In the chaos of day-to-day business, sometimes it's hard to see the forest for the trees. The initial design of an enterprise data warehouse that is supposed to provide strategic information to key analytical thinkers can get bogged down by the overwhelming demand for near real-time information. You spend years designing a rocket ship, and the first thing people want to do is slap roller skates on it and ride it around the parking lot. Your vision of a data warehouse where virtually any information demand can be fulfilled effortlessly is down the drain. Or is it? First we should define the playing field. An enterprise data warehouse (EDW) stores integrated, aggregated information that covers many subject areas and retains months or years of data history. The time span is needed to determine and analyze trends over time. A data mart (DM) is similar in design to a data warehouse, but for a limited subject area(s). An operational data store (ODS) is specifically designed to analyze near real-time data now--to solve day-to-day problems. The content may span many subject areas, but little history is retained because of the temporal nature of the information.

Some examples of ODS data needs within the insurance industry are analyzing the claims backlog by department (to reassign claims processors to overloaded departments), the total accounts payable for the next check run (to move money to the right bank accounts and play the interest float) and what payments have been received in the last 30 days (with check number) for customer service inquiries. Why can't people get near real-time data from the on-line transaction processing (OLTP) system? Does this kind of information belong in an on-line analytical processing (OLAP) data warehouse? The answers are: 1. It's too hard, and 2. Yes (as long as we plan ahead of time). Let's explore why.

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