| Sunday |
08:00 AM |
11:30 AM |
Meeting Room 401 |
|
Tricia Aanderud |
Tutorial |
Think Like a Data Storyteller (Extra Fee) |
What happens when we think like a storyteller? One of the hardest parts about data is capturing people's attention. Numbers may prove your point, but how do you get others to care about them? Data storytelling allows you to mix interesting narratives with data so that you can maximize your impact. This is accomplished through a mixture of data visualization and storytelling theories and best practices. Whether you're an analyst crunching numbers or a manager needing to communicate in a data-driven way—this session introduces the skills needed to create effective data stories. In this workshop, we review data storytelling methods, learn what stories motivate which audiences, learn how to best display data, and create messages that resonate. The topics are focused on general best practices and features techniques using SAS® Visual Analytics.
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| Sunday |
01:00 PM |
03:00 PM |
Meeting Room 503 |
|
Warren Kuhfeld |
Tutorial |
Advanced Output Delivery System Graphics Examples (Extra Fee) |
You can use SG annotation, modify templates, and change dynamic variables to customize graphs in SAS®. Standard graph customization methods include template modification (which most people use to modify graphs that analytical procedures produce) and SG annotation (which most people use to modify graphs that procedures such as PROC SGPLOT produce). However, you can also use SG annotation to modify graphs that analytical procedures produce. You begin by using an analytical procedure, ODS Graphics, and the ODS OUTPUT statement to capture the data that go into the graph. You use the ODS document to capture the values of dynamic variables, which control many of the details of how the graph is created. You can modify the values of the dynamic variables, and you can modify graph and style templates. Then you can use PROC SGRENDER along with the ODS output data set, the captured or modified dynamic variables, the modified templates, and SG annotation to create highly customized graphs. This paper shows you how and introduces SG annotation and axis tables. This tutorial is based on the free web book: http://support.sas.com/documentation/prod-p/grstat/9.4/en/PDF/odsadvg.pdf. Prior experience with ODS Graphics is assumed.
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| Sunday |
04:30 PM |
05:00 PM |
The Quad - Super Demo 5 |
Programming: Data Presentation |
Dan Heath |
SAS Super Demo |
New Features in Output Delivery System Graphics |
|
| Sunday |
04:30 PM |
05:00 PM |
The Quad - Super Demo 2 |
Business Analytics/ Data Visualization: Business Analytics |
Jeff Diamond |
SAS Super Demo |
What's New in SAS® Visual Analytics 8.3 |
Come get a sneak preview of the upcoming release of SAS® Visual Analytics 8.3.
|
| Monday |
10:30 AM |
11:30 AM |
Meeting Room 302 |
Business Analytics/ Data Visualization: Business Analytics |
Tricia Aanderud |
Breakout |
Finding the Treasure: Using Geospatial Data for Better Results with SAS® Visual Analytics |
Traditional business intelligence systems have focused on answering the who, what, and when questions, but organizations need to know the where of data as well. SAS® Visual Analytics makes it easy to plot geospatial data, which can add a completely new element to your data visualizations and analysis. When looking at a tabular report, you notice multiple columns that represent customers, competitors, and demographic information. But when you place the same geocoded data on a map, new insights jump off the page! Perhaps you see where the better customers are, where they are in relation to competitors, and the regions that provide the most market potential based on underlying demographics. These finding are about the where of the data. It's like creating a treasure map. In this session, you learn how to use SAS Visual Analytics geospatial objects, how to create custom geographic data items to use with the maps, and see many examples of how others have incorporated maps for snazzy data presentations.
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| Monday |
10:30 AM |
11:30 AM |
Meeting Room 301 |
Business Analytics/ Data Visualization: Business Analytics |
Suresh Divakar |
Breakout |
Implementing Analytics: Perspectives from the Client Side |
Most organizations are in the midst of transforming themselves from an intuition- or judgement-based culture into a culture where fact-based decision making is the norm. This means using data and analytics, and embedding these into the organization's decision making at all levels. This transformation is both an analytical initiative as well as a change in management. I discuss my experiences in heading Analytics groups in large client organizations in the Consumer Packaged Goods and Pharmaceutical sectors, focusing on the challenges involved in implementing and transforming companies to build a fact-based analytics culture. I first describe how data and analytics are used to formulate marketing strategy and present several real-life use cases to illustrate this point. The presentation discusses the types of analytic work or projects undertaken in detail, both continuously and episodically, and addresses questions such as: Who initiates the analytics work? What is the analytics process? How is analytics activation done? Four analytics use cases related to marketing mix analysis (resource allocation), price optimization, and forecasting assortment optimization are covered. I also discuss learnings and best practices in analytics implementation and activation, such as the importance of getting both top-down and bottom-up buy-ins, the right communication strategy with internal clients, securing quick-wins, getting the right analytic partner or vendor, and so on.
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| Monday |
10:30 AM |
11:00 AM |
Mile High Ballroom Theater B |
Industry-Specific Solutions |
Emily Chapman-McQuiston |
Breakout |
SAS® Visual Investigator and SAS® Visual Analytics: Bridging the Gap between Subject Matter Experts |
Health care payers constantly work in the world of big data. Because of the insights that big data can provide, payers are increasingly required to use it to improve outcomes for their patients, lower costs, and prevent fraud, waste, and abuse (FWA) in their networks. This enormous task requires contributors from many areas of the organization, such as strategically minded executives, clinicians, auditors, and data scientists. Together, SAS® Visual Investigator and SAS® Visual Analytics provide a toolset that enables users to bridge the gaps between the individuals in these different roles. This paper examines how advanced analytics, coupled with visualizations, make complex analytics readily available to subject matter experts across the health care industry.
|
| Monday |
10:30 AM |
11:00 AM |
The Quad - Super Demo 5 |
Programming: Data Presentation |
Sanjay Matange |
SAS Super Demo |
Output Delivery System Graphics Designer |
|
| Monday |
11:00 AM |
12:00 PM |
Mile High Ballroom Theater A |
Administration: SAS Administration |
Mark Schneider |
Breakout |
Comparing SAS® Viya® and SAS® 9.4 Capabilities: A Tale of Two SAS® Platform Engines |
SAS® Viya® extends the SAS® Platform in a number of ways and has opened the door for new SAS® software to take advantage of its capabilities. SAS® 9.4 continues to be a foundational component of the SAS Platform, not only providing the backbone for a product suite that has matured over the last forty years, but also delivering direct interoperability with the next generation analytics engine of SAS Viya. Learn about the core capabilities shared between SAS Viya and SAS 9.4, and about where they are unique. See how the capabilities complement each other in a common environment, and understand when it makes sense to choose between the two and when it makes sense to go with both. In addition to these core capabilities, see how the various SAS software product lines stack up in both, including analytics, visualization, and data management. Some products, like SAS® Visual Analytics, have one version aligned with SAS Viya and a different version with SAS 9.4. Other products, like SAS® Econometrics, leverage the in-memory, distributed processing of SAS Viya, while at the same time including SAS 9.4 functionality like Base SAS® and SAS/ETS® software. Still other products target one engine or the other. Learn which products are available on each, and see functional comparisons between the two. In general, gain a better understanding of the similarities and differences between these two engines behind the SAS Platform, and the ways in which products leverage them.
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| Monday |
11:00 AM |
12:00 PM |
Meeting Room 501 |
Data Management: Data Governance |
Vincent Rejany |
Breakout |
Enable Personal Data Governance for Sustainable Compliance |
In the context of European Union's General Data Protection Regulation (GDPR), one of the challenges for data controllers and data stewards is to identify the personal data categories in an application in a very short amount of time, document them, and then keep an up-to-date view. We propose an approach to automate the governance efforts and to significantly reduce the amount of time and effort needed to have the latest view of the personal data, therefore better servicing customers and answering the regulator. We use several processes developed in SAS® Data Management Studio to identify the personal data and update the governance view within SAS® Business Data Network and SAS® Lineage. We demonstrate several features in other products such as the Personal Data Discovery Dashboard in SAS® Visual Analytics, the Personal Metadata Linker in SAS® Data Management, and SAS® Personal Data Compliance Manager as it applies to Records of Processing Activities and the Data Protection Impact Assessment.
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| Monday |
11:30 AM |
12:30 PM |
Meeting Room 302 |
Business Analytics/ Data Visualization: Data Visualization |
Falko Schulz |
Breakout |
Leverage Custom Geographical Polygons in SAS® Visual Analytics |
Discover how you can explore geographical maps using your own custom map regions. SAS® Visual Analytics supports a number of predefined geo codecs, including various country and subdivision lookups. However, often your own custom polygons or shape files draw exact boundaries for the regional overlay you are trying to explore. From generic sales regions, floor plans, or even pipe lines—there are many use cases for custom polygons in visual data analysis. Using custom regions is now easier than ever with user-interface-driven support for importing and registering these custom providers. This paper demonstrates not only the different types of custom providers that are supported, but also shows how to leverage custom polygons within SAS Visual Analytics by showcasing industry examples.
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| Monday |
12:00 PM |
12:30 PM |
The Quad - Eposter 8 |
User Development: Academic (Teaching) |
Laura Kapitula |
E-Poster |
Reading, Wrangling, Visualizing, and Modeling the Surface Temperature of the Great Lakes |
The Great Lakes are the largest group of freshwater lakes on the planet and contain 21% of the world’s fresh water. Data on the daily temperatures of the individual Great Lakes is stored on the internet in a collection of text files maintained by the National Oceanic and Atmospheric Administration within the U.S. Department of Commerce and is updated daily. SAS® was used to read this data from the internet and create attractive, reproducible, and informative reports. In this paper, we illustrate how SAS can be used to read data from the internet, how to wrangle and combine the data to get it ready for analysis, how to work with Julian dates, and how to use the Output Delivery System to make reproducible reports. We also explore how to make attractive visualizations using the SGPLOT procedure, and how to use polygon files to make an animated map using the GMAP procedure. Furthermore, we show how SAS/STAT® software can be used to explore relationships and build predictive models between lake temperatures, land temperatures, and snowfall amounts in a city to the west of Lake Michigan. These data and examples work very well for illustrating statistical computing techniques to students in a classroom or for tools for self-study.
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| Monday |
12:00 PM |
12:30 PM |
The Quad - Eposter 11 |
Business Analytics/ Data Visualization: Business Analytics |
Sudeep Kunhikrishnan |
E-Poster |
Using Cox Proportional Hazard Model to Predict Failure: Practical Applications in Multiple Scenarios |
This presentation highlights practical applications of Cox proportional hazard modeling in multiple scenarios. Survival analysis helps in estimating time to event for a group of individuals or between two or more groups, and it helps to assess the relationship of co-variables to time to event. Survival analysis provides an added advantage over t test or regression analyses when comparing time to event because survival analysis does not ignore censoring. Survival analysis provides an advantage over logistic regression, while still comparing the proportion of events, because it does not ignore time as a factor. In this presentation, the PHREG procedure (using Cox's partial likelihood method to estimate regression models with censored data) is used to highlight practical applications in scenarios (developing a generalized model that can be used and interpreted in multiple practical business scenarios) such as predicting customer churn, predicting patient longevity on a drug or treatments, and project outcome—breakthrough/incremental innovations. The model results can be used in these scenarios because we model the effect of predictors and covariates on the hazard rate but the baseline hazard rate is unspecified. There is no assumption or knowledge of an absolute risk requirement, and the model can be used on the total population of study. This also helps in ease of comparison between different groups. Sample model result interpretations and applications are discussed in detail.
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| Monday |
12:00 PM |
12:30 PM |
The Quad - Eposter 12 |
Business Analytics/ Data Visualization: Business Analytics |
Romulo Alvim |
E-Poster |
Using SAS® Fraud Framework for Government to Identify Fraud in Brazil's Federal Capital |
Avoiding fraud in bids, identifying links between people and companies, and inhibiting the illegal accumulation of public offices are the main issues to which SAS® Fraud Framework for Government is applied. The Tribunal de Contas do Distrito Federal is the public institution responsible for controlling the public assets and resources of the Federal Capital, promoting ethics in public management in order to guarantee the full exercise of citizenship. It has the constitutional competence to supervise and judge the good and regular application of public resources by administrators and other officials, assisting the Legislative Chamber of the Federal District in the exercise of external control. Identifying undue actions by individuals and companies in public procurement requires data organization and the application of analytical intelligence. Fraud can be discovered by identifying links between people and companies, and in the supply of products and services between groups of companies. In general, a public official of the Federal District cannot work in more than one public office. However, by applying statistical analysis, it was found that several public servants are circumventing this rule and working in other institutions. This work presents the methodology and procedures used with the implementation of a SAS® software solution in the identification of irregularities in the management of the Federal District.
|
| Monday |
12:00 PM |
12:30 PM |
The Quad - Super Demo 1 |
Business Analytics/ Data Visualization: Business Analytics |
Jeff Diamond |
SAS Super Demo |
The Shape of Things |
Custom map data and spatial discovery in SAS® Visual Analytics.
|
| Monday |
12:00 PM |
12:30 PM |
Meeting Room 210 |
Administration: Architecture |
Shelby Katz |
Student Symposium |
An Investigation of the Factors Associated with Opioid Misuse |
The drug epidemic in America is one of the most prevalent, pressing issues the country faces today. America’s opioid crisis consumes the lives of over two million people, while costing the U.S. Economy over $504 billion. Prescription opioids such as codeine, fentanyl, and oxycodone are a few of the drugs that are being prescribed by doctors at an alarming rate. In order for government officials to be able to successfully dissuade citizens from misusing opioids, officials must identify the factors associated with misuse behavior. The primary objective of this study is to examine correlations between predictors to identify variables closely associated with prescription opioid misuse. This study used the data from the 2016 National Survey on Drug Use and Health (NSDUH). The initial set of potential predictors was selected from those believed to be related to opioid misuse and includes variables from four categories: demographic, socioeconomic, psychological, and risk behaviors. The analysis tools include logistic regression, decision trees, and data visualization to illustrate the patterns of association. The data provides evidence that a person who exhibits certain factors is more likely to misuse prescription opioids than one who does not share the same characteristics. The factors that influence the propensity to misuse opioids include mental health, age, race, cigarette use, frequency of alcohol use, frequency of marijuana use, sedative use, and smokeless tobacco use.
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| Monday |
12:30 PM |
01:00 PM |
Mile High Ballroom Theater A |
Business Analytics/ Data Visualization: Data Visualization |
Ed Summers |
Breakout |
Accessible Reporting with SAS® Visual Analytics and SAS® Mobile BI for iOS |
How do you publish beautiful reports that are also accessible for people with disabilities? Use SAS Visual Analytics and SAS Mobile BI for iOS. This presentation will provide an overview of the accessibility features in iOS and demonstrate how users with disabilities access reports using SAS Mobile BI on iPhones and iPads
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| Monday |
12:30 PM |
01:00 PM |
The Quad - Super Demo 5 |
Programming: Data Presentation |
Dan Heath |
SAS Super Demo |
Graphs in the CAS Procedure |
|
| Monday |
01:00 PM |
01:30 PM |
Meeting Room 301 |
Business Analytics/ Data Visualization: Business Analytics |
James Van Scotter |
Breakout |
The Life Expectancy of Phone Numbers in Escort Ads |
Most people would be surprised by the extent to which “the oldest profession” depends on the newest technology. Websites that sell classified ads for escort services enable human traffickers to reach customers while shielding them from law enforcement. The phone numbers that connect potential customers to the victims of human trafficking are an important clue for finding ties to the traffickers and pimps who profit from these activities. Our paper describes how we used SAS® to take the first steps toward using phone numbers to uncover the traffickers. For example, we examined the length of time that phone numbers appeared in ads, whether one phone number was used in different locations at the same time or in different locations at different times, and whether ad categories are associated with the amount of time a phone number remains active. We used a custom web-scraping program to capture the text of nearly 700,000 escort ads from backpage.com. Our analysis focused on ads posted in eight major cities and numerous smaller towns in Louisiana between 2/21/2016 and 1/26/2017. Initial (simulated) results show that about 65% of phone numbers were still active after about 3 months, about 30% were still active after 8 months, and 20-25% were active after 10 months.
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| Monday |
01:00 PM |
01:45 PM |
Meeting Room 506 |
Business Analytics/ Data Visualization: Business Analytics |
Marje Fecht |
Hands-On Workshop |
Easing into Data Exploration, Reporting, and Analytics Using SAS® Enterprise Guide® |
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 SAS Enterprise Guide environment. With all the demands on your time, you need powerful tools that are easy to learn and that deliver end-to-end support for your data exploration, reporting, and analytics needs. This workshop uses the current production version of SAS Enterprise Guide, but the content is still useful to users of earlier versions. 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
productivity tips, and additional tips and tricks
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| Monday |
01:00 PM |
01:15 PM |
The Quad - Super Demo 11 |
Industry-Specific Solutions |
Robert Lill |
Partner Super Demo |
Data Insights for a National Opioid Crisis: Visualizing Public Data to Initiate Improvement Efforts |
The opioid epidemic is now a national emergency. Opioid overdoses are the leading cause of accidental death in the United States claiming over a half a million lives in the last 15 years. We are losing our children, parents, siblings, and neighbors at a rate of over 175 people a day. For 15 minutes, we invite you to take a look at BNL Consulting’s vision—an integration of multiple public data sources shown through a highly interactive solution, our Opioid Dashboard. We explore and gain insight into the opioid crisis,and discuss the power of a solution that includes more than public data sources.
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| Monday |
01:30 PM |
02:00 PM |
The Quad - Eposter 13 |
Business Analytics/ Data Visualization: Data Visualization |
Jaime D'Agord |
E-Poster |
Are Your Color Choices Ruining Your Reports? |
The goal of visualizing data is to communicate information effectively, to provide decision makers a quick and easy way to analyze data, and to help your readers understand data. Doing this might seem as simple as putting data into a graph. However, there’s more to it. Your color choices can make or break a visualization. It’s not just an aesthetic choice, it's a crucial tool to convey information. When used correctly, color sets the tone and helps to create visualizations that tell stories. On the contrary, a badly chosen color palette obscures the information you are trying to portray and, in turn, makes the data visualization less effective. In this poster, we explore color choices using SAS® Visual Analytics 8.1 running on SAS® Viya®.
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| Monday |
01:30 PM |
02:00 PM |
The Quad - Eposter 11 |
Business Analytics/ Data Visualization: Business Analytics |
Su Li |
E-Poster |
Drug Abuse: Is the Number of Breweries Playing A Role? |
The purpose of this paper is to help researchers seek a preventive treatment to predict certain patterns of population on drug abusive behavior. In this study, we use SAS® to build a model to understand the effects of unprecedented factors—political influence and the number of breweries—on human mental status and their decision-making process. The number of drug-induced deaths in our study is used to depict an overall picture of drug abusive behaviors existing in the nation. Crime rates among states and the corresponding number of deaths induced by alcohol are another scope that we consider as an aftermath of drug abusive usage. The model provides us with the probability measure of the correlations among political influence, number of breweries, alcohol-induced deaths, and drug-induced deaths. The study constraints political influence, alcohol-induced death, and number of breweries as independent variables to correlate with drug-induced death as the dependent variable. The model that is developed can help establish multiple criteria to target certain populations before drug abusive behaviors are reported, and make an early intervention if possible.
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| Monday |
01:30 PM |
02:00 PM |
The Quad - Eposter 12 |
Business Analytics/ Data Visualization: Business Analytics |
Prasenjit Shil |
E-Poster |
Improving Financial Reporting Accuracy Using Smart Meter Data |
Utility companies typically bill their customers based on usage during the respective customers' billing cycle despite selling the energy throughout the month. The start and stop date for a billing cycle might not coincide with that of a calendar month. Therefore, to close the accounting books at the end of the calendar month, utilities must estimate customer usage and corresponding revenue during the portion of the calendar month that has not been billed yet. Traditionally, utilities have relied upon either a regression model-based approach or a Prior Unbilled method to estimate the current month's unbilled usage and revenue, which at times can yield financial results outside reasonable limits. However, with the availability of smart meter data on a daily or hourly basis, utilities should be able to accurately calculate the unbilled usage rather than estimating it. The problem is that the daily or hourly meter readings might include erroneous and missing data that needs to be corrected and validated prior to being used in the financial entries. This paper proposes an analytical framework to correct and validate the daily or hourly meter readings using meter taxonomy data, meter operation data, billing cycle information, and tariff class information. Finally, unbilled energy usage and corresponding revenue are calculated and presented using SAS® Visual Analytics.
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| Monday |
01:30 PM |
02:00 PM |
The Quad - Super Demo 5 |
Programming: Data Presentation |
Jeff Phillips |
SAS Super Demo |
The SGMAP Procedure: Geographic Mapping in ODS Graphics |
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| Monday |
01:30 PM |
02:00 PM |
The Quad - Super Demo 2 |
Business Analytics/ Data Visualization: Data Visualization |
Jeff Diamond |
SAS Super Demo |
What's New in SAS® Visual Analytics 8.3 |
Come get a sneak preview of the upcoming release of SAS® Visual Analytics 8.3.
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| Monday |
02:00 PM |
03:00 PM |
Meeting Room 301 |
Business Analytics/ Data Visualization: Data Visualization |
Stephen Overton |
Breakout |
Discovering Insightful Relationships inside the Panama Papers Using SAS® Visual Analytics |
Network analytics is a broad methodology that supports the desire to perform link analysis through visual tools such as SAS® Visual Analytics, SAS® Social Network Analysis, and SAS® Visual Investigator. Link analysis visually displays all possible relationships that exist between entities, based on available data, to provide insight into direct and indirect associations. This can be a very helpful tool to support an investigation as a part of a fraud or anti-money laundering investigation process. Beneath the surface, data management techniques and advanced analytical routines are used to discover relationships, transform data, and build the appropriate data structures to support link analysis. Network statistics can help describe networks more accurately by using quantitative data to define complexities and unusual connections between entities. This paper explores an approach to support network analytics and link analysis by using the Panama Papers as a real-world example. The Panama Papers' leak is the largest leak of confidential data to-date. The data contained within the Panama Papers provides a wealth of knowledge to financial investigation units because it exposes previously unknown relationships between corporate entities and individuals.
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| Monday |
02:00 PM |
03:30 PM |
Meeting Room 506 |
Business Analytics/ Data Visualization: Data Visualization |
Kriss Harris |
Hands-On Workshop |
Animate your Data! |
When reporting your safety data, do you ever feel sorry for the person who has to read all the laboratory listings and summaries? Or have you ever wondered if there is a better way to visualize safety data? Let’s use animation to help the reviewer and to reveal patterns in our safety data, or in any data! This hands-on workshop demonstrates how you can use animation in SAS® 9.4 to report your safety data, using techniques such as visualizing a patient’s laboratory results, vital sign results, and electrocardiogram results and seeing how those safety results change over time. In addition, you learn how to animate adverse events over time, and how to show the relationships between adverse events and laboratory results using animation. You also learn how to use the EXPAND procedure to ensure that your animations are smooth. Animating your data will bring your data to life and help improve lives!
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| Monday |
02:00 PM |
02:30 PM |
Meeting Room 210 |
Business Analytics/ Data Visualization: Data Visualization |
Gregory Terlecky |
Student Symposium |
University CSU: A Web-Based Space Utilization Tool for Investigating Trends in University Room Use |
Understanding utilization of existing space resources across a university campus has applications for both immediate and long-term needs. This knowledge can be used to support plans for upgrades and expansions of the physical plant of the institution. Historical data can help to understand trends in space usage, provided it can be collected and organized uniformly. Additionally, fundamental characteristics that define the potential use of the elements must be included in order to understand trends in use of specialized spaces within the physical plant. With such information in hand, an interactive, web-based dashboard for assessing campus space utilization (CSU) called University CSU was developed as a tool for visualizing and optimizing room use across a university.
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| Monday |
02:30 PM |
03:30 PM |
Meeting Room 502 |
Programming: General Programming |
Philip Mason |
Breakout |
My Top 10 Ways to Use SAS® Stored Processes |
SAS® Stored Processes are a powerful facility within SAS®. Having recently written a book about SAS Stored Processes, I have discovered the 10 best ways to use them so that I can illustrate their different abilities and how to maximize them. I explain how to run almost any code from your web browser, how to use SAS Stored Processes to export data from SAS to systems like Tableau, how to optimize their use for thousands of users, how to build mobile applications, build visualizations like those seen in SAS® Visual Analytics, how to make web services to integrate with other clients, and much more. All of these techniques are not generally well known, although they are not complex. If you don't already understand these techniques, then adding these to your skill set will enable to you to achieve much more.
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| Monday |
02:30 PM |
03:00 PM |
The Quad - Super Demo 5 |
Programming: Data Presentation |
Sanjay Matange |
SAS Super Demo |
Clinical Graphs Using SAS® |
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| Monday |
03:00 PM |
03:30 PM |
Meeting Room 302 |
Business Analytics/ Data Visualization: Data Visualization |
Scott Leslie |
Breakout |
Mile-High Visual Analytics: Ways to Enhance Reports and Dashboards |
The recent versions of SAS® Visual Analytics include enhancements to several useful features that can elevate your reports and dashboards from good to great. This presentation describes workable solutions to common obstacles faced by report developers and data scientists. Topics include the use of parameters, when and how to calculate items, adding dynamic chart titles, and creating hierarchies to add drill-down functionality. This presentation is suitable for users of all experience levels and demonstrates how to optimize SAS Visual Analytics to elevate your reports and dashboards to the next level.
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| Monday |
03:00 PM |
03:30 PM |
Meeting Room 404 |
Analytics: Machine Learning (Data Mining and Predictive Modeling) |
Mia Lyst |
Breakout |
To Show or Not to Show? Using SAS® Solutions to Maximize Patient Scheduling In Medical Clinics |
At busy medical clinics, it is important to maximize the number of patients seen each day. Scheduled patients that fail to show are a common problem in medical clinics and cause a decrease in revenue and disruption in daily operations. In order to combat this issue at several ambulatory clinics in the Southwest, machine learning algorithms were used to help the staff make informed decisions when scheduling patients. Using historical data, predictive models were built to identify patients that are not likely to show up to an appointment. By identifying these patients with high confidence, the staff can quickly fill these open slots to help the clinics serve more people at an efficient pace. A powerful end-to-end SAS® solution was implemented in which SAS® Office Analytics was used to prepare the historical data for modeling and to score new data based on the predictive models, SAS® Enterprise Miner™ was used to build, modify, and validate the machine learning algorithms, and SAS® Visual Analytics provided the mechanism to automatically load scored data into memory and populate informative dashboards. Medical staff is able to review the reports while scheduling patients and track the overall performance of the models. By using this solution, these clinics are able to serve more patients and produce additional revenue that would otherwise be lost. Due to the success of this project, the solution is being implemented at other specialty outpatient clinics across the region.
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| Monday |
03:00 PM |
03:30 PM |
Meeting Room 503 |
Programming: Data Presentation |
Caroline Walker |
Breakout |
Using ODS EXCEL to Integrate Tables, Graphics, and Text into Multi-tabbed Microsoft Excel Reports |
Do you have a complex report involving multiple tables, text items, and graphics that could best be displayed in a multi-tabbed spreadsheet format? The Output Delivery System (ODS) EXCEL destination, introduced in SAS® 9.4, enables you to create Microsoft Excel workbooks that easily integrate graphics, text, and tables, including column labels, filters, and formatted data values. In this paper, we examine the syntax used to generate a multi-tabbed Excel report that incorporates output from the REPORT, PRINT, SGPLOT, and SGPANEL procedures.
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| Monday |
03:00 PM |
03:15 PM |
Meeting Room 205 |
Business Analytics/ Data Visualization: Data Visualization |
Abhilasha Tiwari |
Quick Tip |
Need Dynamic Interactions in Your Dashboards? Use Parameters! |
SAS® Visual Analytics is a robust in-memory environment for creating stunning interactive and dynamic reports. The quick summarization of key performance indicators and the option to navigate the dashboard help in governance and steering operations. Interactive reporting is much easier and fun when combined with the use of parameters. Parameters are variables whose value can be changed and referenced by any other object in the reports. When the value of the parameter changes, any report objects that reference the parameter detect the change accordingly. Parameters not only make the dashboards dynamic, they also make the dashboards intuitive and handy for users. This paper covers how to use parameters to view your data dynamically in a graph or table over a specified span of days.
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| Monday |
03:00 PM |
03:30 PM |
The Quad - Super Demo 1 |
Business Analytics/ Data Visualization: Data Visualization |
Rich Hogan |
SAS Super Demo |
SAS® Visual Analytics On the Go: SAS® Mobile BI |
All of your SAS® Visual Analytics content is already mobile. With SAS® Mobile BI, you can access all of the features of your SAS Visual Analytics reports right on your phone. This demo highlights the features of SAS Mobile BI, including support for the new features available in SAS Visual Analytics 8.2.
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| Monday |
03:00 PM |
03:30 PM |
Meeting Room 210 |
Analytics: Text Analytics |
Bhagmattie Annissa Rodriguez-Ramdhanie |
Student Symposium |
Bridging the Skills Gap between Post-Secondary Education Outcomes and Employment Opportunities |
Monitoring employment qualifications is a difficult task that must be done to ensure that post-secondary education supplies graduates with the essential skills required in the workplace. Because the skill sets in demand vary over time, it is imperative that education accommodate this variation. Predicting these changes is an increasingly difficult task but must be done in order to address the increasing skill gap. This problem can be solved only by an ongoing monitoring system that can associate program outcomes with job requirements. Through the application of SAS® Text Miner, this paper examines automated processes to crawl targeted websites for entry and mid-level positions in order to classify them into topic themes and to highlight the underlying skills required. Using a stratified sample of job postings, the development of an automated text profiler for ongoing performance monitoring is explored. With the use of text cluster and text topic, skill sets can be categorized to better match program outcomes, and a visualization of the relationship between post-secondary program outcomes and the underlying sought-after employment skills can be created. The data can then be used by post-secondary educators to better bridge the skills gap.
|
| Monday |
03:30 PM |
04:30 PM |
Meeting Room 403 |
Analytics: Machine Learning (Data Mining and Predictive Modeling) |
Saratendu Sethi |
Breakout |
Biomedical Image Analytics Using SAS® Viya® |
Biomedical imaging has become the largest driver of health care data growth, generating millions of terabytes of data annually in the US alone. With the release of SAS® Viya® 3.3, SAS has, for the first time, extended its powerful analytics environment to the processing and interpretation of biomedical image data. This new extension, available in SAS® Visual Data Mining and Machine Learning, enables customers to load, visualize, process, and save health care image data and associated metadata at scale. In particular, it accommodates both 2-D and 3-D images, and recognizes all commonly used medical image formats, including the widely used Digital Imaging and Communications in Medicine (DICOM) standard. The visualization functionality enables users to examine underlying anatomical structures in medical images via exquisite 3-D renderings. The new feature set, when combined with other data analytic capabilities available in SAS Viya, empowers customers to assemble end-to-end solutions to significant, image-based health care problems. This paper demonstrates these capabilities with an example problem: diagnostic classification of malignant and benign lung nodules that is based on raw computed tomography (CT) images and radiologist annotation of nodule locations.
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| Monday |
03:30 PM |
04:30 PM |
Meeting Room 301 |
Business Analytics/ Data Visualization: Business Analytics |
Hans-Peter Bischof |
Breakout |
The Hunt for Gravitational Waves and How to Visualize What No Person has Seen Before |
In 1915, Dr. Albert Einstein published his general theory of relativity, which predicted that cataclysmic events create ripples in the fabric of spacetime, called gravitational waves. One hundred years later, on September 14, 2015, scientists of the Laser Interferometer Gravitational-Wave Observatory observed gravitational waves for the first time, confirming a major prediction of Einstein’s general theory of relativity. The signal was named GW150914. The first part of this talk focuses on the experience of a computer scientist surrounded by astrophysicists during the time of the detection and announcement of gravitational waves. The talk focuses on the detectors at the Laser Interferometer Gravitational-Wave Observatory, an engineering marvel, and the activities following the detection until the press conference February 11, 2016. The second part is about the visualization of scientific data. The visualization of scientific data can help to analyze and explore the data in ways that cannot be achieved with analytical methods. The talk explores the design of visualization systems and how to visualize data for the expert and the general audience.
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| Monday |
03:30 PM |
04:30 PM |
Meeting Room 302 |
Business Analytics/ Data Visualization: Data Visualization |
Brian Young |
Breakout |
Tips and Techniques for Designing the Perfect Layout with SAS® Visual Analytics |
Do you want to create better reports but find it challenging to design the best layout to get your idea across to your consumers? Building the perfect layout does not have to be a rocky experience. SAS® Visual Analytics provides a rich set of containers, layout types, size enhancements, and options that enable you to quickly and easily build beautiful reports. Furthermore, you can design reports that work across different device sizes or that are specific to a particular device size. This paper explores how to use the layout system and demonstrates what you can accomplish.
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| Monday |
04:00 PM |
05:00 PM |
Meeting Room 502 |
Programming: General Programming |
Marje Fecht |
Breakout |
Finding and Generalizing a "Best Before" Date |
Do you need a Best Before date that is 18 months from manufacture, and that is represented as a month end date? Do you manually provide dates as input to your processes? Do you struggle to get dates into the right format for database queries, or for your reports and dashboards? This presentation will help you find the right date, and then generalize the coding to avoid manual input, repetitive and messy coding, and frustration. Examples emphasize the easy manipulation of dates, and focus on generalization to support flexible coding, including:
Dynamically identifying date ranges, such as reporting and analytics periods (current calendar year; most recent 6 months; past 90 days; current fiscal year; year over year)
Dynamically generating field names that represent date values or ranges
Controlling the appearance of date values in reports
Generating date-time stamps for file names, without special symbols
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| Monday |
04:30 PM |
05:30 PM |
Meeting Room 302 |
Business Analytics/ Data Visualization: Data Visualization |
Jenine Milum |
Breakout |
SAS® Visual Analytics: Text Analytics Using Word Clouds |
There is a limit to employee skills and capacity to efficiently analyze volumes of textual unstructured data in a manner that provides actionable business insight. SAS® provides several tools that provide the capacity to explore text: SAS® Text Miner, SAS® Sentiment Analysis, and SAS® Visual Analytics. SAS® Visual Analytics word clouds expand analytical capacity by making text analytics and sentiment analysis easier to use, thereby putting these powerful tools in the hands of a much broader audience. Using a point-and-click interface, SAS® Visual Analytics word clouds can explore larger volumes of unstructured data, identify patterns and create usable reports to help define policies, and take actions that improve business operations.
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| Monday |
05:00 PM |
05:30 PM |
Meeting Room 301 |
Business Analytics/ Data Visualization: Data Visualization |
Ed Summers |
Breakout |
Accessibility and ODS Graphics: Seven Simple Steps to Section 508 Compliance Using SAS® 9.4M5 |
How do you create data visualizations that comply with the Section 508 amendment to the United States Workforce Rehabilitation Act, the Web Content Accessibility Guidelines (WCAG), and other accessibility standards? It’s easy when you use the new Output Delivery System (ODS) Graphics accessibility features in SAS® 9.4M5. This paper defines seven simple steps to accessible data visualizations. The accessibility requirements that are satisfied by each step are explained and additional references are provided. It includes sample code for real-world examples that has been tested by the SAS® accessibility team. It also includes a handy one-page checklist that you can print separately for future reference.
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| Monday |
05:00 PM |
05:30 PM |
Mile High Ballroom Theater B |
Industry-Specific Solutions |
Sean Cohen |
Breakout |
Detecting Defective Equipment in the Healthcare Service Industry |
Healthcare equipment service companies confront the challenge of how to identify defective equipment before they accrue a large financial burden to the supplier, downtime for the customer, and significant delay in the treatment of patients. Current methodologies employ sensors to detect large variations and to alert the service provider before failure or break down. However, these sensors are not available in all the machines, especially the older ones, and alerts usually come in at the last minute. Since time is of the essence, engineers scramble to make a quick break fix without looking at a holistic view of the problem and the history of the machine. This situation results in defective equipment being in the market for a significant period of time, causing excessive stress to both the service provider and customer, and impacting the reputation of the company. Using modern visualization tools like SAS® Visual Analytics and JMP®, we can apply meaningful visualizations to analyze large volumes of service event data. Doing so has enabled us to create a useful index that effectively measures customer experience over time and to apply regression techniques in tools like SAS® Enterprise Guide® to quickly identify the defective equipment, thus saving many hours of downtime and unproductive labor.
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| Monday |
05:00 PM |
05:30 PM |
Meeting Room 502 |
Programming: Data Presentation |
Barbara Okerson |
Breakout |
Mapping Roanoke Island Revisited: An OpenStreetMap (OSM) Solution |
In a previous presentation, SAS® was used to illustrate the difficulty of and solutions for mapping small pieces of coastal land, which are often removed from map boundary files, to smooth boundaries. Roanoke Island one of the first areas of the current United States to be mapped (1585) and was used as an example since it is smoothed out of many current maps. While these examples isolated Roanoke Island, they didn't provide detail beyond city names on the map. Originally limited to SAS® Visual Analytics, SAS® now makes background maps that have street and other detail information available for SAS/GRAPH® software using open-source map data from OpenStreetMap (OSM). This paper reviews the previous solutions, and then looks at how to map Roanoke Island using SAS/GRAPH and OSM.
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| Monday |
05:00 PM |
05:30 PM |
Meeting Room 201 |
Administration: SAS Administration |
Trevor Nightingale |
Breakout |
SAS® Environment Manager: A SAS® Viya® Administrator's Swiss Army Knife |
The latest version of SAS® Viya® brings with it a wealth of new capabilities, and administrators are by no means left out of the party. The version of SAS® Environment Manager that accompanies SAS Viya 3.3 significantly ratchets up what a SAS® administrator can accomplish with this next generation HTML5-based web application. See new features in its top-level dashboard, including an at-a-glance widget that provides the health of all SAS Viya machines, services, and service instances in a hierarchical heat map. Witness the new Logged Issues widget highlighting recent errors with details to help diagnose and head-off escalating problems. Learn about all the new SAS Environment Manager menu options, including viewers for the following:
Licensed Products—showing expiration dates and grace periods for all deployed products
Logs—consolidating logging from SAS services with advanced filtering and summarization
Machines—graphing CPU and memory consumption, and listing specific metric checks and supported services
Scheduling—monitoring and editing scheduled jobs
Finally, discover additional features like an advanced data explorer for importing data sources, as well as the ability to create and manage user-defined formats. In general, gain an appreciation for all the new "blades" SAS has added to its multi-purpose Swiss Army knife for administration. Gain an understanding of advanced features and sharpen your skills in monitoring and managing a SAS Viya 3.3 environment.
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| Tuesday |
10:00 AM |
11:00 AM |
Meeting Room 301 |
Business Analytics/ Data Visualization: Data Visualization |
Clayton Bellamy |
Breakout |
Analyzing Electricity Grid Loads with SAS® Enterprise Guide® and SAS® Visual Analytics |
With the introduction of smart meters in the utility industry, utilities have gone from receiving 12 readings per year for each customer's usage to more than 35,000. With thousands and thousands of smart meters deployed, these readings are bulging far past the ability of traditional analytical tools to make sense of them. At OGE Energy Corp., the company's analytics team has used SAS® Enterprise Guide® and SAS® Visual Analytics to leverage this rich data source to monitor loads throughout the utility's distribution grid.
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| Tuesday |
10:00 AM |
11:00 AM |
Meeting Room 302 |
Business Analytics/ Data Visualization: Data Visualization |
Gregor Herrmann |
Breakout |
Is Your Data Viable? Preparing Your Data for SAS® Visual Analytics 8.2 |
We all know that data preparation is crucial before you can derive any value from data through visualization and analytics. SAS® Visual Analytics on SAS® Viya® comes with a new rich HTML5 interface on top of a scalable compute engine that fosters new ways of preparing your data upfront. SAS® Data Preparation that comes with SAS Visual Analytics brings new capabilities like profiling, transposing or joining tables, creating new calculated columns, and scheduling and monitoring jobs. This paper guides you through the enhancements in data preparation with SAS Visual Analytics 8.2 and demonstrates valuable tips for reducing runtimes of your data preparation tasks.
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| Tuesday |
10:00 AM |
11:00 AM |
Meeting Room 406 |
|
Rick Bell |
Hands-On Workshop |
Hands-on Workshop: Exploring SAS® Visual Analytics on SAS® Viya® |
This workshop provides hands-on experience with SAS® Visual Analytics on SAS® Viya®. Workshop participants perform data discovery and learn how to create interactive reports and dashboards. This workshop is intended for both new users of SAS Visual Analytics and seasoned users who want to see new features in SAS Visual Analytics on SAS Viya.
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| Tuesday |
10:30 AM |
11:00 AM |
The Quad - Super Demo 5 |
Programming: Data Presentation |
Jane Eslinger |
SAS Super Demo |
Using ODS LAYOUT Statements to Align Text and Graphics in PDF Output |
|
| Tuesday |
11:00 AM |
12:00 PM |
Meeting Room 402 |
Analytics: Statistics/ Biostatistics |
Raghavendra Rao Kurada |
Breakout |
Fitting Compartment Models Using PROC NLMIXED |
The CMPTMODEL statement is a new enhancement to the NLMIXED procedure in SAS/STAT® 14.3. This statement enables you to fit a large class of pharmacokinetics (PK) models, including one-, two-, and three-compartment models, with intravenous (bolus and infusion) and extravascular (oral) types of drug administration. The CMPTMODEL statement also supports multiple dosages and PK models that have various parameterizations. This paper introduces the new statement and illustrates its usage through examples. Related concepts are also discussed, such as the %PKCONVRT autocall macro (which converts PK data sets that are stored according to industry standard to data sets that can be directly used by PROC NLMIXED), extension to Emax models, prediction, visualization, and fitting Bayesian PK models (by using the MCMC procedure).
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| Tuesday |
11:00 AM |
12:00 PM |
Meeting Room 401 |
Analytics: Statistics/ Biostatistics |
Maribeth Johnson |
Breakout |
Simple Methods for Repeatability and Comparability: Bland-Altman Plots, Bias, and Measurement Error |
While a Pearson correlation coefficient can be a quick and easy measure to compare two measurement methods or examine repeatability, it is not the most appropriate nor does it give you insight into bias. Performing linear regression or tests for differences between the means is also not the best approach for determining whether two methods are comparable or a measurement is repeatable. Examining the difference between the measurements might not offer insight into the accuracy of the methods, and a Pearson correlation coefficient is not a measure of agreement but a measure of association. Altman and Bland (1983) suggested a graphical method and two statistical tests to examine repeatability of measurement or whether two measurement methods produced similar results. The graphical method, called a Bland-Altman plot, is a plot of the difference versus the average of two different measures with y-reference lines at two standard deviations (SD) or three SD limits of the difference. A Bland-Altman plot allows for assessment of the magnitude of disagreement, both error and bias. The statistical tests suggested by Altman and Bland are a test for zero bias and a test of independence of the bias (difference between the methods) and magnitude (average of the methods) of the measure. A systolic blood pressure example is used to show how to perform the statistical tests and create the Bland-Altman plot using SAS/STAT® PROC TTEST, Base SAS® PROC CORR, and ODS Statistical Graphics SGPLOT.
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| Tuesday |
11:00 AM |
12:00 PM |
Meeting Room 302 |
Business Analytics/ Data Visualization: Data Visualization |
Travis Murphy |
Breakout |
Supercharge Your Dashboards with Infographic Concepts Using SAS® Visual Analytics |
A human’s attention span is shorter than that of a gold fish—about eight seconds is all you have to capture their attention and create a reason for a viewer to stay on your dashboard. Therefore, a dashboard’s visual appeal is even more important today than ever before, and this is where infographic concepts make a difference. Infographics deliver information with clarity and simplicity. Data is everywhere, and more report designers are using infographic elements to better communicate insight from the data. The boardroom can now benefit from what has become mainstream on popular news sites and social networks online. This paper shows you how to create infographic-inspired dashboards and reports that can be shared and dynamically explored by your teams using SAS® Visual Analytics on SAS® Viya®. Supercharge your existing dashboards and reports with easy drag-and-drop wizards, while still providing the performance, repeatability, and scalability on massive data that your enterprise demands. This session looks at how the latest enhancements in SAS Visual Analytics enable users to design and create infographic-style dashboards and reports like never before. You learn tips and techniques to get the most from your SAS Visual Analytics software that you can apply back at the office. You will leave this session with the perfect balance of creative ideas and practical examples to better engage your entire organization with high-impact data visualizations.
|
| Tuesday |
11:30 AM |
12:00 PM |
The Quad - Super Demo 5 |
Programming: Data Presentation |
Dan Heath |
SAS Super Demo |
ODS Graphics with SAS® Cloud Analytic Services Data |
|
| Tuesday |
11:30 AM |
12:00 PM |
The Quad - Super Demo 2 |
Business Analytics/ Data Visualization: Data Visualization |
Bradley Morris |
SAS Super Demo |
What’s New in SAS® Report Viewer |
This presentation highlights new features in SAS® Report Viewer 8.2 and 8.3, and demonstrates ways to share that content with others.
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| Tuesday |
12:00 PM |
12:30 PM |
Meeting Room 302 |
Business Analytics/ Data Visualization: Data Visualization |
Brandon George |
Breakout |
Take a Dive into HTML5 |
The SAS® Output Delivery System (ODS) provides several options to save and display SAS graphs and tables. You are likely familiar with some of these ODS statements already, such as ODS RTF for rich text format, ODS PDF for portable document format, and ODS HTML for hypertext markup language. New to SAS® 9.4, users have several additional ODS statements to choose from; among them is ODS HTML5. The ODS HTML5 statement differs from ODS HTML in that it uses HTML version 5 instead of HTML version 4. As such, the user can now take advantage of several new features introduced to the language. ODS HTML5 also uses a different default graphics output device, trading out the old standby portable network graphics (PNG) for the up-and-coming scalable vector graphics format (SVG). Throughout the paper, we focus on some of the differences between ODS HTML and ODS HTML5, with a focus on computer graphics. The goal is to convince you to switch to ODS HTML5 and start using SVG as your graphical output of choice.
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| Tuesday |
12:00 PM |
12:30 PM |
The Quad - Eposter 13 |
Business Analytics/ Data Visualization: Data Visualization |
Raymond Mierwald |
E-Poster |
A SAS® Visual Analytics Solution for the Centers for Medicare and Medicaid Services |
Government agencies have estimated that opioids now kill more Americans than car accidents. In this e-poster session, attendees learn how to use SAS® Studio, SAS® Visual Analytics, and SAS® Visual Statistics to quickly prototype SAS® solutions to better understand the opioid crisis in America for Medicare programs that provide prescription drugs. Using U.S. Federal Government Public Use Files or PUFs, attendees are led through the process of accessing PUF data using APIs, data explorations, clustering and machine learning models, and simple reporting in order to gain insights into this pressing government challenge. Customers who use SAS for population health analytics will find this session particularly useful since it makes extensive use of the Center for Medicare and Medicaid Services (CMS), the Centers for Disease Control and Prevention (CDC), and data from the U.S. Census Bureau. Handouts with step-by-step instructions are provided so that attendees can reproduce the analysis with PUF data on their own and even incorporate it as part of their own work. As a result of attending this session, attendees will gain a better understanding of the opioid epidemic, as well as a clear sense of how prototypes built with SAS can improve the overall quality of a solution.
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| Tuesday |
12:00 PM |
12:30 PM |
The Quad - Eposter 19 |
Business Analytics/ Data Visualization: Business Analytics |
Edmond Cheng |
E-Poster |
Integrating SAS® and Elasticsearch: Performing Text Indexing and Search |
Integrating Elasticsearch document indexing and text search components expands the power of performing textual analysis with SAS® solutions. Information technology, digitization, social connection, modern data storage, and big data accelerate unstructured text data production. Understanding the advantage in processing textual data and extracting underlying information provides valuable insights, setting an edge in competition, as seen in e-commerce, internet companies, communications media, marketing, health care, and across many industrial sectors. This paper covers the benefits of architecting and implementing Elasticsearch applications alongside SAS solutions. The first section presents an overview of Elasticsearch and common use cases. The paper demonstrates indexing SAS data sets into Elasticsearch NoSQL index, writing SAS codes to pass Elasticsearch REST APIs, and storing search query results. The final section demonstrates the use of Elasticsearch Kibana to further complement data visualization and business intelligent reporting capability with SAS analytics.
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| Tuesday |
12:00 PM |
12:30 PM |
The Quad - Eposter 11 |
Business Analytics/ Data Visualization: Business Analytics |
Zihan Fan |
E-Poster |
Understanding the Factors that Affect Customers’ Choice of Sunscreen |
Recently, we found that the sunscreen products on the US market usually have merely UV and broad spectrum protection. So we wondered—what are the main concerns for US consumers that lead them to purchase a specific product? The purpose of this study is to analyze the crucial factors that affect consumers’ preference of sun care products. We came up with several factors, collected the relative data from historical data about sunscreens, and studied the relationship between these factors and sun care product sales in each state in the US. We used SAS® Enterprise Guide® to perform the analysis. We believe that this project can help sunscreen product manufacturers come up with more accurate manufacturing strategies and marketing plans. In our research, we use data from websites and integrate the information in the form we need. According to the data analyzed, we can provide manufacturers the following three suggestions: Firstly, focus on the Southwestern region of the US and explore more types of demand for sunscreen products in this region; Secondly, focus more on daily sunscreen products, rather than other featured types of sun care products; Thirdly, pay attention and put more products on these three major conduits in order to improve their marketing strategy.
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| Tuesday |
12:00 PM |
12:30 PM |
The Quad - Eposter 17 |
Programming: Data Presentation |
Jeff Cheng |
E-Poster |
Using Graph Template Language and R for High-Quality Publication Plots |
The Graph Template Language (GTL) is a powerful SAS® tool to create sophisticated plots. There are many features in GTL that one can use to build plots with high-quality visual effects. Besides SAS, R is also a frequently used tool. This paper explores some GTL techniques for generating a publication-quality graph by creating and combining a pie chart and a bar chart, fine-tuning axis and plot position, and embedding texts for clarifications. Step-by-step instructions for making this graph are shown in both GTL and R to demonstrate how certain graphics elements and effects can be accomplished using either. There are numerous software applications for plotting scientific graphs. Some people use SAS to prepare the data set and rely on other software for plotting the graph. This approach involves converting the SAS data set to other data formats to facilitate use with different software. Companies sometimes contract outside vendors for plotting scientific graphs. However, by taking advantage of the capabilities of SAS and R for generating high-quality publication plots, many of these tasks can be done in-house, which makes a good business case for time and cost savings, and for data protection.
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| Tuesday |
12:30 PM |
01:00 PM |
Mile High Ballroom Theater A |
Analytics: Machine Learning (Data Mining and Predictive Modeling) |
Ilknur Kaynar Kabul |
Breakout |
High-Dimensional Visualization and Unsupervised Exploration with t-SNE and k-nearest neighbors |
Visualizing high-dimensional observations is a key requirement for effective data exploration and interpretation. Recent advances have been made in dimensionality reduction, namely the t-Distributed Stochastic Embedding (t-SNE) method, which is specifically designed for visualization. Through t-SNE, it is possible to overcome the well-known crowding problem for low-dimensional embeddings. This makes t-SNE ideal not only in its own right as an exploratory tool, but also as pre-processing tool for subsequent operations such as similarity search and clustering. In particular, K-nearest neighbor search has been used widely in the context of similarity search in many computer vision and machine learning problems. As data sets grow larger in dimensionality and number of observations, there is increasing need to find the nearest neighbors in a computationally efficient manner. In this presentation, we demonstrate the new TSNE procedure in conjunction with K-nearest neighbor search and clustering in SAS® Visual Data Mining and Machine Learning. The FASTKNN and TSNE procedures are highly parallel and can handle large data sets. They enable you to explore, visualize, search, and group large amounts of data easily and efficiently.
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| Tuesday |
12:30 PM |
01:00 PM |
Meeting Room 302 |
Business Analytics/ Data Visualization: Data Visualization |
Brandon Kirk |
Breakout |
Your Data Visualization Game Is Strong—Take It to Level 8.2 |
Your organization already uses SAS® Visual Analytics, and you have designed reports that show compelling data stories. The newest version of SAS Visual Analytics can give those stories a facelift through its clean, modern HTML5 interface and exciting new visualization features. Learn how to make the transition seamless while also using the move as an opportunity to focus on the most compelling reports. We walk through the methodology and the automation techniques that we used when we moved our own internal SAS Visual Analytics environment from 7.3 to 8.2.
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| Tuesday |
12:30 PM |
01:00 PM |
The Quad - Super Demo 5 |
Programming: Data Presentation |
Sanjay Matange |
SAS Super Demo |
Clinical Graphs Using SAS® |
|
| Tuesday |
01:00 PM |
01:30 PM |
Meeting Room 301 |
Business Analytics/ Data Visualization: Business Analytics |
Renae Rich |
Breakout |
Using Cluster Analysis to Maximize Workplace Design Effectiveness |
While it is generally accepted in the design industry that work spaces should be thoughtfully planned around the workers that occupy them and the types of work they do, space, standards, and budget limit the number of unique workspace options that are feasible. By applying a cluster analysis method to survey data related to the type of work individuals do, job roles are categorized into work style groups with distinct workplace needs and characteristics. These work style profiles are used to inform and develop design strategies to best support the various types of workers in an organization, within spatial and budgetary parameters. This presentation outlines the considerations for selecting and combining variables for analysis, exploration, and creation of the clustering pattern, investigation of unique cluster characteristics, as well as visualization techniques related to this method. Base SAS® procedures used include the FASTCLUS, CANDISC, SGPLOT, and GLM procedures.
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| Tuesday |
01:30 PM |
02:30 PM |
Meeting Room 301 |
Business Analytics/ Data Visualization: Business Analytics |
Evan Guarnaccia |
Breakout |
Working with Real-Time Data Interactively: SAS® Visual Statistics and SAS® Event Stream Processing |
SAS® Event Stream Processing captures the real-time value of data before it is lost. SAS® Visual Statistics provides an easy-to-use interface for exploring the relationships found in data. By combining the in-memory capabilities of these two products, the time lag between analysis and action is significantly reduced. This presentation shows how to model and surface important events in real-time live data.
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| Tuesday |
01:30 PM |
02:00 PM |
The Quad - Eposter 12 |
Business Analytics/ Data Visualization: Data Visualization |
Ted Conway |
E-Poster |
A Periodic Table of Introductory SAS® ODS Graphics Examples |
This e-poster presents "A Periodic Table of Introductory SAS® ODS Graphics Examples," the author’s tongue-in-cheek take on "Towards A Periodic Table of Visualization Methods for Management," the classic 2007 data visualization paper. The poster presents 200+ thumbnails of slides from the author’s introductory SAS Output Delivery System (ODS) Graphics hands-on workshop. When you hold your pointer over a thumbnail, it expands to more fully reveal the SAS code snippets and output images for the programming exercises. Click on an image, and it expands to fill the entire monitor. You might come to be amused, but don't be surprised if you leave with a new SAS ODS Graphics trick or two!
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| Tuesday |
01:30 PM |
02:00 PM |
The Quad - Eposter 11 |
Business Analytics/ Data Visualization: Business Analytics |
Jui Salunkhe |
E-Poster |
Leveraging Multivariate Testing for Digital Marketing Using SAS® Enterprise Guide® |
The most popular method for in-market tests is the A/B test, but the method that has the power to drive much stronger insights is the multivariate test. This paper explains the advantages of adopting multivariate testing in a direct mail campaign for digital marketing over other traditional methods, explaining how multivariate testing uncovers the hidden data insights that go unnoticed due to lack of aggregate testing potential of traditional methods. The paper lays out different approaches to assisting strategic business decisions by taking a narrow approach to campaign data. Consider a direct mail campaign for which the targeted content is sent to customers. The response rate of these customers is analyzed by testing it against the control group using the simple A/B test to determine whether the targeted content causes the response rate to increase. It is observed that customers receiving targeted content are more likely to respond as compared to customers receiving standard content. If this test is re-run using multivariate testing by controlling the best customer effect, it is observed that there is a significant difference between both groups due to best customer effect and not due to targeted content. Multivariate testing has the power to control multiple factors concurrently and to measure the performance of a campaign accurately, in less time, and with minimum effort.
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| Tuesday |
01:30 PM |
02:00 PM |
The Quad - Eposter 17 |
Programming: Data Presentation |
Hezekiah Bunde |
E-Poster |
Outputting Your Data to Microsoft Excel Is Inevitable. So Is the SAS® ODS Excel Destination |
Microsoft Excel is one of the most used tools wherever data is used, stored, or analyzed. Many SAS® users often resort to producing Excel output and working outside of the SAS environment to finesse the deliverables...until now. With the SAS® Output Delivery System (ODS) Excel destination, data manipulation and visualization is now possible. Native Excel files and graphs can now be created and customized. It was just a matter of time before this magnificent tool became a reality. ODS Excel is here to stay! Novice programmers with little or no experience at all with ODS output to experienced professionals will instantly experience its benefits. This e-poster demonstrates with easy-to-follow steps how to deliver your data from SAS to Excel. Users will realize time-saving benefits by pre-defining their preferences and avoid performing manual and repetitive tasks such as creating multiple sheets, adding color, titles, graphs, headers, footers, and so on.
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| Tuesday |
01:30 PM |
02:00 PM |
The Quad - Eposter 13 |
Business Analytics/ Data Visualization: Data Visualization |
Jaime D'Agord |
E-Poster |
Persuading with Data Stories: Is Jaws Just Misunderstood? |
Let’s face it—the media has given sharks a bad rap, portraying them as villains and creating a culture that fears the fin. Sources like the International Shark Attack File, the Global Shark Attack File, and OCEARCH provide us with staggering amounts of data every day. This data gives us the ability to map shark attack occurrences all over the world, to determine the activity that brought on the attack, whether the attack was provoked, and, ultimately whether it was a fatal attack. We can use the data to tell data stories about this animal species that fascinates so many of us. So, the prevailing question is, can we reduce fear over shark attacks using data? We explore this possibility using SAS® Visual Analytics 8.1 on SAS® Viya®.
|
| Tuesday |
01:30 PM |
02:00 PM |
The Quad - Eposter 15 |
Industry-Specific Solutions |
Tracy Song-Brink |
E-Poster |
Using SAS® to Fit AmeriFlux Data to Ecosystem Seasonality Models |
In ecosystem science research, we have several models to define season transitions in ecosystem gross productivity (GEP) and respiration (ER). These models were built with ecosystem data collected years ago. Thanks to the AmeriFlux ecosystem data community, now we have access to ecosystem data from more than 110 sites located across the Americas, compared with 15 sites in 1997. The purpose of this project is to fit the large volume of data that was not available to previous research to our existing models for model evaluation. We used the NLIN procedure for model fitting for each variable of one year at one specific flux data source. For each model fitting process, we used SAS macros to perform Grubbs' test for outlier detection and removal, and the GPLOT procedure for data visualization. SAS® macros were written to automate the process of all input files, variables, and data years. Data from 132 input files with an average size of 4000 observations and 70 variables were processed, and two models were evaluated in this project.
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| Tuesday |
02:00 PM |
02:30 PM |
Meeting Room 401 |
Analytics: Statistics/ Biostatistics |
Fei Wang |
Breakout |
Bayesian Networks for Causal Analysis |
Bayesian networks (BN) are a type of graphical model that represents relationships between random variables. The networks can be very complex with many layers of interactions. Graphical models become BNs when the relationships are probabilistic and uni-directional. Building BNs for causal analyses is a natural and reliable way of expressing (and confirming or refuting) our belief and knowledge about causes and effects. In addition, BNs can be easily reconfigured with minor modifications to facilitate our understanding of probabilistic mechanisms. This paper describes the construction of BNs for causal analyses and how to infer causal structures from observational and interventional data. The paper includes applications of causal BNs for classification using the HP Bayesian Network Classifier node in SAS® Enterprise Miner™. Visualization, inferences, and scenario analyses for the examples are discussed.
|
| Tuesday |
02:00 PM |
03:00 PM |
Meeting Room 302 |
Business Analytics/ Data Visualization: Data Visualization |
Shane Gibson |
Breakout |
Seven Agile Methods that Help Deliver Visualizations Agilely (and without Resorting to Being AdHoc!) |
Using Agile methods to deliver applications is a commonplace approach these days. But when you try to apply Agile techniques to delivering data, analytics, and visualizations, a whole set of new challenges arise that affect whether you are able to deliver a production-ready solution every 2–4 weeks. This session takes you through seven repeatable Agile techniques that deliver completed visualizations every three weeks. Shane covers the WHY, the HOW, and the WHAT for each of these steps. At the end of the session, you will have a set of artifacts that you can start to leverage in your next project. These steps have been discovered, defined, and refined by Shane over the last four years, based on a number of AgileBI customer projects in New Zealand (the land of hobbits and kiwifruit). The seven Agile methods covered in this session are:
Defining information products to set the scope
Modelstorming business events to identify the data requirements
Applying Agile data modeling techniques to structure data quickly
Wireframing to gather visualization requirements
Using Acceptance Test-Driven Development to ensure that you build it right
Delivering three visualization iterations in three weeks
Developing the T-shaped skills required to build an AgileBI team
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| Tuesday |
02:00 PM |
02:30 PM |
The Quad - Super Demo 1 |
Business Analytics/ Data Visualization: Data Visualization |
Rich Hogan |
SAS Super Demo |
SAS® Visual Analytics On the Go: SAS® Mobile BI |
All of your SAS® Visual Analytics content is already mobile. With SAS® Mobile BI, you can access all of the features of your SAS Visual Analytics reports right on your phone. This demo highlights the features of SAS Mobile BI, including support for the new features available in SAS Visual Analytics 8.2.
|
| Tuesday |
02:30 PM |
03:00 PM |
Meeting Room 301 |
Business Analytics/ Data Visualization: Business Analytics |
Atrin Assa |
Breakout |
Back to Basics: Get Better Insights from Data |
Get the most out of your data, big or small. There is more data than ever. But more data doesn't always mean better insights. If you're not careful, more data can sometimes lead you down the wrong path. The worst thing you can do is find patterns that aren't really there. SAS® Visual Analytics gives you the tools to avoid these kinds of problems. With simple drag-and-drop functionality, you can explore your data. You can see the shape of your data. You can see potential relationships in your data. You can get a grasp of the power and limits of your data. With that understanding, you can get better insights. It’s time to move beyond just pretty visualizations, and this paper shows you how.
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| Tuesday |
02:30 PM |
03:00 PM |
The Quad - Super Demo 5 |
Programming: Data Presentation |
Sanjay Matange |
SAS Super Demo |
Infographs Using SAS® |
|
| Tuesday |
02:30 PM |
03:00 PM |
The Quad - Super Demo 2 |
Business Analytics/ Data Visualization: Data Visualization |
Jeff Diamond |
SAS Super Demo |
What's New in SAS® Visual Analytics 8.3 |
Come get a sneak preview of the upcoming release of SAS® Visual Analytics 8.3.
|
| Tuesday |
03:00 PM |
03:30 PM |
Meeting Room 301 |
Business Analytics/ Data Visualization: Business Analytics |
Robby Powell |
Breakout |
Create Awesomeness: Build a Custom App to Extend SAS® Visual Analytics to Get the Results You Need |
SAS® Visual Analytics is an excellent visualization and analytics tool, but sometimes you need the addition of some custom functionality to create the exact tool, content, or analysis your team needs. In this paper, we look at how to extend SAS Visual Analytics 8.2 by adding your own custom app. We look at easy, modern, introductory-level examples using JavaScript that integrate well with SAS®. With further practice, you'll be able to extend SAS Visual Analytics with your own custom functionality to create the customized visual analytics app your team needs for business intelligence and data-driven decisions.
|
| Tuesday |
03:00 PM |
03:30 PM |
Meeting Room 203 |
Industry-Specific Solutions |
Lokendra Devangan |
Breakout |
Optimizing Inventory of Slow-Moving Products Using SAS® Optimization |
Aiming to use data and science to set service-level goals, Advance Auto Parts engaged CoreCompete to deliver a fully integrated service-level (Inventory) optimization system using SAS® Inventory Optimization and SAS/OR® software. The system has the ability to run inventory simulations and execute large-scale optimization for service-level goal optimization, leveraging the batch services on Amazon Web Services. A very large mixed integer optimization problem for inventory cost reduction is solved using the OPTMODEL procedure. The solution has the ability to recommend optimized service-level goals at the SKU/location level. System design integrates a simple Microsoft Excel user interface, data processing in Apache Hadoop, and optimization in SAS® in the cloud and the dashboards in SAS® Visual Analytics in order to review results. The end-to-end process flow for implementing simulations and optimization in large scale is discussed in this paper.
|
| Tuesday |
03:00 PM |
04:00 PM |
Mile High Ballroom Theater C |
Industry-Specific Solutions |
Constantine Boyadjiev |
Breakout |
Suspect Behavior Identification through Sentiment Analysis and Communication Surveillance |
Fraud and misconduct pose significant risks to firms, regardless of industry. These risks include monetary losses, regulatory repercussions, and adverse reputational impacts. Recent individual abuse and collusive market manipulation events have caused regulators to impose severe fines, increase scrutiny, and tighten supervision. Given that malicious behavior is complex and dynamic in nature, identifying and effectively monitoring it is a difficult challenge. Adding to the complexity, multiple data sources (both structured and unstructured) are required to proactively detect and prevent evolving fraudulent behaviors. During this session, Accenture demonstrates the power of using SAS® to deploy big data, advanced analytics, and interactive visualizations, leading to threat identification and prudent risk decision-making. Specifically, the demo showcases the deployment of artificial intelligence and machine learning in the context of surveillance analytics, social network analysis, and deep emotion/sentiment analysis for the detection of misconduct and behavioral risk in security, commercial, and public sector environments.
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| Tuesday |
03:00 PM |
03:30 PM |
Meeting Room 302 |
Business Analytics/ Data Visualization: Data Visualization |
Michael Drutar |
Breakout |
There’s No Reason to Hide! Discovering the Benefits of Using Hidden Columns in SAS® Visual Analytics |
SAS® Visual Analytics 8.2 includes many features that report designers can leverage to create dynamic, interactive reports. One of the most powerful new features available is the new hidden data role that is available for many reporting objects. While initially the concept of hidden columns or data might be a bit foreign, just a little instruction can empower report designers to leverage it and create their most dynamic SAS Visual Analytics reports yet. This paper demonstrates the various methods in which one can use hidden data in report design, and the benefits each of these methods provides to report consumers. Examples include robust mapping between multiple data sources, creating improved color-mapped display rules, and, most importantly, dynamic linking to external web resources based on the values in one or more hidden columns in a list table. Once armed with the knowledge of how to use the hidden data role in SAS Visual Analytics, report designers will be ready to create their most interactive, dynamic, and useful reports yet.
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| Tuesday |
03:00 PM |
04:00 PM |
Meeting Room 403 |
|
Stephanie Thompson |
Breakout |
Using Smart Analytics to Drive a Data-Driven Culture: An Institutional Research Perspective |
Institutional research (IR) is known for handling state and federal reporting for a particular campus. However, this role has greatly expanded over the years to include predictive analytics, data mining, and other types of analyses that might be new to administrators. The ever-increasing number of data sources and technologies are making more and more information available. This panel discusses how IR professionals can help to build a data-driven culture by asking the right questions, finding the relevant data, using the appropriate analytic approach, and creating meaningful visualizations.
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| Tuesday |
03:00 PM |
03:30 PM |
The Quad - Eposter 10 |
Business Analytics/ Data Visualization: Business Analytics |
Adriana Mara Guedes Barbosa |
E-Poster |
A Simple Methodology for Customer Classification in Two Dimensions |
This paper shows a simple way for customer classification in two dimensions. Several variables were used to create only two major characteristics (customer attractiveness and profitability), and then it was possible to identify potential customers for grant credit. This methodology basically uses the REG, GPLOT, and GINSIDE procedures and some DATA steps, enabling the visualization of the results in a simple scatterplot.
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| Tuesday |
03:00 PM |
03:30 PM |
The Quad - Eposter 17 |
Programming: Data Presentation |
Rachana Lele |
E-Poster |
An Unusual Remedy Using the Usual NBINS Option to Rectify Anomalous Histograms in SAS® |
Data visualization is a strong tool for understanding the nature and distribution of collected data. A histogram is one such data visualization tool that can be used to assess normality of the data. Hence, plotting the correct histograms is important since the decision regarding additional analytical methods (parametric or nonparametric) is based on whether the data follows normal distribution. In SAS®, different procedures, such as SGPLOT, SGPANEL, or UNIVARIATE, can be used to generate histograms. However, histograms plotted in SAS using the SGPLOT or SGPANEL procedures show an anomaly when the largest value in a set of data coincides with one of the tick points on the X axis of the histogram. This paper discusses this anomaly and suggests a remedy for solving it. This paper also suggests that the UNIVARIATE procedure can be used to validate the histograms produced using the SGPLOT and SGPANEL procedures. Furthermore, an alternative method for calculating the value to be specified for the BINSTART option in the SGPLOT and SGPANEL procedures that does not alter the histogram produced by the usual method is also suggested. All of the procedures described above were performed using SAS® 9.3.
|
| Tuesday |
03:00 PM |
03:30 PM |
The Quad - Eposter 12 |
Business Analytics/ Data Visualization: Data Visualization |
Deven Chakraborty |
E-Poster |
How Effective is Change of Bowling Pace in Cricket? |
Cricket, similar to baseball in that it uses a bat and ball, is one of the most popular sports in the world, especially in India. At a whopping 411 million unique TV viewers in 2017 (Ahluwalia 2017), the Indian Premier League (IPL) cricket tournament has a follower base 25% larger than the entire population of the United States. T20 cricket, the format of the IPL, is dominated by batsmen, meaning bowlers (pitchers) struggle to keep swashbuckling pinch-hitters from smashing them out of the park. In the past few years, however, many bowlers have developed a strategy in which a ball comes out much slower than their normal pace in an effort to deceive the batsman. But no public data about bowling speeds exist. So, I recorded data from approximately 1,000 balls from last year’s IPL to analyze the effectiveness of these slower balls. It turns out the slower balls are effective because they get batsmen out a statistically significant higher portion of the time than a normal pace ball. But there are many more interesting questions that can be asked: Who has the best slower ball? Is there such thing as too slow for a slower ball? When are bowlers most likely to bowl a slower ball? All of these questions and more can be answered by my data set and can be used by cricket teams around the world to improve their standing in the extremely competitive industry of sport.
|
| Tuesday |
03:00 PM |
03:30 PM |
The Quad - Eposter 15 |
Industry-Specific Solutions |
Hao Sun |
E-Poster |
Mine Mass Fragments Using SAS® and Python for Metabolite Identification of Antibody-Drug Conjugates |
Metabolite identification of antibody-drug conjugate (ADC) and peptide drugs using mass spectrometry is challenging due to the complexity of their metabolism and catabolism reactions and the lack of computing tools for mining complicated fragment patterns from high-resolution mass spectra. Mass fragmentation data are enormous, and thus manual interpretation methods routinely used for small molecules are time-consuming and inefficient. Previously, we reported on an application based on SAS®, AIR Binder, for dynamic visualization, data analysis, and reporting of preclinical and clinical drug metabolism assays (PharmaSUG 2017, MWSUG 2017). We further expanded the application for mining raw accurate mass spectra data. Python scripts were developed for fragment searching and iteration. SAS was then used as a platform to integrate searching results and generate visualization solutions for molecular structure elucidation via SAS macros and various Output Delivery System (ODS) graphics. Searching algorithms for the prediction of accurate mass were based on metabolite “anchor” regions such as “warhead” and linker of ADCs, and “growing” regions that are adjacent amino acids, conjugates with catabolism products, and variations produced by Phase I and II metabolic reactions. Overall, this novel SAS solution in combination with Python scripts was a success for large mass spectra fragment mining of ADC and peptide metabolites with significantly improved productivity and efficiency.
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| Tuesday |
03:00 PM |
03:30 PM |
The Quad - Eposter 11 |
Business Analytics/ Data Visualization: Business Analytics |
Linyi Tang |
E-Poster |
The Ultimate Data-Driven Guide to U.S. Canned Craft Beers Using SAS® Enterprise Miner™ 14.2 |
The explosive growth in the number of available online reviews has provided important guidance for shoppers who are considering the purchase of a product. However, the number of reviews and product choices can be overwhelming. In order to alleviate the problem of information overload, the ability to filter, emphasize, and efficiently deliver relevant information to the customer becomes crucial. Furthermore, product rating prediction based on reviews can be beneficial for online shopping portals to shape their recommendation system and for marketers to generate marketing strategies. Beer is one of the most popular drinks worldwide. In recent years, with the success of microbreweries, the breadth of beer options available is massive. In this study, we provide a data-driven guide to U.S canned craft beers and conduct rating prediction based on online beer reviews. The Text Rule Builder was implemented to extract key words of interest. Similarities between users were explored by using a collaborative filter. Decision tree, linear regression, and k-means clustering were used and evaluated for rating prediction. A linear regression model was selected based on the least mean squared error.
|
| Tuesday |
03:00 PM |
03:30 PM |
The Quad - Super Demo 1 |
Business Analytics/ Data Visualization: Business Analytics |
Jeff Diamond |
SAS Super Demo |
The Shape of Things |
Custom map data and spatial discovery in SAS® Visual Analytics.
|
| Tuesday |
03:30 PM |
04:30 PM |
Meeting Room 301 |
Business Analytics/ Data Visualization: Business Analytics |
Mary Osborne |
Breakout |
Natural Disaster Planning and Recovery Using SAS® Visual Analytics on SAS® Viya® |
Planning for natural disasters, evacuation processes, and recovery are always top priorities for local governments and relief organizations. They must mobilize in an instant to set up shelters and provide supplies for victims. Then, after a disaster, the often lengthy recovery phase begins. They must work to help people return to their homes by ensuring that areas are safe, while also working to restore power and communications. Using SAS® Visual Analytics on SAS® Viya® and integrating with Esri tools, local governments and relief organizations can use location analytics to analyze storm predictions, search for shelter locations in order to plan evacuation routes, and enrich their existing data with Esri demographic data to stay informed and quickly make mission-critical decisions.
|
| Tuesday |
04:00 PM |
04:30 PM |
Meeting Room 302 |
Business Analytics/ Data Visualization: Data Visualization |
Michael Thomas |
Breakout |
Fast Dashboards: Producing Real-Time Dashboards Using SAS® Event Stream Processing |
This presentation covers the issues encountered when creating dashboards for the web using live-streaming data. Technologies covered are SAS® Event Stream Processing, RESTful web services, graphics created using d3.js (D3, or Data-Driven Documents), and open-source dashboards.
|
| Tuesday |
04:30 PM |
05:00 PM |
Meeting Room 301 |
Business Analytics/ Data Visualization: Business Analytics |
Ronald Page |
Breakout |
SAS® in Style: Customizing Solutions and Reports with SAS® Theme Designer |
SAS® Theme Designer enables customers to create their own custom application and report themes and tailor the visual look of the applications they run and the reports they generate. Users can specify colors, fonts, and images, and the visual changes are presented within an embedded preview application or within an actual application (when editing a theme within application context). Users can customize their applications and reports to create a unique organizational theme, or they can customize their applications and reports based on an individual business requirement. This visual customization provides for the customers’ desire for colors, general branding, and logo integration. This paper explores the process of using SAS Theme Designer in conjunction with other applications such as SAS® Visual Analytics. This paper also highlights the process of creating and modifying application themes, and previewing application color, font, and image changes within applications such as SAS Visual Analytics.
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| Tuesday |
04:30 PM |
05:30 PM |
Meeting Room 302 |
Business Analytics/ Data Visualization: Data Visualization |
Louise Hadden |
Breakout |
Wow! You Did That Map With SAS®?! Round II |
This paper explores the creation of complex maps with SAS® software. This presentation explores the wide range of possibilities provided by SAS/GRAPH® software and polygon plots using the ODS Statistical Graphics (SG) procedures, as well as replays and overlays, annotations including spark lines, animations, Zip Code-level processing, and so on. The more recent GfK maps now provided by SAS that underlie newer SAS products such as SAS® Visual Analytics as well as traditional SAS products, are discussed. The differences and similarities in how SAS/GRAPH and SG procedures approach the summarization of data that is not already “pre-digested” to portray on maps are also explored. The new SGMAP procedure is also discussed.
|
| Tuesday |
04:30 PM |
05:00 PM |
The Quad - Super Demo 5 |
Programming: Data Presentation |
Sanjay Matange |
SAS Super Demo |
Animated Graphs |
|
| Tuesday |
05:00 PM |
05:30 PM |
Meeting Room 502 |
Programming: Applications Development |
David Hare |
Breakout |
Integrating SAS® Analytics into Your Web Page |
SAS® Viya® adds enhancements to the SAS® Platform that include the ability to access SAS® services from other applications. Whether your application is in Python, Java, Lua, or R, you can now access SAS analytics and integrate them directly in your application. You can even use REST APIs. In this session, we look at using the REST APIs in SAS Viya to execute SAS® Cloud Analytic Services (CAS) actions and embed them in an application, which in this case is a web page. Examples include uploading a table, performing a SAS analytic procedure and displaying the output, and publishing a report. This method provides you with much greater flexibility and customization for building dashboards and reporting sites. Through this session, you will gain an understanding of how you can embed analytics from SAS Viya into your very own application.
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| Tuesday |
05:00 PM |
05:30 PM |
Meeting Room 301 |
Business Analytics/ Data Visualization: Business Analytics |
Rajiv Ramarajan |
Breakout |
SAS® Visual Analytics 8.2: What’s New in Reporting? |
SAS® Visual Analytics 8.2 extends the unified SAS® user experience to data exploration and reporting, adds new features to report content, and introduces exciting new report objects. The user experience design presents a familiar and consistent user interface for navigation at the report, page, and object levels. New features enrich report content such as improvements in handling data, enhancements in graphs and tables, and more geographic capabilities. New report objects in the release include the Key Value object to provide infographic-like treatment, the parallel coordinates plot for more analytical visualization, and Data-Driven Content to manipulate externally created content within the point-and-click interface of the report.
|
| Wednesday |
10:00 AM |
11:00 AM |
Meeting Room 302 |
Business Analytics/ Data Visualization: Business Analytics |
Murali Nori |
Breakout |
New Location Analysis and Demographic Data Integration with SAS® Visual Analytics and Esri |
Location is an important part of business data. Business always happens somewhere. With smart devices like phones, wearables, and Internet of Things (IoT) devices all producing location information, the ability to analyze where things happen helps create a better understanding of business. SAS continues to expand the location analytics capabilities that are offered in SAS® Visual Analytics on SAS® Viya®. This paper presents the new capabilities that can help you use the location information in business data. There are a variety of situations where location data is vital. Often business data can contain a large number of data points, sometimes in the order of 100,000 or a million. The preferred method for visualizing that many points on a geo map is to use geo-clustering. Another situation is when you are analyzing customers in a given location. Adding demographic will improve insights. If you are performing a task such as analyzing crimes around a location in a city, using travel-time analysis and travel-distance analysis can provide valuable insights. If you need to represent various points of interest on geo maps, using symbols or icons helps make the map clearer. This paper demonstrates how to use these capabilities in SAS Visual Analytics to solve real use cases for demographic data integration with Esri, geo-clustering, symbols, travel-time analysis, and custom regions.
|
| Wednesday |
10:00 AM |
10:30 AM |
Meeting Room 301 |
Business Analytics/ Data Visualization: Business Analytics |
Tim Beese |
Breakout |
Remodeling Your Office: A New Look for the SAS® Add-In for Microsoft Office |
Millions of people spend their weekdays in an office. Occasionally they hang a new picture, or add a new piece of furniture. Eventually their office gets so full they need to reorganize it to find items quicker and work more efficiently. After 15 years, that time has come for the SAS® Add-In for Microsoft Office. This paper reviews the new user interface for the SAS Add-In for Microsoft Office. A redesigned ribbon enables quicker access to the most common operations. Where there used to be several task panes that each served a specific purpose, the task panes are now blended together into one home page that enables users to easily discover, execute, and manage SAS® content. Come see these enhancements and many more in the new version of the SAS Add-In for Microsoft Office.
|
| Wednesday |
10:00 AM |
11:00 AM |
Meeting Room 401 |
Analytics: Statistics/ Biostatistics |
Patricia Berglund |
Breakout |
Using SAS® for Multiple Imputation and Analysis of Longitudinal Data |
This session presents using SAS® to address missing data issues and analysis of longitudinal data. Appropriate multiple imputation and analytic methods are evaluated and demonstrated through an analysis application, using longitudinal survey data with missing data issues. The analysis application demonstrates detailed data management steps required for imputation and analysis, multiple imputation of missing data values, subsequent analysis of imputed data, and finally, interpretation of longitudinal data analysis results. Key SAS tools, including DATA step operations to produce needed data structures and use of the MI, MIANALYZE, MIXED, and SGPLOT procedures are highlighted.
|
| Wednesday |
10:30 AM |
11:00 AM |
Meeting Room 301 |
Business Analytics/ Data Visualization: Business Analytics |
Amanda Pasch |
Breakout |
Using SAS® Visual Analytics to Solve Problems with Reporting Based in Microsoft Excel |
The use of Microsoft Excel as an analytics tool is pervasive and nearly inescapable in many corporate environments. While it can be an ideal tool for many basic ad hoc analyses, it can easily morph into an unscalable, error-prone, and undocumented reporting solution with significant limitations on end-user functionality. When this occurs, SAS® Visual Analytics can provide a new and improved reporting solution that addresses many of the pitfalls you encounter with Excel. This paper uses a real-world example to illustrate the advantages of moving reporting based in Excel to SAS Visual Analytics. Kaiser Permanente's Data and Information Management Enhancement (DIME) team will soon replace a highly manual, single-threaded, reporting solution based in Excel, which supports the needs our regional call center business partners, with a SAS Visual Analytics solution. Several key advantages of the SAS Visual Analytics solution are discussed, including: consolidating a plethora of Excel files into a small list of dynamic reports; greatly reducing the risk of human error in reports; reducing the number of resource hours needed to maintain and enhance reports; and providing users with the advanced functionality needed for effective decision-making.
|
| Wednesday |
11:00 AM |
11:30 AM |
Meeting Room 502 |
Programming: General Programming |
Steven Sober |
Breakout |
My Experiences in Adopting SAS® Cloud Analytic Services into Base SAS® Processes |
The SAS® Platform is greatly enhanced by SAS® Cloud Analytic Services (CAS) and SAS® Viya®. As a Base SAS® programmer, I want to share my experiences in adopting CAS into Base SAS processes that prepare data for modeling, reporting, and visualizations that are enabled for CAS.
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| Wednesday |
11:00 AM |
11:30 AM |
Meeting Room 503 |
Programming: General Programming |
Kriss Harris |
Breakout |
V is for Venn Diagrams |
Would you like to produce Venn diagrams easily? This paper shows how you can produce stunning two-, three-, and four-way Venn diagrams by using the Graph Template Language, in particular the DRAWOVAL and DRAWTEXT statements. From my experience, Venn diagrams have typically been created in the pharmaceutical industry by using Microsoft Excel and Microsoft PowerPoint. Excel is used to first count the numbers in each group, and PowerPoint is used to generate the two- or three-way Venn diagrams. The four-way Venn diagram is largely unheard of. When someone is brave enough to tackle it manually, working out the numbers that should go in each of the 16 groups and entering the right number into the right group is usually done nervously!
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| Wednesday |
11:00 AM |
12:00 PM |
Meeting Room 302 |
Business Analytics/ Data Visualization: Data Visualization |
Jennifer Han |
Breakout |
Working Together for Public Health: Using SAS®, Esri, and Tableau to Enhance Data Presentation |
Numerous clients receive clinical services at local public health departments every day. With the current climate of providing necessary services with fewer resources, data-driven decision-making is essential in public health. Many local public health data systems are antiquated and provide limited reports to public health employees. Innovative means of assessing and visualizing data to enable public health employees to quickly access timely information specific to their clients and community will enhance efforts at the local level. This presentation demonstrates how large volumes of county-level surveillance data and client data can be analyzed using SAS® and meaningfully displayed in a dashboard using Esri ArcGIS maps and Tableau software.
|
| Wednesday |
11:30 AM |
12:00 PM |
Meeting Room 503 |
Programming: Data Presentation |
Sanjay Matange |
Breakout |
Advanced Graphs Using Axis Tables |
A key feature of the graphs that are used for analysis data or for clinical research is the inclusion of textual data in the graph, usually aligned with the X or Y axis. The axis table statements that are available with the SGPLOT procedure make it easy to add such data to the graphs. You can also use axis tables for creating custom axes, multiple axes, and even multidimensional hierarchical axes. This presentation describes how to use axis tables to create complex graphs.
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| Wednesday |
12:00 PM |
12:30 PM |
Meeting Room 302 |
Business Analytics/ Data Visualization: Data Visualization |
Ting Sa |
Breakout |
A Macro That Creates U.S. Census Tracts Keyhole Markup Language Files for Google Map Use |
This paper introduces a macro that can generate the Keyhole Markup Language (KML) files for U.S. census tracts. The generated KML files can be used directly by Google Maps to add customized census tracts layers with user-defined colors and transparencies. When someone clicks on the census tracts layers in Google Maps, customized information is shown. To use the macro, the user needs to prepare only a simple SAS® input data set and download the related KML files from the U.S. census Bureau. The paper includes all the SAS code for the macro and provides examples that show you how to use the macro as well as how to display the KML files in Google Maps.
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| Wednesday |
12:30 PM |
01:00 PM |
Meeting Room 302 |
Business Analytics/ Data Visualization: Data Visualization |
Xiaogang (Isaac) Tang |
Breakout |
Data Visualization from SAS® to Google Maps on Microsoft SharePoint |
Google Maps is a very popular web mapping service developed by Google. Microsoft SharePoint is a popular web application platform and used for content management by companies and organizations. Connecting SAS® with Google Maps and SharePoint combines the power of these three into one. As a continuation of my SAS Global Forum Paper 1062-2017 “Data Visualization from SAS® to Microsoft SharePoint”, this paper expands on how to implement geocoding and data visualization from SAS to Google Maps on Microsoft SharePoint. The paper shows users how to use SAS procedures to create and send XML data files from SAS to SharePoint Document Library. The XML data files serve as data feeds for Google Maps web pages on SharePoint and SAS code examples are included. A couple of examples with different views on data visualization from SAS to Google Maps APIs on SharePoint are provided.
|
| Wednesday |
12:30 PM |
01:00 PM |
Mile High Ballroom Theater D |
Programming: General Programming |
Jessica Fraley |
Breakout |
How Statistically Analyzing System Behaviors with SAS® Visual Analytics Revealed Unknown Data Issues |
Automatic loading, tracking, and analysis of data readiness in SAS® Visual Analytics is easy when you combine SAS® Data Integration Studio with the DATASET and LASR procedures. This paper is a followup to a previous paper presented on the methodology that we use at the University of North Carolina at Chapel Hill to track our data preparation and readiness by using SAS Visual Analytics reporting. This paper covers real-world examples of how our analysis and visualization methods surfaced unknown data integrity issues brought about by anomalous system behaviors. This paper also covers how we recognized the issues, and how creating these SAS Visual Analytics visualizations can help any SAS® customer quickly identify potential data integrity issues that originate from system behaviors.
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| Wednesday |
12:30 PM |
01:30 PM |
Meeting Room 502 |
Programming: Applications Development |
Michael Drutar |
Breakout |
Just Enough SAS® Cloud Analytic Services: CAS Actions for SAS® Visual Analytics Report Developers |
SAS® Visual Analytics includes all of the point-and-click functionality required to load data, manage data, and perform other back-end work necessary to make visualizations efficient and effective. But there are a handful of critical tasks that are sometimes just easier to do with a few lines of code, especially if your end goal is to automate that process. While there is a substantial and growing codebase for SAS® Cloud Analytic Services (CAS) actions and SAS® Viya® procedures, SAS Visual Analytics report developers need to focus on the creation and implementation of reports. What we need is just enough CAS code to simplify the back-end work. This paper presents some of the most common, most useful CAS actions that can be run from any code window in SAS Viya, including SAS® Studio. These bare-bones code examples include: loading a data set to a CAS server, using CAS actions to transform data from a wide to a tall structure, and other formatting operations that make data reporting-ready. Using minimal code, the paper demonstrates how the scored output of a model based in SAS Viya can be lifted into CAS and made ready for SAS Visual Analytics reporting. Finally, the paper describes exactly how coded back-end actions affect SAS Visual Analytics, including how to verify that back-end processes are working and how to ensure that SAS Visual Analytics settings for refresh and automated data re-load are set to take advantage of automated back-end processes implemented in code.
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| Wednesday |
01:00 PM |
01:30 PM |
Meeting Room 301 |
Business Analytics/ Data Visualization: Business Analytics |
Youyou Zheng |
Breakout |
Using Predictive Analytics to Improve a Student Advising System |
For decades, student success has been a focus in higher education. In this paper, institutional researchers discuss the predictive modeling process that could identify students at risk for failing a major STEM course at a top public university. SAS® Enterprise Miner™ and SAS® Visual Analytics were applied to predict and visualize student academic performance. This study provides the possibility to use predictive analytics tools as a Student Early Alert System for decision-making support.
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