Data warehousing and data mining are closely related technologies that support business intelligence and analytics. Data warehousing is the process of collecting, integrating, and organizing data from various sources into a centralized repository that can support complex queries and analysis. Data mining is the process of applying various techniques and algorithms to extract useful information and patterns from the data stored in the data warehouse. Data mining can help discover hidden relationships, trends, anomalies, and insights that can improve decision making and performance. One of the main reasons for building a data warehouse is to enable data mining, as data warehouses provide a consistent, reliable, and comprehensive source of data that can be mined for various purposes. Data warehouses also facilitate data mining by providing data quality, data cleansing, data transformation, data aggregation, and data indexing services that can enhance the accuracy and efficiency of data mining. Data warehouses and data mining are complementary technologies that work together to deliver business value and competitive advantage. References: Data Warehousing and Data Mining 101, Data Warehousing and Data Mining - Topcoder, Difference between Data Warehousing and Data Mining
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