(rhythmic techno music) >> Hey welcome back everybody, Jeff Frick here with theCube. We're at Oracle Park on the shores of McCovey Cove. We're excited to be here, it's a pretty interesting event. Sports Tech Tokyo World Demo Day. It's kind of like an accelerator but not really, it's kind of like Y Combinator but not really, it's a little bit different. But it's a community of tech start-ups focusing on sports with a real angle on getting beyond sports. We're excited to have our next guest, who's an investor and also a mentor, really part of the program to learn more about it and she is Gayatri Sarkar, the managing partner from Hype Capital. Welcome. >> Thank you. Thank you for inviting me here. >> Pretty nice, huh? (laughs) >> Oh, I just love the view. >> So you said before we turned on the cameras, well first off Hype Capital, what do you guys invest in? What's kind of your focus? >> So Hype Capital is part of one of the biggest ecosystems in sports which is Hype Sports Innovation. We have 13 accelerators all around the world. We are just launching the world's first E-sports accelerator with Epsilon and SK Gaming, one of the biggest gaming company. So we are part of the ecosystem for a pretty long time. And now, we have Hype Capital, VC Fund investing in Europe, Israel, and now in U.S. >> So you mentioned that being a mentor is part of this organization. It's something special. I think you're the first person we've had on who's been a mentor. What does that mean, what does that mean for you? But also what does it mean for all the portfolio companies? >> Sure, I'm a mentor at multiple accelerators. But being a part of Sports Tech Tokyo I saw the very inclusive community that is created by them and the opportunity to look at various portfolio companies and also including our portfolio companies as part of it. One of our portfolio company where we had the lead investors, 'Fun with Balls' they're part of this. >> What's it called, Fun with Balls? >> Fun with Balls, very interesting name. >> Good name. (laughs) >> Yeah, they're from Germany and they came all the way from Germany to here. So, yeah, I'm very excited, because as I said it's an inclusive community, and sports is big. So we are looking at opportunities where deep-techs, where it can be translated into various other verticals, but sports can also be one of the use cases, and that's our focus as investors. >> Right, you said your focus was really on AI, machine learning, you have a big data background a tech background. So when you look at the application of AI in sports what are some of the things that you get excited about. >> Yeah, so for me when I'm looking at investments definitely the diversification of sports portfolio. How can I build my portfolio from esports, gaming, behavioral science in sports to AI, ML, AR, opportunities in material science and various other cases. Coming back to your question it's like how can I look into the market and see the opportunities that, okay can I invest in this sector? Like what's the next big trend? And that's where I want to invest. Obviously, product/market fit, promise/market fit because there's a fan engagement experience that you get in sports, not in any other market the network effect is huge, and I think that's what VVC's are very excited in sports and I think this is right now the best time to invest in sports. >> So promise/market fit, I've never heard that before what does that mean when you say promise/market fit? >> Interesting question so promise/market fit was coined by Union Square Venture VC fund. And they think that where there's the network effect or your engagement with your consumers, with your clients, and with your partners can create a very loyal fan base and I think that is very important. You may see that in other technology sector but, not, it is completely unparalelled when it comes to sports. So, I request all the technologies that are actually trying to build they are use cases, they should focus on sports because the fan engagement, the loyal experience the opportunities, you will not get anywhere else. >> Right >> And I think this is the market that I, and other investors are looking for that, if deep-tech investors and deep-tech technologies are coming into this market we see the sports ecosystem not to be a trillion dollar but a multi-trillion dollar market. >> Right, but it's such a unique experience though, right? I mean some people will joke that fans don't necessarily root for the team, they root for the jersey, right? The players come and go, we're here at Oracle Park which was AT&T park, which was SBC Park, which was I can't even remember, Pac bell I think as well. So you know, is it reasonable for a regular company that doesn't have this innate connection to a fanbase that a lot of sports organizations do that's historical, and family-based, and has such deep roots that can survive maybe down years, can survive a crappy product, can survive kind of the dark days and generally they'll be there when things turn back around. Is that reasonable for a regular company to get that relationship with the customer? >> So, you asked me one of the most important questions in the investors relationship, or investors life which is the cyclicality of the industry and I feel like sports is one industry that has survived the cyclicality of that industry. Because, as you say, a crappy product will not survive you have to focus on customer service so you have to focus, that, okay even if you have the best product in the world how can I make my product sticky? These are the qualities that we are looking into when we are investing in entrepreneurs. But the idea is that if we are targeting startups and opportunities, our focus is that okay, you may have the worlds best product but the founder's should have the ability to understand the market. Okay, there are opportunities, if you look at Facebook if you look at various other companies they started with a product that was maybe like okay, friend site, dating site and they pivoted, so you need to understand the economy you need to understand the market and I think that's what we are looking into the entrepreneurs. And, to answer your question, the family offices they are actually part of this whole startup ecosystem they are saying if there is an opportunity because they are big, they are giant and they are working with legacy techs like Microsoft, Amazon. It's very difficult for the legacy techs to be agile and move fast, so it's very important for them if they can place themselves at a 45 degree angle with the startup ecosystem, and they can move faster. So that's the opportunity for them in the sport's startup ecosystem. >> All right, well Gayatri thanks for taking a few minutes and hopefully you can find some new investments here. >> No, thank you so much thank you so much for your time. >> Absolutely, she's Gayatri, I'm Jeff you're watching The Cube, we are at Oracle Park On the shores of historic McCovey Cove I got to get together with Big John and practice this line thanks for watching, and we'll see you next time. (rhythmic techno music)

(upbeat music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at Oracle Park on the shores of McCovey Cove. We're excited to be here. It's a pretty interesting event. Sports Tech Tokyo World Demo Day. It's kind of like an accelerator, but not really. It's kind of like YCombinator, but not really. It's a little bit different, but it's a community of tech start-ups focusing on sports with a real angle on getting beyond sports. We're excited to have our next guest who's an investor and also a mentor, really part of the program to learn more about it, and she is Gayatri Sarkar, the managing partner from HYPE Capital. Welcome. >> Thank you. Thank you for inviting me here. >> Pretty nice, huh? >> Oh, I just love the view. >> So you said before we turned on the cameras... Well, first off, HYPE Capital, what do you guys invest in? What's kind of your focus? >> So HYPE Capital is one of the biggest ecosystem in sports, which is HYPE Sports Innovation. We have 13 accelerators all around the world. We are just launching the world's first Esports accelerator with FC Koeln and SK gaming, one of the biggest gaming company. So we are part of the ecosystem for a pretty long time. And now we have HYPE Capital or VC Fund investing in Europe, Israel, and now in US. >> So you mentioned that being a mentor, as part of this organization, as something special. Think you're the first person we've had on who's been a mentor. What does that mean? What does it mean for you, but also what does it mean for all the portfolio companies? >> Sure. I'm a mentor at multiple accelerators, but being a part of Sports Tech Tokyo, I saw the very inclusive community that is created by them. And the opportunity to look at various portfolio companies and also including our portfolio companies as part of it. One of our portfolio company where we are the lead investors, Fund with Balls, they are part of this. So-- >> What's it called? Fun with Balls? >> Fun with Balls, very interesting name. >> Good name. >> Yeah. (laughing) They're from Germany and they came all the way from Germany to here. So, yeah, I'm very excited because as I said, it's an inclusive community and sports is big. So we are looking at opportunities where deep techs, where it can be translated into various other verticals, but sports can also be one of the use cases. And that's our focus as investors. >> Right. You said your focus is really on AI, machine learning. You have a big data background, a tech background. So when you look at the application of AI in sports, what are some of the things that you get excited about? >> Yeah, so for me, when I'm looking at investments, definitely the diversification of sports portfolio, how can I build my portfolio from Esports gaming, behavioral science in sports to AI, ML, AR opportunities in material science, and various other cases? Coming back to your question, it's like how can I look into the market and see the opportunities that, okay, can I invest in this sector? As I said, what's the next big trend? And that's where I want to invest. Obviously, founder market fit, product market fit, promise market fit because there's the fan engagement experience that you get in sports, not in any other market. The network effect is huge and I think that's what we VCs are very excited in sports. And I think this is, right now, the best time to invest in sports. >> So promise market fit, I've never heard that before. What does that mean when you say promise market fit? >> Interesting question. So promise market fit was coined by Union Square Venture VC Fund. And they think that where there's the network effect, or your engagement with your consumers, with your clients, with your partners, can create a very loyal fan base and I think that's very important. You may see that in other technology sector, but it is completely unparallel when it comes to sports. So I request all the technologies that are actually trying to build their use cases. They should focus on sports because the fan engagement, the loyal experience, they opportunities, you'll not get anywhere else. >> And I think this is the market that I and other investors are looking forward. If deep tech investors and deep tech technologies are coming into this market, we see the sports ecosystem, not to be a trillion-dollar, but a multi-trillion dollar market. >> Right. But it's such a unique experience, though, right? I mean, some people will joke their fans don't necessarily root for the team, they root for the jersey, right? The players come and go. We're here at Oracle Park, which was AT&T Park, which was SBC Park, which was I can't even remember. Pac Bell, I think, as well. So is it reasonable for a regular company that doesn't have this innate, kind of, a connection to a fan base that a lot of sports organizations do that's historical and family-based, and has such deep roots that can survive, maybe, down years, can survive a crappy product, can survive, kind of, the dark days and generally they'll be there when things turn back around. Is that reasonable for a regular company to try to get that relationship with a customer? >> So you asked me one of the most important question in the investor's relationship or investor's life, which is the cyclicality of the industry. And I feel like sports is one industry that has survived the cyclicality of that industry. Because, as you said, a crappy product will not survive. You have to focus on customer service. You have to focus that, okay, even if you have the best product in the world. How can I make my product sticky? I think these are the qualities that we're looking into when we are investing in entrepreneurs. But the idea is that if we are targeting start-ups and opportunities, our focus is that, okay, you may have the world's best product, but the founders should have the ability to understand the market. Okay, there are opportunities. If you look at Facebook, if you look at various other companies, they started with a product, which maybe, okay, friends saw a dating site and they pivoted. So you need to understand the economy. You need to understand the market. And I think that's what we are looking into the entrepreneurs. And as to answering your question, the family offices, they're actually part of this world start-up ecosystems. They're seeing if there's an opportunity, because they're big, they're giant, and they're working with legacy techs like Microsoft, Amazon. It's very difficult for the legacy techs to be agile and move fast. So it's very important for them if they can place themselves at a 45 degree angle with the start-up ecosystem and they can move faster. So that's the opportunity for them in the sports start-up ecosystem. >> All right. Well, Gayatri, thanks for taking a few minutes and hopefully you can find some new investments here-- >> No, thank you so much. >> over the course of the day. >> Thank you so much for your time. >> Absolutely, she's Gayatri, I'm Jeff. You're watching theCUBE. We are at Oracle Park on the shores of historic McCovey Cove. I got to get together with big John and practice this line. (laughing) Thanks for watching. We'll see you next time. (upbeat music) >> Camera Crew: Clear. >> Jeff: John Miller. >> Gayatri: Oh, yeah.

>> From Union Square in downtown San Francisco, it's theCUBE covering PagerDuty Summit '18. Now, here's Jeff Frick. >> Hey, welcome back, everybody, Jeff Frick here with theCUBE. We're at PagerDuty Summit at the Westin St. Francis in Union Square, San Francisco. About a thousand people getting together talking about the evolution of PagerDuty. We're really excited to have Jennifer Tejada here, the CEO just coming off a terrific keynote. And I got to say congratulations on your recent round of funding that made all the news a week ago. It's great to see you. >> Thank you very much. It's great to see you again as always, Jeff. We love having theCUBE with us at Summit. >> Thank you, and I have to say we do hundreds of events over six years I've been doing this. I've never seen a summit picture in the keynote until the Summit. So, you got it worked in twice. I love the message really about taking the team to the top of the mountain, that moment of truth, and then you got to just go for it. You got to be prepared, you got to have the team, and at some point in time, you just got to go. >> Point 'em down. >> Yeah, so let's jump into it. So, big topic, here before it's been kind of DevOps, but you guys are moving beyond that. You're kind of taking this classic play, start as an application, move into a platform space. And you guys now with all these integration announcements, the announcements of BI, the growth obviously, the support from the funding that you just got shows that you guys are well on your way to take what was a pretty special purpose application and take it into a platform play that crosses a whole bunch of other applications. >> Yeah, I'd take it even one step further, we almost started out more like a consumer app. It was really an application built for engineers to make better use of their time on call. And frankly, not being woken up when they didn't need to be, right? >> Right. >> And so, everything about our first product was designed around what does a developer need, what does an Ops person need, what does that look like, et cetera? As opposed to being designed for the CIO, or the CTO, or the company. >> Right, right. >> Right. And I think that that user centricity, that user ethos has served us really well, because we start there. That's our starting point. Who's the community that is using our products and services? How is their role changing? How is their world changing? And what do they need from us? And that was really the foundation of the trust that we built to start to become truly an ecosystem. Because all those users started pushing their data to us. Their monitoring data from their APM environments, or the data from their ticketing platforms, or the data from their cloud services. And with that information comes the power to be able to really create context. >> Right, right. >> And now, with the aggregation of nearly 10 years of data coming from our responders, and how they behave when they're under pressure of the workflows, which ones work better, which ones don't work so well. And the events, the signals that all of technology and the internet of things throws off in realtime all the time. You bring that data together and apply machine learning and artificial intelligence to it, and we really are putting ourselves in a position not only to be the platform that serves a realtime business, and orchestrates teams as sort of the platform for action, but importantly, becomes the trusted engagement for automation or engagement of autonomy. >> Right. >> For a fast-moving business in the future. >> Right, 'cause you talked about realtime and I just want to throw a couple of numbers out that you had from the keynote. 3.6 billion events, so it becomes apparent really fast-- >> So far this year. >> Right, even the people who are at the center of that, that's kind of hard to manage. So, you have to start using intelligence. You have to start to use business intelligence and artificial intelligence to help filter and help that person do their job much better. So, you guys are making a lot of plays there. And we see it all the time. It's not the BI vendors per se, it's the use of this technology in the background to make apps work better. >> And it's the fact that not only do we correlate the signals and turn them into intelligent insights, but we can then route those signals intelligently to the right people, and orchestrate the actual physical work. So, a lot of the technology community has been focused on just that, technology. And our focus is really on people and teams. How do you empower teams closest to the action, closest to where the proverbial stuff hits the fan. >> Right, right. >> I had to really exercise restraint there. To be in a position to make the best possible decision in those tiny micro-moments that matter. And the consumer, like used to wait maybe six minutes for a website to download. Now, if an app doesn't work perfectly in six seconds, maybe three seconds, you're gone. I walk out of the building in our office in San Francisco, and see our employees and they're toggling back and forth between ride sharing apps and food delivery apps, and Tinder, and whatever else is going on. And it's literally like in a couple of minutes, they're working through eight, nine applications at once. And if any one of those does not work the way it's supposed to, they're done. >> Right, right. >> They just move on. And it's one or two times before they'll delete that. >> Right. >> So, the technology community is now responsible for delivering the perfect brand experience digitally every time. And they've got to be empowered to do that with the right tools and services. >> And the expectation is set by the best. That's the funny thing, right? What was the best or cutting edge quickly becomes the expected norm. >> What is the most delightful thing that ever happened to me, well, that's what I want from you. >> Right. >> That is basically the way it works. >> Right, right. And you talked about trust, and trust is such an important part because one of the key pieces that you guys are enabling, you talked about it in your keynote, is letting the person at the front line in that moment of decision have the tools, and the data, and the authority to make the right call. And it's not a escalation up the food chain, waits, and some emails. It's really empowering that individual to get the right thing done. >> And that's a core tenet of DevOps culture. It's actually born and agile, in fact. But what's really interesting about it is it's the way companies need to be run now. If PagerDuty waited for me to make every big decision, we would be back where we were three years ago. >> Right. >> Right. And as a result of being able to empower our teams with great information, very clear understanding of our goals, and the timeframes we expect to achieve those goals, and then context as we progress through our journey to understand how we're doing against those goals, it gives them the power and the intelligence to make better decisions every moment when I obviously can't be there, or their leadership can't be there. And in fact, it means that the most important decisions are getting made where the person's closest to our end customer, the user. >> Right. >> And that makes a ton of sense to me. Even if it's not the way I was taught leadership, or taught to manage. >> Right, well, you clearly get out front and run those people down that big, giant mountain. >> Well, I just, you know-- >> Every time we meet-- >> I got to figure it out, man. >> I learned about Australia last time I saw you speak at the Girls in Tech thing. So, this is great. Another thing that you acknowledge in your keynote I want to get into is that tech people are imperfect. They are imperfect and that's kind of part of what the DevOps ethos is is that that's okay, we're just going to make it better today than it was yesterday. And I think Ray Kurzweil's keynote about exponential growth and just the power of compounding, which so many people miss out on. So, that's really where you're trying to help people solve problems. It isn't to big eureka moment, it's how do we learn, how do we get better, how do we make improvements? >> Well, and a lot of people in the valley talk about failing fast. In order for failing fast to have a benefit for a company, you not only have to be allowed to fail, it has to be okay when you fail, and there has to be an open transparent conversation about what you learned, what went wrong? And that has to be a blameless, high-empathy discussion or it doesn't work. If someone thinks they're going to get fired by marching you through all the details of their failure, they're never going to tell you the truth. So, when we think about incidents as they come up, or something breaking, not working the way it's supposed to, or a business initiative not turning out the way we thought it would, there has to be a blameless conversation so that everybody in that community learns, so we're better the next time around. And that's where the compounding benefits come. >> Right, right, to the whole team, in fact. I thought the quote, I've never seen that quote that you brought up today. Teamwork remains the ultimate competitive advantage because it's both so powerful and so rare. That is a really scary statement, but we see it all the time. In fact, that was in another keynote and there was a behaviorist talking about, how do we get everybody pulling in the same direction? And John also talked about that in terms of incident post-mortems and how do you make sure that you're learning and not just filing reports. >> Totally. >> So, you guys are right in the middle of that. >> I thought John captured it really well when he said, "It's not about the technology. "We spend all of our time monitoring "and talking about the technology. "It's about us. "And it's us that actually makes this technology great "and applies it so effectively to problems, "and challenges, and opportunities "in our world and in our lives." what's also interesting is Patrick Lencioni's paradigm around the first team. So, most employees come into a business and they think the most important world for me in this company is my team, the people in the team who I report to, a leader, and it's just us. Or for leaders, they say it's just the team that reports into me. Your first team is your peer group. Your first team is that, and by first team I mean the most important, highest priority, aligned organism that is going to drive massive change in a business, it's your peer group. It's the people who work across functions to help reduce friction in a business. >> And drive fast outcomes and great results, right? But most people naturally kind of hunker down into their core team and that's the beginning of the silo mentality. >> Right. >> Right. And so, one of the things I love about Patrick's book, and you're going to hear about that tomorrow onstage, is this idea of what it takes to be an ideal team player, to be humble, to be hungry like good is never good enough, and to be smart, to like constantly be learning, to really care deeply about how you continue to push the envelope to get better. >> Right. So, I want to switch gears a little bit from the people in the individual teams to the ecosystem. You had the ton of partners here at the show, and you talked about in the keynote, 300 integrations. >> Yeah. >> And I think some people might be confused, right? Because it's always this wrestle for whose screen am I working on when I'm doing my daily job? But as you said, we're in a lot of different screens, right? And I'm going back from Salesforce. I'm in my G Suite. Maybe I'm jumpin' into Hootsuite for some social stuff. You guys have basically embraced the ecosystem for all these different types of systems, and really kind of plugged into that. I wonder if you can explain a little bit more. 'Cause I'm sure most people might be confused by that. >> You know, I sort of think of us in the same way I would think of like the brain of an Olympic athlete. That athlete, their arms, their legs, their muscles, their pulmonary capability, like the respiratory system is all super important to their performance. But the brain has to accept the signals from all the different parts of the body, and then work through them, correlate them, and then drive action, right? And I sort of think of PagerDuty as sitting at the center of this rapidly changing technology ecosystem, this live organism, and really understanding the signals no matter what, is it raining, is there a pothole in the ground, et cetera? And be able then to drive change in the process on the fly to help the body perform more effectively. The challenge is like if you try and fight with the arms, and the legs, and every other part of the body, they don't work nicely with you. >> Right. >> So, being central to the ecosystem is about being neutral, and agnostic, and really demonstrating you will not only say you will partner, but investing in those partnerships. So, we built first class integrations to companies that may see us as competition, if that's what our customers need. >> Right, 'cause like you said, it's got to be customer-- >> Totally. >> Customer centric first. >> And it's an open ecosystem, and this is what developers, and employees, and tech workers expect. >> Right, and to your point, the amount of data that's flowin' through that nervous system is only getting more. And the amount of noise to get through to the signal-- >> Figure out-- >> To take the right action. >> What really is important. >> Is not getting any easier, right? >> Yeah. >> All right, Jennifer, well thanks again for havin' us. Congratulations on the funding and the great show, and it's always great to catch up. >> Thank you, I have the best job in the world. I feel very lucky. >> All right. >> It's great to see you, Jeff. >> Thank you, all right, she's Jennifer Tejada, I'm Jeff Frick, you're watchin' theCUBE. We're at PagerDuty Summit where they actually show summits on the keynotes screen. Thanks for watchin', we'll see you next time. (bouncy electronic music)

>> From Union Square in downtown San Francisco, it's theCUBE covering PagerDuty Summit '18. Now here's Jeff Frick. >> Hey welcome back everybody. Jeff Frick here with theCUBe. We're at PagerDuty Summit at the Westin St. Francis in Union Square, historic venue. Our second time to this show, there's about 900 people here talking about kind of the future of dev ops, but going a lot further than dev ops. And we're excited to have a couple of CUBE alumni here at the conference from SignalFX. We've got Arjit Mukarji. >> Mukarji, yeah. >> Thank you. And Karthik Rao, co-founder and CEO of Signal FX. Gentlemen, welcome. >> Thank you very much. >> So what do you do at PagerDuty Summit? >> Well we've been partners with PagerDuty for a long time now, we've known them since the very early days, we share a common investor. But we both operate very squarely in the same space, which is companies moving towards dev ops development and deployment methodologies, leveraging cloud and native architectures. We solve a different part of the problem around monitoring and observation and we partner with them very closely around incident management Once a problem is detected, we typically integrate in with PagerDuty and trigger whatever incident management paths that our customers are orchestrating by PagerDuty. So, it's been really an integral part of our entire work flow since we started the company. So we're very close partners with them. >> Yeah, it's interesting 'cause Jen announced they have 300 integrations or 300+ integrations, whatever the number is, and to the outside looking in, it might look like a lot of those are competitive, like there's a lot of work flow and notification types of partners in that ecosystem, but in fact, lots of different people with lots of different slices of the pie. >> That is good. >> Yeah, absolutely. It's a really big problem space that everyone is trying to solve in this day and age. Some of our competitors are in that list, but you know we partner very closely with PagerDuty. As I mentioned earlier, our focus really is around problem detection and leveraging the most intelligent algorithms, statistical models in real time to detect patterns that are occurring in a production environment and triggering an alert, and typically we're integrating in with PagerDuty and PagerDuty deals with the human elements of once something has been detected, how do you manage that incident? How do you router to the appropriate people? One of the things that's really interesting as this world is changing towards these dev ops models is the number of people that have to get involved is substantially greater than it was before. In the old days, you would have an alert go into a knock and you have a specialist group of people with very specific runbooks because your software wasn't changing very often. In today's world, your software is changing sometimes on a daily basis, and it could be changing across dozens of teams, hundreds of teams in larger organizations. And so, there's a problem on the detection side because companies like SignalFX have to do a really great job of detecting problems as they emerge across these disparate teams, across a much, much, much, larger environment with much larger volumes of data and then companies like PagerDuty really have to deal with a far more complex set of requirements around making sure the right people get notified at the right time. And so they're two very different problems and we're very happy to- and have been partnering with them for a number of years now. >> And again, the complexity around the APIs where the app is running, there's so many levels now of new complexity compared to when it was just one app, running on one system, probably in your own data center, probably that you wrote, compared to this kind of API centric multi-cloud world that we live in today. >> That is exactly right because what's happening is our application architectures are changing 'cause we used to have these monoliths, we used to have three tiers and whatnot, and we're replacing that with the micro-services, loosely cabled systems, and whatnot. At the same time, the substrate on which we are running those services, those are also changing. Right, so instead of servers, now we have virtual machines, we have cloud distances and containers and pods and what-have-you. So in a way, we are sort of growing below too in some sense and so that's why sort of monitoring this kind of complex, more numerous environment is becoming a harder challenge. We're doing this for a good cause, because we want to move faster, we want to innovate faster, but at the same time, it's also making the established problems harder, which is sort of what requires newer tools, which sort of brings companies like us into the picture. >> Right, yep. And then just the shear scale, volume, number of data that's flowing through the pipes now on all these different applications is growing exponentially, right? We see time and time again, so it really begs for a smarter approach. >> Absolutely, I mean on two levels right? The number of minutes of software consumption is up exponentially, right? Since the smartphone came out in 2007, you've got billions of people connected to software now, connected all the time, so the load is up order sum magnitude which is driving, even if you didn't change the architectures, you would have to build out substantially more back-end systems, but now the architectures are changing as well, where every physical server is now parceled up into VMs which are parceled up into containers. And so the number of systems are also up by order sum magnitude. And so there's no possible way for a human to respond to individual alerts happening on individual systems, you're just going to drown in noise. So the requirements of this new world really are, you have to have an analytic spaced approach to monitoring and more automation, more intelligence around detecting the patterns that really matter. >> Right. Which is such a great opportunity for artificial intelligence, right, a machine learning. And we talk about it all the time, everyone wants to talk about those, kind of as a vendor-led something that you buy. Yeah, that's kind of okay, but really where the huge benefit is, companies like you guys and PagerDuty using that technology, integrated in with what you deliver on your core to do a much better job in this crazy increasing scale of volume that's run with these machines. >> Yes, because the systems are becoming so complex that even if you asked a human to go and set up the perfect monitoring or perfect alerting, et cetera, it might be quite a hard challenge, right? So, as a result sort of automation, computer intelligence, et cetera needs to be brought in to bear, because again, it's a more complex system, we need higher order systems that have dealed with them. >> Right. >> You are very, very right, yes. And that's a trend we are starting to see within the product, we are actually focusing a lot on sort of data science capabilities which too are sort of making them more and more sort of machine running and automation. In the future, we have capabilities in the product that can look at populations and identify outliers, look at cyclical problems and identify outliers again. So the idea is to make it easy for users to monitor a complex system without having to get into the guts, so to speak. >> Right. >> And to do it on various sorts of data, right? I think you have an interesting use case that we've been experimenting with recently. >> That's right. >> If you want to talk about that. >> Yeah, so I actually have a talk tomorrow, it's called "Interesting One." It's about monitoring social signals, monitoring humans. So we have these systems, we have these metrics platforms and they are quite generic, the tools that we have nowadays and are sort of available to us are quite powerful, and the set of inputs need not be isolated to what the computers are telling me. Why not look at other things, why not look at business signals? In my case, I'm going to talk about monitoring what the humans are doing on Slack as a way for me to know whether there's something of interest that's going on in my infrastructure, in my service that I need to be aware of, right? And you'll be shocked how surprisingly accurate it tends to be. It's just an interesting thing, and it makes one wonder what else is out there for us to sort of look at? Why confine ourselves, right? >> Right. It's funny because we hear about sentiment analysis in social media all the time, but more in the context of Pepsi or a big consumer brand that's trying to figure out how people feel. But to do it inside your own company on your own internal tool, like a Slack, that's a whole different level of insight. >> You'd be surprised at the number of companies that monitor Twitter to understand whether they have an adage. >> That's right. >> Yeah, because in this day and age, users are on Twitter within seconds if something is perceived to be slow, or something is perceived to be down, they're on Twitter. So there are all sorts of other interesting signals to potentially pull from. >> Right, right. Well and guess what, we were just at AT&T Spark yesterday and the 5G's coming and it's 100x more data'll be flowing through the mobiles, so the problem's not going to get any smaller any time soon. >> No. >> Absolutely. >> So what else have you guys been up to since we last spoke? Continuing to grow, making some interesting moves. >> Absolutely- >> Crossing oceans. >> We've been very, very busy, one of the big areas of investment for us has been international growth, so we've been investing quite a bit in Europe. We have just introduced an instance of our service that's based in a European data center. For a lot of our European-based clients, they prefer to have data locality, data residency within the European Union, so that's something new that we just introduced last month, continue to have a ton of momentum, outed AMIA, they're very much on the cloud journey, and embracing cloud and embracing dev ops, so it's really great to see that momentum out there. >> Right, and clearly with GDPR and those types of things, you have to have a presence for certain types of customers, certain types of data. Anything surprising in that move that you didn't expect or? >> No, I don't know, I'll let you. >> Not in that move, but it's just interesting to see how quickly some of these modern technologies are getting adopted and how- one of the things sort of we talk about a lot in our trade is ephemeral, right? So how things are short-lived nowadays, and you used to lease these servers that used to stay in your data center for three years, then you went to Amazon and you leased your instances, which probably lived for a few months or a few days, then they became containers, and the containers sometimes only for a few hours or for- you know. And then, if you think about serverless and whatnot, it's in a whole different level, and the amount of ephemeral that's going on, especially in the more cloud native companies, was a little bit of a surprise in the sense that, it actually poses a very interesting challenge in how do you monitor something that's changing so fast? And we had to have a lot of engineering put in to sort of make that problem more tractable for us. And it continues to be an area of investment. That to me, was something that was a little bit of a surprise when we started off. Much of this doctorization and coordinating was not yet in place, and so that was an interesting technical challenge as well as a surprise. >> Well I'm curious too as instances, right so there's the core instances that are running core businesses that don't change that much, but it's a promotion, it's a this or that, right? It's a spin up app and a spin down app. Are those even going up on the same infrastructure from the first time they do it to the second time they do it. I mean, how much are you learning that you can leverage as people are doing things differently over and over again as their objectives change, their applications change, they're going to go to market around that specific application. That's changing all the time as well. >> Yeah, so I think the challenge there is to sort of build, at least from a technical point of view, from SignalFX point of view, is build something that is versatile enough to handle these different use cases. We've got new use cases, new ways of doing things are going to continue to happen, probably going to keep on accelerating. So the challenge for us is good and bad, is how do we make a platform that is generic, that can be used for anything that may come down the pike, not only just now. At the second time, how do we innovate to continue to be up to speed with the latest of that's what's going on in terms of infrastructure trends, software delivery trends, and whatnot. Because if we're not able to do that, then that puts us sort of behind. >> Right, right. >> So it's a sort of lot of phonetic innovation, but it's also exciting at the same time. >> Right, right, right. And just the whole concept too, where I think what's best practice quickly becomes expected baseline really, really fast. I mean, what's cutting edge, innovative now unfortunately or fortunately, that become the benchmark by which everything else is measured overnight. That's the thing that just amazes me, what was magical yesterday is just expected, boring behavior today. Alright good, so as we get to the end of the year a lot of exciting stuff, you guys said you're going to be at Reinvent, we will see you there. Anything else that you're looking forward to over the next couple months? >> Just, we're really excited about Reinvent's big show for us, and we'll have some good announcements around the show. And yeah, looking forward to just continuing to do what we've been doing and deliver more rally to our customers. >> Love it, just keep working hard. >> Yep. >> Alright. Arjit, hope your throat gets better before your big talk tomorrow. >> Yeah, that's right. >> Alright, thanks for stopping by Karthik, it was great to see you. >> Great to see you. >> I'm Jeff, you're watching theCUBE, we're at PagerDuty Summit at the Westin St. Francis in San Francisco. Thanks for watching, see you next time.

>> From Union Square in downtown San Francisco, it's theCUBE, covering Pager Duty Summit '18. Now, here's Jeff Frick. >> Hey welcome back here everybody, Jeff Frick here with theCUBE. We're at Pager Duty Summit 2018, at the Weston St. Francis, Union Square in San Francisco. It's a beautiful day outside, a really historic building, and we're happy to be here for our second Pager Duty Summit, and our next guest, super excited to have him, we didn't have him last year. He's Alex Solomon, the Co-Founder and CTO of Pager Duty, Alex, great to see you. >> Yeah, thanks for having me. >> Absolutely, so we were just talking a little bit. Before we turned the cameras on, you started this little adventure in '09, so it's been nine years. So what, I'd just love to get your perspective, as you come to something like this, and look at all these people that are here, to see what started as just a germ of an idea, back in your head nine years ago. >> Yeah, it's exciting to see such an amazing conference. We have over 800 people here today, and it's definitely buzzing. I could feel the excitement in the air. >> Right, well Ray Kurzweil is the Keynote, that's getting right up there. >> Exactly. >> Really an amazing story. One of the things that was, that was key to Ray's topic was the accelerating technology curve, in terms of power and performance, and it's not linear, it accelerates, and you guys have seen a huge growth in your business, and your throughput and all the incidents that you're reporting, and now we're talking about BI, and using Machine Learning and Artificial Intelligence to make some sense and to help filter through all this phenomenal amount of throughput. So how are you, how do you see that opportunity, how are you guys grasping the opportunity? What are you going to do with that Machine Learning? >> Sure, so about three months ago, we launched our new Event Intelligence product, which is all about capturing the, all the signals coming out of all of your different tools, things like monitoring tools. Things like ticketing systems, collaboration tools like Slack, and processing all those signals, mostly events and alerts and filtering out the noise. So a lot of alerts and events are not necessarily relevant for someone to get paged about in the middle of the night. Maybe it's a false alert, maybe it's something that has gone up, and will fix itself. >> Right. >> So it's about filtering out all the noise in there, and it's also about automatically clustering, and correlating related events. So we take those events in, and then we group them together into incidents, and we determine the surface area of the problem, which systems are affected, and we page those people, and only those people, so that they can respond to the incident. >> Right. So do you leverage the total learning of the Pager Duty System, in kind of an anonymized way, so you can leverage the multi-billions of dollars worth of incidents to get that type of learning, that was one of Ray's key themes, it helps if you have a billion of something, if you want to start applying some of these Machine Learning techniques. >> Exactly, so the more data you have when you're applying AI and ML, the better the results will be. We have over nine years of data that we've accumulated since founding the company, and we leveraged that for (mumbles) conditions, so for, I'll give you an example. If you're looking at an incident, you just got paged for something, or multiple people got paged, and you're looking at an evolving situation, our algorithms will automatically look in the past, and see has this type of problem happened before? Have you seen this type of incident before? Have you seen these events come in before, that are similar to this and, if so, what happened last time? >> Right. >> Who solved it, what was the resolution, so you can apply that knowledge, to the problem, and resolve it much faster. >> Right. So is it? So you do it both within the existing company, and their ecosystem so yeah, Joe solved it last time, Suzie solved it the time before, as well as to get more of an aggregate, of kind of an anonymized of we see this pattern over and over and over, this might be what you're looking at. >> Yeah, and we haven't done the aggregate picture yet, but it's something that we're very excited about, because we have the potential to become kind of like the Internet weather, where we can tell, based on the number of customers that we have, problems with the Internet, such as, let's say one of the Public Cloud providers is having an issue. Well, they have lots of customers that are Pager Duty customers, and they can now see oh, we have, we're seeing all of this additional incident activity, in this part of the Internet. >> Right. So there's something going on. >> Right. >> And we can start, this is an opportunity that we're very excited about, start telling, being able to tell, oh there's a problem with one of the Cloud providers, there's a problem with one of the main, big infrastructure providers that is commonly used by a lot of different companies. >> Right, because so many of these systems now, are so interdependent. You've got all these APIs, from all these different applications, all these different Cloud providers, and the whole thing stitched together so, trying to figure out where that little, the glitch is, is not necessarily as easy as when you kind of controlled everything. >> Exactly. >> It's funny too, because Jen had a line from her Keynote, which you just triggered. She said, "You know, we're the ones that want to be up, "when the rest of the world seems down." >> Yeah, exactly. >> So, let me expand on that a little bit. So you were the Founder. Jennifer came in a couple years ago, if I recall. I'm sure everyone who loves the classic entrepreneur tale, who liked to watch the show. You founded it, you got it to a certain point, and at some point you decided, you're going to bring in a new CEO. Wonder if you can talk a little bit about how that process went down. Kind of your thoughts as a Founder, of making that transition to see your company go, from this stage to that stage. >> Sure, sure, so yeah, I was the Founding CEO. I built, well me, not just by myself, but with my two Co-founders, and with the great team that we hired, we built the company from zero to over fifty million in annual recurring revenue. The company, when we decided to make that transition, I got into about 200 people or so. So a pretty good scale starting from nothing. >> Yeah, and to a fifty million run right, that's good. >> Yeah, and I'm a first-time entrepreneur, so I haven't done this before, and at that point we had a lot of work to do on the product side of things, like a lot of exciting new things, such as Event Intelligence that we just launched earlier this year, and other products are on Analytics and Visibility, that we're announcing here today. But I found myself spending a lot of my time, inside the building hiring and managing, and I didn't get enough of an opportunity to talk to customers and think about product, and think about the vision, what should we be building next? >> Right, right. >> So I wanted to go and focus more of my time in that direction. I initially started by thinking, maybe I can hire a CEOO, like a Chief Operating Officer to run Sales/Marketing and I can focus on Product and Engineering. Did a lot of due diligence, and talking to other CEOs who had been there, and done that, and realized that while that may solve some problems, it introduces new ones, and that the best bet is to find a World Class CEO. Like the best people out there in the world, are going to command that title, and they're going to want that role. >> Right. >> And I could still get to focus on product, and on talking to customers, and on vision, by replacing myself and taking on a CTO role, so that's what I ultimately decided to do. Had lots of help from the Board, who was very supportive in this decision, and they helped a lot with the interview process for, and the screening process for finding Jennifer. >> Well, you did good. We've known Jennifer for a long time, so I think that was one of your better hires. >> Absolutely. >> In the long history of the company. >> My best hire. Your best hire, very good. Well, and again, congratulations, it's funny you know, you see it over and over right? Overnight successes that are 10 years in the making. You know clearly, your last round of funding was a huge validation on your guy's strategy, and where you're taking the company, and then I just want to call out too, you guys were chosen for the Ernst & Young Co-founder, wait, hold on, the Entrepreneur of the Year Award 2018. Which is funny because you always think of that as maybe a little company just getting started right, or a really early entrepreneur. But you guys have been at this for nine years. Again, really good validation as to where you are in the market, and people realizing the value that you guys are delivering, so congratulations on that. >> Thank you very much. >> Alright Alex, well thanks for taking a few minutes from I'm sure, a very busy couple of days, and it was great to meet you. >> Absolutely, thanks for having me on the show. >> Alright, he's Alex, I'm Jeff. We're at Pager Duty Summit 2018, thanks for watching. (bright music)

(upbeat techno music) >> From Union Square in downtown San Francisco, it's theCUBE, covering PagerDuty Summit '18. Now, here's Jeff Frick. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're at PagerDuty summit in the Westin St. Francis, Union Square, San Francisco. We're excited to have our next guest, this guy likes to get into the weeds. We'll get some into the weeds, not too far in the weeds. Armon Dagar, he's a co-founder and CTO of HashiCorp. Armon, great to see you. >> Thanks so much for having me, Jeff. >> Absolutely, so you're just coming off your session so how did the session go? What did you guys cover? >> It's super good, I mean I think what we wanted to do was sort of take a broader look and not just talk too much just about monitoring and so the talk was really about zero trust networking. Sort of the what, the how, the why. >> Right, right, so that's very important topic. Did Bitcoin come up or blockchain? Or are you able to do zero trust with no blockchain? >> We were able to get through with no blockchain, thankfully I suppose. >> Right. >> But I think kind of the gist of it when we talk about, I think that the challenge is it's still sort of at that nascent point where people are like, okay, zero trust networking I've heard of it, I don't really know what it is or what mental category to put it in. So I think what we tried to do was sort not get too far in the weeds, as you know I tend to do but sort of start high level. >> Right, right. >> And say, what's the problem, right? And I think the problem is we live in this world today of traditional flat networks where, I have a castle and moat, right? I wrap my data center in four walls, all my traffic comes over a drawbridge, and you're either on the outside and you're bad and untrusted or your on the inside and you're good and trusted. And so what happens when a bad guy gets in, right? >> Right. >> It's sort of this all or nothing model, right? >> But now we know, the bad guys are going to get in, right? It's only a function of time, right? >> Right, and I think you see it with the Target breech, the Neiman Marcus breech, the Google breech, right? The list sort of goes on, right? It's like, Equifax, right? It's a bad idea to assume they never get in. (laughing) >> If you assume they get in, so then, if you know the bad guys are going to get in, you got to bake that security in all different levels of your applications, your data, all over the place. >> Exactly. >> So what are some of the things you guys covered in the session? >> So I think the core of it is really saying how do we get to a point where we don't trust our network, where we assume the attacker will get on the network and then what? How do you design around that assumption, right? And what you really have to do is push identity everywhere, right? So every application has to say, I'm a web server and I'm connecting to a database, and is this allowed, right? Is a web server allowed to talk to the database? And that's really the crux of what Google calls Beyond Crop, what other people call sort of zero trust networking, is this idea of identity based where I'm saying it's not IP one talking to IP two, it's web server talking to database. >> Right, right, because then you've got all the role and rules and everything associated at that identity level? >> Bingo, exactly. >> Yeah. >> Exactly, and I think what's made that very hard historically is when we say, what do you have at the network? You have IPs and ports. So how do we get to a point where we know one thing is a web server and one thing's a database, right? >> Right. >> And I think the crux of the challenge there, is kind of three pieces, right? You need application identity. You have to say this is a web server, this is a database. You need to distribute certificates to them and say, you get a certificate that says you're a web server, you get a certificate that says you're a database and you have to enforce that access, right? So everyone can't just randomly talk to each other. >> Right, well then what about context too, right? Because context is another piece that maybe somebody takes advantage of and has access to the identity but is using it in way or there's an interaction that's kind of atypical to what's expected behavior, it just doesn't make sense. So context really matters quite a bit as well. >> Yeah, you're super, super right and I think this is where it gets into not only do we need to assign identity to the applications but how do we tie that back into sort of rich access controls of who's allowed to do what, audit trails of, okay it seems odd, this web server that never connects to this database suddenly out of the blue doing so, why? >> Right, right. >> And do we need to react to it? Do we need to change the rule? Do we need to investigate what's going on? >> Right. >> But you're right. It's like, that context is important of what's expected versus what's unexpected. >> Right, then you have this other X factor called shared infrastructure and hybrid cloud and I've got apps running on AWS, I've got apps running at Google, I've got apps running at Microsoft, I got apps running in the database, I've got some dev here, I've got some prod here. You know that adds another little X factor to the zero trust. (laughing) >> Yeah, I think I aptly heard it called once, we have a service mess on our hands, right? (laughing) >> Right, right. >> We have this stuff so sort of sprawled everywhere now, how do we wrangle it? How do we get our hands around it? And so as much as I think service mess is a play on sort of the language, I think this is where that emerging category of service mesh does make sense. >> Right. >> It's really looking at that and saying, okay, I'm going to have stuff in private cloud, public cloud, maybe multiple public cloud providers, how do I treat all of that in a uniform way? I want to know what's running where. I want to have rules around who can talk to who. >> Right. >> And that's a big focus for us with Console, in terms of, how do we have a consistent way of knowing what's running where a consistent set of rules around who can talk to who. >> Right. >> And do it across all these hybrid environments, right? >> Right, right, but wait, don't buy it yet, there's more. (laughing) Because then I've got all the APIs right? So now you've got all this application integration, many of which are with cloud based applications. So now you've got that complexity and you're pulling all these bits and connections from different infrastructures, different applications, some in house, some outside, so how do you bring some organization to that madness? >> No, that's a super good question. If you ever want to role change, take a look at our marketing department, you've got this down. (laughing) You know, I would say what it comes down to a heterogeneity is going to be fundamental, right? You're going to have folks that are going to operate different tools, different technologies for whatever reasons, right? Might be a historical choice, might be just they have better relations with a particular vendor. So our view has been, how do you inter op with all these things? Part of it is focus on open source. Part of it is focus on API driven. Part of it is focused on you have to do API integrations with all these systems because you're never going to get sort of the end user to standardize everything on a single platform. >> Right, right. It's funny, we were at a show talking about RPA, robotic process automation, and they, they treat those processes as employees in the fact that they give them identities. >> Right. >> So they can manage them. You hire them, you turn 'em on, they work for you for a while and then you might want to turn them off after they're done whatever doing, that you've put them in place for. But literally they were treating them as an employee. >> Right. >> Treating them with like an employee lead identity that they could have all the assigned rules and restrictions to then let the RPA do what it was supposed to do. It's like interesting concept. >> Yeah, and I think it mirrors I think what we see in a lot of different spaces which is what we were maybe managing before was the sort of very physical thing. Maybe it was we called it Robot 1234, right? Or in the same way we might say, this is server at IP 1234. >> Right. >> On our network. And so we're managing this really physical unit, whether it's an IP, a machine, a serial number. How do we take up the level of abstraction and instead say, you know actually all of these machines, whether IP one, IP two, IP three, they're a web server and whether it's robots one, two or three, they're a door attach, right? >> Right, right. >> And so now we start talking about identity and it gives us this more powerful abstraction to sort of talk about these underlying bits. >> Right. >> And I think it sort of follows the history of everything, right? Which is like how do we add new layers of abstraction that let us manage the complexity that we have? >> Right, right, so it's interesting right in Ray Kurzweil's keynote earlier today, hopefully you saw that, he talked about, basically exponential curves and that's really what we're facing so the amount of data, the amount of complexity is only going to increase dramatically. We're trying to virtualize so much of this and abstract it away but then that adds a different layer of management. At the same time, you're going to have a lot more horsepower to work with on the compute side, so is it kind of like the old Wintel, I got a faster PC, it's getting eaten up by more windows? I mean, do you see the automation being able to keep up with kind of the increasing layers of abstraction? >> Yeah, I mean I think there's a grain of that. Are we losing, just because we're getting access to more resources are we using it more efficiently? I think there's some fairness in, with each layer of abstraction we're sort of introduction additional performance cost, sort of to reduce that, but I think overall what we might be doing is increasing the amount of compute tenfold, but adding a 5% additional management fee, so it's still, I think it's still net and net we're able to do much more productive work, go to much bigger scale but only if you have the right abstractions, right? And I think that's where this kind of stuff comes in is, okay great, I'm going to have 10 times as many machines, how do I deal with the fact that my current security model barely works at my current scale? How do I go to 10x the scale? Or if I'm pointing and clicking to provision a machine, how does that work when I'm going to manage a thousand machines, right? >> Yeah. >> You have to bring in additional tooling and automation and sort of think about it at the next higher level. >> Yeah. >> And I think that's all, all part of this process of adopting cloud and sort of getting that leverage. >> It's so interesting, just the whole scale discussion because at the end of the day, right, scale wins and there's a great interview with James Hamilton from AWS, and it's old, but he's talking about kind of scale and he talks about how many server that were sold in this whatever calendar year it was, versus how many mobile phones were sold and it's many ores of magnitude different and the fact that he's thinking in terms of these types of scales as opposed to, you know, which was a big number in the service sales side, but really the scale challenge introduced by these giant clouds and Facebook and the like really changed the game fundamentally in how do you manage these things. >> Totally, totally and I think that's been our view at HashiCorp, is that when you talk about about kinds of the tidal shift of infrastructure from on premise, relatively static VMware centric to AWS, plus Azure, plus Google, plus VMware, it's not just a change of, okay it's of one server here to one server there. It's like going from one server here to 50 servers that I'm changing at every other day rather than every other year, right? >> Right, right. >> And so it's this sort of order of magnitude of scale but also an order of magnitude in terms of sort of the rate of change as well. >> Right, right. >> And I think that puts downward pressure on how do I provision? How do I secure? How do I deploy applications? How do I secure all of this stuff, right? >> Right. >> I think ever layer of the infrastructure gets hit by this change. >> Right, right, alright so you're a smart guy. You're always looking forward. What are some of the things you're working on down the road? Big challenges that you're looking forward to tackling? >> Oh, okay, that's fun. I mean I think the biggest challenge is how do we get this stuff to be simpler for people to use? Because I think what we're going through is you get this sort of see-saw effect, right? Which is okay, we're getting access to all this new hardware, all this new compute, all these new APIs, but it's not getting simpler, right? >> Right, right. >> It's getting exponentially more complicated. >> Right, right. >> And so I think part of it is how do we go back to sort of looking at what's the core of drivers here? It's like, okay well we want to make it easier for people to deliver and deploy their applications, let's go back to sort of, in some sense, the drawing board, say how do we abstract all of these new goodies that we've been given but make it consumable and easy to learn? Because otherwise, you know, what's the point? It's like, here's a catalog of 50,000 things and no one knows how to use any of it. >> Right, right, right. (laughing) Yeah it's funny, I'm waiting for that next abstraction for AWS, instead of the big giant slide that Andy shows every year. (laughing) It's just that I just want to plug in and you figure out. >> Right. >> What connects on the backend. I can't even hardly read that stuff-- >> Maybe AI will save us. >> Let's hope so. Alright Armon, well thanks for taking a few minutes out of your day and sitting down with us. >> My pleasure, thanks so much, Jeff. >> Alright, he's Armon, I'm Jeff, you're watching theCUBE, we're at PagerDuty Summit in downtown San Francisco, thanks for watching. (upbeat techno music)

>> From Union Square in downtown San Francisco, it's theCUBE, covering PagerDuty Summit '18. Now here's Jeff Frick. >> Hey welcome back, everybody, Jeff Frick here with theCUBE. We're at PagerDuty Summit 2018 at the Westin St. Francis in Union Square, San Francisco. Great event, 900 people, we're excited to be here, it's our second year, and now we get to talk to some customers, which we are always excited to do. And our next guest is Alan Alderson. He is the Director of IT Ops for William Hill. Great to see you. >> Afternoon, it's great to be here. >> Absolutely, so for people that aren't familiar with William Hill, what are you guys all about? >> So William Hill offer customers opportunities to place bets on sporting events, presidential elections, snow at Christmas, you name it. We present about a million opportunities every week for customers to have a bet on. >> A million opportunities a week? >> Yeah, so picking on football matches, you know the game of the ramble. So we have opportunities for people to bet playing up to the game, and then once the game kicks off, we transition into what's called in play, so people can then place a bet on who's going to score the next goal, and about another 120 markets within that one game whilst the game's in play. >> Wow, so what's the average duration of the window to put a bet down? >> So generally leading up to the match it's as much time as you want, as soon as the markets are out there you can place the bet before the game kicks off. >> Okay. >> But once the game kicks off, you can, right up until about towards the last few minutes of the game, there'll be markets available to have a bet on. >> Okay, and then what percentage is kind of things that I would guess easily, like sporting events or those types of things, versus you know, whether it's going to snow or not? >> Well we provide the opportunities on the website, so you can have a look and, you know it's snow on Christmas day is a popular bet. People do their research, and they like to have a bet on it. There is a lot of novelty bets. There used to be, you know, life being found on Mars, Elvis being found, et cetera. So there's a lot >> Still taking action on Elvis? >> I don't think so. >> I thought we'd find him. So we're here at PagerDuty Summit. What are you doing here at PagerDuty Summit? >> So I've just come back from a stint in Australia, working for the William Hill business over there. So we introduced PagerDuty over there to help out with just getting the right message out to the right support teams quickly. So we deployed it out there, and we just brought it in to do infrastructure to start with but once we deployed it, it's a bit of a ripple effect. So it was like dropping a pebble into a pool, the ripple effect, and everybody, they seem to be doing all right over there, they use it now for the support models and so those sorts of questions. It's very quick how the other teams decided to latch onto PagerDuty as well. So I since moved back to the UK. So I moved back in January, took on this role back in the Leeds office in the north of England, and one of the first things I said is, guys, start having a look at PagerDuty, we've deployed it successfully in Australia, so let's have a look at what it can do for us. And so management works at William Hill. So I'm not trying to fix anything that's broken. So, it works. But what we can do is increase its speed of how we deal with things. So there's a lot of manual tasks in there that PagerDuty will come in and automate. It will take the pressure off the incident analysts 'cause, you know if there's an incident at two o'clock in the morning, we have 24 by seven business, so if there's an incident overnight, we've got to get on it and start fixing, resolving the incident. And if there's one guy who's trying to call out a number of responders, calling out a duty manager, trying to get comms out, it's a lot of pressure on one person to do that, and when there's pressure mistakes happen. I want PagerDuty to take away the possibility of the mistakes, take the pressure of the incident analyst, so they can focus on resolving the incident and getting service back to our customers as quickly as possible. >> I'm curious though when you said that other people and other groups saw PagerDuty in action. What were some of the other tasks that were not the primary tasks that you brought it in, where people saw value and are implementing it for some other types of activities? >> So initially when we put it in, we put it in purely for service. So for looking at the CPU disk and memory alerts. And we were getting our acknowledgements down from minutes to seconds in Australia. So the other teams are watching in, and within their applications there was a lot of alerts just landing as an email and not getting actioned upon very quickly. So we brought PagerDuty in, they said, can this help out in this space, and they started integrating it into their applications. So through hooking it into their applications they could get the alerts directly from PagerDuty, rather than it going through knocks and service decks et cetera, so it's just a quicker response and get 'em onto the issue quicker. >> And do you have it integrated in with some of your other development tools so it's just kind of part of whole process, or is it more kind of standalone notification system? >> It was integrated straight into ServiceNow and PagerDuty. PagerDuty would integrate with ServiceNow, raise the ticket, and then the things started moving. But the big win was getting the guys the call straight away as that alert happened. Otherwise you're relying on people watching screens, watching queues, waiting for that to happen, and then make the call. So if the call's gone straight to the engineer, he's on it immediately. >> Right, right, right. So what are some of your impressions here? Seeing kind of the ecosystem, what's behind PagerDuty, some great keynotes earlier today, really in terms of, again, the mission it sounds like it's very much in line with what you're trying to do, which is to help teams be more effective. >> Yeah, and what I like about PagerDuty is their passion. You just get a sense of urgency about this place, and you get a sense of passion and commitment, and they want to help people out, and that's what's drawn me to PagerDuty. The guys I worked with in Australia, the guys I worked with in the UK, they just can't do enough for you, and they want to help you succeed as well. You know, you deals with some companies that, they just want to sell you something and move on. These guys are, you know, they look after you, they work with you and they make sure that you're getting the value out of their product. >> It's a pretty interesting culture, 'cause when I talked to Jennifer Tejada a couple of years ago, I used to tease her, I'm like, nobody here knows what a pager is, right? Nobody was born when pagers were >> I had one. >> the rage. >> You had one, yeah, I had one. Shell Oil upside down, I think it says hello, I can't remember, I have to check that. But it's an interesting, there's kind of culture around what a pager represents, and the work that they have duty in there as well, which is a very different kind of level of responsibility when you are the person with the pager on, and that seems to have really carried forward in the way that they deliver the services. >> Yeah, yeah. I mean, on-call has people running, doesn't it? When people, you know when they join a job and go, "Oh you might be expected to be on call", they run a mile, and they think that's not for me. But as we go down more of a DevOps transformation and we get a lot more down the we code it, we own it model, I think it'll change people's perceptions of being on call and just doing the right thing for the business, rather thank, you know, delivering something and expecting the Ops team to fix it all the time and call out the developers at a third line. We should be, we are heading towards being a team, where the alerts go to the right people at the right time, and we get issues resolved as soon as possible. >> Right. I'd just love to get your take on, a lot of talk about digital transformation, and the modernization of IT, and kind of expected behavior on apps going on. You're right in the middle of it. >> Massively in the middle of it. >> Massively in the middle of it, right. I'm sure, what percentage of your bets come in via mobile versus... >> On the digital platform, over 56%. >> A lot, right, a lot. >> And we've got, just said in the last session we had is, we've got competition. So if our app isn't performing, it isn't quick, or it's down, people will go elsewhere. They've got options, they've got choices, and they'll just go elsewhere. And the challenge is getting those customers back. We want to have a stack that just is available and is performing, so we don't drive customers away, or we make sure that things are available at peak times, so when they are wanting to bet on the Super Bowl, the Grand National, the three o'clock kickoffs on a Saturday afternoon in the UK, it's available for them and people can get the bet on as quickly as possible. >> Right. So do you have all your own infrastructure, or do you leverage public cloud? I'm just thinking as you're talking about Super Bowl and some of these other big events, you must have just crazy big spikes. >> You know we've, in the UK it's all on-premise, so we've got to build an infrastructure to cope with that one day of the year, which is Grand National. In the US, we've just opened up in New Jersey. The front end of that stack is in AWS, so we can scale, so when Super Bowl does turn round next January, February, we should be able to scale with the load. >> Right, last question before I let you go. What are your priorities next? What are some of the things that you're working on with your team, to kind of stay at the leading edge of this very competitive space? >> Yeah we're heading into AWS. So we're looking to move into Amazon next year, start migrating some applications in there, and we're looking to get some applications in there the back end of this year, but migrate the existing apps from the start of next year. We're going through a DevOps transformation. We've been doing an agile transformation as well over the last 12 to 18 months, so there's a huge amount of digital transformation going on at William Hill at the moment. It's a very, very exciting place to be. The US expansion, the place has just gone mad, you know. There's a lot going on, it's just a great place to be. >> Yeah, I mean significant changes obviously in the US attitude, I think you guys are a little more progressive on that side of the Atlantic. Big changes happening here. >> 14th of May was a big day, PASPA being repealed has opened up the betting opportunities in any state that wants to regulate. And we are leading the way in that charge at the moment, so it's very exciting. >> All right, well I'm going to let you go so you can get some sleep, 'cause I'm sure you're a very busy man. Alan, thanks for stopping by. >> Thank you very much. >> All right, he's Alan, I'm Jeff, you're watching theCUBE, we're at PagerDuty Summit 2018, thanks for watching.

>> From Union Square in downtown San Francisco, it's theCUBE covering PagerDuty Summit '18, now here's Jeff Frick. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're at PagerDuty Summit 2018 at the Westin St. Francis in Union Square, San Francisco. Been here all day, a lot of excitement, a lot of buzz, some great keynotes including Ray Kurzweil checking in, which was really a cool thing. We're excited to have our next guest, she's Rachel Obstler, she's a VP of products for PagerDuty. She's the one responsible for delivering all this fun new, fun new toys, right? >> Did it all on my own. (laughs) >> All on your own, so Rachel, great to see you. >> Nice to see you, too, Jeff. >> Absolutely, so great event, we were talking, you know, our second year. Last year was cool, it was kind of out by the water, but this is, you know, another kind of historic, classic San Francisco venue. We're surrounded by all the gilding and everything else-- >> Yes. >> Tech companies with all their displays. >> Yeah, there are not a lot of spaces that are big enough to do an event like this-- >> Right. >> In San Francisco proper unless you're going full-on Moscone Center, so... (chuckles) >> You'll be there soon enough, I think. >> Maybe. (laughs) >> So, let's get into it, you announced a bunch of new products here over the last couple days, so what did, let's go through some of those announcements. >> Sure, so we announced two new products. One of them is PagerDuty Visibility, and PagerDuty Visibility is really designed for the person that we call the bow-tie knot in the organization. >> The bow-tie knot. >> The bow-tie knot, so you know, you have a bow-tie, a bow-tie and there's a little knot in the middle-- >> Right. >> So, the bow-tie knot is usually an engineering leader, it's someone who when there's a problem happening, an incident going on, that they're kind of coordinating between keeping track of what's going on on the ground with the responders actually trying to fix it, and telling all the stakeholders what is going on, because stakeholders don't understand things like the server XXX has some problem, right? >> Right, right. >> But at the same time you don't want those executives getting on a call and disrupting the responders who are actually busy working on the issue. >> Right, right. >> So, that person in the bow-tie knot has a lot of manual work to do to make sure they're translating constantly between what's going on and what the executives need to know, and they need to know because if there's an issue with a customer application you want to get in front of it. You want to be able to proactively tell your support team if tickets come in this is what you say. >> Right. >> You want to maybe even send an email to your customers, "We know there's an issue." Update your status page, "We're working on it." You know, tell them as much as you can. It gives them confidence that it's being taken care of. >> Right, so this gives them kind of the God view of all the things that the team is working on in terms of getting resolution to that problem. >> Yeah, it ties the technical services, which are the things that are jargon and gobbledygook, except for the people working on them-- >> Right. >> To what are business services, which are those customer applications that the executives understand and the customers understand, so with that tie you know when a technical service is impacting a customer application, which one it's impacting, and you can also let the right people know who are responsible for that customer application, what they need to know so they can let the customers know. >> Right, so what happened before without having kind of a central place to manage that communication and that visibility? >> That bow-tie knot person did this all manually. >> Just running around gathering facts and figures-- >> Yep. >> And status and updates-- >> Yep, and then-- >> From various points on the compass. >> And then fielding phone calls with people, yelling at them-- >> Right, right. >> And it's a very painful, you know, we talked to a lot of customers. It's a very painful position to be in. >> Right, well that's a good one, and then you have another one, PagerDuty Analytics. >> Yes, so PagerDuty Analytics is really a product more used during peacetime, so Visibility's used during wartime to make sure responders know which customer applications are being impacted, but during peacetime there's a number of operational analytics that you want to know about all the realtime work that you're doing. So, some examples are I had a set of engineers that were on call last week, was it a bad on call? How many times were they woken in the middle of the night, do I need to give someone a day off? Right, so to make sure you manage the health of your team. You may also want to know which of my technical services is causing the most pain for the business, so that might be a monthly or quarterly report, doing like a quarterly business review. So, which technical services do I need to invest in because even a technical service that may not be down that much, if it's impacting multiple critical customer applications it could be causing your business a lot of money. >> Right, right. >> You also may want to know what's your total time that you're spending resolving issues, right? So, how many hours are across all your employees? Are you spending, just reacting to realtime issues that may happen and is that too much? >> Right, and if you can't measure it you can't manage it, right? >> Exactly. >> And it's funny because pulling from Jen's keynote earlier today, I think she talked about, the number was 3.6 billion incidents that have gone through the system just in year-to-date 2018. >> Yep. >> So, the scale is massive. >> Yep. >> But you guys are bringing some artificial intelligence, you're bringing some machine learning to bear because you have to, right? >> That's right. >> This gets way beyond the scope of a person being able to really prioritize and figure out what's a signal, what's a noise, what do they have to really focus on. >> That's exactly right, so in June we launched a product called Event Intelligence, and what it does is it takes in all those signals that PagerDuty takes into the system and then it makes sense of them. So, it says, "Well, these things are related, "let's group them together," so as each new signal comes in it won't create a new incident that someone then needs to run down. It will put it in the existing incident, so the responder keeps getting all the context they need about the incident, but they don't keep getting notified while they're trying to concentrate and fix something. >> (chuckles) They must love you guys, they must love you guys. (laughs) So, then the other piece I found interesting and I think some might find a little confusing is all the number of integrations you guys have-- >> Mm-hm. >> With so many different kind of workflow management and monitoring and a lot of things. How does that work, how does that kind of... I would imagine there's some, you know, competition, cooperation with all these different applications, but as Jen said earlier today if that's what the customer wants that's what you guys got to deliver. >> That's right, and you know, this is a complex ecosystem, there are a lot of different tools in the ecosystem. Naturally, as companies get bigger there will be areas of overlap, but we very strongly believe in an open ecosystem. We want to interoperate with every product that's out there, so we do have a lot of different integrations. We have a lot of integrations with companies where we take data in, so monitoring data that tells you, "Hey, your server's down," or whatever else it is-- >> Right. >> But we also have a lot of integrations with, like, ticketing tools, tools that will, or a customer file's a ticket, so that, you know, you can have the information, this is what's going on in the engineering side right now so the customer support team can stay informed. >> Right. >> And also managing through workflow, a lot of companies use like an ITSM tool to manage through workflows, so integrating with them, integrating with chat tools. We integrate with Slack, you know, so there's a lot of different integrations because you want to make sure that resolving an incident is the smoothest, easiest process it can possibly be because it's stressful enough already. >> Right, right, so a lot of stuff going on here. So, as you look forward don't, you know, congratulations on getting a couple of products out today, but what are some of your priorities as you kind of look at the roadmap, you know, kind of where you guys have things covered. Where do you see some new opportunities to take, you know, some of the tools that you guys have built? >> Yeah, we see a big opportunity in that world of Event Intelligence, so we already have a product but we're going to continue to add more capabilities to it and continue to take advantage of the data in our platform. So, surfacing that data in more intelligent ways through Event Intelligence could also be through Analytics, so for instance, you know, we today can group things together intelligently, we can show you similar incidents, right? This incident looked like something that happened in the past. Well, next maybe we can say, "This looks like something that happened in the past, "and oh, gee, that got really bad. "You might want to pay special attention "because this one may get bad, too." >> Right. >> So, starting to get more predictive, really making sense of all the data that you have from the past history, our 10,500 customers over nine years. It's a lot of data that we can use to help people get more and more efficient with their realtime work. >> Right, and is there an opportunity to kind of use cross-customer data, not, you know, obviously you've got to anonymize it and all those types of issues, but clearly, you know, there's stuff that has happened to other companies that I could probably, you know, benefit in knowing that information around some, you know, some common attributes either around a particular type of infrastructure configuration or whatever. So, have you started to pull that and bake that back into some of the recommendations or... >> Yeah, so one area that we do have available as some data today is benchmarking, so without, as you said, sharing any specific customer data it's very helpful for customers to understand first of all how their individual teams are performing versus their teams, but then also how their teams are performing against the industry. >> Right. >> So, are we fast at responding to incidents? What does best in class look like? How quickly could you actually mobilize a response to a major incident? This is like great data for our customers to have as they move forward in their digital transformation. >> Right, hugely, hugely important. >> Mm-hm. >> So, last word, you said you've, you know, you're relatively new to the company but you're a wily old veteran because you guys are growing so fast. (laughs) Just love to get your impressions. It's your second PagerDuty Summit, you know, kind of the vibe, I think Jen's got a really, very positive and very specific kind of a leadership style. >> Mm-hm. >> Just share your impressions with the show and what's going on inside of PagerDuty. >> It's been great, I've loved every moment that I've worked there. I feel like we're doing things that are really innovative and we're always pushing the envelope trying to go faster and faster, so I'm really excited for the next year. >> Good. >> Can't wait to see what the next Summit looks like. (laughs) >> Yeah, what it's going to look like. Yeah, probably be like 2,000-- >> We're not even done with this one yet. (laughs) >> 2,000 people, I'm sure, all right. Yeah, but the Advanced Planning Committee's already taking notes, right? >> Yeah, right. (laughs) >> All right, well Rachel, thank you for taking a few minutes and congratulations on your product release. I'm sure there were many sleepless nights over the last several months to get that stuff out. >> Thank you, Jeff, great to be here. >> All right, she's Rachel, I'm Jeff. You're watching theCUBE, we're at PagerDuty Summit in San Francisco, thanks for watching. (techy music)

(upbeat techno music) >> From Union Square in downtown San Francisco, it's theCUBE, covering PagerDuty Summit '18. Now, here's Jeff Frick. >> Hey welcome back everybody, Jeff Frick here with theCUBE, we're in downtown San Francisco, actually the Westin St. Francis on Union Square, historical property, this beautiful ballroom, lots of brocade and fancy stuff from another era but we're talking about this era and the era of information. We're here at PagerDuty Summit and we're really excited to have one of the keynote speakers, John Allspaw join us. He is the co-founder of Adaptive Capacity Labs. John, great job on the keynote. >> Thanks, thanks a lot, I'm glad that it landed. >> You know it's funny, we go to literally hundreds of tech conferences a year and so often, the tech is talked about but as you brought up, where is the human factors? Where are the people? Where in all these lovely diagrams, as you pointed out, with beautiful lines and everything is very straight and boxes are very clear, that's not really how the real world works at all. >> No, no, yeah, that's what I find really fascinating which is that, certainly get a lot of attention to incidents when they show up, outages and that sort of thing but we don't get a real shot at understanding how non incidents happen and they happen all the time, right? Outages are being prevented all day long. But it doesn't really get our attention. >> Right. >> And it doesn't get our attention because that seems normal and that's the sort of, this assumption that there's a like a quiet sort of background and that an incident is sort of like a punctuation of something bad and that otherwise sticks up, you know, like Mount Hood, right? >> Right, right. >> But the fact of the matter is there's so much going on. >> Right. >> And that's actually that stuff that's going on, is this activity that people are doing to prevent outages continually and that's what I find fascinating. >> So you really never get like your classic kind of experiment where you can isolate the variables, right? >> No. >> Because they're all completely co-mingled, all the time? >> Yeah, and that's what fascinating. What I like and we always say is that we study cognitive work, and the difference between sort of these types of human factors and cognitive work studies and the difference between that and say sort of classic psychology is classic psychology can be done in a lab. We study cognition in the wild. >> Right, right. >> As they say. The natural laboratory that is the world. >> Right, and the other thing I thought you brought up which was really interesting is really kind of what's the point, right? Is the point just to fix it? Is the point to try to identify this little link and fix it? Or is the point kind of a higher level objective which is to actually learn so that we're doing the things in the future that keep this thing from happening again? And you summarized it really, really well and you talked about the post mortem which you said, "Are you doing this report to be read or are you doing it to be filed?" Very different objectives, going to have a very different report at the end of the process. >> Right, right, right, yeah. I think that the sort of the danger is if we, as an industry, I think we just need to bring some attention to that and the good news is that it's hard work to look at incidents in a different way. It's a way that we're not used to. It's effectively qualitative research. It's difficult but it's not impossible, it can be learned, it can be taught and my hope is that sort of these sorts of bringing attention to the topics will get people to be curious and want to understand more. >> Right and really take it up a notch and I think, again, you have some really easy to implement lessons there, like what are the questions? Document the questions, >> Yeah. >> In the post mortem. >> Yeah. >> Document the concerns in the post mortem. Did those concerns happen? Did they not happen? Why didn't they happen? So really kind of take it up a level from the incident, really, as kind of a catalyst for a conversation and learning but that's really not what the foundational effort should be around, is fixing that little thing? >> Right, right. Well and that's the thing, is if the goal is to fix, and that is the goal, you're going to find something to fix. It may or may not be helpful. What you fix comes from exploring and there are things that shouldn't be fixed, right now. Everybody's making decisions, I mean, this is the entire premise of Agile which is that continual iterative re prioritization, recalibration of what's important so we'll be happy to put effort into that but yet, it seems disingenuous to phone it in. >> Right. >> When it comes to understanding incidents. >> Right, right. You got on to so many things, we could go forever and ever. One of things you talked about and it's often spoke about, is winners write the history books. It was really about the bias that you bring to a problem. What do you think is the most important and what filter and lens are you both looking at the problem, reporting the problem or diagnosing and then reporting the problem which may or may not be root cause, may or may not be the most important thing about that but those biases influences not only is that problem perceived but then documented, resolved and talked about after the fact. Really important. >> Yeah, yeah, absolutely and there's something really paradoxical about that. One of the things that it brings to mind is that I don't think that yet we are in a world where we, when I say we, I mean the software industry, will bring attention to a report on near misses. The scenarios where, you know what? You thought you were in dev but you were in prod and you ran a command that if it had a couple of other parameters, it would have destroyed everything but it turns out that actually, it was this one, you know these couple of characters made it such that it was a near miss. It wasn't a big deal. Is that an incident, right? >> Right, right. >> On the one hand you could say, well there are no customer impact. >> Right. >> So therefore let me look up on my, oh, no, that's not an incident so therefore we shouldn't pay any attention to it. But think of any other sort of high tempo, high consequence domain? >> Right. >> They've learned, aviation is a good example. There are organizations in aviation that will, actually and they find them to be incredibly useful because they're low risk things to pay attention to. It didn't happen this time but we can bring attention to the possibility that it might go poorly the next time. >> Right, so what triggers the action to recognize that you had a near miss? And is that working it's way into best practices dev ops? >> Well, I mean, at my organization, at Etsy, I certainly, full disclosure, I made quite a good number of mistakes at Etsy. This isn't one of them. Getting into habit of what had happened there was people sending PSA e-mails, public service announcements and it was basically the format was, hey everybody, check this out, I was doing this and I went to go do blah, and I almost exploded everybody. So FYI if you're doing this, don't do this. Everything's cool and I'm going to put in these things to sort of help it out, but until we get that done, be really careful about this part, you know, whatever. Even just that, even small things like that, keep the topic of how precarious these scenarios can be in the minds of people who aren't experiencing incidents. >> Right, right. >> Tomorrow you might be that one, or tomorrow you might be, and so here's your colleague like taking the time to spend some effort, could be saving your bacon tomorrow. >> Right. >> You might be in the similar spot. >> Right. How's it codified and how is it communicated. So another concept you touched on, which has a broader implication, but you talked about specifically and really that's diversity of opinions leads to better decision making and you gave some examples of bringing in disparate members of various teams with different experiences, points of view. >> Yeah. >> To pull out things like the esoteric knowledge, to pull out the institutional knowledge. >> Yeah. >> But more importantly, to pull out a different point of view. So we hear about it a lot with diversity of teams, and sects, and culture, et cetera but even with the context of solving an engineering problem diversity and points of view does lead to better problem solving. >> I want to make sort of a crisp clarification. It is the variety of perspectives actually the variety of expertise and the variety of experience, not opinions or perspectives. Perspective you can probably, that's word you can probably go with. I wouldn't say diversity of opinion, that has a connotation that is not concrete enough. >> Okay. >> What we're talking about is cognitive work, how people assess this is something that requires my attention. It requires my attention in these ways based on my experience with this particular type of problem over this different variations of it. >> Right. >> Yeah, I mean the general sense is, but the phrase diversity of opinion generally has like a connotation of the individual attribute of a person. I'm not talking about that. I'm talking about the-- >> These are individual attributes that have been gleaned through experience-- >> It's not an attribute, it's experience. >> It's experience, right okay. >> Right, exactly. An attribute of me is that I'm 5'9", my experience is that I have seen Apache break in a myriad of different, surprising ways, right? (laughter) There's sort of the difference. >> Right, right the difference, okay. But then the other point you brought up even in that conversation was it's always messy, there's always trade-offs, is you know, you get management overhead as soon as you have more than one person working on a problem, right? Now you have communication overhead, you've got management overhead so now you're pulling resources from actually devoting it to the task at hand of trying to solve the problem versus having to devote resources to bring other people up to speed, communicate, et cetera. So it's not even a really easy trade off? >> Oh yeah. >> Or not trade off, I mean but there's consequences to the action. >> Oh yeah, absolutely, absolutely. And again, I think coping with complexity requires an equal amount of complexity, right? You might not say that a baseball team that is very good at doing double plays, right? Which is a pretty hard thing to pull off even in professional baseball. Would you say that the coach represents overhead? I don't know if you would say it that way exactly but there's certainly limitations to the sports metaphor. I like very much a renewed emphasis on building, maintaining and sort of, resolving incidents with software as much more benefiting from collaborative work. >> Right, right. >> Meaning real sort of teamwork. >> Right. >> Not just sort of sparse collaboration. >> Right, right. Well John, it's a fascinating field, we could go on all day long. >> Yes we could. >> Unfortunately, we're going to have to leave it there but really, really enjoyed the conversation. >> Great. >> And also the keynote earlier today. >> Great thanks a lot. Thanks for talking. >> Alright, thank you. He's John, I'm Jeff, you're watching theCUBE. We're at PagerDuty Summit at the Westin St. Francis, Union Square. Thanks for watching. (upbeat techno music)

>> Announcer: Live from downtown San Francisco, it's the Cube, covering IBM Chief Data Officer Strategy Summit 2018. Brought to you by IBM. >> Welcome back to San Francisco everybody. We're at the Parc 55 in Union Square and this is the Cube, the leader in live tech coverage and we're covering exclusive coverage of the IBM CDO strategy summit. IBM has these things, they book in on both coasts, one in San Francisco one in Boston, spring and fall. Great event, intimate event. 130, 150 chief data officers, learning, transferring knowledge, sharing ideas. Cayn Woodruff is here as the principle data scientist at IBM and she's joined by Ritesh Ororo, who is the director of digital analytics at HCL Technologies. Folks welcome to the Cube, thanks for coming on. >> Thank you >> Thanks for having us. >> You're welcome. So we're going to talk about data management, data engineering, we're going to talk about digital, as I said Ritesh because digital is in your title. It's a hot topic today. But Caryn let's start off with you. Principle Data Scientist, so you're the one that is in short supply. So a lot of demand, you're getting pulled in a lot of different directions. But talk about your role and how you manage all those demands on your time. >> Well, you know a lot of, a lot of our work is driven by business needs, so it's really understanding what is critical to the business, what's going to support our businesses strategy and you know, picking the projects that we work on based on those items. So it's you really do have to cultivate the things that you spend your time on and make sure you're spending your time on the things that matter and as Ritesh and I were talking about earlier, you know, a lot of that means building good relationships with the people who manage the systems and the people who manage the data so that you can get access to what you need to get the critical insights that the business needs, >> So Ritesh, data management I mean this means a lot of things to a lot of people. It's evolved over the years. Help us frame what data management is in this day and age. >> Sure, so there are two aspects of data in my opinion. One is the data management, another the data engineering, right? And over the period as the data has grown significantly. Whether it's unstructured data, whether it's structured data, or the transactional data. We need to have some kind of governance in the policies to secure data to make data as an asset for a company so the business can rely on your data. What you are delivering to them. Now, the another part comes is the data engineering. Data engineering is more about an IT function, which is data acquisition, data preparation and delivering the data to the end-user, right? It can be business, it can be third-party but it all comes under the governance, under the policies, which are designed to secure the data, how the data should be accessed to different parts of the company or the external parties. >> And how those two worlds come together? The business piece and the IT piece, is that where you come in? >> That is where data science definitely comes into the picture. So if you go online, you can find Venn diagrams that describe data science as a combination of computer science math and statistics and business acumen. And so where it comes in the middle is data science. So it's really being able to put those things together. But, you know, what's what's so critical is you know, Interpol, actually, shared at the beginning here and I think a few years ago here, talked about the five pillars to building a data strategy. And, you know, one of those things is use cases, like getting out, picking a need, solving it and then going from there and along the way you realize what systems are critical, what data you need, who the business users are. You know, what would it take to scale that? So these, like, Proof-point projects that, you know, eventually turn into these bigger things, and for them to turn into bigger things you've got to have that partnership. You've got to know where your trusted data is, you've got to know that, how it got there, who can touch it, how frequently it is updated. Just being able to really understand that and work with partners that manage the infrastructure so that you can leverage it and make it available to other people and transparent. >> I remember when I first interviewed Hilary Mason way back when and I was asking her about that Venn diagram and she threw in another one, which was data hacking. >> Caryn: Uh-huh, yeah. >> Well, talk about that. You've got to be curious about data. You need to, you know, take a bath in data. >> (laughs) Yes, yes. I mean yeah, you really.. Sometimes you have to be a detective and you have to really want to know more. And, I mean, understanding the data is like the majority of the battle. >> So Ritesh, we were talking off-camera about it's not how titles change, things evolve, data, digital. They're kind of interchangeable these days. I mean we always say the difference between a business and a digital business is how they have used data. And so digital being part of your role, everybody's trying to get digital transformation, right? As an SI, you guys are at the heart of it. Certainly, IBM as well. What kinds of questions are our clients asking you about digital? >> So I ultimately see data, whatever we drive from data, it is used by the business side. So we are trying to always solve a business problem, which is to optimize the issues the company is facing, or try to generate more revenues, right? Now, the digital as well as the data has been married together, right? Earlier there are, you can say we are trying to analyze the data to get more insights, what is happening in that company. And then we came up with a predictive modeling that based on the data that will statically collect, how can we predict different scenarios, right? Now digital, we, over the period of the last 10 20 years, as the data has grown, there are different sources of data has come in picture, we are talking about social media and so on, right? And nobody is looking for just reports out of the Excel, right? It is more about how you are presenting the data to the senior management, to the entire world and how easily they can understand it. That's where the digital from the data digitization, as well as the application digitization comes in picture. So the tools are developed over the period to have a better visualization, better understanding. How can we integrate annotation within the data? So these are all different aspects of digitization on the data and we try to integrate the digital concepts within our data and analytics, right? So I used to be more, I mean, I grew up as a data engineer, analytics engineer but now I'm looking more beyond just the data or the data preparation. It's more about presenting the data to the end-user and the business. How it is easy for them to understand it. >> Okay I got to ask you, so you guys are data wonks. I am too, kind of, but I'm not as skilled as you are, but, and I say that with all due respect. I mean you love data. >> Caryn: Yes. >> As data science becomes a more critical skill within organizations, we always talk about the amount of data, data growth, the stats are mind-boggling. But as a data scientist, do you feel like you have access to the right data and how much of a challenge is that with clients? >> So we do have access to the data but the challenge is, the company has so many systems, right? It's not just one or two applications. There are companies we have 50 or 60 or even hundreds of application built over last 20 years. And there are some applications, which are basically duplicate, which replicates the data. Now, the challenge is to integrate the data from different systems because they maintain different metadata. They have the quality of data is a concern. And sometimes with the international companies, the rules, for example, might be in US or India or China, the data acquisitions are different, right? And you are, as you become more global, you try to integrate the data beyond boundaries, which becomes a more compliance issue sometimes, also, beyond the technical issues of data integration. >> Any thoughts on that? >> Yeah, I think, you know one of the other issues too, you have, as you've heard of shadow IT, where people have, like, servers squirreled away under their desks. There's your shadow data, where people have spreadsheets and databases that, you know, they're storing on, like a small server or that they share within their department. And so you know, you were discussing, we were talking earlier about the different systems. And you might have a name in one system that's one way and a name in another system that's slightly different, and then a third system, where it's it's different and there's extra granularity to it or some extra twist. And so you really have to work with all of the people that own these processes and figure out what's the trusted source? What can we all agree on? So there's a lot of... It's funny, a lot of the data problems are people problems. So it's getting people to talk and getting people to agree on, well this is why I need it this way, and this is why I need it this way, and figuring out how you come to a common solution so you can even create those single trusted sources that then everybody can go to and everybody knows that they're working with the the right thing and the same thing that they all agree on. >> The politics of it and, I mean, politics is kind of a pejorative word but let's say dissonance, where you have maybe of a back-end syst6em, financial system and the CFO, he or she is looking at the data saying oh, this is what the data says and then... I remember I was talking to a, recently, a chef in a restaurant said that the CFO saw this but I know that's not the case, I don't have the data to prove it. So I'm going to go get the data. And so, and then as they collect that data they bring together. So I guess in some ways you guys are mediators. >> [Caryn And Ritesh] Yes, yes. Absolutely. >> 'Cause the data doesn't lie you just got to understand it. >> You have to ask the right question. Yes. And yeah. >> And sometimes when you see the data, you start, that you don't even know what questions you want to ask until you see the data. Is that is that a challenge for your clients? >> Caryn: Yes, all the time. Yeah >> So okay, what else do we want to we want to talk about? The state of collaboration, let's say, between the data scientists, the data engineer, the quality engineer, maybe even the application developers. Somebody, John Fourier often says, my co-host and business partner, data is the new development kit. Give me the data and I'll, you know, write some code and create an application. So how about collaboration amongst those roles, is that something... I know IBM's gone on about some products there but your point Caryn, it's a lot of times it's the people. >> It is. >> And the culture. What are you seeing in terms of evolution and maturity of that challenge? >> You know I have a very good friend who likes to say that data science is a team sport and so, you know, these should not be, like, solo projects where just one person is wading up to their elbows in data. This should be something where you've got engineers and scientists and business, people coming together to really work through it as a team because everybody brings really different strengths to the table and it takes a lot of smart brains to figure out some of these really complicated things. >> I completely agree. Because we see the challenges, we always are trying to solve a business problem. It's important to marry IT as well as the business side. We have the technical expert but we don't have domain experts, subject matter experts who knows the business in IT, right? So it's very very important to collaborate closely with the business, right? And data scientist a intermediate layer between the IT as well as business I will say, right? Because a data scientist as they, over the years, as they try to analyze the information, they understand business better, right? And they need to collaborate with IT to either improve the quality, right? That kind of challenges they are facing and I need you to, the data engineer has to work very hard to make sure the data delivered to the data scientist or the business is accurate as much as possible because wrong data will lead to wrong predictions, right? And ultimately we need to make sure that we integrate the data in the right way. >> What's a different cultural dynamic that was, say ten years ago, where you'd go to a statistician, she'd fire up the SPSS.. >> Caryn: We still use that. >> I'm sure you still do but run some kind of squares give me some, you know, probabilities and you know maybe run some Monte Carlo simulation. But one person kind of doing all that it's your point, Caryn. >> Well you know, it's it's interesting. There are there are some students I mentor at a local university and you know we've been talking about the projects that they get and that you know, more often than not they get a nice clean dataset to go practice learning their modeling on, you know? And they don't have to get in there and clean it all up and normalize the fields and look for some crazy skew or no values or, you know, where you've just got so much noise that needs to be reduced into something more manageable. And so it's, you know, you made the point earlier about understanding the data. It's just, it really is important to be very curious and ask those tough questions and understand what you're dealing with. Before you really start jumping in and building a bunch of models. >> Let me add another point. That the way we have changed over the last ten years, especially from the technical point of view. Ten years back nobody talks about the real-time data analysis. There was no streaming application as such. Now nobody talks about the batch analysis, right? Everybody wants data on real-time basis. But not if not real-time might be near real-time basis. That has become a challenge. And it's not just that prediction, which are happening in their ERP environment or on the cloud, they want the real-time integration with the social media for the marketing and the sales and how they can immediately do the campaign, right? So, for example, if I go to Google and I search for for any product, right, for example, a pressure cooker, right? And I go to Facebook, immediately I see the ad within two minutes. >> Yeah, they're retargeting. >> So that's a real-time analytics is happening under different application, including the third-party data, which is coming from social media. So that has become a good source of data but it has become a challenge for the data analyst and the data scientist. How quickly we can turn around is called data analysis. >> Because it used to be you would get ads for a pressure cooker for months, even after you bought the pressure cooker and now it's only a few days, right? >> Ritesh: It's a minute. You close this application, you log into Facebook... >> Oh, no doubt. >> Ritesh: An ad is there. >> Caryn: There it is. >> Ritesh: Because everything is linked either your phone number or email ID you're done. >> It's interesting. We talked about disruption a lot. I wonder if that whole model is going to get disrupted in a new way because everybody started using the same ad. >> So that's a big change of our last 10 years. >> Do you think..oh go ahead. >> oh no, I was just going to say, you know, another thing is just there's so much that is available to everybody now, you know. There's not this small little set of tools that's restricted to people that are in these very specific jobs. But with open source and with so many software-as-a-service products that are out there, anybody can go out and get an account and just start, you know, practicing or playing or joining a cackle competition or, you know, start getting their hands on.. There's data sets that are out there that you can just download to practice and learn on and use. So, you know, it's much more open, I think, than it used to be. >> Yeah, community additions of software, open data. The number of open day sources just keeps growing. Do you think that machine intelligence can, or how can machine intelligence help with this data quality challenge? >> I think that it's it's always going to require people, you know? There's always going to be a need for people to train the machines on how to interpret the data. How to classify it, how to tag it. There's actually a really good article in Popular Science this month about a woman who was training a machine on fake news and, you know, it did a really nice job of finding some of the the same claims that she did. But she found a few more. So, you know, I think it's, on one hand we have machines that we can augment with data and they can help us make better decisions or sift through large volumes of data but then when we're teaching the machines to classify the data or to help us with metadata classification, for example, or, you know, to help us clean it. I think that it's going to be a while before we get to the point where that's the inverse. >> Right, so in that example you gave, the human actually did a better job from the machine. Now, this amazing to me how.. What, what machines couldn't do that humans could, you know last year and all of a sudden, you know, they can. It wasn't long ago that robots couldn't climb stairs. >> And now they can. >> And now they can. >> It's really creepy. >> I think the difference now is, earlier you know, you knew that there is an issue in the data. But you don't know that how much data is corrupt or wrong, right? Now, there are tools available and they're very sophisticated tools. They can pinpoint and provide you the percentage of accuracy, right? On different categories of data that that you come across, right? Even forget about the structure data. Even when you talk about unstructured data, the data which comes from social media or the comments and the remarks that you log or are logged by the customer service representative, there are very sophisticated text analytics tools available, which can talk very accurately about the data as well as the personality of the person who is who's giving that information. >> Tough problems but it seems like we're making progress. All you got to do is look at fraud detection as an example. Folks, thanks very much.. >> Thank you. >> Thank you very much. >> ...for sharing your insight. You're very welcome. Alright, keep it right there everybody. We're live from the IBM CTO conference in San Francisco. Be right back, you're watching the Cube. (electronic music)

(music playing) >> Narrator: Live, from downtown San Francisco It's the Cube. Covering IBM Chief Data Officer Startegy Summit 2018. Brought to you by: IBM >> Welcome back to San Francisco everybody we're at the Parc 55 in Union Square. My name is Dave Vellante, and you're watching the Cube. The leader in live tech coverage and this is our exclusive coverage of IBM's Chief Data Officer Strategy Summit. They hold these both in San Francisco and in Boston. It's an intimate event, about 150 Chief Data Officers really absorbing what IBM has done internally and IBM transferring knowledge to its clients. Steven Eluk is here. He is one of those internal practitioners at IBM. He's the Vice President of Deep Learning and the Global Chief Data Office at IBM. We just heard from him and some of his strategies and used cases. He's joined by Sumit Gupta, a Cube alum. Who is the Vice President of Machine Learning and deep learning within IBM's cognitive systems group. Sumit. >> Thank you. >> Good to see you, welcome back Steven, lets get into it. So, I was um paying close attention when Bob Picciano took over the cognitive systems group. I said, "Hmm, that's interesting". Recently a software guy, of course I know he's got some hardware expertise. But bringing in someone who's deep into software and machine learning, and deep learning, and AI, and cognitive systems into a systems organization. So you guys specifically set out to develop solutions to solve problems like Steven's trying to solve. Right, explain that. >> Yeah, so I think ugh there's a revolution going on in the market the computing market where we have all these new machine learning, and deep learning technologies that are having meaningful impact or promise of having meaningful impact. But these new technologies, are actually significantly I would say complex and they require very complex and high performance computing systems. You know I think Bob and I think in particular IBM saw the opportunity and realized that we really need to architect a new class of infrastructure. Both software and hardware to address what data scientist like Steve are trying to do in the space, right? The open source software that's out there: Denzoflo, Cafe, Torch - These things are truly game changing. But they also require GPU accelerators. They also require multiple systems like... In fact interestingly enough you know some of the super computers that we've been building for the scientific computing world, those same technologies are now coming into the AI world and the enterprise. >> So, the infrastructure for AI, if I can use that term? It's got to be flexible, Steven we were sort of talking about that elastic versus I'm even extending it to plastic. As Sumit you just said, it's got to have that tooling, got to have that modern tooling, you've got to accommodate alternative processor capabilities um, and so, that forms what you've used Steven to sort of create new capabilities new business capabilities within IBM. I wanted to, we didn't touch upon this before, but we touched upon your data strategy before but tie it back to the line of business. You essentially are a presume a liaison between the line of business and the chief data office >> Steven: Yeah. >> Officer office. How did that all work out, and shake out? Did you defining the business outcomes, the requirements, how did you go about that? >> Well, actually, surprisingly, we have very little new use cases that we're generating internally from my organization. Because there's so many to pick from already throughout the organization, right? There's all these business units coming to us and saying, "Hey, now the data is in the data lake and now we know there's more data, now we want to do this. How do we do it?" You know, so that's where we come in, that's where we start touching and massaging and enabling them. And that's the main efforts that we have. We do have some derivative works that have come out, that have been like new offerings that you'll see here. But mostly we already have so many use cases that from those businesses units that we're really trying to heighten and bring extra value to those domains first. >> So, a lot of organizations sounds like IBM was similar you created the data lake you know, things like "a doop" made a lower cost to just put stuff in the data lake. But then, it's like "okay, now what?" >> Steven: Yeah. >> So is that right? So you've got the data and this bog of data and you're trying to make more sense out of it but get more value out of it? >> Steven: Absolutely. >> That's what they were pushing you to do? >> Yeah, absolutely. And with that, with more data you need more computational power. And actually Sumit and I go pretty far back and I can tell you from my previous roles I heightened to him many years ago some of the deficiencies in the current architecture in X86 etc and I said, "If you hit these points, I will buy these products." And what they went back and they did is they, they addressed all of the issues that I had. Like there's certain issues... >> That's when you were, sorry to interrupt, that's when you were a customer, right? >> Steven: That's when I was... >> An external customer >> Outside. I'm still an internal customer, so I've always been a customer I guess in that role right? >> Yep, yep. >> But, I need to get data to the computational device as quickly as possible. And with certain older gen technologies, like PTI Gen3 and certain issues around um x86. I couldn't get that data there for like high fidelity imaging for autonomous vehicles for ya know, high fidelity image analysis. But, with certain technologies in power we have like envy link and directly to the CPU. And we also have PTI Gen4, right? So, so these are big enablers for me so that I can really keep the utilization of those very expensive compute devices higher. Because they're not starved for data. >> And you've also put a lot of emphasis on IO, right? I mean that's... >> Yeah, you know if I may break it down right there's actually I would say three different pieces to the puzzle here right? The highest level from Steve's perspective, from Steven's teams perspective or any data scientist perspective is they need to just do their data science and not worry about the infrastructure, right? They actually don't want to know that there's an infrastructure. They want to say, "launch job" - right? That's the level of grand clarity we want, right? In the background, they want our schedulers, our software, our hardware to just seamlessly use either one system or scale to 100 systems, right? To use one GPU or to use 1,000 GPUs, right? So that's where our offerings come in, right. We went and built this offering called Powder and Powder essentially is open source software like TensorFlow, like Efi, like Torch. But performace and capabilities add it to make it much easier to use. So for example, we have an extremely terrific scheduling software that manages jobs called Spectrum Conductor for Spark. So as the name suggests, it uses Apache Spark. But again the data scientist doesn't know that. They say, "launch job". And the software actually goes and scales that job across tens of servers or hundreds of servers. The IT team can determine how many servers their going to allocate for data scientist. They can have all kinds of user management, data management, model management software. We take the open source software, we package it. You know surprisingly ugh most people don't realize this, the open source software like TensorFlow has primarily been built on a (mumbles). And most of our enterprise clients, including Steven, are on Redhat. So we, we engineered Redhat to be able to manage TensorFlow. And you know I chose those words carefully, there was a little bit of engineering both on Redhat and on TensorFlow to make that whole thing work together. Sounds trivial, took several months and huge value proposition to the enterprise clients. And then the last piece I think that Steven was referencing too, is we also trying to go and make the eye more accessible for non data scientist or I would say even data engineers. So we for example, have a software called Powder Vision. This takes images and videos, and automatically creates a trained deep learning model for them, right. So we analyze the images, you of course have to tell us in these images, for these hundred images here are the most important things. For example, you've identified: here are people, here are cars, here are traffic signs. But if you give us some of that labeled data, we automatically do the work that a data scientist would have done, and create this pre trained AI model for you. This really enables many rapid prototyping for a lot of clients who either kind of fought to have data scientists or don't want to have data scientists. >> So just to summarize that, the three pieces: It's making it simpler for the data scientists, just run the job - Um, the backend piece which is the schedulers, the hardware, the software doing its thing - and then its making that data science capability more accessible. >> Right, right, right. >> Those are the three layers. >> So you know, I'll resay it in my words maybe >> Yeah please. >> Ease of use right, hardware software optimized for performance and capability, and point and click AI, right. AI for non data scientists, right. It's like the three levels that I think of when I'm engaging with data scientists and clients. >> And essentially it's embedded AI right? I've been making the point today that a lot of the AI is going to be purchased from companies like IBM, and I'm just going to apply it. I'm not going to try to go build my own, own AI right? I mean, is that... >> No absolutely. >> Is that the right way to think about it as a practitioner >> I think, I think we talked about it a little bit about it on the panel earlier but if we can, if we can leverage these pre built models and just apply a little bit of training data it makes it so much easier for the organizations and so much cheaper. They don't have to invest in a crazy amount of infrastructure, all the labeling of data, they don't have to do that. So, I think it's definitely steering that way. It's going to take a little bit of time, we have some of them there. But as we as we iterate, we are going to get more and more of these types of you know, commodity type models that people could utilize. >> I'll give you an example, so we have a software called Intelligent Analytics at IBM. It's very good at taking any surveillance data and for example recognizing anomalies or you know if people aren't suppose to be in a zone. Ugh and we had a client who wanted to do worker safety compliance. So they want to make sure workers are wearing their safety jackets and their helmets when they're in a construction site. So we use surveillance data created a new AI model using Powder AI vision. We were then able to plug into this IVA - Intelligence Analytic Software. So they have the nice gooey base software for the dashboards and the alerts, yet we were able to do incremental training on their specific use case, which by the way, with their specific you know equipment and jackets and stuff like that. And create a new AI model, very quickly. For them to be able to apply and make sure their workers are actually complaint to all of the safety requirements they have on the construction site. >> Hmm interesting. So when I, Sometimes it's like a new form of capture says identify "all the pictures with bridges", right that's the kind of thing you're capable to do with these video analytics. >> That's exactly right. You, every, clients will have all kinds of uses I was at a, talking to a client, who's a major car manufacturer in the world and he was saying it would be great if I could identify the make and model of what cars people are driving into my dealership. Because I bet I can draw a ugh corelation between what they drive into and what they going to drive out of, right. Marketing insights, right. And, ugh, so there's a lot of things that people want to do with which would really be spoke in their use cases. And build on top of existing AI models that we have already. >> And you mentioned, X86 before. And not to start a food fight but um >> Steven: And we use both internally too, right. >> So lets talk about that a little bit, I mean where do you use X86 where do you use IBM Cognitive and Power Systems? >> I have a mix of both, >> Why, how do you decide? >> There's certain of work loads. I will delegate that over to Power, just because ya know they're data starved and we are noticing a complication is being impacted by it. Um, but because we deal with so many different organizations certain organizations optimize for X86 and some of them optimize for power and I can't pick, I have to have everything. Just like I mentioned earlier, I also have to support cloud on prim, I can't pick just to be on prim right, it so. >> I imagine the big cloud providers are in the same boat which I know some are your customers. You're betting on data, you're betting on digital and it's a good bet. >> Steven: Yeah, 100 percent. >> We're betting on data and AI, right. So I think data, you got to do something with the data, right? And analytics and AI is what people are doing with that data we have an advantage both at the hardware level and at the software level in these two I would say workloads or segments - which is data and AI, right. And we fundamentally have invested in the processor architecture to improve the performance and capabilities, right. You could offer a much larger AI models on a power system that you use than you can on an X86 system that you use. Right, that's one advantage. You can train and AI model four times faster on a power system than you can on an Intel Based System. So the clients who have a lot of data, who care about how fast their training runs, are the ones who are committing to power systems today. >> Mmm.Hmm. >> Latency requirements, things like that, really really big deal. >> So what that means for you as a practitioner is you can do more with less or is it I mean >> I can definitely do more with less, but the real value is that I'm able to get an outcome quicker. Everyone says, "Okay, you can just roll our more GPU's more GPU's, but run more experiments run more experiments". No no that's not actually it. I want to reduce the time for a an experiment Get it done as quickly as possible so I get that insight. 'Cause then what I can do I can get possibly cancel out a bunch of those jobs that are already running cause I already have the insight, knowing that that model is not doing anything. Alright, so it's very important to get the time down. Jeff Dean said it a few years ago, he uses the same slide often. But, you know, when things are taking months you know that's what happened basically from the 80's up until you know 2010. >> Right >> We didn't have the computation we didn't have the data. Once we were able to get that experimentation time down, we're able to iterate very very quickly on this. >> And throwing GPU's at the problem doesn't solve it because it's too much complexity or? >> It it helps the problem, there's no question. But when my GPU utilization goes from 95% down to 60% ya know I'm getting only a two-thirds return on investment there. It's a really really big deal, yeah. >> Sumit: I mean the key here I think Steven, and I'll draw it out again is this time to insight. Because time to insight actually is time to dollars, right. People are using AI either to make more money, right by providing better customer products, better products to the customers, giving better recommendations. Or they're saving on their operational costs right, they're improving their efficiencies. Maybe their routing their trucks in the right way, their routing their inventory in the right place, they're reducing the amount of inventory that they need. So in all cases you can actually coordinate AI to a revenue outcome or a dollar outcome. So the faster you can do that, you know, I tell most people that I engage with the hardware and software they get from us pays for itself very quickly. Because they make that much more money or they save that much more money, using power systems. >> We, we even see this internally I've heard stories and all that, Sumit kind of commented on this but - There's actually sales people that take this software & hardware out and they're able to get an outcome sometimes in certain situations where they just take the clients data and they're sales people they're not data scientists they train it it's so simple to use then they present the client with the outcomes the next day and the client is just like blown away. This isn't just a one time occurrence, like sales people are actually using this right. So it's getting to the area that it's so simple to use you're able to get those outcomes that we're even seeing it you know deals close quicker. >> Yeah, that's powerful. And Sumit to your point, the business case is actually really easy to make. You can say, "Okay, this initiative that you're driving what's your forecast for how much revenue?" Now lets make an assumption for how much faster we're going to be able to deliver it. And if I can show them a one day turn around, on a corpus of data, okay lets say two months times whatever, my time to break. I can run the business case very easily and communicate to the CFO or whomever the line of business head so. >> That's right. I mean just, I was at a retailer, at a grocery store a local grocery store in the bay area recently and he was telling me how In California we've passed legislation that does not allow plastic bags anymore. You have to pay for it. So people are bringing their own bags. But that's actually increased theft for them. Because people bring their own bag, put stuff in it and walk out. And he didn't want to have an analytic system that can detect if someone puts something in a bag and then did not buy it at purchase. So it's, in many ways they want to use the existing camera systems they have but automatically be able to detect fraudulent behavior or you know anomalies. And it's actually quite easy to do with a lot of the software we have around Power AI Vision, around video analytics from IBM right. And that's what we were talking about right? Take existing trained AI models on vision and enhance them for your specific use case and the scenarios you're looking for. >> Excellent. Guys we got to go. Thanks Steven, thanks Sumit for coming back on and appreciate the insights. >> Thank you >> Glad to be here >> You're welcome. Alright, keep it right there buddy we'll be back with our next guest. 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prepare for the extraction point we've been briefed on all the important stories and events in the world of emerging information now it's time to extract the data and turn it into action live from the silicon angle studios in the heart of Silicon Valley this is extraction point with John furrier okay we're live back in the palo alto studios i'm john furrier for the extraction point we extract the signal from the noise and my special guest today i'm excited to have here is Paul Martino who is the founder of aggregate knowledge and also storied entrepreneur in Silicon Valley who now lives in Philly with his family comes out here Paul is known for among other things being a great entrepreneur tech geek loves tech loves to build build startups started one of the first social networks with Mark Pincus called tribe started his own company funded by Kleiner Perkins with his partner Chris law called aggregate knowledge which is booming and doing great and now more famous for being the first round investor in zynga company that is exploding with revenue as Kleiner Perkins said is the of all their portfolio comes in the history more than Google's made more money faster than anybody Paul Martino welcome to the extraction point great to see you John as always awesome to see you first I got to start with your now I forgot to mention that you're actually running a venture firm so in addition to being famous with Zynga you're running bullpen capital so first give the folks out there an update and first confirm or deny you were in the first round of Zynga or not yes the the first round of Zynga there were several institutional investors and several individual investors Morocco me Reid Hoffman were individual investors Avalon Union Square accelerator ventures and foundry where the institutional investors in that first round Peter was Peter Thiel yeah Peter was also an individual investor in the first round so that's officially the first round investors of Zynga we have clarified that and that is now hot on the books but now you're you've been successfully founded aggregate knowledge you know have a CEO running that what's the update with aggregate knowledge yeah so great guy runs that company as a guy you need to meet and have on this show Dave jakubowski aggregate knowledge really went in a direction where all of the focus was on providing data and analytics to the major ad agencies and John John Nelson who started organic one of the first agencies is now the CEO of Omnicom digital joined the board and I said look we got to get a guy who's an ad heavy in here and jakubowski was previously the GM of microsoft adcenter and had a senior position at specific media and we brought him in and he's just been kickin butt our greek knowledge has really really made a significant significant contribution in the area of data and analytics for these major agencies and he was very able to bring in a crew of people know exactly how to run that business so you're a big fan of big data then mm-hmm oh yeah we just had a big special yesterday on Big Data mentioned about it so that's cool we're going to get into a lobbyist I was just kind of get the small talk out of the way here your current role is the founder of bullpen capital right so bullpen to me I'm a baseball not I love baseball bullpen means you go the bullpen for relief right yep thank God close the game out hopefully or mid-innings relief so tell us about what bullpen is it's a special fund as I know from reading talk to you to target an expansion of this new seed and explosive new funding environment Bryce plain force right I'll tell you how we got the name at the end too so here's what happened I've been investing with a lot of the so-called super angels and that's kind of a misnomer because they really are actually in some cases actual small venture firms to I've been investing with a lot of them since they got off the ground Josh Kopelman from first round is one of the first investors in aggregate knowledge mike maples was an early advisor to the company I've known Jeff claw be a who run soft tech since he was at Reuters and with the late 90s and so I've worked with these guys done a lot of investing and we were me and my buddies Duncan Davidson rich Melman were sitting around over summer of 09 doing a little bit data analysis right another big data assignment we realized that as more and more these seed funds got created they were creating an inventory of companies that weren't quite ready to go to the traditional venture guy but we're also difficult to bridge from just the seed guys because the see guys at that time didn't have really big funds so wait a minute you've got some really good companies here is to clarify the for the folks out there seed funds don't traditionally have follow-on big funds like a VC firm right that's what you're referring to yeah they tend not to have as bigger reserve so if a big fun writes you a five-million-dollar check and you stub your toe you can probably get some more money to get through the hardships but a lot of the the new super angel funds or smaller funds and you get a five hundred thousand dollar check and if you need another five hundred thousand dollars it can frequently be very difficult because they make so many investments with smaller reserves yeah and so you've got dave McClure clavey a maples first round capital true ventures made the first round truevision more traditional VC then say dave McClure and mike maples and claw VA they're out doing some really good work out their funding really good company spending a lot of time I know I've seen them working their butt off yeah they need some air support right they need some cover the little bullpen is that that's you come in and say hey for your stars they're going to rise up yep and so that's exactly right so what happens is here's what the analysis we did turned out of their portfolio thirty percent of their portfolios in aggregate quickly are really exciting companies you know and they quickly go up to a venture auction and the guys and sandhill rotor excited about it about twenty percent of their deals you know that they don't like too much it's kind of just floating there yeah that you know the entrepreneur wasn't a fit that team didn't execute that left fifty percent of their deals in the middle which they kind of were too early to tell as Mike maple sometimes says they were in an extended learning and discovery phase they hadn't quite figured out what their models yeah and this de pivoting stuff's going on right now the Marcus changes turbulence so these guys are right and so you look you look at some examples and you go well wait a minute for every zynga that goes up into the right immediately go look at the stories of chegg and modcloth and etsy and quite frankly the in-between round on twitter and for everyone Zynga that you find that just hits it out of the park the right way there were four to five companies that went through that hard intermediate round that it was difficult in the environment where you have only a potentially thinly capitalized seed fund in front of you go get through that difficult point I said guys you need a bull pen and way we came up with the name is I'm involved in a deal with Chad Durbin who used to pitch for the Phillies and now as a relief pitcher for the cleveland indians and he was in our office and we were talking about this idea and Chad said yeah it's kind of like you're building a bullpen for the seed guys I'm like that's exactly right that's the name we got to go with and so fortunately I was involved in in this company called showcase you which is actually cool cited suppose for recruiting for college scholarships for a collegiate athletes right you're a high school student you throw 80 miles an hour left hand it and you're in 10th grade how do you figure out where the right scholarships are so Durbin and some of the Phillies where the original investors in this company called showcase you it's actually a cool company as the combine work out online basically fries for the high school kids and because the high school kids sometimes are in tough geographies to get to you're in you're in a small rural area in Nebraska how do they find out that you're the guy who can throw 89 miles an hour great so I mean this VC market so basically you're referring to with bullpen right now is an innie and you've been in our sprayer so you live through classic you know classic financing your last company financed by kleiner perkins and a tribe i forget who financed tribe yet Mayfield was the lead investor may feel again another traditional VC firm all tier 1 VCS although may feel people are you now is slipped a little bit that's some of their key partners who have slipped away but they've all moved on what you're really referring to is there's a new dynamic of entrepreneurship going on now we're now there are some break outcomes that just need a little bit more time to mature in the old model they just be kind of closed down the VC guy would be on the Bora has just a pain in the ass and you know really not growing and do another round it's they get kind of lazy in a way if they got 10 10 boards are on so with the super angels and the fact that does take a lot of cash to start a company you've got more deals getting done so the the Y Combinator the Dave McClure's and chef claw va's in the mike maples and sometimes SiliconANGLE labs which we're doing here is telling you about right we're funding companies the more [ __ ] is funded a better will you come in as you keep them alive longer just wreck the pivot possibly that's right and so what happens is right now the venture industry is being disrupted the same way the venture industry has funded companies that have rupted other industries they are being disrupted in the exact same way and the disruption happened from below as always happens it started in seed stage now in order for the disruption to go all the way through there need to be companies that come after seed stage investors that have the same philosophy and mentality pro entrepreneur easy terms operating people who get their hands dirty to get deals done you need that in the B stage and in the sea stage and here's what our prediction is John our prediction is a few years from now there'll be a company that comes after bullpen that does series c and series d financing or mezzanine financing but the same philosophy is bullpen and then DST s at the end of that chain and you can imagine building companies that go all the way to liquidity that you got money from maples first bullpen second this unnamed company third and you went quasi-public with DST and you've bypassed the entire venture scheme entirely and the entire institutional public markets complete liquidity wealth creation companies creating jobs I mean this is new paradigm I mean this isn't amazing I mean this is a potentially amazing point in the history of us finance the idea that you could go two billion dollar outcomes by passing not only the public markets on the back side but the traditional venture ecosystem on the front side I mean that is a disruption if ever there was one amen I mean hi and with you a hundred percent the other some people who will argue regulation is if market forces first of all I'm a big believer in market forces so I think what you're doing is clearly identifying an opportunity that dynamics are all lying lining up entrepreneurs are validating it and so but the questions are regulations I mean first of all I'm anti-regulation but as you start to get to that liquidity and some are arguing I even wrote a blog post about saying hey you know basically Facebook's public merry go buddy what do you say to those guys this is the change in the history of this financial asustor we want the government regulating this yeah so my co-founder of both i started bullpen with two really good guys Duncan Davison who was the founder covad was advantage point for years asking them to buy government regulation would go bad i mean what happened then because of the I lack warsi like Wars but only that the some extent covet doesn't exist unless the telco 1994 happens through in some ways a creation of the government to good point it's social right but but think about it the arbitrariness of government as opposed to a well-thought-out centralized plan so anyway so Duncan sometimes uses that phrase you know he talks a lot about the way in which the government you know that the worst thing you can ever hear is I'm with the government I'm here to help right i mean that's about the way it goes but his point around the the the new quasi public markets is money we'll find a way yeah and when sarbanes-oxley happens and it's tough to go public and you're a CEO like Pincus who's running one of the great all-time companies in Silicon Valley at Zynga he says you know going public is not an entrance is not an exit it's an entrance that's that's this quote what why would I why do I need that headache I mean I was just talking with Charles beeler who sold for the hell dorado he sold to compel in one of his investments to dell for over a billion dollars and and 3 para nother firm he wasn't on that one that was sold to HP during storage wars he's talking about the lawsuits literally this shakedown of immediately filed lawsuits you know you could have got more money so this is this public markets brutal no doubt no doubt i think what you're doing is a revolution I'm all excited about this new environment again anything with his liquidity wealth creation with the engine of innovation can be powered that's fantastic look back the startups okay get back to where you're playing yeah the history of Silicon Valley was built on the notion of value add some have said over the past 10 years venture capital has not been truly value add and some were arguing value subtract and then just money so what you're talking about here is getting in and helping me stay alive what's the value added side of the equation mean I know that a lot of these folks like like like ourselves here it's looking angle McClure Xavier and maples and true ventures they roll their sleeves up first round capital right before we can only provide so much it kind of expands right you guys are filling in the capital market side right how are you guys helping out on the value add because a lot of those companies may be the next Twitter right you've got a bridge to finance that's right allow them to do the pivot or get the creative energy to grow and they hit that market if they hit that hit it going vertical you got it kind of sometimes nurture it you guys have a strategy for that talk about the so let me let me give you my perspective on that so I think 10 years ago when you're starting a company the name of the venture firm was more important than potentially the partner on your board ten years later the name of the firm matters much less and it's the name of the partner and it's the operating experience that that partner partner brought to bear and you go talk to the 24 year old entrepreneur verse the 34 year old entrepreneur the 24 entrepreneur 24 year old entrepreneur wants a guy like you or a guy like me on his board he wants have been there done that started a company was a CEO exited it got fired hired people fired other people scar tissue scars knowledge experience exactly and if a good friend of mine who's in the traditional business I'll leave his name out of it he sometimes says the following phrase the era of the gentleman VC is over and what he means by the era of the gentleman VC is over is you know if your background is you were a junior associate who came in with a finance degree in an MBA and it never started a company you're not going to get picked by the entrepreneur anymore in 10 years from now almost everyone in the business is going to have a resume that looks more like a Cristal Paul Martino a mark pincus that you name all the people who we've started our companies with if there's a lot more hochberg with track record certainly with with the kind of big companies in the valley just in our generation yet started with netscape google paypal right now i want to see facebook is and then now's inga either the ecosystem is just entered intertwined I mean for every failure that spawns more success right so that's right that's a Silicon Valley way yeah well a tribe was tribe was a perfect example of a successful failure tribe was not a successful outcome but it was in many ways a very successful way to actually pioneer what became social networking you know investments got made into Facebook as a result of that Zynga in aggregate knowledge were both the outcrops of what was learned to some extent the original business case of Zynga was remarkably simple there is a ton of time being spent on social networks and after you get done finding your buddies and looking at photos what do you do and Pincus is original vision to some extent was let's have games to play and that insight doesn't happen that way unless you don't do tribe and go into the trenches and get the scars on your back and your in your your second venture of our adventure right at the tribe was aggregate knowledge was similar concept people are connected I mean you got to be excited though I mean you know you were involved in tribes very early on all the stuff that you dealt with activity streams newsfeed connections the social science you know the one that one of the nicest pieces of validation of this recently was over in q4 of 2010 seven of the patents that me Chris law Elliot low and Brian Waller wrote got issued now they're all owned by Cisco Cisco bought tribe in the end they bought the assets in the and the patent filings but there are patent filings that go back to 2002 on the corner stones and hallmarks of what social networking really is that we wrote back then that have now issued order granted or sitting in the cisco portfolio and well that's kind of like a consolation prize and that there wasn't a big outcome for tribe it is very validating to see that those original claims on really cutting-edge stuff have been had been issued and I'm excited about that you should be proud i'm proud to know your great guy you have great integrity you're going to do well as a venture capitalist i think you people will trust you and you're fair and there's two types of people in this world people who help people people who screw people so you know you really on one side of the other you're you're not in between you're truly on the on the good side I really enjoy you know having chatting with you but let's talk about entrepreneurship from that perspective about patents you know I'm try was an outcome that we all can relate to the peplum with Facebook of what Zuckerberg and and those guys are doing over there that's entrepreneurship so talk to the entrepreneurs out there yeah hey you know what you do some good work it all comes back to you talk about the the Karma of entrepreneurship a failure is not a bad thing it's kind of a punch line these days I'll failures are stepping stone to the next thing but talk about your experience and lets you and i talk about how to deal with faith for those first-time entrepreneurs out there in their 20s what just give them a sense of how to approach their venture and if it fails or succeeds what advice would you give them yeah well like winning and losing is important part of the game I mean certain companies are going to be successful in certain ones art and if you go and start ten unsuccessful companies maybe this isn't exactly the business for you but that said how you the game is important as well and if you're a high integrity guy who gets good investors and you make quality decisions and let's say the market wasn't a fit you're going to get the money the second time because people said you know I work with that guy that guy really did a good job you know they never got it quite right but this is a guy learn the right lessons so when I'm coaching a first-time CEO and i'm the CEO coach of a couple guys now you know i'm looking for someone who's sitting there going hey i not only want to do this to win and be successful but i want to learn i I want to do this better than no one no one walks in and says I learn from my failure I hope I'm successful I mean you let it go and say hey I'm gonna be successful I want to win failure is not an option but failure happens right i mean you know it's bad breaks that mean but but here is the key less I tell this to all of the entrepreneurs I work with you will not be successful if you're making mistakes that were made by those before you if you make novel mistakes you're in good company right and so only ever make a novel mistake I made a good example this is one claw and I started Chris law and I started aggregate knowledge aggregate knowledge was the original business model was around recommendations and there were dead bodies in front of us there was net perceptions there was fire fly and she was in the office this morning with Yazdi one of the founders of [ __ ] cast with it man yeah so predictive analytics residi what did we do we went out and we I flew out and met John riedle University of Minnesota who was the founder of net perceptions I dug up yes d i got these guys on my advisory board and while aggregate knowledge was not successful in the recommendation business and pivoted into the data management thing we made novel mistakes we did not repeat the mistakes of met perceptions and firefly and so i think that's an important important lesson to an entrepreneur if you're going into an area that has dead bodies in front of you you better research them you better know who they are you better know what happened and you better make sure that if you screw it up you at least screw it up in a way which none of us could have predicted yeah that's the only way you're going to get a hall pass on that well let's talk about talk about some of the hot Renisha of activity saw so you're in that sector where you're feeding the seed the super angels in the first rounds early stage guys and it's a good fit what about some of the philosophies on like the firms out there there's of this to this two philosophies I just taught us to an entrepreneur here you met on the way out a street speaker text and there at seven you know under a million dollars in financing hmm series a yeah and then you got in the news yesterday color 41 million dollars building to win magnin flipboard a hundred million dollars i got this is these guys that we know i mean there are yep our generation and a little bit around the same time and certainly they have pedigree so remember the old days the arms race mentality right when the sector at all costs right that's kind of what's going on here i mean some of the command that kind of money there's actually an auction going on what do you make of that I mean bubble is an arms race so so rich Melman inside a bullpen de tu fascinating analysis he looked at the full portfolio of 28 took about 20 of the best super angels by the way the super angles are all different some are micro vc summer buying options etc so so first off super angel is a weird word but it's everybody from Union Square and foundry on one side first round and flooding but any take the top 20 or so of these guys and look at their portfolios what's amazing about their portfolios is the unlike 10 and 20 years ago in prior tech bubbles there are not 20 companies doing the same thing when you categorize them yeah ten percent are in ad tech ten percent our direct-to-consumer consider but like forty percent are one-offs that is this is I think one of the first times in the history of venture that forty percent of the deal flow is a one-off unique business idea that there aren't 30 guys going to do and I think that the importance of that to what happens in this next stage of the tech boom we don't know what that means yet because back in the day well we need to just we're venture firm we need to disk drive company okay so your venture firm you've got your disk drive companies and I'll 20 venture friend knows if drive out and created the herd mentality everyone talks about with venture yep mean I was an opponent on a talk on here in the cube and I don't think I actually put in a blog post but I called the era of entrepreneurship like with open sores and low cost of entry with cloud computing and now mobility the manure of innovation where you know in the manure that's being out in the mark place mushrooms are growing out of it right and these you don't know what's going to be all look the same in a way so how do you tell the good ones from the bad ones so it's hard right so you have a lot of one you have a lot more activity hence angel list hence the super in rice so so the economics and the deal flow are all there the question is how do you get them from being just a one-off looked good on paper flame out the reality yeah well look in my opinion seed stage investing is about investing in people and I think when big firms trying to seed stage investing there's an impedance mismatch a lot of times because they want more evidence they want to know did the market work to the management then this is this is an early stage venture and am I going to want to go in a foxhole with this person and in many ways the good super angels are instinctive investors who are betting on people that they want to be in the foxhole with and yeah did they do it before do they know how to hire people is the market reasonably interesting but guess what they're probably gonna pivot three times so wait a minute at the end of the day you got to invest in people later stage venture is not you can look at discounted cash flows you can look at mezzanine financing you can do traditional measures but if you're going to invest in two people who have a prototype and need five hundred thousand dollars you're investing in people at that point what do you think about the OC angel is I'm a big fan of and recently was added thanks to maybe out there but even though i'm not i don't really co-invest with anyone else other than myself maybe you guys would bullpen but but if that's a phenomenon you don't have angel list which is opening up doors for deal flow companies are getting funded navales getting yeah a ton of activity nivea doing great job with venture hacks i get y combinator which I called the community college of startups they bring in like they open the door and I mean that an actually good way don't mean that negatively I mean they're giving access to entrepreneurs that never had access to the market right and now you have Paul Graham kind of giving the halo effect or thrown the holy water on certain stars and they get magically funded but yesterday at an event and they're they're packed right I've heard from VC saying I'm not invited because I didn't wasn't part of the original investment class so it seems that Y comma day is getting full yeah so do you see that you agree is there will be an over lo y combinator you know kind of like I've TED Conference has you know Ted they'll be you know y combinator Boston little franchises will be like barcamp for sure I mean look and look at techstars they franchise they'd I was over there with Dave Tisch in New York there's TechStars New York after those TechStars older in techstars seattle there is no doubt in my mind that right now there is an over investment in the seed stage meaning that there is a little bit of a seed bubble going on that's not necessarily bad though because in terms of raw dollars there's not a bubble yet Rory who's over at rafi it smells like a bubble it looks like a bubble but when you look at the mechanic when you look at the actual total dollars it's not a bubble rory who has a hinge recent Horowitz been said that that it's a boom not a bubble yeah so don't be confused it looks like bubbles and booms kind of look together the same right I actually I'm not quite sure I had the exact data right but here's the quick summary if you take a look at venture capital investment as a percent of GDP historically it's been something like point one percent of GDP in the bubble back in 99 it went to one percent something like it went 10x higher right now we're still at point one percent but since it's very much centered around the seed stage investing you see this frothiness in the sea but until that number goes from point 1 percent of GDP back up to one percent there's no real bubble because the tonnage of money hasn't come in yet and so so it's starting but this is what a tech boom feels like the early stages are excitement and lots of ideas and lots of flowers blooming and then the big money comes in because John I'll bet you're your brother and your sister and your mom haven't invested in a tech startup back in 99 video there's no public market that supports seven in a way that's a good and bad star basement yeah there's no fraud going on and most of the companies that are out there whether their lifestyle business or seed or bullpen funded are actually generating income the entrepreneur he has any earlier Mike was saying that he could a business deal so people are kind of like saw the old bubble and said shoot I don't want to do that again I gotta have at least revenue right and so companies didn't seem to start out with cash so you know that because you invested it but you know Pincus was getting some cash flow in the door from day one that's right that company was company was profitable the first day it started basically so talk about you know so I'm with Paul Martino by the way with bullpen capital entrepreneur wrote the patents on social networking which he sold the cisco when they sold the company now with bullpen capital huge dynamic you're a company out there this is exactly the positive dynamic you want to see because mainly you know dave mcclure jeff clavier mike maples have been kind of getting their butts handed to them in the press about super angels not having the juice to kind of go anywhere and it's been kind of a negative press there so you know this is the kind of void that's been filled by you guys to show the market that look at this there's a road map here so even though that the McClure's and clubs don't have big funds that there's a path to follow on financing so that the vc's can't shut them down and i've heard some pc say that so a lot of traditional venture guys would like to say that you know this little disruption we nipped it in the butt and it stopped after the seed stage but that's not the history of disruptions the history of disruptions are they start from the bottom then they get ecosystem support and then they grow and they disrupt the incumbents and I think we're halfway there so so the Angel gate thing that Arrington reported on was interesting because you know essentially what happened there it was a lot of him fighting Ron Conway I was not happy you can't be happy about competition I mean this is competition that increases prices right so you know in the short term prices have been inflated on valuations true or false that's true but but but I think I think the whole way angel gate was reported was absurd the most Pro entrepreneurial venture people perhaps in the history of the business are the guys who were supposedly at those tables I mean mike maples Jeff claw VA josh cop and Ron Conway fired his guy that was there I I understand suppose again suppose a key are right these are the most Pro entrepreneurial venture guys in the history of the business so I think that turned into something that it never was yeah well I mean that's the thing you know good for content producers who want page views I got to create some drama and you know as you know SiliconANGLE doesn't have any banner ads on our site quick plug for us we are motivated by content not page views so thanks for coming in today no but seriously I mean there's a there's a black cloud over the super angels has been since Angel gate I've heard privately from VCS that super angels it's been kind of a scuttlebutt they're misaligned just rumors I completely overblown and you know their business model threatens the incumbents and you know someone needed someone needed a piece of fodder to start a you know start a techcrunch discussion right there's no doubt that the market is need in need of a new ecosystem for the early stage because individual angels traditionally were wealthy individuals but now you have people with more experience like yourselves and entrepreneurs from google and facebook etc coming out and doing some things okay so next topic more on a personal kind of professional note k last final question is I know you got to run appreciate your time you're a technologist a lot of folks don't know that you're hardcore computer science guy and our model southern angles computer science meet social science right in your wheelhouse so with that just kind of final parting question what gets you excited technically right now I mean I'll see you have roots in both comps I and social Iran Zynga's early investor roster you got a bullpen capital you're looking at a lot of deals outside of that you as a computer scientist geek mm-hmm what gets you jazz what do you see in the horizon that's not yet on the mega trend roster that kind of you can't put your finger on it truly we might really get a good feeling well so I think you'll be disappointed with this answer because I think it's now cross the chasm to start being one of those mega trends it's called consumerization of enterprise and that's now the buzz word for it but what is it really mean and why do I think it's for real look you've got cool self-service applications for everything you can go do home banking by logging into a portal you can go to an ATM you can go do these things but you know go bring a new laptop into your big stodgy fortune 500 company and you know it's like getting a rectal exam right you know we got to install this we got to give you this private key yet that's TSA it writes like going through TSA exact idea that IT inside of big fortune 500 companies is going to stop being this gatekeeper to new technology I think look how long do you think it'll be until pick your favorite fortune 500 company the IT people know how to deal with the ipad 2 but how many people bought an ipad 2 into the off already everyone and so this to me is going to be the big next deck the next decade are going to be self service offerings for the enterprise getting around a very frustrating gatekeepers inside of you know the IT department etc and that's going to lead to an awesome boom of everything from security to auditing to compliance etc that's the convergence question Paul Martino my friend entrepreneur great guy venture capitals now on the good side helping the seed Super Angel micro VCS great to have you consumerization of IT that hits the cloud mobile social it's everything so that I was buzzword compliant on that great job great to have you know you're busy got to have you in again thanks so much for time that's a wrap thank you very much great thank you John