Save Clients Thousands
Containing Costs with Concept Searching
Our client, a major national bank, was sued by its recently acquired mortgage lending company just after the sub-prime mortgage financing collapse. The case, which arose in 2008, hinged upon the interpretation of a term in the purchase contract related to the valuation of certain types of subprime loans involved in the securitization process.
We identified a set of key documents and concepts in our possession, which we had to use to find similar documents in the opposing party’s production. In trying to do so, we faced multiple challenges.
One challenge was time. We were under pressure to find these documents quickly to conduct depositions and stay on track with our litigation schedule. Also, despite agreements made regarding e-discovery during the Rule 26(f) conference, the opposing party failed to cull down their data set. Instead, they produced 80,000 email messages, many of which were irrelevant to the litigation. Finally, most of these documents lacked critical email metadata allowed for conversation threading. Without all the metadata fields we requested, it was exceedingly difficult to match our key documents to similar documents in the lender’s production or to track them.
We tried to compel the lender to produce the metadata. But the process of compelling the opposing party was met with lengthy debate and hearings with the discovery referee. With depositions looming, we could not remain idle while waiting for the motion to be granted and risk falling behind our litigation schedule. We needed something that would allow us to identify these key documents within the massive data set without resulting in skyrocketing legal costs for our client or a large investment of time.
Using a trusted service provider, we initially attempted to minimize the document collection and pull together similar documents by running near-duplication detection. This failed because the near duplication did not give us the ability to run a targeted search for just the documents or clauses we were seeking. Additionally, the opposition had removed the footer language from the bottom of each email, which caused significant variation in the body of the text file for the key documents we collected and prevented us from locating the near duplicates.
Frustrated, we contacted an e-discovery consultant to help us find a solution that could match key documents between our production and the opposing party’s. We also needed an efficient means of reviewing 80,000 email messages in a matter of days. In addition, we needed the solution to be cost-effective, efficient and scalable, as we had multiple offices and multiple parties that required access to the case information.
The consultant recommended using a Web-based litigation support solution that had a powerful built-in document analytics package. This solution was well-respected in its ability to facilitate the downstream portion of the e-discovery process, specifically aiding attorneys with analysis, review and production.
Our legal staff, however, did not have much time or the desire to learn a new piece of software. To our satisfaction, getting our personnel up-to-speed on this particular solution was completed within a day—45 minutes to be exact. A small learning curve was critical in saving us time while containing costs for our client.
Using traditional culling methods had already fallen short of our goal to find key documents. Fortunately, the software came equipped with embedded text analytics capabilities. The text analytics allowed us to search a data set based on concepts, thus leveraging our knowledge of the facts and making use of the key documents we already found. All we had to do was select relevant lines of text in our key documents—specifically, clauses regarding valuation of sub-prime loans—and then run a concept search against the opponent’s production. The software automatically returned documents that contained similar concepts, even if the wording was not identical or there were variations in the content. In the end, we ended up finding about 1,000 documents similar to our original examples, so we focused our efforts on this data.
Although we had quickly honed in on the most important documents in the case, we still were tasked with reviewing the entirety of the lender’s production. Thanks to the package’s intuitive interface, the review took 10 attorneys only 10 days to complete. This saved the client $50,000 in review costs—half the amount budgeted.
The solution’s scalability also enabled a diverse team of about 40 people, including attorneys, experts, vendors and representatives from our client, to access the system. There were no issues of security because the software enabled us to set permissions so certain information was only viewable to certain people.
After we found the critical documents and reviewed the large set of email messages provided to us by the opposing party, we were able to gauge the weaknesses and strengths of our client’s case. Our case proved to be strong, and so we approached the opposition to negotiate a settlement.
If we had not had the benefit of analytics within the litigation support platform, we never would have gained the early insight into the case that we had. Without that insight, both parties would have had to produce information from an additional 33 custodians, nearly twice as many as the number both parties had already produced. Incorporating more custodians would have necessitated the review of thousands more documents. By taking advantage of the solution’s concept search capabilities, ease-of-use and scalability, we earned our client a favorable settlement and saved them potentially hundreds of thousands of dollars.
Relativity, the solution used in this case study, was recommended and hosted by Premium Hosting Partner Evolve Discovery. Evolve Discovery’s project management and technology team assisted the law firm with the use of analytical technology and online review to streamline workflow and leverage technology. Relativity is a Web-based enterprise application servicing the analysis, review and production stages of the EDRM. Its Relativity Analytics feature set uses CAAT-advanced mathematical modeling to identify where ideas and concepts intersect within documents.
For more information about CAAT, contact Content Analyst Company; headquartered in Reston, VA, they can be reached at 1-888-349-9442, or info@contentanalyst.com.
For more information about Relativity, contact kCura Corporation at info@kcura.com.
To contact Evolve Discovery, please call 415-398-8600 or email Shamir Colloff at scolloff@evolvediscovery.com.