An issue with reporting data quality issues


An organization’s perspective on data quality is often revealed by its data auditing practices. Some organizations practice data quality ignorance by not performing data audits, assuming that if they don’t check it or hear anyone screaming about it, their data quality must be good enough. Other organizations persist on data quality pretense by carefully performing data audits in such a way to project the appearance of high quality data, as sometimes happens with red herrings submitted to avoid raising red flags with regulatory compliance.

Just as important as performing regular data audits is fostering an environment where everyone in the organization can report data quality issues without fear of blame or reprisal. However, improving an organization’s ability to report data quality issues can sometimes have unintended consequences, similar to improving a city’s ability to report crimes.

“If the police report an increased number of burglaries in a neighborhood,” Nate Silver asked in his book The Signal and the Noise: Why Most Predictions Fail but Some Don't, “is that because they are being more vigilant and are catching crimes that they had missed before, or have made it easier to report them? Or is it because the neighborhood is becoming more dangerous?”

As an example of the complex relationship between crime reporting and crime rates, Silver explained that “New York does not allow you to file a police report online, while San Francisco does, as I found out when my rental car was broken into there in a reporting trip for this book. San Francisco is doing a better job of helping citizens and visitors to report and prevent crimes. But perversely, this makes its reported crime rate higher.”

The same thing happens in an organization that makes it easier to report data quality issues. As more issues are reported, the organization’s data quality will be perceived as being worse than it was assumed to be when reporting and auditing were not performed on a regular basis.

But it would be a crime to allow this issue with reporting data quality issues prevent you from being more vigilant about auditing your data and reporting any data quality issues that you find.


About Author

Jim Harris

Blogger-in-Chief at Obsessive-Compulsive Data Quality (OCDQ)

Jim Harris is a recognized data quality thought leader with 25 years of enterprise data management industry experience. Jim is an independent consultant, speaker, and freelance writer. Jim is the Blogger-in-Chief at Obsessive-Compulsive Data Quality, an independent blog offering a vendor-neutral perspective on data quality and its related disciplines, including data governance, master data management, and business intelligence.

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