Thu. Mar 28th, 2024

Describing the eDiscovery Workstream

From the trigger point for audits, investigations, and litigation to the conclusion of cases and matters with the defensible disposition of data, there are countless ways data discovery and legal discovery professionals approach and administer the discipline of eDiscovery.  Based on an aggregation of research from leading eDiscovery educators, developers, and providers, the following eDiscovery Processes and Tasks listing may be helpful as a planning tool for guiding business and technology discussions and decisions related to the conduct of cybersecurity, information governance, and legal discovery-related eDiscovery projects. The processes and tasks highlighted in this listing are not all-inclusive and represent only one of the myriads of approaches to eDiscovery.

The workflow of the electronic discovery process includes initiation, legal hold, collection, ingestion, processing, analytics, predictive coding (technology-assisted review), review, production/export, and data disposition.

Initiation #

The initiation stage involves preliminary planning, project organization, project scoping, and the creation of a statement of work (SOW) and project estimates. The client reviews the SOW and estimates, and upon acceptance, the SOW is executed.

Legal Hold #

The legal hold stage involves scoping the legal hold, identifying and preserving electronic data in accordance with the legal hold plan, issuing legal hold notifications, and documenting the legal hold process.

Collection #

The collection stage involves scoping the collection, identifying the collection assets, developing a collection plan, conducting onsite collection, documenting and certifying the collection, and shipping/transferring the collection.

Ingestion #

The ingestion stage involves processing the received electronic data, conducting an ESI reception audit, creating a chain of custody, reviewing the collection log and notes, and preparing the electronic data for processing.

Processing #

The processing stage involves processing the electronic data according to specification, reporting the processing status to the client, identifying any exception or hold files (EHF), and completing the processing of the electronic data.

Analytics #

The analytics stage involves analyzing the electronic data, preparing an analytics repository, conducting an exploratory analysis, conducting a preliminary filtering hit report, and filtering the electronic data according to specification.

Predictive Coding #

The predictive coding (technology-assisted review) stage involves planning the predictive coding specification, moving the electronic data into a predictive coding application, conducting the predictive coding, and reporting the results of the predictive coding.

Review #

The review stage involves planning and preparing the review technology and staffing, conducting the review, and reporting the results of the review.

Production/Export #

The production/export stage involves planning and executing the production/export of the electronic data, conducting quality control (QC), and delivering the electronic data according to specification.

Data Disposition #

The data disposition stage involves planning the data disposition, evaluating the active discovery, making a decision to conclude the project, and disposing of the data in accordance with the client’s guidance.

The electronic discovery process involves a structured and systematic approach to managing the vast amounts of electronic data involved in modern litigation, ensuring that all relevant information is identified and reviewed in an efficient and effective manner.

The Workstream of eDiscovery: Process and Task Listing #


Complete Checklist: eDiscovery Processes and Tasks


The Workstream of eDiscovery
Process Task
Initiation
Preliminary Planning
Project Organization
Project Scoping
Statement of Work (SOW)
Project Estimates
Client Assessment of SOW and Estimates
Assessment Acceptance
Execute SOW
Legal Hold
Legal Hold Scoping
Legal Hold Asset Requirements
Legal Hold Plan Development
Client Assessment of Legal Hold Plan
Assessment Acceptance
Issue Legal Hold Notifications
Identify and Preserve ESI Per Legal Hold Plan
Track Legal Hold Notifications
Communicate and Execute Legal Hold Release
Release Preserved ESI To Data Retention Policy
Document Legal Hold Process
Collection
Collection Scoping
Collection Asset Requirements
Collection Plan Development
Client Assessment of Collection Plan
Assessment Acceptance
Onsite Collection
Collection Documented and Certified
Collection Shipment/Transfer
Ingestion
Processing Specification Planning
Processing Specifications Defined
Analytics Specification Planning
Analytics Specification Defined
Client Review of Project Plans
Project Plan Acceptance
ESI Received
ESI Reception Audit
Chain of Custody Check
Reception Reporting
Collection Log and Notes Reviewed
Transmittal Letter Reviewed
Client Notification of ESI Reception
Actual and Estimated Volumes Aligned
Modification of Estimates and SOW
Client Upload Approval
ESI Upload into eDiscovery Platform
Processing
ESI Processed According to Specification
Processing Status Reported to Client
Exception and Hold File (EHF) Identification
Client Guidance of EHF Handling Protocol
Further Processing of EHF Per Protocol
ESI Processing Completed
Cyber Discovery (Artificial Intelligence)
Preparation – Initiation of Cyber Discovery Process
Planning – Model and Protocol Planning
Training – Selection, Building Testing, and Training
Tuning – Validation and Evaluation
Discovery – Adaptation, Deployment, and Maintenance
Response – Cyber Discovery Understanding
Analytics
ESI Analyzed According to Specification
Analytics Repository Preparation
Client Exploratory Analysis of ESI
Client Early Case Assessment of ESI
Filtering Planning
Filtering Specification Defined
Client Confirmation of Filtering Specification
Preliminary Filtering Hit Report Preparation
Acceptance of Initial Filtering Results
Complete Filtering Per Specification
Analytics Reduction Completed
Predictive Coding (Technology-Assisted Review)
Predictive Coding Specification Planning
Predictive Coding Specification Defined
Client Review of Predictive Coding Plans
Predictive Coding Plan Acceptance
ESI Moved into Predictive Coding Application
Predictive Coding Accomplished Per Specification
Review
Review Technology Specification Planning
Review Technology Specification Defined
Review Staffing Specification Planning
Review Staffing Specification Defined
Review Technology and Staffing Plan Developed
Client Evaluation of Review Plan
Client Acceptance of Review Plan
Review Technology Prepared According to Specification
Review Staffing Executed According to Specification
ESI Prepared for Review
ESI Volume and Expectation Alignment Verification
ESI Promoted to Review Application
Review Conducted Per Plan
Review QC and Validation
Review Results Reported
Review Results Acceptance
ESI Prepared for Production/Export
Production/Export
Production/Export Specification Planning
Timeframe and Budget Planning/Update
Timeframe and Budget Plan Acceptance
Production/Export Execution
Production/Export QC
Production/Export Delivery Per Specification
Data Disposition
Hosting Requirement Planning
Active Discovery Evaluation
Data Disposition Option Planning
Client Decision To Conclude Project
Data Disposition Per Client Guidance
Project Concluded
AI – Preparation
Cyber Discovery Goals
Data Collection and Ingestion
Data Exploration
Data Processing
AI – Planning Model and Protocol Planning (AI+Experts)
AI – Training
Model and Protocol Selection and Building
Model and Protocol Testing and Training
AI – Tuning
Model and Protocol Validation
Model and Protocol Evaluation
AI – Discovery
Model and Protocol Adaptation (Adjustment)
Model and Protocol Deployment (Execution)
Model and Protocol Monitoring (Monitoring)
AI – Response Cyber Discovery Action
TAR 1.0 – Simple Active Learning
ESI Moved Into Technology-Assisted Review (Predictive Coding) Application
Establish a Random Control Set of ESI
Review and Code Control Set for Relevance
Continue Training (Establish, Review, and Code) Until Sufficient Number of Relevant Documents in Control Set
Establish a Seed Set of ESI (Random and/Judgmental Sampling)
Review and Code Seed Set for Relevance
Apply Machine Learning Algorithm to Suggest Best Learning Documents
Review and Code Suggested Best Learning Documents and Add to Seed Set
Repeat Application of Machine Learning Algorithm With Seed Set Until Stabilization Occurs (Based on Accuracy of Relevance Prediction for Documents in Control Set)
Apply Learning Algorithm to Categorize or Rank All Documents
Prepare for Review All Documents Categorized as Relevant or Ranked Above Cut-off Score
Validate the TAR (Predictive Coding) Process
TAR 1.0 – Simple Passive Learning
ESI Moved Into Technology-Assisted Review (Predictive Coding) Application
Establish a Seed Set of ESI (Random and/Judgmental Sampling)
Review and Code Seed Set for Relevance
Apply Machine Learning Algorithm to Evaluate Whether Documents Are Relevant
Evaluate Effectiveness of Training (Number of Overturns and Machine Learning Unclassifiable Documents)
Continue Training (Establish, Review, and Code) with Larger Seed Set Until Training Effectiveness Deemed Sufficient)
Apply Learning Algorithm to Categorize or Rank All Documents
Prepare for Review All Documents Categorized as Relevant or Ranked Above Cut-off Score
Validate the TAR (Predictive Coding) Process
TAR 2.0 – Continuous Active Learning®
ESI Moved Into Technology-Assisted Review (Predictive Coding) Application
Establish a Seed Set of Relevant Documents (Judgemental Sampling)
Apply Machine Learning Algorithm to Collection to Suggest Most Likely Responsive Documents
Review Suggested Documents and Provide Feedback for Machine Learning Algorithm
Repeat Application of Machine Learning Algorithm Against Collection Until Few, If Any, Suggested Documents Are Relevant
Prepare for Review All Documents Categorized as Relevant
Validate the TAR (Predictive Coding) Process
TAR 3.0 – Cluster-Centric CAL
ESI Moved Into Technology-Assisted Review (Predictive Coding) Application
Form Conceptual Clusters of Collection
Establish a Seed Set of Relevant Documents (Judgemental Sampling)
Apply Machine Learning Algorithm to Cluster Centers and Sort By Relevance Score
Review Small Number of Cluster Centers with Highest Relevance Score and Repeat Application of Machine Learning Algorithm Until Few Relevant Clusters Remain
Apply Machine Learning Algorithm Against Collection
Determine Whether Produce Documents Without Review, Produce Documents with High Relevance Scores Without Review and Perform Standard CAL on Remainder of Documents, or Review All Documents for Production Using Standard CAL
Validate the TAR (Predictive Coding) Process
TAR 4.0 – Hybrid Multimodal IST* Predictive Coding (*Intelligently Spaced Training)
ESI Communications for the Scope Definition of Discovery, Relevance, and Related Review Procedures
Conduct Multimodal Early Case Assessment (ECA)
Random Sample to Determine Prevalence
Training Select (Iterative) to Determine What Documents to Use to Train the Machine (Human Function)
AI Document Ranking (Iterative) to Appropriately Rank Documents (Machine Function)
Multimodal Review (Iterative) to Find New or Irrelevant Document for the Next Round of Training with Multiple Techniques That May Include Search by High Ranked Documents, Mid-Ranked Uncertain Documents, Random and Judgemental Sampling, and Ad Hoc Searches Not Based on Document Ranking
Zero Error Numerics (ZEN) Quality Assurance Tests to Validate the TAR (Predictive Coding) Process
Phased Production, Where Relevant Documents are Reviewed Again and Produced

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