Improving Data Warehouse and Business Information Quality: Methods for Reducing Costs and Increasing Profits (Paperback)

by Larry P. English (Author) "In this chapter I describe the reason why an organization is-or should be-interested in information quality..." (more)
Key Phrases: information architecture quality, information quality problems, information product improvement, New York, Out of the Crisis, Quality Point (more...)

Editorial Reviews

Amazon.com Review
The premise of Improving Data Warehouse and Business Information Quality is that the quality of information stored in a database is just as measurable as the quality of the cars that come off an assembly line. Furthermore, database managers can take steps to ensure that their databases collect the best possible information.

This is a dense book, loaded with management jargon, statistical analysis, and complicated flow diagrams. You won't succeed in skimming casually through it, and you will probably get more out of the book if you have some experience with quantitative management techniques.

Regardless, this book makes excellent reading for those taking the holistic approach to database design, in which a good database considers where the information comes from, how it is used, and what results come from that use. English covers some methods for extracting information from various sources--through surveys and other methods--before launching into an elaborate discussion of information-quality metrics. --David Wall

Product Description
A comprehensive guide to quality improvement from the leading expert in information and data warehouse quality.

Each year, companies lose millions as a result of inaccurate and missing data in their operational databases. This in turn corrupts data warehouses, causing them to fail. With information quality improvement and control systems, like the ones described in this book, your company can reduce costs and increase profits from quality information assets. Written by an internationally recognized expert in information quality improvement, Improving Data Warehouse and Business Information Quality arms you with a comprehensive set of tools and techniques for ensuring data quality both in source databases and the data warehouse. With the help of best-practices case studies, Larry English fills you in on:
* How and when to measure information quality.
* How to measure the business costs of poor quality information.
* How to select the right information quality tools for your environment.
* How to reengineer and cleanse data to improve the information product before it reaches your data warehouse.
* How to improve the information creation processes at the source.
* How to build quality controls into data warehouse processes.

AUTHORBIO: Larry P. English is the leading international expert in the field of information and data warehouse quality. He is a columnist for Data Management Review and a featured speaker at numerous Data Warehousing Conferences. Larry chairs Information Quality Conferences held around the world.

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Product Details

• Paperback: 544 pages
• Publisher: Wiley; 1 edition (March 11, 1999)
• Language: English
• ISBN-10: 0471253839
• ISBN-13: 978-0471253839
• Product Dimensions: 9.1 x 7.4 x 1.3 inches
• Shipping Weight: 1.8 pounds (View shipping rates and policies)
• Average Customer Review: 4.6 out of 5 stars See all reviews (17 customer reviews)
• Amazon.com Sales Rank: #71,085 in Books (See Bestsellers in Books)

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  #13 in	Books > Computers & Internet > Computer Science > Software Engineering > Methodology
  #22 in	Books > Professional & Technical > Engineering > Industrial, Manufacturing & Operational Systems > Quality Control
  #31 in	Books > Computers & Internet > Databases > Data Warehousing

Customer Reviews

Most Helpful Customer Reviews

10 of 10 people found the following review helpful:

5.0 out of 5 stars Excellent ideas for implementing a data quality program, July 21, 1999

By	[e-mail] (Austin, Texas) - See all my reviews

The book is fantastic. English obviously has plenty of front line experience. He doesn't simply state the problem and offer suggestions. He empowers the reader to join the data quality movements by giving them the tools necessary to convince the decision makers that data quality is worth the investment. Chapter Seven, "Measuring Nonquality Information Costs," is worth the price of the book alone, because it gives us a solid ROI model to throw at the bean counters. The writing style is extremely accessable. The book is so well organized that it can be read straight through or employed as a reference.

13 of 14 people found the following review helpful:

5.0 out of 5 stars An important and unique work, February 25, 2001

By	Mike Tarrani "www.tarrani.com" (Deltona, FL USA) - See all my reviews (TOP 500 REVIEWER)    (REAL NAME)    (COMMUNITY FORUM 04)

This is an important and unique work that addresses a big problem: data quality. Why is this a problem? Data warehouses are proliferating at a dizzying rate. Since data warehouses are fed by production databases, many of which are legacy systems, the poor quality of existing data quickly becomes [painfully] apparent. I spent the last half of 2000 bringing data warehouses into production and can attest to this sorry fact. However, the author drives home this point in chapter 1, titled "High Costs of Low-Quality Data" by giving nearly three pages of eye-opening examples from real life. This alone should inspire anyone responsible for data integrity or quality, or who uses data to carefully read this book.

The big question is "what is quality"? Specifically, "what is information quality"? Answers to these basic questions are given early in the book, and sets the tone for what follows. The foundation of data quality is carefully built by how the author applies quality principles to information, which segues into a chapter on improving information quality. It quickly becomes obvious that Mr. English is a Deming fan - although I am more in the Juran camp, I like the way that the author places data and information quality into a recognizable framework.

Things get interesting in the chapters on assessing data and information quality. The two chapters devoted to this subject are strengthened by the chapter on measuring the costs of non quality. This is a great foundation for a business case for data and information quality improvement, which can be expensive.

The rest of the book is a step-by-step approach to getting data quality under control using data reengineering and cleansing; proactive measures for data defect prevention, and how to establish an information quality environment.

Although I found every chapter to be both informative and thought provoking, I particularly liked the concept of information stewardship (this goes far in aligning IT and business, and places roles and responsibilities where they belong), and the chapter on implementing a quality improvement environment. This is especially valuable because it clearly outlines the critical success factors and steps needed to get there.

Who should read this book? Obviously DBAs, data architects and anyone else responsible for designing and implementing data warehouses. It should also be read by key business process owners because they, after all, own the data (or should) and depend on it as the basis for information. In fact, Mr. English's approach and writing make this book highly accessible to non-technical readers, which is probably the book's most valuable aspect. I personally believe that this book is the best on the subject and strongly recommend it.

8 of 8 people found the following review helpful:

4.0 out of 5 stars Deming for data, October 1, 2004

By	Gary Sprandel (Frankfort, Kentucky) - See all my reviews (REAL NAME)

While providing some of the traditional quality assessment measures, Larry English provides a Deming 14-points approach to information quality and continuous data quality improvement. For example, instead or rewarding those who find major quality problems, change the culture to provide quality early in the process. His chapter on "assessing data definition" quality is an important step often neglected. For example, some of us are may be using minimal metadata (perhaps federally mandated standards) that are inadequate for true enterprise wide data definition. The examples included in the book (particularly in "High costs of low quality data") are instructive, and show how someone saturated with thinking about quality (like Larry English), views such simple things as getting a fax at a hotel. If you are planning a data warehouse, this book might fit nicely into the "Enterprise Infrastructure Evaluation" phase in Moss and Atre's "Business Intelligence Roadmap" terminology.

I would have liked more specific methods of detecting low quality in the section on information quality assessment. The final third of the book, on establishing the information quality environment, provides good direction, but seems too optimistic. How does a single database analyst change a corporate culture and how does a small warehouse group influence the quality processes of hundreds of diverse data sources? This is a good, thought-provoking book.