Blazing Saddles or Blazing Trails: e-Discovery vs. Forensic Data Collection — Unraveling the Critical Differences

Understanding e-Discovery Data Collection

Electronic discovery refers to the process of identifying, preserving, collecting, reviewing and producing electronically stored information in legal proceedings. It plays a crucial role in legal matters like cybersecurity litigation and regulatory investigations. When collecting data for e-discovery, attorneys must consider several key factors. First, preserving the integrity of the data is vital to ensure its authenticity and admissibility as evidence. Additionally, data must be collected from various sources and formats, including emails, documents, databases and social media platforms. Metadata, which provides crucial information about the data's creation, modification and storage, and maintaining a proper chain of custody are also essential considerations. To achieve these goals, attorneys rely on techniques such as data sampling, keyword searching, remote collection and cloud-based solutions. Furthermore, data culling and filtering techniques help refine the collected data to focus on the most relevant information.

This is why a standard e-discovery data collection commonly refers to a more informal (but well-documented) process of gathering and preserving ESI in a legally defensible manner. It involves identifying and extracting your potentially relevant data from various sources while ensuring data integrity and maintaining a documented chain of custody. This e-discovery data collection method is typically executed by either internal IT staff familiar with the process of exporting from the various data sources or specialized e-discovery service providers. These professionals must have the technical expertise and knowledge to effectively perform data collection tasks. Attorneys often work closely with these individuals to develop a data collection strategy and provide guidance throughout the process, maintaining compliance with legal requirements and maximizing the relevance and admissibility of the collected data, as standards will vary when using this option.