Every Friday on Twitter people are recommending other tweeps to follow using the #FollowFriday (or simply #FF) hash tag.
My username on twitter is @hlsdk.
Sometimes I notice tweeps I follow are recommending the username @hldsk or @hsldk or other usernames with my five letters swapped.
It could be they meant me? – but misspelled the username. Or they meant someone else with a username close to mine?
As the other usernames wasn’t taken I have taken the liberty to create some duplicate (shame on me) profiles and have a bit of (nerdish) fun with it:
@hsldk
For this profile I have chosen the image being the Swedish Chef from the Muppet show. To make the Swedish connection real the location on the profile is set as “Oresund Region”, which is the binational metropolitan area around the Danish capital Copenhagen and the 3rd largest Swedish city Malmoe as explained in the post The Perfect Wrong Answer.
@hldsk
For this profile I have chosen the image being a gorilla originally used in the post Gorilla Data Quality.
This Friday @hldsk was recommended thrice.
But I think only by two real life individuals: Joanne Wright from Vee Media and Phil Simon who also tweets as his new (one-man-band I guess) publishing company.
What’s the point?
Well, one of my main activities in business is hunting duplicates in party master databases.
What I sometimes find is that duplicates (several rows representing the same real world entity) have been entered for a good reason in order to fulfill the immediate purpose of use.
The thing with Phil and his one-man-band company is explained further in the post So, What About SOHO Homes.
By the way, Phil is going to publish a book called The New Small. It’s about: How a New Breed of Small Businesses is Harnessing the Power of Emerging Technologies.
Liliendahl on Data Quality 
A data quality tweep intentionally creating duplicate Twitter profiles!?!
A real-life data quality expert would at least have the duplicate Twitter profile web addresses point back to Golden Tweep profile, thereby demonstrating the best practice of Duplicate Tweep Matching and Survivorship 🙂
Ouch, no more Mr. Nice Guy comments from Jim.
Two possible explanations of my practice:
• It’s like the shoemakers children: Walking around in really bad shoes.
• I am actually an imposturous Swedish Chef of data quality – börk, börk, börk….
Mr. Nice Comment Guy doesn’t work weekends, therefore Mr. Snarky Comment Guy left that comment 🙂
Or to continue the Muppets references, my Fuzzy Bear comments on weekdays and my Statler and Waldorf comments on weekends 🙂
Henrik,
Thanks for a humorous but instructive post heading into the weekend.
The case also made me think about the “process drivers” / likely “root causes” of inadvertent references one of those “alternate IDs.” It goes back to the reality that even small amounts of automation can enhance data quality.
What I mean is: let’s say I’m I am sending a tweet and want to reference YOU. I have two choices:
1) I can quickly re-type your handle (or I least I intend to type those letters in the right order…)
or
2) I can add a tiny element of automation to my process: I cut and paste your handle…perhaps from a spreadsheet with your correct handle in it (with some other meta data too? I digress). Anyways, that little bit of automation increase DQ in my tweet, and avoids having to reference those other less relevant profiles of yours 😉
Have a great weekend (or Sunday night I guess it is for you).
JF
Thanks Jaime. I agree. Automation oftentimes help with data quality.
also thanks for making me laugh again with the mention from the gorilla account 🙂
And actually what you showed was a delightful customer experience and the ability to interact with your “customers” in whatever channel (intentional or unintentional) they chose to interact with you in. Therefore lifting this argument out of just data quality (I know, how dare I!) you taught a lesson to companies big and small about customer interaction. Everyone is rushing into social media these days, but many companies have just created another badly thought-through silo with very poor data integration.
What this shows is how you can learn from customer behaviour, anticipate the customer mistake, and then drive them back into the correct channel in a nice way, thereby seamlessly continuing that Henrik brand experience… It’s a great eg of both excellent customer experience and the power of data analytics IMHO.