Few IT projects are more frightening than data integration and reconciliation. Actually, let us rephrase that. One thing is more frightening -- when data integration goes bad.
Data projects can go bad in many ways. Here are five of the most common: what went wrong, what happened as a result, and what you can do to avoid having the same thing happen to you. The names of the companies involved have been obscured to protect the guilty. Don't let your own project become someone else's horror story.
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.”
Dan Tynan is contributing editor at InfoWorld.
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