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In my previous blog post How to trim eDiscovery costs part one: Laying the groundwork, I discussed reducing eDiscovery costs by taking the time to set the stage before documents are exchanged. In the next few weeks I will share some more of our practical tips in a series of short articles. Here is Part 2 – Review fewer documents and review efficiently.

Document review is normally the greatest expense in the eDiscovery process. Leveraging analytics to send fewer documents to review will reduce review time. Establishing efficient workflows for your review team will decrease the time spent on review and will result in a higher quality review.

1. Use conceptual analytics

Clustering the data early on can help you quickly pick out the groups of documents that are not important and also help to locate the important documents for prioritizing the review. Giving the litigation team access to the clusters right away is helpful for those lawyers who want an early high-level overview of the data.

2. Email threading and textual near-duplicate identification

Use email threading and textual near-duplicate identification on all of your eDiscovery matters consisting of text rich documents. While this may seem obvious, we are still educating people about its benefits for smaller data sets. Reading the same document more than once is a waste of time and money. Running structured analytics on a smaller case is a quick and straight forward process. 

3. Eliminate near-duplicates

Consider whether you can eliminate some textual near-duplicates from your review set. In certain cases it is possible to bulk code some near-duplicates such as those that are 100% or 99% similar and remove them from the review set. This is suitable for cases where you are conducting a review of productions received from the opposing party. This workflow would not be suitable for a case where small differences in a document such as date or name might be important. This workflow would not be suitable for a dataset containing contracts. You also have to be careful to not remove documents with small extracted text sizes so as to not inadvertently remove relevant images.

4. Review inclusive emails

After email threading, you can gain efficiencies by limiting your review set to inclusive, non-duplicate spares. Depending on the terms in your Discovery Plan and Production Protocol you might be able to limit your production to inclusive, non-duplicate spares.

5. Review workflows

Make sure your reviewers follow workflows that involve viewing related files together. The review will be more efficient if the same reviewer looks at family members, email threads and near-duplicates to a related file.  

6. Customize review batches

The eDiscovery review will be more meaningful and efficient if you create review batches that tell a story rather than batches based on control number. Try batching by conceptual clusters and/or sorting by fields such as date, subject matter, email senders or recipients, subject field or title.

These are just some of the analytics and workflows that can help save on eDiscovery costs. In my next post I will cover more Analytics that you can use, including Active Learning (also referred to as TAR, CAL, assisted review, predictive coding, machine learning). Please reach out to me at [email protected] if you have any questions or comments.

If you would like to read the entire How to trim eDiscovery costs series, the posts can be found here:

How to trim eDiscovery costs part one: Lay the groundwork
How to trim eDiscovery costs part three: Leverage technology by adding tools and apps to your review
How to trim eDiscovery costs part four: set your review team up for success
How to trim eDiscovery costs part five: Archive unnecessary data from your workspace

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