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A system with good tests and coverage will usually allow you to fix things more quickly (assuming the tests are written in a maintainable manner). That, in turn, means better turnaround times, and release cycles that are less stressful. In his book Code Complete (Microsoft Press), Steve McConnell outlines several metrics you can use to test progress. They include the following, among others:

product_key 123 456 123 123 customer_key 111 111 333 777 time_key 102003 102003 102003 102003 C_Month 500 200 710 900 LY_Month 400 180 300 1000 C_YTD 5000 2000 7100 8000 L_YTD 3850 1800 3000 10000

Figure 5-21 This is a Sum Aggregation that has been added to the scorecard Aggregations are automatically available as part of the Details pane for a scorecard

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As the data is loaded into the fact table, each column represents a bucket of information with the time period intelligence built into each column C_MONTH contains sales for just the current month; C_YTD contains sales for each month in the current year If the month is October, it contains sales from January through October Data Integrator has a date generation transform, which generates the time periods needed (quarter, month, year, week, etc) By using our special functions, we can aggregate fact data to these time periods and load the Fact tables in the warehouse with an appropriate indication of the bucket used You can also create aggregations that manage running ytd, mtd, qtd fields You use DI s stateful functions to do this Many analytic applications that build data marts (including SAP BW s Info Cubes) use this kind of structure However, if your company designs and builds your own fact tables, the DBA must program this kind of intelligence into the load routines; unlike in OLAP databases, there may be no simple toggle to achieve time period intelligence (although newer data integration tools may assist with this) A robust ETL tool may help with the process For example, BusinessObjects Data Integrator has a date generation transform that will take an individual date value and create the appropriate year, quarter, and month hierarchies Data Integrator also includes special functions to load data into fact tables to create running year-to-date measures, month-to-date measures, and so on Even with an ETL tool, this kind of design requires more disk space and a stronger understanding of best practices in data warehouse designs DBAs may not have the resources to implement this kind of design or the time (Show me a DBA who is not overworked and overscheduled!) Recall from the first section in this chapter that dimension groupings such as customer age, income level, and product size are also forms of intelligence Analyzing data by dimensions is the bread and butter of most businesses Providing alternative groupings can help reveal.

The number of defects found per class by priority The number of defects per routine number of testing hours per bug found The average number of defects per test case

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Trends are a little different First, it s just as easy to drag the Trend value from the Aggregations node and drop it on the scorecard However, exactly what is it trending The trend that is built into the product only looks at the previous two columns and trends from those In other words, the trend will look at the actual for 2002 and compare it to the actual for 2003, and do the same for the targets (although trends for targets are often not terribly useful) Figure 5-22 shows what happens when the trend is added The sum has been removed, the trend added, and another KPI, Gross Profit Margin, has also been added Notice that while the Actual for sales has risen from 2002 to 2003, the Actual for the gross profit margin has fallen slightly The trend for Sales is up while the trend for Gross Profit Margin is down If a business decides it needs a more powerful trending algorithm than just comparing the last two periods, there are two approaches First, a KPI can be created that includes the MDX necessary to perform the calculation Second, a measure could be built into the Analysis Services cube that performs the trending calculation

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