For customers processing large document collections, which tactic is not useful with Relativity Analytics?

The tactic of using Document Compare to isolate related documents is not particularly useful when processing large document collections within the context of Relativity Analytics. Document Compare is a feature designed to compare two documents side-by-side, highlighting differences and similarities between them. While it can be valuable in specific instances—especially for detailed analysis of a pair of documents—its utility diminishes with larger collections where the focus is typically on broader patterns and trends rather than detailed comparisons of individual document pairs.

In contrast, clustering can effectively group similar documents, making it easier for reviewers to navigate and assess large volumes of information. Setting up multiple review teams allows for division of labor and more efficient handling of large collections. Predictive coding leverages machine learning to streamline document review by allowing the system to identify relevant documents based on previous examples, which is particularly effective for large datasets. These tactics align more closely with the objectives of managing and gleaning insights from large, complex document collections, making them significantly more relevant than Document Compare in this context.