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Database Slideshow:
Bad Customer Data is Common, Costly

By Dennis McCafferty on 2010-09-24


There's a lot of bad data out there when it comes to valuable customer-contact information. Maybe it's a database in which 23 percent of the home addresses aren't valid anymore. Or one with outdated area codes. And, of course, let's not forget good, old-fashioned data-entry errors caused when busy call-center employees input typos. Whatever the source of data dysfunction, it often falls to you and your IT team to fix it. A new survey from Experian QAS reveals that organizations realize that data problems exist, and are taking systemic steps to take corrective action. Still, given the economy, progress is sometimes stalled. "While contact-data quality is becoming a priority as businesses focus on customer loyalty and retention, stakeholders are finding difficulty implementing projects due to cost constraints," says Joel Curry, chief operating officer, Experian QAS. "To overcome this obstacle, organizations should review common-quality issues with data and prioritize projects based on their positive return on investment." An estimated 300 organizations participated in the Experian QAS Data Quality survey, with CIOs, IT managers/directors and other top executives taking part.

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96.2 percent


96.2 percent of respondents say their organizations view data accuracy as an essential issue.

One-third


Nearly one-third of respondents say their organizations do not enforce the accuracy of data, despite the widespread perception of how important this is.

84 percent


84 percent of respondents say their organizations plan to invest – or that they should consider investing – in data-quality initiatives over the next 12 months.

60 percent


60 percent of organizations participating claim at least 6 percent or more of their databases contains inaccurate or missing information.

56 percent


56 percent of those surveyed say outdated information is a common data error.

28 percent


28 percent of respondents say that all departments contribute to data errors.

63 percent


63 percent of respondents say that 5 to 30 percent of their organization's marketing budget is wasted as a result of bad data.

26 percent


26 percent of respondents cite senior management support as a barrier to maintaining accurate data.

39 percent


39 percent of respondents cite budget as a barrier to maintaining accurate data.

48 percent


48 percent of respondents say they use manual processes to measure the accuracy of contact data.

47 percent


47 percent of respondents say they use analysis of response rates from marketing campaigns to measure the accuracy of contact data.

56 percent


56 percent of respondents say they use staff training to maintain and improve contact data.

48 percent


48 percent of respondents say they use software tools to maintain and improve contact data.

Four best ways to ensure 'clean' data


1. Understand your database. Review contact data to determine common errors within. If area codes are constantly wrong, for example, the problem could be systemically fixed.

Four best ways to ensure 'clean' data


2. Remove duplicate records. They inhibit an organization's ability to collect a singular view of each customer or prospect.

Four best ways to ensure 'clean' data


3. Verify data during all capture processes. By knowing data, businesses can determine the best place to implement point-of-capture verification tools. Two common areas where data entry is flawed, for example, are sales and customer-service points.

Four best ways to ensure 'clean' data


4. Enhance and update the data. It improves accuracy while providing additional, fresher customer insight.

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