Large-Scale Document Review Limited by Outdated Two-Dimensional Search Tools Litigators have always been challenged to improve the speed, efficiency and effectiveness of document reviews, while satisfying the obligation to produce requested relevant and non-privileged documents.Historically relying on the scanning of paper documents and implementation of Optical Character Recognition (OCR) for computer-based document review, first- generation tools enabled only small team access to documents, serially – a time and cost prohibitive approach for larger document populations.With the electronic age and proliferation of email as a primary means of communication, second-generation tools emerged to enable review of email threads natively. They also offered a corresponding capacity to remove duplicates within document sets. With these tools, reviewers craft a Boolean search query, examine the results one-by-one and map the documents to an emerging fact timeline. Characteristically, this process is repeated sequentially across a variety of different keywords. Though offering a faster, more consistent and higher quality review, these tools still fall short of accommodating the needs of large-scale reviews.
Ringtail Analytics Creates a New Approach: Three Dimensional Data Reviews and Analysis Ringtail Analytics has a better solution. The product offers the ability to efficiently and effectively review large data populations to not only get to the relevant data, but quickly eliminate data that is not. This is made possible by a revolutionary cube-based visual concept that extends the two-dimensional array of a spreadsheet into a 3D pivot table, much like that found in Microsoft Excel.This third-generation approach provides a means to perform multiple queries simultaneously and map the results to a single three-dimensional dashboard-like interface. Reviewers can enjoy a macro view of data, drilling down quickly to the most interesting batches of documents with just a few mouse clicks, and adding those results to lists requiring more detailed review.
How it Works Ringtail Analytics runs Boolean and concept queries on the document content and metadata behind the scenes—storing that information in an AS 2005 Online Analytical Processing (OLAP) cube. Information stored in “fact” tables contains specific pieces of information. In Ringtail, key phrases of the content and metadata about the document (e.g. creation date, modification date) are stored in these fact tables.By pre-querying the data and caching the results within the cube, subsequent OLAP queries are faster than traditional SQL queries, by a factor of as much as 1000%!