In our previous work, we often needed to perform large numbers of repetitive and data-driven post-campaign analyses to evaluate the performance of marketing campaigns in terms of customer response. These routine tasks were usually carried out manually by using Microsoft Excel, which was tedious, time-consuming, and error-prone. In order to improve the work efficiency and analysis accuracy, we managed to automate the analysis process with SAS^® programming and replace the manual Excel work. Through the use of SAS macro programs and other advanced skills, we successfully automated the complicated data-driven analyses with high efficiency and accuracy. This paper presents and illustrates the creative analytical ideas and programming skills for developing the automatic analysis process, which can be extended to apply in a variety of business intelligence and analytics fields.

In many organizations the amount of data we deal with increases far faster than the hardware and IT infrastructure to support them. As a result, we encounter significant bottlenecks and I/O bound processes. However, clever use of SAS^® software can help us find a way around. In this paper we will look at the clever use of PROC OLAP to show you how to address I/O bound processing spread I/O traffic to different servers to increase cube building efficiency. This paper assumes experience with SAS^® OLAP Cube Studio and/or PROC OLAP.

With the growth in size and complexity of organizations investing in SAS^® platform technologies, the size and complexity of ETL subsystems and data integration (DI) jobs is growing at a rapid rate. Developers are pushed to come up with new and innovative ways to improve process efficiency in their DI jobs to meet increasingly demanding service level agreements (SLAs). The ability to conditionally execute or switch paths in a DI job is an extremely useful technique for improving process efficiency. How can a SAS^® Data Integration developer design a job to best suit conditional execution? This paper discusses a technique for providing a parameterized dynamic execution custom transformation that can be easily incorporated into SAS^® Data Integration Studio jobs to provide process path switching capabilities. The aim of any data integration task is to ensure that all sources of business data are integrated as efficiently as possible. It is concerned with the repurposing of data via transformation, should be a value-adding process, and also should be the product of collaboration. Modularization of common or repeatable processes is a fundamental part of the collaboration process in DI design and development. Switch path a custom transformation built to conditionally execute branches or nodes in SAS Data Integration Studio provides a reusable module for solving the conditional execution limitations of standard SAS Data Integration Studio transformations and jobs. Switch Path logic in SAS Data Integration Studio can serve many purposes in day-to-day business needs for a SAS data integration developer as it is completely reusable

The Washington D.C. aqueduct was completed in 1863, carrying desperately needed clean water to its many residents. Just as the aqueduct was vital and important to its residents, a lifeline if you will, so too is the supply of data to the business. Without the flow of vital information, many businesses would not be able to make important decisions. The task of building my company s first dashboard was brought before us by our CIO; the business had not asked for it. In this poster, I discuss how we were able to bring fresh ideas and data to our business units by converting the data they saw on a daily basis in reports to dashboards. The road to success was long with plenty of struggles from creating our own business requirements to building data marts, synching SQL to SAS^®, using information maps and SAS^® Enterprise Guide^® projects to move data around, all while dealing with technology and other I.T. team roadblocks. Then on to designing what would become our real-time dashboards, fighting for SharePoint single sign-on, and, oh yeah, user adoption. My story of how dashboards revitalized the business is a refreshing tale for all levels.

Business Intelligence (BI) dashboards serve as an invaluable, high-level, visual reference tool for decision-making processes in many business industries. A request was made to our department to develop some BI dashboards that could be incorporated in an academic setting. These dashboards would aim to serve various undergraduate executive and administrative staff at the university. While most business data may lend itself to work very well and easily in the development of dashboards, academic data is typically modeled differently and, therefore, faces unique challenges. In this paper, the authors detail and share the design and development process of creating dashboards for decision making in an academic environment utilizing SAS^® BI Dashboard 4.3 and other SAS^® Enterprise Business Intelligence 9.2 tools. The authors also provide lessons learned as well as recommendations for future implementations of BI dashboards utilizing academic data.

For almost two decades, Western Kentucky University's Office of Institutional Research (WKU-IR) has used SAS^® to help shape the future of the institution by providing faculty and administrators with information they can use to make a difference in the lives of their students. This presentation provides specific examples of how WKU-IR has shaped the policies and practices of our institution and discusses how WKU-IR moved from a support unit to a key strategic partner. In addition, the presentation covers the following topics: How the WKU Office of Institutional Research developed over time; Why WKU abandoned reactive reporting for a more accurate, convenient system using SAS^® Enterprise Intelligence Suite for Education; How WKU shifted from investigating what happened to predicting outcomes using SAS^® Enterprise Miner^™ and SAS^® Text Miner; How the office keeps the system relevant and utilized by key decision makers; What the office has accomplished and key plans for the future.

When creating an OLAP cube, you have the option of specifying a drill-through table, also known as a Show Details table. This quick tip discusses the implications of using your detail table as your drill-through table and explores some viable alternatives.

PROC TABULATE is a powerful tool for creating tabular summary reports. Its advantages, over PROC REPORT, are that it requires less code, allows for more convenient table construction, and uses syntax that makes it easier to modify a table s structure. However, its inability to compute the sum, difference, product, and ratio of column sums has hindered its use in many circumstances. This paper illustrates and discusses some creative approaches and methods for overcoming these limitations, enabling users to produce needed reports and still enjoy the simplicity and convenience of PROC TABULATE. These methods and skills can have prominent applications in a variety of business intelligence and analytics fields.

The Department of Market Monitoring (DMM) at California ISO is responsible for promoting a robust, competitive, and nondiscriminatory electric power market in California by keeping a close watch on the efficiency and effectiveness of the ancillary service, congestion management, and real-time spot markets. We monitor the potential of market participants to exercise undue market power, the behavior of market participants that is consistent with attempts to exercise market power and the market performance that results from the interaction of market structure with participant behavior. In order to perform monitoring activities effectively, DMM collects available data, designs, and implement reporting dashboards that track key market metrics. We are using various SAS^® BI tools to develop and employ metrics and analytic tools applicable to market structure, participant behavior, and market performance. This paper provides details about the effective use of various SAS BI tools to implement an automated real time market monitoring functionality.

SAS^® OLAP technology is used to organize and present summarized data for business intelligence applications. It features flexible options for creating and storing aggregations to improve performance and brings a powerful multi-dimensional approach to querying data. This paper focuses on managing security features available to OLAP cubes through the combination of SAS metadata and MDX logic.

HTML5 has become the de facto standard for web applications. As a result, the lingua franca object notation of the web services that the web applications call has switched from XML to JSON. JSON is remarkably easy to parse in JavaScript, but so far SAS doesn't have any native JSON parsers. The Facebook Graph API dropped XML support a few years ago. This paper shows how we can parse the JSON in SAS by calling an external script, using PROC GROOVY to parse it inside of SAS, or by parsing the JSON manually with a DATA step. We'll extract the data from the Facebook Graph API and import it into an OLAP data mart to report and analyze a marketing campaign's effectiveness.