Data Quality and Gender Bending

We have all heard the story about the man who was sent a mailing for an expectant mother. Obviously this exposed the organization sending it to a good deal of ridicule, but there are plenty of more subtle examples of incorrect targeting based on getting the gender wrong. Today I was amused to get another in a series of emails from gocompare.com addressed to [email protected] The subject was “Eileen, will the ECJ gender ruling affect your insurance premiums?” 🙂 The email went on to explain that from December, insurers in the EU will no longer be able to use a person’s gender to calculate a car insurance quote, “which may be good news for men, but what about women…” They obviously think that my first name is Eileen and therefore I must be female.
Now, I know that my mother had plans to call me Stephanie, but I think that was only because she already had two sons and figured it was going to be third time lucky. Since I actually emerged noisily into the world, I have gotten completely used to Stephen or Steve and never had anyone get it wrong – unlike my last name, Tootill, which has (amongst other variations) been miskeyed as:

• Toothill                    • Tootil
• Tootle                      • Tootal
• Tutil                         • Tooil
• Foothill                    • Toohill
• Toosti                       • Stoolchill

“Stephen” and “Steve” are obviously equivalent, but to suddenly become Eileen is a novel and entertaining experience. In fact, it’s happened more than once so it’s clear that the data here has never been scrubbed to remedy the situation.
Wouldn’t it be useful then if there was some software to scan email addresses to pick out the first and/or last names, or initial letters, so it would be clear that the salutation for [email protected] is not Eileen?

Yes, helpIT systems does offer email validation software, but the real reason for highlighting this is that we just hate it when innovative marketing is compromised by bad data.  That’s why we’re starting a campaign to highlight data quality blunders, with a Twitter hash tag of #DATAQUALITYBLUNDER. Let’s raise the profile of Data Quality and raise a smile at the same time! If you have any examples that you’d like us to share, please comment on this post or send them to [email protected].

Note: As I explained in a previous blog (Phonetic Matching Matters!), the first four variations above are phonetic matches for the correct spelling, whereas the next four are fuzzy phonetic matches. “Toosti” and “Stoolchill” were one-offs and so off-the-wall that it would be a mistake to design a fuzzy matching algorithm to pick them up.

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