The corporation turned to OpenText proprietary technology-assisted review based on continuous active learning (TAR 2.0) and OpenText™ Managed Services to find the relevant documents as quickly and efficiently as possible. Overall, the team achieved 94 percent recall, far greater than that required by the courts. It did so having reviewed only 30 percent of the collection, meeting the corporation’s deadline with room to spare. Additionally, review costs remained substantially lower than they would have been with linear review or keyword search.
The managed review team used CAL from the beginning, without the need for a senior lawyer subject matter expert (SME) to review thousands of documents to train the system. Rather, the team got started right away using the responsive documents already identified for initial training with no time to waste.
The measure of a predictive review is how quickly the algorithm can surface relevant documents. OpenText TAR 2.0 technology picked up the trail almost immediately.
There were almost 5,000 batches in this review. Each blue line represents the percentage of relevant documents in the batch. In the early stages, the number reached 80 to 90 percent. Reviewers stayed as efficient as possible to keep review costs as low as possible.
When keywords are used to cull documents, reviewers typically have to look at as many as nine nonresponsive documents for each responsive one. That means they are wasting their time for about 90 percent of their review efforts. With OpenText TAR 2.0 technology, statistics show the ratio is much narrower, about 2:1, which means the team finishes faster and bills less.
In this case, the team achieved review efficiency of 1.33:1—remarkable efficiency achieved by the combination of the TAR 2.0 engine plus reviewer expertise. In fact, the chart shows batches quickly approached 100 percent responsive and some batches were 100 percent responsive.
The team reviewed 600,000 documents in all and garnered useful results.