FOREWORD By Thomas H. Davenport

There is little doubt that the importance of data to our economy and society has increased markedly over the last couple of decades. We now buy many things on the basis of data, treat medical conditions by data, run our financial system on data, socialize on data, and even run much of our government on data. Products and services increasingly revolve around data. We are so dependent upon data that we agonize about how much of our personal and organizational data leaks out to those who shouldn’t have it.

As a result, interest in how to capitalize on data is at an all-time high. There are many books, conferences, and consulting practices on how to manage “big data” or create analytics from it. Vendors and their customers are increasingly focused on making sense of data, formulating predictions from it, and even converting it into automated recommendations and decisions. In short, dealing with “all things data” is a hallmark of the contemporary era.

Well, not all things. As Tom Redman points out, we seem to have left the topic of data quality behind. Most organizations have made little progress on this issue over the past couple of decades, and confront the problems it creates at every turn. It is as if a sports team devoted all of its energies and focus to offense—scoring points with data—with no orientation to defense, or preventing data-oriented problems. Redman admits that data quality may not be the sexiest aspect of data, but it is certainly among the most important. Without it, the transactions, analyses, recommendations, and decisions made on data are of little value.

Part of the value of this book, then, is simply convincing non-IT professionals of the value of data quality. Redman does that in a variety of ways in the early chapters, from example to metaphor. For many readers who are senior executives, this is worth the price of admission. They can decide that the problem is important enough to do something about it, and commission a concerted effort to address data quality.

Other readers, however, will be the ones charged with doing something about data quality, and they’re in good hands as well with this book. Redman resists the common tendency to reduce data quality—or almost any data management problem—to an engineering exercise. Companies have been trying to engineer—draw diagrams, create abstract models, establish policies—their way around data problems for up to thirty or forty years by now. Few have anything to show for it.

Instead of engineering, Redman realized in his data quality work with companies that people were both the cause of and the solution to data quality problems. Instead of advocating for abstract data architectures, he argues for very tangible organizational architectures. Since he knows that data quality issues are inextricably bound with business processes and organizational structures, he also knows that to address those problems, you have to work with the people who own those processes and structures.

You may have already guessed that this is not simply an IT problem. Redman makes clear from the beginning of the book that data quality is an overall business problem, and can’t be delegated to the IT organization. Since they don’t own the relevant processes and structures, they are generally neither responsible for creating data quality problems nor able to fix them. There are, of course, technologies that can assist with identifying and solving data quality issues, but their power pales in comparison to the human capabilities in organizations.

It’s time to stop reading this Foreword and jump into the real book. Do not hesitate; Redman makes it easy to engage with the issue. There is nothing that will prove too complex or technical for an adult person with a modicum of experience to understand. In other words, you have no excuses not to delve into this book and the issue of data quality in general. You and your organization will both be glad that you did.

Thomas H. Davenport

President’s Distinguished Professor of IT and Management, Babson College

Research Fellow, MIT Initiative on the Digital Economy

Senior Advisor, Deloitte Analytics

Author of Competing on Analytics, Big Data @ Work, and Only Humans Need Apply