The reason is that if the same product is sold in both Australia and Europe, it is counted once in Australia, once in Europe, but only once at the total level. You can clearly tell that the value shown at the total level is not the sum of individual rows. The # Products measure behaves differently. Indeed, Sales Amount is a simple additive measure. Same for time: the sales amount in a year is the sum of the sales in each day of that year. ![]() The sales amount of all the products is the sum of the sales of each individual product. Not only it is additive over continents: it is additive over any dimension. The report clearly shows that Sales Amount is additive: Sales Amount over the entire planet is the sum of sales in each individual continent. Once projected in a matrix, this is the result. We want to produce a report that shows the sales amount and the number of products sold on different continents. Let us start with a very simple report that shows the issue at hand. Choosing the easy way out of introducing additivity in a naturally non-additive calculation means losing the opportunity to generate accurate calculations, and relying on inaccurate values. When users complain about the fact that the rows do not sum up, seasoned BI developers offer a rational explanation of the reasons why the number are not summed: this process often provides a better understanding of how values are computed. ![]() At that point, the total can no longer be computed by summing the rows for a very good reason: it would be inaccurate. When working with business intelligence solutions, sooner or later a developer will author a calculation that is non-additive. ![]() Nonetheless, the total is the sum of rows only for additive measures, which are measures that are naturally computed as a sum. This behavior is extremely natural and mostly effective. The simplest and most intuitive way is to verify whether the total equals the sum of individual rows. When looking at a report, it is natural to double-check the numbers produced.
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