The Honorable Shira Scheindlin once opined against allowing custodians of ESI to collect their data stating “[s]earching for an answer on Google (or Westlaw or Lexis) is very different from searching for all responsive documents in the FOIA or e-discovery context…” and “most custodians cannot be ‘trusted’” to effectuate a legally sufficient collection.  National Day

Aldinger v. Alden State Bank is a good reminder of counsel’s obligation to be cooperative in the discovery process.

Aldinger, an employment discrimination case pending in the United States District Court for the Western District of New York, involved a series of discovery disputes including Plaintiff’s motion to compel Defendant to respond to her First

A recent federal district court decision, Lawson et al. v Love’s Travel Stops & Country Stores, Inc., US Dist Ct, MD Pa, 1:17-CV-1266, Carlson, J., 2019, reminds litigants of the need to tailor discovery requests for electronically stored information (“ESI”).

Before the Court was plaintiffs’ motion to compel defendants’ production of “all” text

The issue of production format when dealing with ESI is often the subject of discussion and disagreement.  If possible, the parties to the litigation should agree at the outset to the production format.   In fact, a conversation about production format, metadata and redactions (among other things) should occur at the preliminary conference and/or the Rule

I am often asked by clients and subscribers to the blog, What is E-discovery?  And so, this week’s post is intended to respond to that question.

E-discovery is the abbreviated term for electronic discovery and refers to the process in which electronic data (as compared to paper or object information) is sought, located, secured, reviewed

De-duplication (“de-duping”) is the process of comparing electronic records based on their content and characteristics and removing duplicate records from the data set so that only one instance of an electronic record is produced when there two or more identical copies. De-duplicating a data set is a smart way to reduce volume and increase efficiencies