Bad Data Needs a Scrub
Kelley Holmes, publisher of Printing News, recently posted on LinkedIn: “Does anyone know who does the printing and marketing for United Healthcare? I’d like to share that their digital campaign data needs to be fixed since clearly my name is not Milton.”
Holmes received a message from a representative of one of our nation’s largest healthcare insurers reading, “Milton, is it time for you to enroll in Medicare?”
I can state with confidence that Ms. Holmes is certainly not a Milton. Although none of us are as young as we used to be, I’m also reasonably certain that she is a number of years away from Medicare eligibility.
Bam! Bad data strikes again. I’ve written stories with titles such as “Big Data Needs A Bath,” filled with egregious examples of incorrect data both in print and in digital media. That should be the end of it, but the bad examples just keep on coming. So here goes…
I don’t know Steve Palmer, nor do I know his company, TRUMPFMEDICALSYSTEMSINC. (All caps and no spacing are the first thing that should have been cleansed from raw data.) I do know that my email somehow became mingled with his data record in some rogue database, as I’ve been receiving daily emailed offers pre-approving me for hundreds of thousands of dollars of credit, no questions asked. What could go wrong?
Email is rife with bad data. There are no hard costs to email, so who cares if your mailing list is 50% bad? Of course, it does make the sender look stupid, and even worse, look like spam.
A recent mailer arrived from a local plumber addressed to my son. Not the son who lives with me and sometimes calls tradesmen for repairs. No, this mailer was addressed to my son who has never lived at this address. He left the nest and left the state before I moved to my current address. That was 25 years ago. How his name became linked to an address in a town in which he’s never lived is beyond me.
Harmless, I suppose, although once again it makes the mailer look foolish. But wait, there’s more.
The same data glitch happened to Penelope. She received a catalog from a hardware company at her new home in a new community. The catalog was addressed to her husband. Her late husband, whose untimely death happened years before she moved to her current address. She was not amused. She was angry.
The hardware company spent a small fortune to print and mail catalogs only to create ill will. Penelope’s husband may have passed away, but bad data is very much alive, pernicious to the point of immortality.
Bad data is an inevitable byproduct of the information age. The sheer volume of data collected is part of the problem, but it is the way it is managed that really causes glitches. Fact is, an awful lot of data is simply collected, but never managed or “cleansed” at all.
Customization and personalization are proven to gain a reader’s attention. Just make sure that the attention is the good kind - leading to responses, purchases or donations - not the ill-will, poor publicity and lawsuits that result when dirty data is used.
There are tremendous opportunities for those who are willing to develop expertise in data cleansing. Today’s data needs its mouth washed out with soap!