Impact analysis could only be done on the specific data elements
that were brought into the warehouse environment. In addition, data was generally loaded
from the golden source or from the operational store that had update or origination rights
over the data element. Unless the warehouse included an element data consolidation, the
various sources of the data were never captured.
While field-to-field mapping of the data warehouse provides enormous benefits, logical impact
analysis has been gaining ground in the past few years. Logical impact analysis utilizes the
models within the environment and follows the linkages that are implied or explicitly stated.
In a simple example, ER tools provide linkages between the logical entities and attributes,
physical tables and fields, and the actual database instances of the model itself. Traditional
metadata repositories have been built around this linkage between the three layers of data
designation: logical, physical, and implementation. UML and other higher-level modeling
languages are looking into this impact analysis at the model level. Most research of impact
analysis in the software environment has focused on the code, but recent research has extended
this to the modeling environment.
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