Five data quality archetypes, part 2

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This is second post in a two-part series. In the first post, I covered three types of employees with data quality issues: the Ignorant, the Aloof, and the Skeptical. Now it's time to address the other two.

The Paranoid

At the other end of the spectrum are the Paranoid. These people won't do anything because of potential data quality ramifications, even if the risks far exceed their rewards.

Back in 2006, I was on project similar to the one I described above. This was a very contentious implementation replete with nasty internal politics and a consulting firm that promised the world. (It ultimately became a full-blown case study in Why New Systems Fail.)

One of the major thorns in my side was a finance director named Nick (not his real name). Nick had marching orders to delay the implementation to the following year. This wouldn't have been a major problem if we were slated to go live in December. This, however, was not the case. We were trying to activate the new system in July. Nick had to push the project a full six months—hardly an easy task when a team of consultants is on the ground to the tune of $50,000/month.

Given Nick's agenda, it wasn't surprising that he routinely used every conversion error as his clarion call for ceasing all work. When we'd convert employee job, benefits, and payroll history, for example, we'd usually hit at least 99 percent accuracy. Our ETL routines were pretty tight. The hospital's data was ten times purer coming out of its legacy system! Despite that success rate, Nick would loudly ask, "How do I know that something else isn't wrong?"

It was a lame argument, yet it found significant traction because the project manager was a newbie who had never run anything like this before. Ever. Ultimately, Nick won—sort of. He was able to push the project's go-live date to November.

The Justly Concerned

Finally, there are those who appreciate the data quality consequences of their actions—and others. At the same time, though, they bring a modicum of perspective to data quality-related issues. Sure, they want to preserve the integrity of enterprise data. That's a given. At the same time, though, they understand that perfect is the enemy of good. They're in touch with their inner Voltaire.

Put differently, new technologies always present new data quality challenges. If an organization remains inert because of the remote possibility of an errant, duplicate, or corrupt record, then it will never adopt important new technologies.

Simon Says

Understanding the differences among groups is a starting point to converting them into the Justly Concerned.

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Want more information on how to maximize your data quality efforts? Download this eBook from TDWI, "Data Quality Challenges and Priorities."

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About Author

Phil Simon

Author, Speaker, and Professor

Phil Simon is a keynote speaker and recognized technology expert. He is the award-winning author of eight management books, most recently Analytics: The Agile Way. His ninth will be Slack For Dummies (April, 2020, Wiley) He consults organizations on matters related to strategy, data, analytics, and technology. His contributions have appeared in The Harvard Business Review, CNN, Wired, The New York Times, and many other sites. He teaches information systems and analytics at Arizona State University's W. P. Carey School of Business. He also runs 5marbles, an Agile software-development shop.

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