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How to handle chat data in eDiscovery and investigations

Water-cooler talk and quick chats in the office disappeared with the pandemic. But online chat—already a prevalent form of communication prior to the pandemic—took off as the preferred way for remote employees to communicate. In the past year, over 600 billion chat messages were sent globally among businesses alone. Microsoft™ Teams™ usage increased by more than 330 percent.

Now a mainstream form of business communication, chat is here to stay as an easy and instant way to connect. But when it comes to litigation, investigations and regulatory compliance, chat presents some unwieldy challenges for eDiscovery and legal teams. Chat data is ephemeral and needs to be captured during its short lifespan. It has its own structure that must be accommodated. And it is inherently designed to encourage long threads and large volumes of data.

With each legal matter comprising more and more data, including chat, expeditious review is key to fulfilling eDiscovery requirements on time and within budget. Here are five key capabilities that legal teams need for handling chat data in eDiscovery and investigations:

  1. Chat data needs to be displayed within eDiscovery platforms with a familiar look and feel to native applications;
  2. Chat data must be able to be reviewed in isolation to maintain the context of chat interactions;
  3. Chat data has to be inclusive of all the activities and events associated with the main body messages;
  4. Chat histories need to be able to be split into blocks of time to help manage the volume of chat data for easier and faster review; and,
  5. Chat data must also be included alongside all other data so insights can be derived across the entire picture of all documents and data.

1. A familiar look and feel

Preserving a familiar look and feel makes it obvious that information comes from chat data. This is important because chat is typically less formal and prone to casual assertions that can have implications within investigations and eDiscovery projects.

Further, the structure of chat data with its associated activities (replies, emoji-based reactions, etc.) contains context. Seeing this context in a familiar layout helps reviewers work through chat data quickly and accurately draw observations. For example, a familiar look and feel for chat data is required to appreciate the context contained within the sequence of responses between custodians.

To achieve a familiar look and feel to the native chat applications, eDiscovery solutions adopt proprietary chat formats within their platforms that replicate the layout of chat data from its various sources.

2. Review in isolation

Because of its unique form, chat data is best reviewed independently of all other data. Switching back and forth between chat data, email and other data within documents makes it harder for reviewers to stay grounded in what they are reviewing. This increases the burden on reviewers, reduces productivity and fuels mistakes in coding.

To enable the discrete review of chat data, eDiscovery platforms need a dedicated smart filter to isolate chat, along with the ability to create review batches dedicated to chat data.

3. Include all chat activities and events

The isolated review of chat data in a familiar layout must also include all associated activities and events related to the core data within messages. eDiscovery platforms must support replies, reactions (including emoticons), edits, deletions, leave / join events and attachments. The related activities and events within a chat conversation often contain significant indicators of the intent of chat body messages and must be considered during review.

Chat attachments, such as Microsoft® Office® documents, often contain key data about intellectual property, product liability, merger and acquisition information, etc. This information is sometimes more significant than the chat conversation itself.

4. Managed chat history

To make the large volumes of chat data more digestible and to narrow the volume of chat that requires eyes-on review, eDiscovery platforms must also be able to break down chat histories into bite-sized blocks of time. This allows reviewers to manage the volume of chat data to home in on relevant data and expedite review efforts.

5. Treat alongside all other data

While this might seem contradictory to the ability to review chat data in isolation, chat must also be treated alongside all other data. If chat data is not incorporated, all of the analytics and visualization tools will show two discrete and independent viewpoints. No aggregate picture with context across all data will be possible.

eDiscovery analysis and review tools that rely on a single inclusive data set for holistic insights include concept grouping, sentiment analysis, fact versus opinion analysis, entity identification, predictive filters, find similar (predictive search) and technology-assisted review, among others.

Visualization tools are critical for reviewers to see a comprehensive view of the entire network of communications and data associations. Including chat data alongside all other data within visualization tools is key to maintaining a holistic view.

These five criteria—a familiar look and feel, the ability to review in isolation, the inclusion of all associated activities and events, the ability to break down chat histories into manageable pieces and being able to assess chat data alongside all other data—are key to effectively handling chat data in eDiscovery and investigations. OpenText™ Axcelerate™ supports all these capabilities, and more.

For information on OpenText™ Axcelerate™ chat capabilities, please visit the Axcelerate product page.

Duncan Bradley

With over 25 years experience in Market Intelligence and Product Marketing, Duncan brings a rich set of perspectives as a subject matter expert in a broad array of tech topics including content process automation and analytics, data privacy and regulatory compliance. As Product Marketing Manager, OpenText Legal Tech, Duncan contributes to product positioning and messaging across OpenText’s suite of Legal Tech products. Duncan holds a MBA from the University of British Columbia, a BA Hons in Political Science from the University of Guelph and an Executive Certificate in Negotiation and Conflict Management from the University of Notre Dame.

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