Data Quality Magic

In previous posts I explained that, at least in regards to data quality, there are no magic beans, tooth fairies, or magic tricks.

In previous posts I explained that, at least in regards to data quality, there are no magic beans, tooth fairies, or magic tricks.

However, and before I am branded a Muggle, I want to assure you that magic does indeed exist in the world of data quality.

The common mistake is looking for data quality magic in the wrong places.  Historically, the quest begins with technology, and perhaps because of Clarke’s Third Law: “Any sufficiently advanced technology is indistinguishable from magic.”

Data quality tools are often believed to be magic, and especially by their salespeople.

But data quality tools are not magic.

The quest continues with methodology, and perhaps because of the Hedgehogian dream of a single, all-encompassing theory, which provides the certainty and control that comes from “just following the framework.”

Data quality methodologies are also often believed to be magic, and especially by our data perfectionists.

But data quality methodologies are not magic.

This is where the quest typically ends, after believing in magic technology and/or magic methodology both fail, but usually not from a lack of repeatedly trying—and repeatedly failing.

So if data quality magic doesn’t come from either technology or methodology, where does it come from?

In the 1988 movie Willow, the title character fails the test to become an apprentice of the village wizard.  The test was to choose which of the wizard’s fingers was the source of his magic—the correct answer was for Willow to choose his own finger.

Data quality magic comes from data quality magicians—from the People working on data quality initiatives, people who are united by trust and collaboration, guided by an adaptive methodology, and of course, enabled by advanced technology.

However, without question, the one and only source of Data Quality Magic comes from Data Quality People.