Slack data can be incredibly valuable, whether in litigation, compliance, or internal investigations. But without the proper tools, Slack data is virtually indecipherable.
"Your engineers will know what to do with these," Slack explains.
But how many clients, and how many law firms, have engineers at all, let alone the engineering bandwidth to create a platform that can process and interpret Slack data? And when it comes to Slack review, very few legal tools are designed to handle Slack data.
As Slack data becomes increasingly rich and complex—full of information message type, edit logs, reactions and more—it becomes even more difficult to handle.
Take comments on a shared file, for example. In Slack, this would appear as a simple message under the file, just a few lines long. When exported from Slack, here's what that those comments look like when exported in JSON format:
You certainly can't review these files on their own. And you can’t toss this into just any discovery software and expect it to be reviewable.
To make sense of data exported from Slack, you need a platform that is designed to process Slack's JSON data and render it in a form at that is easy to review. That doesn't mean just extracting text messages and leaving the rest behind. There is valuable information in Slack's deluge of JSON code, after all!
Your platform should also make that information available—particularly information such as user names, time and date stamps, file types, comments, and edit or deletion records—and include Slack-specific filters so that you can sort conversations by the most relevant criteria for you like participants, channels, and even reactions.
Discovery software like Logikcull can do just that, in a platform that is powerfully simple. In Logikcull, handling Slack data is as easy as sync or upload, search, download.
With Logikcull, you can either pull the data directly from Slack through its direct Slack integration, or upload data previously exported from Slack.
As soon as your chat data is ingested into the platform, it goes through 3,000 automated processing steps: text is rendered and indexed for the most accurate eDiscovery search available, metadata is extracted and preserved to protect against spoliation, quality control tags are applied, and much more.
Slack metadata is instantly turned into filters like conversation participants, channel, sender, deleted and edited messages, and even reactions. You can leverage these filters to cull through chat data and quickly surface the important information.
Slack conversations are also rendered instantly searchable, whether you’re looking for simple text search for keywords or constructing an advanced Power Search based on metadata fields. When it comes time to review Slack documents, Logikcull creates a representation of Slack data similar to how it is displayed in the Slack user interface.
See it in action below:
Once exported from Slack and processed for review in a platform like Logikcull, many of the same discovery best practices that apply to any document review can be employed on Slack data, and you can also leverage features tailor made to handle chat data.
You will also want to be able to quickly identify Slack documents from the rest of your document corpus. In Logikcull, when Slack chats are uploaded, each Slack document is marked with a Slack QC tag. Additionally, you’ll see several chat-specific filters, such as “participants,” “channel,” or “reactions,” which were created with Slack data in mind. These allow you to surface important conversations quickly.
As Slack creates a near-constant stream of communication, it's likely that the vast majority of it will be unnecessary junk. Using powerful culling and search technology, reviewers can easily cull through the vast amounts of data produced by Slack to quickly find and tag the most important information—and cut out the rest.
When review is completed, those Slack documents can be downloaded and produced to others through a secure, permission-based link, allowing you to make sure that your data stays protected throughout your discovery process.
Remember that Slack records more data than the user interface displays. Thus, when a message is deleted in Slack, it will simply disappear—at least, that's what it appears like to users. Similarly, when a message is edited, only the final, altered text is displayed, alongside the parenthetical "(edited)." But, with the proper Slack retention settings, that information can be logged, retained, and surfaced on review.
If a message is deleted, Logikcull displays the deleted information and the time of its attempted destruction. If it is edited, Logikcull shows the original message, the altered version, and the time of the change. That way, review teams can bring clarity to otherwise opaque information—information that could be key to the discovery and investigation processes.
Though Slack presents significant challenges when it comes to litigation and investigations, with the right tools, legal professionals truly can treat Slack as a “searchable log of all conversation and knowledge.”
The first step is to integrate Slack into your regular discovery and investigations process. That means discussing Slack retention and information governance policies with clients. It means adding Slack data to your preservation letters, requests for production, and custodian interviews.
It means looking for indications that Slack might be at issue in a matter, such as the email notifications many users get when receiving a mention or direct message. (Search for the keyword “Slack” or the email address “firstname.lastname@example.org.”)
Most importantly, it means finding a platform that can help you make sense of Slack, one that supports robust a direct integration with Slack for immediate collection, plus dedicated features for chat data culling, document review, tagging, and collaboration, without requiring the intervention of a highly technical IT team—and doesn't charge extraordinary prices for it.
After all, if your Slack discovery solution doesn’t provide comprehensive collection, review, tagging, and collaboration features, then it’s not giving you the tools you need to handle Slack data.
If your Slack discovery process requires you to bring on computer technicians or to pay for a six-figure software setup, then it’s only adding to the problems Slack data creates; it’s not solving them.