eDiscovery data processing is a critical phase in the electronic discovery process, where data collected from various sources is prepared for review and analysis in legal proceedings. Implementing best practices ensures efficiency, accuracy, and defensibility while minimizing costs and risks. Below are eDiscovery data processing best practices:

1. Develop a Clear Data Processing Plan

• Define Objectives: Understand the scope of the case and the specific data needed for review (e.g., relevant custodians, date ranges, keywords).


• Document Processes: Create a detailed workflow for data processing, including steps for data ingestion, filtering, and output, to ensure repeatability and defensibility.


• Engage Stakeholders: Collaborate with legal teams, IT professionals, and vendors early to align on goals, technology, and protocols.

2. Preserve Data Integrity

• Maintain Chain of Custody: Document every step of data handling to prove that data has not been altered or tampered with.


• Use Write-Protection: Employ tools and methods to prevent accidental modification of original data during collection and processing.


• Hash Values: Generate and verify checksums (e.g., MD5 or SHA-1 hashes) for data sets to ensure integrity before and after processing.

3. Filter and Deduplicate Data Early

• Apply Early Case Assessment (ECA): Use ECA tools to analyze data before full processing to identify relevant documents and reduce volume.


• Deduplication: Remove duplicate files (both exact duplicates and near-duplicates) to reduce the volume of data for review, saving time and cost. Decide whether deduplication should be applied globally (across all custodians) or per custodian.


• Apply Date and Keyword Filters: Use date ranges, file type filters, and keyword searches to exclude irrelevant data early in the process.

4. Standardize Data Formats

• Convert to Usable Formats: Process data into standardized, searchable formats (e.g., native files to TIFF or PDF for review, with OCR for scanned documents).


• Extract Metadata: Preserve and extract relevant metadata (e.g., author, date created, file type) for filtering and analysis.


• Handle Unsupported Formats: Identify and address data in unsupported or proprietary formats by using specialized tools or manual conversion processes.

5. Manage Data Volume and Complexity

• Leverage Technology-Assisted Review (TAR): Use machine learning and predictive coding to prioritize relevant documents and reduce manual review time.


• Segment Large Data Sets: Break down large data sets into manageable batches for processing to avoid system overloads and ensure accuracy.


• Exclude Non-Relevant Data: Use file type filters to exclude irrelevant file types (e.g., system files, executables) unless they are specifically required.

6. Address Multilingual and Special Data Types

• Handle Foreign Languages: Use language detection tools and translation software to process and review data in multiple languages.


• Process Multimedia Files: Develop protocols for handling audio, video, and image files, including transcription or content extraction where necessary.


• Deal with Encrypted Data: Identify encrypted or password-protected files and work with IT or custodians to decrypt them legally and securely.

7. Ensure Compliance with Legal and Regulatory Standards

• Adhere to Data Privacy Laws: Comply with regulations such as GDPR, HIPAA, or CCPA, especially when processing personal or sensitive data. Redact personally identifiable information (PII) as needed.


• Follow Court Rules and Guidelines: Ensure processing aligns with Federal Rules of Civil Procedure (FRCP) or other relevant jurisdiction-specific rules.


• Implement Data Security Measures: Use encryption, secure storage, and access controls to protect data during processing and transfer.

8. Quality Control and Validation

• Perform QC Checks: Validate processed data for completeness, accuracy, and consistency (e.g., check for missing files, corrupted data, or incorrect metadata).


• Test Searchability: Confirm that text extraction (OCR) and indexing have been successful and that documents are searchable.


• Audit Logs: Maintain detailed logs of processing activities to provide transparency and support defensibility in case of challenges.

9. Collaborate with Review Teams

• Prepare Data for Review: Organize processed data into a format compatible with the review platform (e.g., load files with proper field mapping).


• Provide Clear Deliverables: Deliver data sets with clear documentation, including processing reports, exception logs, and deduplication summaries.


• Communicate Issues Early: Notify legal teams of any processing challenges (e.g., corrupted files, missing data) and propose solutions.

10. Optimize Cost and Efficiency

• Use Scalable Tools: Employ robust eDiscovery platforms or vendors that can handle large data volumes efficiently.


• Minimize Manual Intervention: Automate repetitive tasks like file conversion, deduplication, and filtering to reduce errors and save time.


• Track Metrics: Monitor processing costs, timelines, and data reduction rates to identify opportunities for improvement in future projects.

11. Work with Experienced Vendors or Tools

• Choose Reliable Software: Select eDiscovery tools with strong processing capabilities (e.g., Relativity, Nuix, DISCO) that support automation and scalability.


• Partner with Experts: If outsourcing, work with reputable eDiscovery vendors who follow industry standards and provide transparent reporting.


• Stay Updated on Technology: Keep abreast of advancements in eDiscovery tools, such as AI-driven analytics, to improve processing outcomes.

12. Prepare for Defensibility

• Document Everything: Maintain detailed records of processing decisions, workflows, and tools used to defend the process if challenged in court.


• Be Transparent: Provide opposing counsel or courts with information about processing methods if required, ensuring compliance with discovery obligations.


• Retain Exception Reports: Keep logs of any data that could not be processed (e.g., corrupt files) and document steps taken to address issues.

By following these best practices, organizations can streamline eDiscovery data processing, reduce costs, improve accuracy, and ensure compliance with legal and regulatory requirements. Tailor these practices to the specific needs of each case, balancing efficiency with thoroughness to achieve the best outcomes.