This paper illustrates a high-level infrastructure discussion with some explanation of the SAS^® codes used to implement a configurable batch framework for managing and updating the data rows and row-level permissions in SAS^® OLAP Cube Studio. The framework contains a collection of reusable, parameter-driven Base SAS^® macros, Base SAS custom programs, and UNIX or LINUX shell scripts. This collection manages the typical steps and processes used for manipulating SAS files and for executing SAS statements. The Base SAS macro collection contains a group of utility macros that includes: concurrent /parallel processing macros, SAS^® Metadata Repository macros, SAS^® Scalable Performance Data Engine table macros, table lookup macros, table manipulation macros, and other macros. There is also a group of OLAP-related macros that includes OLAP utility macros and OLAP permission table processing macros.

SAS^® Visual Analytics provides self-service capabilities for users to analyze, explore, and report on their own data. As users explore their data, there is always a need to bring in more data sources, create new variables, combine data from multiple sources, and even update your data occasionally. SAS Visual Analytics provides targeted user capabilities to access, modify, and enhance data suitable for specific business needs. This paper provides a clear understanding of these capabilities and suggests best practices for self-service data management in SAS Visual Analytics.

In 2013, the University of North Carolina (UNC) at Chapel Hill initiated enterprise-wide use of SAS^® solutions for reporting and data transformations. Just over one year later, the initial rollout was scheduled to go live to an audience of 5,500 users as part of an adoption of PeopleSoft ERP for Finance, Human Resources, Payroll, and Student systems. SAS^® Visual Analytics was used for primary report delivery as an embedded resource within the UNC Infoporte, an existing portal. UNC made the date. With the SAS solutions, UNC delivered the data warehouse and initial reports on the same day that the ERP systems went live. After the success of the initial launch, UNC continues to develop and evolve the solution with additional technologies, data, and reports. This presentation touches on a few of the elements required for a medium to large size organization to integrate SAS solutions such as SAS Visual Analytics and SAS^® Enterprise Business Intelligence within their infrastructure.

The importance of econometrics in the analytics toolkit is increasing every day. Econometric modeling helps uncover structural relationships in observational data. This paper highlights the many recent changes to the SAS/ETS^® portfolio that increase your power to explain the past and predict the future. Examples show how you can use Bayesian regression tools for price elasticity modeling, use state space models to gain insight from inconsistent time series, use panel data methods to help control for unobserved confounding effects, and much more.

SAS^® Visual Analytics is deployed by many customers. IT departments are tasked with efficiently managing the server resources, achieving maximum usage of resources, optimizing availability, and managing costs. Business users expect the system to be available when needed and to perform to their expectations. Business executives who sponsor business intelligence (BI) and analytical projects like to see that their decision to support and finance the project meets business requirements. Business executives also like to know how different people in the organization are using SAS Visual Analytics. With the release of SAS Visual Analytics 7.1, new functionality is added to support the memory management of the SAS^® LASR™ Analytic Server. Also, new out-of-the-box usage and audit reporting is introduced. This paper covers BI-on-BI for SAS Visual Analytics. Also, all the new functionality introduced for SAS Visual Analytics administration and questions about the resource management, data compression, and out-of-the-box usage reporting of SAS Visual Analytics are also discussed. Key product capabilities are demonstrated.

Over the years, the SAS^® Business Intelligence platform has proved its importance in this big data world with its suite of applications that enable us to efficiently process, analyze, and transform huge amounts of business data. Within the data warehouse universe, 'batch execution' sits in the heart of SAS Data Integration technologies. On a day-to-day basis, batches run, and the current status of the batch is generally sent out to the team or to the client as a 'static' e-mail or as a report. From experience, we know that they don't provide much insight into the real 'bits and bytes' of a batch run. Imagine if the status of the running batch is automatically captured in one central repository and is presented on a beautiful web browser on your computer or on your iPad. All this can be achieved without asking anybody to send reports and with all 'post-batch' queries being answered automatically with a click. This paper aims to answer the same with a framework that is designed specifically to automate the reporting aspects of SAS batches and, yes, it is all about collecting statistics of the batch, and we call it - 'BatchStats.'

We regularly speak with organizations running established SAS^® 9.1.3 systems that have not yet upgraded to a later version of SAS^®. Often this is because their current SAS 9.1.3 environment is working fine, and no compelling event to upgrade has materialized. Now that SAS 9.1.3 has moved to a lower level of support and some very exciting technologies (Hadoop, cloud, ever-better scalability) are more accessible than ever using SAS^® 9.4, the case for migrating from SAS 9.1.3 is strong. Upgrading a large SAS ecosystem with multiple environments, an active development stream, and a busy production environment can seem daunting. This paper aims to demystify the process, suggesting outline migration approaches for a variety of the most common scenarios in SAS 9.1.3 to SAS 9.4 upgrades, and a scalable template project plan that has been proven at a range of organizations.

So you are still writing SAS^® DATA steps and SAS macros and running them through a command-line scheduler. When work comes in, there is only one person who knows that code, and they are out--what to do? This paper shows how SAS applies extract, transform, load (ETL) modernization techniques with SAS^® Data Integration Studio to gain resource efficiencies and to break down the ETL black box. We are going to share the fundamentals (metadata foldering and naming standards) that ensure success, along with steps to ease into the pool while iteratively gaining benefits. Benefits include self-documenting code visualization, impact analysis on jobs and tables impacted by change, and being supportable by interchangeable bench resources. We conclude with demonstrating how SAS^® Visual Analytics is being used to monitor service-level agreements and provide actionable insights into job-flow performance and scheduling.

Soon after the advent of the SAS^® hash object in SAS^® 9.0, its early adopters realized that the potential functionality of the new structure is much broader than basic 0(1)-time lookup and file matching. Specifically, they went on to invent methods of data aggregation based on the ability of the hash object to quickly store and update key summary information. They also demonstrated that the DATA step aggregation using the hash object offered significantly lower run time and memory utilization compared to the SUMMARY/MEANS or SQL procedures, coupled with the possibility of eliminating the need to write the aggregation results to interim data files and the programming flexibility that allowed them to combine sophisticated data manipulation and adjustments of the aggregates within a single step. Such developments within the SAS user community did not go unnoticed by SAS R&D, and for SAS^® 9.2 the hash object had been enriched with tag parameters and methods specifically designed to handle aggregation without the need to write the summarized data to the PDV host variable and update the hash table with new key summaries, thus further improving run-time performance. As more SAS programmers applied these methods in their real-world practice, they developed aggregation techniques fit to various programmatic scenarios and ideas for handling the hash object memory limitations in situations calling for truly enormous hash tables. This paper presents a review of the DATA step aggregation methods and techniques using the hash object. The presentation is intended for all situations in which the final SAS code is either a straight Base SAS DATA step or a DATA step generated by any other SAS product.

From large holding companies with multiple subsidiaries to loosely affiliated state educational institutions, security domains are being federated to enable users from one domain to access applications in other domains and ultimately save money on software costs through sharing. Rather than rely on centralized security, applications must accept claims-based authentication from trusted authorities and support open standards such as Security Assertion Markup Language (SAML) instead of proprietary security protocols. This paper introduces SAML 2.0 and explains how the open source SAML implementation known as Shibboleth can be integrated with the SAS^® 9.4 security architecture to support SAML. It then describes in detail how to set up Microsoft Active Directory Federation Services (AD FS) as the SAML Identity Provider, how to set up the SAS middle tier as the relying party, and how to troubleshoot problems.

Business managers are seeing the value of incorporating business information and analytics in daily decision-making with real-time information, when and where it is needed during business meetings and customer engagements. Real-time access of customer and business information reduces the latency in decision-making with confidence and accuracy, increasing the overall efficiency of the company. SAS is introducing new product options with HTML5 and adding advanced features in SAS^® Mobile BI in SAS^® Visual Analytics 7.2 to enhance the reach and experience of business managers to SAS^® analytics and dashboards from SAS Visual Analytics. With SAS Mobile BI 7.2, SAS will push the limits of a business user's ability to author and change the content of dashboards and reports on mobile devices. This presentation focuses on both the new HTML5-based product options and the new advancements made with SAS Mobile BI that empower business users. We present in detail the scope and new features that are offered with the HTML5-based viewer and with SAS Mobile BI from SAS Visual Analytics. Since the new HTML5-based viewer and SAS Mobile BI are the viewer options for business users to visualize and consume the content from SAS Visual Analytics, this presentation demonstrates the two products in detail. Key product capabilities are demoed.

A group tasked with testing SAS^® software from the customer perspective has gathered a number of helpful hints for SAS^® 9.4 that will smooth the transition to its new features and products. These hints will help with the 'huh?' moments that crop up when you are getting oriented and will provide short, straightforward answers. We also share insights about changes in your order contents. Gleaned from extensive multi-tier deployments, SAS^® Customer Experience Testing shares insiders' practical tips to ensure that you are ready to begin your transition to SAS 9.4. The target audience for this paper is primarily system administrators who will be installing, configuring, or administering the SAS 9.4 environment. (This paper is an updated version of the paper presented at SAS Global Forum 2014 and includes new features and software changes since the original paper was delivered, plus any relevant content that still applies. This paper includes information specific to SAS 9.4 and SAS 9.4 maintenance releases.)

SAS^® blogs (hosted at http://blogs.sas.com/content) attract millions of page views annually. With hundreds of authors, thousands of posts, and constant chatter within the blog comments, it's impossible for one person to keep track of all of the activity. In this paper, you learn how SAS technology is used to gather data and report on SAS blogs from the inside out. The beneficiaries include personnel from all over the company, including marketing, technical support, customer loyalty, and executives. The author describes the business case for tracking and reporting on the activity of blogging. You learn how SAS tools are used to access the WordPress database and how to create a 'blog data mart' for reporting and analytics. The paper includes specific examples of the insight that you can gain from examining the blogs analytically, and which techniques are most useful for achieving that insight. For example, the blog transactional data are combined with social media metrics (also gathered by using SAS) to show which blog entries and authors yield the most engagement on Twitter, Facebook, and LinkedIn. In another example, we identified the growing trend of 'blog comment spam' on the SAS blog properties and measured its cost to the business. These metrics helped to justify the investment in a solution. Many of the tools used are part of SAS^® Foundation, including SAS/ACCESS^®, the DATA step and SQL, PROC REPORT, PROC SGPLOT, and more. The results are shared in static reports, automated daily email summaries, dynamic reports hosted in SAS/IntrNet^®, and even a corporate dashboard hosted in SAS^® Visual Analytics.

SAS^® Visual Analytics opens up a world of intuitive interactions, providing report creators the ability to develop more efficient ways to deliver information. Business-related hierarchies can be defined dynamically in SAS Visual Analytics to group data more efficiently--no more going back to the developers. Visualizations can interact with each other, with other objects within other sections, and even with custom applications and SAS^® stored processes. This paper provides a blueprint to streamline and consolidate reporting efforts using these interactions available in SAS Visual Analytics. The goal of this methodology is to guide users down information pathways that can progressively subset data into smaller, more understandable chunks of data, while summarizing each layer to provide insight along the way. Ultimately the final destination of the information pathway holds a reasonable subset of data so that a user can take action and facilitate an understood outcome.

SAS^® customers benefit greatly when they are using the functionality, performance, and stability available in the latest version of SAS. However, the task of moving all SAS collateral such as programs, data, catalogs, metadata (stored processes, maps, queries, reports, and so on), and content to SAS^® 9.4 can seem daunting. This paper provides an overview of the steps required to move all SAS collateral from systems based on SAS^® 9.2 and SAS^® 9.3 to the current release of SAS^® 9.4.

In-database processing refers to the integration of advanced analytics into the data warehouse. With this capability, analytic processing is optimized to run where the data reside, in parallel, without having to copy or move the data for analysis. From a data governance perspective there are many good reasons to embrace in-database processing. Many analytical computing solutions and large databases use this technology because it provides significant performance improvements over more traditional methods. Come learn how Blue Cross Blue Shield of Tennessee (BCBST) uses in-database processing from SAS and Teradata.

The SAS^® Web Application Server is a lightweight server that provides enterprise-class features for running SAS^® middle-tier web applications. This server can be configured to use the SAS^® Web Infrastructure Platform Data Server for a transactional storage database. You can meet the high-availability data requirement in your business plan by implementing a SAS Web Infrastructure Data Server cluster. This paper focuses on how the SAS Web Infrastructure Data Server on the SAS middle tier can be configured for load balancing, and data replication involving multiple nodes. SAS^® Environment Manager and pgpool-II are used to enable these high-availability strategies, monitor the server status, and initiate failover as needed.

Banks can create a competitive advantage in their business by using business intelligence (BI) and by building models. In the credit domain, the best practice is to build risk-sensitive models (Probability of Default, Exposure at Default, Loss-given Default, Unexpected Loss, Concentration Risk, and so on) and implement them in decision-making, credit granting, and credit risk management. There are models and tools on the next level built on these models and that are used to help in achieving business targets, risk-sensitive pricing, capital planning, optimizing of ROE/RAROC, managing the credit portfolio, setting the level of provisions, and so on. It works remarkably well as long as the models work. However, over time, models deteriorate and their predictive power can drop dramatically. Since the global financial crisis in 2008, we have faced a tsunami of regulation and accelerated frequency of changes in the business environment, which cause models to deteriorate faster than ever before. As a result, heavy reliance on models in decision-making (some decisions are automated following the model's results--without human intervention) might result in a huge error that can have dramatic consequences for the bank's performance. In my presentation, I share our experience in reducing model risk and establishing corporate governance of models with the following SAS^® tools: model monitoring, SAS^® Model Manager, dashboards, and SAS^® Visual Analytics.

Organisations find SAS^® upgrades and migration projects come with risk, costs, and challenges to solve. The benefits are enticing new software capabilities such as SAS^® Visual Analytics, which help maintain your competitive advantage. An interesting conundrum. This paper explores how to evaluate the benefits and plan the project, as well as how the cloud option impacts modernisation. The author presents with the experience of leading numerous migration and modernisation projects from the leading UK SAS Implementation Partner.

EBI administrators who are new to SAS^® Visual Analytics and used to the logging capability of the SAS^® OLAP Server might be wondering how they can get their SAS^® LASR™ Analytic Server to produce verbose log files. While the SAS LASR Analytic Server logs differ from those produced by the SAS OLAP Server, the SAS LASR Analytic Server log contains information about each request made to LASR tables and can be a great data source for administrators looking to learn more about how their SAS Visual Analytics deployments are being used. This session will discuss how to quickly enable logging for your SAS LASR Analytic Server in SAS Visual Analytics 6.4. You will see what information is available to a SAS administrator in these logs, how they can be parsed into data sets with SAS code, then loaded back into the SAS LASR Analytic Server to create SAS Visual Analytics explorations and reports.

Sometimes you need to provide multiple administrators with the ability to manage your software. The rationale can be a need to separate roles and responsibilities (such as installer and configuration manager), changing job responsibilities, or even just covering for the primary administrator while on vacation. To meet that need, it's tempting to share the logon credentials of your SAS^® installer account, but doing so can potentially compromise your security and cause a corporate audit to fail. This paper focuses on standard IT practices and utilities, explaining how to diligently manage the administration of your SAS software to help you properly ensure that access is secured and that auditability is maintained.

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Retrospective case-control studies are frequently used to evaluate health care programs when it is not feasible to randomly assign members to a respective cohort. Without randomization, observational studies are more susceptible to selection bias where the characteristics of the enrolled population differ from those of the entire population. When the participant sample is different from the comparison group, the measured outcomes are likely to be biased. Given this issue, this paper discusses how propensity score matching and random effects techniques can be used to reduce the impact selection bias has on observational study outcomes. All results shown are drawn from an ROI analysis using a participant (cases) versus non-participant (controls) observational study design for a fitness reimbursement program aiming to reduce health care expenditures of participating members.

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SAS^® Analytics enables organizations to tackle complex business problems using big data and to provide insights needed to make critical business decisions. A well-architected enterprise storage infrastructure is needed to realize the full potential of SAS Analytics. However, as the need for big data analytics and rapid response times increases, the performance gap between server speeds and traditional hard disk drive (HDD) based storage systems can be a significant concern. The growing performance gap can have detrimental effects, particularly when it comes to critical business applications. As a result, organizations are looking for newer, smarter, faster storage systems to accelerate business insights. IBM FlashSystem Storage systems store the data in flash memory. They are designed for dramatically faster access times and support incredible amounts of input/output operations per second (IOPS) and throughput, with significantly lower latency than HDD-based solutions. Due to their macro-efficiency design, FlashSystem Storage systems consume less power and have significantly lower cooling and space requirements, while allowing server processors to run SAS Analytics more efficiently. Being an all-flash storage system, IBM FlashSystem provides consistent low latency response across IOPS range, as the analytics workload scales. This paper introduces the benefits of IBM FlashSystem Storage for deploying SAS Analytics and highlights some of the deployment scenarios and architectural considerations. This paper also describes best practices and tuning guidelines for deploying SAS Analytics on FlashSystem Storage systems, which would help SAS Analytics customers in architecting solutions with FlashSystem Storage.

Once you have a SAS^® Visual Analytics environment up and running, the next important piece to the puzzle is to keep your users happy by keeping their data loaded and refreshed on a consistent basis. Loading data from the SAS Visual Analytics UI is both a great first start and great for ad hoc data exploring. But automating this data load so that users can focus on exploring the data and creating reports is where to power of SAS Visual Analytics comes into play. By using tried-and-true SAS^® Data Integration Studio techniques (both out of the box and custom transforms), you can easily make this happen. Proven techniques such as sweeping from a source library and stacking similar Hadoop Distributed File System (HDFS) tables into SAS^® LASR™ Analytic Server for consumption by SAS Visual Analytics are presented using SAS Visual Analytics and SAS Data Integration Studio.

A leading killer in the United States is smoking. Moreover, over 8.6 million Americans live with a serious illness caused by smoking or second-hand smoking. Despite this, over 46.6 million U.S. adults smoke tobacco, cigars, and pipes. The key analytic question in this paper is, How would e-cigarettes affect this public health situation? Can monitoring public opinions of e-cigarettes using SAS^® Text Analytics and SAS^® Visual Analytics help provide insight into the potential dangers of these new products? Are e-cigarettes an example of Big Tobacco up to its old tricks or, in fact, a cessation product? The research in this paper was conducted on thousands of tweets from April to August 2014. It includes API sources beyond Twitter--for example, indicators from the Health Indicators Warehouse (HIW) of the Centers for Disease Control and Prevention (CDC)--that were used to enrich Twitter data in order to implement a surveillance system developed by SAS^® for the CDC. The analysis is especially important to The Office of Smoking and Health (OSH) at the CDC, which is responsible for tobacco control initiatives that help states to promote cessation and prevent initiation in young people. To help the CDC succeed with these initiatives, the surveillance system also: 1) automates the acquisition of data, especially tweets; and 2) applies text analytics to categorize these tweets using a taxonomy that provides the CDC with insights into a variety of relevant subjects. Twitter text data can help the CDC look at the public response to the use of e-cigarettes, and examine general discussions regarding smoking and public health, and potential controversies (involving tobacco exposure to children, increasing government regulations, and so on). SAS^® Content Categorization helps health care analysts review large volumes of unstructured data by categorizing tweets in order to monitor and follow what people are saying and why they are saying it. Ultimatel y, it is a solution intended to help the CDC monitor the public's perception of the dangers of smoking and e-cigarettes, in addition, it can identify areas where OSH can focus its attention in order to fulfill its mission and track the success of CDC health initiatives.

Your enterprise SAS^® Visual Analytics implementation is on its way to being adopted throughout your organization, unleashing the production of critical business content by business analysts, data scientists, and decision makers from many business units. This content is relied upon to inform decisions and provide insight into the results of those decisions. With the development of SAS Visual Analytics content decentralized into the hands of business users, the use of automated version control is essential to providing protection and recovery in the event of inadvertent changes to that content. Re-creation of complex report objects accidentally modified by a business user is time-consuming and can be eliminated by maintaining a version control repository of report (and other) objects created in SAS Visual Analytics. This paper walks through the steps for implementing an automated process for version control using SAS^®. This process can be applied to all types of metadata objects used in multiple SAS application development and analysis environments, such as reports and explorations from SAS Visual Analytics, and jobs, tables, and libraries from SAS^® Data Integration Studio. Basic concepts for the process, as well as specific techniques used for our implementation are included. So eliminate the risk of content loss for your business users and the burden of manual version control for your applications developers. Your IT shop will enjoy time savings and greater reliability.

SAS^® Office Analytics, SAS^® Visual Analytics, and SAS^® Studio provide excellent data analysis and report generation. When these products are combined, their deep interoperability enables you to take your analysis and reporting to the next level. Build interactive reports in SAS^® Visual Analytics Designer, and then view, customize and comment on them from Microsoft Office and SAS^® Enterprise Guide^®. Create stored processes in SAS Enterprise Guide, and then run them in SAS Visual Analytics Designer, mobile tablets, or SAS Studio. Run your SAS Studio tasks in SAS Enterprise Guide and Microsoft Office using data provided by those applications. These interoperability examples and more will enable you to combine and maximize the strength of each of the applications. Learn more about this integration between these products and what's coming in the future in this session.

Building a holistic view of the customer is becoming the norm across industries. The financial services industry and retail firms have been at the forefront of striving for this goal. Firm ABC is a large insurance firm based in the United States. It uses multiple campaign management platforms across different lines of business. Marketing campaigns are deployed in isolation. Similarly, responses are tracked and attributed in silos. This prevents the firm from obtaining a holistic view of its customers across products and lines of business and leads to gaps and inefficiencies in data management, campaign management, reporting, and analytics. Firm ABC needed an enterprise-level solution that addressed how to integrate with different types of data sources (both external and internal) and grow as a scalable and agile marketing and analytics organization; how to deploy campaign and contact management using a centralized platform to reduce overlap and redundancies and deliver a more coordinated marketing messaging to customers; how to perform more accurate attribution that, in turn, drives marketing measurement and planning; how to implement more sophisticated and visual self-service reporting that enables business users to make marketing decisions; and how to build advanced analytics expertise in-house. The solution needed to support predictive modeling, segmentation, and targeting. Based on these challenges and requirements, the firm conducted an extensive RFP process and reviewed various vendors in the enterprise marketing and business intelligence space. Ultimately, SAS^® Customer Intelligence and SAS^® Enterprise BI were selected to help the firm achieve its goals and transition to a customer-centric organization. The ability for SAS^® to deliver a custom-hosted solution was one of the key drivers for this decision, along with its experience in the financial services and insurance industries. Moreover, SAS can provide the much-needed flexibility and scala bility, whether it is around integrating external vendors, credit data, and mail-shop processing, or managing sensitive customer information. This presentation provides detailed insight on the various modules being implemented by the firm, how they will be leveraged to address the goals, and what their roles are in the future architecture. The presentation includes detailed project implementation and provides insights, best practices, and challenges faced during the project planning, solution design, governance, and development and production phases. The project team included marketers, campaign managers, data analysts, business analysts, and developers with sponsorship and participation from the C suite. The SAS^® Transformation Project provides insights and best practices that prove useful for business users, IT teams, and senior management. The scale, timing, and complexity of the solution deployment make it an interesting and relevant case study, not only for financial clients, but also for any large firm that has been tasked with understanding its customers and building a holistic customer profile.

In 2012, the Obama campaign used advanced analytics to target voters, especially in social media channels. Millions of voters were scored on models each night to predict their voting patterns. These models were used as the driver for all campaign decisions, including TV ads, budgeting, canvassing, and digital strategies. This presentation covers how the Obama campaign strategies worked, what's in store for analytics in future elections, and how these strategies can be applied in the business world.

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Network diagrams in SAS^® Visual Analytics help highlight relationships in complex data by enabling users to visually correlate entire populations of values based on how they relate to one another. Network diagrams are appealing because they enable an analyst to visualize large volumes and relationships of data and to assign multiple roles to represent key factors for analysis such as node size and color and linkage size and color. SAS Visual Analytics can overlay a network diagram on top of a spatial geographic map for an even more appealing visualization. This paper focuses specifically on how to prepare data for network diagrams and how to build network diagrams in SAS Visual Analytics. This paper provides two real-world examples illustrating how to visualize users and groups from SAS^® metadata and how banks can visualize transaction flow using network diagrams.

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Enrollment management is very important to all colleges. Having the correct tools to help you better understand your enrollment patterns of the past and the future is critical to any school. This session will describe how Valencia College went from manually updating static charts for enrollment management, to building dynamic, interactive visualizations to compare how students register across different calendar-date periods (current versus previous period)grouped by different start-of-registration dates--from start of registration, days into registration, and calendar date to previous year calendar date. This includes being able to see the trend by college campus, instructional method mode (onsite or online ) or by type of session (part of semester, full, and so on) all available in one visual and sliced and diced via check lists. The trend loads 4-6 million rows of data nightly to the SAS^® LASR™ Analytics Server in a snap with no performance issues on the back-end or presentation visual. We will give a brief history of how we used to load data into Excel and manually build charts. Then we will describe the current environment, which is an automated approach through SAS^® Visual Analytics. We will show pictures of our old, static reports, and then show the audience the power and functionality of our new, interactive reports through SAS Visual Analytics.

Smoke detectors operate by comparing actual air quality to expected air quality standards and immediately alerting occupants when smoke or particle levels exceed established thresholds. Just as rapid identification of smoke (that is, poor air quality) can detect harmful fire and facilitate its early extinguishment, rapid detection of poor quality data can highlight data entry or ingestion errors, faulty logic, insufficient or inaccurate business rules, or process failure. Aspects of data quality--such as availability, completeness, correctness, and timeliness--should be assessed against stated requirements that account for the scope, objective, and intended use of data products. A single outlier, an accidentally locked data set, or even subtle modifications to a data structure can cause a robust extract-transform-load (ETL) infrastructure to grind to a halt or produce invalid results. Thus, a mature data infrastructure should incorporate quality assurance methods that facilitate robust processing and quality data products, as well as quality control methods that monitor and validate data products against their stated requirements. The SAS^® Smoke Detector represents a scalable, generalizable solution that assesses the availability, completeness, and structure of persistent SAS data sets, ideal for finished data products or transactional data sets received with standardized frequency and format. Like a smoke detector, the quality control dashboard is not intended to discover the source of the blaze, but rather to sound an alarm to stakeholders that data have been modified, locked, deleted, or otherwise corrupted. Through rapid detection and response, the fidelity of data is increased as well as the responsiveness of developers to threats to data quality and validity.

Managing and organizing external files and directories play an important part in our data analysis and business analytics work. A good file management system can streamline project management and file organizations and significantly improve work efficiency . Therefore, under many circumstances, it is necessary to automate and standardize the file management processes through SAS^® programming. Compared with managing SAS files via PROC DATASETS, managing external files is a much more challenging task, which requires advanced programming skills. This paper presents and discusses various methods and approaches to managing external files with SAS programming. The illustrated methods and skills can have important applications in a wide variety of analytic work fields.