Impact Analysis after Making Changes
This topic is a rough draft and will be completed later.
Most folks who have been involved in some form of data management or analytics are already familiar with the concept of Impact Analysis, but let's briefly remind ourselves using a familiar conversation below:
DBA: We're dropping the INVOICE_001 table from the database.
Report Developer: Waaait a minute -- we're not sure if anyone is using that table
DBA: Well, can you go check?
Report Developer: Well, yes, but it will take a few days to go through and ask the developers -- they're pretty tied up
We all know how this story ends: The DBA never gets her database cleaned up, unwanted or extraneous database objects remain visible to data modelers, the analytics team incorrectly uses certain tables or columns and the leadership, after observing questionable numbers ( "...the numbers are no good!") no longer considers the data as trustworthy. And not too far after that, the BI/analytic platform user base dwindles into shelfware.
So what is 'Impact Analysis', exactly?
It's the ability to identify what reports, dashboards, formulas, variables, users, groups, systems etc. would be affected if any of the reusable parts of the system (e.g. a database table) were modified or removed.