This post, written by Andie Linker, was originally published on the Relativity blog.
Short message data, which features chats and direct messages from Slack and other texting apps, poses some of the most complex challenges when it comes to modern data management. And they’re increasingly common challenges, too; in fact, over 400 people sat in on our session on short message data at Relativity Fest 2022, making it the most-attended breakout session at last year’s conference.
So what makes this data type so complex?
First, there’s simply, and suddenly, so much of it. To put it in my own numbers, I recently got a report that I send over 1,500 Slack messages per month alone. And organizationally at Relativity, we send over 51,000 messages per day. And that’s just us. Take all your clients’ Slack or Teams data and you’re looking at a mountain of chats needing review.
And the challenges just grow from there. Our team estimates that short message data will begin to outnumber emails in RelativityOne next year, so the volumes are growing at an extraordinary clip.
Luckily, at Relativity, we’re innovating to deliver a simpler solution for tackling chat data: Relativity Short Message Format, or RSMF. RSMF takes all of the chat data you might pull from various sources and transforms it into one standard data format. The consistency makes it easier to parse through the messages during a holistic review, and a custom viewer does justice to the unique formatting of these conversations.
Already, 60 percent of RelativityOne customers actively use RSMF on their matters. And they’ve been busy! The amount of RSMF data in RelativityOne has increased five-fold over the past year.
During the ever-popular Relativity Fest session, Relativity product leaders Mike Deuerling and Scott Kohlhoff—alongside RSMF power user and director of operations at CDS, Mark Anderson—walked attendees through some of the key challenges our customers see with short message data and how we’re addressing those challenges with RSMF.
Challenge #1: TMI (too much information) is flooding e-discovery teams.
Volumes of all sorts of chat data sources have exploded in the last three years, with SMS/iMessage and Slack data dominating the data we see flowing into RelativityOne. In the past year, we’ve seen 75 percent of cases in RelativityOne require mobile chat data collection. When case teams need to review all this data, the challenge can feel insurmountable.
Once you get all that data into a review platform, formats differ with each application and they are typically not reviewable in a legible way using traditional platforms. But, with RSMF, we’re able to reduce the complexity of the data to present it in its near-native format. This makes it more reviewable by legal teams and greatly speeds up the process.
Challenge #2: Chat logs motivate TL;DR (too long; didn’t read) reactions from document reviewers.
Searching, filtering, and culling is not easy to do in most native chat platforms. Try searching for an old text you need to find on your iPhone and you’ll see what I mean—and that’s when you’re looking at your own history, which you know better than anyone. That means that e-discovery teams like yours are often pulling in way more data than they need during collection, and need to cull it down before the real review begins—without having any idea where to start or how to pinpoint the meaty parts of a chat log.
Luckily, features built into RelativityOne to help with this early stage of a review—including early case assessment, search, and filtering—work as effectively on RSMF data as they do on other documents. This enables teams to easily get to the chats they need more quickly, without disrupting their favorite workflows or needing to read through each ping individually.
As Mark put it at Fest: “There’s no longer an argument to not collect data; you can filter by date, by custodian, many things. It makes it much easier to get insights out of the data.”
Plus, we’re excited to announce that we’ve released even more functionality to help enrich the metadata within your RSMF files, including support for applications, custodians, participants, and more. Plus, we now also support custom metadata so that you can tailor your RSMF experience to your organization’s workflows.
Challenge #3: Review teams need to find the smoking gun ASAP (as soon as possible).
So, once data is collected, converted, and culled, you still have a ton of messages on your hands that need reviewing. You might have long chat conversations in that data universe, or conversations between two custodians that span months, and you need to identify and produce only the data that is truly relevant to your case.
That’s where persistent filters and slicing come in. Persistent filters allow you to lock certain criteria. Say, for example, you only wanted to see chat messages in a certain date range from certain custodians; you could turn on persistent filters for those two fields, and then continue your searching within those parameters. Once you find the data you need, you can easily use RSMF slicing to cut out the chat you need and create a new group of messages. This will come in handy for preventing inadvertent disclosures when it comes time to produce to opposing counsel.