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.
Chevell Parker, SAS
This work presents a macro for generating a ternary graph that can be used to solve a problem in the ceramics industry. The ceramics industry uses mixtures of clay types to generate a product with good properties that can be assessed according to breakdown pressure after incineration, the porosity, and water absorption. Beyond these properties, the industry is concerned with managing geological reserves of each type of clay. Thus, it is important to seek alternative compositions that present properties similar to the default mixture. This can be done by analyzing the surface response of these properties according to the clay composition, which is easily done in a ternary graph. SAS® documentation does not describe how to adjust an analysis grid in nonrectangular forms on graphs. A macro triaxial, however, can generate ternary graphical analysis by creating a special grid, using an annotated database, and making linear transformations of the original data. The GCONTOUR procedure is used to generate three-dimensional analysis.
IGOR NASCIMENTO, UNB
Zacarias Linhares, IFPI
ROBERTO SOARES, IFPI
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.
Kia Uuskartano, Norwegian Seafood Council
Tor Erik Somby, Norwegian Seafood Council
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.
Mariam Seirafi, Cornerstone Group of Companies
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.
Marlos Bosso, SAS
Electrolux is one of the largest appliance manufacturers in the world. Electrolux North America sells more than 2,000 products to end consumers through 9,000 business customers. To grow and increase profitability under challenging market conditions, Electrolux partnered with SAS® to implement an integrated platform for SAS® for Demand-Driven Planning and Optimization and improve service levels to its customers. The process uses historical order data to create a statistical monthly forecast. The Electrolux team then reviews the statistical forecast in SAS® Collaborative Planning Workbench, where they can add value based on their business insights and promotional information. This improved monthly forecast is broken down to the weekly level where it flows into SAS® Inventory Optimization Workbench. SAS Inventory Optimization Workbench then computes weekly inventory targets to satisfy the forecasted demand at the desired service level. This presentation also covers how Electrolux implemented this project. Prior to the commencement of the project, Electrolux and the SAS team jointly worked to quantify the value of the project and set the right expectations with the executive team. A detailed timeline with regular updates helped provide visibility to all stake holders. Finally, a clear change management strategy was also developed to define the roles and responsibilities after the implementation of SAS for Demand-Driven Planning and Optimization.
Pratapsinh Patil, ELECTROLUX
Aaron Raymond, Electrolux
Sachin Verma, Electrolux
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.
Romain Miralles, Genomic Health
Many organizations need to report forecasts of large numbers of time series at various levels of aggregation. Numerous model-based forecasts that are statistically generated at the lowest level of aggregation need to be combined to form an aggregate forecast that is not required to follow a fixed hierarchy. The forecasts need to be dynamically aggregated according to any subset of the time series, such as from a query. This paper proposes a technique for large-scale automatic forecast aggregation and uses SAS® Forecast Server and SAS/ETS® software to demonstrate this technique.
Michael Leonard, SAS
Ensemble models are a popular class of methods for combining the posterior probabilities of two or more predictive models in order to create a potentially more accurate model. This paper summarizes the theoretical background of recent ensemble techniques and presents examples of real-world applications. Examples of these novel ensemble techniques include weighted combinations (such as stacking or blending) of predicted probabilities in addition to averaging or voting approaches that combine the posterior probabilities by adding one model at a time. Fit statistics across several data sets are compared to highlight the advantages and disadvantages of each method, and process flow diagrams that can be used as ensemble templates for SAS® Enterprise Miner™ are presented.
Wendy Czika, SAS
Ye Liu, SAS Institute
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.
Jeff Stander, SAS
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.
David Ghazaleh, SAS
Injury severity describes the severity of the injury to the person involved in the crash. Understanding the factors that influence injury severity can be helpful in designing mechanisms to reduce accident fatalities. In this research, we model and analyze the data as a hierarchy with three levels to answer the question what road, vehicle and driver-related factors influence injury severity. In this study, we used hierarchical linear modeling (HLM) for analyzing nested data from Fatality Analysis Reporting System (FARS). The results show that driver-related factors are directly related to injury severity. On the other hand, road conditions and vehicle characteristics have significant moderation impact on injury severity. We believe that our study has important policy implications for designing customized mechanisms specific to each hierarchical level to reduce the occurrence of fatal accidents.
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.
Chris Boniface, Census Bureau
We introduce age-period-cohort (APC) models, which analyze data in which performance is measured by age of an account, account open date, and performance date. We demonstrate this flexible technique with an example from a recent study that seeks to explain the root causes of the US mortgage crisis. In addition, we show how APC models can predict website usage, retail store sales, salesperson performance, and employee attrition. We even present an example in which APC was applied to a database of tree rings to reveal climate variation in the southwestern United States.
Joseph Breeden, Prescient Models
Oil and gas wells encounter many types of adverse events, including unexpected shut-ins, scaling, rod pump failures, breakthrough, and fluid influx, to name just a few common ones. This paper presents a real-time event detection system that presents visual reports and alerts petroleum engineers when an event is detected in a well. This system uses advanced time series analysis on both high- and low-frequency surface and downhole measurements. This system gives the petroleum engineer the ability to switch from passive surveillance of events, which then require remediation, to an active surveillance solution, which enables the engineer to optimize well intervention strategies. Events can occur simultaneously or rapidly, or can be masked over long periods of time. We tested the proposed method on data received from both simulated and publicly available data to demonstrate how a multitude of well events can be detected by using multiple models deployed into the data stream. In our demonstration, we show how our real-time system can detect a mud motor pressure failure resulting from a latent fluid overpressure event. Giving the driller advanced warning of the motor state and the potential to fail can save operators millions of dollars a year by reducing nonproductive time caused by these failures. Reducing nonproductive time is critical to reducing the operating costs of constructing a well and to improving cash flow by shortening the time to first oil.
Milad Falahi, SAS
Gilbert Hernandez, SAS Institute
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.
Ethan Merrill, SAS
Bryan Harkola, SAS
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.
Zhouming(Victor) Sun, Medimmune
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.
Vasilij Nevlev, Analytium Ltd
Looking for new ways to improve your business? Try mining your own data! Event log data is a side product of information systems generated for audit and security purposes and is seldom analyzed, especially in combination with business data. Along with the cloud computing era, more event log data has been accumulated and analysts are searching for innovative ways to take advantage of all data resources in order to get valuable insights. Process mining, a new field for discovering business patterns from event log data, has recently proved useful for business applications. Process mining shares some algorithms with data mining but it is more focused on interpretation of the detected patterns rather than prediction. Analysis of these patterns can lead to improvements in the efficiency of common existing and planned business processes. Through process mining, analysts can uncover hidden relationships between resources and activities and make changes to improve organizational structure. This paper shows you how to use SAS® Analytics to gain insights from real event log data.
Emily (Yan) Gao, SAS
Robert Chu, SAS
Xudong Sun, SAS
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.
Christopher Johnson, BrickStreet Insurance
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.
Piyush Singh, TATA Consultancy Services Ltd
Tanuj Gupta, TATA Consultancy Services
Prasoon Sangwan, Tata consultancy services limited
Quality issues can easily go undetected in the field for months, even years. Why? Customers are more likely to complain through their social networks than directly through customer service channels. The Internet is the closest friend for most users and focusing purely on internal data isn't going to help. Customer complaints are often handled by marketing or customer service and aren't shared with engineering. This lack of integrated vision is due to the silo mentality in organizations and proves costly in the long run. Information systems are designed with business rules to find out of spec situations or to trigger when a certain threshold of reported cases is reached before engineering is involved. Complex business problems demand sophisticated technology to deliver advanced analytical capabilities. Organizations can react only to what they know about, but unstructured data is an invaluable asset that we must hatch on. So what if you could infuse the voice of the customer directly into engineering and detect emerging issues months earlier than your existing process? Lenovo just did that. Issues were detected in time to remove problematic parts, eventually leading to massive reductions in cost and time. Lenovo observed a significant decline in call volume to its corporate call center. It became evident that customers' perception of quality relative to the market is a voice we can't afford to ignore. Lenovo Corporate Analytics unit partnered with SAS® to build an end-to-end process that constitutes data acquisition and preparation, analysis using linguistic text analytics models, and quality control metrics for visualization dashboards. Lenovo executives consume these reports and make informed decisions. In this paper, we talk in detail about perceptual quality and why we should care about it. We also tell you where to look for perceptual quality data and best practices for starting your own perceptual quality program through the voice of customer analytics.
Murali Pagolu, SAS
Rob Carscadden, SAS
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.
Meenu Jaiswal, SAS
Ipsita Samantarai, SAS Research & Development (India) Pvt Ltd
As a result of globalization, the durable goods market has become increasingly competitive, with market conditions that challenge profitability for manufacturers. Moreover, high material costs and the capital-intensive nature of the industry make it essential that companies understand demand signals and utilize supply chain capacity as effectively as possible. To grow and increase profitability under these challenging market conditions, a major durable goods company has partnered with SAS to streamline analysis of pricing and profitability, optimize inventory, and improve service levels to its customers. The price of a product is determined by a number of factors, such as the strategic importance of customers, supply chain costs, market conditions, and competitive prices. Offering promotions is an important part of a marketing strategy; it impacts purchasing behaviors of business customers and end consumers. This paper describes how this company developed a system to analyze product profitability and the impact of promotion on purchasing behaviors of both their business customers and end consumers. This paper also discusses how this company uses integrated demand planning and inventory optimization to manage its complex multi-echelon supply chain. The process uses historical order data to create a statistical forecast of demand, and then optimizes inventory across the supply chain to satisfy the forecast at desired service levels.
VARUNRAJ VALSARAJ, SAS
Bahadir Aral, SAS Institute Inc
Baris Kacar, SAS Institute Inc
Jinxin Yi, SAS Institute Inc
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.
Murali Nori, SAS
Himesh Patel, SAS
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.
Khoi To, Office of Planning and Decision Support, Virginia Commonwealth University
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.
Jonathan Wexler, SAS
Steve Sparano, SAS
Every day, businesses have to remain vigilant of fraudulent activity, which threatens customers, partners, employees, and financials. Normally, networks of people or groups perpetrate deviant activity. Finding these connections is now made easier for analysts with SAS® Visual Investigator, an upcoming SAS® solution that ultimately minimizes the loss of money and preserves mutual trust among its shareholders. SAS Visual Investigator takes advantage of the capabilities of the new SAS® In-Memory Server. Investigators can efficiently investigate suspicious cases across business lines, which has traditionally been difficult. However, the time required to collect, process and identify emerging fraud and compliance issues has been costly. Making proactive analysis accessible to analysts is now more important than ever. SAS Visual Investigator was designed with this goal in mind and a key component is the visual social network view. This paper discusses how the network analysis view of SAS Visual Investigator, with all its dynamic visual capabilities, can make the investigative process more informative and efficient.
Danielle Davis, SAS
Stephen Boyd, SAS Institute
Ray Ong, SAS Institute
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.
Justin Jia, Trans Union Canada
Shan Shan Lin, CIBC
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.
Tricia Aanderud, Zencos Consulting LLC
Ryan Kumpfmiller, Zencos Consulting
Nick Welke, Zencos Consulting
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.
Barbara Ross, NA
Jessica Bennett, Snap Finance
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.
Arthur Li, City of Hope
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.
Vishant Bhat, University of Newcastle
Tony Blanch, SAS Consultant
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.
Justin Jia, Trans Union Canada
Shan Shan Lin, CIBC
This paper describes the merging of designed experiments and regularized regression to find the significant factors to use for a real-time predictive model. The real-time predictive model is used in a Weyerhaeuser modified fiber mill to estimate the value of several key quality characteristics. The modified fiber product is accepted or rejected based on the model prediction or the predicted values' deviation from lab tests. To develop the model, a designed experiment was needed at the mill. The experiment was planned by a team of engineers, managers, operators, lab techs, and a statistician. The data analysis used the actual values of the process variables manipulated in the designed experiment. There were instances of the set point not being achieved or maintained for the duration of the run. The lab tests of the key quality characteristics were used as the responses. The experiment was designed with JMP® 64-bit Edition 11.2.0 and estimated the two-way interactions of the process variables. It was thought that one of the process variables was curvilinear and this effect was also made estimable. The LASSO method, as implemented in the SAS® GLMSELECT procedure, was used to analyze the data after cleaning and validating the results. Cross validation was used as the variable selection operator. The resulting prediction model passed all the predetermined success criteria. The prediction model is currently being used real time in the mill for product quality acceptance.
Jon Lindenauer, Weyerhaeuser
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.
Murali Nori, SAS
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.
Nancy Rausch, SAS
Wilbram Hazejager, SAS
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.
Zhu Yanrong, Medtronic
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.
Pamela Dixon, SAS
The Scheduler is an innovative tool that maps linear data (that is, time stamps) to an intuitive three-dimensional representation. This transformation enables the user to identify relationships, conflicts, gaps, and so on, that were not readily apparent in the data's native form. This tool has applications in operations research and litigation-related analyses. This paper discusses why the Scheduler was created, the data that is available to analyze the issue, and how this code can be used in other types of applications. Specifically, this paper discusses how the Scheduler can be used by supervisors to maintain the presence of three or more employees at all times to ensure that all federal- and state- mandated work breaks are taken. Additional examples of the Scheduler include assisting construction foremen to create schedules that visualize the presence of contractors in a logical sequence while eliminating overlap and ensuring cushions of time between contractors and matching items to non-uniform information.
Ariel Kumpinsky, The Claro Group, LLC
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.
Amos Shu, MedImmune
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.
Brandon Kirk, SAS
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.
Khoi To, Office of Planning and Decision Support, Virginia Commonwealth University
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.
Nancy Rausch, SAS
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.
Ethan Merrill, SAS
Bryan Harkola, SAS
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.
Chris Boniface, Census Bureau
Janet Wysocki, U.S. Census Bureau