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 always been around
45 million
. 

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

Frankly,
$600 billion annually is an absolutely staggering number and 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 10 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
. And small
companies are looking to export sales
to pull them out of the recession.

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 names to something
completely new and different just for the heck of it. That
kind of data decay can wreak havoc on a global scale to many companies.

Given
the natural growth of the economy in the United States, whatever the cost of
poor data quality was in 2002, it is undoubtedly more; probably much more
today. 

Given
ever-growing involvement and dependence of companies on the global market, each
new market opened multiplies the challenges. 

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