A Glossary of Key Terms in eDiscovery & Data Management for HR & Recruiting Professionals
In today’s data-driven landscape, HR and recruiting professionals are increasingly tasked with managing vast amounts of sensitive information. Understanding the foundational concepts of eDiscovery and data management is not just about legal compliance; it’s about protecting your organization, safeguarding employee and candidate data, and streamlining your operations. This glossary provides clear, authoritative definitions tailored to your role, helping you navigate the complexities of data retention, legal holds, and information governance with confidence.
eDiscovery (Electronic Discovery)
eDiscovery refers to the process of identifying, collecting, preserving, processing, reviewing, and producing electronically stored information (ESI) in response to a request for production in a lawsuit or investigation. For HR and recruiting, this often involves retrieving employment contracts, performance reviews, applicant resumes, internal communications (emails, chat logs), and HR system data related to specific employees or candidates. Automation can significantly streamline the identification and collection phases, ensuring that relevant data is captured defensibly and efficiently without manual oversight.
Legal Hold (Litigation Hold)
A legal hold is a directive issued by an organization to its employees, mandating the preservation of all potentially relevant electronically stored information (ESI) and physical documents when litigation is reasonably anticipated or initiated. In an HR context, this means that data pertaining to a specific employee, a hiring process, or a policy under dispute must not be altered or deleted, even if it falls outside standard retention policies. Automating legal hold notifications and tracking compliance is critical to avoid spoliation of evidence and ensure defensibility, especially for high-volume recruitment firms.
Data Retention Policy
A data retention policy is a set of guidelines that dictate how long specific types of data should be kept and when they should be securely disposed of. For HR and recruiting, this policy defines the lifecycle of applicant data, employee records, payroll information, background checks, and more, adhering to various legal, regulatory, and business requirements (e.g., EEOC, GDPR, CCPA). A well-defined policy, supported by automation, ensures compliance, reduces storage costs, and minimizes risk by defensibly deleting data once its retention period expires.
Personally Identifiable Information (PII)
Personally Identifiable Information (PII) refers to any data that can be used to identify a specific individual. In HR and recruiting, this includes names, addresses, Social Security numbers, dates of birth, email addresses, phone numbers, and even biometric data. Protecting PII is paramount for privacy compliance (e.g., GDPR, CCPA) and maintaining trust. Automation can help identify, classify, and secure PII within various systems, ensuring appropriate access controls and anonymization where necessary, particularly when handling large candidate databases.
Electronically Stored Information (ESI)
Electronically Stored Information (ESI) encompasses any information created, stored, or transmitted in digital format. This broad category includes emails, instant messages, documents, spreadsheets, databases, voicemails, social media content, and data from HRIS or applicant tracking systems (ATS). During eDiscovery, HR teams are often responsible for ensuring that all relevant ESI is preserved and collected. Understanding the various forms of ESI is crucial for comprehensive data management and ensuring nothing is overlooked during a legal hold.
Metadata
Metadata is “data about data.” It provides contextual information about a file, such as who created it, when it was created, when it was last modified, and its file size. For eDiscovery, metadata is vital because it can reveal important details about the authenticity and integrity of a document. In HR, understanding metadata can be crucial when assessing the timeline of policy changes, employee communications, or the creation dates of candidate resumes, helping to establish facts and timelines in investigations or disputes.
Data Minimization
Data minimization is a principle that advocates for collecting and retaining only the data that is absolutely necessary for a specific purpose. For HR and recruiting, this means collecting only the candidate or employee information required for hiring, employment, or legal obligations, and then deleting it once it’s no longer needed. This practice reduces the risk associated with data breaches, simplifies compliance with privacy regulations, and lowers storage costs. Automation can be used to identify and purge unnecessary data regularly, enforcing minimization policies.
Chain of Custody
Chain of Custody refers to the chronological documentation or paper trail that records the sequence of custody, control, transfer, analysis, and disposition of evidence. In eDiscovery, maintaining an unbroken chain of custody for ESI ensures its integrity and admissibility in court. For HR, this is critical when handling digital evidence in internal investigations (e.g., harassment claims, policy violations), ensuring that data collected from an employee’s computer or communications remains untampered and verifiable from collection to presentation.
Defensible Deletion
Defensible deletion is the systematic and verifiable process of destroying data that is no longer needed, in accordance with established data retention policies and legal obligations. It means not just deleting files, but doing so in a manner that can be proven to be complete, consistent, and compliant with regulations. For HR, automating defensible deletion of old applicant data or former employee records (once retention periods expire) is essential for reducing risk, minimizing storage costs, and demonstrating adherence to privacy laws like GDPR and CCPA.
Data Backup & Recovery
Data backup is the process of creating copies of data to protect against loss, while data recovery is the process of restoring that data in the event of a system failure, data corruption, or disaster. For HR and recruiting, robust backup and recovery strategies are critical for protecting sensitive employee and candidate data, ensuring business continuity, and fulfilling eDiscovery obligations even after a system outage. Automation plays a key role in scheduling regular backups and testing recovery procedures to ensure data integrity and availability.
Records Management
Records management is the systematic control of an organization’s records, throughout their lifecycle, to meet operational needs, legal requirements, and historical preservation. In HR, this involves managing all employee-related documents, from hiring forms and performance reviews to benefits enrollment and termination records. Effective records management, often supported by integrated HRIS and automation, ensures that information is readily accessible when needed (e.g., for audits or eDiscovery) and properly disposed of when no longer required.
Data Governance
Data governance is the overall management of the availability, usability, integrity, and security of data used in an enterprise. It includes defining roles, responsibilities, and processes to ensure that data is accurate, consistent, and handled appropriately throughout its lifecycle. For HR, robust data governance ensures the reliability of employee data, compliance with privacy regulations, and effective use of HR analytics. Automation can enforce governance policies by standardizing data entry, auditing data quality, and managing access permissions across various HR systems.
Predictive Coding (Technology Assisted Review – TAR)
Predictive coding, a form of Technology Assisted Review (TAR), uses machine learning algorithms to help categorize and prioritize documents for legal review. Human reviewers train the system by coding a sample set of documents as “responsive” or “non-responsive,” and the system then applies that learning to the larger dataset. In large-scale eDiscovery relevant to HR (e.g., reviewing thousands of internal emails for discrimination claims), predictive coding can drastically reduce review time and costs, allowing HR and legal teams to focus on the most relevant information efficiently.
Redaction
Redaction is the process of concealing or obscuring sensitive or privileged information within a document before it is disclosed to another party. In an HR context, this might involve redacting personal details of third parties, trade secrets, or protected health information (PHI) from documents that are otherwise relevant for eDiscovery or internal investigations. While historically manual, automation tools can now assist in identifying and redacting patterns of sensitive information across multiple documents, ensuring compliance and privacy.
Custodian
In eDiscovery, a custodian is an individual who has possession, custody, or control over electronically stored information (ESI) that is relevant to a legal matter. This often refers to employees whose data (emails, documents, chat logs) are subject to a legal hold or collection. For HR, identifying the correct custodians early in an eDiscovery process is crucial. Automation can help HR teams track custodian data sources and ensure that all relevant individuals are included in legal hold notifications and data collection efforts, minimizing oversight risks.
If you would like to read more, we recommend this article: HR & Recruiting’s Guide to Defensible Data: Retention, Legal Holds, and CRM-Backup
