E-Discovery: The Changing Game of Review & Analysis

Given the huge growth in data volumes, companies need e-discovery tools to present search results to human reviewers as a relational and prioritized list

May 9, 2009

3 Min Read
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The fifth of a six-part series on e-discovery from the Taneja Group .

In the court case Omega Patents v. Fortin Auto Radio, both the plaintiff and the judge were unhappy with the defendants volume of e-discovery production, which included a few documents and just five emails in a universe of tens of thousands. The defendant subsequently searched for and reviewed more than 17,000 emails, of which 2,000 were produced for plaintiff review. The court was not pleased at the long delay and ruled that the defendant had not proved the undue burden and expense of the subsequent search. The court issued a monetary sanction against the defendant.

Manually sifting through large raw sources of data seriously delays discovery and risks poor results. Too many organizations still depend on a paper-based process where reviewers print, mark up, and pass around documents during the course of review. But given the huge growth in data volumes to be reviewed -- with less and less time to do it -- companies need e-discovery tools to initially analyze, de-duplicate, and present search results to human reviewers as a relational and prioritized list.

This holds true not only for ongoing e-discovery following the initial meeting among parties (called meet-and-confer), but also in the important early case assessment (ECA) phase. ECA is traditionally important in civil litigation as it helps attorneys define an early strategy. But large data stores and shortened timeframes force companies to throw large numbers of attorneys at early case review, leading to high costs and uncoordinated initial reviews. If attorneys are able to review manageable data sets of discoverable electronically stored information (ESI) before the case advances to meet-and-confer, then they can offer a rational plan of settlement or litigation.

Given the unwieldy size of collected data sets, e-discovery tools must work to thin the herd. This includes culling data sets by de-duplicating results, and by analyzing remaining data for priority and relevance. Only then will attorneys be able to meaningfully review and dispense results from a large mass of potential information. In a common example, e-discovery analysis and review tools can de-duplicate a large email repository, and then meaningfully present the complex spider's web of email threads to reviewers. As search teams work to review and further refine result sets, many e-discovery tools can provide alternative presentations of data, typically either relevant extracted snippets or original documents. Snippets can assist search teams in verifying relevance even when faced with large amounts of data. As attorneys review, they can add metadata flags and modify search results online in order to work collaboratively in large teams.Analysis and review is a popular entry point for the legal department's purchase of e-discovery toolsets, and competition here is fierce. Just a few examples include Clearwell Systems Inc. , Kazeon Systems Inc. , CaseCentral , and StoredIQ Corp. ; and there are many others ranging from specialized niche products to large consulting and technology platforms like ONSITE3 .

An automated review process should also go a long way to protect document security. Not all legal staff requires (or should have) all access to all data. Because the review process automates large amounts of initial review, the e-discovery application can confine sensitive documents to role-based reviewers without overloading them.

Next, the sixth and final step in the e-discovery workflow: production. We will also present our conclusions and next steps.

Part one of this series can be found here.

Part two of this series can be found here.Part three of this series can be found here.

Part four of this series can be found here.

Christine Taylor is an analyst with Taneja Group , which provides research and analysis to the storage, server, and knowledge management industries.

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