Data Quality refers to how organizations centralize the discovery, correction, and prevention of data quality issues across the enterprise. Ensuring the integrity of your data, maximizing developer productivity, and gathering complete data for all operational and analytic initiatives are prime objectives of any organization. With trusted, timely and accurate information, your information users will have a solid foundation for decision making.

• Quickly build, centralize, and share business rules
• Deliver trusted data across the enterprise
• Reduce overall total cost of ownership (TCO) with quick integration and easy maintenance
• Extend data quality capabilities to third-party or proprietary applications
• Gain end-user trust with impact analysis and data lineage

The quality of the data that is used by a business is a measure of how well its organizational data practices satisfy business, technical, and regulatory standards. Organizations with high data quality use data as a valuable competitive asset to increase efficiency, enhance customer service, and drive profitability. Alternatively, organizations with poor data quality spend time working with conflicting reports and flawed business plans, resulting in erroneous decisions that are made with outdated, inconsistent, and invalid data.

To avoid the consequences of poor data quality, many organizations implement source system controls to ensure that their data satisfies quality standards at its point of origin. When properly implemented, source quality controls can effectively prevent the proliferation of invalid data. However, source system quality controls alone cannot enforce data quality. They cannot, for example, ensure that data quality is maintained throughout the data life cycle, especially when multiple data sources with varying levels of cleanliness are combined in downstream data integration processes. To address this potential problem, downstream applications must also include steps to ensure that data quality is preserved, if not enhanced, after data leaves the source system.

To meet this challenge, many successful enterprises adopt a flexible data quality strategy that incorporates data quality components directly into their data integration architecture. Successful application of this strategy requires a data integration platform that can implement a broad range of generic and specific business rules and also adhere to a variety of data quality standards.

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