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

Electronic Medical Records

Many healthcare organizations are moving from paper-based processes to electronic medical records. In the United States, federally mandated quality goals to support improved population health management may be achievable only with their adoption. Healthcare providers are aggressively implementing electronic health record systems; the movement is significantly impacting healthcare DW /BI initiatives.

Electronic medical records can present challenges for data warehouse environments because of their extreme variability and potentially extreme volumes. Patients’ medical record data comes in many different forms, ranging from numeric data to freeform text comments entered by a healthcare professional to images and photographs. We’ll further discuss unstructured data in Chapter 21: Big Data Analytics; electronic medical and/or health records may become a classic use case for big data. One thing is certain. The amount and variability of electronic data...