Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information

by Danette McGilvray

Executing Data Quality Ten Steps to Quality Data and Trusted Information (TM) presents a systematic, proven approach to improving and creating data and information quality within the enterprise. Recent studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. This book describes a Ten Step approach that combines a conceptual framework for understanding information quality with the tools, techniques, and instructions for improving and creating information quality. It includes numerous templates, detailed examples, and practical advice for executing every step of the approach. It allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices. The author's trademarked approach, in which she has trained Fortune 500 clients and hundreds of workshop attendees, applies to all types of data and all types of organizations.

Genres: Nonfiction

352 pages, Paperback

First published January 1, 2008

Reviews

[Steven De Landtsheer · Rating 4 out of 5]

I bought this book as I was tackling a new assignment focused on determining data quality in several business systems which had not been addressed before. Although I had extensive experience in both project management and handling business data, I felt a novice in a data quality project.
This book therefore addressed a central need I had, which was to provide a framework for how I could best tackle this challenge. I must say that I feel that the author Danette McGilvray delivers on her goal: ten steps to quality data and trusted information.
It is the strength and to a certain degree the weakness of the book. This is more a reference work than an actual book to read from cover to cover. So while I started first with reading this book and the framework provided great help, I turned to other books for more in depth information and understanding. This is of course more proof of the book delivering on it's promise. The reason I did not give a five star rating is that I find the detailed content underpinning the ten steps sometimes rather vague and therefore not that helpfull. That is the catch of course: where do you start and where do you stop? I think the book would gain by referencing more other works and expertise instead of trying to capture an important activity in just two very general pages lacking the necessary detail.
Overall a very good book and very usefull to keep as a reference work that you can quickly review.

[Mirjam · Rating 4 out of 5]

Een uitstekend boek als eerste stap in DQ-land. Het helpt je praktisch op weg zonder al te veel theoretische ballast. De templates zijn helder en niet al te uitgebreid, zodat je ze kan implementeren zonder meteen bureaucratisch genoemd te worden.

[Edafe · Rating 4 out of 5]

A book that keeps on giving.

I bought this 3 years ago when I began on a master data management project and it is still helping me wrangle data strategy and data quality projects today. Based on the information as an asset, a currency within organisations, this book closely ties business motivations to technical tasks. Content is clear, well-written and in plain English. Each step covers a description, business benefit/context and approach, alongside useful examples and templates. An essential addition to your data toolkit.

[Zhongchen Zhou]

reading on Chapter 3: The Ten Steps Process