A time-saving tool that consolidates different names for the same entity can make all the difference.
One of the many challenges of electronic information and messaging rests in ascertaining the actual identity of the message creator or recipient. Even when only one name is associated with a specific document or communication, the identity journey may have only just begun.
The many forms our monikers take as they weave in and out of the digital realm may hold no import for most exchanges, but they can be critical when it comes to eDiscovery and privilege review, where accurate identification of individuals and/or organizations is key.
It’s difficult enough when common names are shared among many individuals (hello, John Smith?), but the compilation of our own singular name variations and aliases as they live in the realm of digital text and metadata make life no less complicated. In addition, the electronic format of names and email addresses as they appear in headers or other communications can also make a difference. Attempts to consolidate these variations when undertaking document review is painstaking and error-prone.
Not metadata — people.
Enter “name normalization.” Automated name normalization tools come to the rescue by isolating and consolidating information found in the top-level and sub-level email headers. Automated name normalization is designed to scan, identify, and associate the full set of name variants, aliases, and email addresses for any individual referenced in the data set, making it easier to review documents related to a particular individual during a responsive review.
The mindset shift from email sender and recipient information as simply metadata to profiles of individuals is a subtle but compelling one, encouraging case teams and reviewers to consider people-centric ways to engage with data. This is especially helpful when it comes to identifying what may be—and just as importantly what is not—a potentially privileged communication.
Early normalization of names can optimize the privilege workflow.
When and how name normalization is done can make a big difference, especially when it comes to accelerating privilege review. Name normalization has historically been a process executed at the end of a review for the purpose of populating information into a privilege log or a names key. However, performing this analysis early in the workflow can be hugely beneficial.
Normalizing names at the outset of review or during the pre-review stage as data is being processed enables a team to gain crucial intelligence about their data by identifying exactly who is included in the correspondence and what organizations they may be affiliated with. With a set of easy-to-decipher names to work with instead of a mix of full names, nicknames, initials without context, and other random information that may be even more confusing, reviewers don’t have to rely on guesswork to identify people of interest or those whose legally-affiliated or adversarial status may trigger (or break) a privilege call.
Name normalization tools vary, and so do their benefits.
Not all name normalization tools are created equal, so it is important to understand the features and benefits of the one being used. Ideally, the algorithm in use maximizes the display name and email address associations as well as the quality and legibility of normalized name values, with as little cleanup required as possible. Granular fielded output options, including top level and sub-header participants is also helpful, as are simple tools for categorizing normalized name entities based on their function, such as privilege actors (e.g., in-house counsel, outside counsel, legal agent) and privilege-breaking third parties (e.g., opposing counsel, government agencies). The ability to automatically identify and classify organizations as well as people (e.g., government agencies, educational institutions, etc.) is also a timesaver.
Identification of privilege-breaking third parties is important: although some third parties are acting as agents of either the corporation or the law firms in ways that would not break privilege, others likely would. Knowing the difference can allow a team to triage their privilege review by either eliminating documents that include the privilege breakers from the review entirely, significantly reducing the potential privilege pile, or organizing the review with this likelihood in mind, helping to prevent any embarrassing privilege claims that could be rejected by the courts.
Products with such features can provide better privilege identification than is currently the norm, resulting in less volume to manage for privilege log review work later on and curtailing the re-reviews that sometimes occur when new privilege actors or breakers come to light later in the workflow. This information enables a better understanding of any outside firms and attorneys that may not have been included in a list of initial privilege terms and assists in prioritizing the review of documents that include explicit or implied interaction with in-house or outside counsel.
Other privilege review and logging optimizers.
Other analytics features that can accelerate the privilege review process are coming on the scene as AI tools become more accepted for document review.