>> Announcer: 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. Doing that here at AWS Summit here in midtown along with Stu Miniman. I'm John Walls 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 were to give me the elevator speech on that? You said you're the geek who stays up all night and fiddles with stuff. >> 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 2:00 a.m. every night playing around with the data. My wife started getting worried about me. (laughter) But that was the path that I set on. >> You're the guy that looks at the clouds and sees the man's nose, right? >> Yeah exactly, exactly. >> It's just it's in data that's all. >> Yeah, yeah. >> So I hear this concept. But we'll jump in here about continuous intelligence, right? >> Ben: Yeah. >> It's machine data and there's just this constant stream. I mean, how do you see that? How do you define that? And how does that play with how you, 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. You know, we had the $100,000 Sun Microsystem servers. You drop 'em on the floor, somebody gets fired. But it was a very particular problem set. 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 to microservices, 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 it's bigger than anyone ever imagined. I was just looking up some numbers that Barkley says it's going to be 16 zettabytes. I had to look that up. That's a billion terabytes by 2020. That's like watching the whole Netflix catalog 30 million times. (laughter) That's the amount of data that customers are dealing with and that's what's exciting about this space I think. >> So, I remember at Re:Invent. You see Sumo's like the booth when you walk in. They actually had sumo wrestlers one year. (laughter) 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 analytics more? >> Yeah, I got to wrestle one of those sumos. (laughter) He let me win a little bit. (laughter) And then it was over. >> Did you have to wear the outfit? >> Luckily no. That was good for everybody. Yeah, you know, I think ... A few years ago, it was all about big data. And it was all about how much data they 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 weren't actually getting value out of to drive the business. And that's really the next goal I think over the next few years is how can our customers and these companies get more value out of data they have without having to invest in all this costly infrastructure to do it? >> 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. >> Yeah. >> Now, last year at Re:Invent, talked to a lot of the companies. They're working with Hadoop and the like, and they said the data lakes are now in the public cloud. >> Ben: Yes. >> But now I've got edge computing. I kind of have the data side, the public cloud, and 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? >> Ben: Yeah. >> 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, it'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 it. Just like you said. So, before, it might have been about 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 the 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 he going to say? And he used a 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. Making it as easy as possible to get the data in. >> You know, if Stu and I were different clients of yours. What matters to Stu is much different that what matters to me, right? So how do you go about helping determine access to data in a context that I want it, >> Ben: Yeah. as opposed to the data that Stu wants at the time that he wants it? Cause it's just not about finding real time stuff, right? It's about also finding value at it. >> Ben: Yeah. >> 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 in like we just talked about. Another way is actually building a COSMO that matches how you use the data. The typical way that analytics tools have done it 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. It 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 also making easy for their customers to get to that data. And use it in the way they like. So you can 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 scientist this morning. All the ETL challenges >> Yeah. >> that are happening. I know there's a big boost for developers. I expect there's probably something with Lambda >> Yeah. >> 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. I think one thing where you say that with data scientist. I mean we all know that there's a data scientist shortage. We have data scientists at Sumo 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 write inquiries and staying up to 2:00 a.m. writing that. Most people don't. That's (mumble), right? Not surprising. >> Stu: Right. >> 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 interactions. So they can go to a customer and say, hey, we're seeing how you're using a tool. We think you could get value out of these other five things. And work with them in a constructive way. For example, a couple of 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 that other parts of the business are getting access to it beyond dev ops and IT ops, which is huge value to them, right? >> Sumo, I'm just curious. Sumo Logic, umm, where from the name? 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 cause of big data but it also it fit it because he had a >> Alright. >> 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 him I know, if you really wanted to. >> I appreciate that. >> You could have, no doubt. (laughter) Ben Newton. Analytics lead at Sumo Logic joining us here on theCUBE. Back with more from AWS Summit in New York right after this break. (upbeat techno music)