If you are involved in litigation, investigations, or regulatory compliance, you might be wondering what is document review?
Document review is the process of examining documents and electronically stored information to determine relevance, responsiveness, privilege, and confidentiality during discovery. It is one of the most time-intensive and costly stages of eDiscovery, particularly for enterprise organizations managing large and complex data environments.
Under the Federal Rules of Civil Procedure, including Rules 26 and 34, parties are required to produce relevant and proportional information. Effective document review ensures those obligations are met while minimizing risk and unnecessary expense.
Why Document Review Matters in eDiscovery
Document review plays a critical role in shaping legal strategy and outcomes. Inaccurate or incomplete reviews can lead to missed evidence, inadvertent privilege waivers, or disputes that delay litigation.
As enterprise data volumes grow, review challenges increase. Relevant information is no longer limited to email and static files. It now includes chat messages, collaborative documents, shared links, edits, reactions, and task management tools. Without a structured review process, teams may struggle to maintain defensibility and consistency across data sources.
Key Stages of the Document Review Process
A typical legal document review process includes several stages designed to narrow large data sets into actionable information:
- Identifying relevant custodians and data sources
- Filtering and deduplicating collected data
- Reviewing for relevance and responsiveness
- Identifying privileged or protected information
- Preparing documents for production
Each stage must align with proportionality standards under FRCP Rule 26(b) to ensure review efforts remain reasonable and defensible.
Types of Document Review in eDiscovery
Document review in eDiscovery can take several forms depending on the complexity and volume of the data. Traditional manual review involves attorneys or paralegals reading each document to identify relevance, confidentiality, and responsiveness to discovery requests. In contrast, technology-assisted review (TAR) uses machine learning and predictive coding to prioritize documents most likely to be relevant, allowing reviewers to focus on high-value content and streamline the process.
Manual vs Technology-Assisted Review
Manual review gives human reviewers full control over how each document is assessed, which can be important for nuanced legal judgment. However, it can be slow and costly, especially with large datasets. Technology-assisted review accelerates the process by learning from reviewer decisions to automatically classify similar documents, improving efficiency and reducing costs while maintaining accuracy. Many firms now combine both approaches to balance precision with speed in complex eDiscovery cases.
Reviewing Modern Collaboration Data
Traditional review tools were built for email and file repositories. Modern enterprises rely heavily on platforms such as Slack, Microsoft Teams, Google Workspace, Confluence, and Asana. These tools generate conversational and interconnected data that requires contextual review.
Modern Document Review Starts With the Right Technology
Managing document review across complex collaboration platforms and ever-growing data volumes can feel overwhelming. Understanding what document review truly entails is the first step toward efficiency and compliance.
