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Different industries have varying business processes to satisfy their customers. While the data universe often differs, patterns can be developed to improve the data quality. Improved data quality not only increases productivity, it also enhances the value and utility of business information for operational and analytical purposes. Improved data quality enhances the value and utility of business information for operational and analytical purposes. Most data/system architects understand the need for data quality, and the effects of its absence. Data quality services should be a common architectural resource, with consistent and repeatable data interpretation and measurement. A pattern-based approach to organizing data quality services gives a data administrator the ability to choose a pattern than best fits the constraints at hand, modify it if required, and deploy that service within minutes. A reasonable warranty of the data quality can only be given if a measurement of that quality is afforded. A pattern-based approach to web services ensures the ability to measure the quality of the underlying data streams. A data quality solution should deliver capabilities that allow organizations to inspect the impact of applying such patterns against their data domain, providing quick and immediate feedback without requiring integration efforts. The knowledge, that a company's data is meaningful and of satisfactory quality should allow business managers to serve their customers confidently.
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