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.

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.

In an effort to increase transparency and accountability in the US health care system, the Obama administration mandated the Centers for Medicare & Medicaid Services (CMS) to make available data for use by researchers and interested parties from the general public. Among the more well-known uses of this data are analyses published by the Wall Street Journal showing that a large, and in some cases, shocking discrepancy between what hospitals potentially charge the uninsured and what they are paid by Medicare for the same procedure. Analyses such as these highlight both potential inequities in the US health care system and, more importantly, potential opportunities for its reform. However, while capturing the public imagination, analyses such as these are but one means to capitalize on the remarkable wealth of information this data provides. Specifically, data from the public distribution CMS data can help both researchers and the public better understand the burden specific conditions and medical treatments place on the US health care system. It was this simple, but important objective that motivated the present study. Our specific analyses focus on two of what we believe to be important questions. First, using the total number of hospital discharges as a proxy for incidence of a condition or treatment, which have the highest incidence rates nationally? Does their incidence remain stable, or is it increasing/decreasing? And, is there variability in these incidence rates across states? Second, as psychologists, we are necessarily interested in understanding the state of mental health care. To date, and to the best of our knowledge, there has been no study utilizing the public inpatient Medicare provider utilization and payment data set to explore the utilization of mental illness services funded by Medicare.

This paper demonstrates techniques using SAS^® software to combine data from devices and sensors for analytics. Base SAS^®, SAS^® Data Integration Studio, and SAS^® Visual Analytics are used to query external services, import, fuzzy match, analyze, and visualize data. Finally, the benefits of SAS^® Event Stream Processing models and alerts is discussed. To bring the analytics of things to life, the following data are collected: GPS data from countryside walks, GPS and score card data from smart phones while playing golf, and meteorological data feeds. These data are combined to test the old adage that golf is a good walk spoiled. Further, the use of alerts and potential for predictive analytics is discussed.

For me, it's all about avoiding manual effort and repetition. Whether your work involves data exploration, reporting, or analytics, you probably find yourself repeating steps and tasks with each new program, project, or analysis. That repetition adds time to the delivery of results and also contributes to a lack of standardization. This presentation focuses on productivity tips and tricks to help you create a standard and efficient environment for your SAS^® work so you can focus on the results and not the processes. Included are the following: setting up your programming environment (comment blocks, environment cleanup, easy movement between test and production, and modularization) sharing easily with your team (format libraries, macro libraries, and common code modules) managing files and results (date and time stamps for logs and output, project IDs, and titles and footnotes)

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The Health Indicators Warehouse (HIW) is part of the US Department of Health and Human Services' (DHHS) response to make federal data more accessible. Through it, users can access data and metadata for over 1,200 indicators from approximately 180 federal and nonfederal sources. The HIW also supports data access by applications such as SAS^® Visual Analytics through the use of an application programming interface (API). An API serves as a communication interface for integration. As a result of the API, HIW data consumers using SAS Visual Analytics can avoid difficult manual data processing. This paper provides detailed information about how to access HIW data with SAS Visual Analytics in order to produce easily understood visualizations with minimal effort through a methodology that automates HIW data processing. This paper also shows how to run SAS^® macros inside a stored process to make HIW data available in SAS Visual Analytics for exploration and reporting via API calls; the SAS macros are provided. Use cases involving dashboards are also examined in order to demonstrate the value of streaming data directly from the HIW. Both IT professionals and population health analysts will benefit from understanding how to import HIW data into SAS Visual Analytics using SAS^® Stored Processes, macros, and APIs. This can be very helpful to organizations that want to lower maintenance costs associated with data management while gaining insights into health data with visualizations. This paper provides a starting point for any organization interested in deriving full value from SAS Visual Analytics while augmenting their work with HIW data.

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.

This paper is a case study to explore the analytical and reporting capabilities of SAS^® Visual Analytics to perform data exploration, determine order patterns and trends, and create data visualizations to generate extensive dashboard reports using the open source pollution data available from the United States Environmental Protection Agency (EPA). Data collection agencies report their data to EPA via the Air Quality System (AQS). The EPA makes available several types of aggregate (summary) data sets, such as daily and annual pollutant summaries, in CSV format for public use. We intend to demonstrate SAS Visual Analytics capabilities by using pollution data to create visualizations that compare Air Quality Index (AQI) values for multiple pollutants by location and time period, generate a time series plot by location and time period, compare 8-hour ozone 'exceedances' from this year with previous years, and perform other such analysis. The easy-to-use SAS Visual Analytics web-based interface is leveraged to explore patterns in the pollutant data to obtain insightful information. SAS^® Visual Data Builder is used to summarize data, join data sets, and enhance the predictive power within the data. SAS^® Visual Analytics Explorer is used to explore data, to create calculated data items and aggregated measures, and to define geography items. Visualizations such as chats, bar graphs, geo maps, tree maps, correlation matrices, and other graphs are created to graphically visualize pollutant information contaminating the environment; hierarchies are derived from date and time items and across geographies to allow rolling up the data. Various reports are designed for pollution analysis. Viewing on a mobile device such as an iPad is also explored. In conclusion, this paper attempts to demonstrate use of SAS Visual Analytics to determine the impact of pollution on the environment over time using various visualizations and graphs.

In healthcare and other fields, the importance of cell suppression as a means to avoid unintended disclosure or identification of Protected Health Information (PHI) or any other sensitive data has grown as we move toward dynamic query systems and reports. Organizations such as Centers for Medicare & Medicaid Services (CMS), the National Center for Health Statistics (NCHS), and the Privacy Technical Assistance Center (PTAC) have outlined best practices to help researchers, analysts, report writers, and others avoid unintended disclosure for privacy reasons and to maintain statistical validity. Cell suppression is a crucial consideration during report design and can be a substantial hurdle in the dissemination of information. Often, the goal is to display as much data as possible without enabling the identification of individuals and while maintaining statistical validity. When designing reports using SAS^® Visual Analytics, achieving suppression can be handled multiple ways. One way is to suppress the data before loading it into the SAS^® LASR™ Analytic Server. This has the drawback that a user cannot take full advantage of the dynamic filtering and aggregation available with SAS Visual Analytics. Another method is to create formulas that govern how SAS Visual Analytics displays cells within a table (crosstab) or bars within a chart. The logic can be complex and can meet a variety of needs. This presentation walks through examples of the latter methodology, namely, the creation of suppression formulas and how to apply them to report objects.

This workshop shows you how to create powerful interactive visualizations using SAS^® Stored Processes to deliver data to JavaScript objects. We construct some simple HTML to make a simple dashboard layout with a range of connected graphs and some static data. Then, we replace the static data with SAS Stored Processes that we build, which use the STREAM and JSON procedures in SAS^® 9.4 to deliver the data to the objects. You will see how easy it is to build a bespoke dashboard that resembles that in SAS^® Visual Analytics with only SAS Stored Processes and some basic HTML, JavaScript, and CSS 3.

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.

The HIPAA Privacy Rule can restrict geographic and demographic data used in health-care analytics. After reviewing the HIPAA requirements for de-identification of health-care data used in research, this poster guides the beginning SAS^® Visual Analytics user through different options to create a better user experience. This poster presents a variety of data visualizations the analyst will encounter when describing a health-care population. We explore the different options SAS Visual Analytics offers and also offer tips on data preparation prior to using SAS^® Visual Analytics Designer. Among the topics we cover are SAS Visual Analytics Designer object options (including geo bubble map, geo region map, crosstab, and treemap), tips for preparing your data for use in SAS Visual Analytics, and tips on filtering data after it's been loaded into SAS Visual Analytics, and more.

You've heard that SAS^® Output Delivery System (ODS) Graphics provides a powerful and detailed syntax for creating custom graphs, but for whatever reason you still haven't added them to your bag of SAS^® tricks. Let's change that! We will also present a code playground based on Microsoft Office that will enable you to quickly try out a variety of prepared SAS ODS Graphics examples, tweak the code, and see the results--all directly from Microsoft Excel. More experienced users will also find the code playground (which is similar in spirit to Google Code Playground or JSFiddle) useful for compiling SAS ODS Graphics code snippets for themselves and for sharing with colleagues, as well as for creating dashboards hosted by Microsoft Excel or Microsoft PowerPoint that contain precisely sized and placed SAS graphics.

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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.

Metadata is an integral and critical part of any environment. Metadata facilitates resource discovery and provides unique identification of every single digital component of a system, simple to complex. SAS^® Visual Analytics, one of the most powerful analytics visualization platforms, leverages the power of metadata to provide a plethora of functionalities for all types of users. The possibilities range from real-time advanced analytics and power-user reporting to advanced deployment features for a robust and scalable distributed platform to internal and external users. This paper explains the best practices and advanced approaches for designing and managing metadata for a distributed global SAS Visual Analytics environment. Designing and building the architecture of such an environment requires attention to important factors like user groups and roles, access management, data protection, data volume control, performance requirements, and so on. This paper covers how to build a sustainable and scalable metadata architecture through a top-down hierarchical approach. It helps SAS Visual Analytics Data Administrators to improve the platform benchmark through memory mapping, perform administrative data load (AUTOLOAD, Unload, Reload-on-Start, and so on), monitor artifacts of distributed SAS^® LASR™ Analytic Servers on co-located Hadoop Distributed File System (HDFS), optimize high-volume access via FullCopies, build customized FLEX themes, and so on. It showcases practical approaches to managing distributed SAS LASR Analytic Servers, offering guest access for global users, managing host accounts, enabling Mobile BI, using power-user reporting features, customizing formats, enabling home page customization, using best practices for environment migration, and much more.

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.

At a community college, there was a need for college employees to quickly and easily find available classroom time slots for the purposes of course scheduling. The existing method was time-consuming and inefficient, and there were no available IT resources to implement a solution. The Office of Institutional Research, which had already been delivering reports using SAS^® Enterprise BI Server, created a report called Find an Open Room to fill the need. By combining SAS^® programming techniques, a scheduled SAS^® Enterprise Guide^® project, and a SAS^® Web Report Studio report delivered within the SAS^® Information Delivery Portal, a report was created that allowed college users to search for available time slots.

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.

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.

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^® Enterprise Guide^® is an extremely valuable tool for programmers, but it should also be leveraged by managers and executives to do data exploration, get information on the fly, and take advantage of the powerful analytics and reporting that SAS^® has to offer. This can all be done without learning to program. This paper gives an overview of how SAS Enterprise Guide can improve the process of turning real-time data into real-time business decisions by managers.

Have you ever wondered how to get the most from Web 2.0 technologies in order to visualize SAS^® data? How to make those graphs dynamic, so that users can explore the data in a controlled way, without needing prior knowledge of SAS products or data science? Wonder no more! In this session, you learn how to turn basic sashelp.stocks data into a snazzy HighCharts stock chart in which a user can review any time period, zoom in and out, and export the graph as an image. All of these features with only two DATA steps and one SORT procedure, for 57 lines of SAS code.

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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.

Many organizations want to innovate with the analytics solutions from SAS^®. However, many companies are facing constraints in time and money to build an innovation strategy. In this session, you learn how SaasNow can offer you a flexible solution to become innovative again. During this session, you experience how easy it is to deploy a SAS^® Visual Analytics and SAS^® Visual Statistics environment in just 30 minutes in a pay-per-month model. Visitors to this session receive a free voucher to test-drive SaasNow!

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.

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

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

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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Is uniqueness essential for your reports? SAS^® Visual Analytics provides the ability to customize your reports to make them unique by using the SAS^® Theme Designer. The SAS Theme Designer can be accessed from the SAS^® Visual Analytics Hub to create custom themes to meet your branding needs and to ensure a unified look across your company. The report themes affect the colors, fonts, and other elements that are used in tables and graphs. The paper explores how to access SAS Theme Designer from the SAS Visual Analytics home page, how to create and modify report themes that are used in SAS Visual Analytics, how to create report themes from imported custom themes, and how to import and export custom report themes.

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.

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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As part of promoting a data-driven culture and data analytics modernization at its federal sector clientele, Northrop Grumman developed a framework for designing and implementing an in-house Data Analytics and Research Center (DAARC) using a SAS^® set of tools. This DAARC provides a complete set of SAS^® Enterprise BI (Business Intelligence) and SAS^® Data Management tools. The platform can be used for data research, evaluations, and analysis and reviews by federal agencies such as the Social Security Administration (SSA), the Center for Medicare and Medicaid Services (CMS), and others. DAARC architecture is based on a SAS data analytics platform with newer capabilities of data mining, forecasting, visual analytics, and data integration using SAS^® Business Intelligence. These capabilities enable developers, researchers, and analysts to explore big data sets with varied data sources, create predictive models, and perform advanced analytics including forecasting, anomaly detection, use of dashboards, and creating online reports. The DAARC framework that Northrop Grumman developed enables agencies to implement a self-sufficient 'analytics as a service' approach to meet their business goals by making informed and proactive data-driven decisions. This paper provides a detailed approach to how the DAARC framework was established in strong partnership with federal customers of Northrop Grumman. This paper also discusses the best practices that were adopted for implementing specific business use cases in order to save tax-payer dollars through many research-related analytical and statistical initiatives that continue to use this platform.

Pedal-to-the-metal analytics is the notion that analytics can be quickly and easily achieved using web technologies. SAS^® web technologies seamlessly integrate with each other through a web browser and with data via web APIs, enabling organizations to leapfrog traditional, manual analytic and data processes. Because of this integration (and the operational efficiencies obtained as a result), pedal-to-the-metal analytics dramatically accelerates the analytics lifecycle, which consists of these steps: 1) Data Preparation; 2) Exploration; 3) Modeling; 4) Scoring; and 5) Evaluating results. In this paper, data preparation is accomplished with SAS^® Studio custom tasks (reusable drag-and-drop visual components or interfaces for underlying SAS code). This paper shows users how to create and implement these for public health surveillance. With data preparation complete, explorations of the data can be performed using SAS^® Visual Analytics. Explorations provide insights for creating, testing, and comparing models in SAS^® Visual Statistics to predict or estimate risk. The model score code produced by SAS Visual Statistics can then be deployed from within SAS Visual Analytics for scoring. Furthermore, SAS Visual Analytics provides the necessary dashboard and reporting capabilities to evaluate modeling results. In conclusion, the approach presented in this paper provides both new and long-time SAS users with easy-to-follow guidance and a repeatable workflow to maximize the return on their SAS investments while gaining actionable insights on their data. So, fasten your seat belts and get ready for the ride!

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.

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.

Mobile devices are an integral part of a business professional's life. These mobile devices are getting increasingly powerful in terms of processor speeds and memory capabilities. Business users can benefit from a more analytical visualization of the data along with their business context. The new SAS^® Mobile BI contains many enhancements that facilitate the use of SAS^® Analytics in the newest version of SAS^® Visual Analytics. This paper demonstrates how to use the new analytical visualization that has been added to SAS Mobile BI from SAS Visual Analytics, for a richer and more insightful experience for business professionals on the go.

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.

In today's Business Intelligence world, self-service, which allows an everyday knowledge worker to explore data and personalize business reports without being tech-savvy, is a prerequisite. The new release of SAS^® Visual Statistics introduces an HTML5-based, easy-to-use user interface that combines statistical modeling, business reporting, and mobile sharing into a one-stop self-service shop. The backbone analytic server of SAS Visual Statistics is also updated, allowing an end user to analyze data of various sizes in the cloud. The paper illustrates this new self-service modeling experience in SAS Visual Statistics using telecom churn data, including the steps of identifying distinct user subgroups using decision tree, building and tuning regression models, designing business reports for customer churn, and sharing the final modeling outcome on a mobile device.

How can you set up SAS^® Visual Analytics to present reports to the public while still showing different data based on individual access rights? How can a system like that allow for frequent changes in the user base and for individuals' access rights? This session focuses on a recent Norwegian case where SAS^® Visual Analytics 7.3 is used to present reports to a large number of users in the public domain. Report data is controlled on a row-level basis for each user and is frequently changed. This poses key questions on how to design a security architecture that allows for new user and changing access rights while keeping highly available and well-performing reports.

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.

A stored process is a SAS^® program that can be executed as required by different applications. Stored processes have been making SAS users' lives easier for decades. In SAS^® Visual Analytics, stored processes can be used to enhance the user experience, create custom functionality and output, and expand the usefulness of reports. This paper discusses a technique for how data can be loaded on demand into memory for SAS Visual Analytics and accessed by reports as needed using stored processes. Loading tables into memory so that they can be used to create explorations or reports is a common task in SAS Visual Analytics. This task is usually done by an administrator, enabling the SAS Visual Analytics user to have a seamless transition from data to report. At times, however, users need for tables to be partially loaded or modified in memory on demand. The step-by-step instructions in this paper make it easy enough for any SAS Visual Analytics report builder to include these stored processes in their work. By using this technique, SAS Visual Analytics users have the freedom to access their data without having to work through an administrator for every little task, helping everyone be more effective and productive.

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.

SAS^® Visual Analytics can display maps with your location information. However, you might need to display locations that do not match the categories found in the SAS Visual Analytics interface, such as street address locations or non-US postal code locations. You might also wish to display custom locations that are specific to your business or industry, such as the locations of power grid substations or railway mile markers. Alternatively, you might want to validate your address data. This presentation shows how PROC GEOCODE can be used to simplify the geocoding by processing your location information before putting your data into SAS Visual Analytics.

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SAS customers have a growing need for accurate and quality SAS^® maps. SAS has licensed the map data from a third party to satisfy this need. This presentation explores the new map data by discussing the problems and limitations with the old map data, and the features and examples for using the new data.

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SAS^® helps people make better decisions. But what makes a decision better? How can we make sure we are not making worse decisions? There are six tenets to follow to ensure we are making better decisions. Decisions are better when they are: (1) Aligned with your mission; (2) Complete; (3) Faster; (4) Accurate; (5) Accessible; and (6) Recurring, ongoing, or productionalized. By combining all of these aspects of making a decision, you can have confidence that you are making a better decision. The breadth of SAS software is examined to understand how it can be applied toward these tenets. Scorecards are used to ensure that your business stays aligned with goals. Data Management is used to bring together all of the data you have, to provide complete information. SAS^® Visual Analytics offerings are unparalleled in their speed to enable you to make faster decisions. Exhaustive testing verifies accuracy. Modern, easy-to-use user interfaces are adapted for multiple languages and designed for a variety of users to ensure accessibility. And the powerful SAS data flow architecture is built for ongoing support of decisions. Several examples from the SAS^® Solutions OnDemand group are used as case studies in support of these tenets.

Data transformations serve many functions in data analysis, including improving normality of distribution and equalizing variance to meet assumptions and improve effective sizes. Traditionally, the first step in the analysis is to preprocess and transform the data to derive different representations for further exploration. But now in this era of big data, it is not always feasible to have transformed data available beforehand. Analysts need to conduct exploratory data analysis and subsequently transform data on the fly according to their needs. SAS^® Visual Analytics has an expression builder component integrated into SAS^® Visual Data Builder, SAS^® Visual Analytics Explorer, and SAS^® Visual Analytics Designer that helps you transform data on the fly. The expression builder enables you to create expressions that you can use to aggregate columns, perform multiple operations on data, and perform conditional processing. It supports numeric, comparison, Boolean, text, and date time operators, and different functions like Log, Ln, Mod, Exp, Power, Root, and so on. This paper demonstrates how you can use the expression builder that is integrated into the data builder, the explorer, and the designer to create different types of expressions and transform data for analysis and reporting purpose.

Over the years, complex medical data has been captured and retained in a variety of legacy platforms that are characterized by special formats, hierarchical/network relationships, and relational databases. Due to the complex nature of the data structures and the capacity constraints associated with outdated technologies, high-value healthcare data locked in legacy systems has been restricted in its use for analytics. With the emergence of highly scalable, big data technologies such as Hadoop, now it's possible to move, transform, and enable previously locked legacy data economically. In addition, sourcing such data once on an enterprise data hub enables the ability to efficiently store, process, and analyze the data from multiple perspectives. This presentation illustrates how legacy data is not only unlocked but enabled for rapid visual analytics by using Cloudera's enterprise data hub for data transformation and the SAS^® ecosystem.

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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SAS^® Studio includes tasks that can be used to generate SAS^® programs to process SAS data sets. The graph tasks generate SAS programs that use ODS Graphics to produce a range of plots and charts. SAS^® Enterprise Guide 7.1 and SAS^® Add-In for Microsoft Office 7.1 can also make use of these SAS Studio tasks to generate graphs with ODS Graphics, even though their built-in tasks use SAS/GRAPH^®. This paper describes these SAS Studio graph tasks.

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Seeing business metrics in real time enables a company to understand and respond to ever-changing customer demands. In reality, though, obtaining such metrics in real time is not always easy. However, SAS Australia and New Zealand Technical Support solved that problem by using SAS^® Visual Analytics to develop a 16-display command center in the Sydney office. Using this center to provide real-time data enables the Sydney office to respond to customer demands across the entire South Asia region. The success of this deployment makes reporting capabilities and data available for Technical Support hubs in Wellington, Mumbai, Kuala Lumpur, and Singapore--covering a total distance of 12,360 kilometers (approximately 7,680 miles). By sharing SAS Visual Analytics report metrics on displays spanning multiple time zones that cover a 7-hour time difference, SAS Australia and New Zealand Technical Support has started a new journey of breaking barriers, collaborating more closely, and providing fuel for innovation and change for an entire region. This paper is aimed at individuals or companies who want to learn how SAS Australia & New Zealand Technical Support developed its command center and who are inspired to do the same for their offices!