The Once and Future Data Quality Expert

Wednesday, November 11 is World Quality Day 2009.

Contents

IAIDQ IAIDQ Data Quality Expert The Times They Are a-Changin’Share Your Perspectives Related Posts Additional IAIDQ Links

World Quality Day was established by the United Nations in 1990 as a focal point for the quality management profession and as a celebration of the contribution that quality makes to the growth and prosperity of nations and organizations. The goal of World Quality Day is to raise awareness of how quality approaches (including data quality best practices) can have a tangible effect on business success, as well as contribute towards world-wide economic prosperity.

IAIDQ

The International Association for Information and Data Quality (IAIDQ) was chartered in January 2004 and is a not-for-profit, vendor-neutral professional association whose purpose is to create a world-wide community of people who desire to reduce the high costs of low quality information and data by applying sound quality management principles to the processes that create, maintain and deliver data and information.

Wednesday, November 11 is World Quality Day 2009.

IAIDQ

Since 2007 the IAIDQ has celebrated World Quality Day as a springboard for improvement and a celebration of successes. Please join us to celebrate World Quality Day by participating in our interactive webinar in which the Board of Directors of the IAIDQ will share with you stories and experiences to promote data quality improvements within your organization.

In my recent Data Quality Pro article The Future of Information and Data Quality, I reported on the IAIDQ Ask The Expert Webinar with co-founders Larry English and Tom Redman, two of the industry pioneers for data quality and two of the most well-known data quality experts.

Data Quality Expert

As World Quality Day 2009 approaches, my personal reflections are focused on what the title data quality expert has meant in the past, what it means today, and most important, what it will mean in the future.

With over 15 years of professional services and application development experience, I consider myself to be a data quality expert. However, my experience is paltry by comparison to English, Redman, and other industry luminaries such as David Loshin, to use one additional example from many.

Experience is popularly believed to be the path that separates knowledge from wisdom, which is usually accepted as another way of defining expertise.

Oscar Wilde once wrote that “experience is simply the name we give our mistakes.” I agree. I have found that the sooner I can recognize my mistakes, the sooner I can learn from the lessons they provide, and hopefully prevent myself from making the same mistakes again.

The key is early detection. As I gain experience, I gain an improved ability to more quickly recognize my mistakes and thereby expedite the learning process.

James Joyce wrote that “mistakes are the portals of discovery” and T.S. Eliot wrote that “we must not cease from exploration and the end of all our exploring will be to arrive where we began and to know the place for the first time.”

What I find in the wisdom of these sages is the need to acknowledge the favor our faults do for us. Therefore, although experience is the path that separates knowledge from wisdom, the true wisdom of experience is the wisdom of failure.

As Jonah Lehrer explained: “Becoming an expert just takes time and practice. Once you have developed expertise in a particular area, you have made the requisite mistakes.”

But expertise in any discipline is more than simply an accumulation of mistakes and birthdays. And expertise is not a static state that once achieved, allows you to simply rest on your laurels.

In addition to my real-world experience working on data quality initiatives for my clients, I also read all of the latest books, articles, whitepapers, and blogs, as well as attend as many conferences as possible.

The Times They Are a-Changin’

Much of the discussion that I have heard regarding the future of the data quality profession has been focused on the need for the increased maturity of both practitioners and organizations. Although I do not dispute this need, I am concerned about the apparent lack of attention being paid to how fast the world around us is changing.

Rapid advancements in technology, coupled with the meteoric rise of the Internet and social media (blogs, wikis, Twitter, Facebook, LinkedIn, etc.) has created an amazing medium that is enabling people separated by vast distances and disparate cultures to come together, communicate, and collaborate in ways few would have thought possible just a few decades ago.

I don’t believe that it is an exaggeration to state that we are now living in an age where the contrast between the recent past and the near future is greater than perhaps it has ever been in human history. This brave new world has such people and technology in it, that practically every new day brings the possibility of another quantum leap forward.

Although it has been argued by some that the core principles of data quality management are timeless, I must express my doubt. The daunting challenges of dramatically increasing data volumes and the unrelenting progress of cloud computing, software as a service (SaaS), and mobile computing architectures, would appear to be racing toward a high-speed collision with our time-tested (but time-consuming to implement properly) data quality management principles.

The times they are indeed changing and I believe we must stop using terms like Six Sigma and Kaizen as if they were a shibboleth. If these or any other disciplines are to remain relevant, then we must honestly assess them in the harsh and unforgiving light of our brave new world that is seemingly changing faster than the speed of light.

Expertise is not static. Wisdom is not timeless. The only constant is change. For the data quality profession to truly mature, our guiding principles must change with the times, or be relegated to a past that is all too quickly becoming distant.

In celebration of World Quality Day, please share your perspectives regarding the past, present, and most important, the future of the data quality profession. With apologies to T. H. White, I declare this debate to be about the difference between: