(upbeat music) >> Welcome back to Supercloud 2, an open collaborative where we explore the future of cloud and data. Now, you might recall last August at the inaugural Supercloud event we validated the technical feasibility and tried to further define the essential technical characteristics, and of course the deployment models of so-called supercloud. That is, sets of services that leverage the underlying primitives of hyperscale clouds, but are creating new value on top of those clouds for organizations at scale. So we're talking about capabilities that fundamentally weren't practical or even possible prior to the ascendancy of the public clouds. And so today at Supercloud 2, we're digging further into the topic with input from real-world practitioners. And we're exploring the intersection of data and cloud, And importantly, the realities and challenges of deploying technology for a new business capability. I'm pleased to have with me in our studios, west of Boston, Veronika Durgin, who's the head of data at Saks. Veronika, welcome. Great to see you. Thanks for coming on. >> Thank you so much. Thank you for having me. So excited to be here. >> And so we have to say upfront, you're here, these are your opinions. You're not representing Saks in any way. So we appreciate you sharing your depth of knowledge with us. >> Thank you, Dave. Yeah, I've been doing data for a while. I try not to say how long anymore. It's been a while. But yeah, thank you for having me. >> Yeah, you're welcome. I mean, one of the highlights of this past year for me was hanging out at the airport with you after the Snowflake Summit. And we were just chatting about sort of data mesh, and you were saying, "Yeah, but." There was a yeah, but. You were saying there's some practical realities of actually implementing these things. So I want to get into some of that. And I guess starting from a perspective of how data has changed, you've seen a lot of the waves. I mean, even if we go back to pre-Hadoop, you know, that would shove everything into an Oracle database, or, you know, Hadoop was going to save our data lives. And the cloud came along and, you know, that was kind of a disruptive force. And, you know, now we see things like, whether it's Snowflake or Databricks or these other platforms on top of the clouds. How have you observed the change in data and the evolution over time? >> Yeah, so I started as a DBA in the data center, kind of like, you know, growing up trying to manage whatever, you know, physical limitations a server could give us. So we had to be very careful of what we put in our database because we were limited. We, you know, purchased that piece of hardware, and we had to use it for the next, I don't know, three to five years. So it was only, you know, we focused on only the most important critical things. We couldn't keep too much data. We had to be super efficient. We couldn't add additional functionality. And then Hadoop came along, which is like, great, we can dump all the data there, but then we couldn't get data out of it. So it was like, okay, great. Doesn't help either. And then the cloud came along, which was incredible. I was probably the most excited person. I'm lying, but I was super excited because I no longer had to worry about what I can actually put in my database. Now I have that, you know, scalability and flexibility with the cloud. So okay, great, that data's there, and I can also easily get it out of it, which is really incredible. >> Well, but so, I'm inferring from what you're saying with Hadoop, it was like, okay, no schema on write. And then you got to try to make sense out of it. But so what changed with the cloud? What was different? >> So I'll tell a funny story. I actually successfully avoided Hadoop. The only time- >> Congratulations. >> (laughs) I know, I'm like super proud of it. I don't know how that happened, but the only time I worked for a company that had Hadoop, all I remember is that they were running jobs that were taking over 24 hours to get data out of it. And they were realizing that, you know, dumping data without any structure into this massive thing that required, you know, really skilled engineers wasn't really helpful. So what changed, and I'm kind of thinking of like, kind of like how Snowflake started, right? They were marketing themselves as a data warehouse. For me, moving from SQL Server to Snowflake was a non-event. It was comfortable, I knew what it was, I knew how to get data out of it. And I think that's the important part, right? Cloud, this like, kind of like, vague, high-level thing, magical, but the reality is cloud is the same as what we had on prem. So it's comfortable there. It's not scary. You don't need super new additional skills to use it. >> But you're saying what's different is the scale. So you can throw resources at it. You don't have to worry about depreciating your hardware over three to five years. Hey, I have an asset that I have to take advantage of. Is that the big difference? >> Absolutely. Actually, from kind of like operational perspective, which it's funny. Like, I don't have to worry about it. I use what I need when I need it. And not to take this completely in the opposite direction, people stop thinking about using things in a very smart way, right? You like, scale and you walk away. And then, you know, the cool thing about cloud is it's scalable, but you also should not use it when you don't need it. >> So what about this idea of multicloud. You know, supercloud sort of tries to go beyond multicloud. it's like multicloud by accident. And now, you know, whether it's M&A or, you know, some Skunkworks is do, hey, I like Google's tools, so I'm going to use Google. And then people like you are called on to, hey, how do we clean up this mess? And you know, you and I, at the airport, we were talking about data mesh. And I love the concept. Like, doesn't matter if it's a data lake or a data warehouse or a data hub or an S3 bucket. It's just a node on the mesh. But then, of course, you've got to govern it. You've got to give people self-serve. But this multicloud is a reality. So from your perspective, from a practitioner's perspective, what are the advantages of multicloud? We talk about the disadvantages all the time. Kind of get that, but what are the advantages? >> So I think the first thing when I think multicloud, I actually think high-availability disaster recovery. And maybe it's just how I grew up in the data center, right? We were always worried that if something happened in one area, we want to make sure that we can bring business up very quickly. So to me that's kind of like where multicloud comes to mind because, you know, you put your data, your applications, let's pick on AWS for a second and, you know, US East in AWS, which is the busiest kind of like area that they have. If it goes down, for my business to continue, I would probably want to move it to, say, Azure, hypothetically speaking, again, or Google, whatever that is. So to me, and probably again based on my background, disaster recovery high availability comes to mind as multicloud first, but now the other part of it is that there are, you know, companies and tools and applications that are being built in, you know, pick your cloud. How do we talk to each other? And more importantly, how do we data share? You know, I work with data. You know, this is what I do. So if, you know, I want to get data from a company that's using, say, Google, how do we share it in a smooth way where it doesn't have to be this crazy, I don't know, SFTP file moving. So that's where I think supercloud comes to me in my mind, is like practical applications. How do we create that mesh, that network that we can easily share data with each other? >> So you kind of answered my next question, is do you see use cases going beyond H? I mean, the HADR was, remember, that was the original cloud use case. That and bursting, you know, for, you know, Thanksgiving or, you know, for Black Friday. So you see an opportunity to go beyond that with practical use cases. >> Absolutely. I think, you know, we're getting to a world where every company is a data company. We all collect a lot of data. We want to use it for whatever that is. It doesn't necessarily mean sell it, but use it to our competitive advantage. So how do we do it in a very smooth, easy way, which opens additional opportunities for companies? >> You mentioned data sharing. And that's obviously, you know, I met you at Snowflake Summit. That's a big thing of Snowflake's. And of course, you've got Databricks trying to do similar things with open technology. What do you see as the trade-offs there? Because Snowflake, you got to come into their party, you're in their world, and you're kind of locked into that world. Now they're trying to open up. You know, and of course, Databricks, they don't know our world is wide open. Well, we know what that means, you know. The governance. And so now you're seeing, you saw Amazon come out with data clean rooms, which was, you know, that was a good idea that Snowflake had several years before. It's good. It's good validation. So how do you think about the trade-offs between kind of openness and freedom versus control? Is the latter just far more important? >> I'll tell you it depends, right? It's kind of like- >> Could be insulting to that. >> Yeah, I know. It depends because I don't know the answer. It depends, I think, because on the use case and application, ultimately every company wants to make money. That's the beauty of our like, capitalistic economy, right? We're driven 'cause we want to make money. But from the use, you know, how do I sell a product to somebody who's in Google if I am in AWS, right? It's like, we're limiting ourselves if we just do one cloud. But again, it's difficult because at the same time, every cloud provider wants for you to be locked in their cloud, which is why probably, you know, whoever has now data sharing because they want you to stay within their ecosystem. But then again, like, companies are limited. You know, there are applications that are starting to be built on top of clouds. How do we ensure that, you know, I can use that application regardless what cloud, you know, my company is using or I just happen to like. >> You know, and it's true they want you to stay in their ecosystem 'cause they'll make more money. But as well, you think about Apple, right? Does Apple do it 'cause they can make more money? Yes, but it's also they have more control, right? Am I correct that technically it's going to be easier to govern that data if it's all the sort of same standard, right? >> Absolutely. 100%. I didn't answer that question. You have to govern and you have to control. And honestly, it's like it's not like a nice-to-have anymore. There are compliances. There are legal compliances around data. Everybody at some point wants to ensure that, you know, and as a person, quite honestly, you know, not to be, you know, I don't like when my data's used when I don't know how. Like, it's a little creepy, right? So we have to come up with standards around that. But then I also go back in the day. EDI, right? Electronic data interchange. That was figured out. There was standards. Companies were sending data to each other. It was pretty standard. So I don't know. Like, we'll get there. >> Yeah, so I was going to ask you, do you see a day where open standards actually emerge to enable that? And then isn't that the great disruptor to sort of kind of the proprietary stack? >> I think so. I think for us to smoothly exchange data across, you know, various systems, various applications, we'll have to agree to have standards. >> From a developer perspective, you know, back to the sort of supercloud concept, one of the the components of the essential characteristics is you've got this PaaS layer that provides consistency across clouds, and it has unique attributes specific to the purpose of that supercloud. So in the instance of Snowflake, it's data sharing. In the case of, you know, VMware, it might be, you know, infrastructure or self-serve infrastructure that's consistent. From a developer perspective, what do you hear from developers in terms of what they want? Are we close to getting that across clouds? >> I think developers always want freedom and ability to engineer. And oftentimes it's not, (laughs) you know, just as an engineer, I always want to build something, and it's not always for the, to use a specific, you know, it's something I want to do versus what is actually applicable. I think we'll land there, but not because we are, you know, out of the kindness of our own hearts. I think as a necessity we will have to agree to standards, and that that'll like, move the needle. Yeah. >> What are the limitations that you see of cloud and this notion of, you know, even cross cloud, right? I mean, this one cloud can't do it all. You know, but what do you see as the limitations of clouds? >> I mean, it's funny, I always think, you know, again, kind of probably my background, I grew up in the data center. We were physically limited by space, right? That there's like, you can only put, you know, so many servers in the rack and, you know, so many racks in the data center, and then you run out space. Earth has a limited space, right? And we have so many data centers, and everybody's collecting a lot of data that we actually want to use. We're not just collecting for the sake of collecting it anymore. We truly can't take advantage of it because servers have enough power, right, to crank through it. We will run enough space. So how do we balance that? How do we balance that data across all the various data centers? And I know I'm like, kind of maybe talking crazy, but until we figure out how to build a data center on the Moon, right, like, we will have to figure out how to take advantage of all the compute capacity that we have across the world. >> And where does latency fit in? I mean, is it as much of a problem as people sort of think it is? Maybe it depends too. It depends on the use case. But do multiple clouds help solve that problem? Because, you know, even AWS, $80 billion company, they're huge, but they're not everywhere. You know, they're doing local zones, they're doing outposts, which is, you know, less functional than their full cloud. So maybe I would choose to go to another cloud. And if I could have that common experience, that's an advantage, isn't it? >> 100%, absolutely. And potentially there's some maybe pricing tiers, right? So we're talking about latency. And again, it depends on your situation. You know, if you have some sort of medical equipment that is very latency sensitive, you want to make sure that data lives there. But versus, you know, I browse on a website. If the website takes a second versus two seconds to load, do I care? Not exactly. Like, I don't notice that. So we can reshuffle that in a smart way. And I keep thinking of ways. If we have ways for data where it kind of like, oh, you are stuck in traffic, go this way. You know, reshuffle you through that data center. You know, maybe your data will live there. So I think it's totally possible. I know, it's a little crazy. >> No, I like it, though. But remember when you first found ways, you're like, "Oh, this is awesome." And then now it's like- >> And it's like crowdsourcing, right? Like, it's smart. Like, okay, maybe, you know, going to pick on US East for Amazon for a little bit, their oldest, but also busiest data center that, you know, periodically goes down. >> But then you lose your competitive advantage 'cause now it's like traffic socialism. >> Yeah, I know. >> Right? It happened the other day where everybody's going this way up. There's all the Wazers taking. >> And also again, compliance, right? Every country is going down the path of where, you know, data needs to reside within that country. So it's not as like, socialist or democratic as we wish for it to be. >> Well, that's a great point. I mean, when you just think about the clouds, the limitation, now you go out to the edge. I mean, everybody talks about the edge in IoT. Do you actually think that there's like a whole new stove pipe that's going to get created. And does that concern you, or do you think it actually is going to be, you know, connective tissue with all these clouds? >> I honestly don't know. I live in a practical world of like, how does it help me right now? How does it, you know, help me in the next five years? And mind you, in five years, things can change a lot. Because if you think back five years ago, things weren't as they are right now. I mean, I really hope that somebody out there challenges things 'cause, you know, the whole cloud promise was crazy. It was insane. Like, who came up with it? Why would I do that, right? And now I can't imagine the world without it. >> Yeah, I mean a lot of it is same wine, new bottle. You know, but a lot of it is different, right? I mean, technology keeps moving us forward, doesn't it? >> Absolutely. >> Veronika, it was great to have you. Thank you so much for your perspectives. If there was one thing that the industry could do for your data life that would make your world better, what would it be? >> I think standards for like data sharing, data marketplace. I would love, love, love nothing else to have some agreed upon standards. >> I had one other question for you, actually. I forgot to ask you this. 'Cause you were saying every company's a data company. Every company's a software company. We're already seeing it, but how prevalent do you think it will be that companies, you've seen some of it in financial services, but companies begin to now take their own data, their own tooling, their own software, which they've developed internally, and point that to the outside world? Kind of do what AWS did. You know, working backwards from the customer and saying, "Hey, we did this for ourselves. We can now do this for the rest of the world." Do you see that as a real trend, or is that Dave's pie in the sky? >> I think it's a real trend. Every company's trying to reinvent themselves and come up with new products. And every company is a data company. Every company collects data, and they're trying to figure out what to do with it. And again, it's not necessarily to sell it. Like, you don't have to sell data to monetize it. You can use it with your partners. You can exchange data. You know, you can create products. Capital One I think created a product for Snowflake pricing. I don't recall, but it just, you know, they built it for themselves, and they decided to kind of like, monetize on it. And I'm absolutely 100% on board with that. I think it's an amazing idea. >> Yeah, Goldman is another example. Nasdaq is basically taking their exchange stack and selling it around the world. And the cloud is available to do that. You don't have to build your own data center. >> Absolutely. Or for good, right? Like, we're talking about, again, we live in a capitalist country, but use data for good. We're collecting data. We're, you know, analyzing it, we're aggregating it. How can we use it for greater good for the planet? >> Veronika, thanks so much for coming to our Marlborough studios. Always a pleasure talking to you. >> Thank you so much for having me. >> You're really welcome. All right, stay tuned for more great content. From Supercloud 2, this is Dave Vellante. We'll be right back. (upbeat music)

>>From the Bellagio hotel in Las Vegas, it's the cube covering UI path forward for brought to you by UI path >>Live from Las Vegas. It's the cube. We are here with UI path at forward for I'm Lisa Martin, with Dave Volante and a lovely setting at the Bellagio. We're going to be talking about automation from the CFO's perspective. Our next guest is our jet guard group financial officer at landmark group, or just welcome to the program. >>Thank you so much. Thank >>You. Before we dig into your transformation strategy and how automation is a key to that, help the audience understand a little bit about landmark. >>Absolutely. So landmark is one of the largest, uh, non-food primarily retailer in the middle east and Asia, India, and now increasingly in Southeast Asia. So we've got about 50 brands, uh, more than half of them, which are homegrown our own brands and some franchise brands. So about 2,200 stores, uh, across 20 countries, 55,000 employees. Um, so 30 million square feet of retail space >>They company. When was the company founded, >>Uh, 48 years ago, >>Legacy institution you were mentioning before we went live that you guys have been working with UI path since 2017. So talk to me about that legacy institution, embracing cloud digital transformation and automation as a, from a visionary strategic perspective. >>Yeah. So look, I mean, you know, you get so many technologies that are being thrown at you. So I would say you have packed or robotic process automation was just another one like that. So I wouldn't say it was like part of a grand strategy. You know, it comes as it looks like, Hey, this is cool. You know, in the, in the back office, when somebody showed me first 10 desks with nobody sitting on them, it's kind of spooky. So he said, Hey, this, this looks very interesting. So it started off like that, but then it has just grown because we've stayed with it. So we've amongst things in the early part of your parts customers and, and it's been phenomenal, you know, what, uh, what we're able to do with, uh, with, uh, robotic process automation. Uh, I mean, you know, I've been in this industry with my past employers, like Proctor and gamble and Cadbury, Schweppes, and all, and essentially we used to follow the part of, you know, you eliminate all the non-value add you, then try and automate whatever your ERP system, then all allowed you to automate. >>Then what's left, you consolidate, and then you find the right shore, right. It can be offshore or wherever. So that was the sequence. But I think a lot could not be automated because there are huge gaps in the systems that are being offered and you have a mosaic of systems, every company will have. Right. Um, and then we would end up doing lot more offshore or, you know, other kinds of tactics, but then once RPA showed up on the scene, it's suddenly disrupted everything because now whatever the systems can do, or when you have to move data from one system to the other or make sense out of it, that's where this technology sits. And so that's, so that's very, I, you know, we've now got a pretty large, uh, robotic process automation practice. And, and, you know, we are touching started with finance and now we are pretty much enterprise wide. So all the, >>These technologies are coming together, automation, RPA, cloud AI, they're all sort of converging. And as a retailer, I'm curious as to what your cloud strategy is and how that fits and all, there's always a lot of sensitivity from retailers that don't want to be on Amazon, maybe some do. And they say, Hey, we've, we've we compete in other ways, what's your posture in that? >>So we've also been an early adopter of cloud, both. If I talk within the UI path thing, we were, I think the first ones to put it on the cloud, because we just saw, even before you are part, uh, we saw how people could tamper with it, you know, attended robots, you know, on the desktop one. So we went on the cloud and that was good, uh, way back. But overall, the company also has a very pro you know, Val defined cloud strategy. So we are, you know, pretty much all a large part of our systems are on the cloud with Azure. >>Yeah. So, which makes sense, right. As a retailer, go, go with Azure, plus somebody, Microsoft, you know, X, such a lot of Microsoft expertise out there that you can leverage. And I got to ask you because everybody's freaked out on wall street about power automate, you know, competing with UI path. And I've told people they kind of different parts of the spectrum, but I've talked to a lot of customers this week. So yeah, we use both. We use UI path for end-to-end automation. We use power automate for a lot of our personal productivity stuff. How do you guys, do you use, uh, the power automate? How do you see those two? Yeah, >>No, I think, look, it's inevitable. A lot of technologies will keep evolving. I think Microsoft is a fantastic company. I mean, the way they perfected teams right in time, you know, and pretty, always hit, uh, a year before COVID hit teams was not ready, you know? So I think I know power automate is good. We use it, but not as you know, it's not ready for enterprise wide. So I think more, I'm not an expert in power automate yet. Um, you know, what, it kind of seemed more like when it's linked to the office automation versus linking major enterprise wide or >>Which is really where you're headed. Yeah. Talk about the results that you've seen, the higher you're measuring the return and the whole business case. When you evaluate it as CFO, >>See it being a CFO, I wear two hats. Right. I'm trying to help digital transformation. Although I must say I'm not the only one our company has. Every function is these days talking digital. Right. Because it's almost like table stakes. Yeah. Uh, you, you can't be in business a leader and we are like a leader in all the markets we are, and there's no choice, but to be fully digital. Right. Uh, but being a CFO absolutely. You know, you do look at the hard dollars. Right. Um, and initially when you're pushing any technology to any functional head or your colleague or the CEO or the board, they do want to see the dollars because a lot of softwares talk about the soft benefits. Um, I think they gotta pay for themselves. So I think it's like, yes, if I can get the hard dollars and then I can demonstrate softer benefits, whether it is the quality of work, less errors, better compliance, right. >>Or I think employee, uh, work work-life balance, right. I mean, in, in, uh, we are, uh, in a growing company we've been growing for the last four decades and there's a constant struggle to help colleagues maintain better work life balance. So I think once the basic return is off the table, everyone's talking about the quality of work enabling. And I think now we've, we are proudly talking, you know, that, Hey, we've got a lot of people, um, we've hired them. But what we are using of them is their fingers, their eyes, ears, and that's about it. Can we now get them to use their brain? So it's like, Hey, it's a freebie. You got so many people let's start using the gray matter. And that's, I think what this technology does, it takes away the Gronk and you can then tell them, Hey, analyze the data, look at it, better business outcomes. And I think that's where the real value is. >>That is, so we've heard a lot about time saved hours saved. That's kind of the key, a key metric. And you look at that as hard dollars. How, how do you translate that to the income statement? >>So, so let's put it, uh, you know, I was looking at applied science, applied materials presentation, and they had a 150,000 hours saved. Uh, I just did our math. I mean, so we've so far saved 342,000 hours per annum removed out of the system. Right. But I would say not all I can say, I took them to the bottom line. So probably 70% of that, because the rest is probably gone back to people doing more value added stuff. >>So how does it hit the income statement? Is it hit it as new revenue or cost savings or savings reduction in >>Yeah. Or are you don't hire as many as you needed to? Uh, >>Yes. That's the missing link. Yeah. Okay. Absolutely. Is I was going to need to hire or what 1,100 people hire 10 or whatever it is. Okay. Now I'm sorry. Does that, is that, does that get into a debate? Like, cause I can see a lot of people, if we don't do this, we're going to, you know, and then as a CFO, you might say let's defend that a little bit. >>Seek cost avoidance is always debated. Yep. And that's why I said, as long as you can prove that the hard dollars taken to the bottom line are visible and you can put your finger on them, then people become more comfortable saying, okay, as long as you know, I've got my payback, I've got something I can, you know, make sure that my cost line is not going up because it's very easy to do, you know, kind of say, Hey look, all this soft benefits and now your cost has also gone up. So I think once the, the, the hard dollars that you can bank are out of the way, then you can talk about costs avoided, and then you can talk about the softer benefits. Are there, there is no doubt because you try and what we do is we tell people if they're in a cell, okay, we'll shut, shut it down. >>I say, Hey, wait, well, right then, you know, but so you have four years of data on this, so you can prove it. And by the way, soft dollars are where the real money is. I don't mean to denigrate that, but I get into a lot of discussions with CFO's like, okay, show me the hard dollars first and then the hard, the soft dollars or telephone numbers. Yeah. >>Yeah. I think I look at it as an inverted pyramid. Yeah. Where you start with the cost saved, which is the smaller part of the pyramid. And then you get speed, right. Because speed is actually a big thing, which is very difficult to measure. Right? I mean, I'll give you an example in none of our largest markets, right. In the middle of COVID, they announced all products that are being imported, which is for us about 80,000 of them, um, uh, need to have a whole bunch of compliance forms on the government portal, import certifications. And you got like a month to do all that work. So now you'll get an army of 20, 30 people train them. We did nothing. We built the barns and we were ready ahead of competition. And I think, and, and life continues. Now the supply chain officer will sign on the dotted line for you saying he would have had to hire 30 people. And he, it's not easy to hire suddenly, but we were compliant and, and now that's cost avoided. But I would say a big business benefit because we were the first ones to have all our products compliant with the market requirements. That's a >>Great example. >>I think about some of the IDC data that was, did you see that that was presented this morning, looking at, you know, the positive outlook as, as RPA being a jobs creator over time. Talk to me a little bit about how you've navigated that through the organization and even done upskilling of some of those folks so that they're not losing, but they're gaining. >>I think there is, you know, you have to take all these projections with a pinch of salt, you know, I mean, saying you will, the world will save $150 billion and all, I mean, if you add all the soft dollars. Yes. But in reality, you know, I lose joke about it. If you take all the technology initiatives in a company and you add all the MPVs and that they have submitted, that would be larger than the market cap of the company. >>It's true. All the projects add up to more value. >>I think, I think, you know, we don't get carried away by these major projections, but I think some of it is true. I mean, you know, I kind of talk about the Luddites, right? I mean, when the first, you know, weaving machines game in, in Northern England, near Manchester and these Luddites, they were called, they were going around breaking down these machines because they were supposed to take away jobs. Now reality is a lot of people did lose jobs who could not make the transition, could not retrain themselves. It is inevitable. It will happen. But over time I would say yes, there have been lot more employment. So I think both go hand in hand. Um, but yes, the more one can help retrain people, get them to, you know, say, Hey, you don't need to spend the rest of your life. Copy pasting and just doing data entry. Uh, you can look at the data and make sense out of it. How much >>Of that was a part of your strategic vision years ago? >>I think years ago we knew it, but it was more, let's get these, you know, simple. When you have hundreds of people in a, in a back office, how do I get them to do more work or have slate or meet my, you know, my productivity goals? I would say it starts with that. Okay. Uh, if you start, uh, deep down because I, I am, you know, I believe in technology, I knew it, it would happen that we would eventually go from, let's say, robotic process automation to intelligent process automation. Right. Which is coming for us. It's we are able to see it, you try and sell that as the lead in and people shut down >>Because they're seen by intelligent process automation. W what do you mean? And, and >>So it's look, if I've got, uh, my robots and the tech, the RP infrastructure, which is processing whole bunch of transactions right now, if I'm able to add in some machine learning or AI, or what have you on top of it, and then I can read the patterns I can, for example, you know, we, we now have built on top of all the various security in our payment systems. If you've got a bot, which then does a final check, which goes and checks the history of that particular vendor as to what is the typical payments being done to that. And then it flags, if it's V out and it stops the payment, for example, right? So, or it goes and does a whole bunch of tests. We're building constantly building tools. So that's kind of, you know, a bit more intelligent than just a simple copy paste or, or doing a transaction >>Because why that's their job or because they it's a black box. They don't know how that decision is made. Or >>I think a lot of these have been sold previously similar technologies and things that would be, you know, the next best thing since sliced water and people have lost fit. So you got to show them the money and then take them along the journey. If you go too fast and try and give this whole, you know, people are smart enough and it, it turns them off. >>It's one of the failures of the tech industry is the broken promises. I can, I can rattle many off >>Cultural shift. It is. It is. How did you help facilitate that? See, I mean, we, we took, you know, the bottoms up and top down approach, uh, you know, the top down was, uh, I have my whole leadership team and as a joke, we locked them up in the boardroom and we got them to build bonds a long ago. And we said, let each of you, you know, download your bank statement and send yourself, uh, you know, if you say any transaction above 10,000, whatever, um, send, send an email to yourself. So as simple as that, or download the electricity bill and, and send it to your wife, you know, something like that. And half of them were able to build a bot in that couple of hours. The other half looked at it, and obviously are, you know, many of them are not as tech savvy, but it helped build the kind of it's aha moment three years ago that, wow, you know, I can build a bot. Um, for some people it was like, oh, they taught these metallic 10 bots are going to walk into the room. >>I love it. The bottom who's responsible for governance. >>So we've got a, we've got a team across it and finance. Um, I mean, somehow I have kind of, you know, created the skunkworks team. The S the center of excellence sits with me. Um, uh, but overall it's a combination and they now run governance, uh, you know, 24 7, >>Uh, you know, sorry, I got to get my crypto question. I ask every CFO's, when are you going to put crypto in the balance sheet? I know I'm teasing, but what you see companies doing this? Has it ever come up in conversation? Is it sort of tongue in cheek joke? Or what do you make of the crypto? >>Yeah, I think personally I'm a big believer, uh, but not for, uh, for a company. I think the, the benefit case of a company, we are not that, you know, we have enough other face too, you know? Um, uh, I think, uh, it's a bit further out for a company to start taking balance sheet position because that's then a speculation, right? Because, so I'm a believer in the benefit of the blockchain technology. We actually did a blockchain experiment a couple of years ago, moving goods, uh, from China to Dubai and also making the payments through a blockchain to, um, so we see huge benefits. We are working with our bankers on certain other initiatives, but I think on the balance sheet sounds like speculation and use of capital. So yeah, if it brings efficiency, if it brings transparency, which is what blockchains do, uh, I think absolutely it's, it is here to stay >>Last question. And then the last 30 seconds, or so for your peers in any industry who are it was, we saw some of the stats yesterday, the amount of percentage of processes that are automateable that aren't automated. What's your advice, recommendations to peers about pulling automation into their digital transformation strategy? >>I think, um, digital transformation can be hugely aided and accelerated if you first put RPLs, because that is the layer, which goes between the humans and whatever technology is out there or whatever you keep buying. So I think because they will be in every area, new technologies coming up, it's better to put RPA first because you can then get more benefit from whatever other technologies you're bolting on. So I would say it's a predecessor to your broader digital transformation, rather than just a part of it. >>Got it. A predecessor, or just thank you for joining Dave and me on the program today, talking about what you're, how you're transforming landmark. Good luck in your presentation this afternoon. I'm sure a lot of folks will get some great takeaways from your talk. >>Thank you so much. It's been >>Great. Our pleasure for Dave Volante. I'm Lisa Martin live in Las Vegas UI path forward for it. We'll be right back after a break.

>> Announcer: From around the globe it's theCUBE. Presenting the Convergence of File and Object brought to you by Pure Storage. >> Okay now we're going to get the customer perspective on object and we'll talk about the convergence of file and object, but really focusing on the object pieces this is a content program that's being made possible by Pure Storage and it's co-created with theCUBE. Christopher CB Bohn is here. He's a lead architect for MicroFocus the enterprise data warehouse and principal data engineer at MicroFocus. CB welcome good to see you. >> Thanks Dave good to be here. >> So tell us more about your role at Microfocus it's a pan Microfocus role because we know the company is a multi-national software firm it acquired the software assets of HP of course including Vertica tell us where you fit. >> Yeah so Microfocus is you know, it's like I can says it's wide, worldwide company that it sells a lot of software products all over the place to governments and so forth. And it also grows often by acquiring other companies. So there is there the problem of integrating new companies and their data. And so what's happened over the years is that they've had a number of different discreet data systems so you've got this data spread all over the place and they've never been able to get a full complete introspection on the entire business because of that. So my role was come in, design a central data repository and an enterprise data warehouse, that all reporting could be generated against. And so that's what we're doing and we selected Vertica as the EDW system and Pure Storage FlashBlade as the communal repository. >> Okay so you obviously had experience with with Vertica in your previous role, so it's not like you were starting from scratch, but paint a picture of what life was like before you embarked on this sort of consolidated approach to your data warehouse. Was it just dispared data all over the place? A lot of M and A going on, where did the data live? >> CB: So >> Right so again the data is all over the place including under people's desks and just dedicated you know their own private SQL servers, It, a lot of data in a Microfocus is one on SQL server, which has pros and cons. Cause that's a great transactional database but it's not really good for analytics in my opinion. So but a lot of stuff was running on that, they had one Vertica instance that was doing some select reporting. Wasn't a very powerful system and it was what they call Vertica enterprise mode where it had dedicated nodes which had the compute and storage in the same locus on each server okay. So Vertica Eon mode is a whole new world because it separates compute from storage. Okay and at first was implemented in AWS so that you could spin up you know different numbers of compute nodes and they all share the same communal storage. But there has been a demand for that kind of capability, but in an on-prem situation. Okay so Pure storage was the first vendor to come along and have an S3 emulation that was actually workable. And so Vertica worked with Pure Storage to make that all happen and that's what we're using. >> Yeah I know back when back from where we used to do face-to-face, we would be at you know Pure Accelerate, Vertica was always there it stopped by the booth, see what they're doing so tight integration there. And you mentioned Eon mode and the ability to scale, storage and compute independently. And so and I think Vertica is the only one I know they were the first, I'm not sure anybody else does that both for cloud and on-prem, but so how are you using Eon mode, are you both in AWS and on-prem are you exclusively cloud? Maybe you could describe that a little bit. >> Right so there's a number of internal rules at Microfocus that you know there's, it's not AWS is not approved for their business processes. At least not all of them, they really wanted to be on-prem and all the transactional systems are on-prem. And so we wanted to have the analytics OLAP stuff close to the OLTP stuff right? So that's why they called there, co-located very close to each other. And so we could, what's nice about this situation is that these S3 objects, it's an S3 object store on the Pure Flash Blade. We could copy those over if we needed it to AWS and we could spin up a version of Vertica there, and keep going. It's like a tertiary GR strategy cause we actually have a, we're setting up a second, Flash Blade Vertica system geo located elsewhere for backup and we can get into it if you want to talk about how the latest version of the Pure software for the Flash Blade allows synchronization across network boundaries of those Flash Blade which is really nice because if, you know there's a giant sinkhole opens up under our Koll of facility and we lose that thing then we just have to switch to DNS. And we were back in business of the DR. And then the third one was to go, we could copy those objects over to AWS and be up and running there. So we're feeling pretty confident about being able to weather whatever comes along. >> Yeah I'm actually very interested in that conversation but before we go there. you mentioned you want, you're going to have the old lab close to the OLTP, was that for latency reasons, data movement reasons, security, all of the above. >> Yeah it's really all of the above because you know we are operating under the same sub-net. So to gain access to that data, you know you'd have to be within that VPN environment. We didn't want to going out over the public internet. Okay so and just for latency reasons also, you know we have a lot of data and we're continually doing ETL processes into Vertica from our production data, transactional databases. >> Right so they got to be approximate. So I'm interested in so you're using the Pure Flash Blade as an object store, most people think, oh object simple but slow. Not the case for you is that right? >> Not the case at all >> Why is that. >> This thing had hoop It's ripping, well you have to understand about Vertica and the way it stores data. It stores data in what they call storage containers. And those are immutable, okay on disc whether it's on AWS or if you had a enterprise mode Vertica, if you do an update or delete it actually has to go and retrieve that object container from disc and it destroys it and rebuilds it, okay which is why you don't, you want to avoid updates and deletes with vertica because the way it gets its speed is by sorting and ordering and encoding the data on disk. So it can read it really fast. But if you do an operation where you're deleting or updating a record in the middle of that, then you've got to rebuild that entire thing. So that actually matches up really well with S3 object storage because it's kind of the same way, it gets destroyed and rebuilt too okay. So that matches up very well with Vertica and we were able to design the system so that it's a panda only. Now we have some reports that we're running in SQL server. Okay which we're taking seven days. So we moved that to Vertica from SQL server and we rewrote the queries, which were had, which had been written in TC SQL with a bunch of loops and so forth and we were to get, this is amazing it went from seven days to two seconds, to generate this report. Which has tremendous value to the company because it would have to have this long cycle of seven days to get a new introspection in what they call the knowledge base. And now all of a sudden it's almost on demand two seconds to generate it. That's great and that's because of the way the data is stored. And the S3 you asked about, oh you know it, it's slow, well not in that context. Because what happens really with Vertica Eon mode is that it can, they have, when you set up your compute nodes, they have local storage also which is called the depot. It's kind of a cache okay. So the data will be drawn from the Flash Blade and cached locally. And that was, it was thought when they designed that, oh you know it's that'll cut down on the latency. Okay but it turns out that if you have your compute nodes close meaning minimal hops to the Flash Blade that you can actually tell Vertica, you know don't even bother caching that stuff just read it directly on the fly from the from the Flash Blade and the performance is still really good. It depends on your situation. But I know for example a major telecom company that uses the same topologies we're talking about here they did the same thing. They just dropped the cache cause the Flash Blade was able to deliver the data fast enough. >> So that's, you're talking about that's speed of light issues and just the overhead of switching infrastructure is that, it's eliminated and so as a result you can go directly to the storage array? >> That's correct yeah, it's like, it's fast enough that it's almost as if it's local to the compute node. But every situation is different depending on your needs. If you've got like a few tables that are heavily used, then yeah put them in the cache because that'll be probably a little bit faster. But if you're have a lot of ad hoc queries that are going on, you know you may exceed the storage of the local cache and then you're better off having it just read directly from the, from the Flash Blade. >> Got it so it's >> Okay. >> It's an append only approach. So you're not >> Right >> Overwriting on a record, so but then what you have automatically re index and that's the intelligence of the system. how does that work? >> Oh this is where we did a little bit of magic. There's not really anything like magic but I'll tell you what it is I mean. ( Dave laughing) Vertica does not have indexes. They don't exist. Instead I told you earlier that it gets a speed by sorting and encoding the data on disk and ordering it right. So when you've got an append-only situation, the natural question is well if I have a unique record, with let's say ID one, two, three, what happens if I append a new version of that, what happens? Well the way Vertica operates is that there's a thing called a projection which is actually like a materialized columnar data store. And you can have a, what they call a top-K projection, which says only put in this projection the records that meet a certain condition. So there's a field that we like to call a discriminator field which is like okay usually it's the latest update timestamp. So let's say we have record one, two, three and it had yesterday's date and that's the latest version. Now a new version comes in. When the data at load time vertical looks at that and then it looks in the projection and says does this exist already? If it doesn't then it adds it. If it does then that one now goes into that projection okay. And so what you end up having is a projection that is the latest snapshot of the data, which would be like, oh that's the reality of what the table is today okay. But inherent in that is that you now have a table that has all the change history of those records, which is awesome. >> Yeah. >> Because, you often want to go back and revisit, you know what it will happen to you. >> But that materialized view is the most current and the system knows that at least can (murmuring). >> Right so we then create views that draw off from that projection so that our users don't have to worry about any of that. They just get oh and say select from this view and they're getting the latest greatest snapshot of what the reality of the data is right now. But if they want to go back and say, well how did this data look two days ago? That's an easy query for them to do also. So they get the best of both worlds. >> So could you just plug any flash array into your system and achieve the same results or is there anything really unique about Pure? >> Yeah well they're the only ones that have got I think really dialed in the S3 object form because I don't think AWS actually publishes every last detail of that S3 spec. Okay so it had, there's a certain amount of reverse engineering they had to do I think. But they got it right. When we've, a couple maybe a year and a half ago or so there they were like at 99%, but now they worked with Vertica people to make sure that that object format was true to what it should be. So that it works just as if Vertica doesn't care, if it is on AWS or if it's on Pure Flash Blade because Pure did a really good job of dialing in that format and so Vertica doesn't care. It just knows S3, doesn't know what it doesn't care where it's going it just works. >> So the essentially vendor R and D abstracted that complexity so you didn't have to rewrite the application is that right? >> Right, so you know when Vertica ships it's software, you don't get a specific version for Pure or AWS, it's all in one package, and then when you configure it, it knows oh okay well, I'm just pointed at the, you know this port, on the Pure storage Flash Blade, and it just works. >> CB what's your data team look like? How is it evolving? You know a lot of customers I talked to they complain that they struggled to get value out of the data and they don't have the expertise, what does your team look like? How is it, is it changing or did the pandemic change things at all? I wonder if you could bring us up to date on that? >> Yeah but in some ways Microfocus has an advantage in that it's such a widely dispersed across the world company you know it's headquartered in the UK, but I deal with people I'm in the Bay Area, we have people in Mexico, Romania, India. >> Okay enough >> All over the place yeah all over the place. So when this started, it was actually a bigger project it got scaled back, it was almost to the point where it was going to be cut. Okay, but then we said, well let's try to do almost a skunkworks type of thing with reduced staff. And so we're just like a hand. You could count the number of key people on this on one hand. But we got it all together, and it's been a traumatic transformation for the company. Now there's, it's one approval and admiration from the highest echelons of this company that, hey this is really providing value. And the company is starting to get views into their business that they didn't have before. >> That's awesome, I mean, I've watched Microfocus for years. So to me they've always had a, their part of their DNA is private equity I mean they're sharp investors, they do great M and A >> CB: Yeah >> They know how to drive value and they're doing modern M and A, you know, we've seen what they what wait, what they did with SUSE, obviously driving value out of Vertica, they've got a really, some sharp financial people there. So that's they must have loved the the Skunkworks, fast ROI you know, small denominator, big numerator. (laughing) >> Well I think that in this case, smaller is better when you're doing development. You know it's a two-minute cooks type of thing and if you've got people who know what they're doing, you know I've got a lot of experience with Vertica, I've been on the advisory board for Vertica for a long time. >> Right And you know I was able to learn from people who had already, we're like the second or third company to do a Pure Flash Blade Vertica installation, but some of the best companies after they've already done it we are members of the advisory board also. So I learned from the best, and we were able to get this thing up and running quickly and we've got you know, a lot of other, you know handful of other key people who know how to write SQL and so forth to get this up and running quickly. >> Yeah so I mean, look it Pure is a fit I mean I sound like a fan boy, but Pure is all about simplicity, so is object. So that means you don't have to ra, you know worry about wrangling storage and worrying about LANs and all that other nonsense and file names but >> I have burned by hardware in the past you know, where oh okay they built into a price and so they cheap out on stuff like fans or other things in these components fail and the whole thing goes down, but this hardware is super good quality. And so I'm happy with the quality of that we're getting. >> So CB last question. What's next for you? Where do you want to take this initiative? >> Well we are in the process now of, we're when, so I designed a system to combine the best of the Kimball approach to data warehousing and the inland approach okay. And what we do is we bring over all the data we've got and we put it into a pristine staging layer. Okay like I said it's a, because it's append-only, it's essentially a log of all the transactions that are happening in this company, just as they appear okay. And then from the Kimball side of things we're designing the data marts now. So that's what the end users actually interact with. So we're taking the, we're examining the transactional systems to say, how are these business objects created? What's the logic there and we're recreating those logical models in Vertica. So we've done a handful of them so far, and it's working out really well. So going forward we've got a lot of work to do, to create just about every object that the company needs. >> CB you're an awesome guest really always a pleasure talking to you and >> Thank you. >> congratulations and good luck going forward stay safe. >> Thank you, you too Dave. >> All right thank you. And thank you for watching the Convergence of File and Object. This is Dave Vellante for theCUBE. (soft music)

(techno music) >> From Cambridge, Massachusetts, it's theCUBE. Covering MIT Chief Data Officer and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. >> Welcome back to Cambridge, Massachusetts everybody, we're here with theCUBE at the MIT Chief Data Officer Information Quality Conference. I'm Dave Vellante with my co-host Paul Gillin. Susan Wilson is here, she's the vice president of data governance and she's the leader at Informatica. Blake Anders is the corporate vice president of data governance at New York Life. Folks, welcome to theCUBE, thanks for coming on. >> Thank you. >> Thank you. >> So, Susan, interesting title; VP, data governance leader, Informatica. So, what are you leading at Informatica? >> We're helping our customers realize their business outcomes and objectives. Prior to joining Informatica about 7 years ago, I was actually a customer myself, and so often times I'm working with our customers to understand where they are, where they going, and how to best help them; because we recognize data governance is more than just a tool, it's a capability that represents people, the processes, the culture, as well as the technology. >> Yeah so you've walked the walk, and you can empathize with what your customers are going through. And Blake, your role, as the corporate VP, but more specifically the data governance lead. >> Right, so I lead the data governance capabilities and execution group at New York Life. We're focused on providing skills and tools that enable government's activities across the enterprise at the company. >> How long has that function been in place? >> We've been in place for about two and half years now. >> So, I don't know if you guys heard Mark Ramsey this morning, the key-note, but basically he said, okay, we started with enterprise data warehouse, we went to master data management, then we kind of did this top-down enterprise data model; that all failed. So we said, all right, let's pump the governance. Here you go guys, you fix our corporate data problem. Now, right tool for the right job but, and so, we were sort of joking, did data governance fail? No, you always have to have data governance. It's like brushing your teeth. But so, like I said, I don't know if you heard that, but what are your thoughts on that sort of evolution that he described? As sort of, failures of things like EDW to live up to expectations and then, okay guys over to you. Is that a common theme? >> It is a common theme, and what we're finding with many of our customers is that they had tried many of the, if you will, the methodologies around data governance, right? Around policies and structures. And we describe this as the Data 1.0 journey, which was more application-centric reporting to Data 2.0 to data warehousing. And a lot of the failed attempts, if you will, at centralizing, if you will, all of your data, to now Data 3.0, where we look at the explosion of data, the volumes of data, the number of data consumers, the expectations of the chief data officer to solve business outcomes; crushing under the scale of, I can't fit all of this into a centralized data at repository, I need something that will help me scale and to become more agile. And so, that message does resonate with us, but we're not saying data warehouses don't exist. They absolutely do for trusted data sources, but the ability to be agile and to address many of your organizations needs and to be able to service multiple consumers is top-of-mind for many of our customers. >> And the mind set from 1.0 to 2.0 to 3.0 has changed. From, you know, data as a liability, to now data as this massive asset. It's sort of-- >> Value, yeah. >> Yeah, and the pendulum is swung. It's almost like a see-saw. Where, and I'm not sure it's ever going to flip back, but it is to a certain extent; people are starting to realize, wow, we have to be careful about what we do with our data. But still, it's go, go, go. But, what's the experience at New York Life? I mean, you know. A company that's been around for a long time, conservative, wants to make sure risk averse, obviously. >> Right. >> But at the same time, you want to keep moving as the market moves. >> Right, and we look at data governance as really an enabler and a value-add activity. We're not a governance practice for the sake of governance. We're not there to create a lot of policies and restrictions. We're there to add value and to enable innovation in our business and really drive that execution, that efficiency. >> So how do you do that? Square that circle for me, because a lot of people think, when people think security and governance and compliance they think, oh, that stifles innovation. How do you make governance an engine of innovation? >> You provide transparency around your data. So, it's transparency around, what does the data mean? What data assets do we have? Where can I find that? Where are my most trusted sources of data? What does the quality of that data look like? So all those things together really enable your data consumers to take that information and create new value for the company. So it's really about enabling your value creators throughout the organization. >> So data is an ingredient. I can tell you where it is, I can give you some kind of rating as to the quality of that data and it's usefulness. And then you can take it and do what you need to do with it in your specific line of business. >> That's right. >> Now you said you've been at this two and half years, so what stages have you gone through since you first began the data governance initiative. >> Sure, so our first year, year and half was really focused on building the foundations, establishing the playbook for data governance and building our processes and understanding how data governance needed to be implemented to fit New York Life in the culture of the company. The last twelve months or so has really been focused on operationalizing governance. So we've got the foundations in place, now it's about implementing tools to further augment those capabilities and help assist our data stewards and give them a better skill set and a better tool set to do their jobs. >> Are you, sort of, crowdsourcing the process? I mean, you have a defined set of people who are responsible for governance, or is everyone taking a role? >> So, it is a two-pronged approach, we do have dedicated data stewards. There's approximately 15 across various lines of business throughout the company. But, we are building towards a data democratization aspect. So, we want people to be self-sufficient in finding the data that they need and understanding the data. And then, when they have questions, relying on our stewards as a network of subject matter experts who also have some authorizations to make changes and adapt the data as needed. >> Susan, one of the challenges that we see is that the chief data officers often times are not involved in some of these skunkworks AI projects. They're sort of either hidden, maybe not even hidden, but they're in the line of business, they're moving. You know, there's a mentality of move fast and break things. The challenge with AI is, if you start operationalizing AI and you're breaking things without data quality, without data governance, you can really affect lives. We've seen it. In one of these unintended consequences. I mean, Facebook is the obvious example and there are many, many others. But, are you seeing that? How are you seeing organizations dealing with that problem? >> As Blake was mentioning often times what it is about, you've got to start with transparency, and you got to start with collaborating across your lines of businesses, including the data scientists, and including in terms of what they are doing. And actually provide that level of transparency, provide a level of collaboration. And a lot of that is through the use of our technology enablers to basically go out and find where the data is and what people are using and to be able to provide a mechanism for them to collaborate in terms of, hey, how do I get access to that? I didn't realize you were the SME for that particular component. And then also, did you realize that there is a policy associated to the data that you're managing and it can't be shared externally or with certain consumer data sets. So, the objective really is around how to create a platform to ensure that any one in your organization, whether I'm in the line of business, that I don't have a technical background, or someone who does have a technical background, they can come and access and understand that information and connect with their peers. >> So you're helping them to discover the data. What do you do at that stage? >> What we do at that stage is, creating insights for anyone in the organization to understand it from an impact analysis perspective. So, for example, if I'm going to make changes, to as well as discovery. Where exactly is my information? And so we have-- >> Right. How do you help your customers discover that data? >> Through machine learning and artificial intelligence capabilities of our, specifically, our data catalog, that allows us to do that. So we use such things like similarity based matching which help us to identify. It doesn't have to be named, in miscellaneous text one, it could be named in that particular column name. But, in our ability to scan and discover we can identify in that column what is potentially social security number. It might have resided over years of having this data, but you may not realize that it's still stored there. Our ability to identify that and report that out to the data stewards as well as the data analysts, as well as to the privacy individuals is critical. So, with that being said, then they can actually identify the appropriate policies that need to be adhered to, alongside with it in terms of quality, in terms of, is there something that we need to archive. So that's where we're helping our customers in that aspect. >> So you can infer from the data, the meta data, and then, with a fair degree of accuracy, categorize it and automate that. >> Exactly. We've got a customer that actually ran this and they said that, you know, we took three people, three months to actually physically tag where all this information existed across something like 7,000 critical data elements. And, basically, after the set up and the scanning procedures, within seconds we were able to get within 90% precision. Because, again, we've dealt a lot with meta data. It's core to our artificial intelligence and machine learning. And it's core to how we built out our platforms to share that meta data, to do something with that meta data. It's not just about sharing the glossary and the definition information. We also want to automate and reduce the manual burden. Because we recognize with that scale, manual documentation, manual cataloging and tagging just, >> It doesn't work. >> It doesn't work. It doesn't scale. >> Humans are bad at it. >> They're horrible at it. >> So I presume you have a chief data officer at New York Life, is that correct? >> We have a chief data and analytics officer, yes. >> Okay, and you work within that group? >> Yes, that is correct. >> Do you report it to that? >> Yes, so-- >> And that individual, yeah, describe the organization. >> So that sits in our lines of business. Originally, our data governance office sat in technology. And then, our early 2018 we actually re-orged into the business under the chief data and analytics officer when that role was formed. So we sit under that group along with a data solutions and governance team that includes several of our data stewards and also some others, some data engineer-type roles. And then, our center for data science and analytics as well that contains a lot of our data science teams in that type of work. >> So in thinking about some of these, I was describing to Susan, as these skunkworks projects, is the data team, the chief data officer's team involved in those projects or is it sort of a, go run water through the pipes, get an MVP and then you guys come in. How does that all work? >> We're working to try to centralize that function as much as we can, because we do believe there's value in the left hand knowing what the right hand is doing in those types of things. So we're trying to build those communications channels and build that network of data consumers across the organization. >> It's hard right? >> It is. >> Because the line of business wants to move fast, and you're saying, hey, we can help. And they think you're going to slow them down, but in fact, you got to make the case and show the success because you're actually not going to slow them down to terms of the ultimate outcome. I think that's the case that you're trying to make, right? >> And that's one of the things that we try to really focus on and I think that's one of the advantages to us being embedded in the business under the CDAO role, is that we can then say our objectives are your objectives. We are here to add value and to align with what you're working on. We're not trying to slow you down or hinder you, we're really trying to bring more to the table and augment what you're already trying to achieve. >> Sometimes getting that organization right means everything, as we've seen. >> Absolutely. >> That's right. >> How are you applying governance discipline to unstructured data? >> That's actually something that's a little bit further down our road map, but one of the things that we have started doing is looking at our taxonomy's for structured data and aligning those with the taxonomy's that we're using to classify unstructured data. So, that's something we're in the early stages with, so that when we get to that process of looking at more of our unstructured content, we can, we already have a good feel for there's alignment between the way that we think about and organize those concepts. >> Have you identified automation tools that can help to bring structure to that unstructured data? >> Yes, we have. And there are several tools out there that we're continuing to investigate and look at. But, that's one of the key things that we're trying to achieve through this process is bringing structure to unstructured content. >> So, the conference. First year at the conference. >> Yes. >> Kind of key take aways, things that interesting to you, learnings? >> Oh, yes, well the number of CDO's that are here and what's top of mind for them. I mean, it ranges from, how do I stand up my operating model? We just had a session just about 30 minutes ago. A lot of questions around, how do I set up my organization structure? How do I stand up my operating model so that I could be flexible? To, right, the data scientists, to the folks that are more traditional in structured and trusted data. So, still these things are top-of-mind and because they're recognizing the market is also changing too. And the growing amount of expectations, not only solving business outcomes, but also regulatory compliance, privacy is also top-of-mind for a lot of customers. In terms of, how would I get started? And what's the appropriate structure and mechanism for doing so? So we're getting a lot of those types of questions as well. So, the good thing is many of us have had years of experience in this phase and the convergence of us being able to support our customers, not only in our principles around how we implement the framework, but also the technology is really coming together very nicely. >> Anything you'd add, Blake? >> I think it's really impressive to see the level of engagement with thought leaders and decision makers in the data space. You know, as Susan mentioned, we just got out of our session and really, by the end of it, it turned into more of an open discussion. There was just this kind of back and forth between the participants. And so it's really engaging to see that level of passion from such a distinguished group of individuals who are all kind of here to share thoughts and ideas. >> Well anytime you come to a conference, it's sort of any open forum like this, you learn a lot. When you're at MIT, it's like super-charged. With the big brains. >> Exactly, you feel it when you come on the campus. >> You feel smarter when you walk out of here. >> Exactly, I know. >> Well, guys, thanks so much for coming to theCUBE. It was great to have you. >> Thank you for having us. We appreciate it, thank you. >> You're welcome. All right, keep it right there everybody. Paul and I will be back with our next guest. You're watching theCUBE from MIT in Cambridge. We'll be right back. (techno music)

>> Live from San Francisco, it's theCUBE, covering DockerCon '18, brought to you by Docker and it's ecosystem partners. >> Welcome back to theCUBE, we are live at DockerCon 2018 in San Francisco on a spectacular day. I am Lisa Martin with my with my co-host for the day, John Troyer, and we're very pleased to welcome back to theCUBE a distinguished CUBE alumni and Docker veteran, Steve Johnston, Chief Product Officer at Docker. Welcome back. >> Thank you, thank you very much. That's Scott Johnston but that's okay. >> What did I say? Steve? >> Steve. That's okay. >> Oh, I gave you a new name. >> You know, I get that all the time. >> I'm sorry, Scott. >> That's alright. >> This event, between five and six thousand people. >> Yes. >> You were saying in your general session in keynote this morning, that this is the fifth DockerCon. You started a few years ago with just 300 people and when I was walking out of the keynote this morning, I took a photograph, incredible. People as far as the eye can see. It was literally standing room only. >> It's crazy, right? And you think about four years ago, June 2014 when we did our very first DockerCon, here in San Francisco, 300 people, right? And we've gone from 300 to over 5,000 in that time, grown the community, grown the products, grown the partnerships and it's just, it's very humbling, honestly, to be part of something that's literally industry changing. >> You gave some great numbers during your keynote. You talked about 500 customers using Docker Enterprise Edition. >> Yes. >> Some big names. >> Yes. >> MET Life, Visa, PayPal, McKesson, who was on stage and that was a really interesting. McKesson is what, 183 years old? >> Healthcare company, yeah. >> Talking about data, life and death type of data. >> Right. >> Their transformation working with Docker and containers was really pretty impressive. >> It's exciting that companies get their hands on the technology and they start maybe on a small project or a small team but very quickly they see the potential impact of the solution and very quickly, it's almost infectious inside the organization and more and more teams want to jump on, understand how they can use it to help with their applications, their business to get impact in their operations and it just spreads, spreads like wildflower. That was really the story that McKesson was sharing, just how quickly they were seeing the adoption throughout their org. >> I thought that was really interesting and they did point it out on stage, how that developer adoption did help them go to the next level. >> Yes. >> And kind of transform their whole pipeline. >> Yes. >> Now Scott, you've been here the whole line of time and that through line has been, for Docker, that developer experience. >> That's exactly right. >> Now, as Product Lead here, you've got the Docker Desktop side and the Docker EE side and it's clear, there were some great announcements about desktop here, previews today but how do you balance the enterprise side with the developer centric desktop side and that developer experience idea? >> No, it's a great question, John. I'd reshape it almost to say, it's a continuous platform from developer experience to the operation side and you have to stand back and kind of see it as one and less about trading off one versus the other and how do you create an experience that carries all the way through. So a lot of Gareth's demonstration and the Lily Mason play, was showing how you can create apps in Docker very easily as a developer but those same artifacts that they put their apps in to carry all the way through into production, all the way through into operations. So it's about providing a consistent user experience, consistent set of artifacts that can be used by all the different personas that are building software so that they can be successful moving these Docker applications through the entire application development life cycle. Does that make sense? >> It does, thank you. I'd love to get your perspective, when you're talking with enterprises who might have some trepidation about the container journey, they probably know they have to do it to stay agile and competitive. I think in the press release, I believe it was you, that was quoted saying, "An estimated 85% of enterprise organizations are in a multi Cloud world." >> That's right. >> In a multi Cloud strategy. >> That's right. >> So when you're talking with customers, what's that executive conversation like? C level to C level, what are some of the main concerns that you hear and how influential are the developers in that C suite saying, "Hey guys, we've got to go this direction"? >> No, that's right. That's a great question, Lisa and what we hear again, and again, and again, is a realization going on in the C suite, that having software capabilities is strategic to their business, right? That was not always the case, as much as a decade ago, as recently as a decade ago, inside kind of big manufacturing businesses or big verticals that weren't kind of tech first, IT was a back office, right? It was not front and center but now they're seeing the disruption that software can have in other verticals and they're saying, "Wait a minute, we need to make software capabilities a core capability in our business." And who starts that whole cycle? It's the developers, right? If the developers can integrate with the lines of business, understand their objectives, understand how software can help them achieve those objectives, that's where it kicks off the whole process of, "Okay, we're going to build competitive applications. We then need an operations team to manage and deploy those applications to help us deploy them in a competitive way by taking them to the Cloud." So developers are absolutely pivotal in that conversation and core to helping these very large, Fortune 500, hundred year old companies, transform into new, agile, software driven businesses. >> Modernizing enterprise apps has been a theme >> Yes. >> also at Docker for a few years now. >> Yup. >> Up on stage Microsoft demonstrating the results of a multiyear partnership >> That's right. >> between Microsoft and Docker both with Docker integrating well with Windows server as well as, you talked about, Kubernetes now. >> That's right. >> Can you talk a little bit about what the implications of this are? The demo on stage, of course, was a very old enterprise app written in dot net, with just a few clicks, up and running in the Cloud on Kubernetes no less. >> That's right. >> Managed by Docker, that's actually very cool. You want to talk a little bit about, again, your conversations? >> Absolutely. >> Is this all about Cloud native or how much of your conversations are also supporting enterprise apps? >> Tying back to Lisa's question, so how do we help these organizations get started on their transformation? So they realize they need to transform, where do you start? Well guess what? 90% of their IT budget right now is going into these legacy applications and these legacy infrastructures, so if you start there and it can help modernize what they already have and bring it to modern platforms like Docker and Kubernetes, modern platforms like Window Server 2016, it's a modern operating system, modern platforms like Clouds, that's where you can create a lot of value out of existing application assets, reduce your costs, make these apps agile, even though they're thirteen years old and it's a way for the organization to start to get comfortable with the technology, to adopt it in a surface area that's very well known, to see results very, very quickly and then they gain the confidence to then spread it further into new applications, to spread it further into IOT, to spread it further into big data. But you've got to start it somewhere, right? So the MTA, Modernized Traditional Apps, is a very practical, pragmatic but also high, very quick, return way to get started. >> Oh, go ahead. >> Well I just, the other big announcement involving Kubernetes was managing Kubernetes in the Cloud and I wanted to make sure we hit that. >> That's right, that's right. >> Because I think if people aren't paying attention, they're just going to hear multi Cloud and they're going to go on and say, "Well everybody does multi Cloud, Docker's no different, Docker's just kind of catching up." Actually, this tech preview, I think, is a step forward. I think it's something- >> Thank you. >> I haven't actually seen in practice, so I'm kind of curious, again, how you as an engineering leader make those trade offs. Kind of talk a little bit about what you did and how deciding, "Well there's multi Cloud but the devil's in the details." You actually have integrated now with the native Kubernetes in these three Clouds, EKS, AKS and GKE. >> GKE, no that's right. No, it's a great question, John. The wonderful and fascinating but double edged sword of technology is that the race is always moving the abstraction up, right? You're always moving the abstraction up and you're always having to stay ahead and find where you can create real value for your customers. There was two factors that were going on, that you saw us kind of lean in to that and realize there's an opportunity here. One is, the Cloud providers are doing a wonderful job investing in Kubernetes and making it a manage service on their platforms, great. Now, let's take advantage of that because that's a horizontal infrastructure piece. At parallel we were seeing customers want to take advantage of these different Clouds but getting frustrated that every time they went to a different Cloud they were setting up another stack of process and tooling and automation and management and they're like, "Wait a minute. This is going to slow us down if we have to maintain these stacks." So we leaned in to that and said, "Okay, great. Let's take advantage of commoditized infrastructure, hosting Kubernetes. Let's also then take advantage of our ability to ingest and onboard them into Docker Enterprise Edition, and provide a consistent experience user based APIs, so that the enterprise doesn't get tied into these individual silos of tools, processes and stacks." Really, it's the combination of those two that you see a product opportunity emerged that we leaned heavily into and you saw the fruits of this morning. >> I saw a stat on the docker.com website that said that customers migrating to EE containers can reduce total cost by around 50%? >> Yes. >> That's a significant number. >> It's huge, right? You're reducing your cost of maintaining a ten year old app by 50% and you've made it Cloud portable, and you've made it more secure by putting it in the Docker container than outside and so it's like, "Why wouldn't you invest in that?" It shows a way to get comfortable with the technology, free up some cashflow that then you can pour back into additional innovation, so it's really a wonderful formula. That again, is why we start a lot of customers with their legacy applications because it has these types of benefits that gets them going in other parts of their business. >> And as you mentioned, 90% of an enterprise IT budget is spent keeping the lights on. >> That's right. >> Which means 10% for innovation and as we've talked about before, John, it's the aggressively innovating organizations that are the winners. >> That's exactly right and we're giving them tools, we're giving them a road map even, on how they can become an aggressively innovating organization. >> What about the visibility, in terms of, you know, an organization that's got eight different IT platforms, on prem, public Cloud, hybrid- >> Right. >> What are you doing with respect to being able to deliver visibility across containers and multiple clusters? >> That's right. Well that's a big part of today's announcement, was being able ... Every time we ingest one of these clusters, whether it's on prem, whether it's in the Cloud, whether it's a hosted Kubernetes cluster, that gives us that visibility of now we can manage applications across that, we can aggregate the logging, aggregate the monitoring. You can see, are your apps up, down, are they running out of resources? Do you need to load balance them to another cluster? So it's very much aligned with the vision that we shared on stage, which is fully federated management of the applications across clusters which includes visibility and all the tools necessary for that. >> Scott, I wanted to ask about culture and engineering culture >> Thank you. >> The DockerCon here is very, I think we called it humane in our intro, right? There's childcare on site, there's spoustivities, there's other places to take care of the people who are here and give them a great experience and a lot of training, of course, and things like that. But internally, engineering, there's a war for talent. Docker is very small compared to the Googles of the world but yet you have a very ambitious agenda. The theme of choice today, CLI versus GUI, Kubernetes versus Swarm, Lennox and Windows, not versus, Lennox and Windows, you know and, and, and, and now all these different Clouds and on prem. That's very ambitious and each "and" there takes engineering resources, so I'm kind of curious how the engineering team is growing, how you want to build the culture internally and how you use that to attract the right people? >> Well it certainly helps to be the start up that kicked off this entire movement, right? So a lot of credit to Solomon Hykes, our founder, and the original crew that ... Docker was a Skunkworks project in the previous version of our company and they had the vision to bring it forward and bring it to the world in an opensource model which at the time was a brand new language, go language. That was a catalyst that really got the company off and running in 2013/2014. We're staying true to that in that there's still a very strong opensource culture in the company and that attracts a lot of talent, as well as continuing to balance enterprise features and innovation and you see a combination of that on stage. You're also going to see a wonderful combination of that on the show floor, both from our own employees but also from the community. And I think that's the third dimension, John, which is being humble and call it "aware" that innovation doesn't just come from inside our four walls but that we give our engineers license to bring things in from the outside that add value to their projects. The Kubernetes is a great example of that, right? Our team saw the need for orchestration, we had our own IP in the form of Swarm, but they saw the capabilities of Kubernetes is very complimentary to that, or some customers were preferring to deploy that. So, no ifs, ands or buts, let's take advantage of that innovation, bring it inside the four walls and go. So, it's that kind of flexibility and awareness to attract great engineers who want to work on cutting edge, industry building technologies but also who are aware enough of, there's exciting things happening outside with the community and partnering with that community to bring those into the platform as well. >> So Scott, you guys are doing a lot of collaboration internally, but you're also doing a lot of collaboration with customers. How influential are customers to the development of Docker technologies? >> At ground zero, literally and we have at DockerCon, we call it a customer advisor group, where the customers who have been with us, who have deployed with us in production, we have them. And it's a very select group, it's about twelve to sixteen, and they tell us straight talk in terms of where it's working, where we need to improve. They give us feedback on the road map and so that happens every DockerCon, so that's once every six months. But then we actually have targets inside engineering and product management to be out in the field on a regular basis to make sure we're continuing to get that customer feedback. Innovation's a tricky balance, right? Because you want to be out in front and go where folks aren't asking you to, but you know there's opportunity, at the same time here, where they are today, and make sure you're not getting too far ahead. It's the old joke, Henry Ford, where if he's just listened to his customers, he would have made faster horses but instead he was listening to their problems, their real problems which was transportation and his genius, or his innovation, was to give them the Model T, right? We're trying to balance that ourselves inside Docker. Listen to customers but also know where the innovation, where the technology can take you to give you new solutions, hopefully many of which you saw on stage today. >> We did, well Scott, thanks so much for stopping by theCUBE again and sharing some of the exciting announcements that Docker has made and what you're doing to innovate internally and for the external enterprise community. We appreciate your time. >> Thank you, Lisa. Thank you, John. >> We want to thank you for watching theCUBE. Again, Lisa Martin with John Troyer, live in San Francisco at DockerCon 2018. Stick around, John and I will be right back with our next guest. (upbeat techno music)

>> Narrator: Live from San Francisco. It's theCUBE, covering DevNet Create 2017, brought to you by Cisco. >> Welcome back, everyone. We're live in San Francisco for CUBE's special coverage, exclusive coverage, of Cisco Systems DevNet Create. It's an inaugural event for DevNet, a new extension to their developer program. DevNet, which is their classic developer program for the Cisco ecosystem, network guys, so on and so forth, moving packets around, hardware guys. DevNet Create is about developers and dev ops and cloud-native, all the goodness of application developers. Where apps meets infrastructure, certainly with the Cisco acquisition of AppDynamics, a new world order is coming down the pipe. Cisco's moving up the stack. I'm John Furrier, Peter Burris is my co-host, our next guest is Val Bercovici, CUBE alumni and also guest analyst in our studio in Palo Alto, also the cofounder of Peritus.ai, and now you can talk about it. Welcome to theCUBE. >> Thanks, John. We get to talk about it, finally. >> So before we get into your company, and I want to drill into it because the first public CUBE interview, drilling down on what you're working on. What's your take on Cisco's event here, because, I've known Cisco since I moved to Silicon Valley, 18 years, and even before then, and they scaled all the internet connecting networks. There's always been a discussion internally inside Cisco about moving up the stack. And it's always been kind of like a Civil War. Half the company wants to move up the stack, half doesn't, and now, you've been in NetApp, you know this world and its infrastructure, its hardware, its gear, its boxes, network packets. This is a seminal moment for Cisco. They've tried some open source before, but this seems like an all-in bet. Your thoughts? >> It is, and I was just telling Yodi Rahm, before we went live on stage that I think this is like Goldilocks event, right? It's my first. Apparently, it's the first one of its kind here at Cisco, and for me it's not too big. It's not too small. I find it really just the right size, and I find it very well-targeted, in terms of the fellow speakers, panelists that I was on with today in terms of, I see the right amount of laptops, the right amount of code, basically, amongst the attendees on the floor here. So my first impression, 'cause that's all I have so far, is it's a very well-targeted show, and it's not unique anymore. You'll notice Intel kind of pulled back from having one large event, one large annual event, and smaller more targeted events for developers, for operators, for other ISVs, and so forth. >> You're talking about the IDF Intel Developer Forum. >> The IDF, yeah, it's no longer a big, monolithic event. They split it up into more- >> And IBM has also collapsed their shows into one monster show. So little micro-events seem to be the norm. >> Yeah, I wouldn't even call it quite even a micro-event. It's a bit bigger than that, but it's not a VMworld . >> It's not a Dreamforce. >> It's not a COMDEX to VR sales. >> Interesting, I like they did their homework on the panels. So in terms of subject matter, the agenda looks great, but I do agree with you. I like how principals are here. It's not just staff here. It's both people in the trenches at Cisco, and the execs are here. Susie Wee and some other folks, they run Cisco a lot. They're all here. >> And their CTO, this morning, I caught the opening keynote livestream on the way over here. She did a fantastic job describing the role of the infrastructure developer, which is something that is a bit nebulous to nail down, at least it has been in the past, and I'm really glad that Cisco is echoing that, because I think it helps their entire ecosystem, their partner ecosystem, particularly former employers like NetApp of mine. >> I'm usually critical of big companies trying to put their toe in the water with some event that looks like a little cloud washing or you know, here or there, but I think Cisco's got a legit opportunity with programmable infrastructure. And I think, just in general, straight up, they do, because their infrastructure, and Peter and I talked about that. But I think IoT is really the big driver. They could really, that's a network connection. It's at the edge. It requires intelligence. That's a good angle for them. >> It's a great angle. The only beef I have, the gripe I have, is they still call it IoE, I think. If it's going to be Internet of Everything, and it's Internet of nothing, right? I really wish they'd kind of stick to the agreed term, and what they are doing of course is giving- >> But they were first with IoE. They were, you got to give Cisco, when they ran those commercials, what 10 years ago? >> Yeah. >> You know. >> It's a personal in for me. The commercials are fantastic. It's just the term bothers me. >> They got dogma with IoE, come on, get rid of it. Okay, tell me about your company? >> So Peritus.ai, we've realized now there's a chance to go beyond traditional digital disruption of existing industries to cognitive disruption. Let me explain what I mean by that. We're seeing a lot of increasing pace of change in data centers. The conference here, and all the technologies spoken about here, are very foreign to more traditional data center operators, and so the new environments, microservice architectures, or cloud-native apps or so forth. It's a pace of change that we haven't seen before. Agile business and agile software developer models can push code out realistically on a daily basis, whereas the waterfall model and the iCode models in most IT service practitioners practice, that's a manual or quarterly update cycle, with formal change managed practices. >> John: A more settled, structured. >> Yeah, yeah. >> Slow. >> Familiar. >> John: Reliable. >> You know, but it's the past, and the pace of change now is creating stress within IT organizations and stress within the product support organizations of the vendors that they choose to deploy. You couple that with increasing complexity of the environments we have here. We have a lot going on, the ethos of CNCF, which is container packaged, dynamically orchestrated, and micro services architected apps, cloud-native apps. The abstraction layers are masking a lot of complexity there, but the complexity is still there. And you have very good availability if you're able to write to cloud-native principles as a developer, but nevertheless, you still got that .001% of your outages so forth. And the last line of defense towards business continuity is still a human. You still got escalation engineers and support organizations that go through pretty contrived and complicated workflows to triage and diagnose problems, perhaps a case manager to assign a case or subject manager expert, get that back and forth information with the customer and finally resolve the case, and this is what we term cognitive disruption. The maturity of the AI platforms now have reached a point where you can take these complicated workflows that require nuance and inference, and actually apply true machine learning and deep learning to them. And if not entirely automate the resolution of these complex cases, better prepare a scarce resource, an escalation engineer with lots of experience, with more context up front when they encounter the case, so they can close it more quickly, and this has- >> So you're targeting, so if I understand this correctly, you're targeting the personnel in the data center. >> Val: The supportability space. >> Escalation engineers, the human labor, the last mile, if you will, or whatever, first mile, how you look at it. >> Correct. We see APM vendors in this space, we see ITSM vendors in the space. They're partners and even platforms for us. No one really is focused on supportability and automating those workflows using cognitive techniques. >> John: Give an example, give an example. >> The best example I can give actually is firsthand. I'll try and be as generic as I can to protect the innocent, but if you take a look- >> John: NetApp. >> (laughs) It's not even specifically a NetApp case. >> John: Okay, all right. >> If you look at the supply chain upstream, let's talk about electronic supply chain. If a particular manufacturer defect occurs upstream, that defect gets shipped in bundles, purchased by an equipment supplier vendor in bundles, and deployed by customers in bundles. So it's not like one of these one node outage situations is the best case scenario, traditional triple replication, you know. >> John: It's a bad batch basically. >> A bad batch. >> A lot of bad product. >> That can take out not just a node, it can take out a rack. It can take out multiple racks of storage gear, switching gear, server hosts, and so forth. In that case, again, your last line of defense is a human. You basically got to triage and diagnose the problem, could be hardware problem, could be a driver-software problem, could be an upstream OS or database problem. And it's a very stressful environment, a very stressful situation. You can take a look at prior case notes. You can take a look at machine logs and data. You can take a look at product documentation and bill of materials from suppliers, and you can pre-analyze a lot of that, and factor that into your diagnosis, effectively having it almost ready before the case is even opened, so that when the escalation engineer is assigned the case, they don't start from ground zero. They start from third base and almost they're rounding their way to home, and they're able to apply all the prior knowledge, algorithms never sleep. All the prior knowledge in terms of all the cases that have actually been dealt with that match that to a degree. They're never perfect matches, because that's just business process automation. There's a degree of inference required, and using AI techniques, we're able to guess that you know what? I've seen this before. It's very obscure, but it's actually going to be this resolution. >> So AI's technology that you're using in machine learning and data, what problem are you solving specifically? Saving them time, getting them faster resolutions? >> So we're improving the efficiency of support operations. There's always margin pressures within customer support operations. We're fundamentally solving complex system problems. We've reached a point now where business process automation can solve trivial support cases. >> John: Wait a minute, wait a minute, hold on. Expert system's supposed to do this. >> And they did in the past, and now we're evolving beyond the expert. >> Not really. Remember those expert system stays? >> Yeah, I remember LISP and all those early days, so yeah. >> So this kind of sounds like a modern version of an expert system to aid the support engineers to either have a predetermined understanding of options and time to solution. >> So we're able to do so much more than that, right? We're able to create what we call otologies. We're able to categorize all the cases that you've seen in the past, find out whether this new one fits an existing category, if so, if it matches other criteria, if not, defining a category. We're able to orchestrate. Resolution is not just a one-shot deal. Resolution is diagnose the problem, find out if you have some subject matter experts available to resolve the issue, assign it to them, track their progress, close the case, follow up on customer satisfaction. All those things are pretty elaborate workflows that can be highly automated today with cognitive approach-- >> Congratulations on launching. Thanks for spending the time to lay that out. What's next? You've got some seed funding? >> Val: We've got some seed funding. >> You got some in an incubator at the Hive in Palo Alto, which we know quite well. Rob is great, Rob is a great friend. He's done great, he's done great. How many people do have, what are you guys looking to do? What are some of the priorities? >> We are hiring. We're definitely looking to get more data scientists on staff, more full-stack engineers particularly with log experience. We're still looking for a CTO and leadership team. So there's a lot of hiring coming in place. >> John: How many in there now? >> We have about, less than 10 people working right now. >> It's a great opportunity for a classic early-stage opportunity. >> Yeah, early stage opportunity. We're addressing a hot space, and what I love is I personally shifted from being a provider of cloud-native solutions in this market to being a consumer. So I'm seeing exactly how a perfect storm is coming together of machine and deep learning algorithms, running on, orchestrated-- >> John: Both sides of the table. You should talk to Mark Sister. >> Yeah. He's been on both sides. What's it like to be on the other side now? >> It's everything I actually thought it would be, because at the end of the day, I always say, developers are the ultimate pragmatists. So it's not so much about brand loyalty at any particular vendors. What solution, whether it's an open source library, whether it's a commercial library, whether it's a propietary cloud service or something in between. What solution can solve my task, this next task? And composite applications are a very real thing right now. >> So we had a question I posted into the crowd chat, from this social net. I'm going to ask you the same questions. So Burt's watching and maybe you'll find that thread, and I'll add to it later. Here's the question. What challenges still remain as part of implementing DevOps, in your opinion? How did you see the landscape, and how are people addressing them? In your expert opinion, what's the answer to that question? What's your opinion? >> It's a two-faceted answer, at least. The first one, it's not a cliche. It's still a cultural challenge. If you want to actually want to map, it's not even a cultural challenge specifically, it's Conway's Law. Any product output, software output, is a function of your organizational structure that created it. So I find that whether you want to call it culture, whether you want to call it org structure, the org structure's rarely in place to incentivize entire teams to collaborate together throughout a full CICD pipeline process. You've still got incentive structures and org structures in place for people to develop code, unit test it, perhaps even integration test it, but I see more often than I'd like to, isolated or fenced off operations teams that take that and try to make it something real. They might call themselves SREs, and outside recovery engineers, but they're not integrated enough into the development process, in my mind. >> So you're saying the organization structures are also foreclosing their ability be agile, even though they're trying hard, that the incentives are too grounded in there. >> So I still see a lot of skunkworks projects as DevOps projects, and it shouldn't be that way anymore, right? There should be, where there's a legitimate business reason for more agile businesses, there should be a much more formal DevOps structure, as opposed to skunkworks DevOps structure. So that's one challenge, and it's not new, but it's also not resolved. And the other one really is this blind spot for the autonomous data center vision, this blind spot for operations being 100% automated and really just never having to deal with the problem. The blind spot is everything breaks. New technology just happens to break in new ways, but it does fundamentally break, and if your last line of defense is a human or a group of humans, you can expect a very, very different sort of responsiveness and agility as opposed to having something automated. >> Peter and I have been talking all morning the Ford firing of Mark Fields, which was announced yesterday. He quote retired by the Twitter handle of Ford, which is just code words for he got pushed aside. One, we're big fans of Mark Fields, before we covered Ford there in Palo Alto, doing some innovative centers over there, and also a Cloud Foundry customer. So I was actually, took notice of that. We were commenting on not so much the tech, but the guy got fired in less than three years into his journey as chief executive. >> Val: Yeah. >> Now the stock's down 39% so the hammer's coming down from either the family, Ford family, or Wall Street, Peter thinks Wall Street. But this brings up the question, how are you going to be a transformational leader, if you don't have the runway? Back to your org structure. This is, this is-- >> I'm like a broken record. I was thinking that yesterday as I was watching CNBC, and just thinking in my mind, processing what they were announcing. I'm realizing in my head, I bet why, because I don't know, but I bet why, I speculate why he got fired, because he wasn't able to put the org structure and incentives in place to run faster, and that's what the board asked his successor is run faster, and if his successor doesn't put the org structure and the incentives in place to be an agile business. That's the definition of insanity. It's banging your head against the wall. >> If I had to add one more thing to that comment, which by the way I agree with you. If you could configure an asset in a company besides the organizational structure, so you did that, what would your next asset be? More cloud, more data-centric, what would be? >> It might be cliche, but it's totally true, I would have a cloud-first approach to everything. So we don't remember this guy called Obama anymore, but really he did a pretty revolutionary thing, when he brought in a CIO eight, nine years ago, and he made every federal government department defend a capital purchase. And they basically have to go through a multi-hundred page document to defend a capital IT acquisition, but to actually go cloud first or cloud native, didn't require almost any pre-approval at all to get funding. >> So we made it easier incentives to go cloud. >> Created incentives, and I'm a big believer that cloud is not a panacea. >> That helped Amazon, not IBM, as the CIA case now. >> I'm a big believer in life cycles, so it's not like cloud is the rubber stamp solution for every problem, but the beginning innovation phase of every new product line or revenue stream really should be in the cloud right now. The amazing services, forget about IS and all that. Look at the machine language and APIs, IoT APIs, the entire CICD pipelines that are automated and simplistic, the innovation phase for everyone should be in the crowd. Then you got to take a step back, look at that bill, get over your sticker shock, and figure out whether you can afford to stay in a cloud using maybe some of those higher-level proprietary high-margin services and whether you want to re-factor. And that's where professional services kick in, and I think that might be the next great disruption for AI, is re-factoring apps. >> I think one of the things, final question I want to get your thoughts on. Pretend that we're at Cisco and we go back to the ranch, and someone says, "Hey, what's that DevNet Create?" What's our advice to our peers, if we had an opinion that people valued inside Cisco, doubled down on DevNet Create, continue, merge it DevNet? What would your advice be? >> I'm a long time James Governor fan. Developers are the new kingmakers. Actually I think we're in this situation that's not very well understood by business leaders right now, where developers are influencing all the technology infrastructure decisions we're making, but they don't necessarily write the checks. But if you want to run an agile business, a digital business today, you can't do it without happy developers and a good developer experience, so you have to cater to their needs and their biases and so forth, and at shows like this I think, bring Cisco's large ecosystem to bear, where we can figure out how Cisco can maximize the developer experience, how partners, and I'm soon to be a Cisco partner myself at Peritus.ai can maximize their developer experience and just drive more modern business. >> Bring the developer community in with the networking, get those margins connected. Val Bercovici, cofounder of Peritus.ai, this is theCUBE with exclusive coverage of the inaugural event of Cisco's DevNet Create. I'm John Furrier, Peter Burris, returning after this short break. (electric music) >> Hi, I'm April Mitchell, and I'm the senior director.