Data Quality's Important, But Audits Can WaitData Quality's Important, But Audits Can Wait

In July, we ran a story about the "data quality audit," and I got a hankering last week to learn how many of our readers here at Business Intelligence Pipeline actually carry out such audits at their organizations. The answer: Not too many.

A poll on the topic has been up on here on the site for less than a week, but the early going shows that fewer than a fifth of respondents to our admittedly unscientific poll engage in data quality audits as they were defined in the story, which you can read here.

That definition, incidentally: A data quality audit, in a nutshell, is a business rules-based process that incorporates standard deviation to identify variability in sample test results. Michael Gonzales, president of consulting firm The Focus Group Ltd., was the writer who provided tips into how they work. With all the news lately about how data quality is becoming an increasingly sticky issue, his story provides many potentially valuable -- and timely -- insights.

Anyway, here's what I'm getting at: Our site polling mechanism here at the Pipeline does not, unfortunately, allow us to pull together the sort of qualitative responses we'd need in order to get details on how our readers monitor their enterprise data quality. But hey, e-mail does. So if you get a moment, drop me a line. I'd love to hear what you have to say about data quality, and I might use the information you send in a future column. Send me a message, and give me your insights. It doesn't have to be anything detailed (or too revealing of proprietary information) -- a quick note would be great.