Experienced eDiscovery counsel, team leaders, and project managers can name with off the cuff fluidity domains that should be omitted from virtually all eDiscovery matters. The repetitive exercise of filtering unnecessary electronically stored information [ESI] from data collections has become a standard for these veterans. In addition to the known irrelevant domains, like weekly ESPN feeds and Blue Mountain e-cards, these eDiscovery professionals use various approaches for identifying other email domains for wholesale removal from their ESI population before beginning the process of review.  Spam removal from ESI can also improve the relevancy ratio for an ESI data set in any litigation. Some eDiscovery technology solutions provide domain analytics tools that can facilitate the management of email documents for review based on their respective communication domain terminals.

Capital Novus takes a unique approach to domain analytics by providing reviewers with a quick overview of the domains in the data set in a fashion that conveys who communicated with whom trailing corresponding frequency information.

The Capital Novus’s eZVUE and eZReview modules of eZSuite integrate domain analytics tools for analyzing large PST content for e-discovery review filtering.  Domain analytics present content in a format for easy identification of weekly newsfeed, recurring junk sources and privileged emails from ESI. The eZVUE domain analytics contain a listserv of over 250 potentially privileged domains that can prompt culling exercises or creating a review set of privilege emails. The ability to categorize large sets of non-responsive and privileged information through domain filtering can save time, facilitate compliance with e-discovery budgets, and promote review efficiency.

Additionally, as key domains surface as sources for particular review issue relevance, legal teams can dynamically prioritize datasets based on the domain analytics results. For email data from multiple custodians or a company server, the domain communications function provides intelligence regarding communication connections within a certain entity. Domain analytics can facilitate the separation of internal and external communications for further review.

For example, reviewers can build a search based on all emails Company A sent internally or to specific recipient domains. Reviewers can also achieve the reverse by identifying all emails received by Company A from internal sources or specific domains. Furthermore, a subsequent date filter can refine the results. Figure 1.0 displays this option. 

Figure 1.0



Afterwards, additional filters can further prioritize and organize the review set by limiting the data set to certain custodians, full text terms, etc. A host of advanced search options can refine data sets of this nature within eZReview and eZVUE applications.

Domain analytics can cull an ESI dataset or categorize segments for further review. Employing this technology will assist in refining a workflow based on review needs.

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