eDiscovery Daily Blog
Data May Be Doubling Every Couple of Years, But How Much of it is Original? – eDiscovery Best Practices
According to the Compliance, Governance and Oversight Council (CGOC), information volume in most organizations doubles every 18-24 months. However, just because it doubles doesn’t mean that it’s all original. Like a bad cover band singing Free Bird, the rendition may be unique, but the content is the same. The key is limiting review to unique content.
When reviewers are reviewing the same files again and again, it not only drives up costs unnecessarily, but it could also lead to problems if the same file is categorized differently by different reviewers (for example, inadvertent production of a duplicate of a privileged file if it is not correctly categorized).
Of course, we all know the importance of identifying exact duplicates (that contain the exact same content in the same file format) which can be identified through MD5 and SHA-1 hash values, so that they can be removed from the review population and save considerable review costs.
Identifying near duplicates that contain the same (or almost the same) information (such as a Word document published to an Adobe PDF file where the content is the same, but the file format is different, so the hash value will be different) also reduces redundant review and saves costs.
Then, there is message thread analysis. Many email messages are part of a larger discussion, sometimes just between two parties, and, other times, between a number of parties in the discussion. To review each email in the discussion thread would result in much of the same information being reviewed over and over again. Pulling those messages together and enabling them to be reviewed as an entire discussion can eliminate that redundant review. That includes any side conversations within the discussion that may or may not be related to the original topic (e.g., a side discussion about the latest misstep by Anthony Weiner).
Clustering is a process which pulls similar documents together based on content so that the duplicative information can be identified more quickly and eliminated to reduce redundancy. With clustering, you can minimize review of duplicative information within documents and emails, saving time and cost and ensuring consistency in the review. As a result, even if the data in your organization doubles every couple of years, the cost of your review shouldn’t.
So, what do you think? Does your review tool support clustering technology to pull similar content together for review? Please share any comments you might have or if you’d like to know more about a particular topic.
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