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Practical Tips for Gathering and Analyzing Messaging Data - Greg Evans

By Gregory Evans posted 04-18-2022 13:38

Please enjoy this blog from Greg Evans, Senior Product Manager, Relativity. 

The use of collaborative meeting and messaging platforms by organizations of all types has been growing exponentially. This trend has only accelerated since it began prior to 2020.  Finding a large organization that doesn’t use Microsoft Teams, Slack, Google Workspace Chat, Zoom or another similar application is nearly impossible. Today:

  • 66% of companies using Teams are also using Slack
  • 91% of companies are using at least 2 messaging applications
  • The average number of workplace messaging application used is 3.3

As messaging-type platforms become the preferred method of daily communication between employees (relative to email), there is a commensurate increase in the need for eDiscovery professionals to facilitate collection requests involving messaging data as well as support processing, analysis, and efficient review of said data.

When thinking about the journey that massaging-type data makes as it travels across the EDRM, there are a number of challenges that should be taken into consideration when faced with the task of collecting, analyzing, reviewing, and producing such data.

We’ve already touched on points numbers one and two and for the remainder of this discussion we’ll look at specific issues related to the remaining points.


If you are relying on exports out of a given platform, here are some things you need to take into consideration:

  • Ideally, collections should be scoped to targeted custodians, channels, and conversations. Consider using tools that allow collections to be precisely what you need – nothing more, nothing less.
  • Likewise, messaging applications don’t simply contain messages. That would be too simple. Instead, they contain a host of other content – modern attachments, reactions, and images, just to name a few. Make sure your collections account for such data.
  • Once the export has completed, you will need to move that data to a location suitable for processing. Whether that’s locally or up to the cloud, this additional data handling has the potential to lead to mistakes or spoliation of data.
  • If you are required to work with more than one platform, you will need to understand how the exporting functionality works for each platform.

If you’re considering using a tool designed to collect data from a given platform(s), these considerations may be addressed, but you’ll want to do a little due diligence beforehand.

TIP #1: Use a collection tool that can handle multiple platforms and can place collected data where you need it for processing.

Processing & Analysis

Multiple platforms. Multiple data formats. Multiple types of attachments. They all can cause complications during the processing and analysis stage of the EDRM. A few things to consider:

  • If you have multiple platforms – such as texts, Slack messages, and Teams channels – this data can come in all shapes, sizes, and formats.
  • Emails have been around for decades, and processing engines have essentially mastered the threading of such data. Modern messaging applications have unique quirks that need to be accounted for to keep all the relevant data together.

TIP #2: Find a processing and analysis tool that can normalize, organize, and analyze the data and understand the nuances of each source.

Review & Production

There is no set standard for exporting messaging data that platforms adhere to. You may encounter formats such as JSON, HTML, TXT, MSG and although some formats are common (e.g. JSON) the underlying structure of that data will vary from platform to platform.

This has important implications for review.

  • Most reviewers will not be able to make sense of a JSON file and those that can, will find review extremely cumbersome and tedious
  • If individual chat messages were exported as individual files (e.g. msg, txt) reviewers will have a very difficult time piecing together conversation threads.
  • Creating productions (inclusive of those pesky rolling productions) can be cumbersome. Attempting to redact all confidential and privileged information while also producing only what was requested can be an impossible task.

TIP #3: Ensure you have a tool that can convert raw message data into a format suitable for review. This conversion can happen during collection or post-collection.

TIP #4: Find a tool that allows you to bulk redact confidential and privileged data after review. Likewise, make sure your tool allows you to produce only what was requested – instead of full channels of information – lowering the cost and burden of the review.

Key Takeaway

When considering collections, analysis and review of messaging-type data, the most important thing to determine is what format will the messaging data be in post-collection. Are there options for more than one format? Whenever possible you want to ensure post-collection, your data is in a format that will support efficient analysis, review, and production.