>> On January 17th, 2023 join theCUBE community for SuperCloud2 where we explore the intersection of cloud and data. One of our gold sponsors is ChaosSearch and I'm here with Ed Walsh, CEO of the company. Ed, why should people attend SuperCloud2? >> That's good question. Listen, Supercloud is a mega trend, just like you said, data and cloud, I would also add analytics to it and some companies but also some end user enterprise and some companies are using it for great, things you couldn't possibly do without this design principle. In fact, if you're doing anything around cloud, data analytics, you need to look at these things or you're not going to keep up with your data growth. >> Awesome. January 17th, go to SuperCloud.World and register. You don't want to miss the conversations with data mesh founders, Zhamak Dehghani, technologists like Bob Muglia and customers building super clouds like Wal-Mart. Don't miss it.

>>Hello and welcome back to the live coverage of the Cube here. Live in Detroit, Michigan for Cub Con, our seventh year covering all seven years. The cube has been here. M John Fur, host of the Cube, co-founder of the Cube. I'm here with Lisa Mart, my co-host, and our new host, Savannah Peterson. Great to see you guys. We're wrapping up day one of three days of coverage, and our guest analyst is Sario Wall, who's the cube analyst who's gonna give us his report. He's been out all day, ear to the ground in the sessions, peeking in, sneaking in, crashing him, getting all the data. Great to see you, Sarvi. Lisa Savannah, let's wrap this puppy up. >>I am so excited to be here. My first coupon with the cube and being here with you and Lisa has just been a treat. I can't wait to hear what you have to say in on the report side. And I mean, I have just been reflecting, it was last year's coupon that brought me to you, so I feel so lucky. So much can change in a year, folks. You never know where you're be. Wherever you're sitting today, you could be living your dreams in just a few >>Months. Lisa, so much has changed. I mean, just look at the past this year. Events we're back in person. Yeah. Yep. This is a big team here. They're still wearing masks, although we can take 'em off with a cube. But mask requirement. Tech has changed. Conversations are upleveling, skill gaps still there. So much has changed. >>So much has changed. There's so much evolution and so much innovation that we've also seen. You know, we started out the keynote this morning, standing room. Only thousands of people are here. Even though there's a mass requirement, the community that is CNCF Co Con is stronger than I, stronger than I saw it last year. This is only my second co con. But the collaboration, what they've done, their devotion to the maintainers, their devotion to really finding mentors for mentees was really a strong message this morning. And we heard a >>Lot of that today. And it's going beyond Kubernetes, even though it's called co con. I also call it cloud native con, which I think we'll probably end up being the name because at the end of day, the cloud native scaling, you're starting to see the pressure points. You're start to see where things are breaking, where automation's coming in, breaking in a good way. And we're gonna break it all down Again. So much going on again, I've overs gonna be in charge. Digital is transformation. If you take it to its conclusion, then you will see that the developers are running the business. It isn't a department, it's not serving the business, it is the business. If that's the case, everything has to change. And we're, we're happy to have Sarib here with us Cube analysts on the badge. I saw that with the press pass. Well, >>Thank you. Thanks for getting me that badge. So I'm here with you guys and >>Well, you got a rapport. Let's get into it. You, I >>Know. Let's hear what you gotta say. I'm excited. >>Yeah. Went around, actually attend some sessions and, and with the analysts were sitting in, in the media slash press, and I spoke to some people at their booth and the, there are a few, few patterns, you know, which are, some are the exaggeration of existing patterns or some are kind of new patterns emerging. So things are getting complex in open source. The lawn more projects, right. They have, the CNCF has graduated some projects even after graduation, they're, they're exploring, right? Kubernetes is one of those projects which has graduated. And on that front, just a side note, the new projects where, which are entering the cncf, they're the, we, we gotta see that process and the three stages and all that stuff. I tweeted all day long, if you wanna know what it is, you can look at my tweets. But when I will look, actually write right on that actually after, after the show ends, what, what I saw there, these new projects need to be curated properly. >>I think they need to be weed. There's a lot of noise in these projects. There's a lot of overlap. So the, the work is cut out for CNCF folks, by the way. They're sort of managerial committee or whatever you call that. The, the people who are leading it, they're try, I think they're doing their best and they're doing a good job of that. And another thing actually, I really liked in the morning's keynote was that lot of women on the stage and minorities represented. I loved it, to be honest with you. So believe me, I'm a minority even though I'm Indian, but from India, I'm a minority. So people who have Punjab either know that I'm a minority, so I, I understand their pain and how hard it is to, to break through the ceiling and all that. So I love that part as well. Yeah, the >>Activity is clear. Yeah. From day one. It's in the, it's in the dna. I mean, they'll reject anything that the opposite >>Representation too. I mean, it's not just that everyone's invited, it's they're celebrated and that's a very big difference. Yeah. It's, you see conferences offer discounts for women for tickets or minorities, but you don't necessarily see them put them running where their mouth is actually recruit the right women to be on stage. Right. Something you know a little bit about John >>Diversity brings better outcomes, better product perspectives. The product is better with all the perspectives involved. Percent, it might go a little slower, maybe a little debates, but it's all good. I mean, it's, to me, the better product comes when everyone's in. >>I hope you didn't just imply that women would make society. So >>I think John men, like slower means a slower, >>More diversity, more debate, >>The worst. Bringing the diversity into picture >>Wine. That's, that's how good groups, which is, which is >>Great. I mean, yeah, yeah, >>Yeah, yeah. I, I take that mulligan back and say, hey, you knows >>That's >>Just, it's gonna go so much faster and better and cheaper, but that not diversity. Absolutely. >>Yes. Well, you make better products faster because you have a variety >>Of perspectives. The bigger the group, there's more debate. More debate is key. But the key to success is aligning and committing. Absolutely. Once you have that, and that's what open sources has been about for. Oh God, yeah. Generations >>Has been a huge theme in the >>Show generations. All right, so, so, >>So you have to add another, like another important, so observation if you will, is that the security is, is paramount right. Requirement, especially for open source. There was a stat which was presented in the morning that 60% of the projects in under CNCF have more vulnerabilities today than they had last year. So that was, That's shocking actually. It's a big jump. It's a big jump. Like big jump means jump, jump means like it can be from from 40 to 60 or or 50 or 60. But still that percentage is high. What, what that means is that lot more people are contributing. It's very sort of di carmic or ironic that we say like, Oh this project has 10,000 contributors. Is that a good thing? Right. We do. Do we know the quality of that, where they're coming from? Are there any back doors being, you know, open there? How stringent is the process of rolling those things, which are being checked in, into production? You know, who is doing that? I've >>Wondered about that. Yeah. The quantity, quality, efficacy game. Yes. And what a balance that must be for someone like CNCF putting in the structure to try and >>That's >>Hard. Curate and regulate and, and you know, provide some bumpers on the bowling lane, so to speak, of, of all of these projects. Yeah. >>Yeah. We thought if anybody thought that the innovation coming from, or the number of services coming from AWS or Google Cloud or likes of them is overwhelming, look at open source, it's even more >>Overwhelming. What's your take on the supply chain discussion? More code more happening. What are you hearing there? >>The supply chain from the software? Yeah. >>Supply chain software, supply chain security pays. Are people talking about that? What are you >>Seeing? Yeah, actually people are talking about that. The creation, the curation, not creation. Curation of suppliers of software I think is best done in the cloud. Marketplaces Ive call biased or what, you know, but curation of open source is hard. It's hard to know which project to pick. It's hard to know which project will pan out. Many of the good projects don't see the day light of the day, but some decent ones like it becomes >>A marketing problem. Exactly. The more you have out there. Exactly. The more you gotta get above the noise. Exactly. And the noise echo that. And you got, you got GitHub stars, you got contributors, you have vanity metrics now coming in to this that are influencing what's real. But sometimes the best project could have smaller groups. >>Yeah, exactly. And another controversial thing a little bit I will say that is that there's a economics of the practitioner, right? I usually talk about that and economics of the, the enterprise, right? So practitioners in our world, in software world especially right in systems world, practitioners are changing jobs every two to three years. And number of developers doubles every three years. That's the stat I've seen from Uncle Bob. He's authority on that software side of things. Wow. So that means there's a lot more new entrance that means a lot of churn. So who is watching out for the enterprise enterprises economics, You know, like are we creating stable enterprises? How stable are our operations? On a side note to that, most of us see the software as like one band, which is not true. When we talk about all these roles and personas, somebody's writing software for, for core layer, which is the infrastructure part. Somebody's writing business applications, somebody's writing, you know, systems of bracket, some somebody's writing systems of differentiation. We talk about those things. We need to distinguish between those and have principle based technology consumption, which I usually write about in our Oh, >>So bottom line in Europe about it, in your opinion. Yeah. What's the top story here at coupon? >>Top story is >>Headline. Yeah, >>The, the headline. Okay. The open source cannot be ignored. That's a headline. >>And what should people be paying attention to if there's a trend coming out? See any kind of trends coming out or any kind of signal, What, what do you see that people should pay attention to here? The put top >>Two, three things. The signal is that, that if you are a big shop, like you'd need to assess your like capacity to absorb open source. You need to be certain size to absorb the open source. If you are below that threshold, I mean we can talk about that at some other time. Like what is that threshold? I will suggest you to go with the managed services from somebody, whoever is providing those managed services around open source. So manage es, right? So from, take it from aws, Google Cloud or Azure or IBM or anybody, right? So use open source as managed offering rather than doing it yourself. Because doing it yourself is a lot more heavy lifting. >>I I, >>There's so many thoughts coming, right? >>Mind it's, >>So I gotta ask you, what's your rapport? You have some swag, What's the swag look >>Like to you? I do. Just as serious of a report as you do on the to floor, but I do, so you know, I come from a marketing background and as I, I know that Lisa does as well. And one of the things that I think about that we touched on in this is, is you know, canceling the noise or standing out from the noise and, and on a show floor, that's actually a huge challenge for these startups, especially when you're up against a rancher or companies or a Cisco with a very large budget. And let's say you've only got a couple grand for an activation here. Like most of my clients, that's how I ended up in the CU County ecosystem, was here with the A client before. So there actually was a booth over there and I, they didn't quite catch me enough, but they had noise canceling headphones. >>So if you just wanted to take a minute on the show floor and just not hear anything, which I thought was a little bit clever, but gonna take you through some of my favorite swag from today and to all the vendors, you know, this is why you should really put some thought into your swag. You never know when you're gonna end up on the cube. So since most swag is injection molded plastic that's gonna end up in the landfill, I really appreciate that garden has given all of us a potable plant. And even the packaging is plantable, which is very exciting. So most sustainable swag goes to garden. Well done >>Rep replicated, I believe is their name. They do a really good job every year. They had some very funny pins that say a word that, I'm not gonna say live on television, but they have created, they brought two things for us, yet it's replicated little etch sketch for your inner child, which is very nice. And given that we are in Detroit, we are in Motor City, we are in the home of Ford. We had Ford on the show. I love that they have done the custom K eight s key chains in the blue oval logo. Like >>Fords right behind us by the way, and are on you >>Interviewed, we had 'em on earlier GitLab taking it one level more personal and actually giving out digital portraits today. Nice. Cool. Which is quite fun. Get lap house multiple booths here. They actually IPOed while they were on the show floor at CubeCon 2021, which is fun to see that whole gang again. And then last but not least, really embracing the ship wheel logo of a Kubernetes is the robusta accrue that is giving out bucket hats. And if you check out my Twitter at sabba Savvy, you can see me holding the ship wheel that they're letting everyone pose with. So we are all in on Kubernetes. That cove gone 2022, that's for sure. Yeah. >>And this is something, day one guys, we've got three. >>I wanna get one of those >>Hats. We we need to, we need a group photo >>By the end of Friday we will have a beverage and hats on to sign off. That's, that's my word. If I can convince John, >>Don, what's your takeaway? You guys did a great kind of kickoff about last week or so about what you were excited about, what your thoughts were going to be. We're only on day one, There's been thousands of people here, we've had great conversations with contributors, the community. What's your take on day one? What's your, what's your tagline? >>Well, Savannah and I had at we up, we, we were talking about what we might see and I think we, we were right. I think we had it right. There's gonna be a lot more people than there were last year. Okay, check. That's definitely true. We're in >>Person, which >>Is refreshing. I was very surprised about the mask mandate that kind of caught me up guard. I was major. Yeah. Cause I've been comfortable without the mask. I'm not a mask person, but I had to wear it and I was like, ah, mask. But I understand I support that. But whatever. It's >>Corporate travel policy. So you know, that's what it is. >>And then, you know, they, I thought that they did an okay job with the gates, but they wasn't slow like last time. But on the content side, definitely Kubernetes security, top line headline, Kubernetes at scale security, that's, that's to me the bumper sticker top things to pay attention to the supply chain and the role of docker and the web assembly was a surprise. You're starting to see containers ecosystem coming back to, I won't say tension growth in the functionality of containers cuz they have to solve the security problem in the container images. Okay, you got scanning technology so it's a little bit in the weeds, but there's a huge movement going on to fix that problem to scale it so it's not a problem area contain. And then Dr sent a great job with productivity interviews. Scott Johnston over a hundred million in revenue so far. That's my number. They have not publicly said that. That's what I'm reporting from sources extremely well financially. And they, and they love their business model. They make productivity for developers. That's a scoop. That's new >>Information. That's a nice scoop we just dropped there on the co casually. >>You're watching that. Pay attention to that. But that, that's proof. But guess what, Red Hat's got developers too. Yes. Other people have to, So developers gonna go where it's the best. Yeah. Developers are voting with their code, they're voting with their feet. You will see the winners with the developers and that's what we've talked about. >>Well and the companies are catering to the developers. Savannah and I had a great conversation with Ford. Yeah. You saw, you showed their fantastic swag was an E for Ev right behind us. They were talking about the, all the cultural changes that they've really focused on to cater towards the developers. The developers becoming the influencers as you say. But to see a company that is as, as historied as Ford Motor Company and what they're doing to attract and retain developer talent was impressive. And honestly that surprised me. Yeah. >>And their head of deb relations has been working for, for, for 29 years. Which I mean first of all, most companies on the show floor haven't been around for 29 years. Right. But what I love is when you put community first, you get employees to stick around. And I think community is one of the biggest themes here at Cuco. >>Great. My, my favorite story that surprised me and was cool was the Red Hat Lockheed Martin interview where they had edge deployments with micro edge, >>Micro shift, >>Micro >>Shift, new projects under, there's, there are three new projects under, >>Under that was so, so cool because it was an edge story in deployment for the military where lives are on the line, they actually had it working. That is a real world example of Kubernetes and tech orchestrating to deploy the industrial edge. And I think that's proof in my mind that Kubernetes and this ecosystem is gonna move faster through this next wave of growth. Because once things start clicking, you get hybrid on premise to super cloud and edge. That was, that was my favorite cause it was real. That was real >>Story that it can make is literally life and death on the battlefield. Yeah, that was amazing. With what they're doing and what >>They're talking check out the Lockheed Martin Red Hat edge story on Silicon Angle and then a press release all pillar. >>Yeah. Another actually it's impressive, which we knew this which is happening, but I didn't know that it was happening at this scale is the finops. The finops is, I saw your is a discipline which most companies are adopting bigger companies, which are spending like hundreds of millions dollars in cloud average. Si a team size of finops for finops is seven people. And average number of tools is I think 3.5 or around 3.7 or something like that. Average number of tools they use to control the cost. So finops is a very generic term for years. It's not financial operations, it's the financial operations for the cloud cost, you know, containing the cloud costs. So that's a finops that is a very emerging sort of discipline >>To keep an eye on. And well, not only is that important, I talked to, well one of the principles over there, it's growing and they have real big players in that foundation. Their, their events are highly attended. It's super important. It's just, it's the cost side of cloud. And, and of course, you know, everyone wants to know what's going on. No one wants to leave there. Their Amazon on Yeah, you wanna leave the lights on the cloud, as we always say, you never know what the bill's gonna look like. >>The cloud is gonna reach $3 billion in next few years. So we might as well control the cost there. Yeah, >>It was, it was funny to get the reaction I found, I don't know if I was, how I react, I dunno how I felt. But we, we did introduce Super Cloud to a couple of guests and a, there were a couple reactions, a couple drawn. There was a couple, right. There was a couple, couple reactions. And what I love about the super cloud is that some people are like, oh, cringing. And some people are like, yeah, go. So it's a, it's a solid debate. It is solid. I saw more in the segments that I did with you together. People leaning in. Yeah. Super fun. We had a couple sum up, we had a couple, we had a couple cringes, I'll say their names, but I'll go back and make sure I, >>I think people >>Get 'em later. I think people, >>I think people cringe on the, on the term not on the idea. Yeah. You know, so the whole idea is that we are building top of the cloud >>And then so I mean you're gonna like this, I did successfully introduce here on the cube, a new term called architectural list. He did? That's right. Okay. And I wanna thank Charles Fitzgerald for that cuz he called super cloud architectural list. And that's exactly the point of super cloud. If you have a great coding environment, you shouldn't have to do an architecture to do. You should code and let the architecture of the Super cloud make it happen. And of course Brian Gracely, who will be on tomorrow at his cloud cast said Super Cloud enables super services. Super Cloud enables what Super services, super service. The microservices underneath the covers have to be different. High performing, automated. So again, the debate and Susan, the goal is to keep it open. And that's our, that's our goal. But we had a lot of fun with that. It was fun to poke the bear a little bit. So >>What is interesting to see just how people respond to it too, with you throwing it out there so consistently, >>You wanna poke the bear, get a conversation going, you know, let let it go. We'll see, it's been positive so far. >>There, there I had a discussion outside somebody who is from Ford but not attending this conference and they have been there for a while. I, I just some moment hit like me, like I said, people, okay, technologists are horizontal, the codes are horizontal. They will go from four to GM to Chrysler to Bank of America to, you know, GE whatever, you know, like cross vertical within vertical different vendors. So, but the culture of a company is local, right? Right. Ford has been building cars for forever. They sort of democratize it. They commercialize it, right? But they have some intense culture. It's hard to change those cultures. And how do we bring in the new thinking? What is, what approach that should be? Is it a sandbox approach for like putting new sensors on the car? They have to compete with te likes our Tesla, right? Yeah. But they cannot, if they are afraid of deluding their existing market or they're afraid of failure there, right? So it's very >>Tricky. Great stuff. Sorry. Great to have you on as our cube analyst breaking down the stories. We'll document that, that we'll roll out a post on it. Lisa Savannah, let's wrap up the show for day one. We got day two and three. We'll start with you. What's your summary? Quick bumper sticker. What's today's show all about? >>I'm a community first gal and this entire experience is about community and it's really nice to see the community come together, celebrate that, share ideas, and to have our community together on stage. >>Yeah. To me, to me it was all real. It's happening. Kubernetes cloud native at scale, it's happening, it's real. And we see proof points and we're gonna have faster time to value. It's gonna accelerate faster from here. >>The proof points, the impact is real. And we saw that in some amazing stories. And this is just a one of the cubes >>Coverage. Ib final word on this segment was well >>Said Lisa. Yeah, I, I think I, I would repeat what I said. I got eight, nine years back at a rack space conference. Open source is amazing for one biggest reason. It gives the ability to the developing nations to be at somewhat at par where the dev develop nations and, and those people to lift up their masses through the automation. Cuz when automation happens, the corruption goes down and the economy blossoms. And I think it's great and, and we need to do more in it, but we have to be careful about the supply chains around the software so that, so our systems are secure and they are robust. Yeah, >>That's it. Okay. To me for SAR B and my two great co-host, Lisa Martin, Savannah Peterson. I'm John Furry. You're watching the Cube Day one in, in the Books. We'll see you tomorrow, day two Cuban Cloud Native live in Detroit. Thanks for watching.

>>Okay. We're back with Justin Boorman CEO of Starburst. Richard Jarvis is the CTO of EMI health and Teresa tongue is the cloud first technologist from Accenture. We're on July number three. And that is the claim that today's modern data stack is actually modern. So I guess that's the lie or it's it is it's is that it's not modern, Justin, what do you say? >>Yeah, I mean, I think new isn't modern, right? I think it's, the's the new data stack. It's the cloud data stack, but that doesn't necessarily mean it's modern. I think a lot of the components actually are exactly the same as what we've had for 40 years, rather than Terra data. You have snowflake rather than Informatica you have five trend. So it's the same general stack, just, you know, a cloud version of it. And I think a lot of the challenges that it plagued us for 40 years still maintain. >>So lemme come back to you just this, but okay. But, but there are differences, right? I mean, you can scale, you can throw resources at the problem. You can separate compute from storage. You really, you know, there's a lot of money being thrown at that by venture capitalists and snowflake, you mentioned it's competitors. So that's different. Is it not, is that not at least an aspect of, of modern dial it up, dial it down. So what, what do you say to that? >>Well, it, it is, it's certainly taking, you know, what the cloud offers and taking advantage of that, but it's important to note that the cloud data warehouses out there are really just separating their compute from their storage. So it's allowing them to scale up and down, but your data's still stored in a proprietary format. You're still locked in. You still have to ingest the data to get it even prepared for analysis. So a lot of the same sort of structural constraints that exist with the old enterprise data warehouse model OnPrem still exists just, yes, a little bit more elastic now because the cloud offers that. >>So Theresa, let me go to you cuz you have cloud first in your, in your, your title. So what's what say you to this conversation? >>Well, even the cloud providers are looking towards more of a cloud continuum, right? So the centralized cloud, as we know it, maybe data lake data warehouse in the central place, that's not even how the cloud providers are looking at it. They have news query services. Every provider has one that really expands those queries to be beyond a single location. And if we look at a lot of where our, the future goes, right, that that's gonna very much fall the same thing. There was gonna be more edge. There's gonna be more on premise because of data sovereignty, data gravity, because you're working with different parts of the business that have already made major cloud investments in different cloud providers. Right? So there's a lot of reasons why the modern, I guess the next modern generation of the data staff needs to be much more federated. >>Okay. So Richard, how do you deal with this? You you've obviously got, you know, the technical debt, the existing infrastructure it's on the books. You don't wanna just throw it out. A lot of, lot of conversation about modernizing applications, which a lot of times is a, you know, of microservices layer on top of leg legacy apps. Ho how do you think about the modern data stack? >>Well, I think probably the first thing to say is that the stack really has to include the processes and people around the data as well is all well and good changing the technology. But if you don't modernize how people use that technology, then you're not going to be able to, to scale because just cuz you can scale CPU and storage doesn't mean you can get more people to use your data, to generate you more value for the business. And so what we've been looking at is really changing in very much aligned to data products and, and data mesh. How do you enable more people to consume the service and have the stack respond in a way that keeps costs low? Because that's important for our customers consuming this data, but also allows people to occasionally run enormous queries and then tick along with smaller ones when required. And it's a good job we did because during COVID all of a sudden we had enormous pressures on our data platform to answer really important life threatening queries. And if we couldn't scale both our data stack and our teams, we wouldn't have been able to answer those as quickly as we had. So I think the stack needs to support a scalable business, not just the technology itself. >>Oh thank you for that. So Justin let's, let's try to break down what the critical aspects are of the modern data stack. So you think about the past, you know, five, seven years cloud obviously has given a different pricing model. Drisk experimentation, you know that we talked about the ability to scale up scale down, but it's, I'm, I'm taking away that that's not enough based on what Richard just said. The modern data stack has to serve the business and enable the business to build data products. I, I buy that I'm, you know, a big fan of the data mesh concepts, even though we're early days. So what are the critical aspects if you had to think about, you know, the paying, maybe putting some guardrails and definitions around the modern data stack, what does that look like? What are some of the attributes and principles there >>Of, of how it should look like or, or how >>Yeah. What it should be? >>Yeah. Yeah. Well, I think, you know, in Theresa mentioned this in, in a previous segment about the data warehouse is not necessarily going to disappear. It just becomes one node, one element of the overall data mesh. And I, I certainly agree with that. So by no means, are we suggesting that, you know, snowflake or Redshift or whatever cloud data warehouse you may be using is going to disappear, but it's, it's not going to become the end all be all. It's not the, the central single source of truth. And I think that's the paradigm shift that needs to occur. And I think it's also worth noting that those who were the early adopters of the modern data stack were primarily digital, native born in the cloud young companies who had the benefit of, of idealism. They had the benefit of starting with a clean slate that does not reflect the vast majority of enterprises. >>And even those companies, as they grow up mature out of that ideal state, they go by a business. Now they've got something on another cloud provider that has a different data stack and they have to deal with that heterogeneity that is just change and change is a part of life. And so I think there is an element here that is almost philosophical. It's like, do you believe in an absolute ideal where I can just fit everything into one place or do I believe in reality? And I think the far more pragmatic approach is really what data mesh represents. So to answer your question directly, I think it's adding, you know, the ability to access data that lives outside of the data warehouse, maybe living in open data formats in a data lake or accessing operational systems as well. Maybe you want to directly access data that lives in an Oracle database or a Mongo database or, or what have you. So creating that flexibility to really Futureproof yourself from the inevitable change that you will, you won't encounter over time. >>So thank you. So there, based on what Justin just said, I, I might take away there is it's inclusive, whether it's a data Mart, data hub, data lake data warehouse, it's a, just a node on the mesh. Okay. I get that. Does that include Theresa on, on Preem data? Obviously it has to, what are you seeing in terms of the ability to, to take that data mesh concept on pre I mean most implementations I've seen and data mesh, frankly really aren't, you know, adhering to the philosophy there. Maybe, maybe it's data lake and maybe it's using glue. You look at what JPMC is doing. Hello, fresh, a lot of stuff happening on the AWS cloud in that, you know, closed stack, if you will. What's the answer to that Theresa? >>I mean, I, I think it's a killer case for data mesh. The fact that you have valuable data sources, OnPrem, and then yet you still wanna modernize and take the best of cloud cloud is still, like we mentioned, there's a lot of great reasons for it around the economics and the way ability to tap into the innovation that the cloud providers are giving around data and AI architecture. It's an easy button. So the mesh allows you to have the best of both world. You can start using the data products on-prem or in the existing systems that are working already. It's meaningful for the business. At the same time, you can modernize the ones that make business sense because it needs better performance. It needs, you know, something that is, is cheaper or, or maybe just tap into better analytics to get better insights, right? So you're gonna be able to stretch and really have the best of both worlds that, again, going back to Richard's point, that is needful by the business. Not everything has to have that one size fits all set a tool. >>Okay. Thank you. So Richard, you know, you're talking about data as product. Wonder if we could give us your perspectives here, what are the advantages of treating data as a product? What, what role do data products have in the modern data stack? We talk about monetizing data. What are your thoughts on data products? >>So for us, one of the most important data products that we've been creating is taking data that is healthcare data across a wide variety of different settings. So information about patients' demographics about their, their treatment, about their medications and so on, and taking that into a standards format that can be utilized by a wide variety of different researchers because misinterpreting that data or having the data not presented in the way that the user is expecting means that you generate the wrong insight and in any business, that's clearly not a desirable outcome, but when that insight is so critical, as it might be in healthcare or some security settings, you really have to have gone to the trouble of understanding the data, presenting it in a format that everyone can clearly agree on. And then letting people consume in a very structured and managed way, even if that data comes from a variety of different sources in, in, in the first place. And so our data product journey has really begun by standardizing data across a number of different silos through the data mesh. So we can present out both internally and through the right governance externally to, to research is >>So that data product through whatever APIs is, is accessible, it's discoverable, but it's obviously gotta be governed as well. You mentioned appropriately provided to internally. Yeah. But also, you know, external folks as well. So the, so you've, you've architected that capability today >>We have and because the data is standard, it can generate value much more quickly and we can be sure of the security and, and, and value that that's providing because the data product isn't just about formatting the data into the right, correct tables, it's understanding what it means to redact the data or to remove certain rows from it or to interpret what a date actually means. Is it the start of the contract or the start of the treatment or the date of birth of a patient? These things can be lost in the data storage without having the proper product management around the data to say in a very clear business context, what does this data mean? And what does it mean to process this data for a particular use >>Case? Yeah, it makes sense. It's got the context. If the, if the domains on the data, you, you gotta cut through a lot of the, the, the centralized teams, the technical teams that, that data agnostic, they don't really have that context. All right. Let's end, Justin, how does Starburst fit into this modern data stack? Bring us home. >>Yeah. So I think for us, it's really providing our customers with, you know, the flexibility to operate and analyze data that lives in a wide variety of different systems. Ultimately giving them that optionality, you know, and optionality provides the ability to reduce costs, store more in a data lake rather than data warehouse. It provides the ability for the fastest time to insight to access the data directly where it lives. And ultimately with this concept of data products that we've now, you know, incorporated into our offering as well, you can really create and, and curate, you know, data as a product to be shared and consumed. So we're trying to help enable the data mesh, you know, model and make that an appropriate compliment to, you know, the, the, the modern data stack that people have today. >>Excellent. Hey, I wanna thank Justin Teresa and Richard for joining us today. You guys are great. I big believers in the, in the data mesh concept, and I think, you know, we're seeing the future of data architecture. So thank you. Now, remember, all these conversations are gonna be available on the cube.net for on-demand viewing. You can also go to starburst.io. They have some great content on the website and they host some really thought provoking interviews and, and, and they have awesome resources, lots of data mesh conversations over there, and really good stuff in, in the resource section. So check that out. Thanks for watching the data doesn't lie or does it made possible by Starburst data? This is Dave ante for the, and we'll see you next time.

>>Welcome to the cube. I'm Dave Volante. Today, we're going to explore the ebb and flow of data as it travels into the cloud. In the data lake, the concept of data lakes was a Loring when it was first coined last decade by CTO James Dickson, rather than be limited to highly structured and curated data that lives in a relational database in the form of an expensive and rigid data warehouse or a data Mart, a data lake is formed by flowing data from a variety of sources into a scalable repository, like say an S3 bucket that anyone can access, dive into. They can extract water. It can a data from that lake and analyze data. That's much more fine-grained and less expensive to store at scale. The problem became that organizations started to dump everything into their data lakes with no schema on it, right? No metadata, no context to shove it into the data lake and figure out what's valuable. >>At some point down the road kind of reminds you of your attic, right? Except this is an attic in the cloud. So it's too big to clean out over a weekend. We'll look it's 2021 and we should be solving this problem by now, a lot of folks are working on this, but often the solutions at other complexities for technology pros. So to understand this better, we're going to enlist the help of chaos search CEO and Walsh and Thomas Hazel, the CTO and founder of chaos search. We're also going to speak with Kevin Miller. Who's the vice president and general manager of S3 at Amazon web services. And of course they manage the largest and deepest data lakes on the planet. And we'll hear from a customer to get their perspective on this problem and how to go about solving it, but let's get started. Ed Thomas. Great to see you. Thanks for coming on the cube. Likewise face. It's really good to be in this nice face. Great. So let me start with you. We've been talking about data lakes in the cloud forever. Why is it still so difficult to extract value from those data? >>Good question. I mean, a data analytics at scale is always been a challenge, right? So, and it's, uh, we're making some incremental changes. As you mentioned that we need to seem some step function changes, but, uh, in fact, it's the reason, uh, search was really founded. But if you look at it the same challenge around data warehouse or a data lake, really, it's not just a flowing the data in is how to get insights out. So it kind of falls into a couple of areas, but the business side will always complain and it's kind of uniform across everything in data lakes, everything that we're offering, they'll say, Hey, listen, I typically have to deal with a centralized team to do that data prep because it's data scientist and DBS. Most of the time they're a centralized group, sometimes are business units, but most of the time, because they're scarce resources together. >>And then it takes a lot of time. It's arduous, it's complicated. It's a rigid process of the deal of the team, hard to add new data. But also it's hard to, you know, it's very hard to share data and there's no way to governance without locking it down. And of course they would be more self-service. So there's you hear from the business side constantly now underneath is like, there's some real technology issues that we haven't really changed the way we're doing data prep since the two thousands. Right? So if you look at it, it's, it falls, uh, two big areas. It's one. How do data prep, how do you take a request comes in from a business unit. I want to do X, Y, Z with this data. I want to use this type of tool sets to do the following. Someone has to be smart, how to put that data in the right schema. >>You mentioned you have to put it in the right format, that the tool sets can analyze that data before you do anything. And then secondly, I'll come back to that because that's a biggest challenge. But the second challenge is how these different data lakes and data we're also going to persisting data and the complexity of managing that data and also the cost of computing. And I'll go through that. But basically the biggest thing is actually getting it from raw data so that the rigidness and complexity that the business sides are using it is literally someone has to do this ETL process extract, transform load. They're actually taking data request comes in. I need so much data in this type of way to put together their Lilly, physically duplicating data and putting it together and schema they're stitching together almost a data puddle for all these different requests. >>And what happens is anytime they have to do that, someone has to do it. And it's very skilled. Resources are scant in the enterprise, right? So it's a DBS and data scientists. And then when they want new data, you give them a set of data set. They're always saying, what can I add this data? Now that I've seen the reports, I want to add this data more fresh. And the same process has to happen. This takes about 60 to 80% of the data scientists in DPA's to do this work. It's kind of well-documented. Uh, and this is what actually stops the process. That's what is rigid. They have to be rigid because there's a process around that. Uh, that's the biggest challenge to doing this. And it takes in the enterprise, uh, weeks or months. I always say three weeks to three months. And no one challenges beyond that. It also takes the same skill set of people that you want to drive. Digital transformation, data, warehousing initiatives, uh, monitorization being, data driven, or all these data scientists and DBS. They don't have enough of, so this is not only hurting you getting insights out of your dead like that, or else it's also this resource constraints hurting you actually getting smaller. >>The Tomic unit is that team that's super specialized team. Right. Right. Yeah. Okay. So you guys talk about activating the data lake. Yep, sure. Analytics, what what's unique about that? What problems are you all solving? You know, when you guys crew created this, this, this magic sauce. >>No, and it basically, there's a lot of things I highlighted the biggest one is how to do the data prep, but also you're persisting and using the data. But in the end, it's like, there's a lot of challenges that how to get analytics at scale. And this is really where Thomas founded the team to go after this. But, um, I'll try to say it simply, what are we doing? I'll try to compare and stress what we do compared to what you do with maybe an elastic cluster or a BI cluster. Um, and if you look at it, what we do is we simply put your data in S3, don't move it, don't transform it. In fact, we're not we're against data movement. What we do is we literally pointed at that data and we index that data and make it available in a data representation that you can give virtual views to end users. >>And those virtual views are available immediately over petabytes of data. And it re it actually gets presented to the end user as an open API. So if you're elastic search user, you can use all your lesser search tools on this view. If you're a SQL user, Tableau, Looker, all the different tools, same thing with machine learning next year. So what we do is we take it, make it very simple. Simply put it there. It's already there already. Point is at it. We do the hard of indexing and making available. And then you publish in the open API as your users can use exactly what they do today. So that's dramatically. I'll give you a before and after. So let's say you're doing elastic search. You're doing logging analytics at scale, they're lending their data in S3. And then they're,, they're physically duplicating a moving data and typically deleting a lot of data to get in a format that elastic search can use. >>They're persisting it up in a data layer called leucine. It's physically sitting in memories, CPU, uh, uh, SSDs. And it's not one of them. It's a bunch of those. They in the cloud, you have to set them up because they're persisting ECC. They stand up semi by 24, not a very cost-effective way to the cloud, uh, cloud computing. What we do in comparison to that is literally pointing it at the same S3. In fact, you can run a complete parallel, the data necessary. It's being ETL. That we're just one more use case read only, or allow you to get that data and make this virtual views. So we run a complete parallel, but what happens is we just give a virtual view to the end users. We don't need this persistence layer, this extra cost layer, this extra, um, uh, time cost and complexity of doing that. >>So what happens is when you look at what happens in elastic, they have a constraint, a trade-off of how much you can keep and how much you can afford to keep. And also it becomes unstable at time because you have to build out a schema. It's on a server, the more the schema scales out, guess what you have to add more servers, very expensive. They're up seven by 24. And also they become brittle. As you lose one node. The whole thing has to be put together. We have none of that cost and complexity. We literally go from to keep whatever you want, whatever you want to keep an S3, a single persistence, very cost effective. And what we do is, um, costs. We save 50 to 80% why we don't go with the old paradigm of sit it up on servers, spin them up for persistence and keep them up. >>Somebody 24, we're literally asking her cluster, what do you want to cut? We bring up the right compute resources. And then we release those sources after the query done. So we can do some queries that they can't imagine at scale, but we're able to do the exact same query at 50 to 80% savings. And they don't have to do any of the toil of moving that data or managing that layer of persistence, which is not only expensive. It becomes brittle. And then it becomes an I'll be quick. Once you go to BI, it's the same challenge, but the BI systems, the requests are constant coming at from a business unit down to the centralized data team. Give me this flavor of debt. I want to use this piece of, you know, this analytic tool in that desk set. So they have to do all this pipeline. They're constantly saying, okay, I'll give you this data, this data I'm duplicating that data. I'm moving in stitching together. And then the minute you want more data, they do the same process all over. We completely eliminate that. >>The questions queue up, Thomas, it had me, you don't have to move the data. That's, that's kind of the >>Writing piece here. Isn't it? I absolutely, no. I think, you know, the daylight philosophy has always been solid, right? The problem is we had that who do hang over, right? Where let's say we were using that platform, little, too many variety of ways. And so I always believed in daily philosophy when James came and coined that I'm like, that's it. However, HTFS that wasn't really a service cloud. Oddish storage is a service that the, the last society, the security and the durability, all that benefits are really why we founded, uh, Oncotype storage as a first move. >>So it was talking Thomas about, you know, being able to shut off essentially the compute and you have to keep paying for it, but there's other vendors out there and stuff like that. Something similar as separating, compute from storage that they're famous for that. And, and, and yet Databricks out there doing their lake house thing. Do you compete with those? How do you participate and how do you differentiate? >>I know you've heard this term data lakes, warehouse now, lake house. And so what everybody wants is simple in easy N however, the problem with data lakes was complexity of out driving value. And I said, what if, what if you have the easy end and the value out? So if you look at, uh, say snowflake as a, as a warehousing solution, you have to all that prep and data movement to get into that system. And that it's rigid static. Now, Databricks, now that lake house has exact same thing. Now, should they have a data lake philosophy, but their data ingestion is not daily philosophy. So I said, what if we had that simple in with a unique architecture, indexed technology, make it virtually accessible publishable dynamically at petabyte scale. And so our service connects to the customer's cloud storage data, stream the data in set up what we call a live indexing stream, and then go to our data refinery and publish views that can be consumed the lasted API, use cabana Grafana, or say SQL tables look or say Tableau. And so we're getting the benefits of both sides, you know, schema on read, write performance with scheme on, right. Reperformance. And if you can do that, that's the true promise of a data lake, you know, again, nothing against Hadoop, but a schema on read with all that complexity of, uh, software was, uh, what was a little data, swamp >>Got to start it. Okay. So we got to give a good prompt, but everybody I talked to has got this big bunch of spark clusters now saying, all right, this, this doesn't scale we're stuck. And so, you know, I'm a big fan of and our concept of the data lake and it's it's early days. But if you fast forward to the end of the decade, you know, what do you see as being the sort of critical components of this notion of, you know, people call it data mesh, but you've got the analytics stack. Uh, you, you, you're a visionary Thomas, how do you see this thing playing out over the next? >>I love for thought leadership, to be honest, our core principles were her core principles now, you know, 5, 6, 7 years ago. And so this idea of, you know, de centralize that data as a product, you know, self-serve and, and federated, computer, uh, governance, I mean, all that, it was our core principle. The trick is how do you enable that mesh philosophy? We, I could say we're a mesh ready, meaning that, you know, we can participate in a way that very few products can participate. If there's gates data into your system, the CTLA, the schema management, my argument with the data meshes like producers and consumers have the same rights. I want the consumer people that choose how they want to consume that data, as well as the producer publishing it. I can say our data refinery is that answer. You know, shoot, I love to open up a standard, right, where we can really talk about the producers and consumers and the rights each others have. But I think she's right on the philosophy. I think as products mature in this cloud, in this data lake capabilities, the trick is those gates. If you have the structure up front, it gets at those pipelines. You know, the chance of you getting your data into a mesh is the weeks and months that it was mentioning. >>Well, I think you're right. I think the problem with, with data mesh today is the lack of standards you've got. You know, when you draw the conceptual diagrams, you've got a lot of lollipops, which are API APIs, but they're all, you know, unique primitives. So there aren't standards by which to your point, the consumer can take the data the way he or she wants it and build their own data products without having to tap people on the shoulder to say, how can I use this? Where's the data live and, and, and, and, and being able to add their own >>You're exactly right. So I'm an organization I'm generally data will be courageous, a stream it to a lake. And then the service, uh, Ks search service is the data's con uh, discoverable and configurable by the consumer. Let's say you want to go to the corner store? You know, I want to make a certain meal tonight. I want to pick and choose what I want, how I want it. Imagine if the data mesh truly can have that producer of information, you, all the things you can buy a grocery store and what you want to make for dinner. And if you'd static, if you call up your producer to do the change, was it really a data mesh enabled service? I would argue not that >>Bring us home >>Well. Uh, and, um, maybe one more thing with this, cause some of this is we talking 20, 31, but largely these principles are what we have in production today, right? So even the self service where you can actually have business context on top of a debt, like we do that today, we talked about, we get rid of the physical ETL, which is 80% of the work, but the last 20% it's done by this refinery where you can do virtual views, the right our back and do all the transformation need and make it available. But also that's available to, you can actually give that as a role-based access service to your end users actually analysts, and you don't want to be a data scientist or DBA in the hands of a data science. The DBA is powerful, but the fact of matter, you don't have to affect all of our employees, regardless of seniority. If they're in finance or in sales, they actually go through and learn how to do this. So you don't have to be it. So part of that, and they can come up with their own view, which that's one of the things about debt lakes, the business unit wants to do themselves, but more importantly, because they have that context of what they're trying to do instead of queuing up the very specific request that takes weeks, they're able to do it themselves and to find out that >>Different data stores and ETL that I can do things in real time or near real time. And that's that's game changing and something we haven't been able to do, um, ever. Hmm. >>And then maybe just to wrap it up, listen, um, you know, eight years ago is a group of founders came up with the concept. How do you actually get after analytics at scale and solve the real problems? And it's not one thing it's not just getting S3, it's all these different things. And what we have in market today is the ability to literally just simply stream it to S3 by the way, simply do what we do is automate the process of getting the data in a representation that you can now share an augment. And then we publish open API. So can actually use a tool as you want first use case log analytics, Hey, it's easy to just stream your logs in and we give you elastic search puppet services, same thing that with CQL, you'll see mainstream machine learning next year. So listen, I think we have the data lake, you know, 3.0 now, and we're just stretching our legs run off >>Well, and you have to say it log analytics. But if I really do believe in this concept of building data products and data services, because I want to sell them, I want to monetize them and being able to do that quickly and easily, so that can consume them as the future. So guys, thanks so much for coming on the program. Really appreciate it. All right. In a moment, Kevin Miller of Amazon web services joins me. You're watching the cube, your leader in high tech coverage.

>>Welcome back to the cubes cover dot com. Splunk annual conference >>Virtual this year. I'm john for >>your host of the cube as always we're being the best stories. The best guest to you and the best guest today is the ceo Doug merit of course, Top Dog. It's great to see you. Thanks for coming on to be seen. >>So nice. I can't believe it. We had a whole year without seeing each other. >>I love this conference because it's kind of like a studio taking over a full virtual studio multiple sets, cubes here. You have the main stage, you've got rooms upstairs, tons of virtual interactions. Great numbers. Congratulations. >>Thank you. Thank you. We were, we wanted this to be primarily live where we are live, primarily on site. Um, and we pivoted some private marketing team. How quickly they pivoted and I love the environment they've created as I know next year we will be always have virtual now we've all learned but will be on site, which is great. >>It's good to see kind of you guys telling the story a lot, a lot more stories happening and You know, we've been covering splint since 2012 on the Cube. I think longer than aws there was 2013 our first cube seeing Splunk emerge is the trend has been, it's new, it's got value and you operationalize it for customers. Something new happens. You operationalized for customers and it just keeps on the Splunk way, the culture of innovation. It just seems now more than ever. You guys were involved in security early 2015 I think that was the year we started kind of talking about it your first year and now it just feels like something bigger is right here in front of us. It's and people are trying to figure out multi cloud observe ability. We see that what that's a big growth wave coming. What's the wave that's happening? >>So uh the beauty of Splunk and the kind of culture and how we were born was we have this non structured backbone um what I would call the investigative lake where you just dump garbage into it and then get value out of it through the question asking which means you can traverse anywhere because you're not taking a point of view on the data it's usable all over the place. And that's how we went up in security. As we had the I. T. Systems administrators pinging that thing with with questions. And at that point in time the separate teams were almost always part of the I. T. Teams like hey can we ask questions that thing. It's like yeah go ahead. And also they got value. And then the product managers and the app dev guys started asking questions. And so a lot of our proliferation has been because of the underlying back bonus blank the ability for new people to come to the data and find value in the data. Um as you know and as our users know we have tried to stay very focused on the go to market basis on serving the technical triumphant the cyber teams, the infrastructure management, 90 ops teams and the abdomen devoPS teams and on the go to market basis and the solutions we package that is, we're trying to stay super pure to that. That's $90 billion of total addressable market. We're super excited will be well over three billion an error this year, which is amazing is 300 million when I started seven years ago so that 10 x and seven years is great. But three billion and 90 billion like we're all just getting going right now with those Corbyn centers. The were on top of what sean bison as we tell you about, hey, we've got to continue to focus on multi cloud and edge is really important. Machine learning is important. That the lever that we've been focused on for a long time that we'll continue to gain better traction on is making sure that we've got the right data plane and application platform layer so that the rest of the world can participate in building high quality reusable and recyclable applications so that operate operationalization that we have done officially around cyber it and devops and unofficially on a one off basis for marketing and supply chain and logistics and manufacturing that those other use cases can be packaged repeated, sold and supported by the people that really know those domains because we're not manufacturing experts. It's we're honored that portion BMW are using us to get operational insight into the manufacturing floor. But they lead that we just were there is the technical Splunk people to help bring that to life. But there are lots of firms out there, no manufacturing cold process versus the screed and they can create with these packages. They're appropriate for automotive, automotive versus paint versus wineries versus having that. I think the big Accelerant over the next 10 years response, we gotta keep penetrating our core use cases but it would be allowing our ecosystem and so happy Teresa Karlsson's here is just pounding the table and partners to take the other probably 90% of the market that is not covered by by our core market. >>Yeah, I think that's awesome. And the first time we get to the partner 1st and 2nd the rebranding of the ecosystem as it's growing. But you mentioned you didn't know manufacturing as an example where the value is being created. That's interesting because you guys are enabling that value, their adding that because they know their apps then they're experts. That's where the ecosystem is really gonna shine because if you can provide that enablement this control plane as you mentioned, that's going to feed the ecosystem. So the question I have for you is as you guys have become essentially the de facto control playing for most companies because they were using spring for a lot of other great reasons now you have set them up that way is the pattern to just keep building machine learning apps on top of it or more querying what's the what's the customer next level trends that you're seeing. >>So the two core focus areas that we will stay on top of is enriching that data platform and ensure that we continue to provide better at peace and better interfaces so that when people want to build a really interesting automotive parts, supply chain optimization app that they're able to do that, we've got the right A. P. S. We've got the right services, we've got the right separation between the application of platforms so they can get that done, we'll continue to advance that platform so that there's modernization capabilities and there's advertising capabilities and other pieces that they can make their business. The other piece that will stay very focused on is within the cyber realm within I. T. Ops within devops, ensuring that we're leveraging that platform, but baking ml and baking all the advanced edge and other capabilities into those solutions because the cyber teams as where you started with a You know, we really started reporting on cyber 2015, those guys have got such a hard job and while there's lots of people pretending like they're going to come in and serve them, it's the difficulty is there are hundreds of tools and technologies that the average C so deals with and the rate of innovation is not slowing down and those vendors that have a vested interest and I want to maintain my footprint and firewalls, I want to maintain an implant, I want to maintain. It's really hard for them to say, you know what? There are 25 other categories of tools and there's 500 vendors. You gotta play nicely with your competitors and know all those folks if you really want to provide the ml the detection, the remediation, The investigation capabilities. And that's where I'm really excited about the competition. The fake competition in many cases because like, yeah, bring it on. Like I've got 2000 engineers, all they do all day long is focused on the data layer and making sure that we're effective there and I'm not diverting my engineers with any other tasks that I've got a it's hard enough to do what we do in the day layers. Well, >>it's interesting. I just had some notes here, I had one data driven innovation you've been talking about since you've been here. We've been talking about data driven innovation, cybersecurity mentioned for many years, it's almost like the balance of you gotta have tools, but you gotta have the platform. If you have too many tools and no platform, then there's a mix match here and you get hung up with tools and these blind spots. You can't have blind spots, you can't have silos. This is what kind of everyone's pretty much agreeing on right now. It's not a debate. It's more like, okay, I got silos and I got blind spots. Well how do I solve >>the difficulty? And I touched a little bit of the sun my keynote of There are well over 60 and I was using 16 because DB engines categorizes 16 different database tools. But there's actually more if you go deeper. So there's different 16 different categories of database tools. Think relational database, data warehouse, ledger databases, graph database, et cetera over 16 categories those 350 vendors. That's not because we're all stupid in tech like a graph DB is different than a relational database, which is different than what we do with our stimulus index. So there's those categories that many vendors because they're trying to solve different problems within the swim lane that you are in which for us is this non structured, high volume difficult data to manage Now. The problem is how do you create that non broken that end to end view. So you can handle your use cases effectively. Um and then the customer is still going to do with the fact that we're not a relational database engine company. We're not a data warehousing company where we were beginning to use graph DB capabilities within our our solution sets. We're gonna lean on open source other vendors use the tool for the job >>you need. But I think that what you're thinking hitting on my like is this control plane idea. I want to get back to that because if you think about what the modern application developers want is they want devops and deVOps kind of one infrastructures codes there. But if I'm a modern developer, I just want to code, >>I don't want to configure >>the data or the infrastructure. So the data value now is so much more important for the developer, whether that's policy based innovation, get options, some people call it A I ops, these are big trends. This is fairly new in the sense of being mainstream. It's been around for a couple of years, but this time, how do you see the data being much more of a developer input. >>People talk about deVOps is a new thing when I was running on the HR products at Peoplesoft in 2000 and four, we had a deVOPS teams. So that is, you know, there's always been a group of people whether Disney or not that are kind of managing the manufacturing floor for your developers, making sure they got the right tools and databases and what's new is because the ephemeral nature of cloud, that app dev work and devops and everyone that surrounds those or is now 100% data driven because you have ephemeral services, they're popping up and popping down. And if you're not able to trap the data that are each one of those services are admitting and do it on a real time basis and a thorough, complete basis, you can't sample then you are flying blind and that's not gonna work when you've got a critical code push for a feature your customers demanding and if you don't get it out, your competitors are, you need to have assurance that you've done the right things and that the quality and and the actual deployment actually works And that's where what lettuce tubes or ability Three years ago as we roughly started doing our string of acquisitions is we saw that transition from a state full world where it was all transaction engine driven. I've got to insert transaction and engines in a code. Very different engineering problem to I've got to grab data and it's convoluted data. It's chaotic data. It's changing all the time. Well, jeez that sounds and latency >>issues to they're gonna be doing fast. >>I've got to do it. You literally millisecond by millisecond. You've got are are bigger customers were honored because of how we operate. Splunk to serve some of the biggest web properties in the in the globe and they're dealing with hundreds of terabytes to petabytes of data per day that are traversing these pipes and you've got to be able to extract metrics that entire multi petabyte or traces that entire multi pedal extreme and you can't hope you're guessing right by only extracting from portions of it because again, if you missed that data you've missed it forever. So for us that was a data problem, which is why we stepped in and >>other things That data problem these days, it's almost it's the most fun to talk about if you love the problem statement that we're trying to solve. I want to get your reaction something if you don't mind. I was talking to a C. So in the C. I. O. We have a conversation kind of off camera at an event recently and I said what's the biggest challenge that you have? Just curious? I asked him, it's actually it's personnel people are mad at each other. Developers want to go faster because there are ci cd pipeline is devops their coding. They're having to wait for the security groups in some cases weeks and days when they could do it in minutes they want to do it on the in the pipelines, shifting left as some call it and it's kind of getting in the way. So it's kind of like it's not they're not getting along very well uh meaning they're slowing things down. I can say something what they really said, but they weren't getting along. What's your reaction? Because that seems to be a speed scale problem. That's developer centric, not organizational, you've got organizational challenges and being slowed down. >>So uh while we all talk about this converted landscape and how exciting is going to be. You do have diametrically opposed metrics and you're never going to have, it's very difficult to get a single person to have the same allegiance to those diametrically a virgin metrics as you want. So you've got checks and balances and the reality of what the cyber teams need to be doing to ensure that you aren't just coding effective functions with the right delivery timeframe. But that's also secure is I think going to make the security team is important forever and the same thing. You can't just write sloppy code that consumes, that blows your AWS budget or G. C. P budget within the first week of deploying it because you've still got to run a responsible business. So there are different dimensions that we all have to deal with quality time and feature functionality that different groups represent. So we, I believe a converged landscape is important. It's not that we're gonna blow it up and one person is going to do it all if you've got to get those groups talking better and you've got to reduce cycle times now we believe it's plunk is with a common data plane, which is the backbone and then solutions built from that common data plane to serve those groups. You're lessening the lack of understanding and you're reducing the cycle time. So now I can look when I'm publishing the code. If it's done properly, is it also secure And the cyber teams can kind of be flying in saying, hey, wait, wait, wait, we just saw something in the data says we're not quite ready. I'm sorry. I know you want to push, you can't push now, but there'll be a data driven conversation and not this, you shouldn't be waiting a week or two weeks, like we can't operate that scale and you've got to address people with facts and data and logic and that's what we're trying to get done. And you >>guys have a good policy engine, you can put up that up into the pipeline. So awesome. That's great, great insight there. Thanks for sharing. Final question. Um looking back in your time since you've been Ceo the culture kind of hasn't changed at Splunk, it's still they have fun, hard charging laid back a little bit and public company now, he's still got to meet the numbers, but your growing business is good, but there's a lot more coming as a big wave coming talk about the Splunk culture. >>So the core elements of culture that I love that. I think all of us agree you don't want to change one where curiosity driven culture, our tool is an investigative tool, so I never want to lose. I think that threat of grit, determination, tenacity and curiosity is paramount in life and I think literally what we push out represents that and I want our people represent that and I think the fun element is really the quirkiness of the fund, like that is one of the things I love about Splunk but we are a serious company, we are in the data plane of tens of thousands of organizations globally and what we do literally makes a difference on whether they're successful or not. As organizations, we're talking about walmart is example And how one second latency can have a, have a 10% drop off in fulfillment of transaction for wal mart that's like a billion dollars a week if you cannot get their system to perform at the level it needs to so what we do matters and the change that we've been driving that I think is a great enhancement to the culture is as we are now tip into the 50% cloud company, you have the opportunity to measure millisecond by millisecond, second by second, minute by minute, hour by hour and that's a different level of help that you get. You can literally see patterns happening over the course of minutes within customers and that's not something we were born with. We were an on premise solution, we had beautiful tools and it was the C E O. S problem, the CSS problem um and their opportunity to get that feedback. Now we get that feedback so we're trying to measure that crunchiness, the fun, the cool part about Splunk with. We also have got to be very operationally disciplined because we carry a heavy responsibility set from our customers and we're in the middle of that as well as the world knows, we're halfway through our transition to be a cloud first company but I'm excited with the results I'm seeing, so I think curiosity and tenacity go with that operational rigor. Like we should all be growth mindset oriented and very excited about, Hey, can I improve? I guess there's some information that I need that I'm not getting that will make me serve my customers better and that is the tone and tenor. I want to cross all the Splunk of whether in HR legal or engineering or sales or we serve customers and we've got to be so excited every day about getting better feedback and how to serve them better. >>Doug. Thanks for coming on the Cuban, sharing that inside. I know you had to cancel your physical event, pulled off an exceptionally strong virtual event here in person. Thanks for having the Cuban. Thanks for coming on. >>Thank you for being here and I can't wait to do this in person. Next >>to mary the ceo of Splunk here inside the cube cube coverage continues stay with us for more. We've got more interviews all the rest of the day, Stay with us. I'm john for your host. Thanks for watching. Mm >>mm mhm >>mhm >>Yeah

>>From around the globe. It's the queue with digital coverage of dev ops virtual forum brought to you by Broadcom. >>Hi, Lisa Martin here covering the Broadcom dev ops virtual forum. I'm very pleased to be joined today by a cube alumni, Jeffrey Hammond, the vice president and principal analyst serving CIO is at Forester. Jeffrey. Nice to talk with you today. >>Good morning. It's good to be here. Yeah. >>So a virtual forum, great opportunity to engage with our audiences so much has changed in the last it's an understatement, right? Or it's an overstated thing, but it's an obvious, so much has changed when we think of dev ops. One of the things that we think of is speed, you know, enabling organizations to be able to better serve customers or adapt to changing markets like we're in now, speaking of the need to adapt, talk to us about what you're seeing with respect to dev ops and agile in the age of COVID, what are things looking like? >>Yeah, I think that, um, for most organizations, we're in a, uh, a period of adjustment, uh, when we initially started, it was essentially a sprint, you know, you run as hard as you can for as fast as you can for as long as you can and you just kind of power through it. And, and that's actually what, um, the folks that get hub saw in may when they ran an analysis of how developers, uh, commit times and a level of work that they were committing and how they were working, uh, in the first couple of months of COVID was, was progressing. They found that developers, at least in the Pacific time zone were actually increasing their work volume, maybe because they didn't have two hour commutes or maybe because they were stuck away in their homes, but for whatever reason, they were doing more work. >>And it's almost like, you know, if you've ever run a marathon the first mile or two in the marathon, you feel great and you just want to run and you want to power through it and you want to go hard. And if you do that by the time you get to mile 18 or 19, you're going to be gassed. It's sucking for wind. Uh, and, and that's, I think where we're starting to hit. So as we start to, um, gear our development chops out for the reality that most of us won't be returning into an office until 2021 at the earliest and many organizations will, will be fundamentally changing, uh, their remote workforce, uh, policies. We have to make sure that the agile processes that we use and the dev ops processes and tools that we use to support these teams are essentially aligned to help developers run that marathon instead of just kind of power through. >>So, um, let me give you a couple of specifics for many organizations, they have been in an environment where they will, um, tolerate Rover remote work and what I would call remote work around the edges like developers can be remote, but product managers and, um, you know, essentially scrum masters and all the administrators that are running the, uh, uh, the SCM repositories and, and the dev ops pipelines are all in the office. And it's essentially centralized work. That's not, we are anymore. We're moving from remote workers at the edge to remote workers at the center of what we do. And so one of the implications of that is that, um, we have to think about all the activities that you need to do from a dev ops perspective or from an agile perspective, they have to be remote people. One of the things I found with some of the organizations I talked to early on was there were things that administrators had to do that required them to go into the office to reboot the SCM server as an example, or to make sure that the final approvals for production, uh, were made. >>And so the code could be moved into the production environment. And so it actually was a little bit difficult because they had to get specific approval from the HR organizations to actually be allowed to go into the office in some States. And so one of the, the results of that is that while we've traditionally said, you know, tools are important, but they're not as important as culture as structure as organization as process. I think we have to rethink that a little bit because to the extent that tools enable us to be more digitally organized and to hiring, you know, achieve higher levels of digitization in our processes and be able to support the idea of remote workers in the center. They're now on an equal footing with so many of the other levers, uh, that, that, um, uh, that organizations have at their disposal. Um, I'll give you another example for years. >>We've said that the key to success with agile at the team level is cross-functional co located teams that are working together physically co located. It's the easiest way to show agile success. We can't do that anymore. We can't be physically located at least for the foreseeable future. So, you know, how do you take the low hanging fruits of an agile transformation and apply it in, in, in, in the time of COVID? Well, I think what you have to do is that you have to look at what physical co-location has enabled in the past and understand that it's not so much the fact that we're together looking at each other across the table. It's the fact that we're able to get into a shared mindspace, uh, from, um, uh, from a measurement perspective, we can have shared purpose. We can engage in high bandwidth communications. It's the spiritual aspect of that physical co-location that is actually important. So one of the biggest things that organizations need to start to ask themselves is how do we achieve spiritual colocation with our agile teams? Because we don't have the, the ease of physical co-location available to us anymore? >>Well, the spiritual co-location is such an interesting kind of provocative phrase there, but something that probably was a challenge here, we are seven, eight months in for many organizations, as you say, going from, you know, physical workspaces, co-location being able to collaborate face to face to a, a light switch flip overnight. And this undefined period of time where all we were living with with was uncertainty, how does spiritual, what do you, when you talk about spiritual co-location in terms of collaboration and processes and technology help us unpack that, and how are you seeing organizations adopted? >>Yeah, it's, it's, um, it's a great question. And, and I think it goes to the very root of how organizations are trying to transform themselves to be more agile and to embrace dev ops. Um, if you go all the way back to the, to the original, uh, agile manifesto, you know, there were four principles that were espoused individuals and interactions over processes and tools. That's still important. Individuals and interactions are at the core of software development, processes and tools that support those individual and interact. Uh, those individuals in those interactions are more important than ever working software over comprehensive documentation. Working software is still more important, but when you are trying to onboard employees and they can't come into the office and they can't do the two day training session and kind of understand how things work and they can't just holler over the cube, uh, to ask a question, you may need to invest a little bit more in documentation to help that onboarding process be successful in a remote context, uh, customer collaboration over contract negotiation. >>Absolutely still important, but employee collaboration is equally as important if you want to be spiritually, spiritually co-located. And if you want to have a shared purpose and then, um, responding to change over following a plan. I think one of the things that's happened in a lot of organizations is we have focused so much of our dev ops effort around velocity getting faster. We need to run as fast as we can like that sprinter. Okay. You know, trying to just power through it as quickly as possible. But as we shift to, to the, to the marathon way of thinking, um, velocity is still important, but agility becomes even more important. So when you have to create an application in three weeks to do track and trace for your employees, agility is more important. Um, and then just flat out velocity. Um, and so changing some of the ways that we think about dev ops practices, um, is, is important to make sure that that agility is there for one thing, you have to defer decisions as far down the chain to the team level as possible. >>So those teams have to be empowered to make decisions because you can't have a program level meeting of six or seven teams and one large hall and say, here's the lay of the land. Here's what we're going to do here are our processes. And here are our guardrails. Those teams have to make decisions much more quickly that developers are actually developing code in smaller chunks of flow. They have to be able to take two hours here or 50 minutes there and do something useful. And so the tools that support us have to become tolerant of the reality of, of, of, of how we're working. So if they work in a way that it allows the team together to take as much autonomy as they can handle, um, to, uh, allow them to communicate in a way that, that, that delivers shared purpose and allows them to adapt and master new technologies, then they're in the zone in their spiritual, they'll get spiritually connected. I hope that makes sense. >>It does. I think we all could use some of that, but, you know, you talked about in the beginning and I've, I've talked to numerous companies during the pandemic on the cube about the productivity, or rather the number of hours of work has gone way up for many roles, you know, and, and, and times that they normally late at night on the weekends. So, but it's a cultural, it's a mind shift to your point about dev ops focused on velocity, sprints, sprints, sprints, and now we have to, so that cultural shift is not an easy one for developers. And even at this folks to flip so quickly, what have you seen in terms of the velocity at which businesses are able to get more of that balance between the velocity, the sprint and the agility? >>I think, I think at the core, this really comes down to management sensitivity. Um, when everybody was in the office, you could kind of see the mental health of development teams by, by watching how they work. You know, you call it management by walking around, right. We can't do that. Managers have to, um, to, to be more aware of what their teams are doing, because they're not going to see that, that developer doing a check-in at 9:00 PM on a Friday, uh, because that's what they had to do, uh, to meet the objectives. And, um, and, and they're going to have to, to, um, to find new ways to measure engagement and also potential burnout. Um, friend of mine once had, uh, had a great metric that he called the parking lot metric. It was helpful as the parking lot at nine. And how full was it at five? >>And that gives you an indication of how engaged your developers are. Um, what's the digital equivalent equivalent to the parking lot metric in the time of COVID it's commit stats, it's commit rates. It's, um, you know, the, uh, the turn rate, uh, that we have in our code. So we have this information, we may not be collecting it, but then the next question becomes, how do we use that information? Do we use that information to say, well, this team isn't delivering as at the same level of productivity as another team, do we weaponize that data or do we use that data to identify impedances in the process? Um, why isn't a team working effectively? Is it because they have higher levels of family obligations and they've got kids that, that are at home? Um, is it because they're working with, um, you know, hardware technology, and guess what, they, it's not easy to get the hardware technology into their home office because it's in the lab at the, uh, at the corporate office, uh, or they're trying to communicate, uh, you know, halfway around the world. >>And, uh, they're communicating with a, with an office lab that is also shut down and, and, and the bandwidth just doesn't enable the, the level of high bandwidth communications. So from a dev ops perspective, managers have to get much more sensitive to the, the exhaust that the dev ops tools are throwing off, but also how they're going to use that in a constructive way to, to prevent burnout. And then they also need to, if they're not already managing or monitoring or measuring the level of developer engagement, they have, they really need to start whether that's surveys around developer satisfaction, um, whether it's, you know, more regular social events, uh, where developers can kind of just get together and drink a beer and talk about what's going on in the project, uh, and monitoring who checks in and who doesn't, uh, they have to, to, um, work harder, I think, than they ever have before. >>Well, and you mentioned burnout, and that's something that I think we've all faced in this time at varying levels and it changes. And it's a real, there's a tension in the air, regardless of where you are. There's a challenge, as you mentioned, people having, you know, coworker, their kids as coworkers and fighting for bandwidth, because everyone is forced in this situation. I'd love to get your perspective on some businesses that are, that have done this well, this adaptation, what can you share in terms of some real-world examples that might inspire the audience? >>Yeah. Uh, I'll start with, uh, stack overflow. Uh, they recently published a piece in the journal of the ACM around some of the things that they had discovered. Um, you know, first of all, just a cultural philosophy. If one person is remote, everybody is remote. And you just think that way from an executive level, um, social spaces. One of the things that they talk about doing is leaving a video conference room open at a team level all day long, and the team members, you know, we'll go on mute, you know, so that they don't have to, that they don't necessarily have to be there with somebody else listening to them. But if they have a question, they can just pop off mute really quickly and ask the question. And if anybody else knows the answer, it's kind of like being in that virtual pod. Uh, if you, uh, if you will, um, even here at Forrester, one of the things that we've done is we've invested in social ceremonies. >>We've actually moved our to our team meetings on, on my analyst team from, from once every two weeks to weekly. And we have built more time in for social Ajay socialization, just so we can see, uh, how, how, how we're doing. Um, I think Microsoft has really made some good, uh, information available in how they've managed things like the onboarding process. I think I'm Amanda silver over there mentioned that a couple of weeks ago when, uh, uh, a presentation they did that, uh, uh, Microsoft onboarded over 150,000 people since the start of COVID, if you don't have good remote onboarding processes, that's going to be a disaster. Now they're not all developers, but if you think about it, um, everything from how you do the interviewing process, uh, to how you get people, their badges, to how they get their equipment. Um, security is a, is another issue that they called out typically, uh, it security, um, the security of, of developers machines ends at, at, at the corporate desktop. >>But, you know, since we're increasingly using our own machines, our own hardware, um, security organizations kind of have to extend their security policies to cover, uh, employee devices, and that's caused them to scramble a little bit. Uh, so, so the examples are out there. It's not a lot of, like, we have to do everything completely differently, but it's a lot of subtle changes that, that have to be made. Um, I'll give you another example. Um, one of the things that, that we are seeing is that, um, more and more organizations to deal with the challenges around agility, with respect to delivering software, embracing low-code tools. In fact, uh, we see about 50% of firms are using low-code tools right now. We predict it's going to be 75% by the end of next year. So figuring out how your dev ops processes support an organization that might be using Mendix or OutSystems, or, you know, the power platform building the front end of an application, like a track and trace application really, really quickly, but then hooking it up to your backend infrastructure. Does that happen completely outside the dev ops investments that you're making and the agile processes that you're making, or do you adapt your organization? Um, our hybrid teams now teams that not just have professional developers, but also have business users that are doing some development with a low-code tool. Those are the kinds of things that we have to be, um, willing to, um, to entertain in order to shift the focus a little bit more toward the agility side, I think >>Lot of obstacles, but also a lot of opportunities for businesses to really learn, pay attention here, pivot and grow, and hopefully some good opportunities for the developers and the business folks to just get better at what they're doing and learning to embrace spiritual co-location Jeffrey, thank you so much for joining us on the program today. Very insightful conversation. >>My pleasure. It's it's, it's an important thing. Just remember if you're going to run that marathon, break it into 26, 10 minute runs, take a walk break in between each and you'll find that you'll get there. >>Digestible components, wise advice. Jeffery Hammond. Thank you so much for joining for Jeffrey I'm Lisa Martin, you're watching Broadcom's dev ops virtual forum >>From around the globe. It's the queue with digital coverage of dev ops virtual forum brought to you by Broadcom, >>Continuing our conversations here at Broadcom's dev ops virtual forum. Lisa Martin here, please. To welcome back to the program, Serge Lucio, the general manager of the enterprise software division at Broadcom. Hey, Serge. Welcome. Thank you. Good to be here. So I know you were just, uh, participating with the biz ops manifesto that just happened recently. I just had the chance to talk with Jeffrey Hammond and he unlocked this really interesting concept, but I wanted to get your thoughts on spiritual co-location as really a necessity for biz ops to succeed in this unusual time in which we're living. What are your thoughts on spiritual colocation in terms of cultural change versus adoption of technologies? >>Yeah, it's a, it's, it's quite interesting, right? When we, when we think about the major impediments for, uh, for dev ops implementation, it's all about culture, right? And swore over the last 20 years, we've been talking about silos. We'd be talking about the paradox for these teams to when it went to align in many ways, it's not so much about these teams aligning, but about being in the same car in the same books, right? It's really about fusing those teams around kind of the common purpose, a common objective. So to me, the, this, this is really about kind of changing this culture where people start to look at a kind of OKR is instead of the key objective, um, that, that drives the entire team. Now, what it means in practice is really that's, uh, we need to change a lot of behaviors, right? It's not about the Yarki, it's not about roles. It's about, you know, who can do what and when, and, uh, you know, driving a bias towards action. It also means that we need, I mean, especially in this school times, it becomes very difficult, right? To drive kind of a kind of collaboration between these teams. And so I think there there's a significant role that especially tools can play in terms of providing this complex feedback from teams to, uh, to be in that preface spiritual qualification. >>Well, and it talked about culture being, it's something that, you know, we're so used to talking about dev ops with respect to velocity, all about speed here. But of course this time everything changed so quickly, but going from the physical spaces to everybody being remote really does take it. It's very different than you can't replicate it digitally, but there are collaboration tools that can kind of really be essential to help that cultural shift. Right? >>Yeah. So 2020, we, we touch to talk about collaboration in a very mundane way. Like, of course we can use zoom. We can all get into, into the same room. But the point when I think when Jeff says spiritual, co-location, it's really about, we all share the same objective. Do we, do we have a niece who, for instance, our pipeline, right? When you talk about dev ops, probably we all started thinking about this continuous delivery pipeline that basically drives the automation, the orchestration across the team, but just thinking about a pipeline, right, at the end of the day, it's all about what is the meantime to beat back to these teams. If I'm a developer and a commit code, I don't, does it take where, you know, that code to be processed through pipeline pushy? Can I get feedback if I am a finance person who is funding a product or a project, what is my meantime to beat back? >>And so a lot of, kind of a, when we think about the pipeline, I think what's been really inspiring to me in the last year or so is that there is much more of an adoption of the Dora metrics. There is way more of a focus around value stream management. And to me, this is really when we talk about collaboration, it's really a balance. How do you provide the feedback to the different stakeholders across the life cycle in a very timely matter? And that's what we would need to get to in terms of kind of this, this notion of collaboration. It's not so much about people being in the same physical space. It's about, you know, when I checked in code, you know, to do I guess the system to automatically identify what I'm going to break. If I'm about to release some allegation, how can the system help me reduce my change pillar rates? Because it's, it's able to predict that some issue was introduced in the outpatient or work product. Um, so I think there's, there's a great role of technology and AI candidate Lynch to, to actually provide that new level of collaboration. >>So we'll get to AI in a second, but I'm curious, what are some of the, of the metrics you think that really matter right now is organizations are still in some form of transformation to this new almost 100% remote workforce. >>So I'll just say first, I'm not a big fan of metrics. Um, and the reason being that, you know, you can look at a change killer rate, right, or a lead time or cycle time. And those are, those are interesting metrics, right? The trend on metric is absolutely critical, but what's more important is you get to the root cause what is taught to you lean to that metric to degrade or improve or time. And so I'm much more interested and we, you know, fruit for Broadcom. Are we more interested in understanding what are the patterns that contribute to this? So I'll give you a very mundane example. You know, we know that cycle time is heavily influenced by, um, organizational boundaries. So, you know, we talk a lot about silos, but, uh, we we've worked with many of our customers doing value stream mapping. And oftentimes what you see is that really the boundaries of your organization creates a lot of idle time, right? So to me, it's less about the metrics. I think the door metrics are a pretty, you know, valid set metrics, but what's way more important is to understand what are the antiperspirants, what are the things that we can detect through the data that actually are affecting those metrics. And, uh, I mean, over the last 10, 20 years, we've learned a lot about kind of what are, what are the antiperspirants within our large enterprise customers. And there are plenty of them. >>What are some of the things that you're seeing now with respect to patterns that have developed over the last seven to eight months? >>So I think the two areas which clearly are evolving very quickly are on kind of the front end of the life cycle, where DevOps is more and more embracing value stream management value stream mapping. Um, and I think what's interesting is that in many ways the product is becoming the new silo. Uh, the notion of a product is very difficult by itself to actually define people are starting to recognize that a value stream is not its own little kind of Island. That in reality, when I define a product, this product, oftentimes as dependencies on our products and that in fact, you're looking at kind of a network of value streams, if you will. So, so even on that, and there is clearly kind of a new sets, if you will, of anti-patterns where products are being defined as a set of OTRs, they have interdependencies and you have have a new set of silos on the operands, uh, the Abra key movement to Israel and the SRE space where, um, I think there is a cultural clash while the dev ops side is very much embracing this notion of OTRs and value stream mapping and Belgium management. >>On the other end, you have the it operations teams. We still think business services, right? For them, they think about configure items, think about infrastructure. And so, you know, it's not uncommon to see, you know, teams where, you know, the operations team is still thinking about hundreds of thousands, tens of thousands of business services. And so the, the, there is there's this boundary where, um, I think, well, SRE is being put in place. And there's lots of thinking about what kind of metrics can be fined. I think, you know, going back to culture, I think there's a lot of cultural evolution that's still required for true operations team. >>And that's a hard thing. Cultural transformation in any industry pandemic or not is a challenging thing. You talked about, uh, AI and automation of minutes ago. How do you think those technologies can be leveraged by DevOps leaders to influence their successes and their ability to collaborate, maybe see eye to eye with the SRS? >>Yeah. Um, so th you're kind of too. So even for myself, as a leader of a, you know, 1500 people organization, there's a number of things I don't see right. On a daily basis. And, um, I think the, the, the, the technologies that we have at our disposal today from the AI are able to mind a lot of data and expose a lot of, uh, issues that's as leaders we may not be aware of. And some of the, some of these are pretty kind of easy to understand, right? We all think we're agile. And yet when you, when you start to understand, for instance, uh, what is the, what is the working progress right to during the sprint? Um, when you start to analyze the data you can detect, for instance, that maybe the teams are over committed, that there is too much work in progress. >>You can start to identify kind of, interdepencies either from a technology, from a people point of view, which were hidden, uh, you can start to understand maybe the change filler rates he's he is dragging. So I believe that there is a, there's a fundamental role to be played by the tools to, to expose again, these anti parents, to, to make these things visible to the teams, to be able to even compare teams. Right. One of the things that's, that's, uh, that's amazing is now we have access to tons of data, not just from a given customer, but across a large number of customers. And so we start to compare all of these teams kind of operate, and what's working, what's not working >>Thoughts on AI and automation as, as a facilitator of spiritual co-location. >>Yeah, absolutely. Absolutely. It's um, you know, th there's, uh, the problem we all face is the unknown, right? The, the law city, but volume variety of the data, uh, everyday we don't really necessarily completely appreciate what is the impact of our actions, right? And so, um, AI can really act as a safety net that enables us to, to understand what is the impact of our actions. Um, and so, yeah, in many ways, the ability to be informed in a timely matter to be able to interact with people on the basis of data, um, and collaborate on the data. And the actual matter, I think is, is a, is a very powerful enabler, uh, on, in that respect. I mean, I, I've seen, um, I've seen countless of times that, uh, for instance, at the SRE boundary, um, to basically show that we'll turn the quality attributes, so an incoming release, right. And exposing that to, uh, an operations person and a sorry person, and enabling that collaboration dialogue through data is a very, very powerful tool. >>Do you have any recommendations for how teams can use, you know, the SRE folks, the dev ops says can use AI and automation in the right ways to be successful rather than some ways that aren't going to be nonproductive. >>Yeah. So to me, the th there, there's a part of the question really is when, when we talk about data, there are there different ways you can use data, right? Um, so you can, you can do a lot of an analytics, predictive analytics. So I think there is a, there's a tendency, uh, to look at, let's say a, um, a specific KPI, like a, an availability KPI, or change filler rate, and to basically do a regression analysis and projecting all these things, going to happen in the future. To me, that that's, that's a, that's a bad approach. The reason why I fundamentally think it's a better approach is because we are systems. The way we develop software is, is a, is a non-leader kind of system, right? Software development is not linear nature. And so I think there's a D this is probably the worst approach is to actually focus on metrics on the other end. >>Um, if you, if you start to actually understand at a more granular level, what har, uh, which are the things which are contributing to this, right? So if you start to understand, for instance, that whenever maybe, you know, you affect a specific part of the application that translates into production issues. So we, we have, I've actually, uh, a customer who, uh, identified that, uh, over 50% of their unplanned outages were related to specific components in your architecture. And whenever these components were changed, this resulted in these plant outages. So if you start to be able to basically establish causality, right, cause an effect between kind of data across the last cycle. I think, I think this is the right way to, uh, to, to use AI. And so pharma to be, I think it's way more God could have a classification problem. What are the classes of problems that do exist and affect things as opposed to analytics, predictive, which I don't think is as powerful. >>So I mentioned in the beginning of our conversation, that just came off the biz ops manifesto. You're one of the authors of that. I want to get your thoughts on dev ops and biz ops overlapping, complimenting each other, what, from a, the biz ops perspective, what does it mean to the future of dev ops? >>Yeah, so, so it's interesting, right? If you think about DevOps, um, there's no felony document, right? Can we, we can refer to the Phoenix project. I mean, there are a set of documents which have been written, but in many ways, there's no clear definition of what dev ops is. Uh, if you go to the dev ops Institute today, you'll see that they are specific, um, trainings for instance, on value management on SRE. And so in many ways, the problem we have as an industry is that, um, there are set practices between agile dev ops, SRE Valley should management. I told, right. And we all basically talk about the same things, right. We all talk about essentially, um, accelerating in the meantime fee to feedback, but yet we don't have the common framework to talk about that. The other key thing is that we add to wait, uh, for, uh, for jeans, Jean Kim's Lascaux, um, to, uh, to really start to get into the business aspect, right? >>And for value stream mapping to start to emerge for us to start as an industry, right. It, to start to think about what is our connection with the business aspect, what's our purpose, right? And ultimately it's all about driving these business outcomes. And so to me, these ops is really about kind of, uh, putting a lens on this critical element that it's not business and it, that we in fact need to fuse business 19 that I need needs to transform itself to recognize that it's, it's this value generator, right. It's not a cost center. And so the relationship to me, it's more than BizOps provides kind of this Oliver or kind of framework, if you will. That set the context for what is the reason, uh, for it to exist. What's part of the core values and principles that it needs to embrace to, again, change from a cost center to a value center. And then we need to start to use this as a way to start to unify some of the, again, the core practices, whether it's agile, DevOps value, stream mapping SRE. Um, so, so I think over time, my hope is that we start to optimize a lot of our practices, language, um, and, uh, and cultural elements. >>Last question surgeon, the last few seconds we have here talking about this, the relation between biz ops and dev ops, um, what do you think as DevOps evolves? And as you talked to circle some of your insights, what should our audience keep their eyes on in the next six to 12 months? >>So to me, the key, the key, um, challenge for, for the industry is really around. So we were seeing a very rapid shift towards kind of, uh, product to product, right. Which we don't want to do is to recreate kind of these new silos, these hard silos. Um, so that, that's one of the big changes, uh, that I think we need to be, uh, to be really careful about, um, because it is ultimately, it is about culture. It's not about, uh, it's not about, um, kind of how we segment the work, right. And, uh, any true culture that we can overcome kind of silos. So back to, I guess, with Jeffrey's concept of, um, kind of the spiritual co-location, I think it's, it's really about that too. It's really about kind of, uh, uh, focusing on the business outcomes on kind of aligning on driving engagement across the teams, but, but not for create a, kind of a new set of silos, which instead of being vertical are going to be these horizontal products >>Crazy by surge that looking at culture as kind of a way of really, uh, uh, addressing and helping to, uh, re re reduce, replace challenges. We thank you so much for sharing your insights and your time at today's DevOps virtual forum. >>Thank you. Thanks for your time. >>I'll be right back >>From around the globe it's the cube with digital coverage of devops virtual forum brought to you by Broadcom. >>Welcome to Broadcom's DevOps virtual forum, I'm Lisa Martin, and I'm joined by another Martin, very socially distanced from me all the way coming from Birmingham, England is Glynn Martin, the head of QA transformation at BT. Glynn, it's great to have you on the program. Thank you, Lisa. I'm looking forward to it. As we said before, we went live to Martins for the person one in one segment. So this is going to be an interesting segment guys, what we're going to do is Glynn's going to give us a really kind of deep inside out view of devops from an evolution perspective. So Glynn, let's start. Transformation is at the heart of what you do. It's obviously been a very transformative year. How have the events of this year affected the >> transformation that you are still responsible for driving? Yeah. Thank you, Lisa. I mean, yeah, it has been a difficult year. >>Um, and although working for BT, which is a global telecommunications company, um, I'm relatively resilient, I suppose, as a, an industry, um, through COVID obviously still has been affected and has got its challenges. And if anything, it's actually caused us to accelerate our transformation journey. Um, you know, we had to do some great things during this time around, um, you know, in the UK for our emergency and, um, health workers give them unlimited data and for vulnerable people to support them. And that's spent that we've had to deliver changes quickly. Um, but what we want to be able to do is deliver those kinds of changes quickly, but sustainably for everything that we do, not just because there's an emergency. Um, so we were already on the kind of journey to agile, but ever more important now that we are, we are able to do those, that kind of work, do it more quickly. >>Um, and that it works because the, the implications of it not working is, can be terrible in terms of you know, we've been supporting testing centers,  new hospitals to treat COVID patients. So we need to get it right. And then therefore the coverage of what we do, the quality of what we do and how quickly we do it really has taken on a new scale and what was already a very competitive market within the telco industry within the UK. Um, you know, what I would say is that, you know, we are under pressure to deliver more value, but we have small cost challenges. We have to obviously, um, deal with the fact that, you know, COVID 19 has hit most industries kind of revenues and profits. So we've got this kind of paradox between having less costs, but having to deliver more value quicker and  to higher quality. So yeah, certainly the finances is, um, on our minds and that's why we need flexible models, cost models that allow us to kind of do growth, but we get that growth by showing that we're delivering value. Um, especially in these times when there are financial challenges on companies. So one of the things that I want to ask you about, I'm again, looking at DevOps from the inside >>Out and the evolution that you've seen, you talked about the speed of things really accelerating in this last nine months or so. When we think dev ops, we think speed. But one of the things I'd love to get your perspective on is we've talked about in a number of the segments that we've done for this event is cultural change. What are some of the things that you've seen there as, as needing to get, as you said, get things right, but done so quickly to support essential businesses, essential workers. How have you seen that cultural shift? >>Yeah, I think, you know, before test teams for themselves at this part of the software delivery cycle, um, and actually now really our customers are expecting that quality and to deliver for our customers what they want, quality has to be ingrained throughout the life cycle. Obviously, you know, there's lots of buzzwords like shift left. Um, how do we do shift left testing? Um, but for me, that's really instilling quality and given capabilities shared capabilities throughout the life cycle that drive automation, drive improvements. I always say that, you know, you're only as good as your lowest common denominator. And one thing that we were finding on our dev ops journey was that we  would be trying to do certain things quick, we had automated build, automated tests. But if we were taking a weeks to create test scripts, or we were taking weeks to manually craft data, and even then when we had taken so long to do it, that the coverage was quite poor and that led to lots of defects later on in the life cycle, or even in our production environment, we just couldn't afford to do that. >>And actually, focusing on continuous testing over the last nine to 12 months has really given us the ability to deliver quickly across the whole life cycle. And therefore actually go from doing a kind of semi agile kind of thing, where we did the user stories, we did a few of the kind of agile ceremonies, but we weren't really deploying any quicker into production because our stakeholders were scared that we didn't have the same control that we had when we had more waterfall releases. And, you know, when we didn't think of ourselves. So we've done a lot of work on every aspect, um, especially from a testing point of view, every aspect of every activity, rather than just looking at automated tests, you know, whether it is actually creating the test in the first place, whether it's doing security testing earlier in the lot and performance testing in the life cycle, et cetera. So, yeah,  it's been a real key thing that for CT, for us to drive DevOps, >>Talk to me a little bit about your team. What are some of the shifts in terms of expectations that you're experiencing and how your team interacts with the internal folks from pipeline through life cycle? >>Yeah, we've done a lot of work on this. Um, you know, there's a thing that I think people will probably call it a customer experience gap, and it reminds me of a Gilbert cartoon, where we start with the requirements here and you're almost like a Chinese whisper effects and what we deliver is completely different. So we think the testing team or the delivery teams, um, know in our teeth has done a great job. This is what it said in the acceptance criteria, but then our customers are saying, well, actually that's not working this isn't working and there's this kind of gap. Um, we had a great launch this year of agile requirements, it's one of the Broadcom tools. And that was the first time in, ever since I remember actually working within BT, I had customers saying to me, wow, you know, we want more of this. >>We want more projects to have extra requirements design on it because it allowed us to actually work with the business collaboratively. I mean, we talk about collaboration, but how do we actually, you know, do that and have something that both the business and technical people can understand. And we've actually been working with the business , using agile requirements designer to really look at what the requirements are, tease out requirements we hadn't even thought of and making sure that we've got high levels of test coverage. And what we actually deliver at the end of it, not only have we been able to generate tests more quickly, but we've got much higher test coverage and also can more smartly, using the kind of AI within the tool and then some of the other kinds of pipeline tools, actually deliver to choose the right tasks, and actually doing a risk based testing approach. So that's been a great launch this year, but just the start of many kinds of things that we're doing >>Well, what I hear in that, Glynn is a lot of positives that have come out of a very challenging situation. Talk to me about it. And I liked that perspective. This is a very challenging time for everybody in the world, but it sounds like from a collaboration perspective you're right, we talk about that a lot critical with devops. But those challenges there, you guys were able to overcome those pretty quickly. What other challenges did you face and figure out quickly enough to be able to pivot so fast? >>I mean, you talked about culture. You know, BT is like most companies  So it's very siloed. You know we're still trying to work to become closer as a company. So I think there's a lot of challenges around how would you integrate with other tools? How would you integrate with the various different technologies. And BT, we have 58 different IT stacks. That's not systems, that's stacks, all of those stacks can have hundreds of systems. And we're trying to, we've got a drive at the moment, a simplified program where we're trying to you know, reduce that number to 14 stacks. And even then there'll be complexity behind the scenes that we will be challenged more and more as we go forward. How do we actually highlight that to our users? And as an it organization, how do we make ourselves leaner, so that even when we've still got some of that legacy, and we'll never fully get rid of it and that's the kind of trade off that we have to make, how do we actually deal with that and hide that from our users and drive those programs, so we can, as I say, accelerate change,  reduce that kind of waste and that kind of legacy costs out of our business. You know, the other thing as well, I'm sure telecoms is probably no different to insurance or finance. When you take the number of products that we do, and then you combine them, the permutations are tens and hundreds of thousands of products. So we, as a business are trying to simplify, we are trying to do that in an agile way. >>And haven't tried to do agile in the proper way and really actually work at pace, really deliver value. So I think what we're looking more and more at the moment is actually  more value focused. Before we used to deliver changes sometimes into production. Someone had a great idea, or it was a great idea nine months ago or 12 months ago, but actually then we ended up deploying it and then we'd look at the users, the usage of that product or that application or whatever it is, and it's not being used for six months. So we haven't got, you know, the cost of the last 12 months. We certainly haven't gotten room for that kind of waste and, you know, for not really understanding the value of changes that we are doing. So I think that's the most important thing of the moment, it's really taking that waste out. You know, there's lots of focus on things like flow management, what bits of our process are actually taking too long. And we've started on that journey, but we've got a hell of a long way to go. But that involves looking at every aspect of the software delivery cycle. >> Going from, what 58 IT stacks down to 14 or whatever it's going to be, simplifying sounds magical to everybody. It's a big challenge. What are some of the core technology capabilities that you see really as kind of essential for enabling that with this new way that you're working? >>Yeah. I mean, I think we were started on a continuous testing journey, and I think that's just the start. I mean as I say, looking at every aspect of, you know, from a QA point of view is every aspect of what we do. And it's also looking at, you know, we've started to branch into more like AI, uh, AI ops and, you know, really the full life cycle. Um, and you know, that's just a stepping stone to, you know, I think autonomics is the way forward, right. You know, all of this kind of stuff that happens, um, you know, monitoring, uh, you know, watching the systems what's happening in production, how do we feed that back? How'd you get to a point where actually we think about change and then suddenly it's in production safely, or if it's not going to safety, it's automatically backing out. So, you know, it's a very, very long journey, but if we want to, you know, in a world where the pace is in ever-increasing and the demands for the team, and, you know, with the pressures on, at the moment where we're being asked to do things, uh, you know, more efficiently and as lean as possible, we need to be thinking about every part of the process and how we put the kind of stepping stones in place to lead us to a more automated kind of, um, you know, um, the future. >>Do you feel that that planned outcomes are starting to align with what's delivered, given this massive shift that you're experiencing? >>I think it's starting to, and I think, you know, as I say, as we look at more of a value based approach, um, and, um, you know, as I say, print, this was a kind of flow management. I think that that will become ever, uh, ever more important. So, um, I think it starting to people certainly realize that, you know, teams need to work together, you know, the kind of the cousin between business and it, especially as we go to more kind of SAS based solutions, low code solutions, you know, there's not such a gap anymore, actually, some of our business partners that expense to be much more tech savvy. Um, so I think, you know, this is what we have to kind of appreciate what is its role, how do we give the capabilities, um, become more of a centers of excellence rather than actually doing mounds amounts of work. And for me, and from a testing point of view, you know, mounds and mounds of testing, actually, how do we automate that? How do we actually generate that instead of, um, create it? I think that's the kind of challenge going forward. >>What are some, as we look forward, what are some of the things that you would like to see implemented or deployed in the next, say six to 12 months as we hopefully round a corner with this pandemic? >>Yeah, I think, um, you know, certainly for, for where we are as a company from a QA perspective, we are, um, you let's start in bits that we do well, you know, we've started creating, um, continuous delivery and DevOps pipelines. Um, there's still manual aspects of that. So, you know, certainly for me, I I've challenged my team with saying how do we do an automated journey? So if I put a requirement in JIRA or rally or wherever it is and why then click a button and, you know, with either zero touch for one such, then put that into production and have confidence that, that has been done safely and that it works and what happens if it doesn't work. So, you know, that's, that's the next, um, the next few months, that's what our concentration, um, is, is about. But it's also about decision-making, you know, how do you actually understand those value judgments? >>And I think there's lots of the things dev ops, AI ops, kind of that always ask aspects of business operations. I think it's about having the information in one place to make those kinds of decisions. How does it all try and tie it together? As I say, even still with kind of dev ops, we've still got elements within my company where we've got lots of different organizations doing some, doing similar kinds of things, but they're all kind of working in silos. So I think having AI ops as it comes more and more to the fore as we go to cloud, and that's what we need to, you know, we're still very early on in our cloud journey, you know, so we need to make sure the technologies work with cloud as well as you can have, um, legacy systems, but it's about bringing that all together and having a full, visible pipeline, um, that everybody can see and make decisions. >>You said the word confidence, which jumped out at me right away, because absolutely you've got to have be able to have confidence in what your team is delivering and how it's impacting the business and those customers. Last question then for you is how would you advise your peers in a similar situation to leverage technology automation, for example, dev ops, to be able to gain the confidence that they're making the right decisions for their business? >>I think the, the, the, the, the approach that we've taken actually is not started with technology. Um, we've actually taken a human centered design, uh, as a core principle of what we do, um, within the it part of BT. So by using human centered design, that means we talk to our customers, we understand their pain points, we map out their current processes. Um, and then when we mapped out what this process does, it also understand their aspirations as well, you know? Um, and where do they want to be in six months? You know, do they want it to be, um, more agile and, you know, or do they want to, you know, is, is this a part of their business that they want to do one better? We actually then looked at why that's not running well, and then see what, what solutions are out there. >>We've been lucky that, you know, with our partnership, with Broadcom within the payer line, lots of the tools and the PLA have directly answered some of the business's problems. But I think by having those conversations and actually engaging with the business, um, you know, especially if the business hold the purse strings, which in, in, uh, you know, in some companies include not as they do there is that kind of, you know, almost by understanding their, their pain points and then starting, this is how we can solve your problem. Um, is we've, we've tended to be much more successful than trying to impose something and say, well, here's the technology that they don't quite understand. It doesn't really understand how it kind of resonates with their problems. So I think that's the heart of it. It's really about, you know, getting, looking at the data, looking at the processes, looking at where the kind of waste is. >>And then actually then looking at the right solutions. Then, as I say, continuous testing is massive for us. We've also got a good relationship with Apple towards looking at visual AI. And actually there's a common theme through that. And I mean, AI is becoming more and more prevalent. And I know, you know, sometimes what is AI and people have kind of this semantics of, is it true AI or not, but it's certainly, you know, AI machine learning is becoming more and more prevalent in the way that we work. And it's allowing us to be much more effective, be quicker in what we do and be more accurate. And, you know, whether it's finding defects running the right tests or, um, you know, being able to anticipate problems before they're happening in a production environment. >>Well, thank you so much for giving us this sort of insight outlook at dev ops sharing the successes that you're having, taking those challenges, converting them to opportunities and forgiving folks who might be in your shoes, or maybe slightly behind advice enter. They appreciate it. We appreciate your time. >>Well, it's been an absolute pleasure, really. Thank you for inviting me. I have a extremely enjoyed it. So thank you ever so much. >>Excellent. Me too. I've learned a lot for Glenn Martin. I'm Lisa Martin. You're watching the cube >>Driving revenue today means getting better, more valuable software features into the hands of your customers. If you don't do it quickly, your competitors as well, but going faster without quality creates risks that can damage your brand destroy customer loyalty and cost millions to fix dev ops from Broadcom is a complete solution for balancing speed and risk, allowing you to accelerate the flow of value while minimizing the risk and severity of critical issues with Broadcom quality becomes integrated across the entire DevOps pipeline from planning to production, actionable insights, including our unique readiness score, provide a three 60 degree view of software quality giving you visibility into potential issues before they become disasters. Dev ops leaders can manage these risks with tools like Canary deployments tested on a small subset of users, or immediately roll back to limit the impact of defects for subsequent cycles. Dev ops from Broadcom makes innovation improvement easier with integrated planning and continuous testing tools that accelerate the flow of value product requirements are used to automatically generate tests to ensure complete quality coverage and tests are easily updated. >>As requirements change developers can perform unit testing without ever leaving their preferred environment, improving efficiency and productivity for the ultimate in shift left testing the platform also integrates virtual services and test data on demand. Eliminating two common roadblocks to fast and complete continuous testing. When software is ready for the CIC CD pipeline, only DevOps from Broadcom uses AI to prioritize the most critical and relevant tests dramatically improving feedback speed with no decrease in quality. This release is ready to go wherever you are in your DevOps journey. Broadcom helps maximize innovation velocity while managing risk. So you can deploy ideas into production faster and release with more confidence from around the globe. It's the queue with digital coverage of dev ops virtual forum brought to you by Broadcom. >>Hi guys. Welcome back. So we have discussed the current state and the near future state of dev ops and how it's going to evolve from three unique perspectives. In this last segment, we're going to open up the floor and see if we can come to a shared understanding of where dev ops needs to go in order to be successful next year. So our guests today are, you've seen them all before Jeffrey Hammond is here. The VP and principal analyst serving CIO is at Forester. We've also Serge Lucio, the GM of Broadcom's enterprise software division and Glenn Martin, the head of QA transformation at BT guys. Welcome back. Great to have you all three together >>To be here. >>All right. So we're very, we're all very socially distanced as we've talked about before. Great to have this conversation. So let's, let's start with one of the topics that we kicked off the forum with Jeff. We're going to start with you spiritual co-location that's a really interesting topic that we've we've uncovered, but how much of the challenge is truly cultural and what can we solve through technology? Jeff, we'll start with you then search then Glen Jeff, take it away. >>Yeah, I think fundamentally you can have all the technology in the world and if you don't make the right investments in the cultural practices in your development organization, you still won't be effective. Um, almost 10 years ago, I wrote a piece, um, where I did a bunch of research around what made high-performance teams, software delivery teams, high performance. And one of the things that came out as part of that was that these teams have a high level of autonomy. And that's one of the things that you see coming out of the agile manifesto. Let's take that to today where developers are on their own in their own offices. If you've got teams where the team itself had a high level of autonomy, um, and they know how to work, they can make decisions. They can move forward. They're not waiting for management to tell them what to do. >>And so what we have seen is that organizations that embraced autonomy, uh, and got their teams in the right place and their teams had the information that they needed to make the right decisions have actually been able to operate pretty well, even as they've been remote. And it's turned out to be things like, well, how do we actually push the software that we've created into production that would become the challenge is not, are we writing the right software? And that's why I think the term spiritual co-location is so important because even though we may be physically distant, we're on the same plane, we're connected from a, from, from a, a shared purpose. Um, you know, surgeon, I worked together a long, long time ago. So it's been what almost 15, 16 years since we were at the same place. And yet I would say there's probably still a certain level of spiritual co-location between us, uh, because of the shared purposes that we've had in the past and what we've seen in the industry. And that's a really powerful tool, uh, to build on. So what do tools play as part of that, to the extent that tools make information available, to build shared purpose on to the extent that they enable communication so that we can build that spiritual co-location to the extent that they reinforce the culture that we want to put in place, they can be incredibly valuable, especially when, when we don't have the luxury of physical locate physical co-location. Okay. That makes sense. >>It does. I shouldn't have introduced us. This last segment is we're all spiritually co-located or it's a surge, clearly you're still spiritually co located with jump. Talk to me about what your thoughts are about spiritual of co-location the cultural impact and how technology can move it forward. >>Yeah. So I think, well, I'm going to sound very similar to Jeff in that respect. I think, you know, it starts with kind of a shared purpose and the other understanding, Oh, individuals teams, uh, contributed to kind of a business outcome, what is our shared goal or shared vision? What's what is it we're trying to achieve collectively and keeping it kind of aligned to that? Um, and so, so it's really starts with that now, now the big challenge, always these over the last 20 years, especially in large organization, there's been specialization of roles and functions. And so we, we all that started to basically measure which we do, uh, on a daily basis using metrics, which oftentimes are completely disconnected from kind of a business outcome or purpose. We, we kind of reverted back to, okay, what is my database all the time? What is my cycle time? >>Right. And, and I think, you know, which we can do or where we really should be focused as an industry is to start to basically provide a lens or these different stakeholders to look at what they're doing in the context of kind of these business outcomes. So, um, you know, probably one of my, um, favorites experience was to actually weakness at one of a large financial institution. Um, you know, Tuesday Golder's unquote development and operations staring at the same data, right. Which was related to, you know, in calming changes, um, test execution results, you know, Coverity coverage, um, official liabilities and all the all ran. It could have a direction level links. And that's when you start to put these things in context and represent that to you in a way that these different stakeholders can, can look at from their different lens. And, uh, and it can start to basically communicate and, and understand have they joined our company to, uh, to, to that kind of common view or objective. >>And Glen, we talked a lot about transformation with you last time. What are your thoughts on spiritual colocation and the cultural part, the technology impact? >>Yeah, I mean, I agree with Jeffrey that, you know, um, the people and culture, the most important thing, actually, that's why it's really important when you're transforming to have partners who have the same vision as you, um, who, who you can work with, have the same end goal in mind. And w I've certainly found that with our, um, you know, continuing relationship with Broadcom, what it also does though, is although, you know, tools can accelerate what you're doing and can join consistency. You know, we've seen within simplify, which is BTS flagship transformation program, where we're trying to, as it can, it says simplify the number of systems stacks that we have, the number of products that we have actually at the moment, we've got different value streams within that program who have got organizational silos. We were trying to rewrite, rewrite the wheel, um, who are still doing things manually. >>So in order to try and bring that consistency, we need the right tools that actually are at an enterprise grade, which can be flexible to work with in BT, which is such a complex and very dev, uh, different environments, depending on what area of BT you're in, whether it's a consumer, whether it's a mobile area, whether it's large global or government organizations, you know, we found that we need tools that can, um, drive that consistency, but also flex to Greenfield brownfield kind of technologies as well. So it's really important that as I say, for a number of different aspects, that you have the right partner, um, to drive the right culture, I've got the same vision, but also who have the tool sets to help you accelerate. They can't do that on their own, but they can help accelerate what it is you're trying to do in it. >>And a really good example of that is we're trying to shift left, which is probably a, quite a bit of a buzz phrase in their kind of testing world at the moment. But, you know, I could talk about things like continuous delivery direct to when a ball comes tools and it has many different features to it, but very simply on its own, it allows us to give the visibility of what the teams are doing. And once we have that visibility, then we can talk to the teams, um, around, you know, could they be doing better component testing? Could they be using some virtualized services here or there? And that's not even the main purpose of continuous delivery director, but it's just a reason that tools themselves can just give greater visibility of have much more intuitive and insightful conversations with other teams and reduce those organizational silos. >>Thanks, Ben. So we'd kind of sum it up, autonomy collaboration tools that facilitate that. So let's talk now about metrics from your perspectives. What are the metrics that matter? Jeff, >>I'm going to go right back to what Glenn said about data that provides visibility that enables us to, to make decisions, um, with shared purpose. And so business value has to be one of the first things that we look at. Um, how do we assess whether we have built something that is valuable, you know, that could be sales revenue, it could be net promoter score. Uh, if you're not selling what you've built, it could even be what the level of reuse is within your organization or other teams picking up the services, uh, that you've created. Um, one of the things that I've begun to see organizations do is to align value streams with customer journeys and then to align teams with those value streams. So that's one of the ways that you get to a shared purpose, cause we're all trying to deliver around that customer journey, the value with it. >>And we're all measured on that. Um, there are flow metrics which are really important. How long does it take us to get a new feature out from the time that we conceive it to the time that we can run our first experiments with it? There are quality metrics, um, you know, some of the classics or maybe things like defect, density, or meantime to response. Um, one of my favorites came from a, um, a company called ultimate software where they looked at the ratio of defects found in production to defects found in pre production and their developers were in fact measured on that ratio. It told them that guess what quality is your job to not just the test, uh, departments, a group, the fourth level that I think is really important, uh, in, in the current, uh, situation that we're in is the level of engagement in your development organization. >>We used to joke that we measured this with the parking lot metric helpful was the parking lot at nine. And how full was it at five o'clock. I can't do that anymore since we're not physically co-located, but what you can do is you can look at how folks are delivering. You can look at your metrics in your SCM environment. You can look at, uh, the relative rates of churn. Uh, you can look at things like, well, are our developers delivering, uh, during longer periods earlier in the morning, later in the evening, are they delivering, uh, you know, on the weekends as well? Are those signs that we might be heading toward a burnout because folks are still running at sprint levels instead of marathon levels. Uh, so all of those in combination, uh, business value, uh, flow engagement in quality, I think form the backbone of any sort of, of metrics, uh, a program. >>The second thing that I think you need to look at is what are we going to do with the data and the philosophy behind the data is critical. Um, unfortunately I see organizations where they weaponize the data and that's completely the wrong way to look at it. What you need to do is you need to say, you need to say, how is this data helping us to identify the blockers? The things that aren't allowing us to provide the right context for people to do the right thing. And then what do we do to remove those blockers, uh, to make sure that we're giving these autonomous teams the context that they need to do their job, uh, in a way that creates the most value for the customers. >>Great advice stuff, Glenn, over to your metrics that matter to you that really make a big impact. And, and, and also how do you measure quality kind of following onto the advice that Jeff provided? >>That's some great advice. Actually, he talks about value. He talks about flow. Both of those things are very much on my mind at the moment. Um, but there was this, I listened to a speaker, uh, called me Kirsten a couple of months ago. It taught very much around how important flow management is and removing, you know, and using that to remove waste, to understand in terms of, you know, making software changes, um, what is it that's causing us to do it longer than we need to. So where are those areas where it takes long? So I think that's a very important thing for us. It's even more basic than that at the moment, we're on a journey from moving from kind of a waterfall to agile. Um, and the problem with moving from waterfall to agile is with waterfall, the, the business had a kind of comfort that, you know, everything was tested together and therefore it's safer. >>Um, and with agile, there's that kind of, you know, how do we make sure that, you know, if we're doing things quick and we're getting stuff out the door that we give that confidence, um, that that's ready to go, or if there's a risk that we're able to truly articulate what that risk is. So there's a bit about release confidence, um, and some of the metrics around that and how, how healthy those releases are, and actually saying, you know, we spend a lot of money, um, um, an investment setting up our teams, training our teams, are we actually seeing them deliver more quickly and are we actually seeing them deliver more value quickly? So yeah, those are the two main things for me at the moment, but I think it's also about, you know, generally bringing it all together, the dev ops, you know, we've got the kind of value ops AI ops, how do we actually bring that together to so we can make quick decisions and making sure that we are, um, delivering the biggest bang for our buck, absolutely biggest bang for the buck, surge, your thoughts. >>Yeah. So I think we all agree, right? It starts with business metrics, flow metrics. Um, these are kind of the most important metrics. And ultimately, I mean, one of the things that's very common across a highly functional teams is engagements, right? When, when you see a team that's highly functioning, that's agile, that practices DevOps every day, they are highly engaged. Um, that that's, that's definitely true. Now the, you know, back to, I think, uh, Jeff's point on weaponization of metrics. One of the key challenges we see is that, um, organizations traditionally have been kind of, uh, you know, setting up benchmarks, right? So what is a good cycle time? What is a good lead time? What is a good meantime to repair? The, the problem is that this is very contextual, right? It varies. It's going to vary quite a bit, depending on the nature of application and system. >>And so one of the things that we really need to evolve, um, as an industry is to understand that it's not so much about those flow metrics is about our, these four metrics ultimately contribute to the business metric to the business outcome. So that's one thing. The second aspect, I think that's oftentimes misunderstood is that, you know, when you have a bad cycle time or, or, or what you perceive as being a buy cycle time or better quality, the problem is oftentimes like all, do you go and explore why, right. What is the root cause of this? And I think one of the key challenges is that we tend to focus a lot of time on metrics and not on the eye type patterns, which are pretty common across the industry. Um, you know, if you look at, for instance, things like lead time, for instance, it's very common that, uh, organizational boundaries are going to be a key contributor to badly time. >>And so I think that there is, you know, the only the metrics there is, I think a lot of work that we need to do in terms of classifying, descend type patterns, um, you know, back to you, Jeff, I think you're one of the cool offers of waterscrumfall as a, as, as a key pattern, the industry or anti-spatter. Um, but waterscrumfall right is a key one, right? And you will detect that through kind of a defect arrival rates. That's where that looks like an S-curve. And so I think it's beyond kind of the, the metrics is what do you do with those metrics? >>Right? I'll tell you a search. One of the things that is really interesting to me in that space is I think those of us had been in industry for a long time. We know the anti-patterns cause we've seen them in our career maybe in multiple times. And one of the things that I think you could see tooling do is perhaps provide some notification of anti-patterns based on the telemetry that comes in. I think it would be a really interesting place to apply, uh, machine learning and reinforcement learning techniques. Um, so hopefully something that we'd see in the future with dev ops tools, because, you know, as a manager that, that, you know, may be only a 10 year veteran or 15 year veteran, you may be seeing these anti-patterns for the first time. And it would sure be nice to know what to do, uh, when they start to pop up, >>That would right. Insight, always helpful. All right, guys, I would like to get your final thoughts on this. The one thing that you believe our audience really needs to be on the lookout for and to put on our agendas for the next 12 months, Jeff will go back to you. Okay. >>I would say look for the opportunities that this disruption presents. And there are a couple that I see, first of all, uh, as we shift to remote central working, uh, we're unlocking new pools of talent, uh, we're, it's possible to implement, uh, more geographic diversity. So, so look to that as part of your strategy. Number two, look for new types of tools. We've seen a lot of interest in usage of low-code tools to very quickly develop applications. That's potentially part of a mainstream strategy as we go into 2021. Finally, make sure that you embrace this idea that you are supporting creative workers that agile and dev ops are the peanut butter and chocolate to support creative, uh, workers with algorithmic capabilities, >>Peanut butter and chocolate Glen, where do we go from there? What are, what's the one silver bullet that you think folks to be on the lookout for now? I, I certainly agree that, um, low, low code is, uh, next year. We'll see much more low code we'd already started going, moving towards a more of a SAS based world, but low code also. Um, I think as well for me, um, we've still got one foot in the kind of cow camp. Um, you know, we'll be fully trying to explore what that means going into the next year and exploiting the capabilities of cloud. But I think the last, um, the last thing for me is how do you really instill quality throughout the kind of, um, the, the life cycle, um, where, when I heard the word scrum fall, it kind of made me shut it because I know that's a problem. That's where we're at with some of our things at the moment we need to get beyond that. We need >>To be releasing, um, changes more frequently into production and actually being a bit more brave and having the confidence to actually do more testing in production and go straight to production itself. So expect to see much more of that next year. Um, yeah. Thank you. I haven't got any food analogies. Unfortunately we all need some peanut butter and chocolate. All right. It starts to take us home. That's what's that nugget you think everyone needs to have on their agendas? >>That's interesting. Right. So a couple of days ago we had kind of a latest state of the DevOps report, right? And if you read through the report, it's all about the lost city, but it's all about sweet. We still are receiving DevOps as being all about speed. And so to me, the key advice is in order to create kind of a spiritual collocation in order to foster engagement, we have to go back to what is it we're trying to do collectively. We have to go back to tie everything to the business outcome. And so for me, it's absolutely imperative for organizations to start to plot their value streams, to understand how they're delivering value into aligning everything they do from a metrics to deliver it, to flow to those metrics. And only with that, I think, are we going to be able to actually start to really start to align kind of all these roles across the organizations and drive, not just speed, but business outcomes, >>All about business outcomes. I think you guys, the three of you could write a book together. So I'll give you that as food for thought. Thank you all so much for joining me today and our guests. I think this was an incredibly valuable fruitful conversation, and we appreciate all of you taking the time to spiritually co-located with us today, guys. Thank you. Thank you, Lisa. Thank you. Thank you for Jeff Hammond serves Lucio and Glen Martin. I'm Lisa Martin. Thank you for watching the broad cops Broadcom dev ops virtual forum.

>>from the Cube Studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. >>Hi, everybody. This is Dave Volante with Cuban. Welcome to the special digital presentation. We're really digging into how IBM is operational izing and automating the AI and data pipeline not only for its clients, but also for itself. And with me is Julie Lockner, who looks after offering management and IBM Data and AI portfolio really great to see you again. >>Great, great to be here. Thank you. Talk a >>little bit about the role you have here at IBM. >>Sure, so my responsibility in offering >>management and the data and AI organization is >>really twofold. One is I lead a team that implements all of the back end processes, really the operations behind any time we deliver a product from the Data and AI team to the market. So think about all of the release cycle management are seeing product management discipline, etcetera. The other role that I play is really making sure that I'm We are working with our customers and making sure they have the best customer experience and a big part of that is developing the data ops methodology. It's something that I needed internally >>from my own line of business execution. But it's now something that our customers are looking for to implement in their shops as well. >>Well, good. I really want to get into that. So let's let's start with data ops. I mean, I think you know, a lot of people are familiar with Dev Ops. Not maybe not everybody's familiar with data ops. What do we need to know about data? >>Well, I mean, you bring up the point that everyone knows Dev ops. And in fact, I think you know what data ops really >>does is bring a lot of the benefits that Dev Ops did for application >>development to the data management organizations. So when we look at what is data ops, it's a data management. Uh, it is a data management set of principles that helps organizations bring business ready data to their consumers. Quickly. It takes it borrows from Dev ops. Similarly, where you have a data pipeline that associates a business value requirement. I have this business initiative. It's >>going to drive this much revenue or this must cost >>savings. This is the data that I need to be able to deliver it. How do I develop that pipeline and map to the data sources Know what data it is? Know that I can trust it. So ensuring >>that it has the right quality that I'm actually using, the data that it was meant >>for and then put it to use. So in in history, most data management practices deployed a waterfall like methodology. Our implementation methodology and what that meant is all the data pipeline >>projects were implemented serially, and it was done based on potentially a first in first out program management office >>with a Dev Ops mental model and the idea of being able to slice through all of the different silos that's required to collect the data, to organize it, to integrate it, the validate its quality to create those data integration >>pipelines and then present it to the dashboard like if it's a Cognos dashboard >>or a operational process or even a data science team, that whole end to end process >>gets streamlined through what we're pulling data ops methodology. >>So I mean, as you well know, we've been following this market since the early days of Hadoop people struggle with their data pipelines. It's complicated for them, there's a a raft of tools and and and they spend most of their time wrangling data preparing data moving data quality, different roles within the organization. So it sounds like, you know, to borrow from from Dev Ops Data offices is all about streamlining that data pipeline, helping people really understand and communicate across. End the end, as you're saying, But but what's the ultimate business outcome that you're trying to drive? >>So when you think about projects that require data to again cut costs Teoh Artemia >>business process or drive new revenue initiatives, >>how long does it take to get from having access to the data to making it available? That duration for every time delay that is spent wasted trying to connect to data sources, trying to find subject matter experts that understand what the data means and can verify? It's quality, like all of those steps along those different teams and different disciplines introduces delay in delivering high quality data fat, though the business value of data ops is always associated with something that the business is trying to achieve but with a time element so if it's for every day, we don't have this data to make a decision where either making money or losing money, that's the value proposition of data ops. So it's about taking things that people are already doing today and figuring out the quickest way to do it through automation or work flows and just cutting through all the political barriers >>that often happens when these data's cross different organizational boundaries. >>Yes, sir, speed, Time to insights is critical. But in, you know, with Dev Ops, you really bringing together of the skill sets into, sort of, you know, one Super Dev or one Super ops. It sounds with data ops. It's really more about everybody understanding their role and having communication and line of sight across the entire organization. It's not trying to make everybody else, Ah, superhuman data person. It's the whole It's the group. It's the team effort, Really. It's really a team game here, isn't it? >>Well, that's a big part of it. So just like any type of practice, there's people, aspects, process, aspects and technology, right? So people process technology, and while you're you're describing it, like having that super team that knows everything about the data. The only way that's possible is if you have a common foundation of metadata. So we've seen a surgeons in the data catalog market in the last, you know, 67 years. And what what the what? That the innovation in the data catalog market has actually enabled us to be able >>to drive more data ops pipelines. >>Meaning as you identify data assets you captured the metadata capture its meaning. You capture information that can be shared, whether they're stakeholders, it really then becomes more of a essential repository for people don't really quickly know what data they have really quickly understand what it means in its quality and very quickly with the right proper authority, like privacy rules included. Put it to use >>for models, um, dashboards, operational processes. >>Okay. And we're gonna talk about some examples. And one of them, of course, is IBM's own internal example. But help us understand where you advise clients to start. I want to get into it. Where do I get started? >>Yeah, I mean, so traditionally, what we've seen with these large data management data governance programs is that sometimes our customers feel like this is a big pill to swallow. And what we've said is, Look, there's an operator. There's an opportunity here to quickly define a small project, align into high value business initiative, target something that you can quickly gain access to the data, map out these pipelines and create a squad of skills. So it includes a person with Dev ops type programming skills to automate an instrument. A lot of the technology. A subject matter expert who understands the data sources in it's meeting the line of business executive who translate bringing that information to the business project and associating with business value. So when we say How do you get started? We've developed A I would call it a pretty basic maturity model to help organizations figure out. Where are they in terms of the technology, where are they in terms of organizationally knowing who the right people should be involved in these projects? And then, from a process perspective, we've developed some pretty prescriptive project plans. They help you nail down. What are the data elements that are critical for this business business initiative? And then we have for each role what their jobs are to consolidate the data sets map them together and present them to the consumer. We find that six week projects, typically three sprints, are perfect times to be able to a timeline to create one of these very short, quick win projects. Take that as an opportunity to figure out where your bottlenecks are in your own organization, where your skill shortages are, and then use the outcome of that six week sprint to then focus on billing and gaps. Kick off the next project and iterating celebrate the success and promote the success because >>it's typically tied to a business value to help them create momentum for the next one. >>That's awesome. I want to get into some examples, I mean, or we're both Massachusetts based. Normally you'd be in our studio and we'd be sitting here for face to face of obviously with Kobe. 19. In this crisis world sheltering in place, you're up somewhere in New England. I happened to be in my studio, but I'm the only one here, so relate this to cove it. How would data ops, or maybe you have a, ah, a concrete example in terms of how it's helped, inform or actually anticipate and keep up to date with what's happening with both. >>Yeah, well, I mean, we're all experiencing it. I don't think there's a person >>on the planet who hasn't been impacted by what's been going on with this Cupid pandemic prices. >>So we started. We started down this data obscurity a year ago. I mean, this isn't something that we just decided to implement a few weeks ago. We've been working on developing the methodology, getting our own organization in place so that we could respond the next time we needed to be able todo act upon a data driven decision. So part of the step one of our journey has really been working with our global chief data officer, Interpol, who I believe you have had an opportunity to meet with an interview. So part of this year Journey has been working with with our corporate organization. I'm in a line of business organization where we've established the roles and responsibilities we've established the technology >>stack based on our cloud pack for data and Watson knowledge padlock. >>So I use that as the context. For now, we're faced with a pandemic prices, and I'm being asked in my business unit to respond very quickly. How can we prioritize the offerings that are going to help those in critical need so that we can get those products out to market? We can offer a 90 day free use for governments and hospital agencies. So in order for me to do that as a operations lead or our team, I needed to be able to have access to our financial data. I needed to have access to our product portfolio information. I needed to understand our cloud capacity. So in order for me to be able to respond with the offers that we recently announced and you'll you can take a look at some of the examples with our Watson Citizen Assistant program, where I was able to provide the financial information required for >>us to make those products available from governments, hospitals, state agencies, etcetera, >>that's a That's a perfect example. Now, to set the stage back to the corporate global, uh, the chief data office organization, they implemented some technology that allowed us to, in just data, automatically classify it, automatically assign metadata, automatically associate data quality so that when my team started using that data, we knew what the status of that information >>was when we started to build our own predictive models. >>And so that's a great example of how we've been partnered with a corporate central organization and took advantage of the automated, uh, set of capabilities without having to invest in any additional resources or head count and be able to release >>products within a matter of a couple of weeks. >>And in that automation is a function of machine intelligence. Is that right? And obviously, some experience. But you couldn't you and I when we were consultants doing this by hand, we couldn't have done this. We could have done it at scale anyway. It is it is it Machine intelligence and AI that allows us to do this. >>That's exactly right. And you know, our organization is data and AI, so we happen to have the research and innovation teams that are building a lot of this technology, so we have somewhat of an advantage there, but you're right. The alternative to what I've described is manual spreadsheets. It's querying databases. It's sending emails to subject matter experts asking them what this data means if they're out sick or on vacation. You have to wait for them to come back, and all of this was a manual process. And in the last five years, we've seen this data catalog market really become this augmented data catalog, and the augmentation means it's automation through AI. So with years of experience and natural language understanding, we can home through a lot of the metadata that's available electronically. We can calm for unstructured data, but we can categorize it. And if you have a set of business terms that have industry standard definitions through machine learning, we can automate what you and I did as a consultant manually in a matter of seconds. That's the impact that AI is have in our organization, and now we're bringing this to the market, and >>it's a It's a big >>part of where I'm investing. My time, both internally and externally, is bringing these types >>of concepts and ideas to the market. >>So I'm hearing. First of all, one of the things that strikes me is you've got multiple data, sources and data that lives everywhere. You might have your supply chain data in your er p. Maybe that sits on Prem. You might have some sales data that's sitting in a sas in a cloud somewhere. Um, you might have, you know, weather data that you want to bring in in theory. Anyway, the more data that you have, the better insights that you could gather assuming you've got the right data quality. But so let me start with, like, where the data is, right? So So it's it's anywhere you don't know where it's going to be, but you know you need it. So that's part of this right? Is being able >>to get >>to the data quickly. >>Yeah, it's funny. You bring it up that way. I actually look a little differently. It's when you start these projects. The data was in one place, and then by the time you get through the end of a project, you >>find out that it's moved to the cloud, >>so the data location actually changes. While we're in the middle of projects, we have many or even during this this pandemic crisis. We have many organizations that are using this is an opportunity to move to SAS. So what was on Prem is now cloud. But that shouldn't change the definition of the data. It shouldn't change. It's meaning it might change how you connect to it. It might also change your security policies or privacy laws. Now, all of a sudden, you have to worry about where is that data physically located? And am I allowed to share it across national boundaries right before we knew physically where it waas. So when you think about data ops, data ops is a process that sits on top of where the data physically resides. And because we're mapping metadata and we're looking at these data pipelines and automated work flows, part of the design principles are to set it up so that it's independent of where it resides. However, you have to have placeholders in your metadata and in your tool chain, where we're automating these work flows so that you can accommodate when the data decides to move. Because the corporate policy change >>from on prem to cloud. >>And that's a big part of what Data ops offers is the same thing. By the way, for Dev ops, they've had to accommodate building in, you know, platforms as a service versus on from the development environments. It's the same for data ops, >>and you know, the other part that strikes me and listening to you is scale, and it's not just about, you know, scale with the cloud operating model. It's also about what you were talking about is you know, the auto classification, the automated metadata. You can't do that manually. You've got to be able to do that. Um, in order to scale with automation, That's another key part of data office, is it not? >>It's a well, it's a big part of >>the value proposition and a lot of the part of the business case. >>Right then you and I started in this business, you know, and big data became the thing. People just move all sorts of data sets to these Hadoop clusters without capturing the metadata. And so as a result, you know, in the last 10 years, this information is out there. But nobody knows what it means anymore. So you can't go back with the army of people and have them were these data sets because a lot of the contact was lost. But you can use automated technology. You can use automated machine learning with natural, understand natural language, understanding to do a lot of the heavy lifting for you and a big part of data ops, work flows and building these pipelines is to do what we call management by exception. So if your algorithms say 80% confident that this is a phone number and your organization has a low risk tolerance, that probably will go to an exception. But if you have a you know, a match algorithm that comes back and says it's 99% sure this is an email address, right, and you have a threshold that's 98%. It will automate much of the work that we used to have to do manually. So that's an example of how you can automate, eliminate manual work and have some human interaction based on your risk threshold. >>That's awesome. I mean, you're right, the no schema on write said. I throw it into a data lake. Data Lake becomes a data swamp. We all know that joke. Okay, I want to understand a little bit, and maybe you have some other examples of some of the use cases here, but there's some of the maturity of where customers are. It seems like you've got to start by just understanding what data you have, cataloging it. You're getting your metadata act in order. But then you've got you've got a data quality component before you can actually implement and get yet to insight. So, you know, where are customers on the maturity model? Do you have any other examples that you can share? >>Yeah. So when we look at our data ops maturity model, we tried to simplify, and I mentioned this earlier that we try to simplify it so that really anybody can get started. They don't have to have a full governance framework implemented to to take advantage of the benefits data ops delivers. So what we did is we said if you can categorize your data ops programs into really three things one is how well do you know your data? Do you even know what data you have? The 2nd 1 is, and you trust it like, can you trust it's quality? Can you trust it's meeting? And the 3rd 1 is Can you put it to use? So if you really think about it when you begin with what data do you know, write? The first step is you know, how are you determining what data? You know? The first step is if you are using spreadsheets. Replace it with a data catalog. If you have a department line of business catalog and you need to start sharing information with the department's, then start expanding to an enterprise level data catalog. Now you mentioned data quality. So the first step is do you even have a data quality program, right. Have you even established what your criteria are for high quality data? Have you considered what your data quality score is comprised of? Have you mapped out what your critical data elements are to run your business? Most companies have done that for there. They're governed processes. But for these new initiatives And when you identify, I'm in my example with the covert prices, what products are we gonna help bring to market quickly? I need to be able to >>find out what the critical data elements are. And can I trust it? >>Have I even done a quality scan and have teams commented on it's trustworthiness to be used in this case, If you haven't done anything like that in your organization, that might be the first place to start. Pick the critical data elements for this initiative, assess its quality, and then start to implement the work flows to re mediate. And then when you get to putting it to use, there's several methods for making data available. One is simply making a gate, um, are available to a small set of users. That's what most people do Well, first, they make us spreadsheet of the data available, But then, if they need to have multiple people access it, that's when, like a Data Mart might make sense. Technology like data virtualization eliminates the need for you to move data as you're in this prototyping phase, and that's a great way to get started. It doesn't cost a lot of money to get a virtual query set up to see if this is the right join or the right combination of fields that are required for this use case. Eventually, you'll get to the need to use a high performance CTL tool for data integration. But Nirvana is when you really get to that self service data prep, where users can weary a catalog and say these are the data sets I need. It presents you a list of data assets that are available. I can point and click at these columns I want as part of my data pipeline and I hit go and automatically generates that output or data science use cases for it. Bad news, Dashboard. Right? That's the most mature model and being able to iterate on that so quickly that as soon as you get feedback that that data elements are wrong or you need to add something, you can do it. Push button. And that's where data obscurity should should bring organizations too. >>Well, Julie, I think there's no question that this covert crisis is accentuated the importance of digital. You know, we talk about digital transformation a lot, and it's it's certainly riel, although I would say a lot of people that we talk to we'll say, Well, you know, not on my watch. Er, I'll be retired before that all happens. Well, this crisis is accelerating. That transformation and data is at the heart of it. You know, digital means data. And if you don't have data, you know, story together and your act together, then you're gonna you're not gonna be able to compete. And data ops really is a key aspect of that. So give us a parting word. >>Yeah, I think This is a great opportunity for us to really assess how well we're leveraging data to make strategic decisions. And if there hasn't been a more pressing time to do it, it's when our entire engagement becomes virtual like. This interview is virtual right. Everything now creates a digital footprint that we can leverage to understand where our customers are having problems where they're having successes. You know, let's use the data that's available and use data ops to make sure that we can generate access. That data? No, it trust it, Put it to use so that we can respond to >>those in need when they need it. >>Julie Lockner, your incredible practitioner. Really? Hands on really appreciate you coming on the Cube and sharing your knowledge with us. Thank you. >>Thank you very much. It was a pleasure to be here. >>Alright? And thank you for watching everybody. This is Dave Volante for the Cube. And we will see you next time. >>Yeah, yeah, yeah, yeah, yeah

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation LeBron welcome to the cube studio I'm John Ferrier here in Palo Alto in our remote coverage of the tech industry we are in our quarantine crew here getting all the stories in the technology industry from all the thought leaders and all the newsmakers we've got a great story here about data data compliance and really about the platforms around how enterprises are using data I've got two great guests and some news to announce Kieran our CEO is the vice president of business development with elation and William Murphy vice president of technology alliances of big ID got some interesting news a integration partnership between the two companies really kind of compelling especially now as people have to look at the cloud scale what's happening in our world certainly in the new realities of kovin 19 and going forward the role of data new kinds of applications and the speed and agility are gonna require more and more automation more reality around making sure things are in place so guys thanks for coming on appreciate it Kieran William thanks for joining me thank you thank you so let's take a step back elation you guys have been on the cube many times we've been following you guys been a leader and Enterprise catalog a new approach it's a real new technology approach and methodology and team approach to building out the data catalogues so talk about the Alliance here why what's the news why you guys in Creighton is integration partnership well let me start and thank you for having us today you know as you know elation launched the data catalog a category seven years ago and even today we're acknowledging the leader as a leader in that space you know and but we really began with the core belief that ultimately data management will be drive driven more and more by business demand and less by information suppliers so you know another way to think about that is you know how people behave with data will drive how companies manage data so our philosophy put very simply is to start with people and not first not data and our customers really seem to agree with this approach and we've got close to 200 brands using our data you know our tool every single day to drive vibrant data communities and and foster a real data culture in the environment so one of the things that was really exciting to us is the in been in data privacy by large corporate customers to get their arms around this and you know we really strive to improve our ability to use the tool inside you know these enterprises across more use cases so the partnership that we're announcing with big ID today is really you know Big Ideas the leading modern data intelligence platform for privacy and what we're trying to do is to bring bring a level of integration between our two technologies so that enterprises in better manage and scale their their data privacy compliance capability William talked about big ID what you guys are doing you guys also have a date intelligence platform we've been covering gdpr for a very long time I once called I won't say it again because it wasn't really that complimentary but the reality has sit in and they and the users now understand more than ever privacy super important companies have to deal with this you guys have a solution take a minute to explain big-big ID and what you guys are doing yeah absolutely so our founders Demetri Shirota and Nimrod Beck's founded big idea in 2016 Sam you know gdpr was authored and the big reason there is that data changed and how companies and enterprises doubled data was changing pretty much forever that profound change meant that the status quo could no longer exist and so privacy was gonna have to become a day-to-day reality to these enterprises but what big ID realized is that to start to do to do anything with privacy you actually have to understand where your data is what it is and whose it is and so that's really the genesis of what dimitri nimrod created which which is a privacy centric data discovery and intelligence platform that allows our enterprise customers and we have over 70 customers in the enterprise space many within the Fortune hundred to be able to find classify and correlate sensitive data as they defined it across data sources whether its own Prem or in the cloud and this gives our users and kind of unprecedented ability to look into their data to get better visibility which if both allows for collaboration and also allows for real-time decision-making a big place with better accuracy and confidence that regulations are not being broken and that customers data is being treated appropriately great I'm just reading here from the release that I want to get you guys thoughts and unpack some of the concepts on here but the headline is elation strengthens privacy capabilities with big ID part nur ship empowering organizations to mitigate risks delivering privacy aware data use and improved adherence to data privacy regulations it's a mouthful but the bottom line is is that there's a lot of stuff to that's a lot of complexity around these rules and these platforms and what's interesting you mentioned discovery the enterprise discovery side of the business has always been a complex nightmare I think what's interesting about this partnership from my standpoint is that you guys are bringing an interface into a complex platform and creating an easy abstraction to kind of make it usable I mean the end of the day you know we're seeing the trends with Amazon they have Kendre which they announced and they're gonna have a ship soon fast speed of insights has to be there so unifying data interfaces with back-end is really what seems to be the pattern is that the magic going on here can you guys explain what's going on with this and what's the outcome gonna be for customers yeah I guess I'll kick off and we'll please please chime in I think really there's three overarching challenges that I think enterprises are facing is they're grappling with these regulations as as we'll talked about you know number one it's really hard to both identify and classify private data right it's it's not as easy as it might sound and you know we can talk a little bit more about that it's also very difficult to flag at the point of analysis when somebody wants to find information the relevant policies that might apply to the given data that they're looking to it to run an analysis on and lastly the enterprise's are constantly in motion as enterprises change and by new businesses and enter new markets and launch new products these policies have to keep up with that change and these are real challenges to address and you know with Big Idea halation we're trying to really accelerate that compliance right with the the you know the combination of our tools you know reduce the the cost and complexity of compliance and fundamentally keep up through a single interface so that users can know what to do with data at the point of consumption and I think that's the way to think about it well I don't know if you want to add something to that absolutely I think when Karen and I have been working on this for actually many months at this point but most companies don't have a business plan of just saying let's store as much data as possible without getting anything out of it but in order to get something out of it the ability to find that data rapidly and then analyze it so that decision makers make up-to-date decisions is pretty vital a lot of these things when they have to be done manually take a long time they're huge business issues there and so the ability to both automate data discovery and then cataloging across elation and big ID gives those decision makers whether the data steward the data analyst the chief data officer an ability to really dive deeper than they have previously with better speed you know one of the things that we've been talking about for a long time with big data as these data links and they're fairly easy to pull I mean you can put a bunch of data into a corpus and you you act on them but as you start to get across these silos there's a need for you know getting a process down around managing just not only the data wrangling but the policies behind it and platforms are becoming more complex can you guys talk about the product market fit here because there's sass involved so there's also a customer activity what's the product market fit that you guys see with this integration what are some of the things that you're envisioning to emerge out of this value proposition I think I can start I think you're exactly right enterprises have made huge investments in you know historically data warehouses data Mart's data lakes all kinds of other technology infrastructure aimed at making the data easier to get to but they've effectively just layered on to the problem so elations catalog has made it incredibly much more effective at helping organizations to find to understand trust to reuse and use that data so that stewards and people who know about the data can inform users who may need need to run a particular report or conduct a specific analysis can accelerate that process and compress the time the insights much much more than then it's are possible with today's technologies and if you if you overlay that on to the data privacy challenge its compounded and I think you know will it would be great for you to comment on what the data discovery capability it's a big ID do to improve that that even further yeah absolutely so as to companies we're trying to bridge this gap between data governance and privacy and and John as you mentioned there's been a proliferation of a lot of tools whether their data lakes data analysis tools etc what Big Idea is able to do is we're looking across over 70 different types of data platforms whether they be legacy systems like SharePoint and sequel whether they be on pram or in the cloud whether it's data at rest or in motion and we're able to auto populate our metadata findings into relations data catalog the main purpose there being that those data stewards and have access to the most authentic real time data possible so on the terms of the customer value they're going to see what more built in privacy aware features is its speed but you know what I mean the problem is compounded with the data getting that catalog and getting insights out of it but for this partnership is it speed to outcome what does the outcome that you guys are envisioning here for the customer I think it's a combination of speed as you said you know they can much more rapidly get up to speed so an analyst who needs to make a decision about specific data set whether they can use it or not and know at the point of analysis if this data is governed by policies that has been informed by big IDs so the elation catalog user can make a much more rapid decision about how to use that the second piece is the complexity and costs of compliance they can really reduce and start to winnow down their technology footprint because with the combination of the discovery that big ID provides the the the ongoing discovery the big ID provides and the enterprise it data catalog provided violation we give the framework for being able to keep up with these changes in policies as rules and as companies change so they don't have to keep reinventing the wheel every time so we think that there's a significant speed time the market advantage as well as an ability to really consolidate technology footprint well I'll add to that yeah yeah just one moment so elation when they helped create this marketplace seven years ago one of the goals there and I think we're Big Ideas assisting as well as the trusting confidence that both the users of these software's the data store of the analysts have and the data that they're using and then the the trust and confidence are building with their end consumers is much better knowing that there is the this is both bi-directional and ongoing continuously you know I've always been impressed with relations vision it's big vision around the role of the human and data and it's always been impressive and yeah I think the world spinning in that direction you starting to see that now William I want to get your thoughts with big id because you know one of the things is challenging out there from what we're hearing is you know people want to protect the sensitive data obviously with the hacks and everything else and personal information there's all kinds of regulation and believe me state by state nation by nation it's crazy complex at the same time they've got to ensure this compliance tripwires everywhere right so you have this kind of nested complex web of stuff and some real security concerns at the same time you want to make data available for machine learning and for things like that this is the real kind of things that the problem has twisted around so if I'm an enterprise I'm like oh man this is a pain in the butt so how are you guys seeing this evolve because this solution is one step in that direction what are some of the pain points what are some of the examples can you share any insights around how people are overcoming that because they want to get the data out there they want to create applications that are gonna be modern robust and augmented with whether it's augmented AI of some sort or some sort of application at the same time protecting the information and compliance it's a huge problem challenge your thoughts absolutely so to your point regulations and compliance measures both state-by-state and internationally they're growing I mean I think when we saw GDP our four years ago in the proliferation of other things whether it be in Latin America in Asia Pacific or across the United States potentially even at the federal level in the future it's not making it easier to add complexity to that every industry and many companies individually have their own policies in the way that they describe data whether what's sensitive to them is it patent numbers is it loyalty card numbers is it any number of different things where they could just that that enterprise says that this type of data is particularly sensitive the way we're trying to do this is we're saying that if we can be a force multiplier for the individuals within our organization that are in charge of the stewardship over their data whether it be on the privacy side on the security side or on the data and analytics side that's what we want to do and automation is a huge piece of this so yes the ID has a number of patents in the machine learning area around data discovery and classification cluster analysis being able to find duplicate of data out there and when we put that in conjunction with what elations doing and actually gave the users of the data the kind of unprecedented ability to curate deduplicate secure sensitive data all by a policy driven automated platform that's actually I think the magic gear is we want to make sure that when humans get involved their actions can be made how do I say this minimum minimum human interaction and when it's done it's done for a reason of remediation so they're there the second step not the first step here I'll get your thoughts you know I always riff on the idea of DevOps and it's a cloud term and when you apply that the data you talk about programmability scale automation but the humans are making calls whether you're a programmer and devops world or to a data customer of the catalog and halation i'm making decisions with my business I'm a human I'm taking action at the point of design or whatever this is where I think the magic can happen your thoughts on how this evolves for that use case because what you're doing is you're augmenting the value for the user by taking advantage of these things is is that right or am i around the right area yeah I think so I think the one way to think about elation and that analogy is that the the biggest struggle that enterprise business users have and we target the the consumers of data we're not a provider to the information suppliers if you will but the people who had need to make decisions every single day on the right set of data we're here to empower them to be able to do that with the data that they know has been given the thumbs up by people who know about the data connecting stewards who know about the subject matter at hand with the data that the analyst wants to use at the time of consumption and that powerful connection has been so effective in our customers that enabling them to do in our analytical work that they just couldn't dream of before so the key piece here is with the combination with big ID we can now layer in a privacy aware consumption angle which means if you have a question about running some customer propensity model and you don't know if you can use this data or that data the big ID data discovery platform informs the elation catalog of the usage capabilities of that given data set at the moment the analyst wants conduct his or her analysis with the appropriate data set as identified by the stewards and and as endorsed by the steward so that point in time is really critical because that's where the we can we can fundamentally shrink the decision sight yeah it's interesting and so have the point of attack on the user in this case the person in the business who's doing some real work that's where the action is yeah it's a whole nother meaning of actionable data right so you know this seems to where the values quits its agility really it's kind of what we're talking about here isn't it it is very agile on the differentiation between elation and big idea in what we're bringing to the market now is we're also bringing flexibility and you meant that the point of agility there is because we allow our customers to say what their policies are what their sense of gait is define that themselves within our platforms and then go out find that data classify and catalog at etc like that's giving them that extra flexibility the enterprise's today need so that it can make business decisions and faster and I actually operationalize data guys great job good good news it's I think this is kind of a interesting canary in the coal mine around the trends that are going on around how data is evolving what's next how you guys gonna go to market partnership obviously makes a lot of sense technical integration business model integration good fit what's next for you guys I'm sorry I mean I think the the great thing is that you know from the CEO down our organizations are very much aligned in terms of how we want to integrate our two solutions and how we want to go to market so myself and will have been really focused on making sure that the skill sets of the various constituents within both of our companies have the level of education and knowledge to bring these results to bear coupled with the integration of our two technologies well your thoughts yeah absolutely I mean between our CEOs who have a good cadence to care to myself who probably spend too much time on the phone at this point we might have to get him a guest bedroom or something alignments a huge key here ensuring that we've enabled our field to - and to evangelize this out to the marketplace itself and then doing whether it's this or our webinars or or however we're getting the news out it's important that the markets know that these capabilities are out there because the biggest obstacle honestly to adoption it's not that other solutions or build-it-yourself it's just lack of knowledge that it could be easier it could be done better that you could have you could know your data better you could catalog it better great final question to end the segment message to the potential customer out there what it what about their environment that might make them a great prospect for this solution is it is it a known problem is it a blind spot when would someone know to call you guys up in this to ship and leverage this partnership is it too much data as it's just too much many applications across geographies I'm just trying to understand the folks watching when it's an opportunity to call you guys welcome a relation perspective there that can never be too much data they the a signal that may may indicate an interest or a potential fit for us would be you know the need to be compliant with one or more data privacy regulations and as well said these are coming up left and right individual states in the in addition to the countries are rolling out data privacy regulations that require a whole set of capabilities to be in place and a very rigorous framework of compliance those those requirements and the ability to make decisions every single day all day long about what data to use and when and under what conditions are a perfect set of conditions for the use of a data catalog evacuation coupled with a data discovery and data privacy solution like big I well absolutely if you're an organization out there and you have a lot of customers you have a lot of employees you have a lot of different data sources and disparate locations whether they're on prime of the cloud these are solid indications that you should look at purchasing best-of-breed solutions like elation and Big Ideas opposed to trying to build something internally guys congratulations relations strengthening your privacy capabilities with the big ID partnership congratulations on the news and we'll we'll be tracking it thanks for coming I appreciate it thank you okay so cube coverage here in Palo Alto on remote interviews as we get through this kovat crisis we have our quarantine crew here in Palo Alto I'm John Fourier thanks for watching [Music] okay guys

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi everybody this is David on tape with the cube and welcome to the special digital presentation we're really digging into how IBM is operationalizing and automating the AI and data pipeline not only for its clients but also for itself and with me is Julie Lochner who looks after offering management and IBM's data and AI portfolio Julie great to see you again okay great to be here thank you talk a little bit about the role you have here at IBM sure so my responsibility in offering management in the data and AI organization is really twofold one is I lead a team that implements all of the back-end processes really the operations behind anytime we deliver a product from the data AI team to the market so think about all of the release cycle management pricing product management discipline etc the other roles that I play is really making sure that um we are working with our customers and making sure they have the best customer experience and a big part of that is developing the data ops methodology it's something that I needed internally from my own line of business execution but it's now something that our customers are looking for to implement in their shops as well well good I really want to get into that and so let's let's start with data ops I mean I think you know a lot of people are familiar with DevOps not maybe not everybody's familiar with the data Ops what do we need to know about data well I mean you bring up the point that everyone knows DevOps and and then in fact I think you know what data Ops really does is bring a lot of the benefits that DevOps did for application development to the data management organizations so when we look at what is data ops it's a data management it's a it's a data management set of principles that helps organizations bring business ready data to their consumers quickly it takes it borrows from DevOps similarly where you have a data pipeline that associates a business value requirement I have this business initiative it's gonna drive this much revenue or this much cost savings this is the data that I need to be able to deliver it how do I develop that pipeline and map to the data sources know what data it is know that I can trust it so ensuring that it has the right quality that I'm actually using the data that it was meant for and then put it to use so in in history most dated management practices deployed a waterfall like methodology or implementation methodology and what that meant is all the data pipeline projects were implemented serially and it was dawn based on potentially a first-in first-out program management office with a DevOps mental model and the idea of being able to slice through all of the different silos that's required to collect the data to organize it to integrate it to validate its quality to create those data integration pipelines and then present it to the dashboard like if it's a Cognos dashboard for a operational process or even a data science team that whole end-to-end process gets streamlined through what we're calling data ops methodology so I mean as you well know we've been following this market since the early days of a dupe and people struggle with their data pipelines it's complicated for them there's a raft of tools and and and they spend most of their time wrangling data preparing data improving data quality different roles within the organization so it sounds like you know to borrow from from DevOps data OPS's is all about REME lining that data pipeline helping people really understand and communicate across end to end as you're saying but but what's the ultimate business outcome that you're trying to drive so when you think about projects that require data to again cut cost to automate a business process or drive new revenue initiatives how long does it take to get from having access to the data to making it available that duration for every time delay that is spent wasted trying to connect to data sources trying to find subject matter experts that understand what the data means and can verify its quality like all of those steps along those different teams and different disciplines introduces delay in delivering high quality data fast so the business value of data Ops is always associated with something that the business is trying to achieve but with a time element so if it's for every day we don't have this data to make a decision we're either making money or losing money that's the value proposition of data ops so it's about taking things that people are already doing today and figuring out the quickest way to do it through automation through workflows and just cutting through all of the political barriers that often happens when these data's cross different organizational boundaries yeah so speed time to insights is critical but to in and then you know with DevOps you're really bringing together the skill sets into sort of you know one super dev or one super ops it sounds with data ops it's really more about everybody understanding their role and having communication and line-of-sight across the entire organization it's not trying to make everybody a superhuman data person it's the whole it's the group it's the team effort really it's really a team game here isn't it well that's a big part of it so just like any type of practice there's people aspects process aspects and technology right so people process technology and while you're you're describing it like having that super team that knows everything about the data the only way that's possible is if you have a common foundation of metadata so we've seen a surgeons in the data catalog market and last you know six seven years and what what the what that the innovation in the data catalog market has actually enabled us to be able to drive more data ops pipelines meaning as you identify data assets you've captured the metadata you capture its meaning you capture information that can be shared whether they're stakeholders it really then becomes more of a essential repository for people to really quickly know what data they have really quickly understand what it means in its quality and very quickly with the right proper authority like privacy rules included put it to use for models you know dashboards operational processes okay and and we're gonna talk about some examples and one of them of course is ibm's own internal example but but help us understand where you advise clients to start I want to get into it where do I get started yeah I mean so traditionally what we've seen with these large data management data governance programs is that sometimes our customers feel like this is a big pill to swallow and what we've said is look there's an opera there's an opportunity here to quickly define a small project align it to a high-value business initiative target something that you can quickly gain access to the data map out these pipelines and create a squad of skills so it includes a person with DevOps type programming skills to automate an instrument a lot of the technology a subject matter expert who understands the data sources and its meaning a line of business executive who can translate bringing that information to the business project and associating with business value so when we say how do you get started we've developed a I would call it a pretty basic maturity model to help organizations figure out where are they in terms of the technology where are they in terms of organizationally knowing who the right people should be involved in these projects and then from a process perspective we've developed some pretty prescriptive project plans that help you nail down what are the data elements that are critical for this business business initiative and then we have for each role what their jobs are to consolidate the datasets map them together and present them to the consumer we find that six-week projects typically three sprints are perfect times to be able to in a timeline to create one of these very short quick win projects take that as an opportunity to figure out where your bottlenecks are in your own organization where your skill shortages are and then use the outcome of that six-week sprint to then focus on filling in gaps kick off the next project and iterate celebrate the success and promote the success because it's typically tied to a business value to help them create momentum for the next one all right that's awesome I want to now get into some examples I mean or you're we're both massachusetts-based normally you'd be in our studio and we'd be sitting here face-to-face obviously with kovat 19 in this crisis we're all sheltering in place you're up in somewhere in New England I happen to be in my studio believe it but I'm the only one here so relate this to kovat how would data ops or maybe you have a concrete example in in terms of how it's helped inform or actually anticipate and keep up-to-date with what's happening with building yeah well I mean we're all experiencing it I don't think there's a person on the planet who hasn't been impacted by what's been going on with this coded pandemic crisis so we started we started down this data obscurity a year ago I mean this isn't something that we just decided to implement a few weeks ago we've been working on developing the methodology getting our own organization in place so that we could respond the next time we needed to be able to you know act upon a data-driven decision so part of step one of our journey has really been working with our global chief data officer Interpol who I believe you have had an opportunity to meet with an interview so part of this year journey has been working with with our corporate organization I'm in the line of business organization where we've established the roles and responsibilities we've established the technology stack based on our cloud pack for data and Watson knowledge catalog so I use that as the context for now we're faced with a pandemic crisis and I'm being asked in my business unit to respond very quickly how can we prioritize the offerings that are gonna help those in critical need so that we can get those products out to market we can offer a you know 90-day free use for governments and Hospital agencies so in order for me to do that as a operations lead for our team I needed to be able to have access to our financial data I needed to have access to our product portfolio information I needed to understand our cloud capacity so in order for me to be able to respond with the offers that we recently announced you know you can take a look at some of the examples with our Watson citizen assistant program where I was able to provide the financial information required for us to make those products available for governments hospitals state agencies etc that's a that's a perfect example now to to set the stage back to the corporate global chief data office organization they implemented some technology that allowed us to ingest data automatically classify it automatically assign metadata automatically associate data quality so that when my team started using that data we knew what the status of that information was when we started to build our own predictive models and so that's a great example of how we've partnered with a corporate central organization and took advantage of the automated set of capabilities without having to invest in any additional resources or headcount and be able to release products within a matter of a couple of weeks and in that automation is a function of machine intelligence is that right and obviously some experience but but you couldn't you and I when we were consultants doing this by hand we couldn't have done this we could have done it at scale anyways it is it machine intelligence an AI that allows us to do this that's exactly right and as you know our organization is data and AI so we happen to have the a research and innovation teams that are building a lot of this technology so we have somewhat of an advantage there but you're right the alternative to what I've described is manual spreadsheets it's querying databases it's sending emails to subject matter experts asking them what this data means if they're out sick or on vacation you have to wait for them to come back and all of this was a manual process and in the last five years we've seen this data catalog market really become this augmented data catalog and that augmentation means it's automation through AI so with years of experience and natural language understanding we can comb through a lot of the metadata that's available electronically we can comb through unstructured data we can categorize it and if you have a set of business terms that have industry standard definitions through machine learning we can automate what you and I did as a consultant manually in a matter of seconds that's the impact the AI is had in our organization and now we're bringing this to the market and it's a it's a big part of where I'm investing my time both internally and externally is bringing these types of concepts and ideas to the market so I'm hearing first of all one of the things that strikes me is you've got multiple data sources and data lives everywhere you might have your supply chain data and your ERP maybe that sits on Prem you might have some sales data that's sitting in the SAS store in a cloud somewhere you might have you know a weather data that you want to bring in in theory anyway the more data that you have the better insights that you can gather assuming you've got the right data quality but so let me start with like where the data is right so so it sits anywhere you don't know where it's gonna be but you know you need it so that that's part of this right is being able to read it quickly yeah it's funny you bring it up that way I actually look a little differently it's when you start these projects the data was in one place and then by the time you get through the end of a project you find out that it's a cloud so the data location actually changes while we're in the middle of projects we have many or coming even during this this pandemic crisis we have many organizations that are using this as an opportunity to move to SAS so what was on Prem is now cloud but that shouldn't change the definition of the data it shouldn't change its meaning it might change how you connect to it um it might also change your security policies or privacy laws now all of a sudden you have to worry about where is that data physically located and am I allowed to share it across national boundaries right before we knew physically where it was so when you think about data ops data ops is a process that sits on top of where the data physically resides and because we're mapping metadata and we're looking at these data pipelines and automated workflows part of the design principles are to set it up so that it's independent of where it resides however you have to have placeholders in your metadata and in your tool chain where we oughta mating these workflows so that you can accommodate when the data decides to move because of corporate policy change from on-prem to cloud then that's a big part of what data Ops offers it's the same thing by the way for DevOps they've had to accommodate you know building in you know platforms as a service versus on from the development environments it's the same for data ops and you know the other part that strikes me and listening to you is scale and it's not just about you know scale with the cloud operating model it's also about what you're talking about is you know the auto classification the automated metadata you can't do that manually you've got to be able to do that in order to scale with automation that's another key part of data Ops is it not it's well it's a big part of the value proposition and a lot of a part of the business base right then you and I started in this business you know and Big Data became the thing people just move all sorts of data sets to these Hadoop clusters without capturing the metadata and so as a result you know in the last 10 years this information is out there but nobody knows what it means anymore so you can't go back with the army of people and have them query these data sets because a lot of the contact was lost but you can use automated technology you can use automated machine learning with natural under Snatcher Alang guaa Jing to do a lot of the heavy lifting for you and a big part of data ops workflows and building these pipelines is to do what we call management-by-exception so if your algorithms say you know 80% confident that this is a phone number and your organization has a you know low risk tolerance that probably will go to an exception but if you have a you know a match algorithm that comes back and says it's 99 percent sure this is an email address right and you I have a threshold that's 98% it will automate much of the work that we used to have to do manually so that's an example of how you can automate eliminate manual work and have some human interaction based on your risk threshold now that's awesome I mean you're right the no schema on right said I throw it into a data leg the data link becomes the data swap we all know that joke okay I want to understand a little bit and maybe you have some other examples of some of the use cases here but there's some of the maturity of where customers are I mean it seems like you got to start by just understanding what data you have cataloging it you're getting your metadata act in order but then you've got a you've got a data quality component before you can actually implement and get yet to insight so you know where our customers on the on the maturity model do you have any other examples that you can share yeah so when we look at our data ops maturity model we tried to simplify it I mentioned this earlier that we try to simplify it so that really anybody can get started they don't have to have a full governance framework implemented to take advantage of the benefits data ops delivers so what we did we said if you can categorize your data ops programs into really three things one is how well do you know your data do you even know what data you have the second one is and you trust it like can you trust its quality can you trust its meeting and the third one is can you put it to use so if you really think about it when you begin with what data do you know right the first step is you know how are you determining what data you know the first step is if you are using spreadsheets replace it with a data catalog if you have a department line of business catalog and you need to start sharing information with the departments then start expanding to an enterprise level data catalog now you mentioned data quality so the first step is do you even have a data quality program right have you even established what your criteria are for high quality data have you considered what your data quality score is comprised of have you mapped out what your critical data elements are to run your business most companies have done that for they're they're governed processes but for these new initiatives and when you identify I'm in my example with the Kovach crisis what products are we gonna help bring to market quickly I need to be able to find out what the critical data elements are and can I trust it have I even done a quality scan and have teams commented on its trustworthiness to be used in this case if you haven't done anything like that in your organization that might be the first place to start pick the critical data elements for this initiative assess its quality and then start to implement the workflows to remediate and then when you get to putting it to use there's several methods for making data available you know one is simply making a data Mart available to a small set of users that's what most people do well first they make a spreadsheet of the data available but then if they need to have multiple people access it that's when like a data Mart might make sense technology like data virtualization eliminates the need for you to move data as you're in this prototyping phase and that's a great way to get started it doesn't cost a lot of money to get a virtual query set up to see if this is the right join or the right combination of fields that are required for this use case eventually you'll get to the need to use a high performance ETL tool for data integration but Nirvana is when you really get to that self-service data prep where users can query a catalog and say these are the data sets I need it presents you a list of data assets that are available I can point and click at these columns I want as part of my you know data pipeline and I hit go and it automatically generates that output for data science use cases for a Cognos dashboard right that's the most mature model and being able to iterate on that so quickly that as soon as you get feedback that that data elements are wrong or you need to add something you can do it push button and that's where data observation to bring organizations to well Julie I think there's no question that this kovat crisis is accentuated the importance of digital you know we talk about digital transformation a lot and it's it's certainly real although I would say a lot of people that we talk to will say well you know not on my watch or I'll be retired before that all happens will this crisis is accelerating that transformation and data is at the heart of it you know digital means data and if you don't have your data you know story together and your act together then you're gonna you're not going to be able to compete and data ops really is a key aspect of that so you know give us a parting word all right I think this is a great opportunity for us to really assess how well we're leveraging data to make strategic decisions and if there hasn't been a more pressing time to do it it's when our entire engagement becomes virtual like this interview is virtual write everything now creates a digital footprint that we can leverage to understand where our customers are having problems where they're having successes you know let's use the data that's available and use data ops to make sure that we can iterate access that data know it trust it put it to use so that we can respond to those in need when they need it Julie Locker your incredible practitioner really hands-on really appreciate you coming on the Kuban and sharing your knowledge with us thank you okay thank you very much it was a pleasure to be here all right and thank you for watching everybody this is Dave Volante for the cube and we will see you next time [Music]

>> Paige: Hello, everybody, and thank you for joining us today for the virtual Vertica BDC 2020. Today's breakout session is entitled, Vertica next generation architecture. I'm Paige Roberts, open social relationship Manager at Vertica, I'll be your host for this session. And joining me is Vertica Chief Architect, Chuck Bear, before we begin, I encourage you to submit questions or comments during the virtual session. You don't have to wait, just type your question or comment, in the question box that's below the slides and click submit. So as you think about it, go ahead and type it in, there'll be a Q&A session at the end of the presentation, where we'll answer as many questions, as we're able to during the time. Any questions that we don't get a chance to address, we'll do our best to answer offline. Or alternatively, you can visit the Vertica forums to post your questions there, after the session. Our engineering team is planning to join the forum and keep the conversation going, so you can, it's just sort of like the developers lounge would be in delight conference. It gives you a chance to talk to our engineering team. Also, as a reminder, you can maximize your screen by clicking the double arrow button in the lower right corner of the slide. And before you ask, yes, this virtual session is being recorded, and it will be available to view on demand this week, we'll send you a notification, as soon as it's ready. Okay, now, let's get started, over to you, Chuck. >> Chuck: Thanks for the introduction, Paige, Vertica vision is to help customers, get value from structured data. This vision is simple, it doesn't matter what vertical the customer is in. They're all analytics companies, it doesn't matter what the customers environment is, as data is generated everywhere. We also can't do this alone, we know that you need other tools and people to build a complete solution. You know our database is key to delivering on the vision because we need a database that scales. When you start a new database company, you aren't going to win against 30 year old products on features. But from day one, we had something else, an architecture built for analytics performance. This architecture was inspired by the C-store project, combining the best design ideas from academics and industry veterans like Dr. Mike Stonebreaker. Our storage is optimized for performance, we use many computers in parallel. After over 10 years of refinements against various customer workloads, much of the design held up and serendipitously, the fact that we don't store in place updates set Vertica up for success in the cloud as well. These days, there are other tools that embody some of these design ideas. But we have other strengths that are more important than the storage format, where the only good analytics database that runs both on premise and in the cloud, giving customers the option to migrate their workloads, in most convenient and economical environment, or a full data management solution, not just the query tool. Unlike some other choices, ours comes with integration with a sequel ecosystem and full professional support. We organize our product roadmap into four key pillars, plus the cross cutting concerns of open integration and performance and scale. We have big plans to strengthen Vertica, while staying true to our core. This presentation is primarily about the separation pillar, and performance and scale, I'll cover our plans for Eon, our data management architecture, Mart analytic clusters, or fifth generation query executer, and our data storage layer. Let's start with how Vertica manages data, one of the central design points for Vertica was shared nothing, a design that didn't utilize a dedicated hardware shared disk technology. This quote here is how Mike put it politely, but around the Vertica office, shared disk with an LMTB over Mike's dead body. And we did get some early field experience with shared disk, customers, well, in fact will learn on anything if you let them. There were misconfigurations that required certified experts, obscure bugs extent. Another thing about the shared nothing designed for commodity hardware though, and this was in the papers, is that all the data management features like fault tolerance, backup and elasticity have to be done in software. And no matter how much you do, procuring, configuring and maintaining the machines with disks is harder. The software configuration process to add more service may be simple, but capacity planning, racking and stacking is not. The original allure of shared storage returned, this time though, the complexity and economics are different. It's cheaper, even provision storage with a few clicks and only pay for what you need. It expands, contracts and brings the maintenance of the storage close to a team is good at it. But there's a key difference, it's an object store, an object stores don't support the API's and access patterns used by most database software. So another Vertica visionary Ben, set out to exploit Vertica storage organization, which turns out to be a natural fit for modern cloud shared storage. Because Vertica data files are written once and not updated, they match the object storage model perfectly. And so today we have Eon, Eon uses shared storage to hold Vertica data with local disk depot's that act as caches, ensuring that we can get the performance that our customers have come to expect. Essentially Eon in enterprise behave similarly, but we have the benefit of flexible storage. Today Eon has the features our customers expect, it's been developed in tune for years, we have successful customers such as Redpharma, and if you'd like to know more about Eon has helped them succeed in Amazon cloud, I highly suggest reading their case study, which you can find on vertica.com. Eon provides high availability and flexible scaling, sometimes on premise customers with local disks get a little jealous of how recovery and sub-clusters work in Eon. Though we operate on premise, particularly on pure storage, but enterprise also had strengths, the most obvious being that you don't need and short shared storage to run it. So naturally, our vision is to converge the two modes, back into a single Vertica. A Vertica that runs any combination of local disks and shared storage, with full flexibility and portability. This is easy to say, but over the next releases, here's what we'll do. First, we realize that the query executer, optimizer and client drivers and so on, are already the same. Just the transaction handling and data management is different. But there's already more going on, we have peer-to-peer depot operations and other internode transfers. And enterprise also has a network, we could just get files from remote nodes over that network, essentially mimicking the behavior and benefits of shared storage with the layer of software. The only difference at the end of it, will be which storage hold the master copy. In enterprise, the nodes can't drop the files because they're the master copy. Whereas in Eon they can be evicted because it's just the cache, the masters, then shared storage. And in keeping with versus current support for multiple storage locations, we can intermix these approaches at the table level. Getting there as a journey, and we've already taken the first steps. One of the interesting design ideas of the C-store paper is the idea that redundant copies, don't have to have the same physical organization. Different copies can be optimized for different queries, sorted in different ways. Of course, Mike also said to keep the recovery system simple, because it's hard to debug, whenever the recovery system is being used, it's always in a high pressure situation. This turns out to be a contradiction, and the latter idea was better. No down performing stuff, if you don't keep the storage the same. Recovery hardware if you have, to reorganize data in the process. Even query optimization is more complicated. So over the past couple releases, we got rid of non identical buddies. But the storage files can still diverge at the fifth level, because tuple mover operations are synchronized. The same record can end up in different files than different nodes. The next step in our journey, is to make sure both copies are identical. This will help with backup and restore as well, because the second copy doesn't need backed up, or if it is backed up, it appears identical to the deduplication that is going to look present in both backup systems. Simultaneously, we're improving the Vertica networking service to support this new access pattern. In conjunction with identical storage files, we will converge to a recovery system that instantaneous nodes can process queries immediately, by retrieving data they need over the network from the redundant copies as they do in Eon day with even higher performance. The final step then is to unify the catalog and transaction model. Related concepts such as segment and shard, local catalog and shard catalog will be coalesced, as they're really represented the same concepts all along, just in different modes. In the catalog, we'll make slight changes to the definition of a projection, which represents the physical storage organization. The new definition simplifies segmentation and introduces valuable granularities of sharding to support evolution over time, and offers a straightforward migration path for both Eon and enterprise. There's a lot more to our Eon story than just the architectural roadmap. If you missed yesterday's Vertica, in Eon mode presentation about supported cloud, on premise storage option, replays are available. Be sure to catch the upcoming presentation on sizing and configuring vertica and in beyond doors. As we've seen with Eon, Vertica can separate data storage from the compute nodes, allowing machines to quickly fill in for each other, to rebuild fault tolerance. But separating compute and storage is used for much, much more. We now offer powerful, flexible ways for Vertica to add servers and increase access to the data. Vertica nine, this feature is called sub-clusters. It allows computing capacity to be added quickly and incrementally, and isolates workloads from each other. If your exploratory analytics team needs direct access to the source data, they need a lot of machines and not the same number all the time, and you don't 100% trust the kind of queries and user defined functions, they might be using sub-clusters as the solution. While there's much more expensive information available in our other presentation. I'd like to point out the highlights of our latest sub-cluster best practices. We suggest having a primary sub-cluster, this is the one that runs all the time, if you're loading data around the clock. It should be sized for the ETL workloads and also determines the natural shard count. Additional read oriented secondary sub-clusters can be added for real time dashboards, reports and analytics. That way, subclusters can be added or deep provisioned, without disruption to other users. The sub-cluster features of Vertica 9.3 are working well for customers. Yesterday, the Trade Desk presented their use case for Vertica over 300,000 in 5 sub clusters running in the cloud. If you missed a presentation, check out the replay. But we have plans beyond sub-clusters, we're extending sub-clusters to real clusters. For the Vertica savvy, this means the clusters bump, share the same spread ring network. This will provide further isolation, allowing clusters to control their own independent data sets. While replicating all are part of the data from other clusters using a publish subscribe mechanism. Synchronizing data between clusters is a feature customers want to understand the real business for themselves. This vision effects are designed for ancillary aspects, how we should assign resource pools, security policies and balance client connection. We will be simplifying our data segmentation strategy, so that when data that originate in the different clusters meet, they'll still get fully optimized joins, even if those clusters weren't positioned with the same number of nodes per shard. Having a broad vision for data management is a key component to political success. But we also take pride in our execution strategy, when you start a new database from scratch as we did 15 years ago, you won't compete on features. Our key competitive points where speed and scale of analytics, we set a target of 100 x better query performance in traditional databases with path loads. Our storage architecture provides a solid foundation on which to build toward these goals. Every query starts with data retrieval, keeping data sorted, organized by column and compressed by using adaptive caching, to keep the data retrieval time in IO to the bare minimum theoretically required. We also keep the data close to where it will be processed, and you clusters the machines to increase throughput. We have partition pruning a robust optimizer evaluate active use segmentation as part of the physical database designed to keep records close to the other relevant records. So the solid foundation, but we also need optimal execution strategies and tactics. One execution strategy which we built for a long time, but it's still a source of pride, it's how we process expressions. Databases and other systems with general purpose expression evaluators, write a compound expression into a tree. Here I'm using A plus one times B as an example, during execution, if your CPU traverses the tree and compute sub-parts from the whole. Tree traversal often takes more compute cycles than the actual work to be done. Especially in evaluation is a very common operation, so something worth optimizing. One instinct that engineers have is to use what we call, just-in-time or JIT compilation, which means generating code form the CPU into the specific activity expression, and add them. This replaces the tree of boxes that are custom made box for the query. This approach has complexity bugs, but it can be made to work. It has other drawbacks though, it adds a lot to query setup time, especially for short queries. And it pretty much eliminate the ability of mere models, mere mortals to develop user defined functions. If you go back to the problem we're trying to solve, the source of the overhead is the tree traversal. If you increase the batch of records processed in each traversal step, this overhead is amortized until it becomes negligible. It's a perfect match for a columnar storage engine. This also sets the CPU up for efficiency. The CPUs look particularly good, at following the same small sequence of instructions in a tight loop. In some cases, the CPU may even be able to vectorize, and apply the same processing to multiple records to the same instruction. This approach is easy to implement and debug, user defined functions are possible, then generally aligned with the other complexities of implementing and improving a large system. More importantly, the performance, both in terms of query setup and record throughput is dramatically improved. You'll hear me say that we look at research and industry for inspiration. In this case, our findings in line with academic binding. If you'd like to read papers, I recommend everything you always wanted to know about compiled and vectorized queries, don't afraid to ask, so we did have this idea before we read that paper. However, not every decision we made in the Vertica executer that the test of time as well as the expression evaluator. For example, sorting and grouping aren't susceptible to vectorization because sort decisions interrupt the flow. We have used JIT compiling on that for years, and Vertica 401, and it provides modest setups, but we know we can do even better. But who we've embarked on a new design for execution engine, which I call EE five, because it's our best. It's really designed especially for the cloud, now I know what you're thinking, you're thinking, I just put up a slide with an old engine, a new engine, and a sleek play headed up into the clouds. But this isn't just marketing hype, here's what I mean, when I say we've learned lessons over the years, and then we're redesigning the executer for the cloud. And of course, you'll see that the new design works well on premises as well. These changes are just more important for the cloud. Starting with the network layer in the cloud, we can't count on all nodes being connected to the same switch. Multicast doesn't work like it does in a custom data center, so as I mentioned earlier, we're redesigning the network transfer layer for the cloud. Storage in the cloud is different, and I'm not referring here to the storage of persistent data, but to the storage of temporary data used only once during the course of query execution. Our new pattern is designed to take into account the strengths and weaknesses of cloud object storage, where we can't easily do a path. Moving on to memory, many of our access patterns are reasonably effective on bare metal machines, that aren't the best choice on cloud hyperbug that have overheads, page faults or big gap. Here again, we found we can improve performance, a bit on dedicated hardware, and even more in the cloud. Finally, and this is true in all environments, core counts have gone up. And not all of our algorithms take full advantage, there's a lot of ground to cover here. But I think sorting in the perfect example to illustrate these points, I mentioned that we use JIT in sorting. We're getting rid of JIT in favor of a data format that can be treated efficiently, independent of what the data types are. We've drawn on the best, most modern technology from academia and industry. We've got our own analysis and testing, you know what we chose, we chose parallel merge sort, anyone wants to take a guess when merge sort was invented. It was invented in 1948, or at least documented that way, like computing context. If you've heard me talk before, you know that I'm fascinated by how all the things I worked with as an engineer, were invented before I was born. And in Vertica , we don't use the newest technologies, we use the best ones. And what is noble about Vertica is the way we've combined the best ideas together into a cohesive package. So all kidding about the 1940s aside, or he redesigned is actually state of the art. How do we know the sort routine is state of the art? It turns out, there's a pretty credible benchmark or at the appropriately named historic sortbenchmark.org. Anyone with resources looking for fame for their product or academic paper can try to set the record. Record is last set in 2016 with Tencent Sort, 100 terabytes in 99 seconds. Setting the records it's hard, you have to come up with hundreds of machines on a dedicated high speed switching fabric. There's a lot to a distributed sort, there all have core sorting algorithms. The authors of the paper conveniently broke out of the time spent in their sort, 67 out of 99 seconds want to know local sorting. If we break this out, divided by two CPUs and each of 512 nodes, we find that each CPU so there's almost a gig and a half per second. This is for what's called an indy sort, like an Indy race car, is in general purpose. It only handles fixed hundred five records with 10 byte key. There is a record length can vary, then it's called daytona sort, a 10 set daytona sort, is a little slower. One point is 10 gigabytes per second per CPU, now for Verrtica, We have a wide variety ability in record sizes, and more interesting data types, but still no harm in setting us like phone numbers, comfortable to the world record. On my 2017 era AMD desktop CPU, the Vertica EE5 sort to store about two and a half gigabytes per second. Obviously, this test isn't apply to apples because they use their own open power chip. But the number of DRM channels is the same, so it's pretty close the number that says we've hit on the right approach. And it performs this way on premise, in the cloud, and we can adapt it to cloud temp space. So what's our roadmap for integrating EE5 into the product and compare replacing the query executed the database to replacing the crankshaft and other parts of the engine of a car while it's been driven. We've actually done it before, between Vertica three and a half and five, and then we never really stopped changing it, now we'll do it again. The first part in replacing with algorithm called storage merge, which combines sorted data from disk. The first time has was two that are in vertical in incoming 10.0 patch that will be EE5 or resegmented storage merge, and then convert sorting and grouping into do out. There the performance results so far, in cases where the Vertica execute is doing well today, simple environments with simple data patterns, such as this simple capitalistic query, there's a lot of speed up, when we ship the segmentation code, which didn't quite make the freeze as much like to bump longer term, what we do is grouping into the storage of large operations, we'll get to where we think we ought to be, given a theoretical minimum work the CPUs need to do. Now if we look at a case where the current execution isn't doing as well, we see there's a much stronger benefit to the code shipping in Vertica 10. In fact, it turns a chart bar sideways to try to help you see the difference better. This case also benefit from the improvements in 10 product point releases and beyond. They will not happening to the vertical query executer, That was just the taste. But now I'd like to switch to the roadmap first for our adapters layer. I'll start with a story about, how our storage access layer evolved. If you go back to the academic ideas, if you start paper that persuaded investors to fund Vertica, read optimized store was the part that had substantiation in the form of performance data. Much of the paper was speculative, but we tried to follow it anyway. That paper talked about the WS with RS, The rights are in the read store, and how they work together for transaction processing and how there was a supernova. In all honesty, Vertica engineers couldn't figure out from the paper what to do next, incase you want to try, and we asked them they would like, We never got enough clarification to build it that way. But here's what we built, instead. We built the ROS, read optimized store, introduction on steep major revision. It's sorted, ordered columnar and compressed that follows a table partitioning that worked even better than the we are as described in the paper. We also built the last byte optimized store, we built four versions of this over the years actually. But this was the best one, it's not a set of interrelated V tree. It's just an append only, insertion order remember your way here, am sorry, no compression, no base, no partitioning. There is, however, a tuple over which does what we call move out. Move the data from WOS to ROS, sorting and compressing. Let's take a moment to compare how they behave, when you load data directly to the ROS, there's a data parsing operation. Then we finished the sorting, and then compressing right out the columnar data files to stay storage. The next query through executes against the ROS and it runs as it should because the ROS is read optimized. Let's repeat the exercise for WOS, the load operation response before the sorting and compressing, and before the data is written to persistent storage. Now it's possible for a query to come along, and the query could be responsible for sorting the lost data in addition to its other processes. Effect on query isn't predictable until the TM comes along and writes the data to the ROS. Over the years, we've done a lot of comparisons between ROS and WOS. ROS has always been better for sustained load throughput, it achieves much higher records per second without pushing back against the client and hasn't Vertica for when we developed the first usable merge out algorithm. ROS has always been better for predictable query performance, the ROS has never had the same management complexity and limitations as WOS. You don't have to pick a memory size and figure out which transactions get to use the pool. A non persistent nature of ROS always cause headaches when there are unexpected cluster shutdowns. We also looked at field usage data, we found that few customers were using a lot, especially among those that studied the issue carefully. So how we set out on a mission to improve the ROS to the point where it was always better than both the WOS and the profit of the past. And now it's true, ROS is better than the WOS and the loss of a couple of years ago. We implemented storage bundling, better catalog object storage and better tuple mover merge outs. And now, after extensive Q&A and customer testing, we've now succeeded, and in Vertica 10, we've removed the whys. Let's talk for a moment about simplicity, one of the best things Mike Stonebreaker said is no knobs. Anyone want to guess how many knobs we got rid of, and we took the WOS out of the product. 22 were five knobs to control whether it didn't went to ROS as well. Six controlling the ROS itself, Six more to set policies for the typical remove out and so on. In my honest opinion is still wasn't enough control over to achieve excess in a multi tenant environment, the big reason to get rid of the WOS for simplicity. Make the lives of DBAs and users better, we have a long way to go, but we're doing it. On my desk, I keep a jar with the knob in it for each knob in Vertica. When developers add a knob to the product, they have to add a knob to the jar. When they remove a knob, they get to choose one to take out, We have a lot of work to do, but I'm thrilled to report that in 15 years 10 is the first release with a number of knobs ticked downward. Get back to the WOS, I've said the most important thing get rid of it for last. We're getting rid of it so we can deliver our vision of the future to our customer. Remember how he said an Eon and sub-clusters we got all these benefits from shared storage? Guess what can't live in shared storage, the WOS. Remember how it's been a big part of the future was keeping the copies that identical to the primary copy? Independent actions of the WOS took a little at the root of the divergence between copies of the data. You have to admit it when you're wrong. That was in the original design and held up to the a selling point of time, without onto the idea of a separate ROS and WOS for too long. In Vertica, 10, we can finally bid, good reagents. I've covered a lot of ground, so let's put all the pieces together. I've talked a lot about our vision and how we're achieving it. But we also still pay attention to tactical detail. We've been fine tuning our memory management model to enhance performance. That involves revisiting tens of thousands of satellite of code, much like painting the inside of a large building with small paintbrushes. We're getting results as shown in the chart in Vertica nine, concurrent monitoring queries use memory from the global catalog tool, and Vertica 10, they don't. This is only one example of an important detail we're improving. We've also reworked the monitoring tables without network messages into two parts. The increased data we're collecting and analyzing and our quality assurance processes, we're improving on everything. As the story goes, I still have my grandfather's axe, of course, my father had to replace the handle, and I had to replace the head. Along the same lines, we still have Mike Stonebreaker Vertica. We didn't replace the query optimizer twice the debate database designer and storage layer four times each. The query executed is and it's a free design, like charted out how our code has changed over the years. I found that we don't have much from a long time ago, I did some digging, and you know what we have left in 2007. We have the original curly braces, and a little bit of percent code for handling dates and times. To deliver on our mission to help customers get value from their structured data, with high performance of scale, and in diverse deployment environments. We have the sound architecture roadmap, reviews the best execution strategy and solid tactics. On the architectural front, we're converging in an enterprise, we're extending smart analytic clusters. In query processing, we're redesigning the execution engine for the cloud, as I've told you. There's a lot more than just the fast engine. that you want to learn about our new data support for complex data types, improvements to the query optimizer statistics, or extension to live aggregate projections and flatten tables. You should check out some of the other engineering talk that the big data conference. We continue to stay on top of the details from low level CPU and memory too, to the monitoring management, developing tighter feedback cycles between development, Q&A and customers. And don't forget to check out the rest of the pillars of our roadmap. We have new easier ways to get started with Vertica in the cloud. Engineers have been hard at work on machine learning and security. It's easier than ever to use Vertica with third Party product, as a variety of tools integrations continues to increase. Finally, the most important thing we can do, is to help people get value from structured data to help people learn more about Vertica. So hopefully I left plenty of time for Q&A at the end of this presentation. I hope to hear your questions soon.

>>live from Las Vegas. It's the two covering AWS executive Something >>brought to you by Accenture >>everyone to the ex Center Executive Summit here in AWS. Reinvent I'm your host, Rebecca Knight. I'm joined by John. Match it. He is the managing director. Applied Intelligence, North America Attic Center Thank you so much for coming on the Q. So we're gonna have a fun conversation about a I today. We tend to think of a I as this futuristic Star Trek Jetsons kind of thing. But in fact, a i a. I is happening here and now >>it's all around us. I think it's intricate zoologist, sort of blood into the fabric girl of our lives without really even knowing about, I mean, just to get here, Let me lives took a new burst. There's a I in the route navigation. We may have listened to Spotify, and there's a I and the recommendation engine. And if you want to check the weather with Alexa, there's a lot of agents in the natural language processing, and none of that was really impossible 10 years ago. So without even trying, just wake up and I sort of like in your system in your blood. >>So as consumers, we deal with a I every day. But it's all but businesses are also using a I, and it's already having an impact. >>I think >>what is absolutely true it and really interesting is that information is just the new basis of competition. Like like you know, companies used to compete with physical objects and look better cars and blenders and stereos and, you know, thermometers. But today, you know, they're all like on a device, and so information is how they compete. And what's interesting to me about that for our clients is that if you have a good idea, you can probably do it. And so you're limited, really by your own imagination on. So I just as an example of like how things are playing out a lover classroom, the farmer space to make better drugs, and every every form of company I know of is using some sort of machine learning a I to create better pharmaceuticals, the big ones, but also the new entrance. One of the companies that we followed numerator really issued company. What they've been able to do is like in just just a massive amount of data like all day, like good data, bad bias on buying >>its ingesting, this kind of data the data is about. >>It's about like drug efficacy, human health, the human genome like like like doctors visits like all this diverse information. And historically, if you put all that data together just to have a way to actually examine it, there's no way that was too much. Humans can't deal with it, but but But machine learning can. And so what? We just all this date up and we let the robots decided sort of less meaningful. And what's happened is you can now deal with instead, just a very fraction that data, but all of it. And the result, like in pharmaceuticals. Is it wearable? Come with new HIV drugs in six months? It used to be years and millions of dollars, tens of millions of dollars. But now it's, you know, it's months, and so it's really changing the way humans live. And certainly the associated industries. They're producing the drugs. >>So it's as you said, I was already being used to reimagine medicine. So many of the high tech jobs openings today are not necessarily in technology there in pharmaceuticals and automotive's. And these and these involved artificial intelligence, their skills in artificial intelligence. What can you tell us about how a eyes having an impact? And that's what I think. >>This is a really good question. What is interesting is that industry she wouldn't think, or digital companies are now actually digital competitors. I'll give you two examples. One is a lot of clients make liquefied natural gas. Now that that is a mucky business. It's full of science, like geology and chemistry and chemical engineering, and they work with these like small refineries. But the questions like, how we gonna get better if you make you know Ellen G. And so what they do is they use a I, and the way they do that is likely have these small refineries. Each piece of equipment has a sensor on it, so there may be 5000 sensors, and each sensor has three or four like bots looking at it, and one might be looking at vibration heat and and what they're doing is they're making predictions. Millions of predictions every every day about you know whether quality is good. The machine's about to have a problem that safety is jeopardise something like that. And so So you've gone from a place where, you know, the best competitors were chemists to the best competitors are actually using machine learning to make the plants work better. You know, another entry. We see this really was brewing. You know, you don't think no one would think brewing is like a digital business like his beer? The Egyptians may be right, like so everyone knows how to do it. So But think about if you make beer like how you're gonna get better and again do what you do is you begin to touch customers more effectively with better digital marketing, you know? Hey, I tow target to understand who your best customers are, how to make offers to them, had a price head of both new product introduction, and even had a formulate new brands of beer that might appeal to different segments of society. So brewing, like they're all about, like ml in the eye. And they really are, like a digital competitive these days, which I think it's interesting, like no one would have thought about that, you know, is they were consuming beer on a Friday with their friends >>and craft brewing is so hot right now. I mean, it is one of those things. As you said, it is attracting new, different kinds of segments of customers. >>Right? And so the questions like if you are a craft brewer like, how do you go find the people that that you want? So what we're doing is we're way have new digital ways to go touch them very personalized offer like, if you like running, you know we can We can give you an offer like fun run followed by a brew. But we know who you are and what you like your friends like to do to get very specific A CZ we like examined the segments of society to do very personal marketing. It's actually fun, like, you know, it gives you things to go Dio we did one event where he looked at cos we we had a a beer tasting with barbecue teach you no instruction. So if you wanna learn how to cook barbecue and also do a beer tasting can get 20 people together and you have a social experience and you you buy more the product. But what's interesting is like, Well, how do you find those people? How do you reach them? How do you identify these of the right folks? That'll actually participate? And that's where a I comes into play. >>So this is fascinating, and you just you just described a number of different industries and companies beer, brewers, liquefied natural gas, pharmaceuticals that are using a I to transform themselves. What is your What do you recommend for the people out there watching and say, I want to do that? How could I get on >>board or what we advise Companies are clients to really get good at three things, and the first is just to do things differently. So you got to go into your core operations and figure out how you can extract more cash and more profit from your existing operations. And so that's like we talked about natural gas, right? Like you could produce it more profitably and effectively, but that's not enough. The next thing you do step to would be to actually grow your core business. Everyone wants to leave to the new right away, but but you're getting all your cash and your legacy businesses and so like like we saw in the brewing history. If you can find new customers, more profitable customers interact with them, create a better digital experience with them, then you'll grow both your top line in your bottom line. But for our from our perspective, the reason you do both of those things is cash. Then make investments into New Net new businesses on DSO. The last thing you do is to do different things, so find in adjacency and grow. And it's important to talk about the role of a I and that because that's the way you develop outcomes with speed, right? Like you're not gonna build a factory and we're gonna build a service or some sort of, you know, information centric offerings. And so what we like to do is talk about like the wise pivot from your old legacy businesses. We generate cash and you make selective investments in the new and how you regulate that is a really important question, because you're too fast and you start the Lexie businesses like to slow, and you're gonna be sort of left out of the new economy. So doing those three things correctly with the right sort of managing processes is what we advise our clients to focus on. >>So I see all of this from the business side. But do you because you're also a consumer? Do you ever see any sort of concerns about privacy and security in the sense of why does anyone need to know if I like to run or I like barbecue with my beer? I mean, how do you How do you sort of think about those things and and talk to clients about those issues >>too? Well, I think, you know, actually, for censure. Ah, large part of our focus is what we call just ethical a eye on. And so it's important to us to actually have offerings that we think that we're comfortable with that are legally comfortable, but also just societally are acceptable. And it's actually like there's a lot of focus in this area, right, how you do it. And there's actually a lot to learn. Like like what we see, for example, is there could be biased in the data which effects the actual algorithm. So a lot of times were the folks in the algorithm, you need to go back to the data and look at that. But it's something we spend a lot of time on. Its important us because we to our consumers and we care about our privacy. >>So when you talk about the wise pivot and the regulation, this is a This is a big question. There's a lot of bills on the table in Washington. It's certainly dominating our national conversation, how we think about regulating thes new emerging technologies that that present a lot of opportunities, but also a lot of risks. So how how are you, how you are you a tech center thinking about regulation and working with regulators on these issues >>way get involved with talking to the government. They seek independent counsel, so we participate when they're seeking guidance and we'll give our offer. So we're a voice at the table. But you know, what I would say is there's a lot of discussion about privacy and ask. But if you look at, like, at a national level, particularly government, I think there used to be more focused just on the parts that are incontrovertibly not problematic with privacy. So I gave you the example of working with liquefied natural gas. Okay, we need better, eh? I'd run our factories better. There's a lot of a I that goes into those kind of problems or supply chain planning. Like, how do I predict demand more effectively, or where should I put my plants? And A. I is the new way supply chain is done right? And so there's There's very few of the consumer centric problems I think, actually is. A society like 90% of the use cases are gonna be in areas where they don't actually influence for privacy and a lot of art. Our time is actually working on those kind of use cases just to make you know the operations of our organization's Maur more effective than more efficient. >>So we talked about the very beginning of this conversation about the companies that are disrupting old industries. Using a lot of these technologies, I mean, is this is a I A case where you need to be using this you need to be using >>you need to be using it. My view, my personal view is that there is going to be no basis of competition in the future, except for a digital. It just is going to be the case. And so all of our clients, you know, they're at some state of maturity and they're all asking the question like, How did I grow up? I don't get more profitable. Like certainly the street. Once more results on DSO if you want to move quickly in the new space, is you. You you you only have 11 choice. Really? And that that is to get really, really, really good at managing in harnessing digital technologies, inclusive of >>a I >>two to compete in a different way. And so I mean, we're seeing really interesting examples were like, you know, like, retailers are getting into health care, right? Like, you see this like you go into Wal Mart and they have our Walgreens. They have, like a doc in the box, right? So we're seeing. But lots of companies that are making physical things that then turn around and use the developing service and what they used to use their know how they take everything they know about, like like something you know about, like healthcare or how to like, you know, offer service is to customers and retail setting, but then they need to do something different. And now how do I get the data and the know how to then offer, like a new differentiated health service? And so to do that, you know, you have a lot. You have a lot of understanding about your customers, but you need to get all the data sources in place. You may need certain help desk. You know you need ways to aggregate it on, and so you probably need a new partnerships that don't have. You probably need toe manage skill sets that you don't have. You may need to get involved with open source communities. You may need to be involved with universities that where they do research, so you'll need a different kind of partnerships to move a speed then companies have probably used in the past. But when they put all those those eco systems together, onda new emphasis on the required skill sets, they can take their legacy knowledge that's probably physically oriented and then create a service that can create. They can monetize their experience with the new service. What what we find usually doesn't work is just a monetized data. If you have a lot of data, it's not usually worth that much. But if you take the data and you create a new service that people care about, then you can monetize your legacy information that that that's what a lot of our class they're trying to do, think they've very mature and now, like Where do you go? And where they go is something may be nearby to their existing business, but it's not. It's not the same legacy business of the path for years. >>I want to take a little deeper on something you brought up about the skills, and there's a real skills gap in Silicon Valley and in companies in this area. How are you working with companies to make sure that they are attracting the right talent pool and retaining those workers once they have? Um, >>well, so this is, I think, one of the most important questions because, like what? What happened with technology in the past? We would put in these like ear piece systems, and that was a big part of our business, like 15 years ago. And once you learned one of those things, that's a P or oracle or, you know, like whatever your skill set was good for 10 years, You probably you were good. You could just, like, go to the work. But today it just just go down to like the convention center. Look at this vast array of like like >>humanity, humanity >>and new technologies. I mean, half these companies didn't even exist, like, five years ago, right? And so you're still set today is probably only good for a year. So I think the first thing you've got to realise is that there's got to be a new focus on actually cultivating talent as a strategy. It's it's the way to compete like people is your product, if you wanna look at that way. But we're doing actually starting very, uh, where we can very early in the process, like much beyond a corporation. So we work with charter schools over kids, we get them into college, we work with universities, we do a lot of internship. So we're trying to start, like, really early on when you ask a question like, what would our recommendation to the government be were actually advising, like, get kids involved in I t. Like earlier and so so we can get that problem resolved but otherwise, once companies work. I think you know you need your own talent strategy. But part of that might be again, like an eco system play like maybe you don't want all of those people and you'd rather sort of borrow on. And so I think, I think figuring out what your eco system is because I think I think in the future like competition will be like my eco system versus your eco system. And that's that is the way I think it's gonna work. And so thinking in an eco system way is, is what most of our clients need to do. >>Well, it's like you said about the old ways of it was a good idea for a good product versus good ideas. And I just keep looking. Thank you so much, John, for coming on the Cuba Really fascinating conversation >>was my pleasure. Thank you so much. >>I'm Rebecca Knight. Stay tuned for more of the cubes. Live coverage of the Accenture Executive Summit coming up in just a little bit

>> Narrator: From New York, it's theCube. Covering Escape/19. (music plays) >> Welcome back everyone. It's Cube coverage for the first inaugural multicloud conference, Escape 2019. We are here with at Bassam Tabbara who is the CEO of Upbound, hot start-up, has not yet released their product but they're working on it. Good friend of theCube, Cube alumni. Bassam, good to see you again. >> Thank you, glad to be back on the Cube. >> Well we know your guys are beavering away, digging away at the product, building it out. You have a very compelling background coming into the cloud world. You're here at the multicloud, first ever conference >> That's right. >> There is hybrid cloud, but this is like being billed as the first multicloud conference. A lot of technical people here. >> Lots of. >> Lot of industry insiders setting the foundation is one theme I'm hearing and then the other theme is data. >> Yeah. >> These are the two dynamics. What's your take on this multicloud conference opportunity? >> Look, I think it's really interesting, it reflects kind of what's happening. Multicloud's becoming a reality, more and more people are, whether they like it or not, are actually using multiple vendors and they're trying to figure it out so I think it's great that we are now a forum, I mean there are likely to be more. We're doing one of the Atlanta Gitlab at the next KubeCon which is kind of cool. But you know so getting all the right people in here, focusing on the data problem. Where we look at from a universal control plain standpoint. There are lots of people here talking about the economics of this and what it means for venture capital in the next five years and what it means for acquisition patterns and NMA. There are lots of really interesting aspects being covered today. >> Yeah it's a classic inaugural conference where with the organic communities here you have a range of personas. Entrepreneurs, founder, executive, venture capitalists, all kind of having those candid conversations. What to do next. >> That's right. >> Kind of all ger multiclouds here. Questions is, what's it going to be? >> What's it going to be. Well I think I was trying to figure that out. Honestly, anything that makes it easy for enterprises to do this massive lifting and shifting of infrastructure and being able to control their data, deal with multiple vendors, the world is increasingly heterogeneous. That's another way of saying multicloud is just dealing with the heterogeneity. And it's going to be more and more heterogeneous because if you look at the trends, it's hard to imagine that all innovation is going to come out of one cloud company. Right. So if that's not the case then you have people innovating, people creating all sorts of new platforms and infrastructure. Ways of dealing with data, ways of dealing with networking. Or ways of dealing with storage. Data bases and everything else. Now that you've got this innovation happening, whether it's open source communities or not. And then as an enterprise user, I want to consume it, well I have to deal with the heterogeneity. How do I consume it? How do I bring it together? How do I make sense of it? How do I get it all secured? How do I get it all under my compliance department? Those are the opportunities that are on multicloud and it is a reality. So at some level I'd be hard pressed to find someone that says I'm using Amazon or Google or Azure only and not say using a boutique cloud or another service or something else. Everybody's got some set of services that are... >> I mean multicloud and multivendor are two words that you go back to the history of the computer industry >> That's right. multivendor is a heterogeneous environment. There's benefits of that. But all that was based upon the lock-in fear. And you'll be hearing some of that here. So what's your view of lock-in because if value creation is the lock-in, the red hat guys giving a talk about Wal-Mart cloud versus niche clouds, it's all open source so where's the lock-in? >> Yeah I don't know if I would subscribe to this as solving the lock-in problem and every time you use a vendor at some level you're kind of relying on them. If they have a good service you're kind of tied to them right? But the more interesting aspect to me is having a choice. So being able to say I'm going to pick the best data based vendor out there. One that suits my problem and being able to do that without having to let go of the integration aspect of us. If I have to choose a data based SaaS service that I really like but the cost of doing that involves me creating a new vendor or doing some custom automation, custom integration, figuring out monitoring, figuring out logging doing billing, doing metering. All of that stuff so that I can actually just consume one amazing service. That's a really large hurdle to kind of step over. And so, I think part of multicloud is reducing friction for being able to use things that you choose to. >> Do you have any commentary or advice for other founders or other CEOs or even any younger developers because we have a classical trained software developers, they think a certain way. They either were pipe lining it different, not doing Agile, their trained at Agile, but now micro service is a whole nother ballgame. How do you get people to think microservices when they've been classically trained Agile. >> Like Waterfall you're saying? >> Or Waterfall, both, both. >> I think there's a lot happening right now. I would start with looking at some of the best practices around building modern services. Things like Kubernetes and others help. Microservice adoption and all that stuff. But start with, honestly starting with a bunch of open sources probably not a bad place to be. But then find vendors that actually can support in one what you want to do. >> Final question. Tell us about your company. What's going on with you guys. Give an update on Upbound. What's going on? It's going great. We're growing. We launched this project called Crossplain. Like earlier or late last year. It's doing great. We're getting a ton of adoption on it. We're super happy with it. And we're growing the company. We're almost tripled the company this year. Which is fantastic. And working on a SaaS offering that we're exciting about. Hopefully we'll come back here and talk about it when it's... >> And you guys hiring? Looking for people? What's the update there? >> We are. We're hiring on the engineering side, we're hiring on the product side. It's start up so. You never stop hiring. >> Not for the faint of heart. >> Definitely not. >> Bassam, thanks for coming out. >> Yeah absolutely. Always fun. >> Here at the multicloud inaugural event. Escape. Here in New York City. Escape 2019 I'm John Furrier with theCube. Back with more after this short break.

>> Live, from Las Vegas, it's theCUBE covering UiPath Forward Americas 2019. Brought to you by UiPath. >> Welcome back to the Bellagio in Las Vegas, everybody. You're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante. Day one of UiPath Forward III, hashtag UiPathForward. Elena Christopher is here. She's the senior vice president at HFS Research, and Elena, I'm going to recruit you to be my co-host here. >> Co-host! >> On this power panel. Jean Youngers here, CUBE alum, VP, a Six Sigma Leader at Security Benefit. Great to see you again. >> Thank you. >> Dave: And Amy Chandler, who is the Assistant Vice President and Director of Internal Controls, also from Security Benefit. >> Hello. >> Dave: Thanks for coming on theCUBE. >> Thank you. >> Alright Elena, let's start off with you. You follow this market, you have for some time, you know HFS is sort of anointed as formulating this market place, right? >> Elena: We like to think of ourselves as the voice-- >> You guys were early on. >> The voice of the automation industry. >> So, what are you seeing? I mean, process automation has been around forever, RPA is a hot recent trend, but what are you seeing the last year or two? What are the big trends and rip currents that you see in the market place? >> I mean, I think one of the big trends that's out there, I mean, RPA's come on to the scene. I like how you phrase it Dave, because you refer to it as, rightly so, automation is not new, and so we sort of say the big question out there is, "Is RPA just flavor of the month?" RPA is definitely not, and I come from a firm, we put out a blog earlier this year called "RPA is dead. Long live automation." And that's because, when we look at RPA, and when we think about what it's impact is in the market place, to us the whole point of automation in any form, regardless of whether it's RPA, whether it be good old old school BPM, whatever it may be, it's mission is to drive transformation, and so the HFS perspective, and what all of our research shows and sort of justifies that the goal is, what everyone is striving towards, is to get to that transformation. And so, the reason we put out that piece, the "RPA is dead. Long live integrated automation platforms" is to make the point that if you're not- 'cause what does RPA allow? It affords an opportunity for change to drive transformation so, if you're not actually looking at your processes within your company and taking this opportunity to say, "What can I change, what processes are just bad, "and we've been doing them, I'm not even sure why, "for so long. What can we transform, "what can we optimize, what can we invent?" If you're not taking that opportunity as an enterprise to truly embrace the change and move towards transformation, that's a missed opportunity. So I always say, RPA, you can kind of couch it as one of many technologies, but what RPA has really done for the market place today, it's given business users and business leaders the realization that they can have a role in their own transformation. And that's one of the reasons why it's actually become very important, but a single tool in it's own right will never be the holistic answer. >> So Jean, Elena's bringing up a point about transformation. We, Stew Bennett and I interviewed you last year and we've played those clips a number of times, where you sort of were explaining to us that it didn't make sense before RPA to try to drive Six Sigma into business processes; you couldn't get the return. >> Jean: Right. >> Now you can do it very cheaply. And for Six Sigma or better, is what you use for airplane engines, right? >> Right. >> So, now you're bringing up the business process. So, you're a year in, how's it going? What kind of results are you seeing? Is it meeting your expectations? >> It's been wonderful. It has been the best, it's been probably the most fun I've had in the last fifteen years of work. I have enjoyed, partly because I get to work with this great person here, and she's my COE, and helps stand up the whole RPA solution, but you know, we have gone from finance into investment operations, into operations, you know we've got one sitting right now that we're going to be looking at statements that it's going to be fourteen thousand hours out of both time out as well as staff hours saved, and it's going to touch our customer directly, that they're not going to get a bad statement anymore. And so, you know, it has just been an incredible journey for us over the past year, it really has. >> And so okay Amy, your role is, you're the hardcore practitioner here right? >> Amy: That's right. >> You run the COE. Tell us more about your role, and I'm really interested in how you're bringing it out, RPA to the organization. Is that led by your team, or is it kind of this top-down approach? >> Yeah, this last year, we spent a lot of time trying to educate the lower levels and go from a bottom-up perspective. Pretty much, we implemented our infrastructure, we had a nice solid change management process, we built in logical access, we built in good processes around that so that we'd be able to scale easily over this last year, which kind of sets us up for next year, and everything that we want to accomplish then. >> So Elena, we were talking earlier on theCUBE about you know, RPA, in many ways, I called it cleaning up the crime scene, where stuff is kind of really sort of a mass and huge opportunities to improve. So, my question to you is, it seems like RPA is, in some regards, successful because you can drop it into existing processes, you're not changing things, but in a way, this concerns that, oh well, I'm just kind of paving the cow path. So how much process reinvention should have to occur in order to take advantage of RPA? >> I love that you use that phrase, "paving the cow path." As a New Englander, as you know the roads in Boston are in fact paved cow paths, so we know that can lead to some dodgy roads, and that's part of, and I say it because that's part of what the answer is, because the reinvention, and honestly the optimization has to be part of what the answer is. I said it just a little bit earlier in my comments, you're missing an opportunity with RPA and broader automation if you don't take that step to actually look at your processes and figure out if there's just essentially deadwood that you need to get rid of, things that need to be improved. One of the sort of guidelines, because not all processes are created equal, because you don't want to spend the time and effort, and you guys should chime in on this, you don't want to spend the time and effort to optimize a process if it's not critical to your business, if you're not going to get lift from it, or from some ROI. It's a bit of a continuum, so one of the things that I always encourage enterprises to think about, is this idea of, well what's the, obviously, what business problem are you trying to solve? But as you're going through the process optimization, what kind of user experience do you want out of this? And your users, by the way, you tend to think of your user as, it could be your end customer, it could be your employee, it could even be your partner, but trying to figure out what the experience is that you actually want to have, and then you can actually then look at the process and figure out, do we need to do something different? Do we need to do something completely new to actually optimize that? And then again, line it with what you're trying to solve and what kind of lift you want to get from it. But I'd love to, I mean, hopping over to you guys, you live and breathe this, right? And so I think you have a slightly different opinion than me, but-- >> We do live and breathe it, and every process we look at, we take into consideration. But you've also got to, you have a continuum right? If it's a simple process and we can put it up very quickly, we do, but we've also got ones where one process'll come into us, and a perfect example is our rate changes. >> Amy: Rate changes. >> It came in and there was one process at the very end and they ended up, we did a wing to wing of the whole thing, followed the data all the way back through the process, and I think it hit, what, seven or eight-- >> Yeah. >> Different areas-- >> Areas. >> Of the business, and once we got done with that whole wing to wing to see what we could optimize, it turned into what, sixty? >> Amy: Yeah, sixty plus. Yeah. >> Dave: Sixty plus what? >> Bot processes from one entry. >> Yeah. >> And so, right now, we've got 189 to 200 processes in the back log. And so if you take that, and exponentially increase it, we know that there's probably actually 1,000 to 2,000 more processes, at minimum, that we can hit for the company, and we need to look at those. >> Yeah, and I will say, the wing to wing approach is very important because you're following the data as it's moving along. So if you don't do that, if you only focus on a small little piece of it, you don't what's happening to the data before it gets to you and you don't know what's going to happen to it when it leaves you, so you really do have to take that wing to wing approach. >> So, internal controls is in your title, so talking about scale, it's a big theme here at UiPath, and these days, things scale really fast, and boo-boos can happen really fast. So how are you ensuring, you know that the edicts of the organization are met, whether it's security, compliance, governance? Is that part of your role? >> Yeah, we've actually kept internal audit and internal controls, and in fact, our external auditors, EY. We've kept them all at the table when we've gone through processes, when we've built out our change management process, our logical access. When we built our whole process from beginning to end they kind of sat at the table with us and kind of went over everything to make sure that we were hitting all the controls that we needed to do. >> And actually, I'd like to piggyback on that comment, because just that inclusion of the various roles, that's what we found as an emerging best practice, and in all of our research and all of the qualitative conversations that we have with enterprises and service providers, is because if you do things, I mean it applies on multiple levels, because if you do things in a silo, you'll have siloed impact. If you bring the appropriate constituents to the table, you're going to understand their perspective, but it's going to have broader reach. So it helps alleviate the silos but it also supports the point that you just made Amy, about looking at the processes end to end, because you've got the necessary constituents involved so you know the context, and then, I believe, I mean I think you guys shared this with me, that particularly when audit's involved, you're perhaps helping cultivate an understanding of how even their processes can improve as well. >> Right. >> That is true, and from an overall standpoint with controls, I think a lot of people don't realize that a huge benefit is your controls, cause if you're automating your controls, from an internal standpoint, you're not going to have to test as much, just from an associate process owner paying attention to their process to the internal auditors, they're not going to have to test as much either, and then your external auditors, which that's revenue. I mean, that's savings. >> You lower your auditing bill? >> Yeah. Yeah. >> Well we'll see right? >> Yeah. (laughter) >> That's always the hope. >> Don't tell EY. (laughter) So I got to ask you, so you're in a little over a year So I don't know if you golf, but you know a mulligan in golf. If you had a mulligan, a do over, what would you do over? >> The first process we put in place. At least for me, it breaks a lot, and we did it because at the time, we were going through decoupling and trying to just get something up to make sure that what we stood up was going to work and everything, and so we kind of slammed it in, and we pay for that every quarter, and so actually it's on our list to redo. >> Yeah, we automated a bad process. >> Yeah, we automated a bad process. >> That's a really good point. >> So we pay for it in maintenance every quarter, we pay for it, cause it breaks inevitably. >> Yes. >> Okay so what has to happen? You have to reinvent the process, to Elena's? >> Yes, you know, we relied on a process that somebody else had put in place, and in looking at it, it was kind of a up and down and through the hoop and around this way to get what they needed, and you know there's much easier ways to get the data now. And that's what we're doing. In fact, we've built our own, we call it a bot mart. That's where all our data goes, they won't let us touch the other data marts and so forth so they created us a bot mart, and anything that we need data for, they dump in there for us and then that's where our bot can hit, and our bot can hit it at anytime of the day or night when we need the data, and so it's worked out really well for us, and so the bot mart kind of came out of that project of there's got to be a better way. How can we do this better instead of relying on these systems that change and upgrade and then we run the bot and its working one day and the next day, somebody has gone in and tweaked something, and when all's I really need out of that system is data, that's all I need. I don't need, you know, a report. I don't need anything like that, cause the reports change and they get messed up. I just want the raw data, and so that's what we're starting to do. >> How do you ensure that the data is synchronized with your other marts and warehouses, is that a problem? >> Not yet. >> No not yet! (laughter) >> I'm wondering cause I was thinking the exact same question Dave, because on one hand its a nice I think step from a governance standpoint. You have what you need, perhaps IT or whomever your data curators are, they're not going to have a heart attack that you're touching stuff that they don't want you to, but then there is that potential for synchronization issues, cause that whole concept of golden source implies one copy if you will. >> Well, and it is. It's all coming through, we have a central data repository that the data's going to come through, and it's all sitting there, and then it'll move over, and to me, what I most worry about, like I mentioned on the statement once, okay, I get my data in, is it the same data that got used to create those statements? And as we're doing the testing and as we're looking at going live, that's one of our huge test cases. We need to understand what time that data comes in, when will it be into our bot mart, so when can I run those bots? You know, cause they're all going to be unattended on those, so you know, the timing is critical, and so that's why I said not yet. >> Dave: (chuckle) >> But you want to know what, we can build the bot to do that compare of the data for us. >> Haha all right. I love that. >> I saw a stat the other day. I don't know where it was, on Twitter or maybe it was your data, that more money by whatever, 2023 is going to be spent on chat bots than mobile development. >> Jean: I can imagine, yes. >> What are you doing with chat bots? And how are you using them? >> Do you want to answer that one or do you want me to? >> Go ahead. >> Okay so, part of the reason I'm so enthralled by the chat bot or personal assistant or anything, is because the unattended robots that we have, we have problems making sure that people are doing what they're supposed to be doing in prep. We have some in finance, and you know, finance you have a very fine line of what you can automate and what you need the user to still understand what they're doing, right? And so we felt like we had a really good, you know, combination of that, but in some instances, they forget to do things, so things aren't there and we get the phone call the bot broke, right? So part of the thing I'd like to do is I'd like to move that back to an unattended bot, and I'm going to put a chat bot in front of it, and then all's they have to do is type in "run my bot" and it'll come up if they have more than one bot, it'll say "which one do you want to run?" They'll click it and it'll go. Instead of having to go out on their machine, figure out where to go, figure out which button to do, and in the chat I can also send them a little message, "Did you run your other reports? Did you do this?" You know, so, I can use it for the end user, to make that experience for them better. And plus, we've got a lot of IT, we've got a lot of HR stuff that can fold into that, and then RPA all in behind it, kind of the engine on a lot of it. >> I mean you've child proofed the bot. >> Exactly! There you go. There you go. >> Exactly. Exactly. And it also provides a means to be able to answer those commonly asked questions for HR for example. You know, how much vacation time do I have? When can I change my benefits? Examples of those that they answer frequently every day. So that provides another avenue for utilization of the chat bot. >> And if I may, Dave, it supports a concept that I know we were talking about yesterday. At HFS it's our "Triple-A Trifecta", but it's taking the baseline of automation, it intersects with components of AI, and then potentially with analytics. This is starting to touch on some of the opportunities to look at other technologies. You say chat bots. At HFS we don't use the term chat bot, just because we like to focus and emphasize the cognitive capability if you will. But in any case, you guys essentially are saying, well RPA is doing great for what we're using RPA for, but we need a little bit of extension of functionality, so we're layering in the chat bot or cognitive assistant. So it's a nice example of some of that extension of really seeing how it's, I always call it the power of and if you will. Are you going to layer these things in to get what you need out of it? What best solves your business problems? Just a very practical approach I think. >> So Elena, Guy has a session tomorrow on predictions. So we're going to end with some predictions. So our RPA is dead, (chuckle) will it be resuscitated? What's the future of RPA look like? Will it live up to the hype? I mean so many initiatives in our industry haven't. I always criticize enterprise data warehousing and ETL and big data is not living up to the hype. Will RPA? >> It's got a hell of a lot of hype to live up to, I'll tell you that. So, back to some of our causality about why we even said it's dead. As a discrete software category, RPA is clearly not dead at all. But unless it's helping to drive forward with transformation, and even some of the strategies that these fine ladies from Security Benefit are utilizing, which is layering in additional technology. That's part of the path there. But honestly, the biggest challenge that you have to go through to get there and cannot be underestimated, is the change that your organization has to go through. Cause think about it, if we look at the grand big vision of where RPA and broader intelligent automation takes us, the concept of creating a hybrid workforce, right? So what's a hybrid workforce? It's literally our humans complemented by digital workers. So it still sounds like science fiction. To think that any enterprise could try and achieve some version of that and that it would be A, fast or B, not take a lot of change management, is absolutely ludicrous. So it's just a very practical approach to be eyes wide open, recognize that you're solving problems but you have to want to drive change. So to me, and sort of the HFS perspective, continues to be that if RPA is not going to die a terrible death, it needs to really support that vision of transformation. And I mean honestly, we're here at a UiPath event, they had many announcements today that they're doing a couple of things. Supporting core functionality of RPA, literally adding in process discovery and mining capabilities, adding in analytics to help enterprises actually track what your benefit is. >> Jean: Yes. >> These are very practical cases that help RPA live another day. But they're also extending functionality, adding in their whole announcement around AI fabric, adding in some of the cognitive capability to extend the functionality. And so prediction-wise, RPA as we know it three years from now is not going to look like RPA at all. I'm not going to call it AI, but it's going to become a hybrid, and it's honestly going to look a lot like that Triple-A Trifecta I mentioned. >> Well, and UiPath, and I presume other suppliers as well, are expanding their markets. They're reaching, you hear about citizens developers and 100% of the workforce. Obviously you guys are excited and you see a long-run way for RPA. >> Jean: Yeah, we do. >> I'll give you the last word. >> It's been a wonderful journey thus far. After this morning's event where they showed us everything, I saw a sneak peek yesterday during the CAB, and I had a list of things I wanted to talk to her about already when I came out of there. And then she saw more of 'em today, and I've got a pocketful of notes of stuff that we're going to take back and do. I really, truly believe this is the future and we can do so much. Six Sigma has kind of gotten a rebirth. You go in and look at your processes and we can get those to perfect. I mean, that's what's so cool. It is so cool that you can actually tell somebody, I can do something perfect for you. And how many people get to do that? >> It's back to the user experience, right? We can make this wildly functional to meet the need. >> Right, right. And I don't think RPA is the end all solution, I think it's just a great tool to add to your toolkit and utilize moving forward. >> Right. All right we'll have to leave it there. Thanks ladies for coming on, it was a great segment. Really appreciate your time. >> Thanks. >> Thank you. >> Thank you for watching, everybody. This is Dave Vellante with theCUBE. We'll be right back from UiPath Forward III from Las Vegas, right after this short break. (technical music)

>>live from Las Vegas. It's the Q covering you. I pat Forward America's 2019. Brought to you by you, I path Welcome >>back to Las Vegas. Everybody's is Day two of the Cubes coverage of you AI Path forward. Three. This is the third year of North American Conference, and second year the Cube is covered. This Anthony at Batista's here, Cuba. Lami was on last year from from Deloitte. He's a principal there, Anthony. Good to see again. >>Great to be here. Great. >>Yes. So it is. I mean, we've seen the growth of our P A. Generally you AI path, the whole automation were starting to talk about intelligent automation. A. I has its wings, and it's starting toe sore. But give us the update from a year ago. We talked about, you know, accelerating last year. I think it was you had a really good statements around looking, Yes, go on Fast is good, but you wanna accelerate the right things, you know, speeding up for bad processes. Paving the cow path, as I sometimes call it, is really not the way to go. But what's new? >>So I do think there's still some issues around getting programs t to scale and thinking about automation at scale, which has been a major theme here. The conference is still in front of us. People are still figuring out how the climate that curve well, I think is new is way Thought about automation before it was, it was a whore statement was that humans or automation is about going to replace the human on. I really think we've no lights. Always had a campaign about I t a. I that that we kicked off a couple of years ago and said, How do we have automation and humans interact with each other? And I don't just mean attended, attended bots, But how do we actually start to use automation as sort of the glue that hang together a much more rich experience to start to put the components there? So that leads us to the age of with, which is how we how we use technology along with humans, to change their role in there been some great talks. One of my partners earlier they was here with Walmart's, his client on. They talked about how they're redefining the HR processes at Wal Mart on that was That was a really good presentation because they changed the workers work. They didn't replace workers. >>So how was this concept of the age of with how is that different than attended? Boss, can you maybe talk about a possible use case or example? >>So if you think about a call center way, know who's coming in? We used to just look them up and say, Hey, do we know who's calling? Now we can say that we know is calling. Do they have a history with us? Way can use data, and that's another part of the width. Is Dave plus analytics with automation? And we could say, Well, what else do we know about this person to have a history of calling us? They have an open ticket. Have they had some issues or complaints in the past that we can deal with or get in front of on and basically start to put the intelligence in the front end? And that could be unattended, right? That could just be some screen pops around automation way start to introduce natural language. We start to introduce some advanced analytics, and that would be a simple, simple way of enhancing that process. >>So let me double click on that so normally what you would get this year in the other end of the line of the call center. And it's like, Hold on, I'm just reading the notes and you know, they're scanning these notes. It's like an eye test, you know, and they can't. They can't ever get to see. It's a faster for you to just explain. Let me tell you what what I'm imagining is in a different experience where this is happening in near real time, getting pop ups or some other messaging. Is that absolutely experience on how real is this today? >>This Israel. And you know, I I always like to say all them anything. All the main thing is easy if you just take the process, repave the cow path. But it's very real because the natural language components they work up front. Now you can ask some questions you could start to do pre searches on which materials might might help with that type of question. You also can train the process over time. So daily overtime. What's the call satisfaction? Did you actually complete what it was? The call got started about on how quickly you do that so you could train these models and start to use machine learning to actually improve that experience even further. So I think that's left again, back to the whip. It's adding these components. >>I like talking to folks with a consulting background because you know, when you're talking to the vendor community, they get very excited about our why and how you know, lack of disruption to install some software, right? And so that's one of the advantages, I guess, of our P A. As you can pop it into an existing process, good or bad, and get going right away. We've seen this time and time again in the industry. When you have when you have a big force people to change, you know it's slow When you can show Immediate Roo. I start to see these rocket ships at the same time as a consultant, you really want to have a bigger impact on business you don't want to just repeat in automate Bad process is. So how do you work with clients to sort of manage that that insatiable desire for quick R A y, and then the transformative components that. You know, I could maybe defend you from disruption or allow you to be an incumbent disruptor. >>So I think what's interesting is transformation. Use the word we were really good transformation program. So starting to say how that we think of automation first as we do a traditional transformation program is is very near and dear to us now. And instead of saying, Hey, we're gonna bolt the ear piece system and then figure out if we can get some improvement by automating later. We're saying, you know what? Let's sort of double go backwards. Maybe it's a little fashion, but what is this whole process look like? And can we put automation and launch not is a process improvement after lunch? So I think we think of these transformation programmes, But AARP programs for ready and they're doing at automation is now on the tip of the front end of the program rather than afterthought. Reporting used to be >>right, so I mean, >>you guys >>have to be technology agnostic in your business. I mean, we happen to be a U IE path conference, but there, you know, if our p a generally you iPad specifically, it's not a panacea for all problems. I mean, we've talked about a I we talked about other automation process automation capabilities. You've got existing systems. All this stuff has to work together. So so and people always say technology last people process first. You guys lived that, Um So how are you seeing automation evolved in in terms of adoption of the how people are dealing with existing systems and some of the other technologies that you're having to bring together. >>So I think the first thing is, the technology has to work. It has to be bulletproof, resilient. If you're going to put it in these processes and make it make it part of your work life reserving clients or that sort of thing. So first it needs to be bulletproof. That's becoming a given second. I'd like to think that's, well, architected more. Maura's. You bring in a I or other advanced components. You need thio. Be ready to have a changing ecosystem. You know, the best document processing right now might not be the best in six months. So starting to think of your automation solution is that the technical glue and this is allow you to swap out the trade components as you as you refined processes going forward or something new hits the market. So now we're working ecosystem, I think, for the r p a. Vendors that are having great success in a market like you have have they sort of give you that platform, and they give you the off ramps and the on ramps to integrate the other technologies. And like I said, I think that's table stakes in addition, being bulletproof. But the next piece of that is how we get various people involved in the value proposition of creating automation. So various tools and studios, some for the business user that might not be as technical, maybe self designed about it, eh? Process description level on, then maybe a more technical work bench for the technical body builder. So I'm starting to see that in the product suite and somebody announcements here this week. Hallie, we tailor the tools to different users and engage them in that process from one into the other. >>So you mentioned scaling before what the blockers, what's the challenges of scaling? Why's it seemed to be so hard? It's clearly an area of focus here at this event. >>So I think first of all, the technology is is still new to some areas. They're still back and forth with the business or I t led initiative. I think there are some scars and wounds over the last few years of automation where people might have gotten started on the wrong foot. There's even some reduced to learn from. So I think people are looking for the business case. They're getting more comfortable with it. So the job sizes, deal sizes, air getting bigger for the FDA vendors and for us. But I think it's just an evolution. And, like I said, there a lot of stubbed toes early on a nomination. >>What are >>some of the big mistakes that you've seen? People make >>people thinking that it's only a business tool, or only a technology tool or technology to the point that they get started on something that becomes either a real technology problem, a real business problem? Maybe you told the body out in the business, and you attach it to your ear piece system and you cause performance problems or you have security problems on. Then it becomes a real I t problem also seeing the reverse where you know, when I t group will start and say Let's do some automation And they pushed into some departments it might have a fully big business case, might now have good support, and it becomes a technology science project rather than delivery in the real value. >>I was tryingto a week sort of Think about analogies. Analogous ascendance sees in software. I use service now a little bit, but that was kind of a heavy lift. It started an I t. It was very clear. You know, I t You're seeing this massive rapid growth of you ai path fastest growing probably the fastest growing software segment in history and striking to me that we're just now starting to see Cloud come into the play here. If we just you iPad that big announced this week. It's got this new SAS capability, which you would think you would, you know, be born in the cloud. But people have explained why that is. Do you have concerns about the pace of growth and a company like you I path and its competitors their ability to sort of keep up and continue to deliver quality. I mean, a big part of what you guys do is sort of risk management. Well, so how do you manage that risk? >>So I think what you look for if you're going to be in the lion's partner, if you're going the work together and pursue things together first you have to have the basics. It has to be bulletproof. It has to work. When you hit bumps in the road, you have to have escalation pass. That makes sense. And there's growing pains in any firm, or any company that grows grows as quickly as you tap. On the other hand, the question is, your culture is the line. Do you know the fix problems? Do you put your customers first? I think that's what we look like. Look at in the lions, which is how we have a partner with. People have similar DNA about customers first, and you put everything else aside, roll your sleeves up and do the right thing. So that's what we look for in lines like This >>Way. Always talked about the buzzwords of digital transformation, which conferences like this, it is kind of buzzy, but when you talk to customers, they're actually going through digital transformations. And then a couple years ago, they started experimenting. They bought one of everything and they'd run things in parallel with, you know, legacy systems. But now they're starting to place their bets, saying, actually, we've got some use cases that are working. We're gonna double down on the stuff that, you know, we think works. Our p a in some cases fits there. We're gonna unplug some of the legacy stuff and try to deal with our technical debt. But I guess my question is, where do you see our P? A fitting in to that whole digital transformation? Major, I like to think of a matrix where you've got different sets of service is and you've got different industries that are tapping, you know, all data centric that that are tapping these new capabilities and formulating new businesses. News industries. That's how you see this disruption happening. And then the incumbent saying, Hey, we've got assets to we're gonna tap that same matrix and whether it's open source software or cloud or new security paradigms or data and analytics. So where do you see our P? A fitting into that matrix? >>So I think at the glue level. At the architectural level, it can be the orchestrator of the experience of taking a variety technologies integrating them, providing again on ramps and off ramps, doing with a human canoe, looking at screens, analyzing content so it could be the glue that orchestrates those processes orchestrates. Maybe some of the so it was used to be a void between legacy systems and new systems on darky A helps take all that away or level the playing field on. That s So that's has another set of eyes and ears for process integration, our technology integration. And I think that's what it's probably it's best place now. Are there good process tools there? Can we get, you know, community developments? A big discussion right now. I think some people have been successful at it, but it requires a lot of care and feeding and planning to have your community hand the rails or stay between the curbs and do useful things. So I think we're in the beginning of how far can we go with community development? I think the technology is really the glue. >>So community of elven terms of best practice sharing >>and users have developing their own bots. You know, what are the guardrails? Does the process? They're automating matter. Does it introduced a risk? Eyes going to perform. How do you make sure your bots are an evil that people are creating? It's a pretty powerful technology. >>Is their I p in there that you don't want it? We talked about this last year that you don't want to necessarily share with others. So, um, now your role used to have focused specifically in financial service is now you're more horizontal. But how does the light look at this opportunity? Is there is it an automation practice? Is it you cut across all industries with automation, or is it sort of broader than that? >>So my colleague here runs the offering, which is Do we have the people, the training, the tools that delivery centers in the know how to go out and do this kind of work? And we've scaled tremendously in the automation space. The second part is, how do we look to the Jason sees? So we work very closely with our colleagues in a I and ML when we say how we go do the next generation of this out of the gate, How we experiment, how we say, Do you want fries with that as we as we do some of this work. But then we look for the industry in the intersection, and that's where a firm like Lloyd we've got deep, deep industry expertise, way say, well, those intersections where we can go make something happen way come work with our partners are lions you know, partners in making making something happen at an industry specific level, or can we go solve a specific problem? So I think that's what we bring that unique. But we do it both ways. >>It's kind of off off the topic here, but I was talking about that matrix before and again. I'm envisioning technology, horizontal technologies and then vertical industries, and it used to be for decades if you were in it. And if you're in financial service is, you are pretty much stuck in financial service is you had a value chain that was specific to financialservices, and you knew it inside and out, whether it was product development or marketing or sales distribution, whatever it was. That same thing for automobiles on manufacturing, an education on and on and on, and you develop these industry areas of expertise and domain experts with in there. And you guys have built up a global powerhouse doing, But you're seeing a CZ digital. It's cos. Become digital. What's the difference in the business in a digital business? That's how they use data. Data is at the core, and you're now seeing organizations Company's tech company specifically traverse different industries. You're seeing Amazon, you know, in content you're seeing Apple and financialservices other companies getting into health care. >>How is >>that? First of all, you see that and what do you think it was driving that? And how does that affect your business? Or your clients asking youto help you traverse new new industries, get into new industries or defend against others? You know, these big tech companies tryingto with a duel, disruption agenda, trying to take him >>over, and the center of all that you mentioned a little. But the center of that is who the ultimate customers, and we'll experience that they want how they want that experience integrated, so it's not channel by channel anymore. It's which pieces fit together and how I want to buy things and how I want to be serviced. You're getting whole crossed economies around what the consumer wants, unable by technology. I think the other thing that plays into that is you start thinking of the Internet of things and how connected people are. And how do you use monetize and integrate data about particular people and how they want to be served to make that a better experience? I think the consumer ultimately is driving. A lot of that technology is in the billions. >>Yeah, is you think about that picture again. You'd like to use a metaphor of a matrix. I mean, I see our p a is just, you know, one piece of that. You know, there's so many others you mentioned. I o t We talk about a I all the time we talk about Blockchain. It's how you put those different capabilities together and apply them to your business. That really makes the difference. Not that RPG right now feels very tactical, but it's part of a much more strategic agenda. >>Absolutely on again. It could be the glue in an ecosystem of emerging technologies. I do see there's the eyes and ears. The fact that what you get out of the box from regular p. A vendor. Really? Integrate some really, really painful things. Looking at spreadsheets and thinking the guys with green visors column numbers. It's really good at that stuff as, ah, base task. >>Yeah, nothing wrong with tactical and quick. Roo, I So, Anthony, thanks very much for coming on The Cube. Really appreciate your time. >>Thank you. Great to be here >>to welcome. All right, Keep right, everybody. We're back with our next guest. Day two from you. I path forward in Las Vegas. You watching the cue?

>> Live, from Las Vegas, it's theCUBE covering UiPath Forward Americas 2019. Brought to you by UiPath. >> Welcome back to the Bellagio in Las Vegas, everybody. You're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante. Day one of UiPath Forward III, hashtag UiPathForward. Elena Christopher is here. She's the senior vice president at HFS Research, and Elena, I'm going to recruit you to be my co-host here. >> Co-host! >> On this power panel. Jean Youngers here, CUBE alum, VP, a Six Sigma Leader at Security Benefit. Great to see you again. >> Thank you. >> Dave: And Amy Chandler, who is the Assistant Vice President and Director of Internal Controls, also from Security Benefit. >> Hello. >> Dave: Thanks for coming on theCUBE. >> Thank you. >> Alright Elena, let's start off with you. You follow this market, you have for some time, you know HFS is sort of anointed as formulating this market place, right? >> Elena: We like to think of ourselves as the voice-- >> You guys were early on. >> The voice of the automation industry. >> So, what are you seeing? I mean, process automation has been around forever, RPA is a hot recent trend, but what are you seeing the last year or two? What are the big trends and rip currents that you see in the market place? >> I mean, I think one of the big trends that's out there, I mean, RPA's come on to the scene. I like how you phrase it Dave, because you refer to it as, rightly so, automation is not new, and so we sort of say the big question out there is, "Is RPA just flavor of the month?" RPA is definitely not, and I come from a firm, we put out a blog earlier this year called "RPA is dead. Long live automation." And that's because, when we look at RPA, and when we think about what it's impact is in the market place, to us the whole point of automation in any form, regardless of whether it's RPA, whether it be good old old school BPM, whatever it may be, it's mission is to drive transformation, and so the HFS perspective, and what all of our research shows and sort of justifies that the goal is, what everyone is striving towards, is to get to that transformation. And so, the reason we put out that piece, the "RPA is dead. Long live integrated automation platforms" is to make the point that if you're not- 'cause what does RPA allow? It affords an opportunity for change to drive transformation so, if you're not actually looking at your processes within your company and taking this opportunity to say, "What can I change, what processes are just bad, "and we've been doing them, I'm not even sure why, "for so long. What can we transform, "what can we optimize, what can we invent?" If you're not taking that opportunity as an enterprise to truly embrace the change and move towards transformation, that's a missed opportunity. So I always say, RPA, you can kind of couch it as one of many technologies, but what RPA has really done for the market place today, it's given business users and business leaders the realization that they can have a role in their own transformation. And that's one of the reasons why it's actually become very important, but a single tool in it's own right will never be the holistic answer. >> So Jean, Elena's bringing up a point about transformation. We, Stew Bennett and I interviewed you last year and we've played those clips a number of times, where you sort of were explaining to us that it didn't make sense before RPA to try to drive Six Sigma into business processes; you couldn't get the return. >> Jean: Right. >> Now you can do it very cheaply. And for Six Sigma or better, is what you use for airplane engines, right? >> Right. >> So, now you're bringing up the business process. So, you're a year in, how's it going? What kind of results are you seeing? Is it meeting your expectations? >> It's been wonderful. It has been the best, it's been probably the most fun I've had in the last fifteen years of work. I have enjoyed, partly because I get to work with this great person here, and she's my COE, and helps stand up the whole RPA solution, but you know, we have gone from finance into investment operations, into operations, you know we've got one sitting right now that we're going to be looking at statements that it's going to be fourteen thousand hours out of both time out as well as staff hours saved, and it's going to touch our customer directly, that they're not going to get a bad statement anymore. And so, you know, it has just been an incredible journey for us over the past year, it really has. >> And so okay Amy, your role is, you're the hardcore practitioner here right? >> Amy: That's right. >> You run the COE. Tell us more about your role, and I'm really interested in how you're bringing it out, RPA to the organization. Is that led by your team, or is it kind of this top-down approach? >> Yeah, this last year, we spent a lot of time trying to educate the lower levels and go from a bottom-up perspective. Pretty much, we implemented our infrastructure, we had a nice solid change management process, we built in logical access, we built in good processes around that so that we'd be able to scale easily over this last year, which kind of sets us up for next year, and everything that we want to accomplish then. >> So Elena, we were talking earlier on theCUBE about you know, RPA, in many ways, I called it cleaning up the crime scene, where stuff is kind of really sort of a mass and huge opportunities to improve. So, my question to you is, it seems like RPA is, in some regards, successful because you can drop it into existing processes, you're not changing things, but in a way, this concerns that, oh well, I'm just kind of paving the cow path. So how much process reinvention should have to occur in order to take advantage of RPA? >> I love that you use that phrase, "paving the cow path." As a New Englander, as you know the roads in Boston are in fact paved cow paths, so we know that can lead to some dodgy roads, and that's part of, and I say it because that's part of what the answer is, because the reinvention, and honestly the optimization has to be part of what the answer is. I said it just a little bit earlier in my comments, you're missing an opportunity with RPA and broader automation if you don't take that step to actually look at your processes and figure out if there's just essentially deadwood that you need to get rid of, things that need to be improved. One of the sort of guidelines, because not all processes are created equal, because you don't want to spend the time and effort, and you guys should chime in on this, you don't want to spend the time and effort to optimize a process if it's not critical to your business, if you're not going to get lift from it, or from some ROI. It's a bit of a continuum, so one of the things that I always encourage enterprises to think about, is this idea of, well what's the, obviously, what business problem are you trying to solve? But as you're going through the process optimization, what kind of user experience do you want out of this? And your users, by the way, you tend to think of your user as, it could be your end customer, it could be your employee, it could even be your partner, but trying to figure out what the experience is that you actually want to have, and then you can actually then look at the process and figure out, do we need to do something different? Do we need to do something completely new to actually optimize that? And then again, line it with what you're trying to solve and what kind of lift you want to get from it. But I'd love to, I mean, hopping over to you guys, you live and breathe this, right? And so I think you have a slightly different opinion than me, but-- >> We do live and breathe it, and every process we look at, we take into consideration. But you've also got to, you have a continuum right? If it's a simple process and we can put it up very quickly, we do, but we've also got ones where one process'll come into us, and a perfect example is our rate changes. >> Amy: Rate changes. >> It came in and there was one process at the very end and they ended up, we did a wing to wing of the whole thing, followed the data all the way back through the process, and I think it hit, what, seven or eight-- >> Yeah. >> Different areas-- >> Areas. >> Of the business, and once we got done with that whole wing to wing to see what we could optimize, it turned into what, sixty? >> Amy: Yeah, sixty plus. Yeah. >> Dave: Sixty plus what? >> Bot processes from one entry. >> Yeah. >> And so, right now, we've got 189 to 200 processes in the back log. And so if you take that, and exponentially increase it, we know that there's probably actually 1,000 to 2,000 more processes, at minimum, that we can hit for the company, and we need to look at those. >> Yeah, and I will say, the wing to wing approach is very important because you're following the data as it's moving along. So if you don't do that, if you only focus on a small little piece of it, you don't what's happening to the data before it gets to you and you don't know what's going to happen to it when it leaves you, so you really do have to take that wing to wing approach. >> So, internal controls is in your title, so talking about scale, it's a big theme here at UiPath, and these days, things scale really fast, and boo-boos can happen really fast. So how are you ensuring, you know that the edicts of the organization are met, whether it's security, compliance, governance? Is that part of your role? >> Yeah, we've actually kept internal audit and internal controls, and in fact, our external auditors, EY. We've kept them all at the table when we've gone through processes, when we've built out our change management process, our logical access. When we built our whole process from beginning to end they kind of sat at the table with us and kind of went over everything to make sure that we were hitting all the controls that we needed to do. >> And actually, I'd like to piggyback on that comment, because just that inclusion of the various roles, that's what we found as an emerging best practice, and in all of our research and all of the qualitative conversations that we have with enterprises and service providers, is because if you do things, I mean it applies on multiple levels, because if you do things in a silo, you'll have siloed impact. If you bring the appropriate constituents to the table, you're going to understand their perspective, but it's going to have broader reach. So it helps alleviate the silos but it also supports the point that you just made Amy, about looking at the processes end to end, because you've got the necessary constituents involved so you know the context, and then, I believe, I mean I think you guys shared this with me, that particularly when audit's involved, you're perhaps helping cultivate an understanding of how even their processes can improve as well. >> Right. >> That is true, and from an overall standpoint with controls, I think a lot of people don't realize that a huge benefit is your controls, cause if you're automating your controls, from an internal standpoint, you're not going to have to test as much, just from an associate process owner paying attention to their process to the internal auditors, they're not going to have to test as much either, and then your external auditors, which that's revenue. I mean, that's savings. >> You lower your auditing bill? >> Yeah. Yeah. >> Well we'll see right? >> Yeah. (laughter) >> That's always the hope. >> Don't tell EY. (laughter) So I got to ask you, so you're in a little over a year So I don't know if you golf, but you know a mulligan in golf. If you had a mulligan, a do over, what would you do over? >> The first process we put in place. At least for me, it breaks a lot, and we did it because at the time, we were going through decoupling and trying to just get something up to make sure that what we stood up was going to work and everything, and so we kind of slammed it in, and we pay for that every quarter, and so actually it's on our list to redo. >> Yeah, we automated a bad process. >> Yeah, we automated a bad process. >> That's a really good point. >> So we pay for it in maintenance every quarter, we pay for it, cause it breaks inevitably. >> Yes. >> Okay so what has to happen? You have to reinvent the process, to Elena's? >> Yes, you know, we relied on a process that somebody else had put in place, and in looking at it, it was kind of a up and down and through the hoop and around this way to get what they needed, and you know there's much easier ways to get the data now. And that's what we're doing. In fact, we've built our own, we call it a bot mart. That's where all our data goes, they won't let us touch the other data marts and so forth so they created us a bot mart, and anything that we need data for, they dump in there for us and then that's where our bot can hit, and our bot can hit it at anytime of the day or night when we need the data, and so it's worked out really well for us, and so the bot mart kind of came out of that project of there's got to be a better way. How can we do this better instead of relying on these systems that change and upgrade and then we run the bot and its working one day and the next day, somebody has gone in and tweaked something, and when all's I really need out of that system is data, that's all I need. I don't need, you know, a report. I don't need anything like that, cause the reports change and they get messed up. I just want the raw data, and so that's what we're starting to do. >> How do you ensure that the data is synchronized with your other marts and warehouses, is that a problem? >> Not yet. >> No not yet! (laughter) >> I'm wondering cause I was thinking the exact same question Dave, because on one hand its a nice I think step from a governance standpoint. You have what you need, perhaps IT or whomever your data curators are, they're not going to have a heart attack that you're touching stuff that they don't want you to, but then there is that potential for synchronization issues, cause that whole concept of golden source implies one copy if you will. >> Well, and it is. It's all coming through, we have a central data repository that the data's going to come through, and it's all sitting there, and then it'll move over, and to me, what I most worry about, like I mentioned on the statement once, okay, I get my data in, is it the same data that got used to create those statements? And as we're doing the testing and as we're looking at going live, that's one of our huge test cases. We need to understand what time that data comes in, when will it be into our bot mart, so when can I run those bots? You know, cause they're all going to be unattended on those, so you know, the timing is critical, and so that's why I said not yet. >> Dave: (chuckle) >> But you want to know what, we can build the bot to do that compare of the data for us. >> Haha all right. I love that. >> I saw a stat the other day. I don't know where it was, on Twitter or maybe it was your data, that more money by whatever, 2023 is going to be spent on chat bots than mobile development. >> Jean: I can imagine, yes. >> What are you doing with chat bots? And how are you using them? >> Do you want to answer that one or do you want me to? >> Go ahead. >> Okay so, part of the reason I'm so enthralled by the chat bot or personal assistant or anything, is because the unattended robots that we have, we have problems making sure that people are doing what they're supposed to be doing in prep. We have some in finance, and you know, finance you have a very fine line of what you can automate and what you need the user to still understand what they're doing, right? And so we felt like we had a really good, you know, combination of that, but in some instances, they forget to do things, so things aren't there and we get the phone call the bot broke, right? So part of the thing I'd like to do is I'd like to move that back to an unattended bot, and I'm going to put a chat bot in front of it, and then all's they have to do is type in "run my bot" and it'll come up if they have more than one bot, it'll say "which one do you want to run?" They'll click it and it'll go. Instead of having to go out on their machine, figure out where to go, figure out which button to do, and in the chat I can also send them a little message, "Did you run your other reports? Did you do this?" You know, so, I can use it for the end user, to make that experience for them better. And plus, we've got a lot of IT, we've got a lot of HR stuff that can fold into that, and then RPA all in behind it, kind of the engine on a lot of it. >> I mean you've child proofed the bot. >> Exactly! There you go. There you go. >> Exactly. Exactly. And it also provides a means to be able to answer those commonly asked questions for HR for example. You know, how much vacation time do I have? When can I change my benefits? Examples of those that they answer frequently every day. So that provides another avenue for utilization of the chat bot. >> And if I may, Dave, it supports a concept that I know we were talking about yesterday. At HFS it's our "Triple-A Trifecta", but it's taking the baseline of automation, it intersects with components of AI, and then potentially with analytics. This is starting to touch on some of the opportunities to look at other technologies. You say chat bots. At HFS we don't use the term chat bot, just because we like to focus and emphasize the cognitive capability if you will. But in any case, you guys essentially are saying, well RPA is doing great for what we're using RPA for, but we need a little bit of extension of functionality, so we're layering in the chat bot or cognitive assistant. So it's a nice example of some of that extension of really seeing how it's, I always call it the power of and if you will. Are you going to layer these things in to get what you need out of it? What best solves your business problems? Just a very practical approach I think. >> So Elena, Guy has a session tomorrow on predictions. So we're going to end with some predictions. So our RPA is dead, (chuckle) will it be resuscitated? What's the future of RPA look like? Will it live up to the hype? I mean so many initiatives in our industry haven't. I always criticize enterprise data warehousing and ETL and big data is not living up to the hype. Will RPA? >> It's got a hell of a lot of hype to live up to, I'll tell you that. So, back to some of our causality about why we even said it's dead. As a discrete software category, RPA is clearly not dead at all. But unless it's helping to drive forward with transformation, and even some of the strategies that these fine ladies from Security Benefit are utilizing, which is layering in additional technology. That's part of the path there. But honestly, the biggest challenge that you have to go through to get there and cannot be underestimated, is the change that your organization has to go through. Cause think about it, if we look at the grand big vision of where RPA and broader intelligent automation takes us, the concept of creating a hybrid workforce, right? So what's a hybrid workforce? It's literally our humans complemented by digital workers. So it still sounds like science fiction. To think that any enterprise could try and achieve some version of that and that it would be A, fast or B, not take a lot of change management, is absolutely ludicrous. So it's just a very practical approach to be eyes wide open, recognize that you're solving problems but you have to want to drive change. So to me, and sort of the HFS perspective, continues to be that if RPA is not going to die a terrible death, it needs to really support that vision of transformation. And I mean honestly, we're here at a UiPath event, they had many announcements today that they're doing a couple of things. Supporting core functionality of RPA, literally adding in process discovery and mining capabilities, adding in analytics to help enterprises actually track what your benefit is. >> Jean: Yes. >> These are very practical cases that help RPA live another day. But they're also extending functionality, adding in their whole announcement around AI fabric, adding in some of the cognitive capability to extend the functionality. And so prediction-wise, RPA as we know it three years from now is not going to look like RPA at all. I'm not going to call it AI, but it's going to become a hybrid, and it's honestly going to look a lot like that Triple-A Trifecta I mentioned. >> Well, and UiPath, and I presume other suppliers as well, are expanding their markets. They're reaching, you hear about citizens developers and 100% of the workforce. Obviously you guys are excited and you see a long-run way for RPA. >> Jean: Yeah, we do. >> I'll give you the last word. >> It's been a wonderful journey thus far. After this morning's event where they showed us everything, I saw a sneak peek yesterday during the CAB, and I had a list of things I wanted to talk to her about already when I came out of there. And then she saw more of 'em today, and I've got a pocketful of notes of stuff that we're going to take back and do. I really, truly believe this is the future and we can do so much. Six Sigma has kind of gotten a rebirth. You go in and look at your processes and we can get those to perfect. I mean, that's what's so cool. It is so cool that you can actually tell somebody, I can do something perfect for you. And how many people get to do that? >> It's back to the user experience, right? We can make this wildly functional to meet the need. >> Right, right. And I don't think RPA is the end all solution, I think it's just a great tool to add to your toolkit and utilize moving forward. >> Right. All right we'll have to leave it there. Thanks ladies for coming on, it was a great segment. Really appreciate your time. >> Thanks. >> Thank you. >> Thank you for watching, everybody. This is Dave Vellante with theCUBE. We'll be right back from UiPath Forward III from Las Vegas, right after this short break. (technical music)