What method is most effective for determining if new email filters successfully reduced malware detections?

To determine if new email filters have successfully reduced malware detections, data correlation is particularly effective because it involves examining relationships and patterns between different sets of data. In this context, correlating data from before and after the implementation of the new email filters allows for a clear analysis of any changes in malware detection rates. By comparing these two datasets, you can identify whether there has been a significant drop in detections that can be attributed to the filters.

Data correlation can help in identifying trends and establishing a cause-and-effect relationship, which is crucial for assessing the filters' impact on malware detection. It can reveal whether there are fewer malware threats reaching users' inboxes and help validate the effectiveness of the implemented filters through statistical analysis.

While data analysis is a broad term that could apply to many forms of reviewing data, it doesn't specifically focus on the comparisons needed to confirm the filters' effectiveness. Data mining involves exploring large datasets to find patterns but is less focused on the specific comparison of pre- and post-filter performance. Data integrity checks are designed to ensure that data is accurate and consistent but do not analyze the effectiveness of filters on malware detection directly. Thus, data correlation stands out as the most focused and relevant method for this particular task.