Our Insights on eDiscovery

Yesterday, we talked about the use of First Pass Review (FPR) applications to not only conduct first pass review of your own collection, but also to analyze your opponent’s ESI production. Another type of analysis is the use of domain categorization. FirstPass supports domain categorization by providing a list of domains associated with the ESI collection being reviewed, with a count for each domain that appears in emails in the collection. Domain categorization provides several benefits when reviewing your opponent’s ESI.

Yesterday, we talked about the use of First Pass Review (FPR) applications to not only conduct first pass review of your own collection, but also to analyze your opponent’s ESI production. Another type of analysis is the use of fuzzy searching. Attorneys know what terms they’re looking for, but those terms may not often be spelled correctly. Also, opposing counsel may produce a number of image only files that require Optical Character Recognition (OCR), which is usually not 100% accurate.

Yesterday, we talked about the use of First Pass Review (FPR) applications (such as FirstPass™, powered by Venio FPR™) to not only conduct first pass review of your own collection, but also to analyze your opponent’s ESI production. Another type of analysis is the use of synonym searching. Attorneys understand the key terminology their client uses, but they often don’t know the terminology their client’s opposition uses because they haven’t interviewed the opposition’s custodians.

In the past few years, applications that support Early Case Assessment (ECA) (or Early Data Assessment, as I prefer to call it) and First Pass Review (FPR) of ESI have become widely popular in eDiscovery as the analytical and culling benefits of conducting FPR have become obvious. The benefit of these FPR tools to analyze and cull their ESI before conducting attorney review and producing relevant files has become increasingly clear. But, nobody seems to talk about what these tools can do with opponent’s produced ESI.

It is important to include misspellings when searching for relevant ESI to broaden the search to retrieve potentially responsive documents that might be otherwise missed. Another way to identify misspellings is to use a resource that tracks the most typical misspellings for common words.

We all make mistakes. And, forgetting that fact can be costly when searching for, or requesting, relevant documents in eDiscovery. For example, if you’re searching for e-mails that relate to management decisions, can you be certain that “management” is spelled perfectly throughout the collection? Unlikely. It could be spelled “managment” or “mangement” and you would miss those potentially critical emails without an effective plan to look for them.

Several months ago, I provided search strategy assistance to a client that had already agreed upon several searches with opposing counsel. One search related to mining activities, so the attorney decided to use a wildcard of “min*” to retrieve variations like “mine”, “mines” and “mining”. That one search retrieved over 300,000 files with hits. Why? Because there are 269 words in the English language that begin with the letters “min”. How do you ensure that you’re retrieving all variations of your search term?