[{"id":"1","name":"Federal Rule of Evidence 502"},{"id":"53","name":"Conducting Legal Holds"},{"id":"55","name":"Building an eDiscovery Practice & Team"},{"id":"58","name":"Early Data Analysis & Data Reduction Strategies"},{"id":"59","name":"Litigation Data Management & Policies"},{"id":"62","name":"Preservation Obligations & Formats"},{"id":"63","name":"Privilege"},{"id":"67","name":"Budgeting & Project Management"},{"id":"68","name":"Collection of Electronically Stored Information"},{"id":"69","name":"Evidence & Authentication"},{"id":"70","name":"Measuring Results: Surpassing Legal Compliance"},{"id":"71","name":"Introduction to the Federal Rules"},{"id":"72","name":"Search & Review of Electronically Stored Information"},{"id":"73","name":"Using Experts: Rule 30(b)(6) & Testimony"},{"id":"74","name":"An Introduction to the EDI Distance Learning Initiative"},{"id":"75","name":"Records & Information Management in an Organization"},{"id":"76","name":"Introduction to Information Technology: Collaboration & Workspaces"},{"id":"78","name":"Data Remediation"},{"id":"79","name":"Introduction to Information Technology"},{"id":"80","name":"eDiscovery Challenges in Specific Practice Areas: Product Liability Litigation"},{"id":"81","name":"eDiscovery Challenges in Specific Practice Areas: Employment"},{"id":"83","name":"eDiscovery Boot Camp: Challenges to In-House Teams"},{"id":"84","name":"Data Processing & Management"},{"id":"85","name":"Production Obligations & Formats"},{"id":"86","name":"Rule 37(e) & Sanctions"},{"id":"87","name":"The Supply Chain of Custody: Project & Process Management in the eDiscovery Workflow"},{"id":"88","name":"eDiscovery Challenges in Specific Practice Areas: Requesting Parties"},{"id":"89","name":"eDiscovery Challenges in Specific Practice Areas: Antitrust"},{"id":"90","name":"Expectations of the Court: Rule 16 & 26(f) Conferences"},{"id":"91","name":"Introduction to Information Technology: Communications"},{"id":"92","name":"Data Privacy"},{"id":"93","name":"Information Security 101"},{"id":"94","name":"International eDiscovery"},{"id":"95","name":"Third Party Obligations & Procedures"},{"id":"96","name":"The Document Review Process"},{"id":"97","name":"26(f) Conferences and Meet & Confers"},{"id":"98","name":"The EDI Diversity Initiative"},{"id":"99","name":"eDiscovery Challenges in Specific Practice Areas: Government Investigations"},{"id":"100","name":"Write Right"},{"id":"101","name":"Why eDiscovery?"}]

Early Data Analysis & Data Reduction Strategies

1 hour 13 mins

This course outlines the process of early data analysis (EDA). It examines the goals and benefits of EDA as well as the types of tools available. The faculty also shares their expertise regarding how EDA can be used to save time and money—and when it may not be appropriate.

Class Outline:

Early Data Analysis & Data Reduction Strategies 1 hour 13 mins
- The Faculty
- What is Early Data Analysis?
- Why is EDA Important?
- EDA vs. ECA
- Goals & Benefits of EDA
- Traditional Ways to Process ESI
- Using Keywords for Filtering
- EDA Tools
- When to Start EDA
- EDA as an Iterative Process
- Who Should Be Involved in the EDA Process?
- Why is Asking EDA Questions Important?
- When to Stop EDA
- Downsides to EDA
- When EDA May Not Be Appropriate

Class Readings:

Couch v. Wan.pdf
Equity Analytics, LLC v. Lundin.pdf
Eric L. Barnum.pdf
Eurand, Inc. v. Mylan Pharm., Inc..pdf
Qualcomm Inc. v. Broadcom Corp..pdf
United States v. O'Keefe.pdf
Victor Stanley, Inc. v. Creative Pipe, Inc..pdf
Early Data Analysis & Data Reduction Strategies.pdf

Course Comments

Patricia Garnica | 05/22/2018

Please check slide "Who Should be Involved in EDA process?" "conflict" should be "conduct" to match the verbal presentation.

Janice Ann Jaco | 05/07/2017

Loved the analogy for predictive coding, comparing to Pandora or Spotify. Many people can relate to the usefulness of those applications' ability to find and suggest songs you may like based on your favorites. It works the same with documents.

Anthony Stratton | 03/18/2017

The reading materials provided were quite substantive pertaining to the subject matter. Though they took longer to read than the video ran they gave insight into problems that can arise.

Charles Becoat | 03/09/2017

You will not have a successful Early Case Assessment without an Early Data Assessment and keep the process iterative.

David Hedgepeth | 09/02/2016

Very comprehensive. Using predictive coding to reduce data volumes - thereby promoting greater efficiency in the discovery process is extremely interesting, and might merit a (short) course on that subject alone.

Barath Rajagopalan Jayaraman | 04/28/2016

A very concise overview about EDA and Data Reduction strategies. Extremely useful.

Disclaimer: The views and opinions expressed in this online video course are those of the individual faculty members
and do not necessarily reflect the official policy or position of any organization, corporate entity, law firm, government agency or the Electronic Discovery Institute.