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Analytics Tutorial for Tech-Savvy Firms
The legal discovery process can be a long and grueling project. This is particularly true in the modern age of e-discovery, where thousands of digital files are collected and transferred in the blink of an eye.
Now, not every firm will need to use these strategies in their discovery process. The use of analytics certainly isn’t necessary for successful e-discovery, and small firms unable to implement the necessary software may be able to get by without them. However, it’s important to understand how these processes work to keep yourself up to date on the changing trends of the industry, even if you don’t plan on using them.
What are Analytics?
Analytics involves implementing electronic system tools to automatically identify important documents and analyze their content. This process saves tremendous amounts of time and manpower that would otherwise be needed to dig through hundreds of digital files by hand. The various ways that analytic software sorts through these files can be customized and tailored to your unique case. There are several strategies commonly used in e-discovery analytics:
One of the most fundamental forms of analytic review, categorization, involves providing set category names or different content-based category parameters to locate potentially relevant documents. This simple categorization can do wonders for review efficiency, and it is often necessary for large cases that have thousands of files to assess.
Clustering involves separating data sets and categorizing them in a hierarchy, with or without inclusion of descriptive titles. This allows easier categorization and automatic prioritization of documents based on cluster. This strategy can be effective at identifying which documents are most important to your particular review and giving you the option to prioritize their analysis accordingly.
Grouping together timelines of emails for better contextual understanding of the conversation, known as email threading, helps a reviewer with conversational flow when reading through a series of emails. Threading can be used in conjunction with clustering and analytics indexing.
As email is one of our primary forms of digital communication, organization systems for email categorization is often necessary for efficient discovery. Analyzing entire email threads at once instead of individually helps maintain conceptual consistency and contributes to better overall decision making on the part of the reviewer. This type of analytic also allows reviewers to exclude documents based on duplicate contents. This reduces the total number of files to be reviewed for more efficient time use without any loss of relevant data.
Analytics are all about efficiency, and the near duplicate method attains this goal by identifying texts containing similar or duplicate verbiage. This includes texts that have similar content but with small additions or word removals. The near duplicate strategy aggregates all similar documents together to ensure that a disparate team of reviewers aren’t wasting time examining similar documents.
It’s hard to argue that analytic software eases the burden of document review. Though not essential, increased technological efficiency is the direction that the legal world is moving. Being aware of what these tools are and how they’re used is necessary to prevent surprises when litigation time rolls around, even for firms who don’t use them—opposing counsel may not share the same aversion to technology.