With increasing data needs, it becomes more and more unwieldy to ensure that all scheduled jobs are running successfully and on time. Worse, maintaining your reputation as an information provider becomes a precarious prospect as the likelihood increases that your customers alert you of reporting issues before you are even aware yourself. By combining various SAS^® capabilities and tying them together with concise visualizations, it is possible to track jobs actively and alert customers of issues before they become a problem. This paper introduces a report tracking framework that helps achieve this goal and improve customer satisfaction. The report tracking starts by obtaining table and job statuses and then color-codes them by severity levels. Based on the job status, it then goes deeper into the log to search for potential errors or other relevant information to help assess the processes. The last step is to send proactive alerts to users informing them of possible delays or potential data issues.

The new and highly anticipated SAS^® Output Delivery System (ODS) destination for Microsoft Excel is finally here! Available as a production feature in the third maintenance release of SAS^® 9.4 (TS1M3), this new destination generates native Excel (XLSX) files that are compatible with Microsoft Office 2010 or later. This paper is written for anyone, from entry-level programmers to business analysts, who uses the SAS^® System and Microsoft Excel to create reports. The discussion covers features and benefits of the new Excel destination, differences between the Excel destination and the older ExcelXP tagset, and functionality that exists in the ExcelXP tagset that is not available in the Excel destination. These topics are all illustrated with meaningful examples. The paper also explains how you can bridge the gap that exists as a result of differences in the functionality between the destination and the tagset. In addition, the discussion outlines when it is beneficial for you to use the Excel destination versus the ExcelXP tagset, and vice versa. After reading this paper, you should be able to make an informed decision about which tool best meets your needs.

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This paper presents how Norway, the world's second-largest seafood-exporting country, shares valuable seafood insight using the SAS^® Visual Analytics Designer. Three complementary data sources: trade statistics, consumption panel data, and consumer survey data, are used to strengthen the knowledge and understanding about the important markets for seafood, which is a potential competitive advantage for the Norwegian seafood industry. The need for information varies across users and as the amount of data available is growing, the challenge is to make the information available for everyone, everywhere, at any time. Some users are interested in only the latest trade developments, while others working with product innovation are in need of deeper consumer insights. Some have quite advanced analytical skills, while others do not. Thus, one of the most important things is to make the information understandable for everyone, and at the same time provide in-depth insights for the advanced user. SAS Visual Analytics Designer makes it possible to provide both basic reporting and more in-depth analyses on trends and relationships to cover the various needs. This paper demonstrates how the functionality in SAS Visual Analytics Designer is fully used for this purpose, and presents how data from different sources is visualized in SAS Visual Analytics Designer reports located in the SAS^® Information Delivery Portal. The main challenges and suggestions for improvements that have been uncovered during the process are also presented in this paper.

Government funding is a highly sought-after financing medium by entrepreneurs and researchers aspiring to make a breakthrough in the market and compete with larger organizations. The funding is channeled via federal agencies that seek out promising research and products that improve the extant work. This study analyzes the project abstracts that earned the government funding through a text analytic approach over the past three and a half decades in the fields of Defense and Health and Human Services. This helps us understand the factors that might be responsible for awards made to a project. We collected the data of 100,279 records from the Small Business Innovation and Research (SBIR) website (https://www.sbir.gov). Initially, we analyze the trends in government funding over research by the small businesses. Then we perform text mining on the 55,791 abstracts of the projects that are funded by the Department of Defense and the Department of Health. From the text mining, we portray the key topics and patterns related to the past research and determine the changes in their trends over time. Through our study, we highlight the research trends to enable organizations to shape their business strategy and make better investments in the future research.

Although business intelligence experts agree that empowering businesses through a well-constructed semantic layer has undisputed benefits, a successful implementation has always been a formidable challenge. This presentation highlights the best practices to follow and mistakes to avoid, leading to a successful semantic layer implementation by using SAS^® Visual Analytics. A correctly implemented semantic layer provides business users with quick and easy access to information for analytical and fact-based decision-making. Today, everyone talks about how the modern data platform enables businesses to store and analyze big data, but we still see most businesses trying to generate value from the data that they already store. From self-service to data visualization, business intelligence and descriptive analytics are still the key requirements for any business, and we discuss how to use SAS Visual Analytics to address them all. We also describe the key considerations in strategy, people, process, and data for a successful semantic layer rollout that uses SAS Visual Analytics.

Eandis is a rapidly growing energy distribution grid operator in the heart of Europe, with requirements to manage power distribution on behalf of 229 municipalities in Belgium. With a legacy SAP data warehouse and other diverse data sources, business leaders at Eandis faced challenges with timely analysis of key issues such as power quality, investment planning, and asset management. To face those challenges, a new agile way of thinking about Business Intelligence (BI) was necessary. A sandbox environment was introduced where business key-users could explore and manipulate data. It allowed them to have approachable analytics and to build prototypes. Many pitfalls appeared and the greatest challenge was the change in mindset for both IT and business users. This presentation addresses those issues and possible solutions.

For far too long, anti-money laundering and terrorist financing solutions have forced analysts to wade through oceans of transactions and alerted work items (alerts). Alert-centered analysis is both ineffective and costly. The goal of an anti-money laundering program is to reduce risk for your financial institution, and to do this most effectively, you must start with analysis at the customer level, rather than simply troll through volumes of alerts and transactions. In this session, discover how a customer-centric approach leads to increased analyst efficiency and streamlined investigations. Rather than starting with alerts and transactions, starting with a customer-centric view allows your analysts to rapidly triage suspicious activities, prioritize work, and quickly move into investigating the highest risk customer activities.

In the Collection Direction of a well-recognized Colombian financial institution, there was no methodology that provided an optimal number of collection agents to improve the collection task and make possible that more customers be compromised with their minimum monthly payment of their debt. The objective of this paper is to apply the Data Envelopment Analysis (DEA) Optimization Methodology to determine the optimal number of agents to maximize the monthly collection in the bank. We show that the results can have a positive impact to the credit portfolio behavior and reduce the collection management cost. DEA optimization methodology has been successfully used in various fields to solve multi-criteria optimization problems, but it is not commonly used in the financial sector mostly because this methodology requires specialized software, such as SAS^® Enterprise Guide^®. In this paper, we present the PROC OPTMODEL and we show how to formulate the optimization problem, program the SAS^® Code, and how to process adequately the available data.

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Your marketing team would like to pull data from its different marketing activities into one report. What happens in Vegas might stay in Vegas, but what happens in your data does not have to stay there, locked in different tools or static spreadsheets. Learn how to easily bring data from Google Analytics, Facebook, and Twitter into SAS^® Visual Analytics to create interactive explorations and reports on this data along with your other data for better overall understanding of your marketing activity.

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A United States Department of Defense agency with over USD 40 billion in sales and revenue, 25 thousand employees, and 5.3 million parts to source, partnered with SAS^® to turn their disparate PC-based analytic environment into a modern SAS^® Grid Computing server-based architecture. This presentation discusses the challenges of under-powered desktops, data sprawl, outdated software, difficult upgrades, and inefficient compute processing and the solution crafted to enable the agency to run as the Fortune 50 company that its balance sheet (and our nation's security) demand. In the modern architecture, rolling upgrades, high availability, centralized data set storage, and improved performance enable improved forecasting getting our troops the supplies they need, when and where they need them.

Microsoft Office has over 1 billion users worldwide, making it one of the most successful pieces of software on the market today. Imagine combining the familiarity and functionality of Microsoft Office with the power of SAS^® to include SAS content in a Microsoft Office document. By using SAS^® Office Analytics, you can create Microsoft Excel worksheets that are not just static reports, but interactive documents. This paper looks at opening, filtering, and editing data in an Excel worksheet. It shows how to create an interactive experience in Excel by leveraging Visual Basic for Applications using SAS data and stored processes. Finally this paper shows how to open SAS^® Visual Analytics reports into Excel, so the interactive benefits of SAS Visual Analytics are combined with the familiar interface of an Excel worksheet. All of these interactions with SAS content are possible without leaving Microsoft Excel.

Packing a carry-on suitcase for air travel and designing a report for mobile devices have a lot in common. Your carry-on suitcase contains indispensable items for your journey, and the contents are limited by tight space. Your reports for mobile devices face similar challenges--data display is governed by tight real estate space and other factors such as users' shorter attention span and information density come into play. How do you overcome these challenges while displaying data effectively for your mobile users? This paper demonstrates how smaller real estate on mobile devices, as well as device orientation in portrait or landscape mode, influences best practices for designing reports. The use of containers, layouts, filters, information windows, and carefully selected objects enable you to design and guide user interaction effectively. Appropriate selection of font styles, font sizes, and colors reduce distraction and enhance quick user comprehension. By incorporating these recommendations into your report design, you can produce reports that display seamlessly on mobile devices and browsers.

Discover how to answer the Where? question of data visualization by leveraging SAS^® Visual Analytics. Geographical data elements within SAS Visual Analytics provides users the capability to quickly map data by countries and regions, by states or provinces, and by the centroid of US ZIP codes. This paper demonstrates how easy it is to map by these elements. Of course, once your manager sees your new maps they will ask for more granular shapes (such as US counties or US ZIP codes). Respond with Here it is! Follow the steps provided to add custom boundaries by parsing the shape files into consumable data objects and loading these custom boundaries into SAS Visual Analytics.

Transforming data into intelligence for effective decision-making support is critically based on the role and capacity of the Office of Institutional Research (OIR) in managing the institution's data. Presenters share their journey from providing spreadsheet data to developing SAS^® programs and dashboards using SAS^® Visual Analytics. Experience gained and lessons learned are also shared at this session. The presenters demonstrate two dashboards the OIR office developed: one for classroom utilization and one for the university's diversity initiatives. The presenters share the steps taken for creating the dashboard and they describe the process the office took in getting the stakeholders involved in determining the key performance indicators (KPIs) and in evaluating and providing feedback regarding the dashboard. They share their experience gained and lessons learned in building the dashboard.

Debt collection! The two words can trigger multiple images in one's mind--mostly harsh. However, let's try to think positively for a moment. In 2013, over $55 billion of debt were past due in the United States. What if all of these debts were left as is and the fate of credit issuers in the hands of good will payments made by defaulters? Well, this is not the most sustainable model, to say the least. In this situation, debt collection comes in as a tool that is employed at multiple levels of recovery to keep the credit flowing. Ranging from in-house to third-party to individual collection efforts, this industry is huge and plays an important role in keeping the engine of commerce running. In the recent past, with financial markets recovering and banks selling fewer charged-off accounts and at higher prices, debt collection has increasingly become a game of efficient operations backed by solid analytics. This paper takes you into the back alleys of all the data that is in there and gives an overview of some ways modeling can be used to impact the collection strategy and outcome. SAS^® tools such as SAS^® Enterprise Miner™ and SAS^® Enterprise Guide^® are extensively used for both data manipulation and modeling. Decision trees are given more focus to understand what factors make the most impact. Along the way, this paper also gives an idea of how analytics teams today are slowly trying to get the buy-in from other stakeholders in any company, which surprisingly is one of the most challenging aspects of our jobs.

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Electrolux is one of the largest appliance manufacturers in the world. Electrolux North America sells more than 2,000 products to end consumers through 9,000 business customers. To grow and increase profitability under challenging market conditions, Electrolux partnered with SAS^® to implement an integrated platform for SAS^® for Demand-Driven Planning and Optimization and improve service levels to its customers. The process uses historical order data to create a statistical monthly forecast. The Electrolux team then reviews the statistical forecast in SAS^® Collaborative Planning Workbench, where they can add value based on their business insights and promotional information. This improved monthly forecast is broken down to the weekly level where it flows into SAS^® Inventory Optimization Workbench. SAS Inventory Optimization Workbench then computes weekly inventory targets to satisfy the forecasted demand at the desired service level. This presentation also covers how Electrolux implemented this project. Prior to the commencement of the project, Electrolux and the SAS team jointly worked to quantify the value of the project and set the right expectations with the executive team. A detailed timeline with regular updates helped provide visibility to all stake holders. Finally, a clear change management strategy was also developed to define the roles and responsibilities after the implementation of SAS for Demand-Driven Planning and Optimization.

When you create reports in SAS^® Visual Analytics, you automatically have reports that work on mobile devices. How do you ensure that the reports are easy to use and understand on all of your desktops, tablets, and phones? This paper describes how you can design powerful reports that your users can easily view on all their devices. You also learn how to deliver reports to users effortlessly, ensuring that they always have the latest reports. Examples show you tips and techniques to use that create the best possible reports for all devices. The paper provides sample reports that you can download and interactively view on your own devices. These reports include before and after examples that illustrate why the recommended best practices are important. By using these tips and techniques you learn how to design a report once and have confidence that it can be viewed anywhere.

Phishing is the attempt of a malicious entity to acquire personal, financial, or otherwise sensitive information such as user names and passwords from recipients through the transmission of seemingly legitimate emails. By quickly alerting recipients of known phishing attacks, an organization can reduce the likelihood that a user will succumb to the request and unknowingly provide sensitive information to attackers. Methods to detect phishing attacks typically require the body of each email to be analyzed. However, most academic institutions do not have the resources to scan individual emails as they are received, nor do they wish to retain and analyze message body data. Many institutions simply rely on the education and participation of recipients within their network. Recipients are encouraged to alert information security (IS) personnel of potential attacks as they are delivered to their mailboxes. This paper explores a novel and more automated approach that uses SAS^® to examine email header and transmission data to determine likely phishing attempts that can be further analyzed by IS personnel. Previously a collection of 2,703 emails from an external filtering appliance were examined with moderate success. This paper focuses on the gains from analyzing an additional 50,000 emails, with the inclusion of an additional 30 known attacks. Real-time email traffic is exported from Splunk Enterprise into SAS for analysis. The resulting model aids in determining the effectiveness of alerting IS personnel to potential phishing attempts faster than a user simply forwarding a suspicious email to IS personnel.

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

You have SAS^® Enterprise Guide^® installed. You use SAS Enterprise Guide in your day-to-day work. You see how Enterprise Guide can be an aid to accessing data and insightful analytics. You have people you work with or support who are new to SAS^® and want to learn. You have people you work with or support who don't particularly want to code but use the GUI and wizard within Enterprise Guide. And then you have the spreadsheet addict, the person or group who refuse to even sign on to SAS. These people need to consume the data sitting in SAS, and they need to do analysis, but they want to do it all in a spreadsheet. But you need to retain an audit trail of the data, and you have to reduce the operational risk of using spreadsheets for reporting. What do you do? This paper shares some of the challenges and triumphs in empowering these very different groups of people using SAS.

What will your customer do next? Customers behave differently; they are not all average. Segmenting your customers into different groups enables you to provide different communications and interactions for the different segments, resulting in greater customer satisfaction as well as increased profits. Using SAS^® Visual Analytics and SAS^® Visual Statistics to visualize your segments with respect to customer attributes enables you to create more useful segments for customer relationship management and to understand the value and relative importance of different customer attributes. You can segment your customers by using the following methods: 1) business rules; 2) supervised clustering--decision trees and so on; 3) unsupervised clustering; 4) creating segments based on quantile membership. Whatever way you choose, SAS Visual Analytics enables you to graphically represent your customer data with respect to demographic, geographic, and customer behavioral dimensions. This paper covers the four segmentation techniques and demonstrates how SAS Visual Analytics and SAS Visual Statistics can be used for easy and comprehensive understanding of your customers.

With the big data throughputs generated by event streams, organizations can opportunistically respond with low-latency effectiveness. Having the ability to permeate identified patterns of interest throughout the enterprise requires deep integration between event stream processing and foundational enterprise data management applications. This paper describes the innovative ability to consolidate real-time data ingestion with controlled and disciplined universal data access from SAS^® and Teradata.

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

Does the rapidly changing fraud and compliance landscape make it difficult to adapt your applications to meet your current needs? Fraudsters are constantly evolving and your applications need to be able to keep up. No two businesses are ever the same. They all have different business drivers, customer needs, market demands, and strategic objectives. Using SAS^® Visual Investigator, business units can quickly adapt to their ever-changing needs and deliver what end users and customers need to be effective investigators. Using the administrative tools provided, users can quickly and easily adapt the pages and data structures that underpin applications to fit their needs. This presentation walks through the process of updating an insurance fraud detection system using SAS Visual Investigator, including changing the way alerts are routed to users, pulling in additional information, and building out application pages. It includes an example of how an end user can customize a solution due to changing fraud threats.

Injury severity describes the severity of the injury to the person involved in the crash. Understanding the factors that influence injury severity can be helpful in designing mechanisms to reduce accident fatalities. In this research, we model and analyze the data as a hierarchy with three levels to answer the question what road, vehicle and driver-related factors influence injury severity. In this study, we used hierarchical linear modeling (HLM) for analyzing nested data from Fatality Analysis Reporting System (FARS). The results show that driver-related factors are directly related to injury severity. On the other hand, road conditions and vehicle characteristics have significant moderation impact on injury severity. We believe that our study has important policy implications for designing customized mechanisms specific to each hierarchical level to reduce the occurrence of fatal accidents.

Recent years have seen the birth of a powerful tool for companies and scientists: the Google Ngram data set, built from millions of digitized books. It can be, and has been, used to learn about past and present trends in the use of words over the years. This is an invaluable asset from a business perspective, mostly because of its potential application in marketing. The choice of words has a major impact on the success of a marketing campaign and an analysis of the Google Ngram data set can validate or even suggest the choice of certain words. It can also be used to predict the next buzzwords in order to improve marketing on social media or to help measure the success of previous campaigns. The Google Ngram data set is a gift for scientists and companies, but it has to be used with a lot of care. False conclusions can easily be drawn from straightforward analysis of the data. It contains only a limited number of variables, which makes it difficult to extract valuable information from it. Through a detailed example, this paper shows that it is essential to account for the disparity in the genre of the books used to construct the data set. This paper argues that for the years after 1950, the data set has been constructed using a much higher proportion of scientific books than for the years before. An ingenious method is developed to approximate, for each year, this unknown proportion of books coming from the scientific literature. A statistical model accounting for that change in proportion is then presented. This model is used to analyze the trend in the use of common words of the scientific literature in the 20th century. Results suggest that a naive analysis of the trends in the data can be misleading.

In this era of data analytics, you are often faced with a challenge of joining data from multiple legacy systems. When the data systems share a consistent merge key, such as ID or SSN, the solution is straightforward. However, what do you do when there is no common merge key? If one data system has a character value ID field, another has an alphanumeric field, and the only common fields are the names or addresses or dates of birth, a standard merge query does not work. This paper demonstrates fuzzy matching methods that can overcome this obstacle and build your master record through Base SAS^® coding. The paper also describes how to leverage the SAS^® Data Quality Server in SAS^® code.

When attempting to match names and addresses from different files, we often run into a situation where the names are similar, but not exactly the same. Sometimes there are additional words in the names, sometimes there are different spellings, and sometimes the businesses have the same name but are located thousands of miles apart. The files that contain the names might have numeric keys that cannot be matched. Therefore, we need to use a process called fuzzy matching to match the names from different files. The SAS^® function COMPGED, combined with SAS character-handling functions, provides a straightforward method of applying business rules and testing for similarity.

Analysts find the standard linear regression and analysis-of-variance models to be extremely convenient and useful tools. The standard linear model equation form is observations = (sum of explanatory variables) + residual with the assumptions of normality and homogeneity of variance. However, these tools are unsuitable for non-normal response variables in general. Using various transformations can stabilize the variance. These transformations are often ineffective because they fail to address the skewness problem. It can be complicated to transform the estimates back to their original scale and interpret the results of the analysis. At this point, we reach the limits of the standard linear model. This paper introduces generalized linear models (GzLM) using a systematic approach to adapting linear model methods on non-normal data. Why GzLM? Generalized linear models have greater power to identify model effects as statistically significant when the data are not normally distributed (Stroup, xvii). Unlike the standard linear model, the generalized linear model contains the distribution of the observations, the linear predictor or predictors, the variance function, and the link function. A few examples show how to build a GzLM for a variety of response variables that follows a Poisson, Negative Binomial, Exponential, or Gamma distribution. The SAS/STAT^® GENMOD procedure is used to compute basic analyses.

Project management is a hot topic across many industries, and there are multiple commercial software applications for managing projects available. The reality, however, is that the majority of project management software is not applicable for daily usage. SAS^® has a solution for this issue that can be used for managing projects graphically in real time. This paper introduces a new paradigm for project management using the SAS^® Graph Template Language (GTL). SAS clients, in real time, can use GTL to visualize resource assignments, task plans, delivery tracking, and project status across multiple project levels for more efficient project management.

Would you like to be more confident in producing graphs and figures? Do you understand the differences between the OVERLAY, GRIDDED, LATTICE, DATAPANEL, and DATALATTICE layouts? Would you like to know how to easily create life sciences industry standard graphs such as adverse event timelines, Kaplan-Meier plots, and waterfall plots? Finally, would you like to learn all these methods in a relaxed environment that fosters questions? Great--this topic is for you! In this hands-on workshop, you will be guided through the Graph Template Language (GTL). You will also complete fun and challenging SAS graphics exercises to enable you to more easily retain what you have learned. This session is structured so that you will learn how to create the standard plots that your manager requests, how to easily create simple ad hoc plots for your customers, and also how to create complex graphics. You will be shown different methods to annotate your plots, including how to add Unicode characters to your plots. You will find out how to create reusable templates, which can be used by your team. Years of information have been carefully condensed into this 90-minute hands-on, highly interactive session. Feel free to bring some of your challenging graphical questions along!

SAS^® Event Stream Processing is designed to analyze and process large volumes of streaming data in motion. SAS Event Stream Processing provides a browser-based user interface that enables you to create and test event stream processing models in a visual drop-and-drag environment. This environment delivers a highly interactive and intuitive user experience. This paper describes the visual, interactive interface for building models and monitoring event stream activity. It also provides examples to demonstrate how you can easily build a model using the graphical user interface of SAS Event Stream Processing. In these examples, SAS Event Stream Processing serves as the front end to process high-velocity streams. On the back end, SAS^® Real-Time Decision Manager consumes events and makes the final decision to push the suited offer to the customer. This paper explains the concepts of windows, retention, edges, and connector. It also explains how SAS Event Stream Processing integrates with SAS Real-Time Decision Manager.

SAS^® Data Management is not a dating application. However, as a data analyst, you do strive to find the best matches for your data. Similar to a dating application, when trying to find matches for your data, you need to specify the criteria that constitutes a suitable match. You want to strike a balance between being too stringent with your criteria and under-matching your data and being too loose with your criteria and over-matching your data. This paper highlights various SAS Data Management matching techniques that you can use to strike the right balance and help your data find its perfect match, and as a result improve your data for reporting and analytics purposes.

Your organization already uses SAS^® Visual Analytics and you have designed reports for internal use. Now you want to publish a report on your external website. How do you design a report for the general public considering the wide range of education and abilities? This paper defines best practices for designing reports that are universally accessible to the broadest audience. You learn tips and techniques for designing reports that the general public can easily understand and use to gain insight. You also learn how to leverage features that help you comply with your legal obligations regarding users with disabilities. The paper includes recommendations and examples that you can apply to your own reports.

In the aftermath of the 2008 global financial crisis, banks had to improve their data risk aggregation in order to effectively identify and manage their credit exposures and credit risk, create early warning signs, and improve the ability of risk managers to challenge the business and independently assess and address evolving changes in credit risk. My presentation focuses on using SAS^® Credit Risk Dashboard to achieve all of the above. Clearly, you can use my method and principles of building a credit risk dashboard to build other dashboards for other types of risks as well (market, operational, liquidity, compliance, reputation, etc.). In addition, because every bank must integrate the various risks with a holistic view, each of the risk dashboards can be the foundation for building an effective enterprise risk management (ERM) dashboard that takes into account correlation of risks, risk tolerance, risk appetite, breaches of limits, capital allocation, risk-adjusted return on capital (RAROC), and so on. This will support the actions of top management so that the bank can meet shareholder expectations in the long term.

Looking for new ways to improve your business? Try mining your own data! Event log data is a side product of information systems generated for audit and security purposes and is seldom analyzed, especially in combination with business data. Along with the cloud computing era, more event log data has been accumulated and analysts are searching for innovative ways to take advantage of all data resources in order to get valuable insights. Process mining, a new field for discovering business patterns from event log data, has recently proved useful for business applications. Process mining shares some algorithms with data mining but it is more focused on interpretation of the detected patterns rather than prediction. Analysis of these patterns can lead to improvements in the efficiency of common existing and planned business processes. Through process mining, analysts can uncover hidden relationships between resources and activities and make changes to improve organizational structure. This paper shows you how to use SAS^® Analytics to gain insights from real event log data.

A picture is worth a thousand words, but what if there are a billion words? This is where the picture becomes even more important, and this is where infographics step in. Infographics are a representation of information in a graphic format designed to make the data easily understandable, at a glance, without having to have a deep knowledge of the data. Due to the amount of data available today, more infographics are being created to communicate information and insight from all available data, both in the boardroom and on social media. This session shows you how to create information graphics that can be printed, shared, and dynamically explored with objects and data from SAS^® Visual Analytics. Connect your infographics to the high-performance analytical engine from SAS^® for repeatability, scale, and performance on big data, and for ease of use. You will see how to leverage elements of your corporate dashboards and self-service analytics while communicating subjective information and adding the context that business teams require, in a highly visual format. This session looks at how SAS^® Office Analytics enables a Microsoft Office user to create infographics for all occasions. You will learn the workflow that lets you get the most from your SAS Visual Analytics system without having to code anything. You will leave this session with the perfect blend of creative freedom and data governance that comes from leveraging the power of SAS Visual Analytics and the familiarity of Microsoft Office.

Real-time, integrated marketing solutions are a necessity for maintaining your competitive advantage. This presentation provides a brief overview of three SAS products (SAS^® Marketing Automation, SAS^® Real-Time Decision Manager, and SAS^® Event Stream Processing) that form a basis for building modern, real-time, interactive marketing solutions. It presents typical (and also possible) customer-use cases that you can implement with a comprehensive real-time interactive marketing solution, in major industries like finance (banking), telco, and retail. It demonstrates typical functional architectures that need to be implemented to support business cases (how solution components collaborate with customer's IT landscape and with each other). And it provides examples of our experience in implementing these solutions--dos and don'ts, best practices, and what to expect from an implementation project.

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The SAS^® Output Delivery System (ODS) ExcelXP tagset offers users the ability to export a SAS data set, with all of the accompanying functions and calculations, to a Microsoft Excel spreadsheet. For industries, this is particularly useful because although not everyone is a SAS programmer, they would like to have access to and manipulate data from SAS. The ExcelXP tagset is one of several built-in templates for exporting data in SAS. The tagset gives programmers the ability to export functions and calculations into the cells of an Excel spreadsheet. Several options within the tagset enable the programmer to customize the Excel file. Some of these options enable the programmer to name the worksheet, style each table, embed titles and footnotes, and export multiple data tables to the same Excel worksheet.

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Gone are the days when the only method of receiving a loan was by visiting your local branch and working with a loan officer. In today's economy, financial institutions increasingly rely on online channels to interact with their customers. The anonymity that is inherent in this channel makes it a prime target for fraudsters. The solution is to profile the behavior of internet banking in real time and assess each transaction for risk as it is processed in order to prevent financial loss before it occurs. SAS^® Visual Scenario Designer enables you to create rules, scenarios, and models, test their impact, and inject them into real-time transaction processing using SAS^® Event Stream Processing.

Quality issues can easily go undetected in the field for months, even years. Why? Customers are more likely to complain through their social networks than directly through customer service channels. The Internet is the closest friend for most users and focusing purely on internal data isn't going to help. Customer complaints are often handled by marketing or customer service and aren't shared with engineering. This lack of integrated vision is due to the silo mentality in organizations and proves costly in the long run. Information systems are designed with business rules to find out of spec situations or to trigger when a certain threshold of reported cases is reached before engineering is involved. Complex business problems demand sophisticated technology to deliver advanced analytical capabilities. Organizations can react only to what they know about, but unstructured data is an invaluable asset that we must hatch on. So what if you could infuse the voice of the customer directly into engineering and detect emerging issues months earlier than your existing process? Lenovo just did that. Issues were detected in time to remove problematic parts, eventually leading to massive reductions in cost and time. Lenovo observed a significant decline in call volume to its corporate call center. It became evident that customers' perception of quality relative to the market is a voice we can't afford to ignore. Lenovo Corporate Analytics unit partnered with SAS^® to build an end-to-end process that constitutes data acquisition and preparation, analysis using linguistic text analytics models, and quality control metrics for visualization dashboards. Lenovo executives consume these reports and make informed decisions. In this paper, we talk in detail about perceptual quality and why we should care about it. We also tell you where to look for perceptual quality data and best practices for starting your own perceptual quality program through the voice of customer analytics.

As a result of globalization, the durable goods market has become increasingly competitive, with market conditions that challenge profitability for manufacturers. Moreover, high material costs and the capital-intensive nature of the industry make it essential that companies understand demand signals and utilize supply chain capacity as effectively as possible. To grow and increase profitability under these challenging market conditions, a major durable goods company has partnered with SAS to streamline analysis of pricing and profitability, optimize inventory, and improve service levels to its customers. The price of a product is determined by a number of factors, such as the strategic importance of customers, supply chain costs, market conditions, and competitive prices. Offering promotions is an important part of a marketing strategy; it impacts purchasing behaviors of business customers and end consumers. This paper describes how this company developed a system to analyze product profitability and the impact of promotion on purchasing behaviors of both their business customers and end consumers. This paper also discusses how this company uses integrated demand planning and inventory optimization to manage its complex multi-echelon supply chain. The process uses historical order data to create a statistical forecast of demand, and then optimizes inventory across the supply chain to satisfy the forecast at desired service levels.

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

Does lyric complexity impact song popularity, and can analysis of the Billboard Top 100 from 1955-2015 be used to evaluate this hypothesis? The music industry has undergone a dramatic change. New technologies enable everyone's voice to be heard and has created new avenues for musicians to share their music. These technologies are destroying entry barriers, resulting in an exponential increase in competition in the music industry. For this reason, optimization that enables musicians and record labels to recognize opportunities to release songs with a likelihood of high popularity is critical. The purpose of this study is to determine whether the complexity of song lyrics impacts popularity and to provide guidance and useful information toward business opportunities for musicians and record labels. One such opportunity is the optimization of advertisement budgets for songs that have the greatest chance for success, at the appropriate time and for the appropriate audience. Our data has been extracted from open-source hosts, loaded into a comprehensive, consolidated data set, and cleaned and transformed using SAS^® as the primary tool for analysis. Currently our data set consists of 334,784 Billboard Top 100 observations, with 26,869 unique songs. The type of analyses used includes: complexity-popularity relationship, popularity churn (how often Billboard Top 100 resets), top five complexity-popularity relationships, longevity-complexity relationship, popularity prediction, and sentiment analysis on trends over time. We have defined complexity as the number of words in a song, unique word inclusion (compared to other songs), and repetition of each word in a song. The Billboard Top 100 represents an optimal source of data as all songs on the list have some measure of objective popularity, which enables lyric comparison analysis among chart position.

Macroeconomic simulation analysis provides in-depth insights to a portfolio's performance spectrum. Conventionally, portfolio and risk managers obtain macroeconomic scenarios from third parties such as the Federal Reserve and determine portfolio performance under the provided scenarios. In this paper, we propose a technique to extend scenario analysis to an unconditional simulation capturing the distribution of possible macroeconomic climates and hence the true multivariate distribution of returns. We propose a methodology that adds to the existing scenario analysis tools and can be used to determine which types of macroeconomic climates have the most adverse outcomes for the portfolio. This provides a broader perspective on value at risk measures thereby allowing more robust investment decisions. We explain the use of SAS^® procedures like VARMAX and PROC COPULA in SAS/IML^® in this analysis.

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

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Business problems have become more stratified and micro-segmentation is driving the need for mass-scale, automated machine learning solutions. Additionally, deployment environments include diverse ecosystems, requiring hundreds of models to be built and deployed quickly via web services to operational systems. The new SAS^® automated modeling tool allows you to build and test hundreds of models across all of the segments in your data, testing a wide variety of machine learning techniques. The tool is completely customizable, allowing you transparent access to all modeling results. This paper shows you how to identify hundreds of champion models using SAS^® Factory Miner, while generating scoring web services using SAS^® Decision Manager. Immediate benefits include efficient model deployments, which allow you to spend more time generating insights that might reveal new opportunities, expose hidden risks, and fuel smarter, well-timed decisions.

It is of paramount importance for brand managers to measure and understand consumer brand associations and the mindspace their brand captures. Brands are encoded in memory on a cognitive and emotional basis. Traditionally, brand tracking has been done by surveys and feedback, resulting in a direct communication that covers the cognitive segment and misses the emotional segment. Respondents generally behave differently under observation and in solitude. In this paper, a new brand-tracking technique is proposed that involves capturing public data from social media that focuses more on the emotional aspects. For conceptualizing and testing this approach, we downloaded nearly one million tweets for three major brands--Nike, Adidas, and Reebok--posted by users. We proposed a methodology and calculated metrics (benefits and attributes) using this data for each brand. We noticed that generally emoticons are not used in sentiment mining. To incorporate them, we created a macro that automatically cleans the tweets and replaces emoticons with an equivalent text. We then built supervised and unsupervised models on those texts. The results show that using emoticons improves the efficiency of predicting the polarity of sentiments as the misclassification rate was reduced from 0.31 to 0.24. Using this methodology, we tracked the reactions that are triggered in the minds of customers when they think about a brand and thus analyzed their mind share.

In 1965, nearly half of all Americans 65 and older had no health insurance. Now, 50 years later, only 2% lack health insurance. The difference, of course, is Medicare. Medicare now covers 55 million people, about 17% of the US population, and is the single largest purchaser of personal health care. Despite this success, the rising costs of health care in general and Medicare in particular have become a growing concern. Medicare policies are important not only because they directly affect large numbers of beneficiaries, payers, and providers, but also because they affect private-sector policies as well. Analyses of Medicare policies and their consequences are complicated both by the effects of an aging population that has changing cost drivers (such as less smoking and more obesity) and by different Medicare payment models. For example, the average age of the Medicare population will initially decrease as the baby-boom generation reaches eligibility, but then increase as that generation grows older. Because younger beneficiaries have lower costs, these changes will affect cost trends and patterns that need to be interpreted within the larger context of demographic shifts. This presentation examines three Medicare payment models: fee-for-service (FFS), Medicare Advantage (MA), and Accountable Care Organizations (ACOs). FFS, originally based on payment methods used by Blue Cross and Blue Shield in the mid-1960s, pays providers for individual services (for example, physicians are paid based on the fees they charge). MA is a capitated payment model in which private plans receive a risk-adjusted rate. ACOs are groups of providers who are given financial incentives for reducing cost and maintaining quality of care for specified beneficiaries. Each model has strengths and weaknesses in specific markets. We examine each model, in addition to new data sources and more recent, innovative payment models that are likely to affect future trends.

Every day, businesses have to remain vigilant of fraudulent activity, which threatens customers, partners, employees, and financials. Normally, networks of people or groups perpetrate deviant activity. Finding these connections is now made easier for analysts with SAS^® Visual Investigator, an upcoming SAS^® solution that ultimately minimizes the loss of money and preserves mutual trust among its shareholders. SAS Visual Investigator takes advantage of the capabilities of the new SAS^® In-Memory Server. Investigators can efficiently investigate suspicious cases across business lines, which has traditionally been difficult. However, the time required to collect, process and identify emerging fraud and compliance issues has been costly. Making proactive analysis accessible to analysts is now more important than ever. SAS Visual Investigator was designed with this goal in mind and a key component is the visual social network view. This paper discusses how the network analysis view of SAS Visual Investigator, with all its dynamic visual capabilities, can make the investigative process more informative and efficient.

Wouldn't it be fantastic to develop and tune scenarios in SAS^® Visual Scenario Designer and then smoothly incorporate them into your SAS^® Anti-Money Laundering solution with just a few clicks of your mouse? Well, now there is a way. SAS Visual Scenario Designer is the first data-driven solution for interactive rule and scenario authoring, testing, and validation. It facilitates exploration, visualization, detection, rule writing, auditing, and parameter tuning to reduce false positives; and all of these tasks are performed using point and click. No SAS^® coding skills required! Using the approach detailed in this paper, we demonstrate how you can seamlessly port these SAS Visual Scenario Designer scenarios into your SAS Anti-Money Laundering solution. Rewriting the SAS Visual Scenario Designer scenarios in Base SAS^® is no longer required! Furthermore, the SAS Visual Scenario Designer scenarios are executed on the lightning-speed SAS^® LASR™ Analytic Server, reducing the time of the SAS Anti-Money Laundering scenario nightly batch run. The results of both the traditional SAS Anti-Money Laundering alerts and SAS Visual Scenario Designer alerts are combined and available for display on the SAS^® Enterprise Case Management interface. This paper describes the different ways that the data can be explored to detect anomalous patterns and the three mechanisms for translating these patterns into rules. It also documents how to create the scenarios in SAS Visual Scenario Designer; how to test and tune the scenarios and parameters; and how alerts are ported seamlessly into the SAS Anti-Money Laundering alert generation process and the SAS Enterprise Case Management system.

As any airline traveler knows, connection time is a key element of the travel experience. A tight connection time can cause angst and concern, while a lengthy connection time can introduce boredom and a longer than desired travel time. The same elements apply when constructing schedules for airline pilots. Like passengers, pilot schedules are built with connections. Delta Air Lines operates a hub and spoke system that feeds both passengers and pilots from the spoke stations and connects them through the hub stations. Pilot connection times that are tight can result in operational disruptions, whereas extended pilot connection times are inefficient and unnecessarily costly. This paper demonstrates how Delta Air Lines used SAS^® PROC REG and PROC LOGISTIC to analyze historical data in order to build operationally robust and financially responsible pilot connections.

Today, there are 28 million small businesses, which account for 54% of all sales in the United States. The challenge is that small businesses struggle every day to accurately forecast future sales. These forecasts not only drive investment decisions in the business, but also are used in setting daily par, determining labor hours, and scheduling operating hours. In general, owners use their gut instinct. Using SAS^® provides the opportunity to develop accurate and robust models that can unlock costs for small business owners in a short amount of time. This research examines over 5,000 records from the first year of daily sales data for a start-up small business, while comparing the four basic forecasting models within SAS^® Enterprise Guide^®. The objective of this model comparison is to demonstrate how quick and easy it is to forecast small business sales using SAS Enterprise Guide. What does that mean for small businesses? More profit. SAS provides cost-effective models for small businesses to better forecast sales, resulting in better business decisions.

SAS^® provides in-database processing technology in the SQL procedure, which allows the SQL explicit pass-through method to push some or all of the work to a database management system (DBMS). This paper focuses on using the SAS SQL explicit pass-through method to transform Teradata table columns into rows. There are two common approaches for transforming table columns into rows. The first approach is to create narrow tables, one for each column that requires transposition, and then use UNION or UNION ALL to append all the tables together. This approach is straightforward but can be quite cumbersome, especially when there is a large number of columns that need to be transposed. The second approach is using the Teradata TD_UNPIVOT function, which makes the wide-to-long table transposition an easy job. However, TD_UNPIVOT allows you to transpose only columns with the same data type from wide to long. This paper presents a SAS macro solution to the wide-to-long table transposition involving different column data types. Several examples are provided to illustrate the usage of the macro solution. This paper complements the author's SAS paper Performing Efficient Transposes on Large Teradata Tables Using SQL Explicit Pass-Through in which the solution of performing the long-to-wide table transposition method is discussed. SAS programmers who are working with data stored in an external DBMS and would like to efficiently transpose their data will benefit from this paper.

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

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One of the most important factors driving the success of requirements-gathering can be easily overlooked. Your user community needs to have a clear understanding of what is possible: from different ways to represent a hierarchy to how visualizations can drive an analysis to newer, but less common, visualizations that are quickly becoming standard. Discussions about desktop access versus mobile deployment and/or which users might need more advanced statistical reporting can lead to a serious case of option overload. One of the best cures for option overload is to provide your user community with access to template reports they can explore themselves. In this paper, we describe how you can take a single rich data set and build a set of template reports that demonstrate the full functionality of SAS^® Visual Analytics, a suite of the most common, most useful SAS Visual Analytics report structures, from high-level dashboards to statistically deep dynamic visualizations. We show exactly how to build a dozen template reports from a single data source, simultaneously representing options for color schemes, themes, and other choices to consider. Although this template suite approach can apply to any industry, our example data set will be publicly available data from the Home Mortgage Disclosure Act, de-identified data on mortgage loan determinations. Instead of beginning requirements-gathering with a blank slate, your users can begin the conversation with, I would like something like Template #4, greatly reducing the time and effort required to meet their needs.

The success of any marketing promotion is measured by the incremental response and revenue generated by the targeted population known as Test in comparison with the holdout sample known as Control. An unbiased random Test and Control sampling ensures that the incremental revenue is in fact driven by the marketing intervention. However, isolating the true incremental effect of any particular marketing intervention becomes increasingly challenging in the face of overlapping marketing solicitations. This paper demonstrates how a look-alike model can be applied using the GMATCH algorithm on a SAS^® platform to design a truly comparable control group to accurately measure and isolate the impact of a specific marketing intervention.

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As credit unions market themselves to increase their market share against the big banks, they understandably focus on gaining new members. However, they must also retain their existing members. Otherwise, the new members they gain can easily be offset by existing members who leave. Happily, by using predictive analytics as described in this paper, keeping (and further engaging) existing members can actually be much easier and less expensive than enlisting new members. This paper provides a step-by-step overview of a relatively simple but comprehensive approach to reduce member attrition. We first prepare the data for a statistical analysis. With some basic predictive analytics techniques, we can then identify those members who have the highest chance of leaving and the highest value. For each of these members, we can also identify why they would leave, thus suggesting the best way to intervene to retain them. We then make suggestions to improve the model for better accuracy. Finally, we provide suggestions to extend this approach to further engaging existing members and thus increasing their lifetime value. This approach can also be applied to many other organizations and industries. Code snippets are shown for any version of SAS^® software; they also require SAS/STAT^® software.

SharePoint is a web application framework and platform developed by Microsoft, mostly used for content and document management by mid-size businesses and large departments. Linking SAS^® with SharePoint combines the power of these two into one. This paper shows users how to send PDF reports and files in other formats (such as Microsoft Excel files, HTML files, JPEG files, zipped files, and so on) from SAS to a SharePoint Document Library. The paper demonstrates how to configure SharePoint Document Library settings to receive files from SAS. A couple of SAS code examples are included to show how to send files from SAS to SharePoint. The paper also introduces a framework for creating data visualization on SharePoint by feeding SAS data into HTML pages on SharePoint. An example of how to create an infographics SharePoint page with data from SAS is also provided.

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

SAS^® Customer Intelligence 360 enables marketers to create activity maps to delight customers with a consistent experience on digital channels such as web, mobile, and email. Activity maps enable the user to create a customer journey across digital channels and assign conversion measures as individual customers and prospects navigate the customer experience. This session details how those interactions are constructed in terms of what types of interactions tasks are supported and how those interaction tasks can be related to each other in a visual paradigm with built-in goal splits, analytics, wait periods, and A/B testing. Business examples are provided for common omni-channel problems such as cross-selling and customer segmentation. In addition, we cover the integration of SAS^® Marketing Automation and other marketing products, which enables sites to leverage their current segments and treatments for the digital channels.

Over the past two years, the Analytics group at 89 Degrees has completely overhauled the toolset we use in our day-to-day work. We implemented SAS^® Visual Analytics, initially to reduce the time to create new reports, and then to increase access to the data so that users not familiar with SAS^® can create their own explorations and reports. SAS Visual Analytics has become a collaboration tool between our analysts and their business partners, with proportionally more time spent on the interpretation. (We show an example of this in this presentation.) Flush with success, we decided to tackle another area where processing times were longer than we would like, namely weblog data. We were treating weblog data in one of two ways: (1) creating a structured view of unstructured data by saving a handful of predefined variables from each session (for example, session and customer identifiers, page views, time on site, and so on), or (2) storing the granular weblog data in a Hadoop environment and relying on our data management team to fulfill data requests. We created a business case for SAS/ACCESS^® Interface for Hadoop, invested in extra hardware, and created a big data environment that could be accessed directly from SAS by the analysts. We show an example of how we created new variables and used them in a logistic regression analysis to predict combined online and offline shopping rates using online and offline historic behavior. Our next dream is to bring all of that together by linking SAS Visual Statistics to a Hadoop environment other than the SAS^® LASR™ Analytic server. We share our progress, and hopefully our success, as part of the session.

Revolution Analytics reports more than two million R users worldwide. SAS^® has the capability to use R code, but users have discovered a slight learning curve to performing certain basic functions such as getting data from the web. R is a functional programming language while SAS is a procedural programming language. These differences create difficulties when first making the switch from programming in R to programming in SAS. However, SAS/IML^® software enables integration between the two languages by enabling users to write R code directly into SAS/IML. This paper details the process of using the SAS/IML command Submit /R and the R package XML to get data from the web into SAS/IML. The project uses public basketball data for each of the 30 NBA teams over the past 35 years, taken directly from Basketball-Reference.com. The data was retrieved from 66 individual web pages, cleaned using R functions, and compiled into a final data set composed of 48 variables and 895 records. The seamless compatibility between SAS and R provide an opportunity to use R code in SAS for robust modeling. The resulting analysis provides a clear and concise approach for those interested in pursuing sports analytics.

Coronal mass ejections (CMEs) are massive explosions of magnetic field and plasma from the Sun. While responsible for the northern lights, these eruptions can cause geomagnetic storms and cataclysmic damage to Earth's telecommunications systems and power grid infrastructures. Hence, it is imperative to construct highly accurate predictive processes to determine whether an incoming CME will produce devastating effects on Earth. One such process, called stacked generalization, trains a variety of models, or base-learners, on a data set. Then, using the predictions from the base-learners, another model is trained to learn from the metadata. The goal of this meta-learner is to deduce information about the biases from the base-learners to make more accurate predictions. Studies have shown success in using linear methods, especially within regularization frameworks, at the meta-level to combine the base-level predictions. Here, SAS^® Enterprise Miner™ 13.1 is used to reinforce the advantages of regularization via the Least Absolute Shrinkage and Selection Operator (LASSO) on this type of metadata. This work compares the LASSO model selection method to other regression approaches when predicting the occurrence of strong geomagnetic storms caused by CMEs.

With the ever growing global tourism sector, the hotel industry is also flourishing by leaps and bounds, and the standards of quality services by the tourists are also increasing. In today's cyber age where many on the Internet become part of the online community, word of mouth has steadily increased over time. According to a recent survey, approximately 46% of travelers look for online reviews before traveling. A one-star rating by users influences the peer consumer more than the five-star brand that the respective hotel markets itself to be. In this paper, we do the customer segmentation and create clusters based on their reviews. The next process relates to the creation of an online survey targeting the interests of the customers of those different clusters, which helps the hoteliers identify the interests and the hospitality expectations from a hotel. For this paper, we use a data set of 4096 different hotel reviews, with a total of 60,239 user reviews.

This paper describes a Kaiser Permanente Northwest business problem regarding tracking recent inpatient hospital utilization at external hospitals, and how it was solved with the flexibility of SAS^® Enterprise Guide^®. The Inpatient Indicator is an estimate of our regional inpatient hospital utilization as of yesterday. It tells us which of our members are in which hospitals. It measures inpatient admissions, which are health care interactions where a patient is admitted to a hospital for bed occupancy to receive hospital services. The Inpatient Indicator is used to produce data and create metrics and analysis essential to the decision making of Kaiser Permanente executives, care coordinators, patient navigators, utilization management physicians, and operations managers. Accurate, recent hospital inpatient information is vital for decisions regarding patient care, staffing, and member utilization. Due to a business policy change, Kaiser Permanente Northwest lost the ability to track urgent and emergent inpatient admits at external, non-plan hospitals through our referral system, which was our data source for all recent external inpatient admits. Without this information, we did not have complete knowledge of whether a member had an inpatient stay at an external hospital until a claim was received, which could be several weeks after the member was admitted. Other sources were needed to understand our inpatient utilization at external hospitals. A tool was needed with the flexibility to easily combine and compare multiple data sets with different field names, formats, and values representing the same metric. The tool needed to be able to import data from different sources and export data to different destinations. We also needed a tool that would allow this project to be scheduled. We chose to build the model with SAS Enterprise Guide.

Sixty percent of organizations will have Hadoop in production by 2016, per a recent TDWI survey, and it has become increasingly challenging to access, cleanse, and move these huge volumes of data. How can data scientists and business analysts clean up their data, manage it where it lives, and overcome the big data skills gap? It all comes down to accelerating the data preparation process. SAS^® Data Loader leverages years of expertise in data quality and data integration, a simplified user interface, and the parallel processing power of Hadoop to overcome the skills gap and compress the time taken to prepare data for analytics or visualization. We cover some of the new capabilities of SAS Data Loader for Hadoop including in-memory Spark processing of data quality and master data management functions, faster profiling and unstructured data field extraction, and chaining multiple transforms together for improved productivity.

In Brazil, almost 70% of all loans are made based on pre-approved limits, which are established by the bank. Sicredi wanted to improve the number of loans granted through those limits. In addition, Sicredi wanted an application that focuses on the business user; one that enables business users to change system behavior with little or no IT involvement. The new system will be used in three major areas: - In the registration of a new client for whom Sicredi does not have a history. - Upon request by business users, after the customer already has a relationship with Sicredi, without customer request. - In the loan approval process, when a limit has not yet been set for the customer. The limit system will try to measure a limit for the customer based on the loan request, before sending the loan to the human approval system. Due to the impact of these changes, we turned the project into a program, and then split that program into three projects. The first project, which we have already finished, aimed to select an application that meets our requirements, and then to develop the credit measurement for the registration phase. SAS Real-Time Decision Manager was selected because it fulfills our requirements, especially those that pertain to business user operation. A drag-and-drop interface makes all the technical rules more comprehensible to the business user. So far, four months after releasing the project for implementation by the bank's branches, we have achieved more the USD 20 million granted in pre-approved loan limits. In addition, we have reduced the process time for limit measurement in the branches by 84%. The branches can follow their results and goals through reports developed in SAS Visual Analytics.

When business rules are deployed and executed--whether a rule is fired or not--if the rule-fire outcomes are not monitored or investigated for validation or challenged, over time unintended business impacts can occur because of changing data profiles or characteristics of the input data for the rules. Comparing scenarios using modified rules and visually comparing how they might impact your business can aide you in meeting compliance regulations, knowing your customers, and staying relevant or accurate in your particular business context. Visual analysis of rules outcomes is a powerful way to validate what is expected or to compare potential impact that could lead to further investigation and refinement of existing rules. This paper shows how to use SAS^® Visual Analytics and other visualizations to perform various types of meaningful and useful rule-fire outcome analysis with rules created in SAS^® Business Rules Manager. Using visual graphical capabilities can give organizations or businesses a straightforward way to validate, monitor, and keep rules from running wild.

The recent controversy regarding former Secretary Hillary Clinton's use of a non-government, privately maintained email server provides a great opportunity to analyze real-world data using a variety of analytic techniques. This email corpus is interesting because of the challenges in acquiring and preparing the data for analysis as well as the variety of analyses that can be performed, including techniques for searching, entity extraction and resolution, natural language processing for topic generation, and social network analysis. Given the potential for politically charged discussion, rest assured there will be no discussion of politics--just fact-based analysis.

Are you living in Heartbreak Hotel because your boss wants different statistics in the SAME column on your report? Need a currency symbol in one cell of your pre-summarized data, but a percent sign in another cell? Large blocks of text on your report have you all shook up because they wrap badly on your report? Have you hit the wall with PROC PRINT? Well, rock out of your jailhouse with ODS, DATA step, and PROC REPORT. Are you living in Heartbreak Hotel because your boss wants different statistics in the SAME column on your report? Need a currency symbol in one cell of your pre-summarized data, but a percent sign in another cell? Large blocks of text on your report have you all shook up because they wrap badly on your report? Have you hit the wall with PROC PRINT? Well, rock out of your jailhouse with ODS, DATA step, and PROC REPORT. This paper is a sequel to the popular 2008 paper Creating Complex Reports. The paper presents a nuts-and-bolts look at more complex report examples gleaned from SAS^® Community Forum questions and questions from students. Examples will include use of DATA step manipulation to produce PROC REPORT and PROC SGPLOT output as well as examples of ODS LAYOUT and the new report writing interface. And PROC TEMPLATE makes a special guest appearance. Even though the King of Rock 'n' Roll won't be there for the presentation, perhaps we'll hear his ghost say Thank you very much, I always wanted to know how to do that at the end of this presentation.

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Omnichannel, and the omniscient customer experience, is most commonly used as a buzzword to describe the seamless customer experience in a traditional multi-channel marketing and sales environment. With more channels and methods of communication, there is a growing need to establish a more customer-centric way of dealing with all customer interactions, not only 1:1. Telenor, based out of Norway, is one of the world's major mobile operators with in excess of 200 million mobile subscriptions throughout 13 markets across Europe and Asia. The Norwegian home-market is a highly saturated and mature market in which customer demands and expectations are constantly rising. To deal with this and with increased competition, two major initiatives were established together with SAS^®. The initiatives aimed to leverage both the need for real-time analytics and decision management in our inbound channel, and for creating an omnichannel experience across inbound and outbound channels. The projects were aimed at both business-to-consumer (B2C) and business-to-business (B2B) markets. With significant legacy of back-end systems and a complex value chain it was important to both improve the customer experience and simplify customer treatment, all without impacting the back-end system at large. The presentation sheds light on how the projects worked to meet the technical challenges alongside the need for an optimal customer experience. With results far exceeding expectations, the outcome has established the basis for further Customer Lifecycle Management (CLM) initiatives to strengthen both Net Promoter Score/Customer loyalty and revenue.

For marketers who are responsible for identifying the best customer to target in a campaign, it is often daunting to determine which media channel, offer, or campaign program is the one the customer is more apt to respond to, and therefore, is more likely to increase revenue. This presentation examines the components of designing campaigns to identify promotable segments of customers and to target the optimal customers using SAS^® Marketing Automation integrated with SAS^® Marketing Optimization.

Someone has aptly said, Las Vegas looks the way one would imagine heaven must look at night. What if you know the secret to run a plethora of various businesses in the entertainment capital of the world? Nothing better, right? Well, we have what you want, all the necessary ingredients for you to precisely know what business to target in a particular locality of Las Vegas. Yelp, a community portal, wants to help people finding great local businesses. They cover almost everything from dentists and hair stylists through mechanics and restaurants. Yelp's users, Yelpers, write reviews and give ratings for all types of businesses. Yelp then uses this data to make recommendations to the Yelpers about which institutions best fit their individual needs. We have the yelp academic data set comprising 1.6 million reviews and 500K tips by 366K in 61K businesses across several cities. We combine current Yelp data from all the various data sets for Las Vegas to create an interactive map that provides an overview of how a business runs in a locality and how the ratings and reviews tickers a business. We answer the following questions: Where is the most appropriate neighborhood to start a new business (such as cafes, bars, and so on)? Which category of business has the greatest total count of reviews that is the most talked about (trending) business in Las Vegas? How does a business' working hours affect the customer reviews and the corresponding rating of the business? Our findings present research for further understanding of perceptions of various users, while giving reviews and ratings for the growth of a business by encompassing a variety of topics in data mining and data visualization.

Most businesses have benefited from using advanced analytics for marketing and other decision making. But to apply analytical techniques to pharmaceutical marketing is challenging and emerging as it is critical to ensure that the analysis makes sense from the medical side. The drug for a specific disease finally consumed is directly or indirectly influenced by many factors, including the disease origins, health-care system policy, physicians' clinical decisions, and the patients' perceptions and behaviors. The key to pharmaceutical marketing is in identifying the targeted populations for specific diseases and to focus on those populations. Because the health-care environment consistently changes, the predictive models are important to predict the change of the targeted population over time based on the patient journey and epidemiology. Time series analysis is used to forecast the number of cases of infectious diseases; correspondingly, over the counter and prescribed medicines for the specific disease could be predicted. The accurate prediction provides valuable information for the strategic plan of campaigns. For different diseases, different analytical techniques are applied. By taking the medical features of the disease and epidemiology into account, the prediction of the potential and total addressable markets can reveal more insightful marketing trends. And by simulating the important factors and quantifying how they impact the patient journey within the typical health-care system, the most accurate demand for specific medicines or treatments could be discovered. Through monitoring the parameters in the dynamic simulation, the smart decision can be made using what-if comparisons to optimize the marketing result.

The Coordination for the Improvement of Higher Education Personnel (CAPES) is a foundation within the Ministry of Education in Brazil whose central purpose is to coordinate efforts to promote high standards for postgraduate programs inside the country. Structured in a SAS^® data warehouse, vast amounts of information about the National Postgraduate System (SNPG) is collected and analyzed daily. This data must be accessed by different operational and managerial profiles, on desktops and mobile devices (in this case, using SAS^® Mobile BI). Therefore, accurate and fresh data must be maintained so that is possible to calculate statistics and indicators about programs, courses, teachers, students, and intellectual productions. By using SAS programming within SAS^® Enterprise Guide^®, all statistical calculations are performed and the results become available for exploration and presentation in SAS^® Visual Analytics. Using the report designing tool, an excellent user experience is created by integrating the reports into Sucupira Platform, an online tool designed to provide greater data transparency for the academic community and the general public. This integration is made possible through the creation of public access reports with automatic authentication of guest users, presented within iframes inside the Foundation's platform. The content of the reports is grouped by scope, which makes it possible to view the indicators in different forms of presentation, to apply filters (including from URL GET parameters), and to execute stored processes.

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The latest releases of SAS^® Data Integration Studio, SAS^® Data Management Studio and SAS^® Data Integration Server, SAS^® Data Governance, and SAS/ACCESS^® 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, 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.

In the world of big data, real-time processing and event stream processing are becoming the norm. However, there are not many tools available today that can do this type of processing. SAS^® Event Stream Processing aims to process this data. In this paper, we look at using SAS Event Stream Processing to read multiple data sets stored in big data platforms such as Hadoop and Cassandra in real time and to perform transformations on the data such as joining data sets, filtering data based on preset business rules, and creating new variables as required. We look at how we can score the data based on a machine learning algorithm. This paper shows you how to use the provided Hadoop Distributed File System (HDFS) publisher and subscriber to read and push data to Hadoop. The HDFS adapter is discussed in detail. We look at the Streamviewer to see how data flows through SAS Event Stream Processing.