedical researchers to access traditionally unstructured data in a new and exciting way. Pathology reports, progress notes, and many other sections of the patient record that are typically written in a narrative format can now be analyzed by employing natural language processing contextual extraction techniques to identify specific concepts contained within the text. Linking these concepts to a standardized nomenclature (for example, SNOMED CT, ICD-9, ICD-10, and so on) frees analysts to explore and test hypotheses using these observational data. Using SAS^® software, we have developed a solution in order to extract data from the unstructured text found in medical pathology reports, link the extracted terms to biomedical ontologies, join the output with more structured patient data, and view the results in reports and graphical visualizations. At its foundation, this solution employs SAS^® Enterprise Content Categorization to perform entity extraction using both manually and automatically generated concept definition rules. Concept definition rules are automatically created using technology developed by SAS, and the unstructured reports are scored using the DS2/SAS^® Content Categorization API. Results are post-processed and added to tables compatible with SAS^® Visual Analytics, thus enabling users to visualize and explore data as required. We illustrate the interrelated components of this solution with examples of appropriate use cases and describe manual validation of performance and reliability with metrics such as precision and recall. We also provide examples of reports and visualizations created with SAS Visual Analytics.

In this interconnected world, it is becoming ever more important to understand not just details about your data, but also how different parts of your data are related to each other. From social networks to supply chains to text analytics, network analysis is becoming a critical requirement and network visualization is one of the best ways to understand the results. The new SAS^® Visual Analytics network visualization shows links between related nodes as well as additional attributes such as color, size, or labels. This paper explains the basic concepts of networks as well as provides detailed background information on how to use network visualizations within SAS Visual Analytics.

Have you ever seen SAS^® Visual Analytics reports that are somehow more elegant than a standard report? Which qualities make reports easier to navigate, more appealing to the eye, or reveal insights more quickly? These quick tips will reveal several SAS Visual Analytics report design characteristics to help make your reports stand out from the pack. We cover concepts like color palettes, content organization, interactions, labeling, and branding, just to name a few.

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're getting oriented and will provide short, straightforward answers. And we can share insights about changes in your order contents. Gleaned from extensive multi-tier deployments, SAS^® Customer Experience Testing shares insiders' practical tips to ensure you are ready to begin your transition to SAS^® 9.4.

The role of the Data Scientist is the viral job description of the decade. And like LOLcats, there are many types of Data Scientists. What is this new role? Who is hiring them? What do they do? What skills are required to do their job? What does this mean for the SAS^® programmer and the statistician? Are they obsolete? And finally, if I am a SAS user, how can I become a Data Scientist? Come learn about this job of the future and what you can do to be part of it.

SAS^® High-Performance Analytics is a significant step forward in the area of high-speed, analytic processing in a scalable clustered environment. However, Big Data problems generally come with data from lots of data sources, at varying levels of maturity. Teradata s innovative Unified Data Architecture (UDA) represents a significant improvement in the way that large companies can think about Enterprise Data Management, including the Teradata Database, Hortonworks Hadoop, and Aster Data Discovery platform in a seamless integrated platform. Together, the two platforms provide business users, analysts, and data scientists with the ideally suited data management platforms, targeted specifically to their analytic needs, based upon analytic use cases, managed in a single integrated enterprise data management environment. The paper will focus on how several companies today are using Teradata s Integrated Hardware and Software UDA Platform to manage a single enterprise analytic environment, fight the ongoing proliferation of analytic data marts, and speed their operational analytic processes.

There is an increasing interest in exploring healthcare practices and costs for the American working population and their dependents to improve the quality and efficiency of care and to compare healthcare performance. Comparative data is needed to evaluate and benchmark financial and clinical performance. Because of the large amounts of comparative dataavailable, it is useful to use data exploration tools. In this paper, the authors describe theirexperience building a prototype to extract data from MarketScan ResearchDatabases, load the data into SAS^® Visual Analytics, and explore this healthcaredata to understand drug adherence for a diabetes population.

Data governance combines the disciplines of data quality, data management, data policy management, business process management, and risk management into a methodology that ensures important data assets are formally managed throughout an enterprise. SAS^® has developed a cohesive suite of technologies that can be used to implement efficient and effective data governance initiatives, thereby improving an enterprise s overall data management efficiency. This paper discusses data governance use cases and challenges, and provides an example of how to manage the data governance lifecycle to ensure success.

More organizations are understanding the importance of geo-tagged data and the need for tools that can successfully combine location data with business metrics to provide intelligent outputs that are beyond a simple map. SAS^® Visual Analytics provides a robust and powerful platform for achieving location intelligence performed with a combination of SAS^® Analytics and GIS mapping technologies such as that offered by Esri. This paper describes the essentials for achieving location intelligence and demonstrates with industry examples how SAS Visual Analytics makes it possible.

This presentation takes a look at DirectPay, a company that collects and buys consumer claims of all types. It developed a model with SAS^® Enterprise Miner^™ to determine the risk of fraud by a debtor and a debtor's creditworthiness. This model is focused on the added value of more and better data. Since 2010, all credit and fraud scores have been calculated using DirectPay's own data and models. In addition, the presentation explores the use of SAS^® Visual Analytics as both a management information and an analytical tool since early 2013.

Why would a SAS^® administrator need a dashboard? With the evolution of SAS^®9, the SAS administrator s role has dramatically changed. Creating a dashboard on a SAS environment gives the SAS administrator an overview on the environment health, ensures resources are used as predicted, and provides a way to explore. SAS^® Visual Analytics allows you to quickly explore, analyze, and visualize data. So, why not bring the two concepts together? In this session, you will learn tips for designing dashboards, loading what might seem like impossible data, and building visualizations that guide users toward the next level of analysis. Using the dashboard, SAS administrators will learn ways to determine the system health and how to take advantage of external tools, such as the Metacoda software, to find additional insights and explore problem areas.

There are exciting new capabilities available from SAS^® High-Performance Analytics and SAS^® Visual Analytics. Current customers seek a deployment strategy that enables gradual migration to the new technologies. Such a strategy would mitigate the need for 'rip and replace' and would enable resource utilization to evolve along a continuum rather than partitioning resources, which would result in underused computing or storage hardware. New customers who deploy a combination of SAS^® Grid Manager, SAS High-Performance Analytics, and SAS Visual Analytics seek to reduce the cost of computing resources and reduce data duplication and data movement by deploying these solutions on the same pool of hardware. When sharing hardware, it is important to implement resource management in order to help guarantee that resources are available for critical applications and processes. This session discusses various methods for managing hardware resources in a multi-application environment. Specific strategies are suggested, along with implementation suggestions.

Universities in the UK are now subject to League Table reporting by a range of providers. The criteria used by each League Table differ. Universities, their faculties, and individual subject areas want to understand how the different tables are constructed and calculated, and what is required in order to maximize their position in each league table in order to attract the best students to their institution, thereby maximizing recruitment and student-related income streams. The School of Computing and Maths at the University of Derby is developing the use SAS^® Visual Analytics to analyse each league table to provide actionable insights as to actions that can be taken to improve their relative standing in the league tables and also to gain insights into feasible levels of targets relative to the peer groups of institutions. This paper outlines the approaches taken and some of the critical insights developed that will be of value to other higher education institutions in the UK, and suggests useful approaches that might be valuable in other countries.

SAS^® Visual Analytics delivers the power of approachable in-memory analytics in an intuitive web interface. The scalable technology behind SAS Visual Analytics should not benefit just the analyst or data scientist in your organization but indeed everyone regardless of their analytical background. This paper outlines a framework for the creation of a cloud deployment of SAS Visual Analytics using the SAS^® 9.4 platform. Based on proven best practices and existing customer implementations, the paper focuses on architecture, processes, and design for reliability and scalable multi-tenancy. The framework enables your organization to move away from the departmental view of the world and to offer analytical capabilities for consumerization and collaboration across the enterprise.

This workshop provides hands-on experience with SAS^® Visual Analytics. Workshop participants will do the following: explore data with SAS^® Visual Analytics Explorer design reports with SAS^® Visual Analytics Designer

How does the SAS^® server architecture fit within your IT infrastructure? What functional aspects does the architecture support? This session helps attendees understand the logical server topology of the SAS technology stack: resource and process management in-memory architecture in-database processing The session also discusses process flows from data acquisition through analytical information to visual insight. IT architects, data administrators, and IT managers from all industries should leave with an understanding of how SAS has evolved to better fit into the IT enterprise and to help IT's internal customers make better decisions.

Security-conscious organizations have rigorous IT regulations, especially when company data is available on the move. This paper explores the options available to secure a deployment of SAS^® Mobile BI with SAS^® Visual Analytics. The setup ensures encrypted communication from remote mobile clients all the way to backend servers. Additionally, the integration of SAS Mobile BI with third-party Mobile Device Management (MDM) software and Virtual Private Network (VPN) technology enable you to place several layers of security and access control to your data. The paper also covers the out-of-the box security features of the SAS Mobile BI and SAS Visual Analytics administration applications to help you close the loop on all possible areas of exploitation.

Even if you are familiar with security considerations for SAS^® BI deployments, such as metadata and file system permissions, there are additional security aspects to consider when securing any environment that includes SAS^® Visual Analytics. These include files and permissions to the grid machines in a distributed environment, permissions on the SAS^® LASR™ Analytic Servers, and interactions with existing metadata types. We approach these security aspects from the perspective of an administrator who is securing the environment for himself, a data builder, and a report consumer.

SAS^® Visual Analytics enables you to conduct ad hoc data analysis, visually explore data, develop reports, and then share insights through the web and mobile tablet apps. You can now also share your insights with colleagues using the SAS^® Office Analytics integration with Microsoft Excel, Microsoft Word, Microsoft PowerPoint, Microsoft Outlook, and Microsoft SharePoint. In addition to opening and refreshing reports created using SAS Visual Analytics, a new SAS^® Central view enables you to manage and comment on your favorite and recent reports from your Microsoft Office applications. You can also view your SAS Visual Analytics results in SAS^® Enterprise Guide^®. Learn more about this integration and what's coming in the future in this breakout session.

Businesses today are inundated with unstructured data not just social media but books, blogs, articles, journals, manuscripts, and even detailed legal documents. Manually managing unstructured data can be time consuming and frustrating, and might not yield accurate results. Having an analyst read documents often introduces bias because analysts have their own experiences, and those experiences help shape how the text is interpreted. The fact that people become fatigued can also impact the way that the text is interpreted. Is the analyst as motivated at the end of the day as they are at the beginning? Data science involves using data management, analytical, and visualization strategies to uncover the story that the data is trying to tell in a more automated fashion. This is important with structured data but becomes even more vital with unstructured data. Introducing automated processes for managing unstructured data can significantly increase the value and meaning gleaned from the data. This paper outlines the data science processes necessary to ingest, transform, analyze, and visualize three Star Wars movie scripts: A New Hope, The Empire Strikes Back, and Return of the Jedi. It focuses on the need to create structure from unstructured data using SAS^® Data Management, SAS^® Text Miner, and SAS^® Content Categorization. The results are featured using SAS^® Visual Analytics.

Once upon a time, a writer compared a desert to a labyrinth. A desert has no walls or stairways, but you can still find yourself utterly lost in it. And oftentimes, when you think you found that oasis you were looking for, what you are really seeing is an illusion, a mirage. Similarly, logical fallacies and misleading data patterns can easily deceive the unaware data explorer. In this paper, we discuss how they can be recognized and neutralized with the power of the SAS^® Visual Analytics Explorer. Armed with this knowledge, you will be able to safely navigate the dunes to find true insights and avoid false conclusions.

Understanding previous research in key domain areas can help R&D organizations focus new research in non-duplicative areas and ensure that future endeavors do not repeat the mistakes of the past. However, manual analysis of previous research efforts can prove insufficient to meet these ends. This paper highlights how a combination of SAS^® Text Analytics and SAS^® Visual Analytics can deliver the capability to understand key topics and patterns in previous research and how it applies to a current research endeavor. We will explore these capabilities in two use cases. The first will be in uncovering trends in publicly visible government funded research (SBIR) and how these trends apply to future research in nanotechnology. The second will be visualizing past research trends in publicly available NASA publications, and how these might impact the development of next-generation spacecraft.

SAS^® Visual Analytics is a unique tool that provides both exploratory and predictive data analysis capabilities. As the visual part of the name suggests, the rendering of this analysis in the form of visuals (crosstabs, line charts, histograms, scatter plots, geo maps, treemaps, and so on) make this a very useful tool. Join me as I walk you down the path of exploring the capabilities of SAS Visual Analytics 6.3, starting with data stored in a desktop application as multiple Microsoft Excel files. Together, we import the data into SAS Visual Analytics, prepare the data using the data builder, load the data into SAS^® LASR™ Analytic Server, explore data, and create reports.

Everyone has heard about SAS^® Cloud. Now come learn how you can build and manage your own cloud using the same SAS^® virtual application (vApp) technology.

SAS^® Merchandise Planning introduces key changes with the recent 6.4 release and the upcoming 6.5 release. This session highlights the integration to SAS^® Visual Analytics, the analytic infrastructure that enables users to integrate analytic results into their planning decisions, as well as multiple usability enhancements. Included is a look at the first of the packaged analytics that include the Recommended Assortment analytic.

SAS^® Visual Analytics is one of the newer SAS^® products with a lot of excitement surrounding it. But what is SAS Visual Analytics really? By examining the similarities, differences, and synergies between SAS Visual Analytics and other SAS offerings, we can more clearly understand this new product.