If A has an impact on B and B has an impact on C, it is logical to deduce that A has an impact on C. Before modifying A, surely one would be interested to know what changes C would get. Enter impact analysis. In SAS Data Integration Studio, impact analysis identifies jobs, columns or transformation (our C) that would be impacted if a table or a column were to be modified (our A).
Translating A into a real table, let us consider an Employees table shown below.
By using the “Splitter” transformation in DI Studio, we split Employees into 2 tables based on the gender of the employees.
After the Split Job is ran, the FemaleEmployees table will have only 2 rows where Gender=’F’.
Now, let us create another SAS DI Studio job where FemaleEmployees table becomes an input to the job and the output is a sorted table by salary ascending.
This is how SortFemaleEmployees looks like after the job is run.
So far, we have only described the flow of the job processes. Now imagine in a large corporation, you have an Employees table and you are not sure where exactly this table is being used and what end results come out of it. Using SAS Data Integration Studio, you can easily right-click on this table name and click on “Analyze“.
You will get a graphical representation of the process flows from the Employees table to all its appropriate direct and indirect end tables.
It is possible to run impact analysis on a more granular level such as a specific column name in Employees. It is also possible to run reverse impact analysis on the end table, SortedFemaleEmployees, to figure out its sources. But that is a topic for another day!