An independent software research organization has issued a report concluding that poor data quality is costing U.S. businesses more than $600 billion annually, yet most executives are unaware of its dangers.
According to the Seattle-based Data Warehousing Institute, poor quality customer data costs U.S. businesses $611 billion a year in postage, printing and staff overhead. “Frighteningly,” the report notes, “the real cost of poor quality data is much higher. Organizations can frustrate and alienate loyal customers by incorrectly addressing letters or failing to recognize [customers] when they call or visit a store or Web site.”
Despite these dangers, however, “most executives are oblivious to the data quality lacerations that are slowly bleeding their companies to death,” states the report, titled Data Quality and the Bottom Line. The report also found that nearly 50% of survey respondents had no plans to implement measures to improve data quality.
The reports findings were based on survey results from 647 respondents–primarily U.S.-based information technology managers and staff–across a broad range of industries. About 11% of respondents were in financial services companies, while another 9.5% were in insurance.
According to Wayne Eckerson, director of education and research for the Data Warehousing Institute, the definition of data quality is simply removing errors found in data. Such errors are often the result of data entry mistakes, such as transposing letters or numbers. Data are also often wrong due to changes over time, such as when a person dies, he notes.
“Its also how you interpret the data,” Eckerson points out. Within the same organization, different divisions may have different definitions for the same data elements or terms. Classic examples, he says, are what constitutes a “sale,” or how “gross profits” are calculated.
“The problem with data is that its quality quickly degenerates over time,” the report states. “Experts say 2% of records in a customer file become obsolete in one month because customers die, divorce, marry or move. In addition, data entry errors, systems migrations and changes to source systemsgenerate bucket loads of errors.
“Truthfully, though, most data quality problems come up with the name and address data fields,” says Eckerson. He characterizes the cost of mislabeled, misprinted communications as “astounding.”