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.

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.

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.

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.

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.

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.

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!

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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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.

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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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.

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.

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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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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