Designing A Data Warehouse : Supporting Customer Relationship Management
- Paperback | 352 pages
- 175.8 x 233.4 x 22.9mm | 726.57g
- 08 Jan 2001
- Pearson Education (US)
- Prentice Hall
- Upper Saddle River, United States
- w. figs.
Other books in this series
21 Jan 1993
20 Mar 2005
26 Aug 2002
Mixed media product
01 May 2003
01 Jul 2004
Back cover copy
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 book shows you exactly how to build one: Designing Data Warehouses by Dr. Chris Todman.
Table of contents
The Business Dimension. Business Goals. Business Strategy. The Value Proposition. Customer Relationship Management. Summary.
2. An Introduction to Data Warehousing.
Introduction. What Is a Data Warehouse? Dimensional Analysis. Building a Data Warehouse. Problems When Using Relational Databases. Summary.
3. Design Problems We Have to Face Up To.
Dimensional Data Models. What Works for CRM. Summary.
4. The Implications of Time in Data Warehousing.
The Role of Time. Problems Involving Time. Capturing Changes. First-Generation Solutions for Time. Variations on a Theme. Conclusions to the Review of First-Generation Methods.
5. The Conceptual Model.
Requirements of the Conceptual Model. The Identification of Changes to Data. Dot Modeling. Dot Modeling Workshops. Summary.
6. The Logical Model.
Logical Modeling. The Implementation of Retrospection. The Use of the Time Dimension. Logical Schema. Performance Considerations. Choosing a Solution. Frequency of Changed Data Capture. Constraints. Evaluation and Summary of the Logical Model.
7. The Physical Implementation.
The Data Warehouse Architecture. CRM Applications. Backup of the Data. Archival. Extraction and Load. Summary.
8. Business Justification.
The Incremental Approach. The Submission. Summary.
9. Managing the Project.
Introduction. What Are the Deliverables? What Assumptions and Risks Should I Include? What Sort of Team Do I Need? Summary.
10. Software Products.
Extraction, Transformation, and Loading. OLAP. Query Tools. Data Mining. Campaign Management. Personalization. Metadata Tools. Sorts.
11. The Future.
Temporal Databases (Temporal Extensions). OLAP Extensions to SQL. Active Decision Support. External Data. Unstructured Data. Search Agents. DSS Aware Applications.
Appendix A. Wine Club Temporal Classifications.
Appendix B. Dot Model for the Wine Club.
Appendix C. Logical Model for the Wine Club.
Appendix D. Customer Attributes.
About Chris Todman