Teradata Corporation has introduced Teradata Warehouse Miner 5.2, which embeds 50 new statistical data mining functions from its partners into the Teradata Database to accelerate the data mining process. Data mining is a powerful technology that discovers patterns in customer, financial and operational data that can provide valuable business insights.
Teradata Warehouse Miner enhancements are supported by the recently announced SAS Scoring Accelerator for Teradata. An initial benchmark of the SAS Scoring Accelerator for Teradata demonstrated the ability to process the number of records 45 times faster than the traditional scoring method. The SAS Scoring Accelerator for Teradata also eliminates the need for manual translation of the SAS scoring code into SQL, or structured query language. This speed and technology integration significantly increases the development and deployment of more SAS analytical models in Teradata, enabling critical business decisions in real time.
Real-time analytics can dramatically enhance a company's relationships with its consumers. For example, if a consumer calls the customer care center after a recent purchase, the call center representative needs to have an up-to-date history on the consumer's online, phone, direct mail and store buying activities. Advanced analytics applications can look at all these points of contact and, in real time, provide the representative with the "next best offer" or product recommendation customised to meet the consumer's needs. Incomplete or stale analytic information on the consumer's recently purchased activities places the retailer in an embarrassing position or worse, it may drive the consumer to a competitor.
The ability to provide analytics to support the real-time business needs of customers has been achieved by embedding data mining models and methods as user-defined functions (UDFs) within Teradata. Teradata Warehouse Miner's UDF enhancements work like an index from which users can select prepackaged functions which are linked to the detailed data. In a Windows desktop environment, business users can now click, drag and drop the needed analytic function from a list of UDFs and run it against the appropriate detailed data in the warehouse without wasting time by manual coding the function or moving data between systems. The value of UDFs is that they are shareable and reusable among business users, which delivers analytic consistency across the business.
