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
1
Cover
2
Title Page
3
Copyright
4
About the Authors
5
Credits
6
Acknowledgements
29
Index
30
Advertisement
31
End User License Agreement

14
Healthcare

The healthcare industry is undergoing tremendous change as it seeks to both improve patient outcomes, while simultaneously improving operational efficiencies. The challenges are plentiful as organizations attempt to integrate their clinical and administrative information. Healthcare data presents several interesting dimensional design patterns that we’ll explore in this chapter.

Chapter 14 discusses the following concepts:

  • Example bus matrix snippet for a healthcare organization
  • Accumulating snapshot fact table to handle the claims billing and payment pipeline
  • Dimension role playing for multiple dates and physicians
  • Multivalued dimensions, such as patient diagnoses
  • Supertype and subtype handling of healthcare charges
  • Treatment of textual comments
  • Measurement type dimension for sparse, heterogeneous measurements
  • Handling of images with dimensional schemas
  • Facility/equipment inventory utilization as transactions and periodic snapshots