Impact analysis is an important part of software engineering.
When you make changes to a system, it’s important that you know what parts of the system are impacted by your change request. Impact analysis helps identify components that are impacted by the change request. It provides the context or foundation for understanding and implementing changes in the system.
The impact analysis process starts with identifying all existing dependencies between components within your application, then recording them in a diagrammatic form (for example: UML class diagrams). This allows you to see how each component interacts with others, so when new requirements come along they can be quickly evaluated against existing dependencies as well as any potential risks associated with changing those dependencies.
Impact analysis has traditionally been manual and time-consuming.
However, traditional impact analysis methods have been manual and time-consuming, which is not feasible in today’s business environment where data scientists are expected to analyze large amounts of data at scale quickly. To make impact analysis more manageable, we have developed an automated approach that offers several benefits, including:
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Analyzing millions of records within seconds or minutes instead of hours or days.
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Eliminating human errors when performing manual calculations, such as those made in Excel spreadsheets.
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Allowing data scientists to focus on higher-level tasks, like building models, rather than spending hours on mundane tasks like calculating averages or proportions per customer segmentation scheme.