As the coronavirus pandemic has left most knowledge workers clocking in from home, we’ve seen a rapid acceleration of organizations using collaboration tools like Slack to keep their teams in touch. But while collaboration tools have significant benefits for team communication, they’re not without risks.
At Hanzo, we often hear about unexpected challenges that organizations encounter when extracting discoverable data from their Slack channels and other collaboration tools. To help explain those challenges and demonstrate a solution, I recently presented a webinar on how to “Manage Vast Volumes of Dynamic Slack Content for Ediscovery With Ease.”
Here’s what was on the agenda.
Benefits of Collaboration Tools Like Slack
How do you keep your employees connected when they can’t be physically near each other? By giving them collaboration tools that include everything they need for at-a-glance coordination on shared work, including instant messaging, integration with other productivity and project management apps, and in-line file attachments. Companies use these collaborative tools to improve productivity, often with great success. We’ve heard of organizations reducing the volume of emails by about 50 percent and reducing meetings by 25 percent just by adopting a collaboration platform. With shared channels through Slack Connect, companies can even replace their external emails with Slack messages.
Hanzo has helped legal, and compliance teams manage dynamic content for years, so we quickly recognized the emerging challenges that Slack and other collaboration tools would pose.
Challenges of Ediscovery With Collaboration Tools
The proliferation of these platforms has led to one realization and a host of cascading concerns. First, collaboration content is discoverable, so long as it’s relevant and proportional to an issue in dispute in a pending or anticipated litigation matter. See the recent case, Benebone LLC v. Pet Qwerks, Inc., et al. where Slack data discoverability was put to the test regarding proportionality. The short story, it was deemed both relevant and not unduly burdensome. Just like with email, companies must be prepared to preserve, collect, review, and produce Slack data to requesting parties—which unearths a range of related challenges.
The data volume within collaboration tools like Slack can quickly exceed all expectations. We’re talking about millions of messages across hundreds of custodians using thousands of channels. And that data is immensely complex, seeing as how it contains multimedia files as attachments; nonverbal communications such as emojis, pictures, and GIFs; and integrations with other applications such as calendars, video conference systems, and project or ticket management tools like Confluence and Jira. It’s also context-dependent in a way that emails—which are essentially self-contained communications—aren’t; you usually need to read more than a single message to understand what’s happening in a conversation within a collaboration tool.
"Because these collaboration tools have spread so rapidly, they’re often outside of standard information governance pipelines, which means their contents may be unchecked and unmanaged."
Because these collaboration tools have spread so rapidly, they’re often outside of standard information governance pipelines, which means their contents may be unchecked and unmanaged. Preservation is a challenge given that users can, by default, edit and delete their messages. Organizations tend to have limited visibility into the contents of their collaboration tools. When they manage to export data, they find themselves trying to manage unwieldy JSON files rather than readable, review-ready exports.
Overcoming the Challenges
To overcome these challenges, you need to be able to do a few things. You need to know what exists in your collaboration tool and how to find any individual message or piece of information. It would help if you had powerful search tools to grant you access to data based on keywords and metadata fields like custodian, date, and channel. Additionally, it’s best to view conversation threads in their native context. Once you’ve found the data you want, it will save your team significant time and cost to export it in a review-ready format that fits neatly into your existing document review platform.
Even a minimal demo dataset in Slack can have tens of thousands of messages. Organizations using Slack or other collaboration tools must have the ability to winnow that sea of data down to the relevant and important messages and then preserve that data quickly and efficiently, in a format that’s ready to fold into the rest of their ediscovery pipeline.
In case you missed it, the webinar is available on demand at “Manage Vast Volumes of Dynamic Slack Content for Ediscovery With Ease.”
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