8 Core Principles of Data Warehouse Design – #3 Transformation

Transformation isn’t clearly stated in the requirements gathering phase, but it must be derived by carefully analyzing the business requirements. Too many times I’ve seen data warehouses attempted without transformation. The data is often a denormalized mirror of the source data and this does not help your users at all.

After you have modeled the data warehouse in an autonomous fashion (see sections #1 and #2) then you must capture the source data and transform it so that it fits the new model. Transformation of the data is code and ETL that is executed behind the scenes and not visible to end users. It makes use of metadata to execute the necessary transformations.
Some transformations can be simple, such as a two columns titled FIRST_NAME and LAST_NAME being concatenated into the data warehouse as a new column called FULL_NAME. Others can be very complicated, such as determining the primary location of a criminal act based on various reports and numerous sources. In any case, whether simple or complex, make sure you transform the data.

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