From sasCommunity

Chair: MaryAnne Hope

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It is imperative in today’s business world for an organization to be prepared to implement a wide variety of strategies in order to address critical business issues with evidence-based information. Business Analytics encompasses data integration, analysis, data mining, and reporting to provide business solutions using a complete portfolio in the business intelligence domain. The portfolio encompasses applications and practices for the collection and presentation of analytical business information. This section will include a variety of presentations that will discuss SAS analytic capabilities, including statistics, predictive analytics, data and text mining, forecasting and optimization as well as the technology to provide a flexible presentation layer, implement an IT environment, and promote self-service reporting.

Suggested topics include:

• Fundamentals of Business Analytics and Intelligence.
• Query, Analysis and Reporting – providing quick and meaningful answers to important business questions.
• Data Integration and Self-service – serve out ready to use analytic ‘sandboxes’.
• Implementation of Security in the BI environment- who should have access to what.
• Use of Online Analytical Processing (OLAP) – data summarization and presentation.
• Integrated Analytics – use of statistics, data mining, and optimization, predictive modeling techniques to analyze and interpret data.
• Visualization – presenting key results and data trends in an intuitive manner.
• Enterprise Dashboards – depicting concise, meaningful results.
• Microsoft Office Integration.

SAS Global Forum 2010 Paper Topics Submissions

• To post an idea for a paper, click: Here is my idea

Your posting will be listed on this page.

• To add your comments to an existing idea, click on the Edit link beside the topic

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The following subsections list previously suggested topics:

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feedback requested - idea: Calculating Prepayment Rates in an OLAP solution

The idea I have been tossing around is sharing an approach to calculating loan prepayment rates in an OLAP solution, which was implemented a few years ago. It would include the purpose, general approach, the technical details, and lessons learned. Is this anything that would be of interest, or that may be useful for this section? Any/all feedback is welcome.

--Stefanie G. Reay