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Practical Business Intelligence: with SQL Server 2005 by Roger Toren, John C. Hancock

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Summary

It is almost a given that your source data will have errors. Your goal is to get as much useful data into the data warehouse without introducing incorrect data. If a fact isn’t providing a business key for a dimension, the data is still useful in other dimensions, just not in that one missing dimension. You should make use of the error output of most of the data flow transforms to handle imperfect data. You can deal with imperfect data by rejecting it, creating an inferred member if you have a business key without a matching dimension member, or you can simply assign the fact to an unknown or miscellaneous category in the dimension. Fuzzy matching can help increase the number of facts successfully matched to known dimension ...

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