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Teradata and Fair Isaac team to develop decision management systems

27 October 2004  |  479 views  |  0 Source: Teradata

Teradata, a division of NCR Corporation (NYSE:NCR), the industry leader in enterprise data warehousing, and Fair Isaac Corporation (NYSE:FIC), the leading provider of analytics and decision technology, today announced a global strategic alliance.

The companies will combine key capabilities to help organizations across industries speed the development and deployment of analytic applications that automate and improve decisions across the enterprise.

The new alliance will help businesses make real-time decisions using predictive analytics based on current events and historical data. These solutions will leverage Teradata's enterprise data warehouse as the analytic foundation for Fair Isaac's Enterprise Decision Manager software, which is used to create powerful predictive analytic applications. This integration can significantly reduce the time to market of new predictive models and custom analytics by leveraging Teradata's in-database mining techniques. In addition, the time and costs associated with developing applications that leverage these models can be reduced using Fair Isaac's Blaze Advisor rules-based engine and Fair Isaac Model Builder, which are key components of Enterprise Decision Manager.

Enterprise Decision Manager software combines Fair Isaac's industry-leading predictive analytics, business rules and business user control into a complete platform for managing decisions across an enterprise. It is designed to help businesses consistently make more precise and profitable automated decisions and provides the agility to quickly implement changes in decision strategies as business priorities change.

"Fair Isaac has a wealth of experience in areas requiring complex, real-time decision-making, such as risk and fraud management," said Bob Fair, chief marketing officer of Teradata. "This experience, combined with Teradata's expertise in enterprise data warehousing, can provide customers the ability to better manage decisions, comply with regulatory requirements, and reduce associated costs. At the same time, the detailed data within the Teradata database provides Fair Isaac's analytics with a better view of the business, allowing development of analytic models with more predictive intelligence."

"Every day, more businesses are realizing that there is tremendous hidden value in their historical customer data, but extracting valuable, actionable information from this vast amount of data across product silos continues to be a significant challenge for most organizations," said Gresh Brebach, vice president of corporate development at Fair Isaac. "Unlocking that value is what this partnership is all about. Integrating and optimizing Fair Isaac's analytics and Teradata's enterprise data warehousing technologies into a robust infrastructure allows us to bring high-value, custom analytic decision-management applications to market faster and easier. Businesses can finally achieve long-sought benefits through truly effective enterprise decision management."

As part of the alliance agreement, Teradata, the pioneer of in-database data mining, and Fair Isaac will leverage the Teradata Analytic Data Set Generator for the data exploration and pre-processing steps in the data mining process, which can account for up to 75 percent of the effort in building new models and analytics.

Teradata and Fair Isaac will initially leverage their joint industry expertise to target the financial industry with fraud and risk management solutions. Through future joint product development and integration efforts, Fair Isaac plans to bring a range of new analytic applications to market. These applications will be designed to deliver breakthrough value by combining real-time and batch analytics with detailed, customer-level historical data from the Enterprise Data Warehouse. Joint development efforts will also enable deployment of models within the Teradata database via predictive model markup language for efficient parallelized large-scale processing.

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