Impact Analysis

Impact analysis is the practice of determining, before a change is made or after an incident occurs, everything that will be affected downstream. Using the data lineage graph, it answers questions such as: if we rename this column, which pipelines break? If this source table was wrong for three days, which dashboards, reports, and models consumed bad numbers? Who must be notified?

Done manually, impact analysis means emailing teams and hoping someone remembers a dependency. Done with automated lineage, it is a traversal of the graph that returns a complete, current list of affected assets and their owners in seconds.

For a Saudi Data Management Office, impact analysis serves three duties at once. It protects regulatory reporting: a schema change that would silently break a submission to a national platform is discovered before deployment, not after a deadline. It scopes incidents precisely, including assessing whether personal data was affected and which downstream systems must be considered — essential input for PDPL breach-handling decisions under SDAIA's oversight. And it disciplines change management, a recurring expectation across NDMO controls, by attaching evidence of downstream review to every change. The result is fewer surprises and a defensible record of due diligence.