data quality

Because data quality is so important from a business perspective, many large companies employ a data steward to be in charge of data quality. In spite of this, Gartner Inc. forecasted that 25% of critical data within Fortune 1000 companies would continue to be inaccurate through 2007. A Gartner study says that poor quality customer data costs U.S. business an estimated $611 billion dollars a year in postage, printing, and staff overhead.

Getting started with data quality

To explore how data quality is used in the enterprise, here are some additional resources:

Data quality management pitfalls: Three common mistakes to avoid: A data quality expert explains which three areas you need to keep an eye on when implementing a data quality management initiative.

Five steps for weaving data quality management into your enterprise data integration processes: Data quality management is critical for effective enterprise data integration. In this article, get five strategic steps (and potential pitfalls) of data quality management from an expert.

Data quality management and governance tutorial: This is a complete guide to data quality management concepts, strategies and technologies. Get best practices and technology considerations for maintaining accurate, current and concise data.

Read more about data quality: - SearchCRM.com interviewed R. Jeffrey Canter about some of the issues involved in maintaining good data quality. - SearchCRM.com has a quiz that lets you test your knowledge about data quality terminology. - The Data Warehouse Institute cites the $611 billion figure in its article, "Taking Out the (Data) Garbage."

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