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The good news: Accurate data will reap big rewards for the business.

Data cleansing will cramp your budget, no question. And it's not only the cost of the software, which analysts say can range anywhere between $100,000 and $500,000 depending on company size and the amount of data you need to clean. To that, add any custom tools, and don't forget to tack on the cost of employee labor and training. Rybeck, of Emerson Process Management, says her rollout cost roughly $250,000, while Gregory of Hogan & Hartson says his company has spent roughly $100,000 thus far. And though vendors say installing the software should take from three to six months, it will likely take much longer to actually clean the data. It's a significant commitment, which is why most vendors right now cater to large-cap companies. For smaller companies, says Friedman, an outsourced approach might be the way to go. "A midsize company may not have the skills or the resources to be successful" with an in-house initiative, he says. But regardless of company size, "the most important thing is to think of data quality in a broad way—not just in the context of CRM or data warehousing, but as a business discipline."

Even so, as much money and time as it requires, data cleansing isn't going to be hard to justify once business managers understand the rewards. While companies can't always put a dollar figure on the return on their data-cleansing investment, they can cite better control and reduced costs of their marketing campaigns, the ability to cross- and up-sell valuable customers, improve supply chain efficiency and reduce risk.

Riese at Saab Cars, for example, says his data-cleansing initiative has saved the company tens of thousands of dollars because they can now do customized marketing campaigns in-house instead of hiring a third party. And, he says, more accurate matching of potential customers to dealers has helped the company increase sales. "The entire process of assigning a lead to a dealer and then following up on the back end, we have that nailed now. Last year was the best sales year in Saab's history, and I think what we have done has contributed to that."

And it doesn't take a genius to figure out that better data means better forecasting. "Data quality is the backbone of business intelligence," says Gartner's Friedman. Wise of Landstar says his data-quality rollout—which includes cleaning as well as standardizing (or normalizing) data across the company's six business units—will ultimately enable the company to move to a single-customer-view model. This will not only help the company create greater sales opportunities and a more comprehensive view of the company's top customers, it will also reduce risk. "We provide a service and bill later, so if you're extending credit without a single view of the customer, you don't know what your exposure is." Wise says a single view of his company's financial systems is already complete, and his next target is Landstar's customer database, which has more than 15 years' worth of data on hundreds of thousands of customers—many of which actually belong to the same parent company. Wise won't reveal how much he expects to save, but says "it's a significant strategic decision," adding that his department's data-management team has tripled in size in the past three years alone.

For Wadsworth of MarketTouch, the direct marketing company, the return on investment is repeat business. "Our customers wouldn't come back to us if our data were wrong."

How many errors in forecasting can be blamed on bad data now?

Tell Your CFO:

Improving our data will mean reductions in storage and marketing costs.

Tell Your Executive Team:

Better data will allow us to increase our business thanks to more accurate forecasting.

To download a Factsheet on Data Quality, click here.

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