I have earlier used the fairy tales of Hans Christian Andersen on this blog. This time it is the story about the princess on the pea.
The story tells of a prince who wants to marry a princess, but is having difficulty finding a suitable wife. Something is always wrong with those he meets, and he cannot be certain they are real princesses. One stormy night (always a harbinger of either a life-threatening situation or the opportunity for a romantic alliance in Andersen’s stories), a young woman drenched with rain seeks shelter in the prince’s castle. She claims to be a princess, so the prince’s mother decides to test their unexpected guest by placing a pea in the bed she is offered for the night, covered by 20 mattresses and 20 featherbeds. In the morning the guest tells her hosts—in a speech colored with double entendres—that she endured a sleepless night, kept awake by something hard in the bed; which she is certain has bruised her. The prince rejoices. Only a real princess would have the sensitivity to feel a pea through such a quantity of bedding. The two are married, and the pea is placed in the Royal Museum.
Buying a data quality tool is just as hard as it was for a prince to find a real princess in the good old days. How can you be certain that the tool is able to help you finding the difficult not obvious flaws hidden in your already stored data or the data streams coming in?
I think performing a test like the queen did in Andersen’s story is a must, and like the queen didn’t, don’t tell the vendor about the pea. Wait and see if the tool gets black and blue all over by the pea.
Liliendahl on Data Quality 
Hi Henrik,
That is a nice analogy re data quality tools but it also works if we turn it around and say the data quality tool is supposed to find data quality issues in the customer (princess’s) data. We have to be careful that we manage the customer’s expectation for 100% perfect data or the princess’s undisturbed sleep will be replaced by the nightmares of trying to fix all data quality issues. The princess will sleep better if she prioritizes and corrects the major root causes of PDQ.
That is also a great idea about having a “seeded set” of data quality issues for the vendor’s to find rather than just a beauty contest.
Cheers, Gordon
Thanks for commenting Gordon. I agree, the ability to sense the pea must not be confused with expecting capability for 100% perfect data. Even real princesses (and DQ tools) aren’t perfect.
Hi Henrik
A wonderful analogy and beautifully portrayed.
I wonder what double entendre stories are told after a “rough night” with a poor data quality tool?
Regards
John
Thanks John 🙂
Nice Analogy Henrik – I like it.
Rgds Ken