According to Gartner Inc., more than 25 percent of critical data in
Fortune 1000 companies is flawed.
Speaking at the research and advisory firm's Business
Intelligence and Information Management Summit held in Australia in
February, Gartner Research Vice President Andreas Bitterer said that
poor quality, or "dirty data," is often overlooked by
businesses, but it can have a large negative impact on a firm.
"There is not a company on the planet that does not have a
data quality problem," Bitterer said. "And where a company
does recognize they have a problem, they often underestimate the size of
it."
Over the next two years, Gartner predicts, more than 25 percent of
critical data in the world's top firms will continue to be
flawed--the information will be inaccurate, incomplete, or duplicated.
Moreover, Gartner said three-quarters of large enterprises will make
little to no progress toward improving data quality until 2010.
Gartner research shows that poor-quality customer data can cost
businesses dearly in terms of higher customer turnover and excessive
expenses from customer contact processes such as mail-outs, missed sales
opportunities, and even back-office functions such as budgeting,
manufacturing, and distribution.
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Compliance and transparency now top the list of most
companies' data concerns, according to Gartner, but data quality
should be a top concern, as well.
"By introducing data quality initiatives, some companies have
added millions of dollars to their bottom line as they gain benefits
such as increased sales, lower distribution costs, and better
compliance," Bitterer said.
One initiative companies should consider is appointing "data
stewards," or people within the company who are responsible for the
quality of its information. Firms should also manage information as a
corporate asset. Bitterer said businesses also need to invest in
technological data quality solutions that can help them profile,
cleanse, match, and enrich critical information. Gartner said the market
for data quality tools is currently small--$300 million (U.S.) in annual
license revenue--but growing.
According to Gartner, companies should consider data quality issues
including
* Existence (whether the organization has the data)
* Validity (whether the data values fall within an acceptable range
or domain)
* Consistency (whether the same piece of data stored in multiple
locations contains the same values)
* Integrity (the completeness of relationships between data
elements and across data sets)
* Accuracy (whether the data describes the properties of the object
it is meant to model)
* Relevance (whether the data is the appropriate data to support
the business objectives)
Bitterer warns that ensuring data quality is not a one-time
concern, but an ongoing program that requires business-wide commitment
and perhaps even a cultural shift.