Problems with Review? It’s Not the End of the World – eDiscovery Best Practices
December 21, 2012
If you’re reading this, the Mayans were wrong… :-)
If 2012 will be remembered for anything from an eDiscovery standpoint, it will be remembered for the arrival of Technology Assisted Review (TAR), aka Computer Assisted Review (CAR), as a court accepted method for conducting eDiscovery review. Here are a few of the recent TAR cases reported on this blog.
Many associate TAR with predictive coding, but that’s not the only form of TAR to assist with review. How the documents are organized for review can make a big difference in the efficiency of review, not only saving costs, but also improving accuracy by assigning similar documents to the same reviewer. Organizing documents with similar content into “clusters” enables each reviewer to make quicker review decisions (for example, by looking at one document to determine responsiveness and applying the same categorization to duplicates or mere variations of that first document). This also promotes consistency by enabling the same reviewer to review all similar documents in a cluster avoiding potential inadvertent disclosures where one reviewer marks a document as privileged while another reviewer fails to mark a copy of the that same document as such and that document gets produced.
Hot Neuron’s Clustify™ is an example of clustering software that examines the text in your documents, determines which documents are related to each other, and groups them into clusters, labeling each cluster with a set of keywords which provides a quick overview of the cluster, as well as a “representative document” against which all other documents in the cluster are compared.
Clustering can make review more efficient and effective for these types of documents:
- Email Message Threads: The ability to group messages from a thread into a cluster enables the reviewer to quickly identify the email(s) containing the entire conversation, categorize those and either apply the same categorization to the rest or dismiss as duplicative (if so instructed).
- Routine Reports: Periodic reports – such as a weekly accounts receivable report – that are generated can be grouped together in a cluster to enable a single reviewer to make a relevancy determination and quickly apply it to all documents in the cluster.
- Versions of Documents: The content of each draft of a document is often similar to the previous version, so categorizing one version of the document could be quickly applied to the rest of the versions.
- Published Documents: Publishing a file to Adobe PDF format generates an exact copy (from Word, Excel or other application) of the original file in content, but different in format, so these documents won’t be identified as “dupes” based on their HASH value. With clustering, those documents still get grouped together so that those non-HASH dupes are still identified and addressed.
Within the parameters of a review tool like OnDemand®, which manages the review process and delivers documents quickly and effectively for review, clustering documents can speed decision making during review, saving considerable time and review costs, yet improving consistency of document classifications.
So, what do you think? Have you used clustering software to organize documents for review? Please share any comments you might have or if you’d like to know more about a particular topic.CloudNine Discovery. eDiscoveryDaily is made available by CloudNine Discovery solely for educational purposes to provide general information about general eDiscovery principles and not to provide specific legal advice applicable to any particular circumstance. eDiscoveryDaily should not be used as a substitute for competent legal advice from a lawyer you have retained and who has agreed to represent you.