 Live from Midtown Manhattan, the Cube's live coverage of Big Data NYC, a Silicon Angle Wikibon production made possible by Hortonworks. We do Hadoop, and when this go, Hadoop made invincible. And now your co-hosts, John Furrier and Dave Filante. Okay, welcome back everyone. We are live in New York City here at Big Data NYC. Hashtag Big Data NYC for Big Data Week, where we are covering Hadoop World Stratoconference and all the action around Big Data in New York City. That means business, that means technology. I'm John Furrier, the founder of the Cube, where we go out to the events, in this case, creative event, Big Data NYC. Extract a signal from the noise and share that with you. I'm joined with my co-host Dave Filante. And our guest this segment is entrepreneur, executive, now venture capitalist, Chris Lynch with Atlas Ventures. Welcome back to the Cube. Chris, great to see you. Same here. Great to stop meeting like this. This is what? The fourth or fifth year? So you shaved your head again for St. Baldrick, but also grew some hair on the face there. Nice facial hair. We've got the Boston Strong. Get beard. Do your tug. Hashtag. Hashtag. Have a tug. Hashtag. Get beard. You have always been an angel investor, now venture capitalist. So what's your take around what's happening right now? The ecosystem, the community, the technology. What's going on? Well, I think a couple of things are happening, John. First of all, you know, we all know there's this hype cycle, right? So we're hearing a lot of noise in the market about IPOs and, you know, big time exits. I think that there are a couple of bellwethers, right? Tableau would be one, Splunk the other. So clearly the market's hungry for IPOs in the Big Data Space, and we can sustain those. But I think we have to sort out the contenders from the pretenders. There's a lot of people talking a good game, but their business, in my opinion, don't have the discipline, the growth, the linearity that you need to create a quality public company. In what areas? Be specific, if you can, on that area. Sure. I mean, I think that there are a number of companies in the marketplace that have been out of raised. Lots of capital have been around for quite some time now that they've been talking about IPO, but there is any invisible means of how they're going to get there from, you know, a revenue perspective. At the end of the day, you know, hype can only take you so far. It might get you to a filing, but at the end of the day, if you're going to have a sustainable market cap, you're going to have to show the market how you, if a business model, it makes money. Do you think some of these companies are overfunded? Well, I don't know if they're overfunded. I think the market, no one's gated by market here, right? So from a market. So huge. Yeah, the market's so huge. I think at a micro level, I think companies should be investing behind revenue and behind a business model. I do think that there are companies out there that have raised a lot of money and spent a lot of money before really figuring out what business model, you know, they're going to pursue. So Tableau and Splunk are two good examples. I mean, obviously, the stocks are doing well. Huge, multi, multi billion dollar valuations with a lot of growth. They're not making money because they, you know, they're pouring it back into the business. You like that model? I do. For a company where we understand the model, and we can measure and understand when they're going to get profitable and how they get profitable, I think investing in the business makes sense. That's a little bit different than some of the private companies out there, venture-backed companies that are sort of spending money searching for a model. Trying to figure out where the revenue dollars are. I mean, Amazon's kind of the same way. I mean, essentially, you know, they're profitless, but they're killing it, right? So do you see kind of a similar dynamic emerging in the big data field where a couple of large companies will be able to drive growth and valuation and then use that momentum to compete, you know, almost as a competitive weapon in the marketplace? Well, absolutely. I mean, I think one of the advantages and one of the reasons to IPO is to be able to use that currency, you know, to acquire and accumulate assets and resources, particularly in a market, you know, that is a hypergrowth market like big data. What are the sectors that you're tracking where you're making some investments that you're excited about? Sure. So from my perspective, we've talked about this before, but I think there are three areas that have the most, the maximum challenges and opportunities in big data, and their scale, their security, and simplicity. And I think the companies that are focusing on overcoming those obstacles are the ones that are going to really transform the market and really allow the potential of big data to be realized. So I'm investing in companies that are addressing all three of those areas. Some are addressing all of them. Some are addressing them to varying degrees. But I'd say that those three technical areas and then just in general, I'm focused on applications and businesses that can be disruptive, leveraging big data versus, you know, pure data plays, platform plays. So we think about big data week here and big data NYCR event we're having and also the Stratoconference is now jam-packed. They're talking with the Javits Center. Huge event. I mean, are you surprised by the magnitude of the interest and the commercialization? Well, you know, it's interesting. I was thinking on the way over here in the cab. I think the three of us were here, I don't know if it's four or five years ago, if my memory serves me correct. It was obviously very different. You know, it was sort of a grassroots type thing. I think, you know, it's sort of a sign of the times. You know, this feels like, you know, a very commercial, you know, proprietary event, you know, and it sort of lights my competitive fires because it makes me think about sort of the days when I was competing as a startup against, you know, the big, fascist ecosystems of proprietary oligopolies. Big money. Yeah, big money, closed systems. And I think it's kind of ironic, right? Because big data is about open systems and community. And here we are two blocks away from, you know, stuff that you guys, you know, really created. No, we were definitely there. I mean, Hadoop ecosystem, John and Dave at the show, the queue. But when we first started four years ago was, you know, it's cloud era was really the only vendor Hortonworks I've even launched yet. So we were president creations part of that ecosystem. So, you know, the community being part of that community has been real rewarding for us. And we've got a great time. And, you know, we have, we have no complaints around our role in the community. We'd love being here. Granted, we're here at the Warwick, but just it's just a sign of the times. I mean, I think at the end of the day, what's great about our new, the new era of social media is that democratization truly is that people are talking and the community votes with their feet. They vote with their code. And, you know, certainly great endorsement here were sold out in terms of the slots available to beyond great underwriting support from the community validating the queues. It's been fantastic. Can I say it more concisely? Are you doing it for the love, not for the money? We're doing it for the love. I mean, we could meet. I mean, we've had someone said to me last night, you know, you guys can make so much money with this cube. And we're like, yeah, but we're not O'Reilly. We're not in it for just the money. We want to support and grow operations. But there are ways we could make money on this, but we don't. We want to create open source content because really no one's doing it. It's a competitive opportunity for us to be a part of the community and give back, but also more importantly, share information. So, yeah, we could make money. We have some great sponsors who allow us to come to events and drive great content, you know. So that's our model, right? You get guys that come in. They want us at their events, you know, big players like an EMC or an IBM or an HP and they help us get to these smaller events. You know, we did an event at MIT this year. We did an event at Stanford. We've done some stuff with Atlas Venture. We do some stuff with HackReduce. So it allows us, you know, well over half of the events that we do are, you know, just pure editorial. Well, that's the interesting thing. Things are sort of full circle because obviously you're aware that EMC and IBM are very staunch supporters of HackReduce. You know, really, and, you know, they've never tried to commercialize that participation, which I really applaud. And, you know, there was a time when they were sort of part of the proprietary world and they've really opened up those companies. So it's, and I think to great, you know, to their benefit and to the community's benefit. So it's interesting to see an event like this become closed and sort of, you know, sort of just not what, you know, we got a good spot here, right? Yeah, yeah, absolutely. From right where we are. So what's the hot companies out there that you're involved in that you see is coming out of the woodwork that's hot from an investment standpoint, both angel investor and also as a venture investor? Sure, sure. So there are a couple hot ones. So one, of course, is Newtonian, which we just announced that we put four and a half million dollars into the companies where artificial intelligence meets business intelligence. And actually, the bar I gave them was to predict who's going to win the World Series. And back in spring training, they predicted the Red Sox, and it looks like they're getting pretty close to meeting that prediction. Another great one is Data Robot, which you may not have heard a lot about there in stealth mode, but I'm really excited about them. And, you know, Squirrel, I think I heard is one best to show here, you know, from my perspective. Acumulose, let's talk about Squirrel. Squirrel is a company that we know real well in being closest to your investment, seeing that directly emerging right out of the woodwork. Acumulose now the hottest thing. And their role in that was pretty significant, well-documented. So talk about what's new with them. What are you, what's, can you share some data about what traction is, obviously they've got around funding. What else is going on? Sure, sure. So we don't release specific numbers, but I can tell you that quarterly revenues are now measured in millions. And they've got customers in telco, banking, finance, insurance, healthcare. And, you know, I think the sky is the limit for them. I think this is going to be their year. And everybody's talking about security, right? Two years ago, nobody mentioned security. Even, even a year ago, nobody. About last summer, last June, you really started to hear the community talk more and more and more about security. So that has to give companies squirrels. Security is the killer app for big data this year. Yeah, so, and then adapt to the other one, they won Best in Show last year, right? Yep, that's right. How are those guys doing? They're doing terrific. I think some of the guys are out in the audience here. Obviously we've seen, what they say, the best form of flattery is copying somebody. So we've seen within Palo that we had a great idea and the market's trying to fall. But, you know, we think that we've got a two-year lead and certainly thought leadership and we've got some exciting products that are coming out in the next coming months that we really think are going to change the game and distance us from the crowd. And you mentioned Newtonian. That's, I mean, that's a decent chunk of change for an early stage company like that. They're doing some amazing things. If you hit their website and watch that video, it's mind-blowing. Talk a little bit about what they do because it's really unique. Sure, so basically what they do is they provide a data scientist in a box. So they're the next generation of analytics intelligence. They ingest any data type through machine learning. They determine through looking at trends, what questions to ask and they answer the questions. No human beings involved. And then they tell you what action to take based on that. So they kind of reverse engineer the whole pipeline and then ultimately hand it to a human to say, okay, this is what we would recommend. That's right, traditional systems would use analytics. You have to kind of know what you're looking for. Here you don't have to know what you're looking for and they find patterns in data that human beings and legacy systems simply can't find. Okay, Chris Lynch, thanks for coming on theCUBE. We are live in New York City for big data NYC. We get the movers and shakers, newsmakers, entrepreneurs, CEOs, venture capitalists here on theCUBE. We're announcing products. We announced a startup yesterday. This is theCUBE, I'm John with Dave and we'll be right back after this short break.