Silver Creek Systems, a pioneer and leader in enterprise product data quality solutions, announced that its DataLens System increases the quality, usability and value of product data used within Oracle Warehouse Builder 10g and enhances the value and usability of any data warehouse or other business application containing product data. The DataLens System is the first and only data quality solution built from the ground up to effectively deal with the inherent complexity and variability of product data.

Interoperability with Oracle Warehouse Builder 10g allows customers virtually seamless access to the highest product data quality and governance capabilities from within Oracle Extract, Transform and Load and Oracle Data Warehousing environments. This helps allow for inconsistent product information to be seamlessly matched, standardized and classified, thus extending and enhancing the value of the data warehouse and related applications such as product information management (PIM), master data management (MDM) or business intelligence (BI). Silver Creek Systems, a member of Oracle PartnerNetwork, is the only product data quality partner with a validated solution by Oracle.

"Data quality is critical to the success of BI and data warehouse projects, and is also an issue that is very likely to derail MDM initiatives such as PIM and CDI" said Ted Friedman, research vice president at Gartner Inc. "While the importance of customer data quality has been recognized for some time, the quality of product data is rapidly growing in importance as a pain point for large enterprises."

Product data is fundamentally different from other types of data stored in data warehouses. It is typically very attribute-rich and there are few standards, resulting in highly variable descriptions of extremely complex items. Traditional data quality approaches use pattern-based techniques which cannot effectively address dealing with the complexities of product data. The DataLens System used patented semantic-based Content-in-Context technology to go beyond the syntactic patterns and understand the underlying semantics. With this level of understanding, product data can be rapidly adapted to any data warehouse or other enterprise application requirement. By using the DataLens System from within Oracle Warehouse Builder, problems related to product data can be dramatically reduced or eliminated.

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