A traditional data warehouse focuses on strategic decision support. Online analytical processing (OLAP) and data mining techniques are used to explore historical data for trends and insights to develop business strategies. Applications with high returns on investment include customer segmentation, strategic price management, retail category management, asset management and channel management. The business value associated with transforming an organization to quantitatively driven strategy development has been a tremendous success for data warehousing.

As organizations realize the value of data for strategic decision making, the lack of information available for front-line decision making has become dramatically apparent. A well- developed strategy is critical, but ultimate value to an organization is only as good as its execution. As a result, deployment of data warehouse solutions for operational, tactical decision making is becoming an increasingly important use of information assets. A variety of architectural frameworks ­ such as Active Data Warehousing, the Corporate Information Factory and Zero- Latency Enterprise ­ have emerged in recognition of the importance of tactical decision support as an extension of traditional data warehouse capabilities.

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