 Live from Manhattan, it's theCUBE, covering AWS Summit, New York City 2017, brought to you by Amazon Web Services. And welcome back here on theCUBE, the flagship broadcast of SiliconANGLE TV, where our colleague, John Furrier, likes to say we extract the signal from the noise. Join that here at AWS Summit here in Midtown, along with Stu Miniman. I'm John Wallace and we're joined now by Ben Newton, who's the analytics lead at Sumo Logic. And I said, Ben, what is an analytics lead if you had to give me the elevator speech on that? And you said you're the geek who stays up all night and fiddles with stuff. Well, that's why I joined Sumo Logic. I love finding the things that other people didn't find. And when I first joined, I was staying up until 2 a.m., every night playing around with the data. My wife started getting worried about me, but that was the path that I set on. Here the guy looks at the clouds and sees the man's nose. It's in data, that's all. I hear this concept, but we'll jump in here about continuous intelligence. Machine data, and there's just this constant stream. I mean, how do you see that? How do you define that? How does that play with what you do? Yeah, no, absolutely. So I've been around a little while. And when I started out, there was a particular set of problems we were trying to solve. We had the $100,000 Sun Microsystem service. You drop them on the floor, somebody gets fired. But it was a very particular problem to have. What's happened now is that the market is really changing. And so the amount of data is just growing exponentially. So I kind of have my own conjoined triangle slide that I like to show people, but basically, things are getting smaller and smaller and smaller. We're going from these monolithic services, the microservices, the IoT, and the scale is just getting bigger and bigger and bigger. And what that means is that the amount of data being produced is just bigger than anyone ever imagined. I was just looking up some numbers that Barclay says it's going to be a 16 Zetabytes. I had to look that up. That's a billion terabytes by 2020. That's like watching the whole Netflix catalog 30 million times. That's the amount of data that customers are dealing with and that's what's exciting about the space, I guess. So I remember at re-invent, you see Sumos like the booth when you walk in, they actually had Sumo wrestlers one year. Remind me, just wrestling. I've got all that data. How do I take advantage of that? How do I democratize the analytics on data? What are the big challenges? You said customers used to be dropping a server on the floor. How are they getting their arms around this? How are they really leveraging their data and leveraging their analytics? Yeah, I got to wrestle one of those Sumos. That was a, he let me win a little bit. Then it was over. Did you have to wear the outfit? No, luckily no, good for everybody. Yeah, I think a few years ago, it was all about big data. And it was all about how much data could get in. And I think you saw some announcements from AWS today. Really people are getting their hands around it. Now it's all about fast data. Like what can I do in real time? And that's what people are struggling with. They have this massive amount of data that's just sitting there unused and people aren't actually getting value out of it to drive the business. And that's really the next goal or I think over the next few years is how can our customers and companies get more value out of the data they have without having to invest in all this costly infrastructure to do it? Yeah, Ben, I think a few years ago, it was big data. I'm going to take the compute and I'm going to move it to the data. Now, last year at Reinvent, talked to a lot of the companies that were working with the dupe and the like, and they said the data lakes are now in the public clouds. But now I've got edge computing. I kind of have the data center of the public cloud on the edge and I'm never going to get all my data in the same place. So how am I managing all of those various pools of data? How do I make sure I get the right data in the right place so I can make the decisions that I need to when I need to? Yeah, that's a good question. So a lot of what we're trying to do now is trying to help customers get the data in the way they want, just like you said. So before it might have been about like here's our standard way and here's our agent, you go install that. Now we're trying to provide ways for them to get the data in they want. We're providing APIs and basically trying to move towards becoming more of a platform so that customers are sending us with third party tools they like because I was talking to one of my developers and I asked him, if somebody came and said to you, you need to change the way you produce your data to use this product, what is it going to say? And he used the four letter word I can't repeat. That's how they think about it. They don't want to have to change the way they do things. So what we do is we provide lots of different ways of getting from multiple clouds, from multiple tools, open source tools. We don't care and make it as easy as possible to get the data in. You know, if Stu and I were different clients of yours, you know, what matters to Stu is much different than what matters to me, right? So how do you go about helping determine access to data in the context that I wanted, as opposed to the data that Stu wants at the time that he wants it? Because it's just not about finding real time stuff, it's about also finding value at it and helping me put action to it. You know, absolutely, John. So I think there's a couple different ways. One is making it easy to get the data like what he's talked about. Another way is actually building a cost model that matches how you use the data. The typical way the analytics tools have done in the past, including us before, was kind of a one-size-fits-all model. So last year, we announced our unified logs and metric product, which was trying to appeal to long-term trending. And so now what we're moving towards as well is providing a model that allows our customers, we call it Cloud Flex, that allows them to organize their data in the way that makes the most sense. So maybe you want to keep your security data for a year, but you want to keep your operational data for seven days. That's fine. But organizing the way that makes most sense to you and match your cost to your data. I mean, this is the path that I think AWS has really set, that we're basically meeting customers where they're at, allowing them to use it. And the second thing is, is also making it easy for their customers to get to that data. They use it in the way they like. So you make it easy to get in, cost-efficient model, and then make it really easy for the user to get to that data. Ben, who are you working with the most? Maybe you're working across all these, but Amazon was talking a lot about the data scientists this morning, all the ETL challenges they're happening. I know there's a big booth for developers. I expect there's probably something with Lambda that you're involved in, but what are some of those hot-button issues that you're seeing across some of the customer roles? Sure, sure. Well, I mean, I think one thing where you say that with data scientists, I mean, we all know that there's a data science shortage. We have data scientists that assume a logic, they're hard to find. And so part of this is making it, one of the hot-button issues is, can I get people that don't have that background access to the data? And so I may want to geek out and writing queries and staying up to 2 a.m. writing that. Most people don't. That's right, not surprising. So a lot of that is, how can you make it easier for, our developers, for example, that have another job to do. This is not their main job, to get access to that data and use it. And so for example, one of the things we've done for customers we did for ourselves at Sumo is even making that data accessible to other parts of the business. So for example, our sales reps at Sumo Logic actually use that data to drive the customer interaction so they can go to a customer and say, hey, we're seeing how you're using the tool. We think you could get value of these other five things and work with them in a constructive way. For example, a couple other clients I've worked with, they're actually using the data in their marketing departments and their sales departments and putting this up on the wall. So the other parts of the business are getting access to a beyond DevOps and IT ops, which is huge value to them, right? Sumo, I'm just curious, Sumo Logic. Where from, the name? I mean, what's the genesis of that? Well, the official story is that it's about Sumo big data. The real story is that our founder, Christian, loves dogs and he has a dog named Sumo. And so it really fit well. It fit the name because of big data, but also it fit it because he had a dog named Sumo. I'll buy that, just curious. Ben, thanks for being with us. We appreciate the time here on theCUBE and you could have taken them, I know, if you really wanted to. I appreciate that. You could have, no doubt. Ben Newton, our analytics lead at Sumo Logic. Joining us here on theCUBE, back with more from AWS Summit in New York right after this break.