REDUCING COSTS WITH ADVANCED REVIEW STRATEGIES - PRIORITIZATION FOR 100% REVIEW. Bill Tolson Sr. Product Marketing Manager Recommind Inc.

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1 REDUCING COSTS WITH ADVANCED REVIEW STRATEGIES - Bill Tolson Sr. Product Marketing Manager Recommind Inc.

2 Introduction... 3 Traditional Linear Review... 3 Advanced Review Strategies: A Typical Predictive Coding Workflow... 4 Advanced Review Strategies: Prioritization... 5 Advanced Review Strategies: Prioritization for ECA... 5 Advanced Review Strategies: Document Prioritization for 100% Review... 6 Prioritization Review Cost Savings... 7 Conclusion

3 INTRODUCTION Many legal departments are struggling for ways to reduce, or at least stop growth in their legal budgets. One of the obvious targets for cost reduction in any legal department is the cost of responding to ediscovery, including the cost of in-house or external attorney review for relevance and privilege. Per a Compliance, Governance and Oversight Counsel (CGOC) survey, the average legal department spends approximately $3 million per discovery to gather and prepare information for opposing counsel in litigation. The RAND Institute for Civil Justice has published a 2012 study that points out the cost of legal review for privilege and responsiveness costs an average of $0.73 for every dollar spent on ediscovery. The top four cost reduction strategies legal departments are considering are: 1. Bring more evidence collection and analysis in-house to do more ESI processing internally 2. Keep more of the review of ESI in-house rather that utilize outside law firms 3. Explore off-shore review 4. Pressure external law firms for lower rates Many law firms are also looking for ways to reduce the cost of document review based on number 4 above; pressure from their clients to reduce the fees they charge for ediscovery review. The average civil ediscovery matter can include between 3 and 5 GB of potentially responsive ESI per employee. To put that in context, 1 GB of data can contain between 10,000 and 75,000 pages of content. Multiply that by 3 and you are conservatively looking at between 30,000 and 50,000 pages of content that should be reviewed for relevancy and privilege per employee. Now consider that litigation and ediscovery usually includes more than one employee ranging from two to hundreds. TRADITIONAL LINEAR REVIEW Traditional linear review, the process used for discovery review for decades, is a manual, expensive, time-consuming and error-prone process requiring teams of legal professionals to review hundreds of thousands or millions of documents one page at a time to determine relevance to a specific case. This review step drives the largest cost of ediscovery. Traditional linear review is usually accomplished in the following manner (simplified process): 1. Data is collected from affected custodians 2. Data is collected from enterprise repositories 3. Keyword searches are run on collected data to build a potentially responsive data set 3

4 4. The potentially responsive data set of 112 GB (1.12 million documents) is sent to outside counsel for review and tagging 5. Outside counsel assigns a team(s) of attorneys to review 1.12 million documents for privilege and relevance 6. At $70/hour and a review rate of 55 documents per hour, total document review costs $1.425 million The obvious problem with the traditional review method is that keyword searches produce huge amounts of content. Because the 1.12 million documents in the above example are not prioritized in any meaningful way during keyword search, the entire results set has to be reviewed to determine privilege and responsiveness. This process also doesn t allow for early identification of critical documents for early case assessment. Traditional linear review is particularly disposed to errors based on individual reviewer characteristics and the quantity of documents to be reviewed. In the linear review process, documents are usually split up and given to individual reviewers haphazardly; the first 200,000 go to Bob, the second 200,000 go to Judy, the third 200,000 go to Charles in London and so on. Because of this practice and the lack of document prioritization, potentially critical documents are spread across several reviewers and are not reviewed at the same time and by the same person, greatly reducing consistency. ADVANCED REVIEW STRATEGIES: A TYPICAL PREDICTIVE CODING WORKFLOW Predictive Coding is a court-endorsed process which combines people, technology and workflow to find and tag key documents quickly, irrespective of keyword. Predictive Coding produces a computer-generated judgment - with explicit confidence score - about the relevance, or privileged nature of each document. This capability allows counsel and their clients to dramatically expedite the actual review process while concurrently improving accuracy and lowering the risk of missing key documents. In other words, the computer is able to determine relevancy of documents based on examples provided by the case manager. The case manager ends up having to review a small percentage of the total corpus, which is used to train the system. The Predictive Coding process (Figure 1) starts with a person knowledgeable about the matter, typically an attorney, developing an understanding of the corpus while identifying a small number of documents that are representative of the various categories to be reviewed and coded (i.e. relevance, responsiveness, privilege, issue-relation). This subject matter expert uses sophisticated search and analytical tools, including keyword, Boolean and concept search, concept grouping and other automatically populated filters collectively referred to as Predictive Analytics, to identify probative documents for each category to be reviewed and coded. Documents assigned to a category are run through a training process, whereby the system uses each seed set to identify and prioritize all substantively similar documents over the complete corpus. The case team then reviews and codes all computer suggested documents 4

5 to ensure proper categorization and further calibrate the system. This iterative step is repeated until no further computer suggested documents are returned, meaning no additional substantively similar documents remain in the unreviewed portion of the corpus. The final step in the process employs Predictive Sampling methodology to ensure the accuracy and completeness of the Predictive Coding process (i.e. precision and recall) within an acceptable target error rate, typically 95% or 99%. The result in most cases is a highly accurate and completely verifiable and defensible review process with as little as 10% of a corpus being reviewed and coded by human reviewers, producing dramatic cost and time savings. Figure 1 ADVANCED REVIEW STRATEGIES: PRIORITIZATION One of the workflows available with the Axcelerate Review & Analysis platform is the ability to prioritize an entire corpus to determine a relevancy rating for each and every document in the corpus. For example, every document in a data set can be marked with various levels of relevance to the case such as % - definitely responsive, 60-89% - possibly responsive and 0-59% - non-responsive. This prioritization of all documents can be used for early case assessment or for review priority (i.e. which review team gets those documents and at what cost). Advanced Review Strategies: Prioritization for ECA As an output of the prioritization process, those documents tagged as very high priority ( definitely responsive ) can be identified and forwarded to case managers early in the review process to help determine case strategy going forward. In this way counsel can evaluate risks and costs of the case at a very early stage in the case (Figure 2). REDUCING COSTS WITH ADVANCED REVIEW STRATEGIES 5

6 Advanced Review Strategies: Document Prioritization for 100% Review As was mentioned above, an important advantage of the Axcelerate Review & Analysis platform is that it allows for document relevance rating or prioritization. This means that each and every document receives a ranking as to its relevance to the case. This becomes important for those organizations that have chosen to review 100% of the document in a corpus instead of the alternative predictive coding workflow of allowing the system to find and tag all responsive content. An example of this prioritization workflow is shown in Figure 2 below. For example, after culling, an organization is faced with reviewing 400,000 documents to determine relevance and privilege. A traditional linear review would have all 400,000 documents reviewed by the same team. Reviewing 55 documents per hour per reviewer, the 400,000 documents would take 7,273 man-hours to complete. If the prioritization Advanced Review Strategy were used instead, the documents could be grouped by priority or relevance ranking and given to different teams. This practice is important in that those documents tagged as responsive (high priority) can be quickly given to the internal legal team with the most direct case experience for early case assessment. Figure 2: An example of Predictive Coding Prioritization 6

7 Prioritization Review Cost Savings Prioritization can also be an effective cost savings method even when conducting a 100% document review. Using the same 400,000 document example above, the prioritization process will automatically rank each document by relevance to the case and mark them appropriately. In figure 3, the 400,000 documents were tagged and grouped by 3 rankings: 20% definitely responsive, 30% a medium priority and 50% as non-responsive or low priority. With this segregation into 3 groupings or priority ranges, each document group can be handled differently. For example, the 80,000 documents tagged by the predictive coding system as high priority or responsive can be quickly forwarded to the most experienced legal team for early case assessment and review verification. The medium priority group of 120,000 documents can be sent to an external law firm for review and the low priority group of 200,000 documents can be handed off to a contract review team. You might ask so what? I am still reviewing all 400,000 documents so I haven t saved any time at all. Yes but remember, you chose to review all 400,000 documents for whatever reason so review time saving was not the goal, cost savings was. Figure 3: An example of document grouping by relevance 7

8 The review cost savings utilizing prioritization and 100% review comes from each of the three review teams charging a different hourly review rate and a differing docs-per-hour rate. For example with traditional linear review, all 400,000 documents would be reviewed by the same team at the same hourly rate costing a total of $509,091. REVIEW STRATEGY DOCS/HR $/HR $/DOC FORMULA Linear Review (100%) TOTAL REVIEW COST 55 $70 $1.27 (400,000/55)*$70 $509,091 High Priority Documents (20%) 75 $70 $0.93 (80,000/75)*$70 $74,667 Medium Priority Documents (30%) 65 $55 $0.85 (120,000/65)*$55 $101,538 Low Priority Documents (50%) 80 $40 $0.50 (200,000/80)*$00 $100,000 Total Cost of Review Using Prioritization Dollar Savings over Linear Review Percent Savings over Linear Review $276,205 $232,886 46% Table 1: Linear Review verses Prioritization Table 1 shows that by segregating the documents into prioritized sets, the most relevant and smallest group of documents can be directed to those people that need them most for ECA. Those documents deemed not obviously responsive but still with a medium priority can be directed to an experienced but lower cost review team and those deemed non-responsive can still be reviewed in a cost effective manner by directing them to a low cost contract review team. In the above example, a cost saving of $232,886 was realized, a 46% cost savings over linear review. This predictive coding prioritization strategy allows for a 100% review of all potentially relevant documents while still offering measurable cost savings over that of traditional linear 8

9 review while ensuring the most important documents are made available to your legal team in a fraction of the time. Figure 4: An example of document grouping by relevance and its associated cost savings 9

10 CONCLUSION Recommind s Axcelerate Review & Analysis platform, best known for predictive coding technology, has made great strides with industry acceptance over the last several years, especially in the United States. But, many countries (and some organizations within the U.S.) have been hesitant to accept the new predictive coding paradigm of training the system through human guidance to recognize and tag responsive or privileged documents in large data sets thereby reducing human document review time by between 60 and 90%. The Axcelerate Review & Analysis platform works with many different review workflows including the predictive coding process. Prioritization for 100% review is a viable strategy that can and will save cost over traditional linear review. In the above example, if this organization had just 4 ediscovery/edisclosures per year, their annual cost savings over traditional linear review would be $931,544, with a return on investment (ROI) of 191%, in addition to reduced risk levels from higher accuracy rates and complete ECA data sets. Organizations hesitant to utilize predictive coding to its full potential can still exploit Recommind s other Advanced Review Strategies to lower document review cost while ensuring case managers have quick access to responsive content for early case assessment activities. 10

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