Analysis: Data Warehouses and Business Intelligence

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Data Warehouses and Business Intelligence Introduction Enterprise Data Warehouses (EDW) have become the foundation of many enterprises' systems of record, serving as the catalyst of strategic initiatives encompassing Customer Relationship Management (CRM), Supply Chain Management SCM) and the pervasive adoption of analytics and Business Intelligence (BI) throughout enterprises. The role of databases continues to be an ancillary one, supporting the overall structural and data integrity of the EDW and increasing its value to the overall enterprise (Phillips, 1997). The advances made over the last decade in the areas of Extra, Transact & Load (ETL) have made it possible to create EDW frameworks and platforms more efficiently, creating greater accuracy in overall database and data warehouse performance as a result (Ballou, Tayi, 1999). The creation and use of an EDW to further drive an organization to its objectives requires that the differences between databases and data warehouses be defined, in addition to a clear, concise definition of just what data warehouse technologies are. Finally, the relationship between data warehouses and business intelligence (BI) including analytics needs analysis and validation. Each of these three areas are discussed in this analysis. Defining the Differences Between Databases and Data Warehouses A database is often very transaction-driven, with a specific time horizon of data that is captured and analyzed in creating reports,
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