Book Image

The Data Warehouse Toolkit - Third Edition

By : Ralph Kimball, Margy Ross
5 (1)
Book Image

The Data Warehouse Toolkit - Third Edition

5 (1)
By: Ralph Kimball, Margy Ross

Overview of this book

The volume of data continues to grow as warehouses are populated with increasingly atomic data and updated with greater frequency. Dimensional modeling has become the most widely accepted approach for presenting information in data warehouse and business intelligence (DW/BI) systems. The goal of this book is to provide a one-stop shop for dimensional modeling techniques. The book is authored by Ralph Kimball and Margy Ross, known worldwide as educators, consultants, and influential thought leaders in data warehousing and business intelligence. The book begins with a primer on data warehousing, business intelligence, and dimensional modeling, and you’ll explore more than 75-dimensional modeling techniques and patterns. Then you’ll understand dimension tables in-depth to get a good grip on retailing and moved towards the topics of inventory. Moving ahead, you’ll learn how to use this book for procurement, order management, accounting, customer relationship management, and many more business sectors. By the end of this book, you’ll be able to gather all the essential knowledge, practices, and patterns for designing dimensional models.
Table of Contents (31 chapters)
Free Chapter
Title Page
About the Authors
End User License Agreement

Customer Dimension Attributes

The conformed customer dimension is a critical element for effective CRM. A well-maintained, well-deployed conformed customer dimension is the cornerstone of sound CRM analysis.

The customer dimension is typically the most challenging dimension for any DW/BI system. In a large organization, the customer dimension can be extremely deep (with many millions of rows), extremely wide (with dozens or even hundreds of attributes), and sometimes subject to rapid change. The biggest retailers, credit card companies, and government agencies have monster customer dimensions whose size exceeds 100 million rows. To further complicate matters, the customer dimension often represents an amalgamation of data from multiple internal and external source systems.

In this next section, we focus on numerous customer dimension design considerations. We’ll begin with name/address parsing and other common customer attributes, including coverage of dimension outriggers, and...