IT and litigation professionals call for better ways to obtain deep-dive information about big data and large litigation data sets – in order to evaluate and make critical decisions relating to data management, policy and rule enforcement, and the collection and subsequent treatment of litigation data. Organizational leaders want more information to assist with file management, document retention and archiving, compliance enforcement and monitoring, protection of personal information, and litigation readiness, legal hold and collection. Litigation professionals want better contextual information about litigation data and greater insight into content in order to prioritize document review and estimate and minimize the scope and cost of the eDiscovery project.
More ways to visualize data create more options by which to accomplish enterprise data goals and to cull out, prioritize, and structure litigation review – creating more flexibility over the greatest possible variety of data and sources, and covering a greater diversity of purposes and stakeholders. Multiple ways to view data, for multiple uses, tracking the natural workflows of information professionals and litigators drive speed and greater accuracy on critical decisions:
- Analytics-driven file analysis can significantly reduce the time and resources necessary to organize, manage, and archive enterprise data.
- Multidimensional views into the content of enterprise data help enforce and monitor compliance and protect personal information as required by domestic and international regulations.
- Analytics provide greater actionable insight empowering organizations to become more litigation ready and better able to quickly respond to legal requirements to identify, preserve, and collect potentially relevant information.
- Applied prior to processing and review of a litigation data set, analytics can significantly reduce time and cost of the matter by removing clearly irrelevant and duplicative documents (typically by an average 70-90%).
- During litigation review, analytics can be used to cluster related documents for batching and bulk tagging, to QC for inconsistent coding decisions on related documents, and on new issues arising as the legal team learns more about the case.
- After formal review, analytics accommodates last-minute concerns and critical changes to the set of documents that will be produced.
eZAnalytics engine is the data analysis engine powering nayaEdge and the eDiscovery eZSuite modules (eZProcess, eZVUE and eZReview). It provides multidimensional views of not only standard metadata (document properties, file types, email addresses), but also of people/locations/organizations, concepts/thematic patterns, and frequently used phrases – identified within the core document content – while also removing noise such as headers or disclaimers. eZAnalytics engine identifies email communication threads, most frequent communications (considering the senders and receivers of email), and near-duplicates. Using eZAnalytics engine, data analysis can be performed on a micro or macro level, at any stage of enterprise data management (click here for more information) and the eDiscovery lifecycle (click here to learn more).
A Content Intelligence and Data Mining Engine