Once a number gets into public circulation, sometimes it never changes no matter what happens. How many people in America don’t have health insurance? For about 10 years, the number bandied about has been around 45 million. (Factcheck.org 2009) 


The data quality arena has its own 10-year-old, commonly accepted number–the cost of poor data quality on American businesses. The statistic most often cited is $600 billion annually. Its source is 2002 study by The Data Warehousing Institute.


Frankly, $600 billion per year is an absolutely staggering number. According to that 2002 report, it was only the tip of the iceberg; representing postage (remember that?), printing and staff overhead associated with name-and-address data problems. If that number is even close to accurate, it is safe to speculate that the cost is even greater now. Over the past ten years, the economy has become increasingly global. 


For example, last year General Motors sold more cars in China than it did in the United States. (LA Times, Jan. 25, 2011). And small companies are looking to export sales to pull them out of the recession. (USA Today, Feb. 3, 2011).


Working internationally complicates and intensifies data quality problems. Not only do different divisions of companies have to communicate with each other efficiently, they have to manage data that comes in different languages, different formats and reflects different cultural traditions. 


Consider a country like Sweden. According to The New York Times, the new trend is when couples get married; they both change their surnames to something completely new and different because they were tired of their names. That kind of data decay can wreak havoc on a global scale to global companies.


Given the natural growth of the economy in the United States, whatever the cost to business of poor data quality was in 2002, it is undoubtedly more, probably much more, now. Given ever-growing involvement and dependence of companies on the global market, each new market opened multiplies the challenges. 


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