When I begin to invest my time reading a professional text, I wonder to what degree I can trust the material. I question whether it will be relevant for my challenge. And I hope that the author or authors have applied expertise that makes the pages in front of me worthy of my personal commitment. In a short number of short paragraphs I will address these questions, and describe how this book can best be leveraged.

I am a practicing data management executive, and I had the honor and privilege of leading the author and the contributors to this book through a very large-scale, extremely successful global data quality program design, implementation, and operation for one of the world's great financial services companies. The progressive topics of this book have been born from a powerful combination of academic/intellectual expertise and learning from applied business experience.

I have since moved from financial services to healthcare and am currently responsible for building an enterprise-wide data management program and capability for a global industry leader. I am benefiting greatly from the application of the techniques outlined in this book to positively affect the reliability, usability, accessibility, and relevance for my company's most important enterprise data assets. The foundation for this journey must be formed around a robust and appropriately pervasive data quality program.

Competing with High Quality Data chapter topics, such as how to construct a Data Quality Operating Model, can be raised to fully global levels, but can also provide meaningful lift at a departmental or data domain scale. The same holds true for utilizing Statistical Process Controls, ­Critical Data Element Identification and Prioritization, and the other valuable capability areas discussed in the book.

The subject areas also lead the reader from the basics of ­organizing an effort and creating relevance, all the way to utilizing sophisticated advanced techniques such as Data Quality Scorecards, Information ­System ­Testing, Statistical Data Tracing, and Developing Multivariate Diagnostic Systems. Experiencing this range of capability is not only important to accommodate readers with different levels of experience, but also because the data quality improvement journey will often need to start with rudimentary base level improvements that later need to be pressed forward into finer levels of tuning and precision.

You can have confidence in the author and the contributors. You can trust the techniques, the approaches, and the systematic design brought forth throughout this book. They work. And they can carry you from data quality program inception to pervasive and highly precise levels of execution.