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

Facility/Equipment Inventory Utilization

In addition to financial and clinical data, healthcare organizations are also keenly interested in more operationally oriented metrics, such as utilization and availability of their assets, whether referring to patient beds or surgical operating theatres. In Chapter 4, we discussed product inventory data as transaction events as well as periodic snapshots. Facility or equipment inventories in a healthcare organization can be handled similarly.

For example, you can envision a bed utilization periodic snapshot with every bed’s status at regularly recurring points in time, perhaps at midnight, the start of every shift, or even more frequently throughout the day. In addition to a snapshot date and potentially time-of-day, this factless fact table would include foreign keys to identify the patient, attending physician, and perhaps an assigned nurse on duty.

Conversely, you can imagine treating the bed inventory data as a transaction fact table...