In the context of process analysis, what does filtering allow you to do?

Filtering is a crucial feature in process analysis that enables users to concentrate on specific segments of the data, thereby uncovering insights relevant to particular criteria or conditions. By applying filters, analysts can isolate certain cases, trends, or anomalies that contribute to a more precise understanding of process performance. For instance, if you want to analyze the efficiency of a specific department, filtering allows you to exclude all other data that is not pertinent to that department's operations, leading to clearer insights.

This focused approach is instrumental in improving decision-making as it allows stakeholders to retrieve only the information they need to assess a particular aspect of the process without the noise of irrelevant data. Ultimately, filtering enhances the analytical capabilities by facilitating a deeper dive into the data that matters most for specific inquiries or objectives.

The other options, while they touch on aspects related to data, do not accurately represent the primary purpose of filtering. Viewing all possible cases is more about the complete data set without the selective narrowing that filtering provides. Manipulating underlying data sources directly or permanently modifying the data set goes beyond filtering, as these actions involve changing the original data rather than simply modifying how data is displayed or analyzed in the context of a report or analysis.