Designing A Data Warehouse: Supporting Customer Relationship Management

• A complete methodology for building CRM-focused data warehouses
• Planning, ROI, conceptual and logical models, physical implementation, project management, and beyond
• For database developers, architects, consultants, project managers, and decision-makers

Today's next-generation data warehouses are being built with a clear goal: to maximize the power of Customer Relationship Management. To make CRM-focused data warehousing work, you need new techniques, and new methodologies. In this book, Dr. Chris Todman--one of the world's leading data warehouse consultants--delivers the first start-to-finish methodology for defining, designing, and implementing CRM-focused data warehouses. Todman covers all this, and more:

• Critical design challenges unique to CRM-focused data warehousing
• A new look at data warehouse conceptual models, logical models, and physical implementation
• The crucial implications of time in data warehouse modeling and querying
• Project management: deliverables, assumptions, risks, and team-building--including a full breakdown of work
• Estimating the ROI of CRM-focused data warehouses up front
• Choosing software for loading, extraction, transformation, querying, data mining, campaign management, personalization, and metadata
• DW futures: temporal databases, OLAP SQL extensions, active decision support, integrating external and unstructured data, search agents, and more

If you want to leverage the full power of your CRM system, you need a data warehouse designed for the purpose. One bookshows you exactly how to build one: "Designing Data Warehouses" by Dr. Chris Todman.

There is no point any longer in just building a huge database and allowing people to query it. It has to be fully integrated into the organization and, most importantly, it must be customer centric in design. It has to support the campaign management system and the customer care system in the organization. Traditional methods for designing data warehouses do not produce a customer centric model. This is in large part why 70% of data warehouse projects are deemed to have failed; they did not deliver sufficient business benefit to justify the investment (according to a research project in the U.S. by Kaisha).

Data modeling is a key area in the development of a customer centric solution and so is the representation of time. That is: just how is the customer's detailed history properly represented so that we can undertake proper predictive analysis to, for instance, try to determine which customers are likely to churn. Having identified the issues, this book lays out a solution in the form of a complete methodology.