In the beginning, data warehousing boiled down to creating ways to effectively integrate data from across a firm's various systems. Prior to the development of data warehousing, disparate systems prohibited analysts from effectively performing their jobs. As data warehouse technology, requirements and processes have matured, more and more organizations are now preparing for the next major phase of their business intelligence (BI) infrastructure. Phase two encompasses many different aspects, including upgrades to: extract, transform and load (ETL) infrastructure; the data model or the data architecture; information access tools; functionality such as campaign management, business performance management or predictive modeling; and any other major change in architecture, technology or business functionality.

It is important to enter this new phase of data warehousing with a different mind-set. Instead of thinking of the problem as a data integration issue, today's data warehouse professionals are thinking more in terms of analytical functionality. Organizations that are focusing on data integration issues perform long, expensive projects and leave information-starved users with little-improved functionality, albeit with extra data delivered in a slightly more timely fashion. This column offers a strategy to follow when approaching the new phase of data warehousing and includes specific tools to use when executing the strategy.

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