IMPROVING DOCUMENT REVIEW WITH NEW GENERATION OF TECHNOLOGY ASSISTED REVIEW
CD: Could you provide an overview of the extent to which technology assisted review (TAR) is becoming a fixture in the discovery process?
Burton: The volume of data created and exchanged is increasing every day, and the amount of data collected during a typical case has increased dramatically in recent years. Doing a full manual review is no longer feasible and the use of keywords alone to narrow that data is no longer sufficient. Following the 2016 UK rulings on the use of TAR, parties are far more open to the use of intelligent TAR tools to analyse, investigate and model the data to quickly identify potentially relevant documents. Combining TAR 1.0 and 2.0 workflows with new data visualisations and entity extraction for finding metadata in the document content, has meant that far more is achievable by looking at the data from the top down, rather than on a document-by-document basis.
CD: How would you characterise the benefits and drawbacks of TAR? Is linear review becoming a thing of the past?
Burton: Linear review does not scale well and the days of throwing more staff and more hours at a case are behind us. The legal standpoint is to perform intelligent and proportional review of the data and TAR provides a defensible solution to this. By leveraging TAR 1.0 or 2.0 workflows, it is possible to cull the data so that only potentially relevant documents are identified and reviewed. This reduces the time it takes to review the majority of the relevant documents and therefore reduces costs. However, this method relies on the assumption that not all relevant material is required to make the case. If you are not permitted to cull the data population, then TAR can be used to prioritise the documents so that the potentially relevant documents are reviewed first. The drawback to this process, however, is that this will add the analytics costs on top of the existing document review.
Jan-Mar 2019 issue