With the increasing amount of educational data, educational data mining has become more and more important for uncovering the hidden patterns within institutional data so as to support institutional decision making (Luan 2012). However, only very limited studies have been done on educational data mining for institutional decision support. At the University of Connecticut (UCONN), organic chemistry is a required course for undergraduate students in a STEM discipline. It has a very high DFW rate (D=Drop, F=Failure, W=Withdraw). Take Fall 2014 as an example: the average DFW% for the Organic Chemistry lectures was 24% at UCONN, and there were over 1200 students enrolled in this class. In this study, undergraduate students enrolled during School Year 2010 2011 were used to build up the model. The purpose of this study was to predict student success in the future so as to improve the education quality in our institution. The Sample, Explore, Modify, Model, and Assess (SEMMA) method introduced by SAS was applied to develop the predictive model. The freshmen SAT scores, campus, semester GPA, financial aid, and other factors were used to predict students' performance in this course. In the predictive modeling process, several modeling techniques (decision tree, neural network, ensemble models, and logistic regression) were compared with each other in order to find an optimal one for our institution.

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.

A major issue in America today is the growing gap between the rich and the poor. Even though the basic concept has entered the public consciousness, the effects of highly concentrated wealth are hotly debated and poorly understood by the general public. The goal of this paper is to get a fair picture of the wealth gap and its ill effects on American society. Before visualizing the financial gap, an exploration and descriptive analysis is carried out. By considering the data (gross annual income, taxable income, and taxes paid), which is available on the website of United States Census Bureau, we try to find out the actual spending capacity of the people in America. We visualize the financial gap on the basis of the spending capacity. With the help of this analysis we try to answer the following questions. Why is it important to have a fair idea of this gap? At what rate is the average wealth of the American population increasing? How does it affect the tax system? Insights generated from answering these questions will be used for further analysis.

As you know, real world data (RWD) provides highly valuable and practical insights. But as valuable as RWD is, it still has limitations. It is encounter-based, and we are largely blind to what happens between encounters in the health-care system. The encounters generally occur in a clinical setting that might not reflect actual patient experience. Many of the encounters are subjective interviews, observations, or self-reports rather than objective data. Information flow can be slow (even real time is not fast enough in health care anymore). And some data that could be transformative cannot be captured currently. Select Internet of Things (IoT) data can fill the gaps in our current RWD for certain key conditions and provide missing components that are key to conducting Analytics of Healthcare Things (AoHT), such as direct, objective measurements; data collected in usual patient settings rather than artificial clinical settings; data collected continuously in a patient s setting; insights that carry greater weight in Regulatory and Payer decision-making; and insights that lead to greater commercial value. Teradata has partnered with an IoT company whose technology generates unique data for conditions impacted by mobility or activity. This data can fill important gaps and provide new insights that can help distinguish your value in your marketplace. Join us to hear details of successful pilots that have been conducted as well as ongoing case studies.

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.

Understanding customer behavior profiles is of great value to companies. Customer behavior is influenced by a multitude of elements-some are capricious, presumably resulting from environmental, economic, and other factors, while others are more fundamentally aligned with value and belief systems. In this paper, we use unstructured textual cheque card data to model and estimate latent spending behavioral profiles of banking customers. These models give insight into unobserved spending habits and patterns. SAS^® Text Miner is used in an atypical manner to determine the buying segments of customers and the latent buying profile using a clustering approach. Businesses benefit in the way the behavioral spend model is used. The model can be used for market segmentation, where each cluster is seen as a target marketing segment, leads optimization, or product offering where products are specifically compiled to align to each customer's requirements. It can also be used to predict future spend or to align customer needs with business offerings, supported by signing customers onto loyalty programs. This unique method of determining the spend behavior of customers makes it ideal for companies driving retention and loyalty in their customers.

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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.

Rapid advances in technology have empowered musicians all across the globe to share their music easily, resulting in intensified competition in the music industry. For this reason, musicians and record labels need to be aware of factors that can influence the popularity of their songs. The focus of our study is to determine how themes, topics, and terms within song lyrics have changed over time and how these changes might have influenced the popularity of songs. Moreover, we plan to run time series analysis on the numeric attributes of Billboard Top 100 songs in order to determine the appropriate combination of relevant attributes that influences a song's popularity. The findings of our study can potentially benefit musicians and record labels in understanding the necessary lyrical construction, overall themes, and topics that might enable a song to reach the highest chart position on the Billboard Top 100. The Billboard Top 100 is an optimal source of data, as it is an objective measure of popularity. Our data has been collected from open sources. Our data set consists of all 334,784 Billboard Top 100 observations for the years 1955-2015, with metadata covering all 26,869 unique songs that have appeared on the chart for that period. Our expanding lyric data set currently contains 18,002 of those songs, which were used to conduct our analysis. SAS^® Enterprise Miner and SAS^® Sentiment Analysis Studio were the primary tools of our analysis.

In this paper, we introduce a SAS/IML^® program of Classification Accuracy and Classification Consistency (CA/CC) that provides useful resources to test analysts or psychometricians. Our program optimizes functions of SAS^® by offering the CA/CC statistics not only with dichotomous items, but also with polytomous items. Classification Decision (CD) is a method to categorize examinees into achievement groups based on cut scores (Quinn and Cheng, 2013). CD has been predominantly used in educational and vocational situations such as admissions, selection, placement, or certification. This method needs to be accurate because its use has been important to examinees' professional and academic futures. Classification Accuracy and Classification Consistency (CA/CC) statistics are indices representing the precision of CD, and they need to be reported in order to affirm the validity of the CD. Classification Accuracy is referred to as the degree to which the classification of observed scores matches with the classification of true scores, and Classification Consistency is defined as the degree to which examinees are classified in the same category when taking two parallel test forms (Lee, 2010). Under item response theory (IRT), there are two methods to calculate CA/CC: Rudner (2001) and Lee (2010) approaches. This research deals with these two approaches for CA/CC with the examinee level.

An interactive voice response (IVR) system is a powerful tool that automates routine inbound call tasks. Companies leverage this system and make substantial savings by cutting down call center costs while customers from do-it-yourselfers to non-tech-savvies take advantage of this technology rather than wait in line to speak to a Customer Care Representative (CSR). The flip side of the coin is that customers often see IVR as a barrier to overcome in order to talk to a real person. So it is important that IVR is managed in such a way that it is mutually beneficial for both a business and their customers. If managing IVR is critical, then measuring Customer Satisfaction (CSAT) scores is paramount as it helps in understanding customers better. The first section of this paper discusses analysis of different use cases on how CSAT scores correlate with customers' journeys inside IVR. West Corporation's leading financial services client offers a survey to their customers, and customers rate questions on a scale of 1 to 10 based on their IVR experience (10 being extremely satisfied). Analysis of survey ratings using SAS^® helped Operations understand challenges faced by customers traversing different sections of the IVR. The second section of the paper discusses how the research helped Operations to identify a population specification error that occurred while surveying customers. The error was rectified, and IVR CSAT scores improved by 3%.

We've learned a great deal about how to develop great reports and about business intelligence (BI) tools and how to use them to create reports, but have we figured out how to create true BI reports? Not every report that comes out of a BI tool provides business intelligence! In pursuit of the perfect BI report, this paper explores how we can combine the best of lessons learned about developing and running traditional reports and about applying business analytics in order to create true BI reports that deliver integrated analytics and intelligence.

For all business analytics projects big or small, the results are used to support business or managerial decision-making processes, and many of them eventually lead to business actions. However, executives or decision makers are often confused and feel uninformed about contents when presented with complicated analytics steps, especially when multi-processes or environments are involved. After many years of research and experiment, a web reporting framework based on SAS^® Stored Processes was developed to smooth the communication between data analysts, researches, and business decision makers. This web reporting framework uses a storytelling style to present essential analytical steps to audiences, with dynamic HTML5 content and drill-down and drill-through functions in text, graph, table, and dashboard formats. No special skills other than SAS^® programming are needed for implementing a new report. The model-view-controller (MVC) structure in this framework significantly reduced the time needed for developing high-end web reports for audiences not familiar with SAS. Additionally, the report contents can be used to feed to tablet or smartphone users. A business analytical example is demonstrated during this session. By using this web reporting framework based on SAS Stored Processes, many existing SAS results can be delivered more effectively and persuasively on a SAS^® Enterprise BI platform.

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.

Microsoft SharePoint is a popular web application framework and platform that is widely used for content and document management by companies and organizations. Connecting SAS^® with SharePoint combines the power of these two into one. As a continuation of my SAS^® Global Forum Paper 11520-2016 titled Releasing the Power of SAS^® into Microsoft SharePoint, this paper expands on how to implement data visualization from SAS to SharePoint. This paper shows users how to use SAS/GRAPH^® software procedures, Output Delivery System (ODS), and emails to create and send visualization output files from SAS to SharePoint Document Library. Several SAS code examples are included to show how to create tables, bar charts (with PROC GCHART), line plots (with PROC SGPLOT) and maps (with PROC GMAP) from SAS to SharePoint. The paper also demonstrates how to create data visualization based on JavaScript by feeding SAS data into HTML pages on SharePoint. A couple of examples on how to export SAS data to JSON formats and create data visualization in SharePoint based on JavaScript are provided.

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.

In highly competitive markets, the response rates to economically reasonable marketing campaigns are as low as a few percentage points or less. In that case, the direct measure of the delta between the average key performance indicators (KPIs) of the treated and control groups is heavily 'contaminated' by non-responders. This paper focuses on measuring promotional marketing campaigns with two properties: (1) price discounts or other benefits, which are changing profitability of the targeted group for at least the promotion periods, and (2) impact of self-responders. The paper addresses the decomposition of the KPI measurement between responders and non-responders for both groups. Assuming that customers who rejected promotional offers will not change their behavior and that non-responders of both treated and control groups are not biased, the delta of the average KPIs for non-responders should be equal to zero. In practice, this component might be significantly deviated from zero. It might be caused by an initial nonzero delta of KPI values despite a random split between groups or by existence of outliers, especially for non-balanced campaigns. In order to address the deviation of the delta from zero, it might require running additional statistical tests comparing not just the means but the distributions of KPIs as well. The decomposition of the measurement between responders and non-responders for both groups can then be used in differential modeling.

Whether you have been programming in SAS^® for years, or you are new to it, or you have dabbled with SAS^® Enterprise Guide^® before, this hands-on workshop sheds some light on the depth, breadth, and power of the SAS Enterprise Guide environment. With all the demands on your time, you need powerful tools that are easy to learn and that deliver end-to-end support for your data exploration, reporting, and analytics needs. Included in this workshop are 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; and productivity tips. This workshop uses SAS Enterprise Guide 7.1, but most of the content is applicable to earlier versions.

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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.

The mean-variance model might be the most famous model in the financial field. It can determine the optimal portfolio if you know every asset's expected return and its covariance matrix. The tangency portfolio is a type of optimal portfolio, which means that it has the maximum expected return (mean) and the minimial risk (variance) among all portfolios. This paper uses sample data to get the tangency portfolio using SAS/IML^® code.

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Allowing SAS^® users to leverage SAS prompts when running programs is a very powerful tool. Using SAS prompts makes it easier for SAS users to submit parameter-driven programs and for developers to create robust, data-driven programs. This presentation demonstrates how to create SAS prompts from SAS^® Enterprise Guide^® and shows how to roll them out to users so that they can take advantage of them from SAS Enterprise Guide, the SAS^® Add-In for Microsoft Office, and the SAS^® Stored Process Web Application.

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.

In this technology-driven era, multi-channel communication has become a pivotal part of an effective customer care strategy for companies. Old ways of delivering customer service are no longer adequate. To survive a tough competitive market and retain current customer base, companies are spending heavily to serve customers in the manner in which they wish to be served. West Corporation helps their clients in designing a strategy that would provide their customers with a connected inbound and outbound communication experience. This paper illustrates how the Data Science team at West Corporation has measured the effect of outbound short message service (SMS) notification in reducing inbound interactive voice response (IVR) call volume and improving customer satisfaction for a leading telecom services company. As part of a seamless experience, customers have the option of receiving outbound SMS notifications at several stages while traversing inside IVR. Notifications can involve successful payment and appointment confirmations, outage updates in the area, and an option of receiving text with details to reset Wi-Fi password and activate new devices. This study was performed on two groups of customers one whose members opted to receive notifications and one whose members did not opt in. Also, analysis was performed using SAS^® to understand repeat caller behaviors within both groups. The group that opted to receive SMS notifications were less likely to call back than those who did not opt in.

A growing need in higher education is the more effective use of analytics when evaluating the success of a postsecondary institution. The metrics currently used are simplistic measures of graduation rates, publications, and external funding. These measures offer a limited view of the effectiveness of postsecondary institutions. This paper provides a global perspective of the academic progress of students and the business of higher education. It presents innovative metrics that are more effective in evaluating postsecondary-institutional effectiveness.

SAS^® Enterprise Guide^® continues to add easy-to-use features that enable you to work more efficiently. For example, you can now debug your DATA step code with a DATA step debugger tool; upload data to SAS^® Viya with a point-and-click task; control process flow execution behavior when an error occurs; export results to Microsoft Excel and Microsoft PowerPoint destinations with the click of a button; zoom views; filter the data grid with your own WHERE clause; easily define case-insensitive filters; and automatically get the latest product updates. Come see these and more new features and enhancements in SAS Enterprise Guide 7.11, 7.12, and 7.13.

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.

This paper will build on the knowledge gained in the Intro to SAS^® ODS Graphics. The capabilities in ODS Graphics grow with every release as both new paradigms and smaller tweaks are introduced. After talking with the ODS developers, a selection of the many wonderful capabilities was selected. This paper will look at that selection of both types of capabilities and provide the reader with more tools for their belt. Visualization of data is an important part of telling the story seen in the data. And while the standards and defaults in ODS Graphics are very well done, sometimes the user has specific nuances for characters in the story or additional plot lines they want to incorporate. Almost any possibility, from drama to comedy to mystery, is available in ODS Graphics if you know how. We will explore tables, annotation and changing attributes, as well as the BLOCK plot. Any user of Base SAS on any platform will find great value from the SAS ODS Graphics procedures. Some experience with these procedures is assumed, but not required.

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This presentation teaches the audience how to use ODS Graphics. Now part of Base SAS^®, ODS Graphics are a great way to easily create clear graphics that enable any user to tell their story well. SGPLOT and SGPANEL are two of the procedures that can be used to produce powerful graphics that used to require a lot of work. The core of the procedures is explained, as well as some of the many options available. Furthermore, we explore the ways to combine the individual statements to make more complex graphics that tell the story better. Any user of Base SAS on any platform will find great value in the SAS ODS Graphics procedures.

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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.

A leading global information and communications technology solution company provides a broad range of telecom products across the world. Their finished products share commonality in key components, and, in most cases, are assembled after the customer orders are realized. Each finished product typically consists of a large number of key components, and the stockout of any components causes a delay of customer orders. For these reasons, the optimal inventory policy of one component should be determined in conjunction with those of other components. Currently the company uses business experience to manage inventory across their supply chain network for all of the components and finished products. However, the increasing variety of products and business expansion raise difficulties in inventory management. The company wants to explore a systematic approach to optimizing inventory policies, assuring customer service level and minimizing total inventory cost. This paper describes using SAS/OR^® software and SAS^® inventory optimization technologies to model such a multi-echelon assembly system and optimize inventory policies for key components and finished products.

One of the first maps of the present United States was John White's 1585 map of the Albemarle Sound and Roanoke Island, the site of the Lost Colony and the site of my present home. This presentation looks at advances in mapping through the ages, from the early surveys and hand-painted maps, through lithographic and photochemical processes, to digitization and computerization. Inherent difficulties in including small pieces of coastal land (often removed from map boundary files and data sets to smooth a boundary) are also discussed. The paper concludes with several current maps of Roanoke Island created with SAS^®.

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.

Financial institutions are faced with a common challenge to meet the ever-increasing demand from regulators to monitor and mitigate money laundering risk. Anti-Money Laundering (AML) Transaction Monitoring systems produce large volumes of work items, most of which do not result in quality investigations or actionable results. Backlogs of work items have forced some financial institutions to contract staffing firms to triage alerts spanning back months. Moreover, business analysts struggle to define interactions between AML models and to explain what attributes make a model productive. There is no one approach to solve this issue. Analysts need several analytical tools to explore model relationships, improve existing model performance, and add coverage for uncovered risk. This paper demonstrates an approach to improve existing AML models and focus money laundering investigations on cases that are more likely to be productive using analytical SAS^® tools including SAS^® Visual Analytics, SAS^® Enterprise Miner , SAS^® Studio, SAS/STAT^® software, and SAS^® Enterprise Guide^®.

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.

Bayesian inference has become ubiquitous in applied science because of its flexibility in modeling data and advances in computation that allow special methods of simulation to obtain sound estimates when more mathematical approaches are intractable. However, when the sample size is small, the choice of a prior distribution becomes difficult. Computationally convenient choices for prior distributions can overstate prior beliefs and bias the estimates. We propose a simple form of prior distribution, a mixture of two uniform distributions, that is weakly informative, in that the prior distribution has a relatively large standard deviation. This choice leads to closed-form expressions for the posterior distribution if the observed data follow a normal, binomial, or Poisson distribution. The explicit formulas are easily encoded in SAS^®. For a small sample size of 10, we illustrate how to elicit the mixture prior and indicate that the resulting posterior distribution is insensitive to minor misspecification of input values. Weakly informative prior distributions suitable for small sample sizes are easy to specify and appear to provide robust inference.

Using zero inflated beta regression and gradient boosting, a solution to forecast the gross revenue of credit card products was developed. This solution was based on 1) A set of attributes from invoice information. 2) Zero inflated beta regression for forecasts of interchange and revolving revenue (by using PROC NLMIXED and by building data processing routines (with attributes and a target variable)). 3) Gradient boosting models for different product forecasts (annuity, insurance, etc.) using PROC TREEBOOST, exploring its parameters, and creating a routine for selecting and adjusting models. 4) Construction of ranges of revenue for policies and monitoring. This presentation introduces this credit card revenue forecasting solution.

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'.

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.

Dealing with analysts and managers who do not know how to or want to use SAS^® can be quite tricky if everything you are doing uses SAS. This is where stored processes using SAS^® Enterprise Guide^® comes in handy. Once you know what they want to get out of the code, prompts can be defined in a smart and flexible way to give all users (whether they are SAS or not) full control over the output of the code. The key is having code that requires minimal maintenance and for you to be very flexible so that you can accommodate anything that the user comes up with. This session provides examples of credit risk stress testing where loss forecasting results were presented using different levels. Results were driven by a stored process prompt using a simple DATA step, PROC SQL, and PROC REPORT. This functionality can be used in other industries where data is shown using different levels of granularity.

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.

At the University of Central Florida (UCF), Student Development and Enrollment Services (SDES) combined efforts with Institutional Knowledge Management (IKM), which is the official source of data at UCF, to venture in a partnership to bring to life an electronic version of the SDES Dashboard at UCF. Previously, SDES invested over two months in a manual process to create a booklet with graphs and data that was not vetted by IKM; upon review, IKM detected many data errors plus inconsistencies in the figures that had been manually collected by multiple staff members over the years. The objective was to redesign this booklet using SAS^® Web Report Studio. The result was a collection of five major reports. IKM reports use SAS^® Business Intelligence (BI) tools to surface the official UCF data, which is provided to the State of Florida. Now it just takes less than an hour to refresh these reports for the next academic year cycle. Challenges in the design, implementation, usage, and performance are presented.

Every sourcing and procurement department has limited resources to use for realizing productivity (cost savings). In practice, many organizations simply schedule yearly pricing negotiations with their main suppliers. They do not deviate from that approach unless there is a very large swing in the underlying commodity. Using cost data gleaned from previous quotes and SAS^® Enterprise Guide^®, we can put in place a program and methodology that move the practice from gut instinct to quantifiable and justifiable models that can easily be updated on a monthly basis. From these updated models, we can print a report of suppliers or categories that we should consider for cost downs, and suppliers or categories that we should work on to hold current pricing. By having all cost models, commodity data, and reporting functions within SAS Enterprise Guide, we are able to not only increase the precision and effectiveness of our negotiations, but also to vastly decrease the load of repetitive work that has been traditionally placed on supporting analysts. Now the analyst can execute the program, send the initial reports to the management team, and be leveraged for other projects and tasks. Moreover, the management team can have confidence in the analysis and the recommended plan of action.

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.

You might scream in pain or cry with joy that SAS^® software can directly produce output in Microsoft Excel as .xlsx workbooks. Excel is an excellent vehicle for delivering large amounts of summary information that needs to be partitioned for human review, exploratory filtering, and sorting. SAS supports ODS EXCEL as a production destination. This paper discusses using the ODS EXCEL statement and the TABULATE and REPORT procedures in the domain of summarizing cross-sectional data extracted from a medical claims database. The discussion covers data preparation, report preparation, and tabulation statements such as CLASS, CLASSLEV, and TABLE. The effects of STYLE options and the TAGATTR suboption for inserting features that are specific to Excel such as formulas, formats, and alignment are covered in detail. A short discussion of reusing these concepts in PROC REPORT statements such as DEFINE, COMPUTE, and CALL DEFINE are also covered.

Visualizing the movement of people over time in an animation can provide insights that tables and static graphs cannot. There are many options, but what if you want to base the visualization on large amounts of data from several sources? SAS^® is a great tool for this type of project. This paper summarizes how visualizing movement is accomplished using several data sets, large and small, and using various SAS procedures to pull it together. The use of a custom shape file is also highlighted. The end result is a GIF, which can be shared, that provides insights not available with other methods.

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%.

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.

Increasingly, customers are using social media and other Internet-based applications such as review sites and discussion boards to voice their opinions and express their sentiments about brands. Such spontaneous and unsolicited customer feedback can provide brand managers with valuable insights about competing brands. There is a general consensus that listening to and reacting to the voice of the customer is a vital component of brand management. However, the unstructured, qualitative, and textual nature of customer data that is obtained from customers poses significant challenges for data scientists and business analysts. In this paper, we propose a methodology that can help brand managers visualize the competitive structure of a market based on an analysis of customer perceptions and sentiments that are obtained from blogs, discussion boards, review sites, and other similar sources. The brand map is designed to graphically represent the association of product features with brands, thus helping brand managers assess a brand's true strengths and weaknesses based on the voice of customers. Our multi-stage methodology uses the principles of topic modeling and sentiment analysis in text mining. The results of text mining are analyzed using correspondence analysis to graphically represent the differentiating attributes of each brand. We empirically demonstrate the utility of our methodology by using data collected from Edmunds.com, a popular review site for car buyers.

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.

In the world of gambling, superstition drives behavior, which can be difficult to explain. Conflicting evidence suggests that slot machines, like BCLC's Cleopatra, perform well regardless of where they are placed on a casino floor. Other evidence disputes this, arguing that performance is driven by their strategic placement (for example, in high-traffic areas). We explore and quantify the location sensitivity of slot machines by leveraging SAS^® to develop robust models. We test various methodologies and data import techniques (such as casino CAD floor plans) to unlock some of the nebulous concepts of player behavior, product performance, and superstition. By demystifying location sensitivity, key drivers of performance can be identified to aid in optimizing the placement of slot machines.

Global trade and more people and freight moving across international borders present border and security agencies with a difficult challenge. While supporting freedom of movement, agencies must minimize risks, preserve national security, guarantee that correct duties are collected, deploy human resources to the right place, and ensure that additional checks do not result in increased delays for passengers or cargo. To meet these objectives, border agencies must make the most efficient use of their data, which is often found across disparate intelligence sources. Bringing this data together with powerful analytics can help them identify suspicious events, highlight areas of risk, process watch lists, and notify relevant agents so that they can investigate, take immediate action to intercept illegal or high-risk activities, and report findings. With SAS^® Visual Investigator, organizations can use advanced analytical models and surveillance scenarios to identify and score events, and to deliver them to agents and intelligence analysts for investigation and action. SAS Visual Investigator provides analysts with a holistic view of people, cargo, relationships, social networks, patterns, and anomalies, which they can explore through interactive visualizations before capturing their decision and initiating an action.