Which feature helps reviewers quickly identify relevant information?

Cluster analysis is a feature that allows reviewers to group similar documents based on shared characteristics, such as keywords, themes, or patterns. This technique is particularly useful in large data sets where identifying relevant information can be time-consuming. By clustering documents, reviewers can efficiently narrow down their focus to a specific subset that is most pertinent to their needs. This process not only enhances the speed of review but also improves accuracy by ensuring that all related documents are considered together.

In contrast, the other options—like document merging, exporting, and metadata review—serve different purposes in the review process. Document merging focuses on combining information for easier access or review. Document exporting pertains to transferring files for use outside the review system, and metadata review involves looking at data about the documents, such as dates or authors, which aids in understanding context but doesn't directly streamline the identification of relevant content in the way that cluster analysis does. Thus, cluster analysis stands out for its direct role in enhancing the reviewer’s ability to pinpoint valuable information swiftly.