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Dirty Data

By Mitch Betts

December 17, 2001 12:00 PM ET

Computerworld - There are many ways that a supplier named IBM could be entered into a supply chain database: IBM Corp., I.B.M. Corporation, International Business Machines Corp. or a host of other variations.

Any one of those monikers might work well enough for a specific transaction. But if a company wants to see how much business it's doing with IBM overall, the name variations become a problem. The company might be doing $100 million worth of business with IBM, yet a database query might show only $20 million, depending on which name is used in the query.

The result is that the company wouldn't have a complete, accurate view of its suppliers so it could negotiate better deals and volume discounts.

"We see 20% duplicate supplier records," says Craig Verran, assistant vice president for supply chain solutions at The Dun & Bradstreet Corp. in Murray Hill, N.J., which helps companies clean up their supplier files.

That's just one small example of how uncleansed data gives a company the wrong picture of its supply chain.

"Companies are making bad operational decisions every day of the week [and losing money] because of bad data quality," says Ted Friedman, an analyst at Gartner Inc. in Stamford, Conn.

Housecleaning

Rules of thumb for a data quality initiative:

First, establish the processes to prevent errors. Then clean up the existing errors.

Start with critical data suppliers: Insist they provide accurate and current data.

Focus on the most important data.

Meet the most important data needs of the most important customers.

Don’t be seduced by cleanup tools. They’re no substitute for preventing errors.

Pick specific software tools to solve specific problems rather than general tools for general problems.
Source: Data Quality: The Field Guide, by Thomas C. Redman (Digital Press, 2001).

Poor data management is costing global businesses more than $1.4 billion per year in billing, accounting and inventory snafus, according to a survey of 599 companies by PricewaterhouseCoopers in New York. One-third of the companies say that "dirty data" forced them to delay or scrap a new system.

"We have had major [supply chain] software projects fail for lack of good data," says Donald Carlson, director of data and configuration management at Motorola Inc.'s semiconductor products group in Austin, Texas. In a presentation at a recent data quality conference, he recalled how a new supply chain planning system had to be scrapped because bills of material were hit by a triple whammy: incomplete data, inaccurate data and different data formats in different countries.

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