eDiscovery Leaders Live: Jeremy Schaper of BlueStar Case Solutions
Jeremy Schaper, Chief Technology Officer of BlueStar Case Solutions, joins George Socha, Senior Vice President of Brand Awareness at Reveal, for ACEDS #eDiscoveryLeadersLive.
Jeremy Schaper is a partner and co-founder of BlueStar and serves as the company's Chief Technology Officer. He has over eighteen years of experience in legal industry software instruction and consulting and has consulted with legal professionals and teams at many top law firms and corporations throughout the United States. Jeremy has managed large-scale eDiscovery projects and has served as an eDiscovery and social media special master to the court. He also has acted as an expert witness and consultant for numerous cases including FDIC cases involving failed banks.
Jeremy started the discussion talking about the topic of the moment, ChatGPT. From there he turned to how he and his colleagues help eDiscovery clients work with M365 and Google Workspace content, including what is and is not searchable within those systems and ways to craft effective search terms to get through that content. Next, Jeremy discussed challenges posed by short messages and way to address those challenges. Finally, Jeremy gave his thoughts on an ideal solution for dealing with short messages.
Key Highlights
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- [1:55] How Jeremy got into all of this and what he does today.
- [4:01] ChatGPT – how they have been using it.
- [5:08] Embracing ChatGPT.
- [6:25] Potential uses of ChatGPT.
- [7:43] Working with ChatGPT-generated-content in discovery.
- [8:40] What might be next with ChatGPT.
- [9:18] Helping clients with M365 and Google Workspace.
- [12:18] What M365 does not index.
- [13:33] Working on M365 with inhouse counsel, corporate IT, and corporate security.
- [15:09] Helping them address their security concerns – the value of oversight.
- [16:15] What comes after collecting data.
- [17:10] Challenges searching Google Workspace.
- [18:33] Helping clients formulate effective search terms.
- [22:26] Alternatives to search terms – using platforms such as Reveal AI to make suggestions.
- [23:03] Working with short messages.
- [25:23] The efficacy – or ineffectiveness – of retrofitting short messages as documents.
- [27:01] His ideal solution for dealing with short messages.
Key Quotes
- “It’s more of how we can embrace [ChatGPT] and what can we see future-wise…. For me, I see it as something we definitely have to pay attention to.”
- “I see uses in [ChatGPT] for possible QC, eventually to pinpoint more documents and to help us evolve our review, just like CAL, just like technology-assisted review, adding it into that fold in a way that it can help us target and also find errors in our production or help us evolve our thinking in terms of what we’re looking for.”
- “One of the big gotchas with both [M365 and Google Workspace] is running terms on those programs might not be your best-case scenario. They don’t index everything, so when you’re trying to find data, when you’re doing date filtering, that’s great; when you’re looking for specific custodians, that’s great; maybe some high-level searches to pull data. But understanding what happens to the data you can’t search, keep that in mind.”
- “I wouldn’t run [eDiscovery] search terms on Google Workspace because it isn’t one that identifies the non-searchable data and there’s definitely non-searchable data…. I would say let’s just do date filtering, let’s target specific custodians, but definitely not run search terms in there.”
- “[When working with short messages], that’s what all of us want, we want a document…. That’s what we want and we’re trying to fit text messages into that mold.”