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Data warehouses and enterprise-class platforms such as the Oracle Exadata Database Machine are usually deployed for one of two reasons to better determine where to reduce bottom-line business costs or to increase top-line business growth While IT management might initially want to focus on the ability to meet business Service Level Agreements or reduced IT costs by deploying this platform, this may not provide enough justification to get funding Discussing the business analysis needs with lines-of-business communities might be thought of as difficult by some in IT To help you along, the following questions might serve as a starting point in uncovering key business initiatives that could fund a data warehouse project on this platform:

Part III:

3 From the source tree, drag a measure to the Measure drop zone in the work area The measure is added to the crosstab and the analysis is created For this example, we add the Revenue measure As shown next, you can see that from the prior year-to-date to the current year-to-date, overall revenue has increased by almost $12 million However, revenue has decreased by more than $340,000 for Outdoor Protection A potential question is beginning to unfold: Why is revenue down for Outdoor Protection This is where you can begin to use some of the tools that Analysis Studio has to offer to see what might be happening This generates the next business question that needs to be answered

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How is the data warehouse being used by the lines of business today How accessible is the current data Is it trusted and is it available in time to make key business decisions What business key performance indicators (KPIs) and data will be needed in the future What would be the potential business value in delivery of these additional KPIs and data What is the mix of reporting, ad hoc query, analysis, and modeling today How might that mix change in the future How might data loading characteristics change in the future

With Analysis Studio, you can use many tools to answer your business questions One of the first things that you may want to do is navigate through your data Navigating through a dimensional data source is a lot like accessing data in a filing cabinet: You keep digging through the folders until you get to the data you need In a dimensional data source, this is called drilling-down and drilling-up

When you drill-down or drill-up through data, you can see what items lie beneath other items, much like items stored in folders inside a filing cabinet When you drill-down, you see the children of the selected parent Again, a child is the detailed information that lies beneath the current level, or parent, in the hierarchy For example, suppose the Geography dimension of a package has been added to an analysis Within the dimension, the first member and the highest level is Country, which contains all the countries in which the company sells products If we click a country link in the analysis, the United States for example, the next level down, Provinces/States, displays in the analysis This level contains the states in the United States in which the company sells products If we now click a state link, Pennsylvania for example, the next level below that displays in the analysis, which is Cities This level shows all of the cities in Pennsylvania in which the company sells products When you drill-up, you see less detailed information or the parent of the child that you are currently viewing For example, if we drill-up on the Cities member, we see the Provinces/States, or if we drill-up on Provinces/States, we see Countries

Table 7-3 lists some of the key data warehousing initiatives currently under way in selected industries that are driven by real business needs and, when linked to performance and availability criteria, can lead to an understanding of how to properly size the platform The initiatives change over time based on changing business conditions In shrinking economies or market segments, organizations most often focus on cost savings In growing economies or market segments, the focus is more often on business growth For government agencies that are typically much less influenced by economic conditions, the data warehousing initiatives tend to be about better delivery of information to sponsors and constituents to provide proof points as to the value that the agency provides

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