Gartner warns firms of 'dirty data'.

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.