The 2010 movie Tooth Fairy was a box office bust—and deservedly so for obvious reasons. The studio executives couldn’t handle the tooth, er I mean, the truth, which is before Jim Piddock stole, modified, and sold my idea, the original plot centered around Dwayne “The DQ Expert” Johnson, who is a dentist by day, but at night becomes a crime fighter battling poor data quality, who is known only as The Tooth Fairy of Data Quality.
Okay, so obviously the real truth that’s all too easy to handle is that nobody really stole my idea for a movie about a data quality crime fighter who uses the tag line: “Can you smell the bad data The DQ Expert is cleansing?”
However, some of the organizations that I discuss data quality with seem like they really do believe in The Tooth Fairy of Data Quality.
No, they don’t literally put their poor quality data under their pillow at night, going to sleep believing when they wake up the next morning that they will magically have high quality data—or at least get $1 for every bad data record.
But they do often act as if they believe that simply loading all of their existing data into a shiny new system, like say an enterprise data warehouse (EDW) or a master data management (MDM) hub, will magically resolve all of their enterprise-wide data issues, resulting in brightly smiling, happy business users.
Data Quality Fairy Tales
Please post a comment below and share your experiences dealing with this or any other fairy tales about data quality that you have encountered. Perhaps we could even collectively create a new literary or movie genre for Data Quality Fairy Tales.
Anatomy of an OCDQ Blog Post
Since I am often asked by my readers where I get the wacky ideas for some of my data quality blog posts, I thought I would share the Twitter-aided thought process that lead—really quite inevitably—to the writing of this particular blog post:
Therefore, special thanks to Robert Karel of Forrester Research and Steve Sarsfield of Talend for “inspiring” this blog post.
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