DQ-Tip: “Don't pass bad data on to the next person...”
Jim Harris in Books, Data Quality tagged DQ-Tip, Thomas Redman Wednesday, September 16, 2009 at 8:50PM Data Quality (DQ) Tips is a new regular segment. Each DQ-Tip is a clear and concise data quality pearl of wisdom.
“Don't pass bad data on to the next person. And don't accept bad data from the previous person.”
This DQ-Tip is from Thomas Redman's excellent book Data Driven: Profiting from Your Most Important Business Asset.
In the book, Redman explains that this advice is a rewording of his favorite data quality policy of all time.
Assuming that it is someone else's responsibility is a fundamental root case for enterprise data quality problems. One of the primary goals of a data quality initiative must be to define the roles and responsibilities for data ownership and data quality.
In sports, it is common for inspirational phrases to be posted above every locker room exit door. Players acknowledge and internalize the inspirational phrase by reaching up and touching it as they head out onto the playing field.
Perhaps you should post this DQ-Tip above every break room exit door throughout your organization?
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Reader Comments (5)
I follow the sentiment of the tip in so much as we should all take responsibility for quality and never knowingly propagate bad practice but I think the tip should be further qualified:
"Don't pass on bad data without telling the recipient..."
Bad data can be useful in helping root cause analysis.
If I can refer back to a comment in Adventures in Data Profiling (Part 1):
'However, as Bill Inmon recently discussed in his B-eye-Network article Some Perspectives on Quality : “…keeping incorrect data even when it is known to be incorrect often provides valuable clues as to how data needs to be corrected.”'
It is too simplistic to merely reject data. The provider needs time to do the root cause analysis and fix it at source.
Excellent point (as always) Phil,
Thanks for sharing your perspectives on quality :-)
I agree that Redman's advice is heavy on sentiment and therefore somewhat impractical for day-to-day practice.
But, as you also pointed out, the sentiment of the tip is an incredibly important one.
Best Regards...
Jim
On Twitter, Jill Dyché commented:
"A.K.A.: Avoid viral sucky-data."
From the LinkedIn Group for Enterprise Data Quality, Walter Howard commented:
"Data quality is context sensitive. What is good enough for me may not be good enough for you. This is always an issue when people or organizations repurpose data. It is a worthy idea to keep in mind when building data structures and is certainly a supporting reason to have a centralized data management organization. This organization can look at the enterprise rules for a given piece of data as opposed to the normal myopic project view I see at most companies."
From the LinkedIn Group for Enterprise Data Quality, Henrik Liliendahl Sørensen commented:
"I agree with Walter that the multi-purpose aspect of data (information) quality is of great importance. With Master Data we often have several different purposes with these data and we could even consider future purposes. This makes the real world alignment of data more and more attractive."
More on the subject from Henrik's blog post: Fit for what purpose?