Further, while “dirty” data can be a troublesome and, at times, painfully obvious problem, it shouldn’t be first on your to-do list, as it’s a situation that’s more easily rectified once the rest of the process has been undertaken.
“Cleaning up data can sometimes be made into a big job,” says Hughes. “There is no company that is so huge that it couldn’t be done in three months or less. And it should be done rapidly. You don’t make money while you’re cleaning data.”
Which is to say that you should first design your database, then quickly clean and standardize the data, and finally perform merge/purge, NCOA and other appends.
It’s About the Users
You can do all the deep thinking you want about your database design, but you can’t get a true sense of what your database should be able to do without talking to the people who’ll be using it. Users tend to be a company’s marketing department, but a portion of the discovery process should be devoted to finding out all the users in a company.
“Part of what folks like me do is walk around and talk to the folks who’ll be using the database,” says Grossman.
From these types of discussions, the real value of your eventual database will emerge. For example, as you’ll read in the RadioShack case study that follows, the retailer interviewed 70 of its internal data users to find out their big needs, pains and concerns before moving ahead with its database rebuild.
Just as important as the users’ input is their access to the refurbished database.
“People should be accessing the database online,” says Hughes. “Everybody in the company with a need to know should have access to the database.”
A data overhaul can increase mail efficiency, as in the case of LexisNexis. It can help you, as in the case of Aspen Skiing Company, more easily combine your house data with outside sources. And it can allow you to disperse accurate intelligence to far-flung branches as in the cases of RadioShack and The Parable Group.