After reading this article and talking to a friend that
is a marketing database admin. It makes me wonder what people on
the other end of the phone think about me after I'm done talking to them.
What about you? At the same time how is your company handling the information
that is being entered into your company database? What personnel
put into customer profiles can come back and get you, as seen below.
1. The "Dear Idiot" letter
Be careful where you get your data – it may come back to haunt you.
This tale of terror comes from the customer call center of a large
financial services institution. As in nearly all help desks, service
reps take calls and enter customer information into a shared database.
This
particular database had a salutation field that was editable. Instead
of being constrained to Mr., Ms., Dr., etc., the field could accept 20
or 30 characters of whatever the rep typed. As service reps listened to
the complaints of angry customers, some of them began adding their own,
not entirely kind, notes to each record, like, "what an idiot this
customer is."
This
went on for years. No one noticed because no other system in the
organization pulled data from that salutation field. Then, one day, the
marketing department decided to launch a direct mail campaign to
promote a new product. They came up with a brilliant idea. Instead of
purchasing a list, why not use the service desk database?
So the letters went out: "Dear Idiot Customer John Smith."
Strangely, no customers signed up for the new service. It wasn't until the organization began examining its outgoing mail
that it figured out why. The moral of this story?
"We
don't own our data any more," says Arvind Parthasarathi, vice president
of product management and data quality for data integration specialists
Informatica. "The world is so interconnected that it's likely someone
will pick up your information and use it in a way you never
anticipated. Because you're pulling data from everywhere, you need to
make sure you have the right level of data quality management before
you use it for anything new."
What constitutes the "right level" will vary
depending on how you use the data. "In the direct mail industry,
getting 70 to 80 percent of your data correct is probably good enough,"
he adds. "In the pharmaceutical industry, you want to be at 99 percent
or better. But no company really wants, needs, or will pay for perfect
data; it's just too expensive. The issue always is, how will it be used
and at what point is it good enough?"