Posted on: May 24, 2017in Blog
Unique eDiscovery Challenges with Mobile Device Data and How to Solve Them
The core issue in Healthwerks, Inc. v. Stryker Spine, et. al, (Case No. 14-cv-93-pp.) involves alleged breaches of non-compete contracts. However, a "late-filed motion" by Plaintiff to compel additional discovery prompted Honorable US District Judge Pamela Pepper to issue a ruling relating to the after-the-buzzer admissibility of text message evidence. Through the motion, Plaintiff argued that Defendant failed to discover and produce relevant text messages from key witnesses.
This is certainly a punishable offense in today's eDiscovery environment where business communications frequently sprawl across company-owned and personal mobile devices. Plaintiff further claimed that the manner in which Defendant produced 21 text messages from an immaterial witness who joined Plaintiff's organization after the filing of the lawsuit, made it difficult to locate the text messages among the thousands of pages of other discovery materials. A 49-page email "spat" between the parties' counsel was generously provided for the court's consideration.
Perhaps predictably, Judge Pepper's decision to deny the motion to compel additional discovery had little to do with the minutiae of the discovery specifically of text messages. Instead, it focused on the undisputed realities that fact discovery had concluded some 6 months prior to the motion to compel, "extensive discovery" had already been accomplished, and the trial date was only one month away.
Challenges with Mobile Discovery
Producing Mobile Device Data
My interest in this decision stems from the ongoing challenges parties face when discovering text message evidence that is stored on mobile devices. Typically, after a mobile device is collected, the text messages are exported to a spreadsheet where each row in the spreadsheet contains the metadata and content associated with a single text message. If that spreadsheet is subsequently converted into flat TIFF images and then buried between thousands or millions of other produced pages, it may indeed be difficult to locate those specific text messages.
What if you could take that spreadsheet and load it into your review platform, enabling you to review and produce only relevant data? Learn more about the text message integrator tool and how you can use it on your next case for a more efficient workflow.
Even if a generous producing party provided the native spreadsheet, reviewers must now rummage through it for relevant information; however, there could be tens of thousands of completely irrelevant messages scattered among the pertinent ones because it is very time-consuming for the producing party to restrict the production to only the individual messages that have passed attorney review for relevance.
Reviewing Text Messages for Relevance and Privilege
It may be easy to use the spreadsheet software's functionality to sort the messages on the device by date, but what if devices were collected from 50 custodians and the reviewers need to look at conversations across devices and mark individual messages for relevance and issues?
Imagine that a reviewer for the producing party located a text message with a mix of privileged and responsive content; how will the non-privileged and relevant portion of the message be produced without altering the spreadsheet that contains all the other messages? Even if you've managed to segregate the relevant non-privileged messages and are gearing up for production, what will become of the pictures, videos, and contact cards that were attached to the original messages on the device? We all know that we have a duty to produce the messages and attachments as they are kept in the ordinary course of business, or in a readily accessible and useable form. Producing the messages and attachments in a manner that degrades their usefulness as evidence may land us on the receiving end of a motion to compel.
Text Message Integration
It's clear that mobile device data discovery presents unique challenges for producing and receiving parties alike. Luckily, there are tools available to facilitate the process by preparing mobile device evidence to be reviewed and produced just like any other form of document-based electronic evidence. Through this process, mobile device material can be searched across devices and custodians, filtered by complex keywords, subjected to conceptual analytics, and ranked by means of predictive coding – all within the same platform that hosts traditional types of ESI such as email, spreadsheets, and word-processing files.
Reviewers can even run searches for specific Emojis to locate messages that convey a certain emotion. Redactions, Bates labels, and confidentiality branding can also be applied to individual messages during production, while messages and attachments are kept together with a family relationship throughout the process to ensure the integrity of the data set.
Some tools even enable searching, filtering, sorting and analysis of text messages across multiple devices at once – and even can facilitate searches for specific Emojis. The most robust tools can support the application of redactions and Bates label to individual text messages. To avoid spoliation, make sure that the tool you use can maintain message attachments with a family relationship to the parent.
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