If You’ve Got Discoverable Data In Slack, You’re Gonna Want To Read This


The collaboration platform Slack appears intent on disrupting the use of email messages. And that’s fine — surely, plenty of people in corporate legal operations could use a little less volume in their inboxes. But, as with most of the new and nontraditional data sources that have been cropping up recently, data in Slack presents significant discovery challenges.

First, while you can set retention and deletion policies to govern Slack data natively, there is no way to apply a defensible legal hold and preserve Slack data in place. This makes balancing the duty to preserve with information governance a real battle. Second, it’s not uncommon for companies to copy all of the data every time they need to preserve it. This creates multiple copies of massive data sets — increasing both risk and discovery costs. Finally, in Slack it’s common to have discussion threads between multiple people who join and leave channels at any time. They also edit and delete posts, and the conversation threads are dynamic, as well as packed with abbreviations, misspellings, slang, emojis, gifs, links, and videos — all of which make it hard to find relevant information, not to mention challenging to view the information in context to discern its meaning.

I had a project a little over a year ago that involved a boatload of Slack data that was part of the potentially relevant discoverable material. The issues in the case revolved around who knew what and when, and lots of “discussion” on relevant topics had been held in the Slack workspace. The organization involved had created so many channels in the Slack workspace that it was difficult to assess what was relevant and what was not. We collected all the data just to be sure and decided to figure it out later.

Parsing through the multitude of Slack channels to identify the responsive messages was time-consuming. A document-centric discovery approach did not serve well because content in Slack is not like the data associated with typical electronic documents. Electronic documents usually have an identifiable beginning and end. Slack requires us to rethink what a document is in order to get to the relevant data.

The problem was that we had enormous strings of Slack messages, and it was in an unfamiliar format. The data was exported from Slack in JavaScript Object Notation (JSON) file format. It’s a file type that stores data and objects and is mostly used for transmitting structured data between web-based applications and file servers. JSON is similar to the XML file format, but it’s more lightweight, less verbose, and consequently, it’s faster to parse JSON data than XML. JSON files can usually be viewed with a web browser or text editor, but again, the data is cumbersome and difficult to read. So, we had a ton of JSON data exported from Slack, and we realized we lacked a workflow for examining this data.

Fortunately, our friends at Hanzo developed Hanzo Hold, a software platform designed from the very experience I faced on my project. Well-known for their web-capture technology, Hanzo launched Hanzo Hold for Slack in mid-2019. The tool enables users to defensibly preserve and collect data in a Slack workspace.

I asked Brad Harris, vice president of product, who recently jumped over to Hanzo from Zapproved, what brought about the development of Hanzo Hold. “It grew out of the experience of users who were looking for a better way to collect and preserve Slack data in a targeted and defensible manner,” he said. As so often happens in e-discovery, Hanzo “had a client who wanted a better approach that would tie in with their matter-centric workflows and we put our engineers to work.”

Hanzo Hold is more than a “hold” tool because it is both a preservation repository and viewer for assessing the value of Slack data early in the process. Hanzo Hold takes a unique approach to preservation, building a skeleton of the entire Slack environment to inform and later guide what is actually collected, reviewed and produced. The needed relevant data is ingested into the Hanzo platform along with the associated metadata, and it is indexed. This enables users to run simple queries on the Slack data to identify and produce the relevant material. And you can them produce the data by custodian, by channel, or even by date range.

Hanzo Hold represents to me what is right about e-discovery. There’s a problem and the solution is built to resolve that problem. I could have used Hanzo Hold on my project; if you’ve got Slack data responsive to discovery demands, I suggest you look into it.


Mike Quartararo

Mike Quartararo is the President of the Association of Certified E-Discovery Specialists (ACEDS), a professional member association providing training and certification in e-discovery. He is also the author of the 2016 book Project Management in Electronic Discovery and a consultant providing e-discovery, project management and legal technology advisory and training services to law firms and Fortune 500 corporations across the globe. You can reach him via email at mquartararo@aceds.org. Follow him on Twitter @mikequartararo.



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