Traditionally, Business Intelligence leverages only some of the most basic statistical techniques available. BI is still largely using 17th-century statistical techniques: counts, sums, averages and extrema. At most, we might use techniques that were used by Gauss and Galton in the 19th century (e.g., standard deviations and quantiles).
In traditional BI, when we’re slicing and dicing, we take data that’s defined over some complex dimensional space and project it down onto a smaller dimensional space that’s easy to understand. Like virtually everything in statistics, we’re doing this mostly through regression and clustering.
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