The modern business intelligence (BI) environment must address a daunting array of challenges: a broad spectrum of analytical applications, increasing data volumes, increasing data disparity, and refresh rates ranging from periodic to continuous. These challenges escalate the complexity of data quality processing, which is fundamental to the promise of BI. Traditional data quality tools are limited in their scope and scale to address the data quality issues implicated by the increasing data volumes and disparity.

The speed with which you can cleanse, transform, and absorb data into warehouse structures and BI applications is critical to the success of many organizations. With global companies operating 24 x 7, there is literally no time available for traditional data warehousing technologies. It is here where virtually all traditional approaches to data warehousing are made obsolete, including data quality.

With Teradata Warehouse Miner, solution strategists and architects can design and implement comprehensive, adaptive data quality solutions. These solutions can complement existing ETL processes just like traditional data quality technology, or Teradata Warehouse Miner can be the foundation to a pure Teradata platform solution that does cleansing and transformation. In either case, proper implementation of Teradata Warehouse Miner with its in-database capability ensures the necessary data for analytics and delivers on the promise of BI.

This paper, written by Michael Gonzales of The Focus Group, defines the architecture necessary for an enterprise-wide data quality strategy: architecture based on Teradata Warehouse Miner for providing a predictable and timely source of quality data.