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previously hidden patterns For example, let s take a wine merchant Users can analyze sales by type of wine and rating Users also can see which customers buy the most wine Now break that customer list down by corporate customers versus individual consumers, and the analysis will reveal which group of customers generates the most business If the dimension table contained income information for each corporate customer, the wine merchant can better understand if large corporate customers are more profitable than small business owners The same logic applies to product information: what are sales for soft drinks in plastic bottles versus aluminum cans Unfortunately, this kind of dimensional information is often not captured, or when it is captured, it is stored in departmental databases and spreadsheets While Desktop Intelligence users can incorporate personal data sources into their reports, I would advocate that more dimensional information be stored in sources that are accessible to more users If companies use a star or snowflake schema in data marts, then a data mart can contain both a standard customer dimension and a customer dimension with business-specific groupings and hierarchies BusinessObjects XI can handle multiple dimensions with alternate hierarchies in each Further, dimensional information in relational tables allows users to create lists and easily do attribute analysis with the information, a type of analysis that not all OLAP databases support and that is not possible when such information remains in personal workspaces only

data warehouse, this still may be accessible via Web Intelligence It requires some creativity and some organizational issues, but here is a work-around: Web Intelligence in XI Release 2 allows for synchronized multiple data sources in a document With this capability, the central data warehouse team does not have to load the departmental dimensional data into the central data warehouse to make it accessible to end users They do, however, have to allow the department to build a universe off the departmental data source End users can then easily combine the data from the central data warehouse universe with the dimensional data in the universe from the departmental data source

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There are always hurdles. Most will come from within the organizational structure, and some will be technical. The technical ones are easier to fix, because it s just a matter of finding the right solution. The organizational ones need care and attention and a psychological approach.

Relational tables are not a good place to physically store ratios and variances, as the ratio always needs to be recalculated with an aggregated numerator and an aggregated denominator To follow on the earlier example, let s say you want to calculate the percentage variance between the current year-to-date sales (C_YTD) and last year s year-to-date sales (LY_YTD) For each row in the database, you can correctly store the variance

product_key 123 123 123 customer_key 111 333 777 time_key 102003 102003 102003 C_YTD 5000 7100 8000 L_YTD 3850 3000 10000 % Variance 2987% 13667% 2000%

Figure 5-22 One of the strengths of KPIs is that they can identify trends The Trend included with the Aggregations is shown here, but it can only identify a trend over the last two periods

It s important not to surrender to a feeling of temporary failure when an iteration goes bad, tests go slower than expected, and so on. It s sometimes hard to get going, and you ll need to persist for at least a couple of months to start feeling comfortable with the new process and to iron out all the kinks. Have management commit to continuing for at least three months even if things don t go as planned. It s important to get their agreement up front. You don t want to be running around trying to convince people in the middle of a stressful first month. Now that we ve looked at ways of ensuring things go right, let s look at some things that can lead to failure.

However, recall that Web Intelligence users will create reports that dynamically group information by dimensions Thus a business user may ask for a variance analysis by product In order to allow this, the universe designer includes the SQL SUM aggregate in the object

Part II:

definition This incorrectly sums the individual variance rows, suggesting that sales are 14654 percent higher than last year s sales This is wrong!

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