Analytics can save valuable time and money with respect to eDiscovery projects used to identify documents for early case assessment, litigation, investigations, due diligence, or audits. In addition, traditional key word search terms have limitations and analytics can be used to recognize patterns that might not otherwise be picked up on during a key word search and review. Here are some types of analytics being used for eDiscovery.

1. E-Discovery Analytics
W W W . N E X T R A S O L U T I O N S . C O M
D A T A V I S U A L I Z A T I O N
Provides greater context and insight to help absorb
information more easily by displaying data graphically. Users
can visualize emerging trends and see summary of the data.
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E M A I L T H R E A D I N G
Finds subsets of emails that include all replies and "sideways"
conversations. Allows users to review more efficiently by only
reviewing messages with complete conversation.
C L U S T E R I N G
Clustering involves grouping documents into related
"buckets." The buckets identify key words that show what
documents have in common at a conceptual level.
P R E D I C T I V E C O D I N G
Uses algorithms to apply machine learning and code
documents into categories. Subject matter experts code a seed
set of documents which is applied to a larger document set.
C O N C E P T U A L S E A R C H I N G
Allows users to find similar documents without relying on
traditional key word search terms or strings. Users can
organize based on conceptual relevance of the documents.
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