Course Description Once a data warehouse is providing data access and tools that are satisfying analytical queries are in production, what do you do next? When patterns are seen, relationships are uncovered, causality is quantified, inferences are drawn, how is the information leveraged in the enterprise quickly enough to gain or maintain an advantage? What turns insight into value?

The real return on the data warehouse investment isn’t realized until you can “close the loop” in the analytical process. Unlike traditional computing architectures, closing the loop requires a specialized architecture, one that promotes exploration, creativity and organizational learning. That is what we call the Extended Decision Support Architecture.

This session will use case sudies to examine proven methods employed by data warehouse early adopters to drive the data warehousing effort to extraordinary results. Among the strategies and tools covered will be:

1. Looking over the horizon with agents and including the propagation of timely analysis
2. How to identify candidate subject areas, based on available resources
3. How to find and implement organizational catalysts
4. Tracking progress to measure the results of actions taken, thereby “closing the loop”
5. How to fine-tune the analytical process through organizational learning

Unless your focus is purely technical, and we hope it isn’t, you can benefit from this course by learning about successful efforts to integrate tools, learning and action. No technical background is necessary. It is assumed that the attendee is familiar with the basic concepts of data warehousing
 
Agenda Neil Raden
•  How people work, what they need to be successful, and how data warehouses can support those needs
•  Problems that data warehouses can (and can’t) solve
•  The architecture needed to be effective
•  Case studies of some smart companies that learned how to get    fantastic leverage from their data warehouse efforts
•  And, as usual, we’ll lampoon some well-known ideas, products and approaches just for fun

Creating a decision support architecture
•  First Principles: “adequate” information systems; improvement, not control; learning; performance is everything
•  What the architecture is designed to do
•  How to create quick and enduring value

Case Study #1: Pricing – How to make your company really rich!
•  Brief overview of pricing economics
•  Finding the selling price
•  The “loop” in pricing: perceived value creation process
•  The application in detail
•  A look at profitability: an activity-based method, using relevance and quality, to find the real bottom line
 

 Case Study #2: Cosmetics/Fragrance “Sell Through”
Subject area: fresh weekly sales by item by store, reported by Monday morning of the following week

Architectural blueprint for “closing the loop” to:
•  Predict actual demand at retail
•  Fine-tune sales efforts
•  Optimize promotions
•  Launch new products
•  Switch to auto replenishment
•  Promote organizational learning

Integrating Tools in the Extended Decision Support Architecture
•  The closed loop is still a difficult systems integration job
•  Using agents, alerts and triggers for maximum leverage
•  Web provides a favorable economic model for expanding the audience
•  Using “groupware” drive the decision support value chain (what about Domino, Exchange, et.al.?)
•  Evaluating the fit of technologies: the Organizational Impact Analysis

 is an active practitioner in data warehousing projects, a riveting speaker and frequent contributor to books, magazines, journals and conferences. He is one of the most well-known authorities in the world on decision support, data warehousing, and the intersection of technology and management. Neil is the President and founder of Archer Decision Sciences, an international consulting organization that provides management consulting.