Providing Scalable, Cost Saving Solutions to Improve Conditional Awareness of Your Enterprise Wide Assets.
August 17, 2008
Jeremy Wilson Attends the TDWI World Conference August 17 - 22, 2008
ESRG attended 3 days of The Data Warehouse Institute’s (TDWI) Business Intelligence Conference. The conference is one of many ways in which ESRG remains current with the latest industry best practices and products for use in integrating Business Intelligence processes and tools with ESRG Products and Process. The following are the specific topics of interest which ESRG participated in:
• A Systems-Thinking Approach to Business Analytics
Many of today’s BI programs focus intensely on analytics. The business wants scorecards, dashboards, and analytic applications, and the technology to deliver them is mature. Still, many IT organizations struggle to deliver analytics, and the results frequently fail to meet expectations. The problem, it seems, lies in requirements gathering—a more difficult and complex task for business analytics than for simple reporting. This course describes how the models and methods of systems thinking meet the challenges of analytic requirements.
• Business Intelligence Roadmap: The Complete Lifecycle for Decision-Support Applications
The content of the methodology is presented as a framework of 16 development steps. Each development step begins with a list of things to consider, then highlights the major activities, and concludes with deliverables, roles, and responsibilities of project team members.
• Predictive Analytics: A Business Perspective
Traditionally, organizations use data tactically—to manage operations. For a competitive edge, leading organizations use data strategically—to expand the business, to improve profitability, to reduce costs, and to market more effectively. The mining of data for predictive indicators creates information assets that an organization can leverage to achieve these strategic objectives. Predictive analytics is a new component in an enterprise’s decision-support system (DSS) architecture. It complements and interlocks with other ‘retrospective’ DSS capabilities.
• Predictive Analytics: Making It Work
Typically, organizations approach analytics from a technology perspective. Analytical tools receive a great deal of attention for their features and capabilities. This course illustrates the importance of an appropriate conceptual approach to predictive analytics, and the critical role of data handling on performance. Unlike OLAP, predictive analytics focuses on group behavior, probabilistic expectations, and low-incidence/high-impact occurrences.
