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

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Geographically Weighted Negative Binomial Regression (GWNBR) was developed by Silva and Rodrigues (2014). It is a generalization of the Geographically Weighted Poisson Regression (GWPR) proposed by Nakaya and others (2005) and of the Poisson and negative binomial regressions. This paper shows a SAS^® macro to estimate the GWNBR model encoded in SAS/IML^® software and shows how to use the SAS procedure GMAP to draw the maps.

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

Traditional approaches to assessing undergraduate assignments in the field of software-related courses, including Analytics and Data Science courses, involve the course tutors in reading the students' code and getting the students to physically demonstrate their artifacts. However, this approach tends to assess only the technical skills of solving the set task. It generally fails to assess the many soft skills that industry is looking for, as identified in the e-skills UK (Tech Partnership)/SAS^® report of November 2014 and the associated infographic poster. This presentation describes and evaluates the effectiveness of a different approach to defining the assessment task and summatively assessing the work of the students in order to effectively evaluate and mark both the soft skills, including creativity, curiosity, storytelling, problem solving, and communication, and the technical skills. This approach works effectively at all levels of undergraduate and masters courses. The session is structured to provide adequate time for audience participation to discuss the approach and its applicability.

The surge of data and data sources in marketing has created an analytical bottleneck in most organizations. Analytics departments have been pushed into a difficult decision: either purchase black-box analytical tools to generate efficiencies or hire more analysts, modelers, and data scientists. Knowledge gaps stemming from restrictions in black-box tools or from backlogs in the work of analytical teams have resulted in lost business opportunities. Existing big data analytics tools respond well when dealing with large record counts and small variable counts, but they fall short in bringing efficiencies when dealing with wide data. This paper discusses the importance of an agile modeling engine designed to deliver productivity, irrespective of the size of the data or the complexity of the modeling approach.

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SAS^® Grid Manager, as well as other grid computing technologies, have a set of great capabilities that we, IT professionals, love to have in our systems. This technology increases high availability, allows parallel processing, facilitates increasing demand by scale out, and offers other features that make life better for those managing and using these environments. However, even when business users take advantage of these features, they are more concerned about the business part of the problem. Most of the time business groups hold the budgets and are key stakeholders for any SAS Grid Manager project. Therefore, it is crucial to demonstrate to business users how they will benefit from the new technologies, how the features will improve their daily operations, help them be more efficient and productive, and help them achieve better results. This paper guides you through a process to create a strong and persuasive business plan that translates the technology features from SAS Grid Manager to business benefits.

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As SAS^® programmers, we often develop listings, graphs, and reports that need to be delivered frequently to our customers. We might decide to manually run the program every time we get a request, or we might easily schedule an automatic task to send a report at a specific date and time. Both scenarios have some disadvantages. If the report is manual, we have to find and run the program every time someone request an updated version of the output. It takes some time and it is not the most interesting part of the job. If we schedule an automatic task in Windows, we still sometimes get an email from the customers because they need the report immediately. That means that we have to find and run the program for them. This paper explains how we developed an on-demand report platform using SAS^® Enterprise Guide^®, SAS^® Web Application Server, and stored processes. We had developed many reports for different customer groups, and we were getting more and more emails from them asking for updated versions of their reports. We felt we were not using our time wisely and decided to create an infrastructure where users could easily run their programs through a web interface. The tool that we created enables SAS programmers to easily release on-demand web reports with minimum programming. It has web interfaces developed using stored processes for the administrative tasks, and it also automatically customizes the front end based on the user who connects to the website. One of the challenges of the project was that certain reports had to be available to a specific group of users only.

As Data Management professionals, you have to comply with new regulations and controls. One such regulation is Basel Committee on Banking Supervision (BCBS) 239. To respond to these new demands, you have to put processes and methods in place to automate metadata collection and analysis, and to provide rigorous documentation around your data flows. You also have to deal with many aspects of data management including data access, data manipulation (ETL and other), data quality, data usage, and data consumption, often from a variety of toolsets that are not necessarily from a single vendor. This paper shows you how to use SAS^® technologies to support data governance requirements, including third party metadata collection and data monitoring. It highlights best practices such as implementing a business glossary and establishing controls for monitoring data. Attend this session to become familiar with the SAS tools used to meet the new requirements and to implement a more managed environment.

SAS^® Embedded Process offers a flexible, efficient way to leverage increasing amounts of data by injecting the processing power of SAS^® directly where the data lives. SAS Embedded Process can tap into the massively parallel processing (MPP) architecture of Hadoop for scalable performance. Using SAS^® In-Database Technologies for Hadoop, you can run scoring models generated by SAS^® Enterprise Miner™ or, with SAS^® In-Database Code Accelerator for Hadoop, user-written DS2 programs in parallel. With SAS Embedded Process on Hadoop you can also perform data quality operations, and extract and transform data using SAS^® Data Loader. This paper explores key SAS technologies that run inside the Hadoop parallel processing framework and prepares you to get started with them.

Do you create complex reports using PROC REPORT? Are you confused by the COMPUTE BLOCK feature of PROC REPORT? Are you even aware of it? Maybe you already produce reports using PROC REPORT, but suddenly your boss needs you to modify some of the values in one or more of the columns. Maybe your boss needs to see the values of some rows in boldface and others highlighted in a stylish yellow. Perhaps one of the columns in the report needs to display a variety of fashionable formats (some with varying decimal places and some without any decimals). Maybe the customer needs to see a footnote in specific cells of the report. Well, if this sounds familiar then come take a look at the COMPUTE BLOCK of PROC REPORT. This paper shows a few tips and tricks of using the COMPUTE DEFINE block with conditional IF/THEN logic to make your reports stylish and fashionable. The COMPUTE BLOCK allows you to use data DATA step code within PROC REPORT to provide customization and style to your reports. We'll see how the Census Bureau produces a stylish demographic profile for customers of its Special Census program using PROC REPORT with the COMPUTE BLOCK. The paper focuses on how to use the COMPUTE BLOCK to create this stylish Special Census profile. The paper shows quick tips and simple code to handle multiple formats within the same column, make the values in the Total rows boldface, trafficlighting, and how to add footnotes to any cell based on the column or row. The Special Census profile report is an Excel table created with ODS tagsets.ExcelXP that is stylish and fashionable, thanks in part to the COMPUTE BLOCK.

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If your organization already deploys one or more software solutions via Amazon Web Services (AWS), you know the value of the public cloud. AWS provides a scalable public cloud with a global footprint, allowing users access to enterprise software solutions anywhere at any time. Although SAS^® began long before AWS was even imagined, many loyal organizations driven by SAS are moving their local SAS analytics into the public AWS cloud, alongside other software hosted by AWS. SAS^® Solutions OnDemand has assisted organizations in this transition. In this paper, we describe how we extended our enterprise hosting business to AWS. We describe the open source automation framework from which SAS Soultions onDemand built our automation stack, which simplified the process of migrating a SAS implementation. We'll provide the technical details of our automation and network footprint, a discussion of the technologies we chose along the way, and a list of lessons learned.

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

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

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

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Microsoft Visual Basic Scripting Edition (VBScript) and SAS^® software are each powerful tools in their own right. These two technologies can be combined so that SAS code can call a VBScript program or vice versa. This gives a programmer the ability to automate SAS tasks; traverse the file system; send emails programmatically via Microsoft Outlook or SMTP; manipulate Microsoft Word, Microsoft Excel, and Microsoft PowerPoint files; get web data; and more. This paper presents example code to demonstrate each of these capabilities.

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Considering the fact that SAS^® Grid Manager is becoming more and more popular, it is important to fulfill the user's need for a successful migration to a SAS^® Grid environment. This paper focuses on key requirements and common issues for new SAS Grid users, especially if they are coming from a traditional environment. This paper describes a few common requirements like the need for a current working directory, the change of file system navigation in SAS^® Enterprise Guide^® with user-given location, getting job execution summary email, and so on. The GRIDWORK directory has been introduced in SAS Grid Manager, which is a bit different from the traditional SAS WORK location. This paper explains how you can use the GRIDWORK location in a more user-friendly way. Sometimes users experience data set size differences during grid migration. A few important reasons for data set size difference are demonstrated. We also demonstrate how to create new custom scripts as per business needs and how to incorporate them with SAS Grid Manager engine.

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In this paper, a SAS^® macro is introduced that can help users find and access their folders and files very easily. By providing a path to the macro and letting the macro know which folders and files you are looking for under this path, the macro creates an HTML report that lists the matched folders and files. The best part of this HTML report is that it also creates a hyperlink for each folder and file so that when a user clicks the hyperlink, it directly opens the folder or file. Users can also ask the macro to find certain folders or files by providing part of the folder or file name as the search criterion. The results shown in the report can be sorted in different ways so that it can further help users quickly find and access their folders and files.

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

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

For SAS^® Enterprise Guide^® users, sometimes macro variables and their values need to be brought over to the local workspace from the server, especially when multiple data sets or outputs need to be written to separate files in a local drive. Manually retyping the macro variables and their values in the local workspace after they have been created on the server workspace would be time-consuming and error-prone, especially when we have quite a number of macro variables and values to bring over. Instead, this task can be achieved in an efficient manner by using dictionary tables and the CALL SYMPUT routine, as illustrated in more detail below. The same approach can also be used to bring macro variables and their values from the local to the server workspace.

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

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

When analyzing data with SAS^®, we often encounter missing or null values in data. Missing values can arise from the availability, collectibility, or other issues with the data. They represent the imperfect nature of real data. Under most circumstances, we need to clean, filter, separate, impute, or investigate the missing values in data. These processes can take up a lot of time, and they are annoying. For these reasons, missing values are usually unwelcome and need to be avoided in data analysis. There are two sides to every coin, however. If we can think outside the box, we can take advantage of the negative features of missing values for positive uses. Sometimes, we can create and use missing values to achieve our particular goals in data manipulation and analysis. These approaches can make data analyses convenient and improve work efficiency for SAS programming. This kind of creative and critical thinking is the most valuable quality for data analysts. This paper exploits real-world examples to demonstrate the creative uses of missing values in data analysis and SAS programming, and discusses the advantages and disadvantages of these methods and approaches. The illustrated methods and advanced programming skills can be used in a wide variety of data analysis and business analytics fields.

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

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Inspired by Christianna William's paper on transitioning to PROC SQL from the DATA step, this paper aims to help SQL programmers transition to SAS^® by using PROC SQL. SAS adapted the Structured Query Language (SQL) by means of PROC SQL back with SAS^®6. PROC SQL syntax closely resembles SQL. However, there are some SQL features that are not available in SAS. Throughout this paper, we outline common SQL tasks and how they might differ in PROC SQL. We also introduce useful SAS features that are not available in SQL. Topics covered are appropriate for novice SAS users.

SAS^® software provides many DATA step functions that search and extract patterns from a character string, such as SUBSTR, SCAN, INDEX, TRANWRD, etc. Using these functions to perform pattern matching often requires you to use many function calls to match a character position. However, using the Perl regular expression (PRX) functions or routines in the DATA step improves pattern-matching tasks by reducing the number of function calls and making the program easier to maintain. This talk, in addition to discussing the syntax of Perl regular expressions, demonstrates many real-world applications.

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Many inquisitive minds are filled with excitement and anticipation of response every time one posts a question on a forum. This paper explores the factors that impact the response time of the first response for questions posted in the SAS^® Community forum. The factors are contributors' availability, nature of topic, and number of contributors knowledgeable for that particular topic. The results from this project help SAS^® users receive an estimated response time, and the SAS Community forum can use this information to answer several business questions such as following: What time of the year is likely to have an overflow of questions? Do specific topics receive delayed responses? Which days of the week are the community most active? To answer such questions, we built a web crawler using Python and Selenium to fetch data from the SAS Community forum, one of the largest analytics groups. We scraped over 13,443 queries and solutions starting from January 2014 to present. We also captured several query-related attributes such as the number of replies, likes, views, bookmarks, and the number of people conversing on the query. Using different tools, we analyzed this data set after clustering the queries into 22 subtopics and found interesting patterns that can help the SAS Community forum in several ways, as presented in this paper.

In a data warehousing system, change data capture (CDC) plays an important part not just in making the data warehouse (DWH) aware of the change but also in providing a means of flowing the change to the DWH marts and reporting tables so that we see the current and latest version of the truth. This and slowly changing dimensions (SCD) create a cycle that runs the DWH and provides valuable insights in the history and for the decision-making future. What if the source has no CDC? It would be an ETL nightmare to identify the exact change and report the absolute truth. If these two processes can be combined into a single process where just one single transform does both jobs of identifying the change and applying the change to the DWH, then we can save significant processing times and value resources of the system. Hence, I came up with a hybrid SCD with CDC approach for this. My paper focuses on sources that DO NOT have CDC in their sources and need to perform SCD Type 2 on such records without worrying about data duplications and increased processing times.

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

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Representational State Transfer (REST) is being used across the industry for designing networked applications to provide lightweight and powerful alternatives to web services such as SOAP and Web Services Description Language (WSDL). Since REST is based entirely on HTTP, SAS^® provides everything you need to make REST calls and to process structured and unstructured data alike. This paper takes a look at how some enhancements in the third maintenance release of SAS^® 9.4 can benefit you in this area. Learn how the HTTP procedure and other SAS language features provide everything you need to simply and securely use REST.

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

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

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

This paper discusses a set of practical recommendations for optimizing the performance and scalability of your Hadoop system using SAS^®. Topics include recommendations gleaned from actual deployments from a variety of implementations and distributions. Techniques cover tips for improving performance and working with complex Hadoop technologies such as Kerberos, techniques for improving efficiency when working with data, methods to better leverage the SAS in Hadoop components, and other recommendations. With this information, you can unlock the power of SAS in your Hadoop system.

All public schools in the United States require health and safety education for their students. Furthermore, almost all states require driver education before minors can obtain a driver's license. Through extensive analysis of the Fatality Analysis Reporting System data, we have concluded that from 2011-2013 an average of 12.1% of all individuals killed in a motor vehicle accident in the United States, District of Columbia, and Puerto Rico were minors (18 years or younger). Our goal is to offer insight within our analysis in order to better road safety education to prevent future premature deaths involving motor vehicles.

VBA has been described as a glue language, and has been widely used in exchanging data between Microsoft products such as Excel and Word or PowerPoint. How to trigger the VBA macro from SAS^® via DDE has been widely discussed in recent years. However, using SAS to send parameters to a VBA macro was seldom reported. This paper provides a solution for this problem. Copying Excel tables to PowerPoint using the combination of SAS and VBA is illustrated as an example. The SAS program rapidly scans all Excel files that are contained in one folder, passes the file information to VBA as parameters, and triggers the VBA macro to write PowerPoint files in a loop. As a result, a batch of PowerPoint files can be generated by just one mouse-click.

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

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

Specifying colors based on group value is a quite popular practice in visualizing data, but it is not so easy to do, especially when there are multiple group values. This paper explores three different methods to dynamically assign colors to plots based on their group values. They are combining EVAL and IFN functions in the plot statements; bringing the DISCRETEATTRMAP block into the plot statements; and using the macro from the SAS^® sample 40255.

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

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For SAS^® users, PROC TABULATE and PROC REPORT (and its compute blocks) are probably among the most common procedures for calculating and displaying data. It is, however, pretty difficult to calculate and display changes from one column to another using data from other rows with just these two procedures. Compute blocks in PROC REPORT can calculate additional columns, but it would be challenging to pick up values from other rows as inputs. This presentation shows how PROC TABULATE can work with the lag(n) function to calculate rates of change from one period of time to another. This offers the flexibility of feeding into calculations the data retrieved from other rows of the report. PROC REPORT is then used to produce the desired output. The same approach can also be used in a variety of scenarios to produce customized reports.

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Multivariate statistical analysis plays an increasingly important role as the number of variables being measured increases in educational research. In both cognitive and noncognitive assessments, many instruments that researchers aim to study contain a large number of variables, with each measured variable assigned to a specific factor of the bigger construct. Recalling the educational theories or empirical research, the factor of each instrument usually emerges the same way. Two types of factor analysis are widely used in order to understand the latent relationships among these variables based on different scenarios. (1) Exploratory factor analysis (EFA), which is performed by using the SAS^® procedure PROC FACTOR, is an advanced statistical method used to probe deeply into the relationship among the variables and the larger construct and then develop a customized model for the specific assessment. (2) When a model is established, confirmatory factor analysis (CFA) is conducted by using the SAS procedure PROC CALIS to examine the model fit of specific data and then make adjustments for the model as needed. This paper presents the application of SAS to conduct these two types of factor analysis to fulfill various research purposes. Examples using real noncognitive assessment data are demonstrated, and the interpretation of the fit statistics is discussed.

The latest releases of SAS^® Data Integration Studio, SAS^® Data Management Studio and SAS^® Data Integration Server, SAS^® Data Governance, and SAS/ACCESS^® software provide a comprehensive and integrated set of capabilities for collecting, transforming, and managing your data. The latest features in the product suite include capabilities for working with data from a wide variety of environments and types including Hadoop, cloud, RDBMS, files, unstructured data, streaming, and others, and the ability to perform ETL and ELT transformations in diverse run-time environments including SAS^®, database systems, Hadoop, Spark, SAS^® Analytics, cloud, and data virtualization environments. There are also new capabilities for lineage, impact analysis, clustering, and other data governance features for enhancements to master data and support metadata management. This paper provides an overview of the latest features of the SAS^® Data Management product suite and includes use cases and examples for leveraging product capabilities.

For many organizations, the answer to whether to manage their data and analytics in a public or private cloud is going to be both. Both can be the answer for many different reasons: common sense logic not to replace a system that already works just to incorporate something new; legal or corporate regulations that require some data, but not all data, to remain in place; and even a desire to provide local employees with a traditional data center experience while providing remote or international employees with cloud-based analytics easily managed through software deployed via Amazon Web Services (AWS). In this paper, we discuss some of the unique technical challenges of managing a hybrid environment, including how to monitor system performance simultaneously for two different systems that might not share the same infrastructure or even provide comparable system monitoring tools; how to manage authorization when access and permissions might be driven by two different security technologies that make implementation of a singular protocol problematic; and how to ensure overall automation of two platforms that might be independently automated, but not originally designed to work together. In this paper, we share lessons learned from a decade of experience implementing hybrid cloud environments.

Even if you're not a GIS mapping pro, it pays to have some geographic problem-solving techniques in your back pocket. In this paper we illustrate a general approach to finding the closest location to any given US zip code, with a specific, user-accessible example of how to do it, using only Base SAS^®. We also suggest a method for implementing the solution in a production environment, as well as demonstrate how parallel processing can be used to cut down on computing time if there are hardware constraints.

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Do you write reports that sometimes have missing categories across all class variables? Some programmers write all sorts of additional DATA step code in order to show the zeros for the missing rows or columns. Did you ever wonder whether there is an easier way to accomplish this? PROC MEANS and PROC TABULATE, in conjunction with PROC FORMAT, can handle this situation with a couple of powerful options. With PROC TABULATE, we can use the PRELOADFMT and PRINTMISS options in conjunction with a user-defined format in PROC FORMAT to accomplish this task. With PROC SUMMARY, we can use the COMPLETETYPES option to get all the rows with zeros. This paper uses examples from Census Bureau tabulations to illustrate the use of these procedures and options to preserve missing rows or columns.

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