If you and your organization want to go beyond just talking about data as one of your most valuable assets, Danette lays out clearly how to begin treating data like one-offering the most robust, comprehensive approach to data quality found anywhere. Her years of expertise pack this book with a practical, structured methodology and necessary guidance to help any organization achieve the level of data quality necessary to thrive in the Information Age. --Douglas B. Laney, data and analytics strategist and author of Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage The need for high-quality data has never been greater! Managers need to guide their organizations, employees need to do their work, and we all need to take care of our families. All much harder in the face of a global pandemic and its consequences. Data could be our best, most powerful weapon. McGilvray's Ten Steps is a proven guide to help attack the underlying issues. I've been a big fan, for a long-time, of the first edition of Executing Data Quality Projects. The second edition features terrific updates to help people and teams tackle the really important problems. --Tom Redman, the Data Doc, Data Quality Solutions Great books do not sit on your shelf, pristine and beautiful, without so much as a crease in them. The best books occupy precious desk space, dog-eared and highlighted. By this standard, Danette McGilvray's book, Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (TM), will be absolutely ravaged, and never more than arms-length away. The power of the content and techniques she has brought into one volume is a testament to the book itself: by applying the principles covered inside, the author has assembled a collection of knowledge and tools to help readers at every point in their data quality journey. This is not a book you will read once and put on a shelf -- this will be a faithful companion guiding you daily. --Anthony J. Algmin, Founder, Algmin Data Leadership Within my field of expertise, computer security, I hadn't had much exposure to the concept of Data Quality. Now that I've been introduced to it, however, I am convinced that data quality is essential to computer security and that security professionals will never successfully defend systems until they incorporate it into their practice. To get started, I recommend reading McGilvray's book Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (TM). I literally tell people that this book changed my (professional) life. Not only did it do a great job of teaching core data quality concepts in a way that even a newbie like myself could understand, digest, and apply, but the Ten Steps themselves, the real meat of the book, are amazingly actionable. The overwhelming emphasis on practicality and contextualization creates a framework that can be used in almost every possible environment to improve an organization's data quality. --Seth James Nielson, PhD, Founder and Chief Scientist, Crimson Vista, Inc There is nothing better than learning from a practitioner. An architect can consider a design, draw a blueprint and write a book about how wonderful their buildings meet human needs. But they may never hit a nail with a hammer. But when someone writes, not only from what they know but what they have done, now you have something. The second edition of Danette's data quality book fits that description. Not only did Danette write a great book on data quality in 2008, she learned more, made changes, evolved, and then decided to do it again. The second edition is just as important and excellent as the first. It is required reading for a data practitioner and needs to be on your bookshelf - and put to use. --John Ladley, Data Thought Leader and Practitioner, Consultant and Mentor for Business and Data Leaders I've known the Ten Steps and Danette for 10 years. Through the decade, many data practitioners in China apply the method to real data quality and data governance projects and programs. By doing so, the organizations benefit from higher data quality. The Ten-Steps Process itself has evolved and I believe more data, more people and more organizations will get more value from the deep thought and experience embedded in this book. The legacy of this book to the data community cannot be overstated. --Chen Liu, CEO of DGWorkshop (???) If we don't recognize that we are living in a knowledge economy in which data has significant value, it's time we did. Yet, further research by the University of South Australia and Experience Matters on three continents shows irrefutably that data is not managed well. Amongst many other findings, its value and benefits aren't measured. Boards and executives don't understand why information assets are important and, unlike financial assets, nobody is held truly accountable for their management. Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (TM) is a knowledgeable and pragmatic guide to managing data well. I highly recommend it for anyone who wants to make money out of their data and / or improve their service delivery. And that will be the vast majority of people reading this. --James Price, Managing Director, Experience Matters When Danette said that she was working on a second edition of Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (TM), my first thought was, Why? The first edition was so good, and the process is sound. But I am pleased to say that the second edition is even better. In the second edition, McGilvray has clarified and updated the process steps and supporting templates and incorporated valuable examples and case studies (The Ten Steps in Action), while accounting for the evolution of technology and data production over the past decade. The presentation is clear and crisp. People who are new to data quality management should read this book cover to cover. Experienced practitioners should have it on their desks at all times for reference. --Laura Sebastian-Coleman, Author, Measuring Data Quality for Ongoing Improvement
