Name: Overcoming the Chat Chaos: How to Collect & Review Data from Slack, Teams & Zoom
Uploaded: 2025-04-24T14:16:10.189Z
Duration: 43 min 20 s
Description: Overcoming the Chat Chaos: How to Collect & Review Data from Slack, Teams & Zoom

Transcript for "Overcoming the Chat Chaos: How to Collect & Review Data from Slack, Teams & Zoom": Hello, everyone, and welcome to this webinar where we're going to talk about ediscovery and overcoming the chat chaos. And we're focusing today on collection and review of data from Slack and Microsoft Teams. My name is Daniel Schuuring. I'm the director of product strategy at Review, and I will be your host today together with Andrew. Thanks, Daniel. Hi. Andrew Punter, strategic sales engineer, work presenting and demonstrating all the wonderful things that Reveal have to offer. So, yeah, excited to be here. Thanks, Daniel. Thanks, Andrew. And just for you, if you have any questions, on the right of your screen, you will see a Q and A section. If you click on there, you can ask questions there and we will try to do our best to answer those questions during the session or probably at the end of the session so we can focus on your questions. So let's get started. Yeah, a few words about Reveal. Reveal is a software developer. We develop software solutions for eDiscovery. And over the years, we have built a full end to end system to support all your e discovery needs, varying from, halt to review to production processing, and everything what is needed in a proper eDiscovery solution. One of the key things where you focus on is leveraging AI as much as possible within our solution, to help you and make our systems more efficient and effective, and, make sure that you have results sooner, using, the AI technologies that we have available for you. No. I'm going a little bit too fast. Over the years, like I said, Reveal has grown significantly also by acquiring technology and companies. So this moment, just to give you an insight in the offering that we have is that we have a quite extensive lineup of products. And we start here with Onna, which we will discuss today as well. Onna is a, platform for connectivity to collect data, make data searchable, and, perform early case assessment. We have an extensive legal hold solution to help you with notifications and the whole legal hold workflow. We have two review platforms, Logical, which is a easy to use platform. You can start with us basically tomorrow if you want, and we have a fully AI powered review environment review. And, last but not least, we also offer, trial director, which is a presentation platform to help you present your cases in court. And all these combined are the full end to end eDiscovery workflow system that we offer. Today's focus will be on Onna and Logical, which is basically kind of a unique collaboration between two products. Onna for connecting, to various data sources. I think Onna supports over 25 data sources to get data from, and make the data searchable and processing it. The combination with Logical, a very simple and easy to use review platform, which allows you to quickly filter your data and review your data and perform productions. Now the two of these we will combine these today to show you how we can effectively handle chat conversations and the collection of chat for investigation purposes. A little bit more about Onna. Already mentioned that we have quite a few connectors also for chat data, including Slack, Teams, Google, but also Zoom, Bloomberg, quite a few connectivity solutions. Besides that, we offer an open API. So if you have really a specific system where you would like to collect data from, Onna offers you the capability to build your own collectors for that. Now besides the collection pros, collection of data, Onna also processes your data, makes it searchable, and allows you to quickly, browse your data and do fast selections of your data to move your data to Logikcull for review. With Logikcull, already mentioned that it's a very simple and easy to use solution for review, which, yeah, some basic training you could start right away using this software. It has all the features you need to properly process your data and make sure that your data becomes, is available for review. And using this filtering technology in there, you can really quickly cool down your data to the data that you need for your review and need for your case for your investigation. And, of course, Andrew will show you that as well. And especially the filtering, I think, is really important also for chat data. When we collect chat data from various sources, you'll see that we will collect quite some metadata, which can be used for filtering to quickly dive into specific chat conversations with certain people or with certain information in it. If we don't come out to collection of chat data, there's, yeah, quite some challenges there. And I think, yeah, what, most people understand is that there are quite a few systems for chat. And if I look at my personal experience, I use Slack a lot with my, immediate colleagues, but I also know that if I want to reach some colleagues fast and I want to get get a reply fast, that I should send them a chat message in Teams. I don't know how you, what your experience is, Andrew. But I know that some of our colleagues have their preference for the chat system, and they will respond faster on a certain system than they will do on others. So, sometimes good to understand which one is the the favorite of a person to get a false reply. So if you want to collect my chat data, it's definitely scattered through Slack and through Teams. So, yeah, and I think, that happens in a lot of cases. Especially if systems are available, people will start using the ones that they like. So, yeah, it's, it can be hard to, find in all these different systems the the proper data. Yeah. There's also a lot of data involved in chat. I think even more nowadays, at least for me, I think I get more communication going through chat than over email these days. Also with the documents attached to Slack data or links to data that is that are important. Definitely within chat, there's a lot of different way people use the communication. Think about emoticons, reactions that you can do to certain, yeah, conversations and messages. You don't always have to reply. You can also say, give an okay or a thumbs up to message to agree with somebody. So all that information is really, really important that that is part of your collection to understand the meaning of what people were saying in their communication. Yeah. One thing I think really interesting is the that what we call the modern attachments, just to explain what it is. Many collaboration tools offer you the option to share a document and work together in a document. I'm a little bit older, and I know in the past, if you would share a document, you would attach a document to an email and then send it. And you would get then the corrected document or the document with the feedback back over email, and you would save that as a new version on your disk. But that's all old school, I guess. And nowadays, we tend to share a link of the document because link the document is stored maybe in OneDrive, maybe a Google Workspace and we can work together on the same version on the same document, in the same location, even at the same time. So instead of sharing the actual documents, we're sharing links these days. But if you want to collect data, you do want to collect the document that is part of these links that are shared in, chats. So those are a few things that you need to keep in mind, and our solution supports that, that we can also collect the linked documents to our system. Yeah. Of course permissions, preservation complexity, you wanna make sure that what you do is defensible, that everything is all the text, but also that all the data is collected. So especially with all the the changes and the way people are able to create new chats, new chat groups, but also new sites where they share data. And, also, why actively going out and collecting chat data, we are making it very simple and easy to collect that data. If you want to use the native system to export all your chats, that might be quite cumbersome to do that, due to, let's say, missing functionality or the format you get the data in. You can, of course, export all your chats using a JSON format, but it's not as readable as you would like to, to see that. So quite some challenges there. And also with the, the ratio of chat data, there are lots of chats. There's a lot of metadata involved. You want to, yeah, zoom in on the data quickly, zoom in on the right chats that you're looking for, the messages that contain the relevant data. So you also want to have, let's say, proper filtering. Of course, you can have threats, people replying to messaging a threat or replying to a message in the chat itself, making it hard to read all that. Also, it's really important to understand the the order of the chats and, yeah, quite some information is irrelevant and that's of course where Logikcull comes in with the advanced scrolling options to quickly get rid of data that is, let's say, not relevant, like the the birthday wishes, people reacting with memes, and all kinds of other stuff, which may make reading, all the chats a little bit, yeah, maybe even painful, because there's a lot of, yeah, data in the chat that might also need will be really work related. So just give you an overview of some of these challenges and Andrew, will guide you through how we can solve these challenges with, with the Onna and logical solution. Thanks, Daniel. Yeah. Let me stop sharing my screen. Yeah. So those are really good, really good points in terms of the the the challenges that chat poses to classic discovery. It's really not the same as emails. There is so much more complexity about it. So as I go through the demo, I'm gonna be talking you to through, two kind of aspects. So how do we work with a logical only world, and then how do we work with an Onna and logical world? So the last demo we talked about on the last webinar, how do we collect and how do we use both of these tools, both independently and then collaboratively? And the same is said for if we talk about chat data. Now Logikcull is a fantastic resource when it comes to chat data. It's super easy to get data in. It's really easy to search over the chat data. It looks fantastic in the UI, and you can review it in a defensible way, but also similar to how it would be seen on its native platform. What I'm gonna show you first off is how we import chat data. I've talked a lot about Slack and how our integration with Slack is incredible. As you can see in Blue Peter fashion, here's one I made earlier of a chat, collection that's already gone into Onna. Sorry. Logikcull. I'm gonna talk to you now about Teams. Again, another challenging data source to bring into your, solution. So here with Teams, with, with Microsoft, you can export these as individual emails, which is great, but also a bit of a pain. So I can come into here. I can I can identify my good friend, Daniel Schuuring, and I can do a date range? So let's identify kind of going back a bit, from November through to December. And I can also have a great idea of how what I want from my Teams universe. Right? Do I want different channels? Do I only want standard channels or or private channels? Do I want to include, direct messages and multi party threads, those kind of things? And, also, do I want to include those pesky attachments? Coming to here, I can also have that preemptive identification of how many documents are gonna come into here. So let's identify and bring his data in very, very easily. So here, I can also get that nice thing within Logikcull. Right? How many documents is this gonna affect before I go in? And I can click on and start this upload. And you'll see that data is uploading and transferring real time. So really, really cool. Now that's fantastic if I look at, a very specific use case. Right? I'm looking at a few custodians where I know exactly where and when the data is. But what happens if I need to, for example, respond back to a DSA, and I'm I'm I'm looking at Slack or Teams data across a vast number of people, but I'm only interested in a very specific thing that's keyword responsive. Well, that's something that we now need to look at Onna because we now need to do that broad collection but on a targeted subset. And Onna really plays well in this in this in this strategy. If I use, Onna, I can connect into loads of different data sources all at once and search across them all. The key principles around Onna are freefold. So we connect, we find, and we act on them. So connection, awesome. Let me identify if I jump into my Slack project, which I'll be using later. If I click on the plus, you can see all the different no code connectors I can do. In Logikcull, we only had four. Four fantastic connectors, but we only had four connectors nonetheless. Here, I can search over Slack data, Microsoft data, Google data, all in one fell swoop. I can connect them up, and I can use that to build up an index and see what's going on, and therefore, search and review over much fewer documents. So the way we're seeing Onna and logical working in a corporation is owners almost come in and be in that that that repository, that data layer, that archive, especially for things like Slack. Now as Daniel alluded to, if I were to do classic discovery on Slack, I would have to export the JSON out of, Slack itself, which is not a very readable format for me to to go and look into. It's also I I have a real problem targeting that collection. I can't filter down just using the Slack, machine. But what I can do in here is I can say, yep. This is really interesting. I'm gonna do a one time collection, and I'm gonna say let's let's identify that area again. So I'm gonna say the first first twenty twenty one through to, let's say, the end of that quarter. So the thirtieth twenty twenty one. And now I also now have the really cool way. I can have the ability to filter down like we saw in Logikcull, that kind of Slack universe. I can change between direct messages, public messages, private messages. And here, I also have the ability to add, modern attachments. Right? I can link into my Google Workspace and grab those links, and they will appear alongside the messages. This is super powerful. This is really, really cool. I can filter down between all my users, and I can see from within my Slack connection what users are being done. So maybe I want all of the users at that time because I know during this period, there was an event, but I don't know what it was. I can also now load and filter down different workspaces. Okay? Well, now we can see all the workspaces within this instant. Now I know that a lot of these here, for example, that is just not gonna be interesting, so or not relevant. The other one is if I'm looking to sync and build up that archive ahead of searching, ahead of, reviewing, is I can add in future workspaces. So I don't need to collect from the start. I can just set up my own collection and say, hey. If future workspaces appear, I want those to be included in this index. This brings me to a point where I've now identified lots of different areas, lots of different things within my data sources. I can also do this really cool way of identifying all the things across my data. So it doesn't just have to be Slack or Teams or chat. It can be comprehensive. Let's take a data for example. I need to identify where my name or a name is appearing in anywhere. So let's I'm gonna say, for example, Lydia Holloway. I want to know where her name appears across different datasets. I can easily search over a keyword across lots of different data. I'm now into 35 files. Best thing here, look at all the different sources I've identified. Slack, Teams, Microsoft as well. I'm gonna say I'm only interested in the Slack ones. And if I scroll down, I can also see participants, different channels, and see how these different documents are coming. If I jump into a document themselves, this is fantastic when it comes to review. Okay? I can now see exactly where I am. One of the problems Daniël was talking about is how do I know where I am? How do I know where chronology happens? Well, we're gonna tell you exactly where and when this data was. Right? I'm gonna say I'm in this private channel. I'm in a private channel with these participants on this date in UTC. Again, if we have multiple people across different time zones, we're making sure we have the what is a day in terms of from the machine. Also, I can see I can review down. I can see Lydia starting this thread and Isaiah responding just within this thread. I can see reactions, emojis. I can see and the best thing about this, I can see edits. I can see where someone has gone back and edited a message with the original message and now the edited message. Many people believe that edited and deleted messages within Slack are gone. No. They are still stored, and they are available for collection because they're important discovery. Now what now I've identified this. I can use this to start searching on. What I can do here is take this data, this dataset, and and do something with it. Now within my workspaces under Slack for end, full sync, I came and I searched, for Bridget. I identified her distinct data, and I've come up with about 50 files that need review. They need to be actioned on. So I have multiple options here. I can review within Onna. I can come in to see these documents and see what these documents look like. Within Onna, I can have a preliminary ECA review. I can tag these documents with a very rudimentary tag, but I think that's about as far as I can go. What if I need to then use this in an investigation or, or disclose this in court or give this back to the data subject? This is now where the beauty between Onna and logical place. I've gone from a very large data corpus. I've used this the search and index in power of Onna to identify a corpus that is now gonna be relevant and needs to be reviewed within Logikcull. And I can simply connect them too. I can export directly to Logikcull. I've, accessed my account, and I've logged in via Onna, and it's gonna bring me back the list of the list of projects that I have access to. So as we are talking about our chat webinar, I've identified this, and I'm gonna put it in my Onna folder. Now the cool thing about this is whilst we push from owner to logical, we have a very speedy data transfer. I can see the data being collected and processed. I can actually see well, first of all, I can also see my upload for for mister Schuuring has has completed, so we can see what he's been messaging back in that day. But I can also come into here and see that export directly from owner, which is a really powerful, easy mechanism. I can identify, review, which is really important. Now I can use the full power of Logikcull to prepare my documents ready for production. I still have, as we've noticed before, my searching bar at the top. This is now maybe a little bit more redundant because we've already done a lot of that search from within owner. But the cool things here, let's focus on these filters. Those different conversations I can have. I can see my also my difference between my Slack and my Teams, conversations and channels. K? I can see how those are filtered. I can see the participants, how who they're doing to, even the senders. Now the really awesome thing is I can also see reactions. Now reactions actually become a massive part of reviewing chat data. Before Logikcull was acquired by Reveal, there's a there's a great story about how they were, being hope they'd been used by one of their clients. Now their their client had an employment dispute where they, they sent a amendment to a contract to a specific employee over Slack. This employee did not uphold the amendment and was subsequently fired. Upon receiving news of his termination, he filed a legal suit to say he never received this amendment to his contract. They did discovery and found out that underneath the, the change of, contract, he had actually given a thumbs up. And the judge, said that this was acknowledgement of the contract, and therefore, he was, he was fairly dismissed. So bonkers, business strategy, but shows the power that reactions and chat messages have. Now if I want to come and look at some of these documents and see how they are showing, maybe I'm interested in documents in the finance team. If I filter on here, I'm down to five documents. I can come and see how these documents are showing. And, again, similar to owner, we can see our our documents appearing. We can see that nice chain of documents, that nice chain of threads. But you'll also notice that it's ever so slightly different in owner because it looks like there's a load of spelling mistakes. Well, that's not just spelling mistakes. That's actually where we've auto identified PII. This becomes really, really crucial, especially in Europe where we we, we respond to data privacy, and we respect, data privacy especially in disclosures. Now this can become a major problem when you're having to review and redact this. Well, Logikcull has a fantastic solution. I can easily redact all of these documents for PII with a couple of clicks. Instantly, I have my documents sanitized, ready to be disclosed. I can continue tagging, redacting, and going through all these different documents and making sure they're ready for disclosure. So, hopefully, between this, you have now seen the power of both of our tools and how they can be used in conjunction, especially around the complexity that chat data has to offer. With Onna, we can search across huge numbers of documents and huge different data sources and all the arms that potentially businesses have in different data sources to find that truly relevant material. How we filter in down from, to get the signal out of the noise exactly as Daniel put? And then with Logikcull, we have the review power to prepare all these documents for disclosure. Daniel, back to you. You're me, Daniel. Yes. Thank you, Andrew. And, also, thanks for letting me know not to give a thumbs up to any messages from HR before you know it. You have approved something. Right? Absolutely. But interesting story about the, the employee disputes. And, yeah, it's very helpful to also be able to filter on reactions and all the, the metadata in the, chat conversations. Just a quick, yeah, summary of what we have discussed. So Andrew has been showing Onna, the smart chat data chat collection and with all the capabilities for all the integrations with the various tools like Slack, Teams, the handling of modern attachments, the ability to find or to collect attachments that are embedded as a link in the chats. Not something we showed, live, but what Onna can do is a real time sync of your whole Slack repository and make everything searchable and ready for ediscovery and building yourself an ediscovery ready archive. Of course, you can do it, reactive and do a collection when you need the actual data, but you can also do this proactive also for compliance purposes to have all the data available instantly. And, of course, yeah, making sure that we do everything dependable, a built in chain of custody. Have, of course, permissions control because on the you know, you can set a lot of permissions to make sure that's only those, that are allowed to view the data are allowed to do so. So that's very important, of course. You don't want to open up everything to everybody. And then Logikcull, yeah, I think still very important, the filtering options there that Andrew displayed, the ability to view the documents and also have it in a nice readable format. So you can also, download the information in a proper format and share it if that's needed with the other parties in maybe your investigation or in your dispute that you are having. And, yeah, I think also you you saw that the the interface of Logikcull is quite simple. Just by a few clicks, you can open your documents, filter it down, and then be able to search it and redact it. And especially, I think the redaction is quite unique capability. Chet can contain lots of personal data and leveraging AI to, find all the personal identifiable information is a big help in becoming more efficient to, review the data. Andrew was already also was also mentioning the, the DSARs, the data certificate access requests. We have done a full webinar also on DSARs, where we discuss the issues with DSARs and how you can handle them efficiently using, own ad Logikcull. But chat is indeed becoming more and more part of the DSR request because lots of information is discussed over chat, and we see indeed, the number of collections from chat increasing compared to, let's say, the email. Email is still being collected, of course, but chat is gaining, definitely. So time for the q and a. One last word. Andrew and I have been doing quite a few webinars on internal investigations using Onna and, Logikcull on these hours. So also please check out our website and go to the event section, and you can see there all the recorded sessions that we have done and view those. So let's move over to the q and a section and let's see which questions are popping up. So, Andrew, I see a few questions coming in. Let's see the first one that I see, and I think I can even put them on screen here. How secure is the data in transit and at rest? Yeah. Great question. Is probably concerned, and we get data from various locations. Is that all safe? Oh, absolutely. Yeah. We we use, AWS's instances, as well as, Google's for the owner, and we have a full, security, site where you can check out any of our security accreditations, on our trust center. If you want any more of that information, let myself or Daniel know, and we can provide links to the appropriate, content. Alright. The other one that I see, I think we answered that already slightly, but I see here a new brand name that we haven't mentioned yet, a new tool, WhatsApp business. I know we have some partners that help us to, collect data from, let's say, more mobile sources. Can you tell more about that, Andrew? Absolutely. Yeah. So, Zoom and Google Chat are both connected, and supported via our in app connectors in owner. WhatsApp business, you can either create your own connector, as I mentioned earlier, or you can use one of our partners, for example, ModeOne, is a fantastic cloud mobile cloud connector which could help you connect and collect data over a Cloud without having to physically have the mobile phone itself. It's pretty cool. Check it out. Yes. Maybe also good to mention as well that our solution supports rsmf, which is a default standard within the e discovery industry for, chat messages to move them between different systems. There are many tools out there that can capture from your mobile device, yeah, let's say, chat data and export that to RSMF that can be imported into Onna and to to, Logikcull for the review purposes. Yeah, and I think we handled this question in the previous webinars. If you go to our website, you will see that there are quite a few webinars that Andrew and I did also discussing how to review documents, email, and, you know, let's say, Word, PDF, PowerPoints, all those types of documents that you come across. And I think, yeah, definitely that data can be reviewed alongside the chat data that we discussed today. I think this is an interesting one. I think this is probably referring to the data that we sync with, with Onna. So Onna can indeed sync the information, from your Teams or from your Slack system. And what happens if the data is deleted? It's a really interesting, concept about deletion. So if you if you delete a message, for example, in Slack, whilst it will be deleted from your screen, it won't be deleted in perpetuity. There'll be a record of that message. As I showed, you can see the original message. You can see the edits. You can see the deleted and then the new message over the top of it. But it also kind of this this whole area of deletion, comes into play when, for example, Onna is collecting data and syncing. So we have this concept of a also syncing archive where, for example, over Teams, in, file shares, and so far, you can see how the message or the document message, etcetera, lives until it's deleted, and then it will be flagged as deleted. It will still exist within your, Onna archive if you wanted to. You can delete it subsequently, should you should you also want it to mirror completely. But it also kind of brings up that idea of version control as well. And within Onna, you can see how documents change over time over different syncs, and if there's different versions of that document or that message as well. Okay. Great. Thanks. And, yeah, also referring to the synchronization between your chat data and, Onna in this case? Because in Logikcull, what we do is a one time collection of data. But, you know, Onna, you can set up the a real time sync or a sync. How real time is that? So without sounding saying it depends, it does depend. It depends on the size of your Slack, universe and how much is being synced. So the idea is once owner has finished the sync, it will then restart the sync to then make sure it captures. It's very near real time, so you're looking at being able to see messages within the last kind of minutes, hours, as opposed to days and weeks. So it's it's it's continuous syncing. Okay. And then I think the the last question, I think we may have go for that as well, but I think it's good to clarify that because, yeah, there are, of course, private channels. You and I have a chat. It's our private channel. And, also, I know in Slack, you can create private channels and public channels. Can we get all that data, or do we Yeah. So we we private? We can we can basically see anything that the, the account that you're using to connect into can see. So if that is a administration account, they you will be able to collect from, private messages, DMs, etcetera, in in the business. So it's all about what the business account can see. So absolutely, you can connect from there. Alright. And I think those were all the questions that were asked. So again, thanks, Andrew for your support in this webinar. And as as mentioned before, if you would like to receive more information, on the top you will see a link to Reveal demo. If you hit that button, you'll be taken to, we will register. If you would like to receive a demo, we will reach out to you. But you can also visit our website, refilldata.com, and find more information there and also you'll find their options to to contact us. Thanks for your attention and joining us today and hopefully we'll hear from you. Thank you. Bye. Bye.