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.

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.

Manufacturers of any product from toys to medicine to automobiles must create items that are, above all else, safe to use. Not only is this essential to long-term brand value and corporate success, but it's also required by law. Although perfection is the goal, defects are bound to occur, especially in advanced products such as automobiles. Automobiles are the largest purchase most people make, next to a house. When something that costs tens of thousands of dollars runs into problems, you tend to remember. Recalls in part reflect growing pains after decades of consolidation in the auto industry. Many believe that recalls are the culmination of years of neglect by manufacturers and the agencies that regulate them. For several reasons, automakers are acting earlier and more often in issuing recalls. In the past 20 years, the number of voluntarily recalled vehicles has steadily grown. The automotive-recall landscape changed dramatically in 2000 with the passage of the federal TREAD Act. Before that, federal law required that automakers issue a recall only when a consumer reported a problem. TREAD requires that companies identify potential problems and promptly notify the NHTSA. This is largely due to stricter laws, heavier fines, and more cautious car makers. This study helps automobile manufacturers understand customers who are talking about defects in their cars and to be proactive in recalling the product at the right time before the Government acts.

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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.

Uber has changed the face of taxi ridership, making it more convenient and comfortable for riders. But, there are times when customers are dissatisfied because of a shortage of Uber vehicles, which ultimately leads to Uber surge pricing. It's a very difficult task to forecast the number of riders at different locations in a city at different points in time. This gets more complicated with changes in weather. In this paper, we attempt to estimate the number of trips per borough on a daily basis in New York City. We add an exogenous factor weather to this analysis to see how it impacts the changes in the number of trips. We fetched six months worth of data (approximately 9.7 million records) of Uber rides in New York City ranging from January 2015 to June 2015 from GitHub. We gathered weather data (about 3.5 million records) for New York City for the same period from the National Climatic Data Center. We analyzed Uber data and weather data together to estimate the change in the number of trips per borough due to changing weather conditions. We built a model to predict the number of trips per day for a one-week-ahead forecast for each borough of New York City. As part of a further analysis, we got the number of trips on a particular day for each borough. Using time series analysis, we forecast the number of trips that might be required in the near future (probably one week).

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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.

The use of telematics data within the insurance industry is becoming prevalent as insurers use this data to give discounts, categorize drivers, and provide feedback to improve customers' driving. The data captured through in-vehicle or mobile devices includes acceleration, braking, speed, mileage, and many other events. Data elements are analyzed to determine high-risk events such as rapid acceleration, hard braking, quick turning, and so on. The time between these successive high-risk events is a function of the mileage driven and time in the telematics program. Our discussion highlights how we treated these high-risk events as recurrent events and analyzed them using the RELIABILITY procedure within SAS/QC^® software. The RELIABILITY procedure is used to determine a nonparametric mean cumulative function (MCF) of high-risk events. We illustrate the use of the MCF for identifying and categorizing average driver behavior versus individual driver behavior. We also discuss the use of the MCF to evaluate how a loss event or driver feedback can affect future driving behavior.

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.

It's essential that SAS^® users enhance their skills to implement best-practice programming techniques when using Base SAS^® software. This presentation illustrates core concepts with examples to ensure that code is readable, clearly written, understandable, structured, portable, and maintainable. Attendees learn how to apply good programming techniques including implementing naming conventions for data sets, variables, programs, and libraries; code appearance and structure using modular design, logic scenarios, controlled loops, subroutines and embedded control flow; code compatibility and portability across applications and operating platforms; developing readable code and program documentation; applying statements, options, and definitions to achieve the greatest advantage in the program environment; and implementing program generality into code to enable its continued operation with little or no modifications.

This paper shows how to use Base SAS^® to create unique datetime stamps that can be used for naming external files. These filenames provide automatic versioning for systems and are intuitive and completely sortable. In addition, they provide enhanced flexibility compared to generation data sets, which can be created by SAS^® or by the operating system.

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.

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.

The SAS^® 9.4 SGPLOT procedure is a great tool for creating all types of graphs, from business graphs to complex clinical graphs. The goal for such graphs is to convey the data in a simple and direct manner with minimal distractions. But often, you need to grab the attention of a reader in the midst of a sea of data and graphs. For such cases, you need a visual that can stand out above the rest of the noise. Such visuals insert a decorative flavor into the graph to attract the eye of the reader and to encourage them to spend more time studying the visual. This presentation discusses how you can create such attention-grabbing visuals using the SGPLOT procedure.

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.

For all healthcare systems, considerable attention and resources are directed at gauging and improving patient satisfaction. Dignity Health has made considerable efforts in improving most areas of patient satisfaction. However, improving metrics around physician interaction with patients has been challenging. Failure to improve these publicly reported scores can result in reimbursement penalties, damage to Dignity's brand and an increased risk of patient harm. One possible way to improve these scores is to better identify the physicians that present the best opportunity for positive change. Currently, the survey tool mandated by the Centers for Medicare and Medicaid Services (CMS), the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS), has three questions centered on patient experience with providers, specifically concerning listening, respect, and clarity of conversation. For purposes of relating patient satisfaction scores to physicians, Dignity Health has assigned scores based on the attending physician at discharge. By conducting a manual record review, it was determined that this method rarely corresponds to the manual review (PPV = 20.7%, 95% CI: 9.9% -38.4%). Using a variety of SAS^® tools and predictive modeling programs, we developed a logistic regression model that had better agreement with chart abstractors (PPV = 75.9%, 95% CI: 57.9% - 87.8%). By attributing providers based on this predictive model, opportunities for improvement can be more accurately targeted, resulting in improved patient satisfaction and outcomes while protecting fiscal health.

Applying solutions for recommending products to final customers in e-commerce is already a known practice. Crossing consumer profile information with their behavior tends to generate results that are more than satisfactory for the business. Natura's challenge was to create the same type of solution for their sales representatives in the platform used for ordering. The sales representatives are not buying for their own consumption, but rather are ordering according to the demands of their customers. That is the difference, because in this case the analysts does not have information about the behavior or preferences of the final client. By creating a basket product concept for their sales representatives, Natura developed a new solution. Natura developed an algorithm using association analysis (Market Basket) and implemented this directly in the sales platform using SAS^® Real-Time Decision Manager. Measuring the results in indications conversion (products added in the requests), the amount brought in by the new solution was 53% higher than indications that used random suggestions, and 38% higher than those that used business rules.

Creating an environment that enables and empowers self-service and agile analytic capabilities requires a tremendous amount of working together and extensive agreements between IT and the business. Business and IT users are struggling to know what version of the data is valid, where they should get the data from, and how to combine and aggregate all the data sources to apply analytics and deliver results in a timely manner. All the while, IT is struggling to supply the business with more and more data that is becoming available through many different data sources such as the Internet, sensors, the Internet of Things, and others. In addition, once they start trying to join and aggregate all the different types of data, the manual coding can be very complicated and tedious, can demand extraneous resources and processing, and can negatively impact the overhead on the system. If IT enables agile analytics in a data lab, it can alleviate many of these issues, increase productivity, and deliver an effective self-service environment for all users. This self-service environment using SAS^® analytics in Teradata has decreased the time required to prepare the data and develop the statistical data model, and delivered faster results in minutes compared to days or even weeks. This session discusses how you can enable agile analytics in a data lab, leverage SAS analytics in Teradata to increase performance, and learn how hundreds of organizations have adopted this concept to deliver self-service capabilities in a streamlined process.

Randomized control trials have long been considered the gold standard for establishing causal treatment effects. Can causal effects be reasonably estimated from observational data too? In observational studies, you observe treatment T and outcome Y without controlling confounding variables that might explain the observed associations between T and Y. Estimating the causal effect of treatment T therefore requires adjustments that remove the effects of the confounding variables. The new CAUSALTRT (causal-treat) procedure in SAS/STAT^® 14.2 enables you to estimate the causal effect of a treatment decision by modeling either the treatment assignment T or the outcome Y, or both. Specifically, modeling the treatment leads to the inverse probability weighting methods, and modeling the outcome leads to the regression methods. Combined modeling of the treatment and outcome leads to doubly robust methods that can provide unbiased estimates for the treatment effect even if one of the models is misspecified. This paper reviews the statistical methods that are implemented in the CAUSALTRT procedure and includes examples of how you can use this procedure to estimate causal effects from observational data. This paper also illustrates some other important features of the CAUSALTRT procedure, including bootstrap resampling, covariate balance diagnostics, and statistical graphics.

Implementation of state transition models for loan-level portfolio evaluation was an arduous task until now. Several features have been added to the SAS^® High-Performance Risk engine that greatly enhance the ability of users to implement and execute these complex, loan-level models. These new features include model methods, model groups, and transition matrix functions. These features eliminate unnecessary and redundant calculations; enable the user to seamlessly interconnect systems of models; and automatically handle the bulk of the process logic in model implementation that users would otherwise need to code themselves. These added features reduce both the time and effort needed to set up model implementation processes, as well as significantly reduce model run time. This paper describes these new features in detail. In addition, we show how these powerful models can be easily implemented by using SAS^® Model Implementation Platform with SAS^® 9.4. This implementation can help many financial institutions take a huge leap forward in their modeling capabilities.

Credit card fraud. Loan fraud. Online banking fraud. Money laundering. Terrorism financing. Identity theft. The strains that modern criminals are placing on financial and government institutions demands new approaches to detecting and fighting crime. Traditional methods of analyzing large data sets on a periodic, batch basis are no longer sufficient. SAS^® Event Stream Processing provides a framework and run-time architecture for building and deploying analytical models that run continuously on streams of incoming data, which can come from virtually any source: message queues, databases, files, TCP\IP sockets, and so on. SAS^® Visual Scenario Designer is a powerful tool for developing, testing, and deploying aggregations, models, and rule sets that run in the SAS^® Event Stream Processing Engine. This session explores the technology architecture, data flow, tools, and methodologies that are required to build a solution based on SAS Visual Scenario Designer that enables organizations to fight crime in real time.

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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Because many SAS^® users either work for or own companies that house big data, the threat that malicious software poses becomes even more extreme. Malicious software, often abbreviated as malware, includes many different classifications, ways of infection, and methods of attack. This E-Poster highlights the types of malware, detection strategies, and removal methods. It provides guidelines to secure essential assets and prevent future malware breaches.

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

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

The clock is ticking! Is your company ready for May 25, 2018 when the General Data Protection Regulation that affects data privacy laws across Europe comes into force? If companies fail to comply, they incur very large fines and might lose customer trust if sensitive information is compromised. With data streaming in from multiple channels in different formats, sizes, and wavering quality, it is increasingly difficult to keep track of personal data so that you can protect it. SAS^® Data Management helps companies on their journey toward governance and compliance involving tasks such as detection, quality assurance, and protection of personal data. This paper focuses on using SAS^® Federation Server and SAS^® Data Management Studio in the SAS^® data management suite of products to surface and manage that hard-to find-personal data. SAS Federation Server provides you with a universal way to access data in Hadoop, Teradata, SQL Server, Oracle, SAP HANA, and other types of data without data movement during processing. The advanced data masking and encryption capabilities of SAS Federation Server can be use when virtualizing data for users. Purpose-built data quality functions are used to perform identification analysis, parsing, and matching and extraction of personal data in real time. We also provide insight to how the exploratory data analysis capability of SAS^® Data Management Studio enables you to scan through your investigation hub to identify and categorize personal data.

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.

Every organization, from the most mature to a day-one start-up, needs to grow organically. A deep understanding of internal customer and operational data is the single biggest catalyst to develop and sustain the data. Advanced analytics and big data directly feed into this, and there are best practices that any organization (across the entire growth curve) can adopt to drive success. Analytics teams can be drivers of growth. But to be truly effective, key best practices need to be implemented. These practices include in-the-weeds details, like the approach to data hygiene, as well as strategic practices, like team structure and model governance. When executed poorly, business leadership and the analytics team are unable to communicate with each other they talk past each other and do not work together toward a common goal. When executed well, the analytics team is part of the business solution, aligned with the needs of business decision-makers, and drives the organization forward. Through our engagements, we have discovered best practices in three key areas. All three are critical to analytics team effectiveness. 1) Data Hygiene 2) Complex Statistical Modeling 3) Team Collaboration

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.

A new ODS destination for creating Microsoft Excel workbooks is available starting in the third maintenance release for SAS^® 9.4. This destination creates native Microsoft Excel XLSX files, supports graphic images, and offers other advantages over the older ExcelXP tagset. In this presentation, you learn step-by-step techniques for quickly and easily creating attractive multi-sheet Excel workbooks that contain your SAS^® output. The techniques can be used regardless of the platform on which SAS software is installed. You can even use them on a mainframe! Creating and delivering your workbooks on demand and in real time using SAS server technology is discussed. Using earlier versions of SAS to create multi-sheet workbooks is also discussed. Although the title is similar to previous presentations by this author, this presentation contains new and revised material not previously presented.

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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.

People typically invest in more than one stock to help diversify their risk. These stock portfolios are a collection of assets that each have their own inherit risk. If you know the future risk of each of the assets, you can optimize how much of each asset to keep in the portfolio. The real challenge is trying to evaluate the potential future risk of these assets. Different techniques provide different forecasts, which can drastically change the optimal allocation of assets. This talk presents a case study of portfolio optimization in three different scenarios historical standard deviation estimation, capital asset pricing model (CAPM), and GARCH-based volatility modeling. The structure and results of these three approaches are discussed.

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^®.

Whether you are a current SAS^® Marketing Optimization user who wants to fine tune your scenarios, a SAS^® Marketing Automation user who wants to understand more about how SAS Marketing Optimization might improve your campaigns, or completely new to the world of marketing optimizations, this session covers ideas and insights for getting the highest strategic impact out of SAS Marketing Optimization. SAS Marketing Optimization is powerful analytical software, but like all software, what you get out is largely predicated by what you put in. Building scenarios is as much an art as it is a science, and how you build those scenarios directly impacts your results. What questions should you be asking to establish the best objectives? What suppressions should you consider? We develop and compare multiple what-if scenarios and discuss how to leverage SAS Marketing Optimization as a business decisioning tool in order to determine the best scenarios to deploy for your campaigns. We include examples from various industries including retail, financial services, telco, and utilities. The following topics are discussed in depth: establishing high-impact objectives, with an emphasis on setting objectives that impact organizational key performance indicators (KPIs); performing and interpreting sensitivity analysis; return on investment (ROI); evaluating opportunity costs; and comparing what-if scenarios.

The analytical data life cycle consists of 4 stages: data exploration, preparation, model development, and model deployment. Traditionally, these stages can consume 80% of the time and resources within your organization. With innovative techniques such as in-database and in-memory processing, managing data and analytics can be streamlined, with an increase in performance, economics, and governance. This session explores how you can optimize the analytical data life cycle with some best practices and tips using SAS^® and Teradata.

Production forecasts that are based on data analytics are able to capture the character of the patterns that are created by past behavior of wells and reservoirs. Future trends are a reflection of past trends unless operating principles have changed. Therefore, the forecasts are more accurate than the monotonous, straight line that is provided by decline curve analysis (DCA). The patterns provide some distinct advantages: they provide a range instead of an absolute number, and the periods of high and low performance can be used for better planning. When used together with DCA, the current method of using data driven production forecasting can certainly enhance the value tremendously for the oil and gas industry, especially in times of volatility in the global oil and gas industry.

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.

SAS^® Enterprise Guide^® empowers organizations, programmers, business analysts, statisticians, and end users with all the capabilities that SAS has to offer. This hands-on workshop presents the SAS Enterprise Guide graphical user interface (GUI). It covers access to multi-platform enterprise data sources, various data manipulation techniques that do not require you to learn complex coding constructs, built-in wizards for performing reporting and analytical tasks, the delivery of data and results to a variety of mediums and outlets, and support for data management and documentation requirements. Attendees learn how to use the graphical user interface to access SAS^® data sets and tab-delimited and Microsoft Excel input files; to subset and summarize data; to join (or merge) two tables together; to flexibly export results to HTML, PDF, and Excel; and to visually manage projects using flow diagrams.

SAS^® Enterprise Guide^® empowers organizations, programmers, business analysts, statisticians, and users with all the capabilities that SAS^® has to offer. This hands-on workshop presents the SAS Enterprise Guide graphical user interface (GUI), access to multi-platform enterprise data sources, various data manipulation techniques without the need to learn complex coding constructs, built-in wizards for performing reporting and analytical tasks, the delivery of data and results to a variety of mediums and outlets, and support for data management and documentation requirements. Attendees learn how to use the GUI to access SAS data sets and tab-delimited and Excel input files; how to subset and summarize data; how to join (or merge) two tables together; how to flexibly export results to HTML, PDF, and Excel; and how to visually manage projects using flow diagrams.

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This session introduces how Equifax uses SAS^® to develop a streamlined model automation process, including development and performance monitoring. The tool increases modeling efficiency and accuracy of the model, reduces error, and generates insights. You can integrate more advanced analytics tools in a later phase. The process can apply in any given business problem in risk and marketing, which helps leaders to make precise and accurate business decisions.

We live in a world of data; small data, big data, and data in every conceivable size between small and big. In today's world, data finds its way into our lives wherever we are. We talk about data, create data, read data, transmit data, receive data, and save data constantly during any given hour in a day, and we still want and need more. So, we collect even more data at work, in meetings, at home, on our smartphones, in emails, in voice messages, sifting through financial reports, analyzing profits and losses, watching streaming videos, playing computer games, comparing sports teams and favorite players, and countless other ways. Data is growing and being collected at such astounding rates, all in the hope of being able to better understand the world around us. As SAS^® professionals, the world of data offers many new and exciting opportunities, but it also presents a frightening realization that data sources might very well contain a host of integrity issues that need to be resolved first. This presentation describes the available methods to remove duplicate observations (or rows) from data sets (or tables) based on the row's values and keys using SAS.

Have you heard of SAS^® Customer Intelligence 360, the program for creating a digital marketing SasS offering on a multi-tenant SAS cloud? Were you mesmerized by it but found it overwhelming? Did you tell yourself, I wish someone would show me how to do this ? This paper is for you. This paper provides you with an easy, step-by-step procedure on how to create a successful digital web, mobile, and email marketing campaign. In addition to these basics, the paper points to resources that allow you to get deeper into the application and customize each object to satisfy your marketing needs.

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.

Self-driving cars are no longer a futuristic dream. In the recent past, Google has launched a prototype of the self-driving car, while Apple is also developing its own self-driving car. Companies like Tesla have just introduced an Auto Pilot version in their newer version of electric cars which have created quite a buzz in the car market. This technology is said to enable aging or disabled people to remain mobile, while also increasing overall traffic saftery. But many people are still skeptical about the idea of self-driving cars, and that's our area of interest. In this project, we plan to do sentiment analysis on thoughts voiced by people on the Internet about self-driving cars. We have obtained the data from http://www.crowdflower.com/data-for-everyone which contain these reviews about the self-driving cars. Our dataset contains 7,156 observations and 9 variables. We plan to do descriptive analysis of the reviews to identify key topics and then use supervised sentiment analysis. We also plan to track and report how the topics and the sentiments change over time.

Imagine if you will a program, a program that loves its data, a program that loves its data to be in the same directory as the program itself. Together, in the same directory. True love. The program loves its data so much, it just refers to it by filename. No need to say what directory the data is in; it is the same directory. Now imagine that program being thrust into the world of the server. The server knows not what directory this program resides in. The server is an uncaring, but powerful, soul. Yet, when the program is executing, and the program refers to the data just by filename, the server bellows nay, no path, no data. A knight in shining armor emerges, in the form of a SAS^® macro, who says lo, with the help of the SAS^® Enterprise Guide^® macro variable minions, I can gift you with the location of the program directory and send that with you to yon mighty server. And there was much rejoicing. Yay. This paper shows you a SAS macro that you can include in your SAS Enterprise Guide pre-code to automatically set your present working directory to the same directory where your program is saved on your UNIX or Linux operating system. This is applicable to submitting to any type of server, including a SAS Grid Server. It gives you the flexibility of moving your code and data to different locations without having to worry about modifying the code. It also helps save time by not specifying complete pathnames in your programs. And can't we all use a little more time?

In 2012, US Customs scanned nearly 4% and physically inspected less than 1% of the 11.5 million cargo containers that entered the United States. Laundering money through trade is one of the three primary methods used by criminals and terrorists. The other two methods used to launder money are using financial institutions and physically moving money via cash couriers. The Financial Action Task Force (FATF) roughly defines trade-based money laundering (TBML) as disguising proceeds from criminal activity by moving value through the use of trade transactions in an attempt to legitimize their illicit origins. As compared to other methods, this method of money laundering receives far less attention than those that use financial institutions and couriers. As countries have budget shortfalls and realize the potential loss of revenue through fraudulent trade, they are becoming more interested in TBML. Like many problems, applying detection methods against relevant data can result in meaningful insights, and can result in the ability to investigate and bring to justice those perpetuating fraud. In this paper, we apply TBML red flag indicators, as defined by John A. Cassara, against shipping and trade data to detect and explore potentially suspicious transactions. (John A. Cassara is an expert in anti-money laundering and counter-terrorism, and author of the book Trade-Based Money Laundering. ) We use the latest detection tool in SAS^® Viya , along with SAS^® Visual Investigator.

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.

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.

This project described the method to classify movie genres based on synopses text data by two approaches: term frequency, and inverse document frequency (tf-idf) and C4.5 decision tree. Using the performance comparison of the classifiers by manipulating the different parameters, the strength and improvement of this method in substantial text analysis were also interpreted. As the result, these two approaches are powerful to identify movie genres.

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Does your job require you to create reports in Microsoft Excel on a quarterly, monthly, or even weekly basis? Are you creating all or part of these reports by hand, referencing another sheet containing rows and rows and rows of data? If so, stop! There is a better way! The new ODS destination for Excel enables you to create native Excel files directly from SAS^®. Now you can include just the data you need, create great-looking tabular output, and do it all in a fraction of the time! This paper shows you how to use the REPORT procedure to create polished tables that contain formulas, colored cells, and other customized formatting. Also presented in the paper are the destination options used to create various workbook structures, such as multiple tables per worksheet. Using these techniques to automate the creation of your Excel reports will save you hours of time and frustration, enabling you to pursue other endeavors.

Machine learning is not just for data scientists. Business analysts can use machine learning to discover rules from historical decision data or from historical performance data. Decision tree learning and logistic regression scorecard learning are available for standard data tables, and Associations Analysis is available for transactional event tables. These rules can be edited and optimized for changing business conditions and policies, and then deployed into automated decision-making systems. Users will see demonstrations using real data and will learn how to apply machine learning to business problems.

As part of the Talanx Group, HDI Insurance has been one of the leading insurers in Brazil. Recently HDI Brazil implemented an innovative and integrated solution to prevent fraud in the Auto Claims process based on SAS^® Fraud Framework and SAS^® Real-time Decision Manager. A car fix or a refund is approved immediately after the claim registration for those customers who have no suspicious information. On the other hand, the high-scored claims are checked by the inspectors using SAS^® Social Network Analysis. In terms of analytics, the solution has a hybrid approach working with predictive models, business rules, anomalies, and network relationship. The main benefits are a reduction in the amount of fraud, more accuracy in determining the claims to be investigated, a decrease in the false-positive rate, and the use of a relationship network to investigate suspicious connections.

A Middle Eastern company is responsible for daily distribution of over 230 million liters of oil products. For this distribution network, a failure scenario is defined as occurring when oil transport is interrupted or slows down, and/or when product demands fluctuate outside the normal range. Under all failure scenarios, the company plans to provide additional transport capacity at minimum cost so as to meet all point-to-point product demands. Currently, the company uses a wait-and-see strategy, which carries a high operating cost and depends on the availability of third-party transportation. This paper describes the use of the OPTMODEL procedure to implement a mixed integer programming model to model and solve this problem. Experimental results are provided to demonstrate the utility of this approach. It was discovered that larger instances of the problem, with greater numbers of potential failure scenarios, can become computationally extensive. In order to efficiently handle such instances of the problem, we have also implemented a Benders decomposition algorithm in PROC OPTMODEL.

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.

Students now have access to a SAS^® learning tool called SAS^® University Edition. This online tool is freely available to all, for non-commercial use. This means it is basically a free version of SAS that can be used to teach yourself or someone else how to use SAS. Since a large part of my body of writings has focused upon moving data between SAS and Microsoft Excel, I thought I would take some time to highlight the tasks that permit movement of data between SAS and Excel using SAS University Edition. This paper is directed toward sending graphs to Excel using the new ODS EXCEL destination.

More than ever, customers are demanding consistent and relevant interaction across all channels. Businesses are having to develop omnichannel marketing capabilities to please these customers. Implementing omnichannel marketing is often difficult, especially when using digital channels. Most products designed solely for digital channels lack capabilities to integrate with traditional channels that have on-premises processes and data. SAS^® Customer Intelligence 360 is a new offering that enables businesses to leverage both cloud and on-premises channels and data. This is possible due to the solution's hybrid cloud architecture. This paper discusses the SAS Customer Intelligence 360 approach to the hybrid cloud, and covers key capabilities on security, throughput, and integration.

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.

The latest releases of SAS^® Data Management software provide a comprehensive and integrated set of capabilities for collecting, transforming, and managing your data. The latest features in the product suite include capabilities for working with data from a wide variety of environments and types including Hadoop, cloud data sources, RDBMS, files, unstructured data, streaming, and others, and the ability to perform ETL and ELT transformations in diverse run-time environments including SAS^®, database systems, Hadoop, Spark, SAS^® Analytics, cloud, and data virtualization environments. There are also new capabilities for lineage, impact analysis, clustering, and other data governance features for enhancements to master data and support metadata management. This paper provides an overview of the latest features of the SAS^® Data Management product suite and includes use cases and examples for leveraging product capabilities.

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.