eDiscovery Daily Blog

• March 14, 2024

Since the inception of legal processes in ancient societies, documents have been pivotal in evidence and discovery. In recent years, with the rise of email and electronically stored information (ESI), technology companies have standardized review and production processes around these data, converting them into the standard source of discovery: documents. This process has been functional and the data quite usable, until the advent of short messaging in business and personal communications.

Starting with the end in mind, the format we seek to produce for the requesting party is still a document. This format makes the most sense, as it can display short messages as a fluid document showing the conversation, with redactions of confidential messages, alongside all other relevant data. However, the processing and review process for short messages often follows the same document conversion and review process, even though it is the least efficient way to review this data.

But why is the processing and review process subject to the same document conversion and review process when it is the least efficient way to review these data?

Issues with document review of short messages are:

• Context of cross-channel conversations – Many people will start conversations in one channel like email, switch to a corporate chat application like Slack or MS Teams and may end in a 30-minute phone conversation. When the data is in a linear document review workflow it is extremely difficult to stitch those conversations together.
• High costs in document review – Whether the message is converted to individual documents or conversations are converted to 24-hour conversations or any variation, reviewing document by document and redacting within those documents is tedious and costly as the time to do so is significant.
• Data volumes impact hosting costs – The conversion of short message metadata into documents creates a high data footprint that is expensive with “cost per gigabyte” hosting models. This is good for service and software providers, but not for the end client.

Given the growing volumes and diverse types of data dominating investigations and discovery processes, it is crucial to explore more efficient strategies for analysis and review. These strategies should provide better contextual understanding while mitigating costs. It is evident that short message communications differ significantly from emails and e-files, requiring a different approach in the processing and review phases of the EDRM.

The most efficient and effective approach to short message discovery is to render conversations in a “near-native” view for analysis and review where the conversation looks like a document, but the underlying data is the metadata and all media as a part of the conversations.

Example of a Near-Native conversation segment

Benefits of a near-native review of short messages are:

• Easy import of data to a review platform: Rather than processing and loading documents, short message collections can be directly imported into the review platform.
• Tag items at the message level: This allows for more granular tagging and categorization of messages.
• Produce only relevant messages: By reviewing messages at the message level, only relevant messages need to be produced, reducing unnecessary data production.
• Automatic redactions: The review platform can automatically redact confidential or privileged messages, reducing the manual effort required.
• Display all media inline: Videos, GIFs, and audio files can be displayed in line with messages, providing a more comprehensive view of the communication.
• Export options: The review platform should offer export options that accommodate all document and load file types, ensuring compatibility with various systems and processes.

Here’s what this can look like with the right technology solution:

Visual of conversation using CloudNine

If you want to learn more about handling short message data efficiently and effectively in eDiscovery, set up time to learn more about CloudNine Analyst here.