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
User avatar
Patricia Garnica   |   05/22/2018

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

User avatar
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.

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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.

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Charles Becoat   |   03/09/2017

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

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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.

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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.