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.

Amazon Web Services (AWS) as a platform for analytics and data warehousing has gained significant adoption over the years. With SAS^® Visual Analytics being one of the preferred tools for data visualization and analytics, it is imperative to be able to deploy SAS Visual Analytics on AWS. This ensures swift analysis and reporting on large amounts of data with SAS Visual Analytics by minimizing the movement of data across environments. This paper focuses on installing SAS Visual Analytics 7.2 in an Amazon Web Services environment, migration of metadata objects and content from previous versions to the deployment on the cloud, and ensuring data security.

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.

At Royal Bank of Scotland, business intelligence users require sophisticated security permissions both at object level and data (row) level in order to comply with data security, audit, and regulatory requirements. When we rolled out SAS^® Visual Analytics to our two main stakeholder groups, this was identified as a key requirement as data is no longer restricted to the desktop but is increasingly available on mobile devices such as tablets and smart phones. Implementing row-level security (RLS) controls, in addition to standard security measures such as authentication, is a most effective final layer in your data authorization process. RLS procedures in leading relational database management systems (RDBMSs) and business intelligence (BI) software are fairly commonplace, but with the emergence of big data and in-memory visualization tools such as SAS^® Visual Analytics, those RLS procedures now need to be extended to the memory interface. Identity-driven row-level security is a specific RLS technique that enables the same report query to retrieve different sets of data in accordance with the varying security privileges afforded to the respective users. This paper discusses an automated framework approach for applying identity-driven RLS controls on SAS^® Visual Analytics and our plans to implement a generic end-to-end RLS framework extended to the Teradata data warehouse.

Organizations are collecting more structured and unstructured data than ever before, and it is presenting great opportunities and challenges to analyze all of that complex data. In this volatile and competitive economy, there has never been a bigger need for proactive and agile strategies to overcome these challenges by applying the analytics directly to the data rather than shuffling data around. Teradata and SAS^® have joined forces to revolutionize your business by providing enterprise analytics in a harmonious data management platform to deliver critical strategic insights by applying advanced analytics inside the database or data warehouse where the vast volume of data is fed and resides. This paper highlights how Teradata and SAS strategically integrate in-memory, in-database, and the Teradata relational database management system (RDBMS) in a box, giving IT departments a simplified footprint and reporting infrastructure, as well as lower total cost of ownership (TCO), while offering SAS users increased analytic performance by eliminating the shuffling of data over external network connections.

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.

This paper demonstrates the deployment of SAS^® Visual Analytics 7.2 with a distributed SAS^® LASR™ Analytic Server and an internet-facing web tier. The key factor in this deployment is to establish the secure web server connection using a third-party certificate to perform the client and server authentication. The deployment process involves the following steps: 1) Establish the analytics cluster, which consists of SAS^® High-Performance Deployment of Hadoop and the deployment of high-performance analytics environment master and data nodes. 2) Get the third-party signed certificate and the key files. 3) Deploy the SAS Visual Analytics server tier and middle tier. 4) Deploy the standalone web tier with HTTP protocol configured using secure sockets. 5) Deploy the SAS^® Web Infrastructure Platform. 6) Perform post-installation validation and configuration to handle the certificate between the servers.

The SAS^® Visual Analytics autoload feature loads any files placed in the autoload location into the public SAS^® LASR™ Analytic Server. But what if we want to autoload the tables into a private SAS LASR Analytic Server and we want to load database tables into SAS Visual Analytics and we also want to reload them every day so that the tables reflect any changes in the database? One option is to create a query in SAS^® Visual Data Builder and schedule the table for load into the SAS LASR Analytic Server, but in this case, a query has to be created for each table that has to be loaded. It would be rather efficient if all tables could be loaded simultaneously; that would make the job a lot easier. This paper provides a custom solution for autoloading the tables in one step, so that tables in SAS Visual Analytics don't have to be reloaded manually or multiple queries don't have to be rerun whenever there is a change in the source data. This solution automatically unloads and reloads all the tables listed in a text file every day so that the data available in SAS Visual Analytics is always up-to-date. This paper focuses on autoloading the database tables into the private SAS LASR Analytic server in SAS Visual Analytics hosted in a UNIX environment and uses the SASIOLA engine to perform the load. The process involves four steps: initial load, unload, reload, and schedule unload and reload. This paper provides the scripts associated for each step. The initial load needs to be performed only once. Unload and reload have to be performed periodically to refresh the data source, so these steps have to be scheduled. Once the tables are loaded, a description is also added to the tables detailing the name of the table, time of the load, and user ID performing the load, which is similar to the description added when loading the tables manually into the SAS LASR Analytic Server using SAS Visual Analytics Administrator.

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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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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.

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.

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.

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.

As more of your work is performed in an off-premises cloud environment, understanding how to get the data you want to analyze and report on becomes important. In addition, working in a cloud environment like Amazon Web Services might be something that is new to you or to your organization when you use a product like SAS^® Visual Analytics. This presentation discusses how to get the best use out of cloud resources, how to efficiently transport information between systems, and how to continue to leverage data in an on-premises database management system (DBMS) in your future cloud system.

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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.

Do you want to see and experience how to configure SAS^® Enterprise Miner™ single sign-on? Are you looking to explore setting up Integrated Windows Authentication with SAS^® Visual Analytics? This hands-on workshop demonstrates how you can configure Kerberos delegation with SAS^® 9.4. You see how to validate the prerequisites, make the configuration changes, and use the applications. By the end of this workshop you will be empowered to start your own configuration.

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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At Royal Bank of Scotland, one of our key organizational design principles is to 'share everything we can share.' In essence, this promotes the cross-departmental sharing of platform services. Historically, this was never enforced on our Business Intelligence platforms like SAS^®, resulting in a diverse technology estate, which presents challenges to our platform team for maintaining software currency, software versions, and overall quality of service. Currently, we have SAS^® 8.2 and SAS^® 9.1.3 on the mainframe, SAS^® 9.2, SAS^® 9.3, and SAS^® 9.4 across our Windows and Linux servers, and SAS^® 9.1.3 and SAS^® 9.4 on PC across the bank. One of the benefits to running a multi-tenant SAS environment is removing the need to procure, install, and configure a new environment when a new department wants to use SAS. However, the process of configuring a secure multi-tenant environment, using the default tools and procedures, can still be very labor intensive. This paper explains how we analyzed the benefits of creating a shared Enterprise Business Intelligence platform in SAS alongside the risks and organizational barriers to the approach. Several considerations are presented as well as some insight into how we managed to convince our key stakeholders with the approach. We also look at the 'custom' processes and tools that RBS has implemented. Through this paper, we encourage other organizations to think about the various considerations we present to decide if sharing is right for their context to maximize the return on investment in SAS.

You've heard all the talk about SAS^® Visual Analytics--but maybe you are still confused about how the product would work in your SAS^® environment. Many customers have the same points of confusion about what they need to do with their data, how to get data into the product, how SAS Visual Analytics would benefit them, and even should they be considering Hadoop or the cloud. In this paper, we cover the questions we are asked most often about implementation, administration, and usage of SAS Visual Analytics.

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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!

The North Carolina Community College System office can quickly and easily enable colleges to compare their program's success to other college programs. Institutional researchers can now spend their days quickly looking at trends, abnormalities, and other colleges, compared to spending their days digging for data to load into a Microsoft Excel spreadsheet. We look at performance measures and how programs are being graded using SAS^® Visual Analytics.

SAS^® 9.4 allows for extensive customization of configuration settings. These settings are changed as new products are added into a deployment and upgrades to existing products are deployed into the SAS^® infrastructure. The ability to track which configuration settings change during the addition of a specific product or the installation of a particular platform maintenance release can be very useful. Often, customers run a SAS deployment step and wonder what exactly changed and in which files. The use of version control systems is becoming increasingly popular for tracking configuration settings. This paper demonstrates how to use Git, a hugely popular, open-source version control system to manage, track, audit, and revert changes to your SAS configuration infrastructure. Using Git, you can quickly list which files were changed by the addition of a product or maintenance package and inspect the differences. You can then revert to the previous settings if that becomes desirable.

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.

SAS^® Visual Analytics offers many new and exciting ways to look at your data. Users are able to load data at will to explore and ask questions of their data. But what happens if you need to prevent users from viewing all data? What can you do to prevent users from loading too much data? What happens if a user loads data that exceeds the hardware capacity of your environment? This session covers practical ways to limit available resources and secure SAS LASR data. Real-world scenarios are covered and attendees can participate in an open discussion.

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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.

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.

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.

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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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.

At the University of Central Florida (UCF), we recently invested in SAS^® Visual Analytics, along with the updated SAS^® Business Intelligence platform (from 9.2 to 9.4), a project that took over a year to be completed. This project was undertaken to give our users the best and most updated tools available. This paper introduces the SAS Visual Analytics environment at UCF and includes projects created using this product. It answers why we selected SAS Visual Analytics for development over other SAS^® applications. It explains the technical environment for our non-distributed SAS Visual Analytics: RAM, servers, benchmarking, sizing, and scaling. It discusses why we chose the non-distributed mode versus distributed mode. Challenges in the design, implementation, usage, and performance are also presented, including the reasons why Hadoop was not adopted.

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UNC General Administration's Division of Institutional Research (UNCGA IR) collects student-level data on enrollment, graduation, courses, grades, financial aid, facilities, and personnel from the 16 public universities. The university system is transitioning from data collection and processing of flat files submitted via SAS/IntrNet^® to an Oracle Data Mart that retrieves the data from the campuses' source systems. After collecting the data, either via flat files or data mart, SAS^® data sets are created and used for analysis and reporting for public and private universities, policy makers, elected officials, and the general public. After a complete turnover in programming staff, UNCGA IR decided to make some major upgrades and improvements to a server and system that were quickly becoming unsupported and outdated. This presentation covers the following topics: a background of our office functions, including our SAS environment; the decision-making process to upgrade to a SAS^® BI and SAS^® Visual Analytics environment, using SAS^® 9.3 and eventually SAS^® 9.4; the factors in determining which processes should be moved to a PHP interface, to SAS^® Stored Processes, and to SAS Visual Analytics (including whether the user was internal or external, whether the user had access to secure data, and how much time was involved in making the changes); the process and challenges of implementing the changes; and the lessons learned. This presentation has a universal audience but is specifically intended for anyone who has gone through or plans to go through a SAS upgrade, has inherited spaghetti code and used it to do their job, or has used SAS/IntrNet, SAS Visual Analytics, SAS Stored Processes, or a PHP interface to initiate SAS programs.

Sensitive data requires elevated security requirements and the flexibility to apply logic that subsets data based on user privileges. Following the instructions in SAS^® Visual Analytics: Administration Guide gives you the ability to apply row-level permission conditions. After you have set the permissions, you have to prove through audits who has access and row-level security. This paper provides you with the ability to easily apply, validate, report, and audit all tables that have row-level permissions, along with the groups, users, and conditions that will be applied. Take the hours of maintenance and lack of visibility out of row-level secure data and build confidence in the data and analytics that are provided to the enterprise.

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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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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!

SAS^® Web Application Server goes down and the user is presented with an error message. The error messages in the SAS^® 9.4 middle tier are the default ones that are shipped with the underlying VMware vFabric Web Server and are seen by many users as too technical and uninformative. This paper describes an application called 'Errors' that was developed at the Royal Bank of Scotland that has been implemented across its 9.4 estate to provide a much better user experience for when things go wrong. In addition, regardless of communications, users always try to access an application if it is available. This paper goes into detail about a feature of the Errors application that RBS uses to prevent this. This feature is used to control access to the web applications during scheduled outage windows and it provides capability for IP and location-based access as well as others. This paper also documents features and capabilities that RBS would like to introduce to the application.

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