[Music] thecube's coverage of red hat summit 2022 thecube has been covering red hat summit for a number of years of course the last two years were virtual coverage now the red hat summit is one of the industry's most premier events and and typically red hat summits are many thousands of people i think the last one i went to was eight or nine thousand people very heavy developer conference this year red hat has taken a different approach it's a hybrid event it's kind of a vip event at the westin in boston with a lot more executives here than we would normally expect versus developers but a huge virtual audience my name is dave vellante i'm here with my co-host paul gillin paul this is a location that you and i have broadcast from many times and um of course 2019 the summer of 2019 ibm acquired red hat and um we of course we did red hat summit that year but now we're seeing a completely new red hat and a new ibm and you wouldn't know ibm owned red hat for what they've been talking about at this conference we just came out of the keynote where uh in the in the hour-long keynote ibm was not mentioned once and only appeared the logo only appeared once on the screen in fact so this is uh very much red hat being red hat not being a subsidiary at ibm and perhaps that's justified given that ibm's track record with acquisitions is that they gradually envelop the acquired company and and it becomes part of the ibm board yeah they blue wash the whole thing right it's ironic because ibm think is going on right across the street arvin krishna is here but no presence here and i think that's by design i mean it reminds me of when you know emc owned vmware you know the vmware team didn't want to publicize that they had an ecosystem of partners that they wanted to cater to and they wanted to treat everybody equally even though perhaps behind the scenes they were forced to do certain things that they might not have necessarily wanted to because they were owned by another company and i think that you know certainly ibm's done a good job of leaving the brand separate but when they talk about the con the conference calls ibm's earnings calls you certainly get a heavy dose of red hat when red hat was acquired by ibm it was just north of three billion dollars in revenue obviously ibm paid 34 billion dollars for the company actually by today's valuations probably a bargain you know despite the market sell-off in the last several months uh but now we've heard public statements from arvind kushner that that red hat is a 5 billion plus revenue company it's a little unclear what's in there of course when you listen to ibm earnings you know consulting is their big business red hat's growing at 21 but when i remember paul when red hat was acquired stu miniman and i did a session and i said this is not about cloud this is about consulting and modernizing applications and sure there's some cloud in there with openshift but from a financial standpoint ibm was able to take red hat and jam it right into its application modernization initiatives so it's hard to tell how much of that 5 billion is actually you know legacy red hat but i guess it doesn't matter anymore it's working ibm mathematics is notoriously opaque they if the business isn't going well it'll tend to be absorbed into another number in the in the earnings report that that does show some growth so we've heard uh certainly ibm talks a lot about red hat on its earnings calls it's very clear that red hat is the growth engine within ibm i'd say it's a bit of the tail wagging the dog right now where red hat really is dictating where ibm goes with its hypercloud strategy which is the foundation not only of its technology portfolio but of its consulting business and so red hat is really in the driver's seat of of hybrid cloud and that's the future for ibm and you see that very much at this conference where uh red hat is putting out its uh series of announcements today about improvements to his hybrid cloud the new release of route 9 red hat enterprise linux 9 improvements to its hybrid cloud portfolio it very much is going its own way with that and i sense that ibm is going to go along with wherever red hat chooses to go yeah i think you're absolutely right if by the way if you go to siliconangle.com paul just published a piece on red hat reds hats their roll out of their parade which of course is as you pointed out led by enterprise linux but to your point about hybrid cloud it is the linchpin of of certainly ibm strategy but many companies hybrid cloud strategies if you think about it openshift in particular it's it's the modern application development environment for kubernetes you can get kubernetes you can buy eks you can get that for free in a lot of places but you have to do dozens and dozens of things and acquire dozens of services to do what openshift does to get the reliability the recoverability the security and that's really red hat's play and they're the the thing about red hat combining with linux their linux heritage they're doing that everywhere it's going to open shift everywhere red hat everywhere whether it's on-prem in aws azure google out to the edge you heard paul cormier today saying he expects that in the next several years hardware is going to become one of the most important you know factors i agree i think we're going to enter a hardware renaissance you've seen the work that we've done on arm i think 2017 was when red hat and arm announced kind of their initial collaboration could have even been before that today we're hearing a lot about intel and nvidia and so affinity with all of these alternative processes i think they did throw in today in the keynote power and so i think i heard that that was the other ibm branding they sort of tucked that in there but the point is red hat runs everywhere so it's fundamental to building out hybrid cloud and that is fundamental to a lot of company strategies and red hat has been all over kubernetes with openshift it's i mean it's a drum beat here uh the openshift strategy is what really makes hybrid cloud possible because kubernetes is what makes it possible to shift workloads seamlessly from platform to platform you make an interesting point about hardware we have seen kind of a renaissance in hardware these last couple of years as these specific chipsets and uh and even full-scale processors have come to market we're seeing several in the ai area right now where startups are developing full-blown chipsets and and systems uh just for ai processing and nvidia of course that's that's really kind of their stock and trade these days so uh a a company that can run across all of those different platforms a platform like like rel which can run all across those different platforms is going to have a leg up on on anybody else and the implications for application development are considerable when you when you think about we talk about a lot about these alternative processes when flash replaced the spinning disk that had a huge impact on how applications are developed developers now didn't have to wait for that that disc to spin even though it's spinning very fast it's mechanical compared to electrons forget it and and the second big piece here is how memory is actually utilized the x86 you know traditional x86 you know memory everything goes through that core processor intel for years grabbed more and more function and you're seeing now that function become dispersed in fact a lot of people think we're moving from a processor-centric world to a connect centric world meaning connecting all these piece parts alternative processors memory controllers you know storage controllers io network interface cards smartnics and things like that where the communication across those resources is now where a lot of the innovation is going you see you're seeing a lot of that and now of course applications can take advantage of that especially now at the edge which is just a whole new frontier the edge certainly is part of that equation when you look at machine learning at training machine learning models the cpu actually does relatively little work most of it is happening in gpus in these parallel processes that are going on and the cpu is kind of acting as a traffic cop and you see that in the edge as well it's the same model at the edge where more of the intelligence is going to be out in discrete devices spread across the network and the cpu is going to be less of a uh you know less of a engine of intelligence at the same time though we've got cpus with we've got 100 core cpus are on the horizon and there are even 200 and 300 core cpus that we may see in the next uh in the next couple of years so cpus aren't standing still they are evolving to become really kind of super traffic cops for all of these other processors out in the network and on the edge so it's a very exciting time to be in hardware because so much innovation is happening really at the microprocessor level well we saw this you and i lived through the pc era and we saw a whole raft of applications come about as a result of the microprocessor the shift of the microprocessor-based economy we're going to see so we are seeing something similar with mobile and the edge you know just think about some of the numbers if you think about the traditional moore's law doubling a number of transistors every let's call it two years 18 to 24 months pat gelsinger at intel promises that intel is on that pace still but if you look at the apple m1 ultra they increased the transistor density 6x in the last 15 months okay so where is this another data point is the historical moore's law curve is 40 that's moderating to somewhere down you know down in the low 30s if you look at the apple a series i mean that thing is on average increasing performance at 110 a year when you add up into the combinatorial factors of the cpu the neural processing unit the gpu all the accelerators so we are seeing a new era the thing i i i wanted to bring up paul is you mentioned ai much of the ai work that's done today is modeling that's done in the cloud and when we talk about edge we think that the future of ai is ai inferencing in real time at the edge so you may not even be persisting that data but you're going to create a lot of data you're going to be operating on that data in streams and it's going to require a whole new new architectural thinking of hardware very low cost very low power very high performance to drive all that intelligence at the edge and a lot of that data is going to stay at the edge and and that's we're going to talk about some of that today with some of the ev innovations and the vehicle innovations and the intelligence in these vehicles yeah and in talking in its edge strategy which it outlined today and the announcements that are made today red hat very much uh playing to the importance of being able to run red hat enterprise linux at the edge the idea is you do these big machine learning models centrally and then you you take the you take what results from that and you move it out to smaller processors it's the only way we can cope with it with the explosion of data that will be uh that these sensors and other devices will be generating so some of the themes we're hearing in the uh announcements today that you wrote about paul obviously rel9 is huge uh red hat enterprise linux version nine uh new capabilities a lot of edge a lot of security uh new cross portfolio capabilities for the edge security in the software supply chain that's a big conversation especially post solar winds managed ansible when you think about red hat you really i think anyway about three things rel which is such as linux it powers the internet powers everything uh you think of openshift which is application development you think about ansible which is automation so itops so that's one of the announcements ansible on azure and then a lot of hybrid cloud talk and you're gonna hear a lot of talk this week about red hat's cloud services portfolio packaging red hat as services as managed services that's you know a much more popular delivery mechanism with clients because they're trying to make it easy and this is complicated stuff and it gets more complicated the more features they add and the more the more components of the red hat portfolio are are available it's it's gonna be complex to build these hybrid clouds so like many of these so thecube started doing physical events last summer by the way and so this is this is new to a lot of people uh they're here for the first time people are really excited we've definitely noticed a trend people are excited to be back together paul cormier talked about that he talked about the new normal you can define the new normal any way you want so paul cormier gave the uh the the intro keynote bidani interviewed amex stephanie cheris interviewed accenture both those firms are coming out stephanie's coming on with the in accenture as well matt hicks talked about product innovation i loved his reference to ada lovelace that was very cool he talked about uh serena uh ramyanajan a famous mathematician who nobody knew about when he was just a kid these were ignored individuals in the 1800s for years and years and years in the case of ada lovelace for a century even he asked the question what if we had discovered them earlier and acted on them and been able to iterate on them earlier and his point tied that to open source very brilliantly i thought and um keynotes which i appreciate are much shorter much shorter intimate they did a keynote in the round this time uh which i haven't seen before there's maybe a thousand people in there so a much smaller group much more intimate setting not a lot of back and forth but uh but there is there is a feeling of a more personal feel to this event than i've seen it past red hat summits yeah and i think that's a trend that we're going to see more of where the live audience is kind of the on the ground it's going to the vip audience but still catering to the virtual audience you don't want to lose them so that's why the keynotes are a lot tighter okay paul thank you for setting up red hat summit 2022 you're watching the cube's coverage we'll be right back wall-to-wall coverage for two days right after this short break [Music] you

>>from >>around the >>globe. It's the Cube with digital coverage of pager duty. Summit 2020. Brought to you by pager duty. Hey, welcome back to Brady. Jeffrey here with the Cube in Palo Alto studios today. And we're talking about an upcoming event. It's one of our favorites. This will be the fourth year that we've been doing it. And it's pager duty summit. And we're excited to have from the pager duty team. She's Caroline Gus, the VP of corporate marketing from pager duty. Caroline, Great to see you. >>Hi, Jeff. Great to see you again. >>Absolutely. So, you know, I was thinking before we turn on the cameras we've been doing pager duty for I think this will be like, say, our fourth year that first year was in the cool, um, cruise ship terminal pier. I gotta written appear 27 which was which was nice. And then the last two years, you've been in the, you know, historic Westin ST Francis in downtown San Francisco, which is a cool old venue, but oh, my goodness. You guys were busting at the seams last year. So this year, year to go virtual. There's a whole bunch of new things that that you could do in virtual that you couldn't do in physical space. At least when you're busting out of the seems so First off, Welcome and >>talk a little >>bit about planning for virtual versus planning for a physical event from, you know, head of marketing perspective. >>Absolutely. I mean, the first thing that's changed for us is the number of people that can come. It's five x the number of people that were able to join us, the Western last year. So we have, uh, we we expect to have 10,000 people registered on attending age duty summit. The second thing is thea share number of sessions that we can put on. Last year, I think we had around 25 sessions. This year we have between 40 and 50 on again. That's because we're not constrained by space and physical meeting rooms, so it's being a really exciting process for us. We've built a fantastic agenda on. It's very much personalized, you know, developers come to our event. They love our event for the opportunity to learn mixed with their peers, get best practices and hands on experience. So we have many more of those types of sessions when we have done previously, and that things like labs and Bird of Feather Sessions and Emma's. But we've also built a whole new track of content this year for executives. Page Julie has, um, many of the Fortune 500 on 4100 customers. We work very closely with CEO CTO, so we have built sessions that are really designed specifically for that audience on I think for us it's really opened up. The potential of this event made it so much broader and more appealing than we were able to do when we were, As you say, you know, somewhat confined by the location in downtown San Francisco. >>I think it's such an interesting point. Um, because before you were constrained, right, If you have X number of rooms over a couple of days, you know you've got to make hard decisions on breakouts and what could go in and what can't go in. And, you know, will there be enough demand for these for this session versus another session? Or from the perspective of an attendee, you know, do they have to make hard tradeoffs? I could only attend one session at one oclock on Tuesday and I got to make hard decisions. But this is, you said really opens up the opportunities. I think you said you doubled. You doubled your sessions on and you got five X a number of registrations. So I think, you know, way too many people think about what doesn't happen in digital vs talking about the things that you can do that are impossible in physical. >>Yeah, I think at the very beginning. Well, first of all, we held our Amir summit events in London in July. So that was great because we got Thio go through this experience once already. And what we learned was the rial removal of hurdles in this process. So, to your point about missing the session because you're attending another session, we were calling this sort of the Pelton version of events where you have live sessions. It's great to be there, live participate in the live Q and A, but equally you have an entire on demand library. So if you weren't able to go because there was something else at the same time, this is available on demand for you. So we are actually repeating live sessions on two consecutive day. So on the Monday we're on everything on the Tuesday I ask because show up again for life Q and A at the end of their sessions. But after that it's available forever on an on demand library. So for us, it was really removing hurdles in terms of the amount of content, the scheduling of the content on also the number of people that content in attend, no geographical boundaries anymore. It used to be that a customer of ours would think, Well, I'll send one or two people to the page duty summit. They could learn all the great innovation from page duty, and they'll bring it back to the team that's completely changed. You know, we have tens of 20 signing up on. All of them are able to get that experience firsthand. >>That's really interesting. I didn't didn't even think about, you know, kind of whole teams being able to attend down instead of just certain individuals because of budget constraints, or you can't send your whole team, you know, a way for a conference in a particular area. But the piece to that you're supporting that were over and over is that the net new registrants goes up so dramatically in terms of the names and and and who those individuals are because a lot of people just couldn't attend for for various reasons, whether it's cost, whether it's, uh, geography, whether it's they just can't take time off from from from leaving their primary job. So it's a really interesting opportunity to open up, um, the participation to such a much bigger like you said five x five X, and increase in the registration. That's pretty good number. >>That's right. Yeah. I mean, that crossed boundaries gone away. This event is free on DWhite. That's actually meant is, as I say, you know, larger teams from the same company are attending. Uh, In addition, we have a number of attendees who are not actually paid to duty customers right now to previously. This was very much a community event for, you know, our page duty users on now we actually have a large number of I asked, interested future customers that will be coming to the event. So that's really important for us. And also, I think, for our sponsor partners as well, because it's bordering out the audience for both of us. So let's >>talk about sponsors for a minute, because, um, one of the big things in virtual events that people are talking about quite often is. Okay, I can do the keynotes, and I could do the sessions. And now I have all these breakout sessions for, um, you know, training and certification and customer stories, etcetera. But when it comes to sponsors, right sponsors used, you know, go to events to set up a booth and hand out swag and wander badge. Right? And it really was feeding kind of a top level down funnel. That was really important. Well, now those have gone away. Physical events. So from the sponsor perspective, you know, what can they expect? What? What do you know the sponsor experience at pager duty Summit. Since I don't have a little tiny booth at the Westin ST Francis given out swag this year. >>Yeah. So one important thing is the agenda and how we're involving our sponsors in our agenda this time, something that we learned is we used to have very long keynotes. You know, the keynote could be an hour long on involved multiple components and people would stay in that room for a now er on did really stay and watch sessions all day. So we learned in the virtual format that we need to be shorter and more precise in our sessions on that opened up the opportunity to bring in more of our partners, our sponsorship partners. So zendesk Salesforce, Microsoft some examples. So they actually get to have their piece of both of our keynote sessions and of our technical product sessions. I'm really explain both the partnership with pager duty, but also they're called technology and the value that they provide customers. So I think that the presence of sponsors in content is much higher than it was before on we are still repeating the Expo format, so we actually do have on Expo Hall that any time there's breaking between sessions, you could go over to the Expo ball, and it actually runs throughout as well, and you can go in and you can talk to the teams. You can see product demos, so it's very much a virtual version of the Expo Hall where you went and you want around and you picked up a bit of swag, >>so you mentioned keynotes and and Jennifer and and the team has always had a fantastic keynotes. I mean, I just saw Jennifer being interviewed with Frank's Luqman and and Eric Juan from Zoom By by Curry, which was pretty amazing. I felt kind of jealous that I didn't get to do that. But, um, talk tell us a little bit about some of the speakers I know there'll be some some, you know, kind of big rally moment speakers as well as some that are more down to technical track or another track. Give us some highlights on on some of the people. I will be sharing the stage with Jennifer. >>Absolutely, I said. I think what's really unique about Page duty Summit is that we designed types of content for different types of attendees. So if you're a developer, your practitioner, we have something like this from Jones of Honeycombs, who's talking about who builds the tools that we all rely on today, and how do they collaborate to build them together in this virtual world? Or we have J. Paul Reed from Netflix talking about how to handle the stress of being involved in incidents, So that's really sessions for our core audience of developers who are part of our community and pager duty really helps them day to day with with that job. And then we have the more aspirational senior level speakers who could really learn from a ZA leader. So Bret Taylor, president and CEO of Salesforce, will be joining us on the main stage. You'll be talking about innovation and trust in today's world on. Then we have Derrick Johnson. He is president of N A A. C P, and he'll be talking about community engagement and particularly voter engagement, which is such an important topic for us right now. Aan den. We have leaders from within our customers who are really talking about the way they use pager duty thio drive change in their organization. So an example would be porches, bro. He runs digital for Fox on, and he's gonna be talking about digital acceleration. How large organization like Fox can really accelerate for this digital first world that we find ourselves living in right now, >>right? Well, you guys have such a developer focus because pager duty, the product of solution, has to integrate with so many other, um, infrastructure, you know, monitoring and, uh, and all of all those different systems because you guys were basically at the front line, you know, sending them the signals that go into those systems. So you have such a broad, you know, kind of ecosystem of technology partners. I don't know if people are familiar with all the integrations that you guys have built over the years, which is such a key piece of your go to market. >>That's right. I mean, we we like to say we're at the center of the digital ecosystem. We have 203 170 integrations on. That's important because we want anyone to be able to use page duty no matter what is in their technology stack technology stacks today are more complex than they've ever been before, particularly with businesses having to shift to this digital first model since we all began shelter in place, you know, we all are living through digital on working and learning through digital on DSO. The technology stacks that power that are more complicated than ever before. So by having 370 integrations, we really know that we conserve pretty much any set of services that your business. It's using. >>Yeah, we've all seen all the means right about who's who's pushing your digital transformation. You know, the CEO, the CEO or or covert. And we all know the answer to toe what's accelerated that whole process. So okay, but so before I let you go, I don't even think we've mentioned the date. So it's coming up Monday, September, September 21st through Thursday, September 24th not at the West End Online and again. What air? What are you hoping? You're kind of the key takeaways for the attendees after they come to the summit? >>Yeah, a couple of things. I mean, first of all, I think will be a sense of belonging. Three attendees, the uses, a pager duty. They are really the teams that are at the forefront of keeping our digital services working on. But what that means is responding to incidents we've actually seen. Ah, 38% increase in the volume of incidents on our platform since covert and shelter in place began. Wait 30 >>38% increase in incidents since mid March. >>That's correct. Since the beginning of on bear in mind incidents. Prior to that in the six months prior, they were pretty flat. There wasn't instant growth. But what we've also seen is a 20% improvement in the time that it takes to resolve an incident from five minutes down to four minutes. So what that really means is that the pager duty community is working really hard. They're improving their practices. Hopefully our platform, our platform is a key part of how, but these are some people under pressure, so I hope that people can come and they can experience a sense of belonging. They can learn from each other about experiences. How do you manage the stress of that situation on what are some of the great innovations that make your job easier in the year ahead? The second thing that we don't for that community is that we are offering certification for P. D. You page due to university for free this year. It's of course, with a value of $7500. Last year, you would attend page duty summit on you would sit through your sessions and you would learn and you would get certified. So this year it's offered for free. You take the course during summit. But you can also carry on if you miss anything for 30 days after. So we're really feeling that, you know, we're giving back there, offering a great program for certification and improved skills completely free to help our community in this in this time of pressure, >>right? Right. Well, it is a very passionate community, and, you know, we go to so many events and you can you can really tell it's palatable, you know, kind of what the where the tight communities are and where people are excited to see each other and where they help each other, not necessarily only at the event, but you know, throughout the year. And I think you know a huge shout out to Jennifer on the culture that she's built there because it is very warm. It's very inclusive, is very positive. And and that energy, you know, kind of goes throughout the whole company and ice the teaser. You know this in something that's built around a device that most of the kids today don't even know what a pager is, and just the whole concept of carrying a pager and being on call right and being responsible. It's a very different way to kind of look at the world when you're the one that has that thing on your hip and it's buzzing and someone's expecting, Ah, return call and you gotta fix something So you know, a huge shout out to keep a positive and you're smiling nice and big culture in a job where you're basically fixing broken things most of the time. >>Yeah, absolutely. I mean, there's, I think, a joke that we make you know these things only break on Friday night or your wedding anniversary or Thanksgiving. But one of the announcements we're most excited about this year is the level of automation on artificial intelligence that we're building into our platform that is really going to reduce the number of interruptions that developers get when they are uncle. >>Yeah, I look forward to more conversations because we're gonna be doing a bunch of Cube interviews like Normal and, uh, you know, applied artificial intelligence, I think, is where all the excitement is. It's not a generic thing. It's where you applied in a specific application to get great business outcomes. So I look forward to that conversation and hopefully we'll be able to talk again and good luck to you and the team in the last few weeks of preparation. >>Thanks so much, Jeff. I've enjoyed talking to you. Thanks for having me. >>Alright. You too. And we'll see you later. Alright. She is Caroline. I'm Jeff. You're watching the Cube. Thanks for watching. We'll see you next time.

>> [Announcer] Live from Times Square in New York City, it's theCUBE covering IBM's Change the Game: Winning with AI, brought to you by IBM. >> Hello everybody, welcome to theCUBE's special presentation. We're covering IBM's announcements today around AI. IBM, as theCUBE does, runs of sessions and programs in conjunction with Strata, which is down at the Javits, and we're Rob Thomas, who's the General Manager of IBM Analytics. Long time Cube alum, Rob, great to see you. >> Dave, great to see you. >> So you guys got a lot going on today. We're here at the Westin Hotel, you've got an analyst event, you've got a partner meeting, you've got an event tonight, Change the game: winning with AI at Terminal 5, check that out, ibm.com/WinWithAI, go register there. But Rob, let's start with what you guys have going on, give us the run down. >> Yeah, it's a big week for us, and like many others, it's great when you have Strata, a lot of people in town. So, we've structured a week where, today, we're going to spend a lot of time with analysts and our business partners, talking about where we're going with data and AI. This evening, we've got a broadcast, it's called Winning with AI. What's unique about that broadcast is it's all clients. We've got clients on stage doing demonstrations, how they're using IBM technology to get to unique outcomes in their business. So I think it's going to be a pretty unique event, which should be a lot of fun. >> So this place, it looks like a cool event, a venue, Terminal 5, it's just up the street on the west side highway, probably a mile from the Javits Center, so definitely check that out. Alright, let's talk about, Rob, we've known each other for a long time, we've seen the early Hadoop days, you guys were very careful about diving in, you kind of let things settle and watched very carefully, and then came in at the right time. But we saw the evolution of so-called Big Data go from a phase of really reducing investments, cheaper data warehousing, and what that did is allowed people to collect a lot more data, and kind of get ready for this era that we're in now. But maybe you can give us your perspective on the phases, the waves that we've seen of data, and where we are today and where we're going. >> I kind of think of it as a maturity curve. So when I go talk to clients, I say, look, you need to be on a journey towards AI. I think probably nobody disagrees that they need something there, the question is, how do you get there? So you think about the steps, it's about, a lot of people started with, we're going to reduce the cost of our operations, we're going to use data to take out cost, that was kind of the Hadoop thrust, I would say. Then they moved to, well, now we need to see more about our data, we need higher performance data, BI data warehousing. So, everybody, I would say, has dabbled in those two area. The next leap forward is self-service analytics, so how do you actually empower everybody in your organization to use and access data? And the next step beyond that is, can I use AI to drive new business models, new levers of growth, for my business? So, I ask clients, pin yourself on this journey, most are, depends on the division or the part of the company, they're at different areas, but as I tell everybody, if you don't know where you are and you don't know where you want to go, you're just going to wind around, so I try to get them to pin down, where are you versus where do you want to go? >> So four phases, basically, the sort of cheap data store, the BI data warehouse modernization, self-service analytics, a big part of that is data science and data science collaboration, you guys have a lot of investments there, and then new business models with AI automation running on top. Where are we today? Would you say we're kind of in-between BI/DW modernization and on our way to self-service analytics, or what's your sense? >> I'd say most are right in the middle between BI data warehousing and self-service analytics. Self-service analytics is hard, because it requires you, sometimes to take a couple steps back, and look at your data. It's hard to provide self-service if you don't have a data catalog, if you don't have data security, if you haven't gone through the processes around data governance. So, sometimes you have to take one step back to go two steps forward, that's why I see a lot of people, I'd say, stuck in the middle right now. And the examples that you're going to see tonight as part of the broadcast are clients that have figured out how to break through that wall, and I think that's pretty illustrative of what's possible. >> Okay, so you're saying that, got to maybe take a step back and get the infrastructure right with, let's say a catalog, to give some basic things that they have to do, some x's and o's, you've got the Vince Lombardi played out here, and also, skillsets, I imagine, is a key part of that. So, that's what they've got to do to get prepared, and then, what's next? They start creating new business models, imagining this is where the cheap data officer comes in and it's an executive level, what are you seeing clients as part of digital transformation, what's the conversation like with customers? >> The biggest change, the great thing about the times we live in, is technology's become so accessible, you can do things very quickly. We created a team last year called Data Science Elite, and we've hired what we think are some of the best data scientists in the world. Their only job is to go work with clients and help them get to a first success with data science. So, we put a team in. Normally, one month, two months, normally a team of two or three people, our investment, and we say, let's go build a model, let's get to an outcome, and you can do this incredibly quickly now. I tell clients, I see somebody that says, we're going to spend six months evaluating and thinking about this, I was like, why would you spend six months thinking about this when you could actually do it in one month? So you just need to get over the edge and go try it. >> So we're going to learn more about the Data Science Elite team. We've got John Thomas coming on today, who is a distinguished engineer at IBM, and he's very much involved in that team, and I think we have a customer who's actually gone through that, so we're going to talk about what their experience was with the Data Science Elite team. Alright, you've got some hard news coming up, you've actually made some news earlier with Hortonworks and Red Hat, I want to talk about that, but you've also got some hard news today. Take us through that. >> Yeah, let's talk about all three. First, Monday we announced the expanded relationship with both Hortonworks and Red Hat. This goes back to one of the core beliefs I talked about, every enterprise is modernizing their data and application of states, I don't think there's any debate about that. We are big believers in Kubernetes and containers as the architecture to drive that modernization. The announcement on Monday was, we're working closer with Red Hat to take all of our data services as part of Cloud Private for Data, which are basically microservice for data, and we're running those on OpenShift, and we're starting to see great customer traction with that. And where does Hortonworks come in? Hadoop has been the outlier on moving to microservices containers, we're working with Hortonworks to help them make that move as well. So, it's really about the three of us getting together and helping clients with this modernization journey. >> So, just to remind people, you remember ODPI, folks? It was all this kerfuffle about, why do we even need this? Well, what's interesting to me about this triumvirate is, well, first of all, Red Hat and Hortonworks are hardcore opensource, IBM's always been a big supporter of open source. You three got together and you're proving now the productivity for customers of this relationship. You guys don't talk about this, but Hortonworks had to, when it's public call, that the relationship with IBM drove many, many seven-figure deals, which, obviously means that customers are getting value out of this, so it's great to see that come to fruition, and it wasn't just a Barney announcement a couple years ago, so congratulations on that. Now, there's this other news that you guys announced this morning, talk about that. >> Yeah, two other things. One is, we announced a relationship with Stack Overflow. 50 million developers go to Stack Overflow a month, it's an amazing environment for developers that are looking to do new things, and we're sponsoring a community around AI. Back to your point before, you said, is there a skills gap in enterprises, there absolutely is, I don't think that's a surprise. Data science, AI developers, not every company has the skills they need, so we're sponsoring a community to help drive the growth of skills in and around data science and AI. So things like Python, R, Scala, these are the languages of data science, and it's a great relationship with us and Stack Overflow to build a community to get things going on skills. >> Okay, and then there was one more. >> Last one's a product announcement. This is one of the most interesting product annoucements we've had in quite a while. Imagine this, you write a sequel query, and traditional approach is, I've got a server, I point it as that server, I get the data, it's pretty limited. We're announcing technology where I write a query, and it can find data anywhere in the world. I think of it as wide-area sequel. So it can find data on an automotive device, a telematics device, an IoT device, it could be a mobile device, we think of it as sequel the whole world. You write a query, you can find the data anywhere it is, and we take advantage of the processing power on the edge. The biggest problem with IoT is, it's been the old mantra of, go find the data, bring it all back to a centralized warehouse, that makes it impossible to do it real time. We're enabling real time because we can write a query once, find data anywhere, this is technology we've had in preview for the last year. We've been working with a lot of clients to prove out used cases to do it, we're integrating as the capability inside of IBM Cloud Private for Data. So if you buy IBM Cloud for Data, it's there. >> Interesting, so when you've been around as long as I have, long enough to see some of the pendulums swings, and it's clearly a pendulum swing back toward decentralization in the edge, but the key is, from what you just described, is you're sort of redefining the boundary, so I presume it's the edge, any Cloud, or on premises, where you can find that data, is that correct? >> Yeah, so it's multi-Cloud. I mean, look, every organization is going to be multi-Cloud, like 100%, that's going to happen, and that could be private, it could be multiple public Cloud providers, but the key point is, data on the edge is not just limited to what's in those Clouds. It could be anywhere that you're collecting data. And, we're enabling an architecture which performs incredibly well, because you take advantage of processing power on the edge, where you can get data anywhere that it sits. >> Okay, so, then, I'm setting up a Cloud, I'll call it a Cloud architecture, that encompasses the edge, where essentially, there are no boundaries, and you're bringing security. We talked about containers before, we've been talking about Kubernetes all week here at a Big Data show. And then of course, Cloud, and what's interesting, I think many of the Hadoop distral vendors kind of missed Cloud early on, and then now are sort of saying, oh wow, it's a hybrid world and we've got a part, you guys obviously made some moves, a couple billion dollar moves, to do some acquisitions and get hardcore into Cloud, so that becomes a critical component. You're not just limiting your scope to the IBM Cloud. You're recognizing that it's a multi-Cloud world, that' what customers want to do. Your comments. >> It's multi-Cloud, and it's not just the IBM Cloud, I think the most predominant Cloud that's emerging is every client's private Cloud. Every client I talk to is building out a containerized architecture. They need their own Cloud, and they need seamless connectivity to any public Cloud that they may be using. This is why you see such a premium being put on things like data ingestion, data curation. It's not popular, it's not exciting, people don't want to talk about it, but we're the biggest inhibitors, to this AI point, comes back to data curation, data ingestion, because if you're dealing with multiple Clouds, suddenly your data's in a bunch of different spots. >> Well, so you're basically, and we talked about this a lot on theCUBE, you're bringing the Cloud model to the data, wherever the data lives. Is that the right way to think about it? >> I think organizations have spoken, set aside what they say, look at their actions. Their actions say, we don't want to move all of our data to any particular Cloud, we'll move some of our data. We need to give them seamless connectivity so that they can leave their data where they want, we can bring Cloud-Native Architecture to their data, we could also help move their data to a Cloud-Native architecture if that's what they prefer. >> Well, it makes sense, because you've got physics, latency, you've got economics, moving all the data into a public Cloud is expensive and just doesn't make economic sense, and then you've got things like GDPR, which says, well, you have to keep the data, certain laws of the land, if you will, that say, you've got to keep the data in whatever it is, in Germany, or whatever country. So those sort of edicts dictate how you approach managing workloads and what you put where, right? Okay, what's going on with Watson? Give us the update there. >> I get a lot of questions, people trying to peel back the onion of what exactly is it? So, I want to make that super clear here. Watson is a few things, start at the bottom. You need a runtime for models that you've built. So we have a product called Watson Machine Learning, runs anywhere you want, that is the runtime for how you execute models that you've built. Anytime you have a runtime, you need somewhere where you can build models, you need a development environment. That is called Watson Studio. So, we had a product called Data Science Experience, we've evolved that into Watson Studio, connecting in some of those features. So we have Watson Studio, that's the development environment, Watson Machine Learning, that's the runtime. Now you move further up the stack. We have a set of APIs that bring in human features, vision, natural language processing, audio analytics, those types of things. You can integrate those as part of a model that you build. And then on top of that, we've got things like Watson Applications, we've got Watson for call centers, doing customer service and chatbots, and then we've got a lot of clients who've taken pieces of that stack and built their own AI solutions. They've taken some of the APIs, they've taken some of the design time, the studio, they've taken some of the Watson Machine Learning. So, it is really a stack of capabilities, and where we're driving the greatest productivity, this is in a lot of the examples you'll see tonight for clients, is clients that have bought into this idea of, I need a development environment, I need a runtime, where I can deploy models anywhere. We're getting a lot of momentum on that, and then that raises the question of, well, do I have expandability, do I have trust in transparency, and that's another thing that we're working on. >> Okay, so there's API oriented architecture, exposing all these services make it very easy for people to consume. Okay, so we've been talking all week at Cube NYC, is Big Data is in AI, is this old wine, new bottle? I mean, it's clear, Rob, from the conversation here, there's a lot of substantive innovation, and early adoption, anyway, of some of these innovations, but a lot of potential going forward. Last thoughts? >> What people have to realize is AI is not magic, it's still computer science. So it actually requires some hard work. You need to roll up your sleeves, you need to understand how I get from point A to point B, you need a development environment, you need a runtime. I want people to really think about this, it's not magic. I think for a while, people have gotten the impression that there's some magic button. There's not, but if you put in the time, and it's not a lot of time, you'll see the examples tonight, most of them have been done in one or two months, there's great business value in starting to leverage AI in your business. >> Awesome, alright, so if you're in this city or you're at Strata, go to ibm.com/WinWithAI, register for the event tonight. Rob, we'll see you there, thanks so much for coming back. >> Yeah, it's going to be fun, thanks Dave, great to see you. >> Alright, keep it right there everybody, we'll be back with our next guest right after this short break, you're watching theCUBE.

>> Live, from Times Square, in New York City, it's theCUBE covering IBM's Change the Game: Winning with AI. Brought to you by IBM. >> Welcome back to the Big Apple, everybody. I'm Dave Vellante, and you're watching theCUBE, the leader in live tech coverage, and we're here covering a special presentation of IBM's Change the Game: Winning with AI. IBM's got an analyst event going on here at the Westin today in the theater district. They've got 50-60 analysts here. They've got a partner summit going on, and then tonight, at Terminal 5 of the West Side Highway, they've got a customer event, a lot of customers there. We've talked earlier today about the hard news. Seth Dobern is here. He's the Chief Data Officer of IBM Analytics, and he's joined by Shreesha Rao who is the Senior Manager of IT Applications at California-based Niagara Bottling. Gentlemen, welcome to theCUBE. Thanks so much for coming on. >> Thank you, Dave. >> Well, thanks Dave for having us. >> Yes, always a pleasure Seth. We've known each other for a while now. I think we met in the snowstorm in Boston, sparked something a couple years ago. >> Yep. When we were both trapped there. >> Yep, and at that time, we spent a lot of time talking about your internal role as the Chief Data Officer, working closely with Inderpal Bhandari, and you guys are doing inside of IBM. I want to talk a little bit more about your other half which is working with clients and the Data Science Elite Team, and we'll get into what you're doing with Niagara Bottling, but let's start there, in terms of that side of your role, give us the update. >> Yeah, like you said, we spent a lot of time talking about how IBM is implementing the CTO role. While we were doing that internally, I spent quite a bit of time flying around the world, talking to our clients over the last 18 months since I joined IBM, and we found a consistent theme with all the clients, in that, they needed help learning how to implement data science, AI, machine learning, whatever you want to call it, in their enterprise. There's a fundamental difference between doing these things at a university or as part of a Kaggle competition than in an enterprise, so we felt really strongly that it was important for the future of IBM that all of our clients become successful at it because what we don't want to do is we don't want in two years for them to go "Oh my God, this whole data science thing was a scam. We haven't made any money from it." And it's not because the data science thing is a scam. It's because the way they're doing it is not conducive to business, and so we set up this team we call the Data Science Elite Team, and what this team does is we sit with clients around a specific use case for 30, 60, 90 days, it's really about 3 or 4 sprints, depending on the material, the client, and how long it takes, and we help them learn through this use case, how to use Python, R, Scala in our platform obviously, because we're here to make money too, to implement these projects in their enterprise. Now, because it's written in completely open-source, if they're not happy with what the product looks like, they can take their toys and go home afterwards. It's on us to prove the value as part of this, but there's a key point here. My team is not measured on sales. They're measured on adoption of AI in the enterprise, and so it creates a different behavior for them. So they're really about "Make the enterprise successful," right, not "Sell this software." >> Yeah, compensation drives behavior. >> Yeah, yeah. >> So, at this point, I ask, "Well, do you have any examples?" so Shreesha, let's turn to you. (laughing softly) Niagara Bottling -- >> As a matter of fact, Dave, we do. (laughing) >> Yeah, so you're not a bank with a trillion dollars in assets under management. Tell us about Niagara Bottling and your role. >> Well, Niagara Bottling is the biggest private label bottled water manufacturing company in the U.S. We make bottled water for Costcos, Walmarts, major national grocery retailers. These are our customers whom we service, and as with all large customers, they're demanding, and we provide bottled water at relatively low cost and high quality. >> Yeah, so I used to have a CIO consultancy. We worked with every CIO up and down the East Coast. I always observed, really got into a lot of organizations. I was always observed that it was really the heads of Application that drove AI because they were the glue between the business and IT, and that's really where you sit in the organization, right? >> Yes. My role is to support the business and business analytics as well as I support some of the distribution technologies and planning technologies at Niagara Bottling. >> So take us the through the project if you will. What were the drivers? What were the outcomes you envisioned? And we can kind of go through the case study. >> So the current project that we leveraged IBM's help was with a stretch wrapper project. Each pallet that we produce--- we produce obviously cases of bottled water. These are stacked into pallets and then shrink wrapped or stretch wrapped with a stretch wrapper, and this project is to be able to save money by trying to optimize the amount of stretch wrap that goes around a pallet. We need to be able to maintain the structural stability of the pallet while it's transported from the manufacturing location to our customer's location where it's unwrapped and then the cases are used. >> And over breakfast we were talking. You guys produce 2833 bottles of water per second. >> Wow. (everyone laughs) >> It's enormous. The manufacturing line is a high speed manufacturing line, and we have a lights-out policy where everything runs in an automated fashion with raw materials coming in from one end and the finished goods, pallets of water, going out. It's called pellets to pallets. Pellets of plastic coming in through one end and pallets of water going out through the other end. >> Are you sitting on top of an aquifer? Or are you guys using sort of some other techniques? >> Yes, in fact, we do bore wells and extract water from the aquifer. >> Okay, so the goal was to minimize the amount of material that you used but maintain its stability? Is that right? >> Yes, during transportation, yes. So if we use too much plastic, we're not optimally, I mean, we're wasting material, and cost goes up. We produce almost 16 million pallets of water every single year, so that's a lot of shrink wrap that goes around those, so what we can save in terms of maybe 15-20% of shrink wrap costs will amount to quite a bit. >> So, how does machine learning fit into all of this? >> So, machine learning is way to understand what kind of profile, if we can measure what is happening as we wrap the pallets, whether we are wrapping it too tight or by stretching it, that results in either a conservative way of wrapping the pallets or an aggressive way of wrapping the pallets. >> I.e. too much material, right? >> Too much material is conservative, and aggressive is too little material, and so we can achieve some savings if we were to alternate between the profiles. >> So, too little material means you lose product, right? >> Yes, and there's a risk of breakage, so essentially, while the pallet is being wrapped, if you are stretching it too much there's a breakage, and then it interrupts production, so we want to try and avoid that. We want a continuous production, at the same time, we want the pallet to be stable while saving material costs. >> Okay, so you're trying to find that ideal balance, and how much variability is in there? Is it a function of distance and how many touches it has? Maybe you can share with that. >> Yes, so each pallet takes about 16-18 wraps of the stretch wrapper going around it, and that's how much material is laid out. About 250 grams of plastic that goes on there. So we're trying to optimize the gram weight which is the amount of plastic that goes around each of the pallet. >> So it's about predicting how much plastic is enough without having breakage and disrupting your line. So they had labeled data that was, "if we stretch it this much, it breaks. If we don't stretch it this much, it doesn't break, but then it was about predicting what's good enough, avoiding both of those extremes, right? >> Yes. >> So it's a truly predictive and iterative model that we've built with them. >> And, you're obviously injecting data in terms of the trip to the store as well, right? You're taking that into consideration in the model, right? >> Yeah that's mainly to make sure that the pallets are stable during transportation. >> Right. >> And that is already determined how much containment force is required when your stretch and wrap each pallet. So that's one of the variables that is measured, but the inputs and outputs are-- the input is the amount of material that is being used in terms of gram weight. We are trying to minimize that. So that's what the whole machine learning exercise was. >> And the data comes from where? Is it observation, maybe instrumented? >> Yeah, the instruments. Our stretch-wrapper machines have an ignition platform, which is a Scada platform that allows us to measure all of these variables. We would be able to get machine variable information from those machines and then be able to hopefully, one day, automate that process, so the feedback loop that says "On this profile, we've not had any breaks. We can continue," or if there have been frequent breaks on a certain profile or machine setting, then we can change that dynamically as the product is moving through the manufacturing process. >> Yeah, so think of it as, it's kind of a traditional manufacturing production line optimization and prediction problem right? It's minimizing waste, right, while maximizing the output and then throughput of the production line. When you optimize a production line, the first step is to predict what's going to go wrong, and then the next step would be to include precision optimization to say "How do we maximize? Using the constraints that the predictive models give us, how do we maximize the output of the production line?" This is not a unique situation. It's a unique material that we haven't really worked with, but they had some really good data on this material, how it behaves, and that's key, as you know, Dave, and probable most of the people watching this know, labeled data is the hardest part of doing machine learning, and building those features from that labeled data, and they had some great data for us to start with. >> Okay, so you're collecting data at the edge essentially, then you're using that to feed the models, which is running, I don't know, where's it running, your data center? Your cloud? >> Yeah, in our data center, there's an instance of DSX Local. >> Okay. >> That we stood up. Most of the data is running through that. We build the models there. And then our goal is to be able to deploy to the edge where we can complete the loop in terms of the feedback that happens. >> And iterate. (Shreesha nods) >> And DSX Local, is Data Science Experience Local? >> Yes. >> Slash Watson Studio, so they're the same thing. >> Okay now, what role did IBM and the Data Science Elite Team play? You could take us through that. >> So, as we discussed earlier, adopting data science is not that easy. It requires subject matter, expertise. It requires understanding of data science itself, the tools and techniques, and IBM brought that as a part of the Data Science Elite Team. They brought both the tools and the expertise so that we could get on that journey towards AI. >> And it's not a "do the work for them." It's a "teach to fish," and so my team sat side by side with the Niagara Bottling team, and we walked them through the process, so it's not a consulting engagement in the traditional sense. It's how do we help them learn how to do it? So it's side by side with their team. Our team sat there and walked them through it. >> For how many weeks? >> We've had about two sprints already, and we're entering the third sprint. It's been about 30-45 days between sprints. >> And you have your own data science team. >> Yes. Our team is coming up to speed using this project. They've been trained but they needed help with people who have done this, been there, and have handled some of the challenges of modeling and data science. >> So it accelerates that time to --- >> Value. >> Outcome and value and is a knowledge transfer component -- >> Yes, absolutely. >> It's occurring now, and I guess it's ongoing, right? >> Yes. The engagement is unique in the sense that IBM's team came to our factory, understood what that process, the stretch-wrap process looks like so they had an understanding of the physical process and how it's modeled with the help of the variables and understand the data science modeling piece as well. Once they know both side of the equation, they can help put the physical problem and the digital equivalent together, and then be able to correlate why things are happening with the appropriate data that supports the behavior. >> Yeah and then the constraints of the one use case and up to 90 days, there's no charge for those two. Like I said, it's paramount that our clients like Niagara know how to do this successfully in their enterprise. >> It's a freebie? >> No, it's no charge. Free makes it sound too cheap. (everybody laughs) >> But it's part of obviously a broader arrangement with buying hardware and software, or whatever it is. >> Yeah, its a strategy for us to help make sure our clients are successful, and I want it to minimize the activation energy to do that, so there's no charge, and the only requirements from the client is it's a real use case, they at least match the resources I put on the ground, and they sit with us and do things like this and act as a reference and talk about the team and our offerings and their experiences. >> So you've got to have skin in the game obviously, an IBM customer. There's got to be some commitment for some kind of business relationship. How big was the collective team for each, if you will? >> So IBM had 2-3 data scientists. (Dave takes notes) Niagara matched that, 2-3 analysts. There were some working with the machines who were familiar with the machines and others who were more familiar with the data acquisition and data modeling. >> So each of these engagements, they cost us about $250,000 all in, so they're quite an investment we're making in our clients. >> I bet. I mean, 2-3 weeks over many, many weeks of super geeks time. So you're bringing in hardcore data scientists, math wizzes, stat wiz, data hackers, developer--- >> Data viz people, yeah, the whole stack. >> And the level of skills that Niagara has? >> We've got actual employees who are responsible for production, our manufacturing analysts who help aid in troubleshooting problems. If there are breakages, they go analyze why that's happening. Now they have data to tell them what to do about it, and that's the whole journey that we are in, in trying to quantify with the help of data, and be able to connect our systems with data, systems and models that help us analyze what happened and why it happened and what to do before it happens. >> Your team must love this because they're sort of elevating their skills. They're working with rock star data scientists. >> Yes. >> And we've talked about this before. A point that was made here is that it's really important in these projects to have people acting as product owners if you will, subject matter experts, that are on the front line, that do this everyday, not just for the subject matter expertise. I'm sure there's executives that understand it, but when you're done with the model, bringing it to the floor, and talking to their peers about it, there's no better way to drive this cultural change of adopting these things and having one of your peers that you respect talk about it instead of some guy or lady sitting up in the ivory tower saying "thou shalt." >> Now you don't know the outcome yet. It's still early days, but you've got a model built that you've got confidence in, and then you can iterate that model. What's your expectation for the outcome? >> We're hoping that preliminary results help us get up the learning curve of data science and how to leverage data to be able to make decisions. So that's our idea. There are obviously optimal settings that we can use, but it's going to be a trial and error process. And through that, as we collect data, we can understand what settings are optimal and what should we be using in each of the plants. And if the plants decide, hey they have a subjective preference for one profile versus another with the data we are capturing we can measure when they deviated from what we specified. We have a lot of learning coming from the approach that we're taking. You can't control things if you don't measure it first. >> Well, your objectives are to transcend this one project and to do the same thing across. >> And to do the same thing across, yes. >> Essentially pay for it, with a quick return. That's the way to do things these days, right? >> Yes. >> You've got more narrow, small projects that'll give you a quick hit, and then leverage that expertise across the organization to drive more value. >> Yes. >> Love it. What a great story, guys. Thanks so much for coming to theCUBE and sharing. >> Thank you. >> Congratulations. You must be really excited. >> No. It's a fun project. I appreciate it. >> Thanks for having us, Dave. I appreciate it. >> Pleasure, Seth. Always great talking to you, and keep it right there everybody. You're watching theCUBE. We're live from New York City here at the Westin Hotel. cubenyc #cubenyc Check out the ibm.com/winwithai Change the Game: Winning with AI Tonight. We'll be right back after a short break. (minimal upbeat music)

(upbeat music) >> Live from Time Square in New York City, it's The Cube. Covering IBM's change the game, winning with AI. Brought to you by IBM. >> Hi everybody, welcome back to The Big Apple. My name is Dave Vellante. We're here in the Theater District at The Westin Hotel covering a Special Cube event. IBM's got a big event today and tonight, if we can pan here to this pop-up. Change the game: winning with AI. So IBM has got an event here at The Westin, The Tide at Terminal 5 which is right up the Westside Highway. Go to IBM.com/winwithAI. Register, you can watch it online, or if you're in the city come down and see us, we'll be there. Uh, we have a bunch of customers will be there. We had Rob Thomas on earlier, he's kind of the host of the event. IBM does these events periodically throughout the year. They gather customers, they put forth some thought leadership, talk about some hard dues. So, we're very excited to have John Thomas here, he's a distinguished engineer and Director of IBM Analytics, long time Cube alum, great to see you again John >> Same here. Thanks for coming on. >> Great to have you. >> So we just heard a great case study with Niagara Bottling around the Data Science Elite Team, that's something that you've been involved in, and we're going to get into that. But give us the update since we last talked, what have you been up to?? >> Sure sure. So we're living and breathing data science these days. So the Data Science Elite Team, we are a team of practitioners. We actually work collaboratively with clients. And I stress on the word collaboratively because we're not there to just go do some work for a client. We actually sit down, expect the client to put their team to work with our team, and we build AI solutions together. Scope use cases, but sort of you know, expose them to expertise, tools, techniques, and do this together, right. And we've been very busy, (laughs) I can tell you that. You know it has been a lot of travel around the world. A lot of interest in the program. And engagements that bring us very interesting use cases. You know, use cases that you would expect to see, use cases that are hmmm, I had not thought of a use case like that. You know, but it's been an interesting journey in the last six, eight months now. >> And these are pretty small, agile teams. >> Sometimes people >> Yes. use tiger teams and they're two to three pizza teams, right? >> Yeah. And my understanding is you bring some number of resources that's called two three data scientists, >> Yes and the customer matches that resource, right? >> Exactly. That's the prerequisite. >> That is the prerequisite, because we're not there to just do the work for the client. We want to do this in a collaborative fashion, right. So, the customers Data Science Team is learning from us, we are working with them hand in hand to build a solution out. >> And that's got to resonate well with customers. >> Absolutely I mean so often the services business is like kind of, customers will say well I don't want to keep going back to a company to get these services >> Right, right. I want, teach me how to fish and that's exactly >> That's exactly! >> I was going to use that phrase. That's exactly what we do, that's exactly. So at the end of the two or three month period, when IBM leaves, my team leaves, you know, the client, the customer knows what the tools are, what the techniques are, what to watch out for, what are success criteria, they have a good handle of that. >> So we heard about the Niagara Bottling use case, which was a pretty narrow, >> Mm-hmm. How can we optimize the use of the plastic wrapping, save some money there, but at the same time maintain stability. >> Ya. You know very, quite a narrow in this case. >> Yes, yes. What are some of the other use cases? >> Yeah that's a very, like you said, a narrow one. But there are some use cases that span industries, that cut across different domains. I think I may have mentioned this on one of our previous discussions, Dave. You know customer interactions, trying to improve customer interactions is something that cuts across industry, right. Now that can be across different channels. One of the most prominent channels is a call center, I think we have talked about this previously. You know I hate calling into a call center (laughter) because I don't know Yeah, yeah. What kind of support I'm going to get. But, what if you could equip the call center agents to provide consistent service to the caller, and handle the calls in the best appropriate way. Reducing costs on the business side because call handling is expensive. And eventually lead up to can I even avoid the call, through insights on why the call is coming in in the first place. So this use case cuts across industry. Any enterprise that has got a call center is doing this. So we are looking at can we apply machine-learning techniques to understand dominant topics in the conversation. Once we understand with these have with unsupervised techniques, once we understand dominant topics in the conversation, can we drill into that and understand what are the intents, and does the intent change as the conversation progress? So you know I'm calling someone, it starts off with pleasantries, it then goes into weather, how are the kids doing? You know, complain about life in general. But then you get to something of substance why the person was calling in the first place. And then you may think that is the intent of the conversation, but you find that as the conversation progresses, the intent might actually change. And can you understand that real time? Can you understand the reasons behind the call, so that you could take proactive steps to maybe avoid the call coming in at the first place? This use case Dave, you know we are seeing so much interest in this use case. Because call centers are a big cost to most enterprises. >> Let's double down on that because I want to understand this. So you basically doing. So every time you call a call center this call may be recorded, >> (laughter) Yeah. For quality of service. >> Yeah. So you're recording the calls maybe using MLP to transcribe those calls. >> MLP is just the first step, >> Right. so you're absolutely right, when a calls come in there's already call recording systems in place. We're not getting into that space, right. So call recording systems record the voice calls. So often in offline batch mode you can take these millions of calls, pass it through a speech-to-text mechanism, which produces a text equivalent of the voice recordings. Then what we do is we apply unsupervised machine learning, and clustering, and topic-modeling techniques against it to understand what are the dominant topics in this conversation. >> You do kind of an entity extraction of those topics. >> Exactly, exactly, exactly. >> Then we find what is the most relevant, what are the relevant ones, what is the relevancy of topics in a particular conversation. That's not enough, that is just step two, if you will. Then you have to, we build what is called an intent hierarchy. So this is at top most level will be let's say payments, the call is about payments. But what about payments, right? Is it an intent to make a late payment? Or is the intent to avoid the payment or contest a payment? Or is the intent to structure a different payment mechanism? So can you get down to that level of detail? Then comes a further level of detail which is the reason that is tied to this intent. What is a reason for a late payment? Is it a job loss or job change? Is it because they are just not happy with the charges that I have coming? What is a reason? And the reason can be pretty complex, right? It may not be in the immediate vicinity of the snippet of conversation itself. So you got to go find out what the reason is and see if you can match it to this particular intent. So multiple steps off the journey, and eventually what we want to do is so we do our offers in an offline batch mode, and we are building a series of classifiers instead of classifiers. But eventually we want to get this to real time action. So think of this, if you have machine learning models, supervised models that can predict the intent, the reasons, et cetera, you can have them deployed operationalize them, so that when a call comes in real time, you can screen it in real time, do the speech to text, you can do this pass it to the supervise models that have been deployed, and the model fires and comes back and says this is the intent, take some action or guide the agent to take some action real time. >> Based on some automated discussion, so tell me what you're calling about, that kind of thing, >> Right. Is that right? >> So it's probably even gone past tell me what you're calling about. So it could be the conversation has begun to get into you know, I'm going through a tough time, my spouse had a job change. You know that is itself an indicator of some other reasons, and can that be used to prompt the CSR >> Ah, to take some action >> Ah, oh case. appropriate to the conversation. >> So I'm not talking to a machine, at first >> no no I'm talking to a human. >> Still talking to human. >> And then real time feedback to that human >> Exactly, exactly. is a good example of >> Exactly. human augmentation. >> Exactly, exactly. I wanted to go back and to process a little bit in terms of the model building. Are there humans involved in calibrating the model? >> There has to be. Yeah, there has to be. So you know, for all the hype in the industry, (laughter) you still need a (laughter). You know what it is is you need expertise to look at what these models produce, right. Because if you think about it, machine learning algorithms don't by themselves have an understanding of the domain. They are you know either statistical or similar in nature, so somebody has to marry the statistical observations with the domain expertise. So humans are definitely involved in the building of these models and claiming of these models. >> Okay. >> (inaudible). So that's who you got math, you got stats, you got some coding involved, and you >> Absolutely got humans are the last mile >> Absolutely. to really bring that >> Absolutely. expertise. And then in terms of operationalizing it, how does that actually get done? What tech behind that? >> Ah, yeah. >> It's a very good question, Dave. You build models, and what good are they if they stay inside your laptop, you know, they don't go anywhere. What you need to do is, I use a phrase, weave these models in your business processes and your applications. So you need a way to deploy these models. The models should be consumable from your business processes. Now it could be a Rest API Call could be a model. In some cases a Rest API Call is not sufficient, the latency is too high. Maybe you've got embed that model right into where your application is running. You know you've got data on a mainframe. A credit card transaction comes in, and the authorization for the credit card is happening in a four millisecond window on the mainframe on all, not all, but you know CICS COBOL Code. I don't have the time to make a Rest API call outside. I got to have the model execute in context with my CICS COBOL Code in that memory space. >> Yeah right. You know so the operationalizing is deploying, consuming these models, and then beyond that, how do the models behave over time? Because you can have the best programmer, the best data scientist build the absolute best model, which has got great accuracy, great performance today. Two weeks from now, performance is going to go down. >> Hmm. How do I monitor that? How do I trigger a loads map for below certain threshold. And, can I have a system in place that reclaims this model with new data as it comes in. >> So you got to understand where the data lives. >> Absolutely. You got to understand the physics, >> Yes. The latencies involved. >> Yes. You got to understand the economics. >> Yes. And there's also probably in many industries legal implications. >> Oh yes. >> No, the explainability of models. You know, can I prove that there is no bias here. >> Right. Now all of these are challenging but you know, doable things. >> What makes a successful engagement? Obviously you guys are outcome driven, >> Yeah. but talk about how you guys measure success. >> So um, for our team right now it is not about revenue, it's purely about adoption. Does the client, does the customer see the value of what IBM brings to the table. This is not just tools and technology, by the way. It's also expertise, right? >> Hmm. So this notion of expertise as a service, which is coupled with tools and technology to build a successful engagement. The way we measure success is has the client, have we built out the use case in a way that is useful for the business? Two, does a client see value in going further with that. So this is right now what we look at. It's not, you know yes of course everybody is scared about revenue. But that is not our key metric. Now in order to get there though, what we have found, a little bit of hard work, yes, uh, no you need different constituents of the customer to come together. It's not just me sending a bunch of awesome Python Programmers to the client. >> Yeah right. But now it is from the customer's side we need involvement from their Data Science Team. We talk about collaborating with them. We need involvement from their line of business. Because if the line of business doesn't care about the models we've produced you know, what good are they? >> Hmm. And third, people don't usually think about it, we need IT to be part of the discussion. Not just part of the discussion, part of being the stakeholder. >> Yes, so you've got, so IBM has the chops to actually bring these constituents together. >> Ya. I have actually a fair amount of experience in herding cats on large organizations. (laughter) And you know, the customer, they've got skin in the IBM game. This is to me a big differentiator between IBM, certainly some of the other technology suppliers who don't have the depth of services, expertise, and domain expertise. But on the flip side of that, differentiation from many of the a size who have that level of global expertise, but they don't have tech piece. >> Right. >> Now they would argue well we do anybodies tech. >> Ya. But you know, if you've got tech. >> Ya. >> You just got to (laughter) Ya. >> Bring those two together. >> Exactly. And that's really seems to me to be the big differentiator >> Yes, absolutely. for IBM. Well John, thanks so much for stopping by theCube and explaining sort of what you've been up to, the Data Science Elite Team, very exciting. Six to nine months in, >> Yes. are you declaring success yet? Still too early? >> Uh, well we're declaring success and we are growing, >> Ya. >> Growth is good. >> A lot of lot of attention. >> Alright, great to see you again, John. >> Absolutely, thanks you Dave. Thanks very much. Okay, keep it right there everybody. You're watching theCube. We're here at The Westin in midtown and we'll be right back after this short break. I'm Dave Vellante. (tech music)

>> Live from Times Square in New York City, it's The Cube covering IBM's Change the Game: Winning with AI, brought to you by IBM. >> Hello everybody, welcome to The Cube's special presentation. We're covering IBM's announcements today around AI. IBM, as The Cube does, runs of sessions and programs in conjunction with Strata, which is down at the Javits, and we're Rob Thomas, who's the General Manager of IBM Analytics. Long time Cube alum, Rob, great to see you. >> Dave, great to see you. >> So you guys got a lot going on today. We're here at the Westin Hotel, you've got an analyst event, you've got a partner meeting, you've got an event tonight, Change the game: winning with AI at Terminal 5, check that out, ibm.com/WinWithAI, go register there. But Rob, let's start with what you guys have going on, give us the run down. >> Yeah, it's a big week for us, and like many others, it's great when you have Strata, a lot of people in town. So, we've structured a week where, today, we're going to spend a lot of time with analysts and our business partners, talking about where we're going with data and AI. This evening, we've got a broadcast, it's called Winning with AI. What's unique about that broadcast is it's all clients. We've got clients on stage doing demonstrations, how they're using IBM technology to get to unique outcomes in their business. So I think it's going to be a pretty unique event, which should be a lot of fun. >> So this place, it looks like a cool event, a venue, Terminal 5, it's just up the street on the west side highway, probably a mile from the Javits Center, so definitely check that out. Alright, let's talk about, Rob, we've known each other for a long time, we've seen the early Hadoop days, you guys were very careful about diving in, you kind of let things settle and watched very carefully, and then came in at the right time. But we saw the evolution of so-called Big Data go from a phase of really reducing investments, cheaper data warehousing, and what that did is allowed people to collect a lot more data, and kind of get ready for this era that we're in now. But maybe you can give us your perspective on the phases, the waves that we've seen of data, and where we are today and where we're going. >> I kind of think of it as a maturity curve. So when I go talk to clients, I say, look, you need to be on a journey towards AI. I think probably nobody disagrees that they need something there, the question is, how do you get there? So you think about the steps, it's about, a lot of people started with, we're going to reduce the cost of our operations, we're going to use data to take out cost, that was kind of the Hadoop thrust, I would say. Then they moved to, well, now we need to see more about our data, we need higher performance data, BI data warehousing. So, everybody, I would say, has dabbled in those two area. The next leap forward is self-service analytics, so how do you actually empower everybody in your organization to use and access data? And the next step beyond that is, can I use AI to drive new business models, new levers of growth, for my business? So, I ask clients, pin yourself on this journey, most are, depends on the division or the part of the company, they're at different areas, but as I tell everybody, if you don't know where you are and you don't know where you want to go, you're just going to wind around, so I try to get them to pin down, where are you versus where do you want to go? >> So four phases, basically, the sort of cheap data store, the BI data warehouse modernization, self-service analytics, a big part of that is data science and data science collaboration, you guys have a lot of investments there, and then new business models with AI automation running on top. Where are we today? Would you say we're kind of in-between BI/DW modernization and on our way to self-service analytics, or what's your sense? >> I'd say most are right in the middle between BI data warehousing and self-service analytics. Self-service analytics is hard, because it requires you, sometimes to take a couple steps back, and look at your data. It's hard to provide self-service if you don't have a data catalog, if you don't have data security, if you haven't gone through the processes around data governance. So, sometimes you have to take one step back to go two steps forward, that's why I see a lot of people, I'd say, stuck in the middle right now. And the examples that you're going to see tonight as part of the broadcast are clients that have figured out how to break through that wall, and I think that's pretty illustrative of what's possible. >> Okay, so you're saying that, got to maybe take a step back and get the infrastructure right with, let's say a catalog, to give some basic things that they have to do, some x's and o's, you've got the Vince Lombardi played out here, and also, skillsets, I imagine, is a key part of that. So, that's what they've got to do to get prepared, and then, what's next? They start creating new business models, imagining this is where the cheap data officer comes in and it's an executive level, what are you seeing clients as part of digital transformation, what's the conversation like with customers? >> The biggest change, the great thing about the times we live in, is technology's become so accessible, you can do things very quickly. We created a team last year called Data Science Elite, and we've hired what we think are some of the best data scientists in the world. Their only job is to go work with clients and help them get to a first success with data science. So, we put a team in. Normally, one month, two months, normally a team of two or three people, our investment, and we say, let's go build a model, let's get to an outcome, and you can do this incredibly quickly now. I tell clients, I see somebody that says, we're going to spend six months evaluating and thinking about this, I was like, why would you spend six months thinking about this when you could actually do it in one month? So you just need to get over the edge and go try it. >> So we're going to learn more about the Data Science Elite team. We've got John Thomas coming on today, who is a distinguished engineer at IBM, and he's very much involved in that team, and I think we have a customer who's actually gone through that, so we're going to talk about what their experience was with the Data Science Elite team. Alright, you've got some hard news coming up, you've actually made some news earlier with Hortonworks and Red Hat, I want to talk about that, but you've also got some hard news today. Take us through that. >> Yeah, let's talk about all three. First, Monday we announced the expanded relationship with both Hortonworks and Red Hat. This goes back to one of the core beliefs I talked about, every enterprise is modernizing their data and application of states, I don't think there's any debate about that. We are big believers in Kubernetes and containers as the architecture to drive that modernization. The announcement on Monday was, we're working closer with Red Hat to take all of our data services as part of Cloud Private for Data, which are basically microservice for data, and we're running those on OpenShift, and we're starting to see great customer traction with that. And where does Hortonworks come in? Hadoop has been the outlier on moving to microservices containers, we're working with Hortonworks to help them make that move as well. So, it's really about the three of us getting together and helping clients with this modernization journey. >> So, just to remind people, you remember ODPI, folks? It was all this kerfuffle about, why do we even need this? Well, what's interesting to me about this triumvirate is, well, first of all, Red Hat and Hortonworks are hardcore opensource, IBM's always been a big supporter of open source. You three got together and you're proving now the productivity for customers of this relationship. You guys don't talk about this, but Hortonworks had to, when it's public call, that the relationship with IBM drove many, many seven-figure deals, which, obviously means that customers are getting value out of this, so it's great to see that come to fruition, and it wasn't just a Barney announcement a couple years ago, so congratulations on that. Now, there's this other news that you guys announced this morning, talk about that. >> Yeah, two other things. One is, we announced a relationship with Stack Overflow. 50 million developers go to Stack Overflow a month, it's an amazing environment for developers that are looking to do new things, and we're sponsoring a community around AI. Back to your point before, you said, is there a skills gap in enterprises, there absolutely is, I don't think that's a surprise. Data science, AI developers, not every company has the skills they need, so we're sponsoring a community to help drive the growth of skills in and around data science and AI. So things like Python, R, Scala, these are the languages of data science, and it's a great relationship with us and Stack Overflow to build a community to get things going on skills. >> Okay, and then there was one more. >> Last one's a product announcement. This is one of the most interesting product annoucements we've had in quite a while. Imagine this, you write a sequel query, and traditional approach is, I've got a server, I point it as that server, I get the data, it's pretty limited. We're announcing technology where I write a query, and it can find data anywhere in the world. I think of it as wide-area sequel. So it can find data on an automotive device, a telematics device, an IoT device, it could be a mobile device, we think of it as sequel the whole world. You write a query, you can find the data anywhere it is, and we take advantage of the processing power on the edge. The biggest problem with IoT is, it's been the old mantra of, go find the data, bring it all back to a centralized warehouse, that makes it impossible to do it real time. We're enabling real time because we can write a query once, find data anywhere, this is technology we've had in preview for the last year. We've been working with a lot of clients to prove out used cases to do it, we're integrating as the capability inside of IBM Cloud Private for Data. So if you buy IBM Cloud for Data, it's there. >> Interesting, so when you've been around as long as I have, long enough to see some of the pendulums swings, and it's clearly a pendulum swing back toward decentralization in the edge, but the key is, from what you just described, is you're sort of redefining the boundary, so I presume it's the edge, any Cloud, or on premises, where you can find that data, is that correct? >> Yeah, so it's multi-Cloud. I mean, look, every organization is going to be multi-Cloud, like 100%, that's going to happen, and that could be private, it could be multiple public Cloud providers, but the key point is, data on the edge is not just limited to what's in those Clouds. It could be anywhere that you're collecting data. And, we're enabling an architecture which performs incredibly well, because you take advantage of processing power on the edge, where you can get data anywhere that it sits. >> Okay, so, then, I'm setting up a Cloud, I'll call it a Cloud architecture, that encompasses the edge, where essentially, there are no boundaries, and you're bringing security. We talked about containers before, we've been talking about Kubernetes all week here at a Big Data show. And then of course, Cloud, and what's interesting, I think many of the Hadoop distral vendors kind of missed Cloud early on, and then now are sort of saying, oh wow, it's a hybrid world and we've got a part, you guys obviously made some moves, a couple billion dollar moves, to do some acquisitions and get hardcore into Cloud, so that becomes a critical component. You're not just limiting your scope to the IBM Cloud. You're recognizing that it's a multi-Cloud world, that' what customers want to do. Your comments. >> It's multi-Cloud, and it's not just the IBM Cloud, I think the most predominant Cloud that's emerging is every client's private Cloud. Every client I talk to is building out a containerized architecture. They need their own Cloud, and they need seamless connectivity to any public Cloud that they may be using. This is why you see such a premium being put on things like data ingestion, data curation. It's not popular, it's not exciting, people don't want to talk about it, but we're the biggest inhibitors, to this AI point, comes back to data curation, data ingestion, because if you're dealing with multiple Clouds, suddenly your data's in a bunch of different spots. >> Well, so you're basically, and we talked about this a lot on The Cube, you're bringing the Cloud model to the data, wherever the data lives. Is that the right way to think about it? >> I think organizations have spoken, set aside what they say, look at their actions. Their actions say, we don't want to move all of our data to any particular Cloud, we'll move some of our data. We need to give them seamless connectivity so that they can leave their data where they want, we can bring Cloud-Native Architecture to their data, we could also help move their data to a Cloud-Native architecture if that's what they prefer. >> Well, it makes sense, because you've got physics, latency, you've got economics, moving all the data into a public Cloud is expensive and just doesn't make economic sense, and then you've got things like GDPR, which says, well, you have to keep the data, certain laws of the land, if you will, that say, you've got to keep the data in whatever it is, in Germany, or whatever country. So those sort of edicts dictate how you approach managing workloads and what you put where, right? Okay, what's going on with Watson? Give us the update there. >> I get a lot of questions, people trying to peel back the onion of what exactly is it? So, I want to make that super clear here. Watson is a few things, start at the bottom. You need a runtime for models that you've built. So we have a product called Watson Machine Learning, runs anywhere you want, that is the runtime for how you execute models that you've built. Anytime you have a runtime, you need somewhere where you can build models, you need a development environment. That is called Watson Studio. So, we had a product called Data Science Experience, we've evolved that into Watson Studio, connecting in some of those features. So we have Watson Studio, that's the development environment, Watson Machine Learning, that's the runtime. Now you move further up the stack. We have a set of APIs that bring in human features, vision, natural language processing, audio analytics, those types of things. You can integrate those as part of a model that you build. And then on top of that, we've got things like Watson Applications, we've got Watson for call centers, doing customer service and chatbots, and then we've got a lot of clients who've taken pieces of that stack and built their own AI solutions. They've taken some of the APIs, they've taken some of the design time, the studio, they've taken some of the Watson Machine Learning. So, it is really a stack of capabilities, and where we're driving the greatest productivity, this is in a lot of the examples you'll see tonight for clients, is clients that have bought into this idea of, I need a development environment, I need a runtime, where I can deploy models anywhere. We're getting a lot of momentum on that, and then that raises the question of, well, do I have expandability, do I have trust in transparency, and that's another thing that we're working on. >> Okay, so there's API oriented architecture, exposing all these services make it very easy for people to consume. Okay, so we've been talking all week at Cube NYC, is Big Data is in AI, is this old wine, new bottle? I mean, it's clear, Rob, from the conversation here, there's a lot of substantive innovation, and early adoption, anyway, of some of these innovations, but a lot of potential going forward. Last thoughts? >> What people have to realize is AI is not magic, it's still computer science. So it actually requires some hard work. You need to roll up your sleeves, you need to understand how I get from point A to point B, you need a development environment, you need a runtime. I want people to really think about this, it's not magic. I think for a while, people have gotten the impression that there's some magic button. There's not, but if you put in the time, and it's not a lot of time, you'll see the examples tonight, most of them have been done in one or two months, there's great business value in starting to leverage AI in your business. >> Awesome, alright, so if you're in this city or you're at Strata, go to ibm.com/WinWithAI, register for the event tonight. Rob, we'll see you there, thanks so much for coming back. >> Yeah, it's going to be fun, thanks Dave, great to see you. >> Alright, keep it right there everybody, we'll be back with our next guest right after this short break, you're watching The Cube.

>> 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.

(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)

live from San Francisco California it's the queue at vmworld 2014 brought to you by vmware cisco EMC HP and nutanix now here are your hosts John furrier and Stu minimum okay welcome back around here live in San Francisco for VMware 2014 this is our fifth year with the cube extracting the city from the noise at vmworld always a pleasure and we have the chief marketing officer Robin Matlock here inside the queue of my Coast stupid minute for this segment Robin welcome back to the cube thank you great keynote this morning you opened it up in front of a packed house for Pat Gelsinger and delivered an amazing keynote before we get some icky knows what some of the stats with the show here obviously vmworld it just keeps getting bigger and bigger and bigger every year well you know it's amazing the energy is fantastic here this year we're going strong we have well over twenty two thousand attendees the solutions exchange is packed there's about 250 companies that are they're exhibiting we have all kinds of breakout sessions and content I mean if you just walk around here the energy is just really thrive and the theme is no limit so I got to get some a back story on the theme I'll see no limits breaking through this is the transformation market the sign is just break it was a quick taste of wow how this all came together yeah what's the meaning behind the pictures are they're all on the hall you know it's really fun the themes that every year actually put just tremendous effort into them they can really be stressful but at the end when you land or right when it feels so good this whole notion of concrete you know in breaking through and that there's something on the other side that is truly infinite for us that just really spoke to our business it spoke to what our customers are going through and it truly spoke to the potential of this incredible you know this incredible industry you know i was when i think of the No Limits I think about the space jump the Red Bull I think about some of the things with it within the cloud that developers are doing you know Pat mentioned uber they have no asses of mass evaluation of hurts and to cumbies combined this is the kind of dream that entrepreneurs think about is like this is this inflection point stuff right so is that was that some of the vibe you guys were thinking absolutely and I think when we look at where we are in our journey relative to cloud relative to a software-defined world we're really passionate that you know the customers and the attendees of this conference are very well positioned to truly break through some of the silos that have been holding us back for a long time and we are at Crossroads um you know we believe vehemently that the data center is destined to be software-defined and that many of these attendees are well positioned to take us on that journey so I got to ask you because I see you're involved in the brain trust and all the formulation of the strategy the company and out of how to communicate it's always a challenge when it's like a moving train of innovation but you have some new things going on this year first of all nothing new on strategy it's the same marching orders with with Pats cadence hybrid cloud you know March to that cadence ops ii server defined data center but now AirWatch comes on over the top how did that affect things for you or did it it's just more of more the same so actually they bring in there some of that security and the apps piece of the business did that change some of the thinking and all I know it's an interesting question but I think at the end of the day the three strategic priorities for VMware have been very consistent now for multiple years you know largely under Pat's leadership it's about a software-defined world that's the software-defined data center it's about extending that to the hybrid cloud and it's always been about end-user computing I think the air watch acquisition just took it up a couple notches really the world of mobility we're big advocates and believers that the mobile workforce is exploding but there's a really strong connective value between what's happening at the infrastructure layer and what we can do to enable that mobile workforce so I think it was very consistent with the strategy but I do think the air guac acquisition is changing the game it's certainly producing Pat was giving us a little taste on the cube talk about the steams of the show today we had Pat had bill father's Carl up sure do a little Q&A a little little cube action almost on stage with Bill and what's what's tomorrow did you guys bring it up by thieves share with the folks out here Shey lay the land here what's the what's the contracts for tomorrow so today what we try to do is really telex the expanse of entire story what's going on holistically and you know the Karl part of it was a lot about getting our customers to really talk about what's working for them I think that's really important because we laid out a vision for VMware um you know a couple years ago and it's important to make that tangible and real and I hope the customers were able to bring that to life for people tomorrow is all about the technical under the hood let's get you know inside and really understand how the technologies are delivering against that vision and we're going to go through the whole thing it's going to cover the infrastructure it's going to talk about the hybrid cloud and we're going to talk a lot about mobility well the geeks want under the hood I mean it gets a gig show the end of the day it's very content rich at vmworld as we know it super busy a lot of parties going off as Deb going on certainly the business transactions are happening but it's still a geek show you guys have preserved that here right you know if we ask ourselves every year you know how how and should or shouldn't we evolve vmworld and i tell you we're really resolved at the end of the day this is largely a practitioner show they come for technological information education certifications and we have no desire to take a square pose and put in a round hole I mean it works so well for this audience let's just give this crowd what they need and I want to do more of it year after year yeah and we can always tell how good the conferences are in terms of content based upon how much Twitter activity there is in terms of like if people are just talking a lot on Twitter and not say anything that means it's kind of a boring show when there's not a lot of Twitter activity mostly it's text sessions people have too busy running around between between the events I mean are you guys seeing the sessions packet but we haven't had a chance to go out there what's happening yeah well to be really honest I haven't at a moment to scan too much but from what I'm hearing they are overflowing and frankly they were booked you know even before we showed up today because we do give people the schedule builder and a chance to book their sessions so I know that they are all full we're doing repeats we're trying to get you know more breakouts so people can deal with Wednesday and Thursday as things settle down but all the reports I'm getting so far is that we are pretty much over sold and oversubscribed yeah so buds do you Robin I was just gonna say you know is my fifth year now coming to vmworld it's all we impressive just the passion of the people in the virtualization community it's such a good community everybody gives back I really like what you guys did with the charity event that's going I mean what's a destination give by 25,000 with 250 oh not twenty five thousand two hundred and hundred and fifty thousand dollars that that's fantastic you know I got to talk to the hands-on lab guys today and things were running so smooth and so many people do it because as John said the geeks really love to geek out here I noticed it looked like on the badge it had you know the show spread out beyond just the north south and the West you brought the analysts kind of off to off to a hotel because they don't need to be in the center of all the geeks and everything the show floor is cranking as usual so you know it sounds like you still have the core and just pieces add on to it yeah i mean the core of the program if you were to look at breakout sessions keynotes labs that's going to stay right here in moscone but the reality is we're bursting out of the scenes and we love San Francisco we loved the venue but we have to take advantage of all the hotel space around so we got things at the w we got things at the westin we got things at the marriott we got things at the Intercontinental I mean we're or everywhere frankly but you're right we are having to kind of spread out a little bit so I got to ask you about the 10-year anniversary because that was a pretty epic event and you mentioned you made a comment on stage where'd that world go and i love the Golden Gate Bridge metaphor you put together what's changed for you over the past year it seems to be like it seems like seven years ago internet years it seems like a decade ago almost from last year yeah a lots changed and you share your perspective yeah I think a lot has changed I think on though um to be almost all for the good in my view I think you know VMware had built such a business on kind of one core platform which was compute virtualization and over the last several years we've really broadened our wings right and we are now dealing with networking and storage and security and automation and cloud and mobility and I think the diversity that that brings um from a customer perspective from an ecosystem perspective from our routes to market perspective I mean certainly it is definitely a charge because there's just so much tremendous diversity it also means we got a lot of things to cover so you know I think with that comes a responsibility to make sure our customers can understand all these different diverse you know offerings what's your objective for the show what's your preferred outcomes you can look back and just fast forward to thursday evening friday morning you know you're in a hot tub relaxing maybe it's saturday or monday morning what do you want to have happen what's your ideal outcome for vmworld beyond the fact that i like my feet attached to my body because right now i'm afraid they might fall off but let's say personal attributes aside you know i really hope that these attendees you know 22,000 plus people get on those airplanes fly home and feel like they had one of the most invigorating educational inspirational experiences professionally that they're going to have all year I hope that they got to the content that was relevant for them that they were able to navigate and you know really spend time in the areas of focus for them and I hope that people met dozens and dozens of new people that will only help them broaden their career so I have this little prop I brought because I was attended the VIP event you guys had an amazing event mark injuries since the NBC was broadcasting there Joe Tucci was there and then you know opening up your new facility which could have been around for a while so we've got some new new areas got these hot pens there so I'm going to ask you about the culture and the brand future brand for vmware I mean it's an amazing campus eco-friendly beautiful design high quality is this the brand of VMware that you seeing vision for me and you what's your vision for the brand I mean it's evolving in in real time for the company it is evolving but at the same time I think our brand and what we stand for as a company is also very stable it's great that you came to that event and saw the final unveiling of the last building as we finished it up and certainly it's a beautiful campus and it's green you know it's very you know natural woods and doing all kinds of things to protect the environment I think at the core of VMware there's you know five key values and those values are sustaining the test of time you know it's about innovation it's about community it's about people it's about integrity and it's about our customers and I think really no matter what products and services and solutions we wrap around our company I think we still stand for the same core values and I hope that never changes so I got to ask you out the community I think it's one of those things and you know something to pat about how doctor is implemented community aspect of the open source of their product and made them success you guys have had great community over the years really part of the backbone of vmware versus other companies some don't even have a heartbeat to a community you guys have a great thriving ecosystem how do you maintain that as we get more connected with the crowdsourcing with the Twitter expansion and all the people talking and it's not just forums anymore it's and more it's it's it's a virtual event every day it's like vmworld every day out there how do you handle that what's your vision and how you going to get your arms around that going forward well it's yeah I think it's really critical first of all just like anything whether you're talking about technologies you're talking about engaging with customers you have to evolve you can't use the same techniques that you use last year really to propel you next year so I think it's all about making sure you understand how our customers choosing to engage and then embrace that for us our social channels are really important our communities are really important and we're all about enabling facilitation and engagement and I think we're really that's kind of philosophically how we go about our whole social strategy it's all about enablement so that's a personal question for you to you always loved your eye for you know detail remember the first VMware we did you had pointed out the vmware stickers which ended up being perfect camera location ibly I like her I like this Robin woman she's awesome but what are you excited about now I mean what are you personally motivated upon right now what gets you really excited about the tech industry about what you what you're involved in what's the what's the one thing that get you so excited you know frankly I'm extremely proud to be the CMO of VMware I think there was a great company and I think we're part of something truly meaningful I think there was a time when maybe we weren't going to be as relevant we and by we I don't mean to see him or I mean this this whole thing that maybe we weren't going to be as relevant in the next decade but we collectively as a mystery are making bold moves we're doubling down on software we're pushing the boundaries of the data center we're getting out beyond compute we're going to storage or going to networking we're looking at security we're layering in automation and I think we are really securing our future as an industry that we are relevant and we need a seat at the table a strategic seat at the table and I'm thrilled to be a part and you certainly the global footprint the virtualization has been a great part of enabling that that mindset great to have you on the cube any other tidbits about the show you'd like to share the folks you know I think the main thing is just get involved and try some things that are different push your own personal boundaries explore there's so much content there's so many networking opportunities there's breakouts and I think definitely sampling a little bit of everything and making sure that you go home exhausted and then I'll be happy but certainly is exhausting show but Pat brought up the whole brave concept that's really about bold moves writing that's about that's kind of the whole theme here right yeah I think you know the notion of bravery is in the sense that given that things are changing so rapidly and the world is so dynamic and fluid as a business climate it's going to take some calculated risk you're going to have to really decide where are you partnering where are you betting what kind of steps are you going to take and I think action is key and the one thing it probably isn't going to work is status quo Robin Matlock the chief marketing officer for VMware keynote speech this morning set the table for Pat Gelsinger great jobs at the big picture laid out everything out the holistic vision of VMware continues to thrive thanks for coming down the cube always great to have you it's the Cubist retin from the noise we'll be right back with our next guest after the short break great thanks John you