Just kidding—I would never reveal a superhero’s secret identity.
Although I was never a big fan of the book, the title of this blog post is inspired by The Scarlet Letter by Nathaniel Hawthorne, where the novel’s protagonist Hester Prynne is forced to wear The Scarlet Letter A as a badge of shame for committing the act of adultery, which lead to the birth of her daughter Pearl.
The book came to mind while I was reading the commendable comments received last week on The Poor Data Quality Jar, where a recurring theme was the valid criticism of the “public humiliation” aspect of having employees put money into the jar when they contribute to the organization's poor data quality.
Using such an approach to help organizations illustrate the costs of poor data quality is equivalent to making the offenders wear The Scarlet DQ as a badge of shame, which will only make it far more likely that data quality issues will not be reported at all.
But even without my “swear jar” inspired idea, I think that the fear of public humiliation is what prevents poor data quality from being acknowledged by many organizations, which often leads to a major data quality related crisis that “no one saw coming.”
For example, if you are in need of some quiet time alone for taking a good power nap in a conference room, then try scheduling a meeting to discuss known data quality issues and their root causes. If your organization is like most, then you could probably book one of those really nice conference rooms with the big comfy reclining chairs—because nobody will attend your meeting.
Data quality can be somewhat of a taboo topic. Many organization assume that their data quality must be “good enough” otherwise “how could we possibly still be in business?” Nobody likes to talk about data quality for one simple reason:
If data quality issues exist (and they do), then no one wants to be blamed for causing or failing to fix them.
It’s as if everyone is afraid that they will be forced to wear The Scarlet DQ.
This is one of the many human dynamics that can render even the best technology and proven methodology completely useless.
What Say You?
Please post a comment and share your recommendations about how to foster an environment in which poor data quality can be reported freely without fear of blame or reprisal. All viewpoints are welcome. Nathaniel Hawthorne references are not required.