A Guide to Data Quality Management

By eweek  |  Posted 2003-12-01 Print this article Print

Once only direct marketers worried about getting names and addresses right. Now many companies, from transportation to technology, recognize the need to clean up dirty data. Check out this detailed Baseline guide about the growing data-quality mark

Direct marketers have long reckoned with cleaning dirty databases loaded with duplicate names, misspelled addresses and other shortcomings. Today, companies in more diverse industries—from technology to manufacturing—are jumping on the information-quality bandwagon to get the most out of managing customer relations and other projects relying on lots of data.

RSA Security, like countless other companies, found out the hard way about the perils of dirty data. The security-software firm had just installed customer-relationship-management software from Siebel Systems in 2001 to make it easier for its 300-person sales force to sell authentication products to enterprise and electronic-commerce customers.

Problem was, the Siebel application was fed by several incompatible systems, each with its own way of identifying customers. And RSA had no way—short of manually going through every record—of being sure salespeople were being given complete and correct information on all customers. If, for example, the companys order-entry system reported information on a customer named James Smith, and the Web server had information on a Jim Smith, the Siebel system probably wouldnt catch on that the two Mr. Smiths were really one customer.
"We had no way of filtering all the nonsense data out, so we ended up with a lot of duplicate and just-plain-wrong data in the application," says John Ma, manager of information-system applications at RSA. "It was quite confusing to the sales folks."

So Mas team tried data-quality software to scan the Siebel database for duplicates and errors and automatically correct them. RSA selected the Data Quality Connector for Siebel, a program from Group 1 Software specifically designed to work with Siebel customer-relationship management software. The Group 1 software identified a whopping 40,000 of the 160,000 customer records in the Siebel database as duplicates or errors and eliminated them. Once the data was cleaned up, says Ma, 95 percent of RSAs salespeople started using Siebel applications.

Thats helped the company do a better job of turning prospects into customers, Ma says. And its showing. In the third quarter, 700 of RSAs 4,000 customers were new. The project may have benefited the companys bottom line, too. For the three months ended Sept. 30, the company reported earnings of $3.65 million compared to a loss of $8.22 million for the same period in 2002.

In addition to Group 1, companies such as Harte-Hanks Trillium, Ascential and Firstlogic offer software that uses complicated matching algorithms to comb through databases and spot problems in records based on a set of user-defined criteria.

The strength, particularly of products like Group 1s DataSight suite and Firstlogics IQ Suite, is correcting name and address records. But software packages can also fix other, unrelated problems, such as making sure that customer e-mail addresses are right or that customer-contact histories are complete. Most data-quality software can also append records with missing or relevant information such as four-digit ZIP code extensions or data for geographic location. And most packages can be set up to operate in real time—catching errors as, say, customers enter contact information into Web-based applications—as well as in batch mode.
Next Page: Data quality market spending expected to grow.


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