Automatic loading, tracking, and visualization of data readiness in SAS^® Visual Analytics is easy when you combine SAS^® Data Integration Studio with the DATASET and LASR procedures. This paper illustrates the simple method that the University of North Carolina at Chapel Hill (Enterprise Reporting and Departmental Systems) uses to automatically load tables into the SAS^® LASR Analytic Servers, and then store reportable data about the HDFS tables created, the LASR tables loaded, and the ETL job execution times. This methodology gives the department the ability to longitudinally visualize system loading performance and identify changes in system behavior, as well as providing a means of measuring how well we are serving our customers over time.

Microsoft Windows 10 is a new operating system that is increasingly being adopted by enterprises around the world. SAS has planned to expand SAS^® Mobile BI, which is currently available on Apple iOS and Google Android, to the Microsoft Windows 10 platform. With this new application, customers can download business reports from SAS^® Visual Analytics to their desktop, laptop, or Microsoft Surface device, and use these reports both online and offline in their day-to-day business life. With Windows 10, users have the option of pinning a report to the desktop for quick access. This paper demonstrates this new SAS mobile application. We also demonstrate the cool new functionality on iOS and Android platforms, and compare them with the Windows 10 application.

Many organizations that use SAS^® Visual Analytics must conform with accessibility requirements such as Section 508, the Americans with Disabilities Act, and the Accessibility for Ontarians with Disabilities Act. SAS Visual Analytics provides a number of different ways to view reports, including the SAS^® Report Viewer and SAS^® Mobile BI native applications for Apple iOS and Google Android. Each of these options has its own strengths and weaknesses when it comes to accessibility a one-size-fits-all approach is unlikely to work well for the people in your audience who have disabilities. This paper provides a comprehensive assessment of the latest versions of all SAS Visual Analytics report viewers, using Web Content Accessibility Guidelines (WCAG) 2.0 as a benchmark to evaluate accessibility. You can use this paper to direct the end users of your reports to the viewer that best meets their individual needs.

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Location information plays a big role in business data. Everything that happens in a business happens somewhere, whether it s sales of products in different regions or crimes that happened in a city. Business analysts typically use the historic data that they have gathered for years for analysis. One of the most important pieces of data that can help answer more questions qualitatively, is the demographic data along with the business data. An analyst can match the sales or the crimes with the population metrics like gender, age groups, family income, race, and other pieces of information, which are part of the demographic data, for better insight. This paper demonstrates how a business analyst can bring the demographic and lifestyle data from Esri into SAS^® Visual Analytics and join the data with business data. The integration of SAS Visual Analytics with Esri allows this to happen. We demonstrate different methods of accessing Esri demographic data from SAS Visual Analytics. We also demonstrate how you can use custom shape files and integrate with Esri Portal for ArcGIS.

SAS^® Visual Analytics provides a robust platform to perform business intelligence through a high-end and advanced dashboarding style. In today's technology era, dashboards not only help in gaining insight into an organization's operations, but they also are a key performance indicator. In this paper, I discuss five important and frequently used objects in SAS Visual Analytics. These objects are used to get the most out of dashboards in an effective and efficient way. This paper covers the use of dates (as a format) in the date slider and gauges, cascading filters, custom graphs, linking reports within sections of the same report or with other reports, and associating buttons with graphs for dynamic functionality.

The University of Memphis has been a SAS^® customer since the mainframe computing era. Our deployments have included various SAS products involving web-based applications, client/server implementations, desktop installations, and virtualized services. This paper uses an information technology (IT) perspective to discuss how the University has leveraged SAS, as well as the latest benefits and challenges for our most recent deployment involving SAS^® Visual Analytics.

Whether you are an existing SAS^® Visual Analytics user or you are exploring SAS Visual Analytics for the first time, the first release of SAS^® Visual Analytics 8.1 on SAS^® Viya has something exciting for everyone. The latest version is a clean, modern HTML5 interface. SAS^® Visual Analytics Designer, SAS^® Visual Analytics Explorer, and SAS^® Visual Statistics are merged into a single web application. Whether you are designing reports, exploring data, or running interactive, predictive models, everything is integrated into one seamless experience. The application delivers on the same basic promise: get pertinent answers from any-size data. The paper walks you through key features that you have come to count on, from auto charting, to display rules, and more. It acclimates you to the new interface and highlights a few exciting new features like web content and donut pie charts. Finally, the paper touches upon the ability to promote your existing reports to the new environment.

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.

Many organizations are using SAS^® Visual Analytics for their daily reporting. But as more users gain access to the visual tool, it is easy to lose track of what data is being used, what reports are being accessed, and what elements of the system are classified as critical. With SAS Visual Analytics comes a governance exercise that all organizations should provision for, as otherwise it jeopardizes its maintenance and performance. This paper explores the three different auditing areas that can be configured with SAS Visual Analytics and the different metrics that are associated with them. It presents how to configure the auditing, the data sources that are being populated on the background, and how to exploit them to expand your reports beyond the pre-created audit reports. Consideration is also given to the IT and infrastructure side of enabling auditing mechanisms, with data volumes and archiving practices being at the heart of the discussion.

A lot of time and effort goes into creating presentations or dashboards for the purposes of management business reviews. Data for the presentation is produced from a variety of tools, and the output is cut and pasted into Microsoft PowerPoint or Microsoft Excel. Time is spent not only on the data preparation and reporting, but also on the finishing and touching up of these presentations. In previous years, SAS^® Global Forum authors have described the automation capabilities of SAS^® and Microsoft Office. The default look and feel of SAS output in Microsoft PowerPoint and Microsoft Excel is not always adequate for the more polished requirement of an executive presentation. This paper focuses on how to combine the capabilities of SAS^® Enterprise Guide^®, SAS^® Visual Analytics, and Microsoft PowerPoint into a finished, professional presentation. We will build and automate a beautiful finished end product that can be refreshed by anyone with the click of a mouse.

Whether you are calculating a credit risk, a health risk, or something entirely different, you need instant, on-the-fly risk score calculation across multiple industries. This paper demonstrates how you can produce individualized risk scores through interactive dashboards. Your risk scores are backed by powerful SAS^® analytics because they leverage score code that you produce in SAS^® Visual Statistics. Advanced topics, including the use of calculated items and parameters in your dashboards, as well as how to develop SAS^® Stored Processes capable of accepting parameters that are passed through your SAS^® Visual Analytics Dashboard are covered in detail.

Coming off a recent smart grid implementation, OGE Energy Corp. was collecting more data than at any time in its history. This data held the potential to help the organization uncover new insights and chart new paths. Find out how OGE Energy is building a culture of data analytics by using SAS^® tools, a distributed analytics model, and an analytics center of excellence.

Today it is vital for an organization to manage, distribute, and secure content for its employees. In most cases, different groups of employees are interested in different content, and some content should not be available to everyone. It is the SAS^® administrator's job to design a metadata group structure that makes managing content easier. SAS enables you to create any metadata group organizational structure imaginable, and it is common to define a metadata group structure that mimics the organization's hierarchy. Circular group memberships are frequently the cause of unexpected issues with SAS web applications. A circular group relationship can be as simple as two groups being members of one another. You might not be aware that you have defined this type of recursive association between groups. The paper identifies some problems that are caused by recursive group memberships and provides tools to investigate your metadata group structure that help identify recursive metadata group relationships. We explain the process of extracting group associations from the SAS^® Metadata Server, and we show how to organize this data to investigate group relationships. We use a stored process to generate a report and SAS^® Visual Analytics to generate a network diagram that provides a graphical representation of an organization's group relationship structure, to easily identify circular group structures.

Do your reports effectively communicate the message you intended? Are your reports aesthetically pleasing? An attractive report does not ensure the accurate delivery of a data story, nor does a logical data story guarantee visual appeal. This paper provides guidance for SAS^® Visual Analytics Designer users to facilitate the creation of compelling data stories. The primary goal of a report is to enable readers to quickly and easily get answers to their questions. Achieving this goal is strongly influenced by the choice of visualizations for the data, the quantity and arrangement of the information that is included, and the use or misuse of color. This paper describes how to guide readers' movement through a report to support comprehension of the data story; provides tips on how to express quantitative data using the most appropriate graphs; suggests ways to organize content through the use of visual and interactive design techniques; and instructs report designers about the meaning of colors, presenting the notion that even subtle changes in color can evoke feelings that are different from those intended. A thoughtfully designed report can educate the viewer without compromising visual appeal. Included in this paper are recommendations and examples which, when applied to your own work, will help you create reports that are both informative and beautiful.

SAS^® Visual Analytics is a very powerful tool for users to visually explore data, but in some organizations not all data should be available for everybody. And although it is relatively easy to scale up a SAS Visual Analytics environment when the need for data increases, it still would be beneficial to set up a structure where the organization can keep control over who actually has the right to load data versus providing everybody the right to load data into a SAS Visual Analytics environment. Within this breakout session a potential solution is shown by providing a high-level overview of the SAS Visual Analytics data access management solution at ING bank in the Netherlands for the Risk Services Organization.

As a report designer using SAS^® Visual Analytics, your goal is to create effective data visualizations that quickly communicate key information to report readers. But what makes a dashboard or report effective? How do you ensure that key points are understood quickly? One of the most common questions asked about SAS Visual Analytics is: what are the best practices for designing a report? Experts like Stephen Few and Edward Tufte have written extensively about successful visual design and data visualization. This paper focuses mainly on a different aspect of visual reports-the speed with which online reports render. In today's world, instant results are almost always expected. And the faster your report renders, the sooner decisions can be made and actions taken. Based on proven best practices and existing customer implementations, this paper focuses on server-side performance, client-side performance, and design performance. The end result is a set of design techniques that you can put into practice immediately and optimize your report performance.

Do you ever wonder how to create a report with weighted averages, or one that displays the last day of the month by default? Do you want to take advantage of the one-click relative-time calculations available in SAS^® Visual Analytics, or learn a few other creative ways to enhance your report? If your answer is yes, then this paper is for you. We not only teach you some new tricks, but the techniques covered here will also help you expand the way you think about SAS Visual Analytics the next time you are challenged to create a report.

This workshop provides hands-on experience with SAS Viya Data Mining and Machine Learning through the programming interface to SAS Viya. Workshop participants will learn how to start and stop a CAS session; move data into CAS; prepare data for machine learning; use SAS Studio tasks for supervised learning; and evaluate the results of analyses.

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.

Capacity management is concerned with managing, controlling, and optimizing the hardware resources on a technology platform. Its primary goal is to ensure that IT resources are right-sized to meet current and future business requirements in a cost-effective manner. In other words, keeping those hardware vendors at bay! A SAS^® LASR Analytic Server, with its dependence on in-memory resources, necessitate a revisit to the traditional IT server capacity management practices. A major UK-based financial services institution operates a multi-tenanted Enterprise SAS^® platform. The tenants share platform resources and as such, require quotas enforced with system limits and costs for their resource utilization, aligned to the business outcomes and agreed-upon service level agreements (SLAs). This paper discusses the implementation of system, operational, and development polices applicable in a multi-tenanted SAS platform, in order to optimize an investment in the analytic platform provided by SAS LASR Analytic Server and to be in control as to when capacity uplifts are required.

Exploring, analyzing, and presenting information are strengths of SAS^® Visual Analytics. However, when we need to expand the viewing of this information to an unlimited public outside the boundaries of the organization, we must aggregate geographic information to facilitate interaction and the use of information. This application uses JavaScript and CSS programming language, integrated with SAS^® programming, to present information about 4,239 programs of postgraduate study in Brazil. This information was evaluated by the Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES, Brazil) with cartographic precision, enabling the visualization of the data generated in SAS Visual Analytics integrated with Google Maps and Google Street View. Users can select from Brazilian postgraduate programs, know information about the program, learn about theses and dissertations, and see the location of the institution and the campus. The application that is presented can be accessed at http://goo.gl/uAjvGw.

SAS^® Visual Analytics has two offerings, SAS^® Visual Statistics and SAS^® Visual Data Mining and Machine Learning, that provide knowledge workers and data scientists an interactive interface for data partition, data exploration, feature engineering, and rapid modeling. These offerings are powered by the SAS^® Viya platform, thus enabling big data and big analytic problems to be solved. This paper focuses on the steps a user would perform during an interactive modeling session.

Statistical analysis is like detective work, and a data set is like the crime scene. The data set contains unorganized clues and patterns that can, with proper analysis, ultimately lead to meaningful conclusions. Using SAS^® tools, a statistical analyst (like any good crime scene investigator) performs a preliminary analysis of the data set through visualization and descriptive statistics. Based on the preliminary analysis, followed by a detailed analysis, both the crime scene investigator (CSI) and the statistical analyst (SA) can use scientific or analytical tools to answer the key questions: What happened? What were the causes and effects? Why did this happen? Will it happen again? Applying the CSI analogy, this paper presents an example case study using a two-step process to investigate a big-data crime scene. Part I shows the general procedures that are used to identify clues and patterns and to obtain preliminary insights from those clues. Part II narrows the focus on the specific statistical analyses that provide answers to different questions.

Walt Disney once said, 'Of all of our inventions for mass communication, pictures still speak the most universally understood language.' Using data visualization to tell our stories makes analytics accessible to a wider audience than we can reach through words and numbers alone. Through SAS^® Visual Analytics, we can provide insight to a wide variety of audiences, each of whom see the data through a unique lens. Determining the best data and visualizations to provide for users takes concentrated effort and thoughtful planning. This session discusses how Western Kentucky University uses SAS Visual Analytics to provide a wide variety of users across campus with the information they need to visually identify trends, even some they never expected to see, and to answer questions they might not have thought to ask.

SAS^® BI Dashboard is an important business intelligence and data visualization product used by many customers worldwide. They still rely on SAS BI Dashboard for performance monitoring and decision support. SAS^® Visual Analytics is a new-generation product, which empowers customers to explore huge volumes of data very quickly and view visualized results with web browsers and mobile devices. Since SAS Visual Analytics is used by more and more regular customers, some SAS BI Dashboard customers might want to migrate existing dashboards to SAS Visual Analytics to take advantage of new technologies. In addition, some customers might hope to deploy the two products in parallel and keep everyone on the same page. Because the two products use different data models and formats, a special conversion tool is developed to convert SAS BI Dashboard dashboards into SAS Visual Analytics dashboards and reports. This paper comprehensively describes the guidelines, methods, and detailed steps to migrate dashboards from SAS BI Dashboard to SAS Visual Analytics. Then the converted dashboards can be shown in supported viewers of SAS Visual Analytics including mobile devices and modern browsers.

Creating your first suite of reports using SAS^® Visual Analytics is like being a kid in a candy store with so many options for data visualization, it is difficult to know where to start. Having a plan for implementation can save you a lot of time in development and beyond, especially when you are wrangling big data. This paper helps you make sure that you are parallelizing work (where possible), maximizing your data insights, and creating a polished end product. We provide guidelines to common questions, such as How many objects are too many ? or When should I use multiple tabs versus report linking? to start any data visualizer off on the right foot.

The report brings a simple and intuitive overview on behavior of technical provision and rentability of health insurance segments, based on historical data of a major insurance company. The profitability analysis displays indicators consisting of claims, prices, and quantity of insureds and their performance separated by gender, region, and different products. The report's user can simulate more accurate premiums by inputting information about medical costs increasing and target claims rate. The technical provision view identifies the greatest impacts on the provision, such as claims payments, legal expense estimates, and future claims payments and reports. Also, it compares the real health insurance costs with the provision estimated on a previous period. Therefore, the report enables the user to get a unique panorama of health insurance underwriting and evaluate its results in order to make strategic decision for the future.

Web Intelligence is a business objects web-based application used by the FDA for accessing and querying data files, and ultimately creating reports from multiple databases. The system allows querying of different databases using common business terms, and in the case of the FDA's Center for Food Safety and Applied Nutrition (CFSAN), careful review of dietary supplement information. However, in order to create timely and efficient reports for detection of safety signals leading to adverse events, a more efficient system is needed to obtain and visually display the data. Using SAS^® Visual Analytics and SAS^® Enterprise Guide^® can assist with timely extraction of data from multiple databases commonly used by CFSAN and create a more user friendly interface for management's review to help make key decisions in prevention of adverse events for the public.

SAS^® Visual Analytics provides a complete platform for analytics visualization and exploration of the data. There are several interactive visualizations such as charts, histograms, heat maps, decision tree, and Sankey diagrams. A Sankey diagram helps in performing path analytics and offers a better understanding of complex data. It is a graphic illustration of flows from one set of values to another as a series of paths, where the width of each flow represents the quantity. It is a better and more efficient way to illustrate which flows represent advantages and which flows are responsible for the disadvantages or losses. Sankey diagrams are named after Matthew Henry Phineas Riall Sankey, who first used this in a publication on energy efficiency of a steam engine in 1898. This paper begins with information regarding the essentials or parts of Sankey: nodes, links, drop-off links, and path. Later, the paper explains the method for creating a meaningful visualization (with the help of examples) with a Sankey diagram by looking into the data roles and properties, describing ways to manage the path selection, exploring the transaction identifier values for a path selection, and using the spotlight tool to view multiple data tips in SAS Visual Analytics. Finally, the paper provides recommendation and tips to work effectively and efficiently with the Sankey diagram.

The University of Central Florida (UCF) Institutional Knowledge Management (IKM) office provides data analysis and reporting for all UCF divisions. These projects are logged and tracked through the Oracle PeopleSoft content management system (CMS). In the past, projects were monitored via a weekly query pulled using SAS^® Enterprise Guide^®. The output would be filtered and prioritized based on project importance and due dates. A project list would be sent to individual staff members to make updates in the CMS. As data requests were increasing, UCF IKM needed a tool to get a broad overview of the entire project list and more efficiently identify projects in need of immediate attention. A project management dashboard that all IKM staff members can access was created in SAS^® Visual Analytics. This dashboard is currently being used in weekly project management meetings and has eliminated the need to send weekly staff reports.

In SAS^® Visual Analytics, we demonstrate a search functionality that enables users to filter a LASR table for records containing a search string. The search is performed on selected character fields that are defined for the table. The search string can be portions of words. Each additional string to search for narrows the search results.

SAS^® provides an extensive set of graphs for different needs. But as a SAS programmer or someone who uses SAS^® Visual Analytics Designer to create reports, the number of possible scenarios you have to address outnumber the available graphs. This paper demonstrates how to create your own advanced graphs by intelligently combining existing graphs. This presentation explains how you can create the following types of graphs by combining existing graphs: a line-based graph that shows a line for each group such that each line is partly solid and partly dashed to show the actual and predicted values respectively; a control chart (which is currently not available as a standard graph) that lets you show how values change within multiple upper and lower limits; a line-based graph that gives you more control over attributes (color, symbol, and so on) of specific markers to depict special conditions; a visualization that shows the user only a part of the data at a given instant, and lets him move a window to see other parts of the data; a chart that lets the user compare the data values to a specific value in a visual way to make the comparison more intuitive; and a visualization that shows the overall data and at the same time shows the detailed data for the selected category. This paper demonstrates how to use the technique of combining graphs to create such advanced charts in SAS^® Visual Analytics and SAS^® Graph Builder as well as by using SAS procedures like the SGRENDER procedure.

Socioeconomic status (SES) is a major contributor to health disparities in the United States. Research suggests that those with a low SES versus a high SES are more likely to have lower life expectancy; participate in unhealthy behaviors such as smoking and alcohol consumption; experience higher rates of depression, childhood obesity, and ADHD; and experience problems accessing appropriate health care. Interpreting SES can be difficult due to the complexity of data, multiple data sources, and the large number of socioeconomic and demographic measures available. When SES is expanded to include additional social determinants of health (SDOH) such as language barriers and transportation barriers to care; access to employment and affordable housing; adequate nutrition, family support and social cohesion; health literacy; crime and violence; quality of housing; and other environmental conditions, the ability to measure and interpret the concept becomes even more difficult. This paper presents an approach to measuring SES and SDOH using publicly available data. Various statistical modeling techniques are used to define state-specific composite SES scores at local areas-ZIP Code and Census Tract. Once developed, the SES/SDOH models are applied to health care claims data to evaluate the relationship between health services utilization, cost, and social factors. The analysis includes a discussion of the potential impact of social factors on population risk adjustment.

Every visualization tells a story. The effectiveness of showing data through visualization becomes clear as these visualizations will tell stories about differences in US mortality using the National Longitudinal Mortality Study (NLMS) data, using the Public-Use Microdata Samples (PUMS) of 1.2 million cases and 122 thousand records of mortality. SAS^® Visual Analytics is a versatile and flexible tool that easily displays the simple effects of differences in mortality rates between age groups, genders, races, places of birth (native or foreign), education and income levels, and so on. Sophisticated analyses including logistical regression (with interactions), decision trees, and neural networks that are displayed in a clear, concise manner help describe more interesting relationships among variables that influence mortality. Some of the most compelling examples are: Males who live alone have a higher mortality rate than females. White men have higher rates of suicide than black men.

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As a SAS^® Visual Analytics administrator, how do you efficiently manage your SAS^® LASR environment? How do you ensure reliable data availability to your end users? How do you ensure that your users have the proper permissions to perform their tasks in SAS Visual Analytics? This paper covers some common management issues in SAS Visual Analytics, why and how they might arise, and how to resolve them. It discusses methods of programmatically managing your SAS^® LASR Analytic Server and tables, as well as using SAS^® Visual Analytics Administrator. Furthermore, it provides a better understanding of the roles in SAS Visual Analytics and demonstrates how to set up appropriate user permissions. Using the methods discussed in this paper can help you improve the end-user experience as well as system performance.

Oi S.A. (Oi) is a pioneer in providing convergent services in Brazil. It currently has the greatest network capillarity and WiFi availability Brazil. The company offers fixed lines, mobile services, broadband, and cable TV. In order to improve service to over 70 million customers, The Customer Intelligence Department manages the data generated by 40,000 call center operators. The call center produces more than a hundred million records per month, and we use SAS^® Visual Analytics to collect, analyze, and distribute these results to the company. This new system changed the paradigm of data analysis in the company. SAS Visual Analytics is user-friendly and enabled the data analysis team to reduce IT time. Now it is possible to focus on business analysis. Oi started developing its SAS Visual Analytics project in June 2014. The test period lasted only 15 days and involved 10 people. The project became relevant to the company. It led us to the next step, in which 30 employees and 20 executives used the tool. During the last phase, we applied that to a larger scale with 300 users, including local managers, executives, and supervisors. The benefits brought by the fast implementation (two months) are many. We reduced the time it takes to produce reports by 80% and the time to complete business analysis by 40%.

Your SAS^® Visual Analytics users begin to create and share reports. As an administrator, you want to track performance of the reports over time, analyzing timing metrics for key tasks such as data query and rendering, relative to total user workload for the system. Logging levels can be set for the SAS Visual Analytics reporting services that provide timing metrics for each report execution. The log files can then be mined to create a data source for a time series plot in SAS Visual Analytics. You see report performance over time with peak workloads and how this impacts the user experience. Isolation on key metrics can identify performance bottlenecks for improvement. First we look at how logging levels are modified for the reporting services and focus on tracking a single user viewing a report. Next, we extract data from a long running log file to create a report performance data source. Using SAS Visual Analytics, we analyze the data with a time series plot, looking at times of peak work load and how the user experience changes.

SAS^® Theme Designer provides a rich set of colors and graphs that enables customers to create a custom application and report themes. Users can also preview their work within SAS^® Visual Analytics. The features of SAS Theme Designer enable the user to bring a new look and feel to their entire application and to their reports. Users can customize their reports to use a unique theme across the organization, yet they have the ability to customize these reports based on their individual business requirements. Providing this capability involves meeting the customers demands from the theming perspectives of customization, branding, and logo, and making them seamless within their application. This paper walks users through the process of using SAS Theme Designer in SAS Visual Analytics. It further highlights the following features of SAS Theme Designer: creating and modifying application and report themes, previewing output in SAS Visual Analytics, and importing and exporting themes for reuse.

SAS^® Visual Analytics gives customers the power to quickly and easily make sense of any data that matters to them. SAS^® Visual Analytics 7.4 delivers requested enhancements to familiar features. These enhancements include dynamic text, custom geographical regions, improved PDF printing, and enhanced prompted filter controls. There are also enhancements to report parameters and calculated data items. This paper provides an overview of the latest features of SAS Visual Analytics 7.4, including use cases and examples for leveraging these new capabilities.

Whether you are a new SAS^® administrator or you are switching to a Linux environment, you have a complex mission. This job becomes even more formidable when you are working with a system like SAS^® Visual Analytics that requires multiple users loading data daily. Eventually a user has data issues or creates a disruption that causes the system to malfunction. When that happens, what do you do next? In this paper, we go through the basics of a SAS Visual Analytics Linux environment and how to troubleshoot the system when issues arise.

High-quality analytics works best with the best-quality data. Preparing your data ranges from activities like text manipulation and filtering to creating calculated items and blending data from multiple tables. This paper covers the range of activities you can easily perform to get your data ready. High-performance analytics works best with in-memory data. Getting your data into an in-memory server, as well as keeping it fresh and secure, are considerations for in-memory data management. This paper covers how to make small or large data available and how to manage it for analytics. You can choose to perform these activities in a graphical user interface or via batch scripts. This paper describes both ways to perform these activities. You ll be well-prepared to get your data wrangled into shape for analytics!