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

Cleaning and Conforming Data

Cleaning and conforming data are critical ETL system tasks. These are the steps where the ETL system adds value to the data. The other activities, extracting and delivering data, are obviously necessary, but they simply move and load the data. The cleaning and conforming subsystems actually change data and enhance its value to the organization. In addition, these subsystems can be architected to create metadata used to diagnosis what’s wrong with the source systems. Such diagnoses can eventually lead to business process reengineering initiatives to address the root causes of dirty data and improve data quality over time.

Improving Data Quality Culture and Processes

It is tempting to blame the original data source for any and all errors that appear downstream. If only the data entry clerks were more careful! We are only slightly more forgiving of keyboard-challenged salespeople who enter customer and product information into their order forms. Perhaps...