Nabeel S. Qureshi: evaluation

Transcript context

…Okay. There's a lot here. First of all, you talk about Gotham and Foundry. I know that we'll link to videos of people checking these out, but just what's the simplest way to understand what these two products do? So Gotham is optimized for military and defense use cases and intel as well. I would say they both have some things in common. So they both have, I would describe this almost as a pyramid where the bottom layer is data ingestion, the middle layer is data mapping, and then the top layer is anything that's user facing. So any UI component. And then if you think of Foundry for a second, there's different tools that allow you to ingest data to it. There's different tools that allow you to easily build data pipelines and clean up data, which everybody has to do. And then there's a bunch of tooling that allows you to build compelling UIs on top, do point and click analytics, do notebook style workflows, however technical you are. So that's, I mean, when it's a platform, it's a suite of things that has a common data backing but contains a bunch of different applications.So I think that is somewhat true of Gotham as well. But when you log in, you see this unified interface. So what is the actual difference then? I would say with Gotham, you're looking much more at workflows like that involve maps, for example. So when you're doing a military operation, a lot of the time you are going to be looking at a map and you are going to be monitoring the movement of troops or tanks or whatever it is. Another big difference is the idea of graph-based analysis. So Gotham, one of the use cases was finding combing through networks of terrorists and basically finding the bad guys. So being able to do queries that are graph-based was important. So it's like, "Who is everybody that Lenny called in the last week?" Imagine all the nodes fanning out from there. And then it's like, "Okay. Well, this one looks interesting. Let's zoom in on that. What is this person's location? " So it's this very graph-based way of thinking that also applies to things like fraud. So Gotham has been deployed against fraud, but if you look at Foundry, it doesn't actually emphasize that component so much because it turns out, let's say you're a B2B SaaS company, you're probably not doing that much graph-based analysis. You're doing things that look a lot more like classic SQL queries, tables, that kind of stuff. So Foundry is a lot more traditional in that way. That was an amazing explanation. For the first time, I am starting to understand what these products do. Basically, it's just sucks in a bunch of data, cleans it up so you can actually trust it and then helps you interact with it in various use cases, maps, graphs, tables.…

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