>>Hello everyone and welcome back to Motor City, Michigan. We're live from the Cube and my name is Savannah Peterson. Joined this afternoon with my co-host John Ferer. John, how you doing? Doing >>Great. This next segment's gonna be awesome about application modernization, scaling pluses. This is what's gonna, how are the next generation software revolution? It's gonna be >>Fun. You know, it's kind of been a theme of our day today is scale. And when we think about the complex orchestration platform that is Kubernetes, everyone wants to scale faster, quicker, more efficiently, and our guests are here to tell us all about that. Please welcome to Char and Andy, thank you so much for being here with us. You were on the Red Hat OpenShift team. Yeah. I suspect most of our audience is familiar, but just in case, let's give 'em a quick one-liner pitch so everyone's on the same page. Tell us about OpenShift. >>I, I'll take that one. OpenShift is our ES platform is our ES distribution. You can consume it as a self-managed platform or you can consume it as a managed service on on public clouds. And so we just call it all OpenShift. So it's basically Kubernetes, but you know, with a CNCF ecosystem around it to make things more easier. So maybe there's two >>Lights. So what does being at coupon mean for you? How does it feel to be here? What's your initial takes? >>Exciting. I'm having a fantastic time. I haven't been to coupon since San Diego, so it's great to be back in person and see old friends, make new friends, have hallway conversations. It's, it's great as an engineer trying to work in this ecosystem, just being able to, to be in the same place with these folks. >>And you gotta ask, before we came on camera, you're like, this is like my sixth co con. We were like, we're seven, you know, But that's a lot of co coupons. It >>Is, yes. I mean, so what, >>Yes. >>Take us status >>For sure. Where we are now. Compare and contrast co. Your first co con, just scope it out. What's the magnitude of change? If you had to put a pin on that, because there's a lot of new people coming in, they might not have seen where it's come from and how we got here is maybe not how we're gonna get to the next >>Level. I've seen it grow tremendously since the first one I went to, which I think was Austin several years ago. And what's great is seeing lots of new people interested in contributing and also seeing end users who are trying to figure out the best way to take advantage of this great ecosystem that we have. >>Awesome. And the project management side, you get the keys to the Kingdom with Red Hat OpenShift, which has been successful. Congratulations by the way. Thank you. We watched that grow and really position right on the wave. It's going great. What's the update on on the product? Kind of, you're in a good, good position right now. Yeah, >>No, we we're feeling good about it. It's all about our customers. Obviously the fact that, you know, we have thousands of customers using OpenShift as the cloud native platform, the container platform. We're very excited. The great thing about them is that, I mean you can go to like OpenShift Commons is kind of a user group that we run on the first day, like on Tuesday we ran. I mean you should see the number of just case studies that our customers went through there, you know? And it is fantastic to see that. I mean it's across so many different industries, across so many different use cases, which is very exciting. >>One of the things we've been reporting here in the Qla scene before, but here more important is just that if you take digital transformation to the, to its conclusion, the IT department and developers, they're not a department to serve the business. They are the business. Yes. That means that the developers are deciding things. Yeah. And running the business. Prove their code. Yeah. Okay. If that's, if that takes place, you gonna have scale. And we also said on many cubes, certainly at Red Hat Summit and other ones, the clouds are distributed computer, it's distributed computing. So you guys are focusing on this project, Andy, that you're working on kcp. >>Yes. >>Which is, I won't platform Kubernetes platform for >>Control >>Planes. Control planes. Yes. Take us through, what's the focus on why is that important and why is that relate to the mission of developers being in charge and large scale? >>Sure. So a lot of times when people are interested in developing on Kubernetes and running workloads, they need a cluster of course. And those are not cheap. It takes time, it takes money, it takes resources to get them. And so we're trying to make that faster and easier for, for end users and everybody involved. So with kcp, we've been able to take what looks like one normal Kubernetes and partition it. And so everybody gets a slice of it. You're an administrator in your little slice and you don't have to ask for permission to install new APIs and they don't conflict with anybody else's APIs. So we're really just trying to make it super fast and make it super flexible. So everybody is their own admin. >>So the developer basically looks at it as a resource blob. They can do whatever they want, but it's shared and provisioned. >>Yes. One option. It's like, it's like they have their own cluster, but you don't have to go through the process of actually provisioning a full >>Cluster. And what's the alternative? What's the what's, what's the, what's the benefit and what was the alternative to >>This? So the alternative, you spin up a full cluster, which you know, maybe that's three control plane nodes, you've got multiple workers, you've got a bunch of virtual machines or bare metal, or maybe you take, >>How much time does that take? Just ballpark. >>Anywhere from five minutes to an hour you can use cloud services. Yeah. Gke, E Ks and so on. >>Keep banging away. You're configuring. Yeah. >>Those are faster. Yeah. But it's still like, you still have to wait for that to happen and it costs money to do all of that too. >>Absolutely. And it's complex. Why do something that's been done, if there's a tool that can get you a couple steps down the path, which makes a ton of sense. Something that we think a lot when we're talking about scale. You mentioned earlier, Tohar, when we were chatting before the cams were alive, scale means a lot of different things. Can you dig in there a little bit? >>Yeah, I >>Mean, so when, when >>We talk about scale, >>We are talking about from a user perspective, we are talking about, you know, there are more users, there are more applications, there are more workloads, there are more services being run on Kubernetes now, right? So, and OpenShift. So, so that's one dimension of this scale. The other dimension of the scale is how do you manage all the underlying infrastructure, the clusters, the name spaces, and all the observability data, et cetera. So that's at least two levels of scale. And then obviously there's a third level of scale, which is, you know, there is scale across not just different clouds, but also from cloud to the edge. So there is that dimension of scale. So there are several dimensions of this scale. And the one that again, we are focused on here really is about, you know, this, the first one that I talk about is a user. And when I say user, it could be a developer, it could be an application architect, or it could be an application owner who wants to develop Kubernetes applications for Kubernetes and wants to publish those APIs, if you will, and make it discoverable and then somebody consumes it. So that's the scale we are talking about >>Here. What are some of the enterprise, you guys have a lot of customers, we've talked to you guys before many, many times and other subjects, Red Hat, I mean you guys have all the customers. Yeah. Enterprise, they've been there, done that. And you know, they're, they're savvy. Yeah. But the cloud is a whole nother ballgame. What are they thinking about? What's the psychology of the customer right now? Because now they have a lot of choices. Okay, we get it, we're gonna re-platform refactor apps, we'll keep some legacy on premises for whatever reasons. But cloud pretty much is gonna be the game. What's the mindset right now of the customer base? Where are they in their, in their psych? Not the executive, but more of the the operators or the developers? >>Yeah, so I mean, first of all, different customers are at different levels of maturity, I would say in this. They're all on a journey how I like to describe it. And in this journey, I mean, I see a customers who are really tip of the sphere. You know, they have containerized everything. They're cloud native, you know, they use best of tools, I mean automation, you know, complete automation, you know, quick deployment of applications and all, and life cycle of applications, et cetera. So that, that's kind of one end of this spectrum >>Advanced. Then >>The advances, you know, and, and I, you know, I don't, I don't have any specific numbers here, but I'd say there are quite a few of them. And we see that. And then there is kind of the middle who are, I would say, who are familiar with containers. They know what app modernization, what a cloud application means. They might have tried a few. So they are in the journey. They are kind of, they want to get there. They have some other kind of other issues, organizational or talent and so, so on and so forth. Kinds of issues to get there. And then there are definitely the quota, what I would call the lag arts still. And there's lots of them. But I think, you know, Covid has certainly accelerated a lot of that. I hear that. And there is definitely, you know, more, the psychology is definitely more towards what I would say public cloud. But I think where we are early also in the other trend that I see is kind of okay, public cloud great, right? So people are going there, but then there is the so-called edge also. Yeah. That is for various regions. You, you gotta have a kind of a regional presence, a edge presence. And that's kind of the next kind of thing taking off here. And we can talk more >>About it. Yeah, let's talk about that a little bit because I, as you know, as we know, we're very excited about Edge here at the Cube. Yeah. What types of trends are you seeing? Is that space emerges a little bit more firmly? >>Yeah, so I mean it's, I mean, so we, when we talk about Edge, you're talking about, you could talk about Edge as a, as a retail, I mean locations, right? >>Could be so many things edges everywhere. Everywhere, right? It's all around us. Quite literally. Even on the >>Scale. Exactly. In space too. You could, I mean, in fact you mentioned space. I was, I was going to >>Kinda, it's this world, >>My space actually Kubernetes and OpenShift running in space, believe it or not, you know, So, so that's the edge, right? So we have Industrial Edge, we have Telco Edge, we have a 5g, then we have, you know, automotive edge now and, and, and retail edge and, and more, right? So, and space, you know, So it's very exciting there. So the reason I tag back to that question that you asked earlier is that that's where customers are. So cloud is one thing, but now they gotta also think about how do I, whatever I do in the cloud, how do I bring it to the edge? Because that's where my end users are, my customers are, and my data is, right? So that's the, >>And I think Kubernetes has brought that attention to the laggards. We had the Laed Martin on yesterday, which is an incredible real example of Kubernetes at the edge. It's just incredible story. We covered it also wrote a story about it. So compelling. Cuz it makes it real. Yes. And Kubernetes is real. So then the question is developer productivity, okay, Things are starting to settle in. We've got KCP scaling clusters, things are happening. What about the tool chains? And how do I develop now I got scale of development, more code coming in. I mean, we are speculating that in the future there's so much code in open source that no one has to write code anymore. Yeah. At some point it's like this gluing things together. So the developers need to be productive. How are we gonna scale the developer equation and eliminate the, the complexity of tool chains and environments. Web assembly is super hyped up at this show. I don't know why, but sounds good. No one, no one can tell me why, but I can kind of connect the dots. But this is a big thing. >>Yeah. And it's fitting that you ask about like no code. So we've been working with our friends at Cross Plain and have integrated with kcp the ability to no code, take a whole bunch of configuration and say, I want a database. I want to be a, a provider of databases. I'm in an IT department, there's a bunch of developers, they don't wanna have to write code to create databases. So I can just take, take my configuration and make it available to them. And through some super cool new easy to use tools that we have as a developer, you can just say, please give me a database and you don't have to write any code. I don't have to write any code to maintain that database. I'm actually using community tooling out there to get that spun up. So there's a lot of opportunities out there. So >>That's ease of use check. What about a large enterprise that's got multiple tool chains and you start having security issues. Does that disrupt the tool chain capability? Like there's all those now weird examples emerging, not weird, but like real plumbing challenges. How do you guys see that evolving with Red >>Hat and Yeah, I mean, I mean, talking about that, right? The software, secure software supply chain is a huge concern for everyone after, especially some of the things that have happened in the past few >>Years. Massive team here at the show. Yeah. And just within the community, we're all a little more aware, I think, even than we were before. >>Before. Yeah. Yeah. And, and I think the, so to step back, I mean from, so, so it's not just even about, you know, run time vulnerability scanning, Oh, that's important, but that's not enough, right? So we are talking about, okay, how did that container, or how did that workload get there? What is that workload? What's the prominence of this workload? How did it get created? What is in it? You know, and what, what are, how do I make, make sure that there are no unsafe attack s there. And so that's the software supply chain. And where Red Hat is very heavily invested. And as you know, with re we kind of have roots in secure operating system. And rel one of the reasons why Rel, which is the foundation of everything we do at Red Hat, is because of security. So an OpenShift has always been secure out of the box with things like scc, rollbacks access control, we, which we added very early in the product. >>And now if you kind of bring that forward, you know, now we are talking about the complete software supply chain security. And this is really about right how from the moment the, the, the developer rights code and checks it into a gateway repository from there on, how do you build it? How do you secure it at each step of the process, how do you sign it? And we are investing and contributing to the community with things like cosign and six store, which is six store project. And so that secures the supply chain. And then you can use things like algo cd and then finally we can do it, deploy it onto the cluster itself. And then we have things like acs, which can do vulnerability scanning, which is a container security platform. >>I wanna thank you guys for coming on. I know Savannah's probably got a last question, but my last question is, could you guys each take a minute to answer why has Kubernetes been so successful today? What, what was the magic of Kubernetes that made it successful? Was it because no one forced it? Yes. Was it lightweight? Was it good timing, right place at the right time community? What's the main reason that Kubernetes is enabling all this, all this shift and goodness that's coming together, kind of defacto unifies people, the stacks, almost middleware markets coming around. Again, not to use that term middleware, but it feels like it's just about to explode. Yeah. Why is this so successful? I, >>I think, I mean, the shortest answer that I can give there really is, you know, as you heard the term, I think Satya Nala from Microsoft has used it. I don't know if he was the original person who pointed, but every company wants to be a software company or is a software company now. And that means that they want to develop stuff fast. They want to develop stuff at scale and develop at, in a cloud native way, right? You know, with the cloud. So that's, and, and Kubernetes came at the right time to address the cloud problem, especially across not just one public cloud or two public clouds, but across a whole bunch of public clouds and infrastructure as, and what we call the hybrid clouds. I think the ES is really exploded because of hybrid cloud, the need for hybrid cloud. >>And what's your take on the, the magic Kubernetes? What made it, what's making it so successful? >>I would agree also that it came about at the right time, but I would add that it has great extensibility and as developers we take it advantage of that every single day. And I think that the, the patterns that we use for developing are very consistent. And I think that consistency that came with Kubernetes, just, you have so many people who are familiar with it and so they can follow the same patterns, implement things similarly, and it's just a good fit for the way that we want to get our software out there and have, and have things operate. >>Keep it simple, stupid almost is that acronym, but the consistency and the de facto alignment Yes. Behind it just created a community. So, so then the question is, are the developers now setting the standards? That seems like that's the new way, right? I mean, >>I'd like to think so. >>So I mean hybrid, you, you're touching everything at scale and you also have mini shift as well, right? Which is taking a super macro micro shift. You ma micro shift. Oh yeah, yeah, exactly. It is a micro shift. That is, that is fantastic. There isn't a base you don't cover. You've spoken a lot about community and both of you have, and serving the community as well as your engagement with them from a, I mean, it's given that you're both leaders stepping back, how, how Community First is Red Hat and OpenShift as an organization when it comes to building the next products and, and developing. >>I'll take and, and I'm sure Andy is actually the community, so I'm sure he'll want to a lot of it. But I mean, right from the start, we have roots in open source. I'll keep it, you know, and, and, and certainly with es we were one of the original contributors to Kubernetes other than Google. So in some ways we think about as co-creators of es, they love that. And then, yeah, then we have added a lot of things in conjunction with the, I I talk about like SCC for Secure, which has become part security right now, which the community, we added things like our back and other what we thought were enterprise features needed because we actually wanted to build a product out of it and sell it to customers where our customers are enterprises. So we have worked with the community. Sometimes we have been ahead of the community and we have convinced the community. Sometimes the community has been ahead of us for other reasons. So it's been a great collaboration, which is I think the right thing to do. But Andy, as I said, >>Is the community well set too? Are well said. >>Yes, I agree with all of that. I spend most of my days thinking about how to interact with the community and engage with them. So the work that we're doing on kcp, we want it to be a community project and we want to involve as many people as we can. So it is a heavy focus for me and my team. And yeah, we we do >>It all the time. How's it going? How's the project going? You feel good >>About it? I do. It is, it started as an experiment or set of prototypes and has grown leaps and bounds from it's roots and it's, it's fantastic. Yeah. >>Controlled planes are hot data planes control planes. >>I >>Know, I love it. Making things work together horizontally scalable. Yeah. Sounds like cloud cloud native. >>Yeah. I mean, just to add to it, there are a couple of talks that on KCP at Con that our colleagues s Stephan Schemanski has, and I, I, I would urge people who have listening, if they have, just Google it, if you will, and you'll get them. And those are really awesome talks to get more about >>It. Oh yeah, no, and you can tell on GitHub that KCP really is a community project and how many people are participating. It's always fun to watch the action live to. Sure. Andy, thank you so much for being here with us, John. Wonderful questions this afternoon. And thank all of you for tuning in and listening to us here on the Cube Live from Detroit. I'm Savannah Peterson. Look forward to seeing you again very soon.

(upbeat music) >> Hello everyone. This is Dave Vellante. We're diving into the deep end with AMD and Oracle on the topic of mySQL HeatWave performance. And we want to explore the important issues around machine learning. As applications become more data intensive and machine intelligence continues to evolve, workloads increasingly are seeing a major shift where data and AI are being infused into applications. And having a database that simplifies the convergence of transaction and analytics data without the need to context, switch and move data out of and into different data stores. And eliminating the need to perform extensive ETL operations is becoming an industry trend that customers are demanding. At the same time, workloads are becoming more automated and intelligent. And to explore these issues further, we're happy to have back in theCUBE Nipun Agarwal, who's the Senior Vice President of mySQL HeatWave and Kumaran Siva, who's the Corporate Vice President Strategic Business Development at AMD. Gents, hello again. Welcome back. >> Hello. Hi Dave. >> Thank you, Dave. >> Okay. Nipun, obviously machine learning has become a must have for analytics offerings. It's integrated into mySQL HeatWave. Why did you take this approach and not the specialized database approach as many competitors do right tool for the right job? >> Right? So, there are a lot of customers of mySQL who have the need to run machine learning on the data which is store in mySQL database. So in the past, customers would need to extract the data out of mySQL and they would take it to a specialized service for running machine learning. Now, the reason we decided to incorporate machine learning inside the database, there are multiple reasons. One, customers don't need to move the data. And if they don't need to move the data, it is more secure because it's protected by the same access controlled mechanisms as rest of the data There is no need for customers to manage multiple services. But in addition to that, when we run the machine learning inside the database customers are able to leverage the same service the same hardware, which has been provisioned for OTP analytics and use machine learning capabilities at no additional charge. So from a customer's perspective, they get the benefits that it is a single database. They don't need to manage multiple services. And it is offered at no additional charge. And then as another aspect, which is kind of hard to learn which is based on the IP, the work we have done it is also significantly faster than what customers would get by having a separate service. >> Just to follow up on that. How are you seeing customers use HeatWaves machine learning capabilities today? How is that evolving? >> Right. So one of the things which, you know customers very often want to do is to train their models based on the data. Now, one of the things is that data in a database or in a transaction database changes quite rapidly. So we have introduced support for auto machine learning as a part of HeatWave ML. And what it does is that it fully automates the process of training. And this is something which is very important to database users, very important to mySQL users that they don't really want to hire or data scientists or specialists for doing training. So that's the first part that training in HeatWave ML is fully automated. Doesn't require the user to provide any like specific parameters, just the source data and the task which they want to train. The second aspect is the training is really fast. So the training is really fast. The benefit is that customers can retrain quite often. They can make sure that the model is up to date with any changes which have been made to their transaction database. And as a result of the models being up to date, the accuracy of the prediction is high. Right? So that's the first aspect, which is training. The second aspect is inference, which customers run once they have the models trained. And the third thing, which is perhaps been the most sought after request from the mySQL customers is the ability to provide explanations. So, HeatWave ML provides explanations for any model which has been generated or trained by HeatWave ML. So these are the three capabilities- training, inference and explanations. And this whole process is completely automated, doesn't require a specialist or a data scientist. >> Yeah, that's nice. I mean, training obviously very popular today. I've said inference I think is going to explode in the coming decade. And then of course, AI explainable AI is a very important issue. Kumaran, what are the relevant capabilities of the AMD chips that are used in OCI to support HeatWave ML? Are they different from say the specs for HeatWave in general? >> So, actually they aren't. And this is one of the key features of this architecture or this implementation that is really exciting. Um, there with HeatWave ML, you're using the same CPU. And by the way, it's not a GPU, it's a CPU for both for all three of the functions that Nipun just talked about- inference, training and explanation all done on CPU. You know, bigger picture with the capabilities we bring here we're really providing a balance, you know between the CPU cores, memory and the networking. And what that allows you to do here is be able to feed the CPU cores appropriately. And within the cores, we have these AVX instruc... extensions in with the Zen 2 and Zen 3 cores. We had AVX 2, and then with the Zen 4 core coming out we're going to have AVX 512. But we were able to with that balance of being able to bring in the data and utilize the high memory bandwidth and then use the computation to its maximum we're able to provide, you know, build pride enough AI processing that we are able to get the job done. And then we're built to build a fit into that larger pipeline that that we build out here with the HeatWave. >> Got it. Nipun you know, you and I every time we have a conversation we've got to talk benchmarks. So you've done machine learning benchmarks with HeatWave. You might even be the first in the industry to publish you know, transparent, open ML benchmarks on GitHub. I mean, I, I wouldn't know for sure but I've not seen that as common. Can you describe the benchmarks and the data sets that you used here? >> Sure. So what we did was we took a bunch of open data sets for two categories of tasks- classification and regression. So we took about a dozen data sets for classification and about six for regression. So to give an example, the kind of data sets we used for classifications like the airlines data set, hex sensors bank, right? So these are open data sets. And what we did was for on these data sets we did a comparison of what would it take to train using HeatWave ML? And then the other service we compared with is that RedShift ML. So, there were two observations. One is that with HeatWave ML, the user does not need to provide any tuning parameters, right? The HeatWave ML using RML fully generates a train model, figures out what are the right algorithms? What are the right features? What are the right hyper parameters and sets, right? So no need for any manual intervention not so the case with Redshift ML. The second thing is the performance, right? So the performance of HeatWave ML aggregate on these 12 data sets for classification and the six data sets on regression. On an average, it is 25 times faster than Redshift ML. And note that Redshift ML in turn involves SageMaker, right? So on an average, HeatWave ML provides 25 times better performance for training. And the other point to note is that there is no need for any human intervention. That's fully automated. But in the case of Redshift ML, many of these data sets did not even complete in the set duration. If you look at price performance, one of the things again I want to highlight is because of the fact that AMD does pretty well in all kinds of workloads. We are able to use the same cluster users and use the same cluster for analytics, for OTP or for machine learning. So there is no additional cost for customers to run HeatWave ML if they have provision HeatWave. But assuming a user is provisioning a HeatWave cluster only to run HeatWave ML, right? That's the case, even in that case the price performance advantage of HeatWave ML over Redshift ML is 97 times, right? So 25 times faster at 1% of the cost compared to Redshift ML And all these scripts and all this information is available on GitHub for customers to try to modify and like, see, like what are the advantages they would get on their workloads? >> Every time I hear these numbers, I shake my head. I mean, they're just so overwhelming. Um, and so we'll see how the competition responds when, and if they respond. So, but thank you for sharing those results. Kumaran, can you elaborate on how the specs that you talked about earlier contribute to HeatWave ML's you know, benchmark results. I'm particularly interested in scalability, you know Typically things degrade as you push the system harder. What are you seeing? >> No, I think, I think it's good. Look, yeah. That's by those numbers, just blow me, blow my head too. That's crazy good performance. So look from, from an AMD perspective, we have really built an architecture. Like if you think about the chiplet architecture to begin with, it is fundamentally, you know, it's kind of scaling by design, right? And, and one of the things that we've done here is been able to work with, with the HeatWave team and heat well ML team, and then been able to, to within within the CPU package itself, be able to scale up to take very efficient use of all of the course. And then of course, work with them on how you go between nodes. So you can have these very large systems that can run ML very, very efficiently. So it's really, you know, building on the building blocks of the chiplet architecture and how scaling happens there. >> Yeah. So it's you're saying it's near linear scaling or essentially. >> So, let Nipun comment on that. >> Yeah. >> Is it... So, how about as cluster sizes grow, Nipun? >> Right. >> What happens there? >> So one of the design points for HeatWave is scale out architecture, right? So as you said, that as we add more data set or increase the size of the data, or we add the number of nodes to the cluster, we want the performance to scale. So we show that we have near linear scale factor, or nearly near scale scalability for SQL workloads in the case of HeatWave ML, as well. As users add more nodes to the cluster so the size of the cluster the performance of HeatWave ML improves. So I was giving you this example that HeatWave ML is 25 times faster compared to Redshift ML. Well, that was on a cluster size of two. If you increase the cluster size of HeatWave ML to a larger number. But I think the number is 16. The performance advantage over Redshift ML increases from 25 times faster to 45 times faster. So what that means is that on a cluster size of 16 nodes HeatWave ML is 45 times faster for training these again, dozen data sets. So this shows that HeatWave ML skills better than the computation. >> So you're saying adding nodes offsets any management complexity that you would think of as getting in the way. Is that right? >> Right. So one is the management complexity and which is why by features like last customers can scale up or scale down, you know, very easily. The second aspect is, okay What gives us this advantage, right, of scalability? Or how are we able to scale? Now, the techniques which we use for HeatWave ML scalability are a bit different from what we use for SQL processing. So in the case of HeatWave ML, they really like, you know, three, two trade offs which we have to be careful about. One is the accuracy. Because we want to provide better performance for machine learning without compromising on the accuracy. So accuracy would require like more synchronization if you have multiple threads. But if you have too much of synchronization that can slow down the degree of patterns that we get. Right? So we have to strike a fine balance. So what we do is that in HeatWave ML, there are different phases of training, like algorithm selection, feature selection, hyper probability training. Each of these phases is analyzed. And for instance, one of the ways techniques we use is that if you're trying to figure out what's the optimal hyper parameter to be used? We start up with the search space. And then each of the VMs gets a part of the search space. And then we synchronize only when needed, right? So these are some of the techniques which we have developed over the years. And there are actually paper's filed, research publications filed on this. And this is what we do to achieve good scalability. And what that results to the customer is that if they have some amount of training time and they want to make it better they can just provision a larger cluster and they will get better performance. >> Got it. Thank you. Kumaran, when I think of machine learning, machine intelligence, AI, I think GPU but you're not using GPU. So how are you able to get this type of performance or price performance without using GPU's? >> Yeah, definitely. So yeah, that's a good point. And you think about what is going on here and you consider the whole pipeline that Nipun has just described in terms of how you get you know, your training, your algorithms And using the mySQL pieces of it to get to the point where the AI can be effective. In that process what happens is you have to have a lot of memory to transactions. A lot of memory bandwidth comes into play. And then bringing all that data together, feeding the actual complex that does the AI calculations that in itself could be the bottleneck, right? And you can have multiple bottlenecks along the way. And I think what you see in the AMD architecture for epic for this use case is the balance. And the fact that you are able to do the pre-processing, the AI, and then the post-processing all kind of seamlessly together, that has a huge value. And that goes back to what Nipun was saying about using the same infrastructure, gets you the better TCO but it also gets you gets you better performance. And that's because of the fact that you're bringing the data to the computation. So the computation in this case is not strictly the bottleneck. It's really about how you pull together what you need and to do the AI computation. And that is, that's probably a more, you know, it's a common case. And so, you know, you're going to start I think the least start to see this especially for inference applications. But in this case we're doing both inference explanation and training. All using the the CPU in the same OCI infrastructure. >> Interesting. Now Nipun, is the secret sauce for HeatWave ML performance different than what we've discussed before you and I with with HeatWave generally? Is there some, you know, additive engine additive that you're putting in? >> Right? Yes. The secret sauce is indeed different, right? Just the way I was saying that for SQL processing. The reason we get very good performance and price performance is because we have come up with new algorithms which help the SQL process can scale out. Similarly for HeatWave ML, we have come up with new IP, new like algorithms. One example is that we use meta-learn proxy models, right? That's the technique we use for automating the training process, right? So think of this meta-learn proxy models to be like, you know using machine learning for machine learning training. And this is an IP which we developed. And again, we have published the results and the techniques. But having such kind of like techniques is what gives us a better performance. Similarly, another thing which we use is adaptive sampling that you can have a large data set. But we intelligently sample to figure out that how can we train on a small subset without compromising on the accuracy? So, yes, there are many techniques that you have developed specifically for machine learning which is what gives us the better performance, better price performance, and also better scalability. >> What about mySQL autopilot? Is there anything that differs from HeatWave ML that is relevant? >> Okay. Interesting you should ask. So mySQL Autopilot is think of it to be an application using machine learning. So mySQL Autopilot uses machine learning to automate various aspects of the database service. So for instance, if you want to figure out that what's the right partitioning scheme to partition the data in memory? We use machine learning techniques to figure out that what's the right, the best column based on the user's workload to partition the data in memory Or given a workload, if you want to figure out what is the right cluster size to provision? That's something we use mySQL autopilot for. And I want to highlight that we don't aware of any other database service which provides this level of machine learning based automation which customers get with mySQL Autopilot. >> Hmm. Interesting. Okay. Last question for both of you. What are you guys working on next? What can customers expect from this collaboration specifically in this space? Maybe Nipun, you can start and then Kamaran can bring us home. >> Sure. So there are two things we are working on. One is based on the feedback we have gotten from customers, we are going to keep making the machine learning capabilities richer in HeatWave ML. That's one dimension. And the second thing is which Kamaran was alluding to earlier, We are looking at the next generation of like processes coming from AMD. And we will be seeing as to how we can more benefit from these processes whether it's the size of the L3 cache, the memory bandwidth, the network bandwidth, and such or the newer effects. And make sure that we leverage the all the greatness which the new generation of processes will offer. >> It's like an engineering playground. Kumaran, let's give you the final word. >> No, that's great. Now look with the Zen 4 CPU cores, we're also bringing in AVX 512 instruction capability. Now our implementation is a little different. It was in, in Rome and Milan, too where we use a double pump implementation. What that means is, you know, we take two cycles to do these instructions. But the key thing there is we don't lower our speed of the CPU. So there's no noisy neighbor effects. And it's something that OCI and the HeatWave has taken full advantage of. And so like, as we go out in time and we see the Zen 4 core, we can... we see up to 96 CPUs that that's going to work really well. So we're collaborating closely with, with OCI and with the HeatWave team here to make sure that we can take advantage of that. And we're also going to upgrade the memory subsystem to get to 12 channels of DDR 5. So it should be, you know there should be a fairly significant boost in absolute performance. But more important or just as importantly in TCO value for the customers, the end customers who are going to adopt this great service. >> I love their relentless innovation guys. Thanks so much for your time. We're going to have to leave it there. Appreciate it. >> Thank you, David. >> Thank you, David. >> Okay. Thank you for watching this special presentation on theCUBE. Your leader in enterprise and emerging tech coverage.

>>Hey everyone. Welcome to this cube conversation. I'm your host, Lisa Martin Rajko same joins me now the chief product officer at elation. Raj. Great to have you on the cube. Welcome. >>It's great to be here, Lisa. And I've been a fan for a while and excited to have a chance to talk with you live. >>And we've got some exciting stuff to talk about elation in terms of the success in the enterprise market. I see more than 25% of the fortune 100 doing great. There is customers elation and snowflake. Before we get into your exciting news. Talk to me a little bit about the evolution of the partnership. >>Yeah, no, absolutely. So, you know, we've always been a, a close partner and integrator with snowflake and last year snowflake became an investor in elation and they participated in our series D round. And the thing I'm most excited about beyond that is we were announced in the snowflake summit back in June to be their data governance partner of the year for the second year running. And so we've always had a closer relationship with snowflake, both at the go to market level and at the product level. And you know, the stuff that we're about to talk about is a Testament to that. >>Absolutely. It is. So talk to us before we get into the announcement. What you're seeing in the market as organizations are really becoming much more serious about being data driven and building a data culture. What are you seeing with respect to enterprises as well as those smaller folks? >>Yeah, no, it, it, it's, it's a great question. I mean, you, you hear the T tropes data is the new oil data is like water it's essential. And we're seeing that very consistently across every customer, every segment, every geo that we, that we talk to, I, I think the challenges that organizations are seeing that are leading to the amazing growth that we've seen at elation are there's so much data. They don't know where it resides. You've got silos or islands of knowledge that exist across the, the enterprise. And they need a data intelligence platform to bring it all together, to help them make sense of it and ultimately build a data culture that, you know, it lets their employees make data driven decisions as opposed to relying on gut. And so those are some of the macro trends that we're seeing and with the migration of data to the cloud and in particular snowflake, it seemed like a huge opportunity for us to partner even more closely with, with snowflake. And we're, we're excited about the progress that we've seen with them thus far. >>All right, let's get right into it. So first of all, define a data culture and then talk to us about how elation and snowflake are helping organizations to really achieve that. >>Yeah. You know, it, it's interesting. The, the company vision that we have at elation is to empower a curious and rational world. And you know, what that really means is we want to deliver solutions that drive curiosity and drive rational behavior. So making, making decisions based on data and insights, as opposed to gut, or, you know, the, the highest paid, you know, person's opinion or what have you. And so delivering a data culture, building a data culture, which is something we hear from all the CDOs that we talk to is, Hey, elation, help us drive data literacy across the organization, provide that single source of reference. So if anybody has a question about, do we have data that answers this, or, you know, what kind of performance are we seeing in this product area? Give me a starting point for my data exploration journey. And that's really where elation and our data intelligence solutions kind of come into the play. >>So unpack elation cloud service for snowflake. Talk to us about what it is, why you're doing it, what the significance of this partnership and this solution is delivering. >>Absolutely. So the elation cloud service for snowflake is a brand new offering that we just brought to market. And the intent really was, you know, we've had massive success in the global 2000. You mentioned the, the progress that we've had with fortune 100 customers, we see the need for data, culture, and data literacy and governance in organizations, you know, that are massive global multinational enterprises all the way down to divisions of an organization, or even, you know, mid-market and SMB companies. And so we thought there was a huge opportunity to really drive data culture for those organizations that are adopting snowflake, but still need that data intelligence overlay across the, the data that's in the snowflake cloud. And so what we did is we launched the elation cloud service for snowflake as a free trial, and then, you know, low cost purchase solution that, you know, can be adopted for less than a hundred thousand dollars a year. >>Got it. So tar from a target market perspective that lower end of the market for, of course, you know, these days, Raj, as we talk about every company, regardless of size, regardless of industry and location has to be a data company getting there and, and, and, and really defining and going on a journey to get there is really complex. So you're going now down market to meet those customers where they are, how will elation cloud service for snowflake help those customers, those smaller customers really become data driven and, and, and adopt a data culture. >>Yeah. Yeah. It's, it's a great question. I, I think the biggest goal that we had was making it really simple and easy for them to begin this journey. So, you know, we are now live in the snowflake partner connect portal. And if someone wants to experience the power of elation cloud service for snowflake, they just need to go to that portal, click the elation tile. And literally within less than two minutes, a brand new instance of elation is spun up. Their snowflake data is automatically being cataloged as part of this trial. And they have 14 days to go through this experience and, and get a sense of the power of elation to give them insights into what's in their snowflake platform, what governance options they can layer on top of their snowflake data cloud and how the data is transforming across their organization. >>So talk to me about who you're talking to within a customer. I was looking at some data that elation provided to me, and I see that according to Gartner data culture is priority number one for chief data officers, but for those smaller organizations, do they have chief data officers? Is that responsibility line still with the CIO? Who are you engaging with? >>Yeah, it's very, very, really great question. I, I think the larger organizations that we sell to definitely have a, a CDO and, you know, CDO sometimes is the chief data and analytics officer in smaller organizations, or even in divisions of big companies that, that, you know, might be target customers for ACS, for snowflake could be a, a VP of analytics could be head of marketing. Operations could be a data engineering function, so that might roll up into the it. And so I think that's, what's interesting is we, we wanted to take the friction out of the, the experience process and the trial process, and whoever is responsible for the snowflake instance and, and leveraging snowflake for, for data and analytics, they can explore and understand what the, a power elation layered on top of snowflake can provide for them. >>Okay. So another, another thing that I uncovered in researching for this segment is McKenzie says data, culture is decision culture. I thought that was a really profound statement, but it's also such a challenge to get there is organizations of all sizes are on various points in their journey to become data driven. What does that mean? How, how well, how do elation and help customers really achieve that data culture so that they can really have that decision culture so they can make faster, better data based decisions? >>Yeah, it, so I, I think a huge part of it, like if we think about our, our, our big area of focus, how do we enable users to find, understand trust, govern, and use data within snowflake in this instance? And so step one to drive data culture is how, how do you provide a single source of reference a, a, a search box, frankly, you know, Google for your, for your data environment, so that you can actually find data, then how do you understand it? You know, what's in there. What does it mean? What are the relationships between these data objects? Can I trust this? Is this sandbox data, or is this production data that can be used for reporting and analytics? How do I govern the data? So I know who's using it, who should use it, what policies are there. And so if, if we go through the set of features that we've built into ation cloud service for snowflake, it enables us to deliver on that promise result at the very end, resulting in the ability to explore the data that exists in the snowflake platform as well. >>Let's go ahead and unpack that. Now, talk to me about some of the key capabilities of the solution and what it's enabling organizations to achieve. >>Yeah, so, you know, it starts with cataloging the data itself. You know, we, we, we are the data catalog company. We basically define that category. And so step one is how do we connect to snowflake and automatically ingest all the metadata that exists within that snowflake cloud, as well as extract the lineage relationships between tables. So you can understand how the data is transforming within the snowflake data cloud. And so that provides visibility to, to begin that fine journey. You know, how, how do I actually discover data on the understand and trust front? I think where things get really interesting is we've integrated deeply with Snowflake's new data governance features. So they've got data policies that provide things like row level security and, and data masking. We integrate directly with those policies, extract them, ingest them into elation so that they can be discovered, can be easily applied or added to other data sets within snowflake directly from the elation UI. >>So now you've got policies layered on top of your data environment. Snowflake's introduced, tagging and classification capabilities. We automatically extract and ingest those tags. They're surfaced in inhalation. So if somebody looks for a data set that they're not familiar with, they can see, oh, here are the policies that this data set is applied to. Here are the tags that are applied. And so snow elation actually becomes almost like a user interface to the data that exists within that snowflake platform. And then maybe just two other things with the lineage that we extract. One of the most important things that you can deliver for users is impact analysis. Hey, if I'm gonna deprecate this table, or if I'm gonna make a change to what this table definition is, what are the downstream objects and users that should know about that? So, Hey, if this table's going away and my Tableau report over here is gonna stop working, boy, it'd be great to be able to get visibility into that before that change is made, we can do that automatically within the elation UI and, and really just make it easier for somebody to govern and manage the data that exists within the snowflake data cloud. >>So easier to govern and manage the data. Let's go up a level or two. Sure. Talk to me about some of the business outcomes that this solution is gonna help organizations to achieve. We talked about every company these days has to be a data company. Consumers expect this very personalized, relevant experience. What are you thinking? Some of the outcomes are gonna be that this technology and this partnership is gonna unlock. >>Yeah, no, I, I, I think step one, and this has always been a huge area of focus for us is just simply driving business productivity. So, you know, the, the data that we see in talking to CDOs and CDOs is the onboarding and, and getting productive the time it takes to onboard and, and get a data analyst productive. It, it can be nine to 12 months. And, you know, we all know the battle for talent these days is significant. And so if we can provide a solution, and this is exactly what we do that enables an organization to get a data analyst productive in weeks instead of months, or, or, you know, potentially even a year, the value that that analyst can deliver to the organization goes up dramatically because they're spending less time looking for data and figuring out who knows what about the data. >>They can go to elation, get those insights and start answering business questions, as opposed to trying to wrangle or figure out does the data exist. And, and, and where does it exist? So that's, that's one key dimension. I'd say the other one that, that I'd highlight is just being able to have a governance program that is monitored managed and well understood. So that, you know, whether it's dealing with CCPA or GDPR, you know, some of the regulatory regimes, the, the ability for an organization to feel like they have control over their data, and they understand where it is who's using it and how it's being used. Those are hugely important business outcomes that CIOs and CDOs tell us they need. And that's why we built the lation cloud service for snowflake >>On the first front. One of the things that popped into my mind in terms of really enabling workforce productivity, workforce efficiency, getting analysts ramped up dramatically faster also seems to me to be something that your customers can leverage from a talent attraction and retention perspective, which in today's market is critical. >>I, I so glad you mentioned that that's, that's actually one of the key pillars that we highlight as well is like, if you give great tools to employees, they're gonna be happier. And, and you'll be a, a preferred employer and people are gonna feel like, oh, this is an organization that I wanna work at because they're making my job easier and they're making it easier for me to deliver value and be productive to the organization. And that's, it's absolutely critical this, this, this war for talent that everybody talks about. It's real and great self-service tools that are empowering to employees are the things that are gonna differentiate companies and allow them to, to unleash the power of data, >>Unleash the power of data, really use it to the competitive advantage that it can and should be used for. When we look at, when you look at customers that are on that journey, that data catalog journey, they, you probably see such a variety of, of locations about where they are in that journey. Do you see a common thread when you're in customer conversations? Is there kind of a common denominator that you, you speak to where you, you really know elation and snowflake here is absolutely the right thing. >>Yeah, no, it, it, it's a good question. I would actually say the fact that a customer is on snowflake. I they're already, you know, a step up on that maturity curve. You know, one of the big use cases that we see with customers that is, is leading to the need for data intelligence solutions that, you know, like that elation can deliver is digital transformation and, and, and cloud migration, you know, we've got legacy data. On-prem, we know we need to move to the cloud to get better agility, better scaling, you know, perhaps, you know, reduced costs, et cetera. And so I think step one, on that, that qualification criteria or that maturity journey is, Hey, if you're already in snowflake, that's a great sign because you're, you're recognizing the power of a data cloud platform and, and, and warehouse like snowflake. And so that's a, a, a great signal to us that this is a customer that wants to, you know, really better understand how they can get value out of, out of their solution. I think the next step on that journey is a recognition that they're not utilizing the data that they have as effectively as they can and should be, and they're not, and, and their employees are still struggling with, you know, where does the data exist? Can I trust it? It, you know, it, who do I know tends to be more important than do I have a tool that will help me understand the data. And so customers that are asking those sorts of questions are ideal customers for the elation cloud service for snowflake solution. >>So enabling those customers to get their hands on it, there's a free trial. Talk to us about that. And where can the audience go to actually click and try? >>Absolutely. So, you know, we'll, we'll be doing our usual marketing and, and promotion of this, but what I'm super excited about, you know, again, I mentioned earlier, you know, this is part of our, our cloud native multi 10 and architecture. We are live in the snowflake partner connect portal. And so if you are logged into snowflake and are an admin, you can go to the partner connect portal and you will see a tile. I think it's alphabetically, sorted and elation starts with a so pretty easy to find. I don't think you'll have to do too much searching. And literally all you have to do is click on that tile, answer a couple quick questions. And in the background in about two minutes, your elation instance will get spun up. We'll we will have sample data sets in there. There's some guided tours that you can walk through to kind of get a feel for the power of snowflake. >>So policy center lineage, you know, tags, our intelligent SQL tool that allows you to smartly query the snowflake data cloud and publish queries, share queries with others, collaborate on them for, for greater insights. And there's, you know, as you would expect with any, you know, online free trial, you know, we've got a built in chat bot. So if you have a question, wanna get a better sense of how a particular feature works or curious about how elation might work. In other areas, you can, you know, ask a question to the chat bot and we've got product specialists on the back end that can answer questions. So we really wanna make that journey as, as seamless and easy as, as possible. And hopefully that results in enough interests that the customer wants to, to, or the, the trial user wants to become a customer. And, and that's where our great sales organization will kind of take the Baton from there. >>And there's the, there's the objective there, and I'm sure Raj folks can find out about the free trial and access it. You, you mentioned through the marketplace, more information on elian.com. I imagine they can go there to access it as well, >>A hundred percent elation.com. We're on Twitter, we're on LinkedIn, but yeah, if you have any questions, you know, you can just search for elation cloud service for snowflake, or just go to the elation.com website. Absolutely. >>All right. Elation cloud service for snowflake. Congratulations on the launch to you and the entire elation team. We look forward to hearing customer success stories and really seeing those business outcomes realize in the next few months, Raj, thanks so much for your time. >>Thank you so much, Lisa. It's great to talk to you. >>Likewise, Raj gin. I'm Lisa Martin. Thank you for watching this cube conversation. Stay right here for more great action on the cube, the leader in live tech coverage.

>>Welcome back to Vegas. Lisa Martin here covering snowflake summit 22. We are live at Caesar's forum. A lot of guests here about 10,000 attendees, actually 10,000 plus a lot of folks here at the momentum and the buzz. I gotta tell you the last day and a half we've been covering this event is huge. It's probably some of the biggest we've seen in a long time. We're very pleased to welcome back. One of our cube alumni to the program, Ron Tayo principal and chief data officer at Deloitte one. It's great to have you joining us. >>Yeah, no, thank you. Super excited to be here with you today. >>Isn't it great to be back in person? Oh, >>I love it. I mean the, the energy, the, you know, connections that we're making definitely, definitely loving and loving the experience. >>Good experience, but the opportunity to connect with customers. Yes. I'm hearing a lot of conversations from snowflake folks from their partners like Deloitte from customers themselves. Like it's so great to be back in person. And they're really talking about some of the current challenges that are being faced by so many industries. >>That's right. Oh, that, that is, you know, I would say as a consultant, you know, it all comes down to that personal connection and that relationship. And so I am, I'm all for this and love, you know, being able to connect with our customers. >>Yeah. Talk to me about the Deloitte snowflake partnership. Obviously a ton of news announced from snowflake yesterday. Snowflake is a rocket ship. Talk to us about the partnership, what you guys do together, maybe some joint customer examples. >>Yeah. I mean, so snowflake is a strategic Alliance partner. We won the, you know, SI partner of the year award and for us, the, the shift and the opportunity to help our clients modernize and achieve a level of data maturity in their journey is, is strategically it's super important. And it's really about how do we help them leverage, you know, snowflake has underlying data platform to ultimately achieve, you know, broader goals around, you know, their business strategy. And our approach is always very much connected to overarching business strategies and sense of, is it a finance transformation, a supply chain transformation, a customer transformation, and what are the goals of those transformations and how do we ensure that data is a critical component to enabling that and with, you know, technologies and vendors and partners like snowflake, allowing us to even do that at a faster, better, cheaper pace only increases the overall business case and the value and the impact that it generates. >>And so we are super, super excited about our partnership with snowflake and we believe, you know, the journey is very, very bright. You know, we, this is the future, you know, often tell folks that, you know, data has and will continue to be more valuable than sort of the systems that own it and manage it. And I think we're starting to see that. I think the topic that I discussed today around data collaboration and data sharing is an example of how we're starting to see, you know, the importance and the value of data, you know, become way more important and more of the focus around the strategy for, for organizations >>As the chief data officer, what do data sharing and data collaboration mean to somebody in your position and what are some of the conversations you have with customer other CDOs at customer organizations? >>Yeah, so, so my role is, is sort of twofold. I, I am responsible for our internal data strategy. So when you think about Deloitte as a professional service organization, across four unique businesses, I am a customer of snowflake in our own data modernization journey, and we have our own strategy on how and what we share, not only internally across our businesses, but also externally across, you know, our partners. So, so I bring that perspective, but then I also am a client service professional and serve our clients in their own journey. So I often feel very privileged in, in the opportunity to be able to sort of not only share my own experience from a Deloitte perspective, but also in how we help our clients >>Talk about data maturation. You mentioned, you know, the volume of data just only continues to grow. We've seen so much growth in the last two years alone of data. We've seen all of us be so dependent on things like media and entertainment and retail, eCommerce, healthcare, and life sciences. What, how do you define data maturation and how does Deloitte and snowflake help companies create a pathway to get there? >>Yeah. Yeah. So I would say step one for us is all about the overarching business strategy. And when you sort of double click on the big, broad business strategy and what that means from a data strategy perspective, we have to develop business models where there is an economical construct to the value of data. And it's extremely important specifically when we talk about sharing and collaborating data, I would say the, the, the, the assumption or the, or, or, or, or the posture typically seems to be, it's a one way relationship, our strategy and what we're pushing, you know, again, not only internally within ourselves, but also with our clients, is it has to be a bidirectional relationship. And so you, you hear of, of the concepts of, you know, the, the, the data clean room where you have two partners coming together and agreeing with certain terms to share data bidirectionally. Like I do believe that is the future in how we need to do, you know, more data collaboration, more data sharing at a scale that we've not quite seen. Yes. Yet >>The security and privacy areas are increasingly critical. We've seen the threat landscape change so dramatically the last couple of years, it's not, will we get hit by a cyber talk? It's when yes. For every industry, right? The privacy legislation that just we've seen it with GDPR, CCPA is gonna become CPR in California, other states doing the same thing. How do you help customers kind of balance that line of being able to share data equitably between organizations between companies do so in a secure way, and in a way that ensures data privacy will be maintained. >>Yeah. Yeah. So first absolutely recognizing, evolving, recognize the evolving regulatory landscape. You mentioned, you know, California, there's actually now 22 states that have a, is it 22 now? Right? Yeah. 22 states that have a privacy act enacted. And our projection is in the next 12 to 18 months, all states will have one. And so absolutely a, a perceived challenge, but one that I think is, is addressable. And, and I think that gets to the spirit of the question for us. There's, there's four dimensions that an organization needs to work through when it comes to data sharing. The first one is back to the, the business goal and objective, like, is there truly a business need? And is there value in sharing data? And it needs to have a very solid business model. Okay. So, so that's the first step. The second step is what are the legal terms? >>What are the legal terms? What can you do? What can't you do? Do you have primary rights, secondary rights? The third dimension is around risk. What is the risk and exposure, not only from a data security perspective, but what is the risk if someone uses a data inappropriately, and then the fourth one is around ethics and the ethical use of data. And we see lots of examples where an organization has consent has rights to the data, but the way they used it might have not necessarily been, you know, among the kind of ethical framing. And so for us, those four dimensions is what guides us and our clients in developing a very robust data, sharing data collaboration framework that ensures it's connected to the overall business strategy, but it provides enough of the guardrails to minimize legal and ethical risk. So >>With that in mind, what do the customer conversations look like? Cause you gotta have a lot of players, the business folks, the data folks, every line of business needs data for its functions. Talk to us about how the customer conversations and projects have evolved as data is increasingly important to every line of business. >>Yes. I would say the biggest channel, or maybe the, the, the denominator at this point that we're seeing bring the, let's say diversity of needs to more common denominator has been AI. So every organization at this point is driving massive AI programs. And in order to really scale AI, you know, the, the algorithm cannot execute without data. Yeah. And so for us, at least in our experience with our customers, AI has almost been the, the, the mechanism to have these conversations across the different business stakeholders and do it in a way that, you know, you're not necessarily boiling the ocean, cuz I think that's the other element that makes this a bit hard is, well, what, what data do you want me to share and for what purpose? And when you start to bring it into sort of more individual swim lanes and, and, and our experience with our customers is AI has sort of been that mechanism to say, am I automating, you know, our factory floor? Am I bringing AI and how we engage and serve our customers? Right? Like it be, it be begins to sort of bring a little bit more of, of that repeatability at a, at an individual level. So that's been a, a really good strategy for us in our customers >>In terms of the customer's strategy and kind of looking forward, what are some of the things that excite you about the, the future of data collaboration, especially given all of the news that snowflake announced just yesterday? >>Yes. Yeah. I think for me, and this is both the little bit of the ambition, as well as the push, it's no longer a question of should it's it's how and for what? And so, so yes, I mean the, the, the snowflake data cloud is a network that allows us to integrate, you know, disparate and unique data assets that have never, you know, been possible before. Right. So we're in this network, it's now a matter of figuring out how to use that and for what purpose. And so I, I go back to, we, each individual organization needs to be figuring out the how, and for what not, when this is the future, we all need it. Yeah. And we just need to figure out how that fits in our individual businesses >>In terms of the, how that's such an interesting, I love how you bring that up. It's not, it's not when it's definitely how, because there's gonna be another competing business or several right there in the rear view mirror, ready to take your place. Yep. If you don't act quickly, how does Deloitte and snowflake help customers achieve the, how quickly enough to be able to really take advantage of data sharing and data collaboration so that they can be very competitive? >>Yeah. So there's two main, maybe even three driving forces in this. What we see is when there's a common purpose across director, indirect competitors and the need to share data. So I think the poster child of this was the pandemic, and we started to see organizations again, either competitively or non-com competitively share data in ways for a greater good, right. When there was a purpose, we believe when that element exists, the ability to share data is going to increase. We believe the next big sort of common purpose out there in the world is around ESG. And so that's gonna be a big driver for sharing data. So that's one element. The other one is the concept of developing integrated value chains. So when you think about any individual business and sort of where they are in that piece of the value chain, developing more integrated value across, let's say a manufacturer of goods with a distributor of those goods that ultimately get to an end customer. >>They're not sharing data in a meaningful way to really maximize their overall, you know, profitability. And so that's another really good, meaningful example that we're seeing is where there's value across, you know, a, what appears to be a siloed set of steps, and really looking at it more as an integrated value chain, the need to share data is the only way to unlock that. And so that's, that's the second one. The, the third one I would say is, is around the need to address the consumer across sort of the multiple personas that we all individually sit. Right? So I go into a bank and I'm, I'm a client. I walk into a retail store and I'm a customer. I walk into my physician's office and I'm a patient at the end of the day. I am still the same person. I am still one. And so that consumer element and the convergence of how we are engaging and serving that consumer is the third, big shift that is really going to bring data collaboration and sharing to the next level. >>Do you think snowflake is, is the right partner of the defacto for delight to do that with? >>Absolutely. I think, you know, the head start of the cloud, the data cloud platform and the network that it's already established with all the sort of data privacy and security constraints around it. Like that's a big, that's a big, you know, check right. That we don't have to worry about. It's there for sure. >>Awesome. Sounds like a great partnership, Juan. Thank you so much for joining me on the program. It's great to have you back on the cube in person sharing what Deloitte and snowflake are doing and how you're really helping to transform organizations across every industry. We appreciate >>Your insights. Yeah. No, thank you for having me here. My pleasure. Always a pleasure. Thank you. >>All right. For Juan. I am Lisa Martin. You're watching the cube live from snowflake summit 22 at Caesar's forum. You write back with our next guest.

(upbeat music) >> Hello, and welcome to theCUBE's presentation of the a AWS startup showcase, data as code is the theme. This is season two episode two of the ongoing series covering the exciting startups from the AWS ecosystem and we talk about data analytics. I'm your old John Furrier with the cube, and we have Javier De La Torre. who's the founder and chief strategy officer of Carto, which is doing some amazing innovation around geographic information systems or GIS. Javier welcome to the cube for this showcase. >> Thank you. Thank you for having me. >> So, you know, one of the things that you guys are bringing to the table is spatial analytic data that now moves into spatial relations, which is, you know, we know about geofencing. You're seeing more data coming from satellites, ground stations, you name it. Things are coming into the market from a data perspective, that's across the board and geo's one of them GIS systems. This is what you guys are doing in the rise of SQL in particular with spatial. This is a huge new benefit to the world. Can you take a minute to explain what Carto's doing and what spatial SQL is? >> Sure. Yeah. So like you said, like data, obviously we know is growing very fast and as you know now, being leveraged by many organizations in many different ways. There's one part of data, one dimension that is location. We like to say that everything happens somewhere. So therefore everything can be analyzed and understood based on the location. So we like to put an example, if all your neighbors get an alarm in their homes, the likelihood that you will get an alarm increases, right? So that's obvious we are all affected by our surroundings. What is spatial analytics, this type of analytics does is try to uncover those spacial relations so that you can model, you can predict where something is going to happen, or, you know, like, or optimize it, you know, like where else you want it to happen, right? So that's at the core of it. Now, this is something that as an industry has been done for many years, like the GIS or geographic information systems have existed for a long time. But now, and this is what Carto really brings to the table. We're looking at really the marketizing it, so that it's in the hands of any analyst, our vision is that you need to go five years, to a geography school to be able to do this type of spatial analysis. And the way that we want to make that happen is what we call with the rise of a spatial SQL. We add these capabilities around spatial analytics based on the language that is very, very popular for a analysts, which is SQL. So what we do is enables you to do this spatial analysis on top of the well known and well used SQL methods. >> It's interesting the cloud native and the cloud scale wave and now data as code has shown that the old school, the old guard, the old way of doing things, you mentioned data warehousing, okay, as one. BI tools in particular have always been limited. And the scope of the limitation was the environment was different. You have to have domain expertise, rich knowledge of the syntax. Usually it's for an application developer, not for like real time and building it into the CICD pipeline, or just from a workflow standpoint, making it available. The so-called democratization, this is where this connects. And so I got to ask you, what are you most excited about in the innovations at Carto? Can you share some of the things that people might know about or might not know about that's happening at Carto, that takes advantage of this cloud native wave because companies are now on this bandwagon. >> Yeah, no, it is. And cloud native analytics is probably the most disruptive kind of like trend that we've seen over the few years, in our particular space on the spatial it has tremendous effects on the way that we provide our service. So I'd like to kind of highlight four main reasons why cloud analytics, cloud native is super important to us. So the first one is obviously is a scalability, the working with the sizes of data that we work now in terms of location was just not possible or before. So for someone that is performing now analysis on autonomous car, or you're like that has any sensorized GPS on a device and is collecting hundreds of billions of points. If you want to do analysis on that type of data, cloud native allows you to do that in a scalable way, but it also is very cost effective. That is something that you'll see very quickly when your data grows a lot, which is that this computing storage separation, the idea that is store your data at cloud prices, but then use them with these data warehouses that we work in this private, makes for a very, very cost effective solution. But then, you know, there is other two, obviously one of them being SQL and spatial SQL that like means we like to say that SQL is becoming the lingua franca for analytics. So it's used by many products that you can connect through the usage of SQL, but I think like you coming towards why I think it's even more interesting it's like, in the cloud the concept like we all are serving, we are all living in the same infrastructure enables us that we can distribute a spatial data sets to a customer that they can join it on their database on SQL without having to move the data from one another, like in the case of Redshift or Amazon Redshift car connects and you using something called a spectrum, we can connect live to data that is stored on S3. And I think that is going to disrupt a lot the way that we think about data distributions and how cost effective it is. I think, it has a lot of your like potential on it. And in that sense what Carto is providing on top of it in the format of formats like parquet, which is a very popular with big data format. We adding geo parquet, we are specializing this big data technology for doing the spatial analysis. And that to me it is very exciting because it's putting some of the best tools at the hands of doing the space analytics for something that we're not able to do before. So to me, this is one area that I'm very, very excited. >> Well, I want to back up for a second. So you mentioned parquet and the standards around that format. And also you mentioned Redshift, so let me get this right. So you just saying that you can connect into Redshift. So I'm a customer and I have Redshift I'm using, I got my S3, I'm using Redshift for analysis. You're saying you can plug right into Redshift. >> Yes. And this is a very, very, very important part because what Carto does is leverage Redshift computing infrastructure to essentially kind of like do all the analysis. So what we do is we bring a spatial analysis where the data is, where Redshift is versus in the past, what we will do is take the data where the analysis was and that sense, it's at the core of cloud native. >> Okay. This is really where I see the exciting shift where data as code now becomes a reality is that you bring the... It redefines architecture, the script is flipped. The architecture has been redefined. You're making the data move to the environments that needs to move when it has to, if it doesn't have to move you bring compute to it. So you're seeing new kinds of use cases. So I have to ask you on the use cases and examples for Carto AWS customers with spatial analytics, what are some of the examples on how your clients are using cloud native spatial analytics or Carto? >> Yeah. So one, for example, that we've seen a lot, on the AWS ecosystem, obviously because of its suites and its position. We work together with another service in the AWS ecosystem called Amazon Location. So that actually provides you access to maps and SDKs for navigation. So it means that you are like a company that is delivering food or any other goods in the city. We have like hundreds or thousands of drivers around the city moving, doing all these deliveries. And each of these drivers they have an app and they're collecting actively their location, their position, right? So you get all the data and then it gets stored on something like a Redshift data cluster on S3 as well. There's different architectures in there, but now you essentially have like a full log of the activity that is happening on the ground from your business. So what Carto does on top of that data is you connect your data into Carto. And now you can do analysis, for example, for finding out where you user may be placed, another distribution center, you know, for optimizing your delivering routes, or like if you're in the restaurant business where you might want to have a new dark kitchen, right? So all this type of analysis based on, since I know where you're doing your operations, I can post analyze the data and then provide you a different way that you can think about solving your operation. So that's an example of a great use case that we're seeing right now. >> Talk to me about about the traditional BI tools out there, because you mentioned earlier, they lack the specific capabilities. You guys bring that to the table. What about the scalability limitations? Can you talk about where that is? Is there limitations there, obviously, if they don't have the capabilities, you can't scale that's one, but you know, as you start plugging into Redshift, scale and performance matters, what's the issue there? Can you unpack that a little bit real quick? >> Yeah. It goes back to the particulars of the spacial data, location data, like in the use case, like I was describing you very quickly are going to end up with really a lot of your like terabytes, if not petabytes of data very quickly, if you're start aggregating all this data, because it gets created by sensors. So volumes in our world kind of tends to grow a lot now. So when you work with BI tools, there's two things that you have to take in consideration. BI tools are great for seeing things like for example, if all you want to see is where your customers are, a BI tool is great. Seeing, creating a map and seeing your customers. That's totally in the world of BI. But if you want to understand why your customers are there, or where else could they be, you're going to need to perform what we call a spatial analysis. You're going to have to create a spatial model. You're going to have to, and for that BI tools will not give you that that's one side, the other it talks about the volumes that I was describing. Most of these BI tools can handle certain aggregations. Like, for example, if you are reading, if you're connecting your, let's say 10 billion data set to a BI tool, the BI tool will do some aggregations because you cannot display 10,000 rows on a BI tool and that's okay, you get aggregations and that works. But when it comes to a map, you cannot aggregate the data on the map. You actually want to see all the data on the map, and that's what Carto provides you. It allows you to make maps that sees all the data, not just aggregated by county or aggregated by other kind of like area, you see all your data on the map. >> You know, what's interesting is that location based service has been around for a long time. You know, when mobile started even hitting the scene, you saw it get better mashups, Google Maps, all this Google API mashups, things like that. You know, developers are used to it, but they could never get to the promised land on the big data side, because they just didn't have the compute. But now you add in geofencing, geo information, you now have access to this new edge like data, right? So I have to ask you on the mobile side, are you guys working with any 5G or edge providers? Because I can almost imagine that the spatial equation gets more complicated and more data full when you start blowing out edge data, like with 5G, you got more, more things happening at the edge. It's only going to fill in more data points. Can you share that's how that use case is going with mobile, mobile carriers or 5G? >> Yeah, that's totally, yeah. It's totally the case. Well, first, even before, you know, like we are there, we actually helping a lot of telcos on actually planning the 5G deployment. Where do you place your antennas is a very, very important topic when you're like talking about 5G. Because you know, like 5G networks require a lot of density. So it's a lot about like, okay, where do I start deploying my infrastructure to ensure the customers like meet, like have the best service and the places where I want to kind of like go first So like... >> You mean like the RF maps, like understanding how RF propagates. >> Well, that's one signal, but the other is like, imagine that your telco is more interested on, you know, let's say on a certain kind of like consumer profile, like young people that are using the one type of service. Well, we know where these demographics kind of lives. So you might want to start kind of like deploying your 5G in those areas, right. Versus if you go to more commercial and more kind of like residential areas, there might be other demographics. So that's one part around market analysis. Then the second part is once these 5G networks are in place, you're right. I mean, one of the premises that kind of like these news technologies give us is because the network is much smarter. You can have all these edge cases, there's much more location data that can be collected. So what we see now is a rise on the amount of what we call telemetry. That for example, the IOT space can make around location. And that's now enabled because of 5G. So I think 5G is going to be one of those trends that are going to make like more and more data coming into, I mean, more location, data available for analysis. >> So how does that, I mean, this is a great conversation because everyone can realize they're at a stadium and they see multiple bars but they can't get bandwidth. So they got a back haul problem or not enough signal. Everyone knows when they're driving their car, they know they can relate to the consumer side of it. So I get how the spatial data grows. What's the impact to Carto and specifically the cloud, because if you have more data coming in, you need the actionable insight. So I can see the use case, oh, put the antenna here. That's an actionable business decision, more content, more revenue, more happy customers, but where else is the impact to you guys and the spatial piece of it? >> Yeah. Well, I mean like there's many, many factors, right? So one of them, for example, on the telco, one of the things where we realize impact is that it gives the visibility to the operator, for example, around the quality of service. Like, okay, are my customers getting the quality of services where I want? Or like you said, like if there sitting outside a concert the quality of service in one particular area is dropping very fast. So the idea of like being able to now in real time, kind of like detect location issues, like I'm having an issue in this place. That means that then now I can act, I can drive up bandwidth, put more capacity et cetera right. So I think the biggest impact that we are seeing we are going to see on the upcoming years is that like more and more use cases going towards real time. So where, like before it was like, well, now that it has happened, I'm going to analyze it. I'm going to look at, you know, like how I could do better next time towards a more of like an industry where Carto ourselves, we are embedded in more real time type of, you know, like analytics where it's okay, if this happens, then do that, right. So it's going to be more personalized at the level that like in the code environment, it has to be art of a full kind of like pipeline kind of like type of analysis. That's already programmatically prepared to act on real time. >> That's great and it's a good segue. My next question, as more and more companies adopt cloud native analytics, what trends are you seeing out of the key to watch? Obviously you're seeing more developers coming on site, on the scene, open sources growing, what's the big cloud native analytics trends for Carto and geographic information. >> Yeah. So I think you know like the, we were talking before the cloud native now is unstoppable, but one of the things that we are seeing that is still needs to be developed and we are seeing progress is around a standardization, for example, around like data sets that are provided by different providers. What I mean with that is like, you as an organization, you're going to be responsible for your data like that you create on your cloud, right. On S3, or, you know and then you going to have a competing engine, like Redshift and you're going to have all that set up, but then you also going to have to think about like, okay, how do I ingest data from third party providers that are important for my analysis? So for example, Carto provides a lot of demographics, human mobility. we aggregate and clean up and prepare lot of spacial data so that we can then enrich your business. So for us, how we deliver that into your cloud native solution is a very important factor. And we haven't seen yet enough standardization around that. And that's one of the things, what we are pushing, you know, with the concept of geo Parquet of standardizing that body. That's one, then there is another, this is more what I like to say that you know, we are helping companies figure out their own geographies. What we mean by that is like most companies, when they start thinking about like how they interact, on the space, on the location, some of them will work like by zip codes and other by cities, they organize their operations based on a geography in a way, or technically what we call a geographic support system. Well, nowadays, like the most advance companies are defining their geographies in a continuous spectrum in what we call global grid system or spatial indexes that allows them to understand the business, not just as a set of regions, but as a continuous space. And that is now possible because of the technologies that we are introducing around spatial indexes at the cloud native infrastructure. And it provides a great a way to match data with resources and operate at scale. To me those two trends are going to be like very, very important because of the capabilities that cloud native brings to our spatial industry. >> So it changes the operation. So it's data as ops, data as code, is data ops, like infrastructures code means cloud DevOps. So I got to ask you because that's cool. Spatial index is a whole another way to think of it, rather than you go hyper local, super local, you get local zones for AWS and regions. Things are getting down to the granular levels I see that. So I have to ask you, what does data as code mean to you and what does it mean to Carto? Because you're kind of teasing at this new way because it's redefining the operation, the data operations, data engineering. So data as code is real. What does that mean to you? >> No, I think we already seeing it happening to me and to Carto what I will describe data as code is when an organization has moved from doing an analysis after the fact, like where they're like post kind of like analysis in a way to where they're actually kind of like putting analytics on their operational cycle. So then they need to really code it. They need to make these analysis, put them and insert them into the architecture bus, if you want to say of the organization. So if I get a customer, happens to be in this location, I'm going to trigger that and then this is going to do that. Or if this happens, I'm need to open up. And this is where if an organization is going to react in more real time, and we know that organizations need to drive in that direction, the only way that they can make that happen is if they operationalize analytics on their daily operations. And that can only happen with data as code. >> Yeah. And that's interesting. Look at ML ops, AI ops, people talk about that. This is data, so developers meets operations, that's the cloud, data meets code that's operations, that's data business. >> You got it. And add to that, the spacial with Carto and we go it. >> Yeah, because every piece of data now is important. And the spatial's key real quick before we close out, what is the index thing? Explain the benefit real quick of a spatial index. >> Yes. So the spatial index is well everybody can understand how we organize societies politically, right? Our countries, you have like states and then you have like counties and you have all these different kind, what we call administrative boundaries, right? That's a way that we organize information too, right? A spatial index is when you divide the world, not in administrative boundaries, but you actually make a grid. Imagine that you just essentially make a grid of the world. right? And you make that grid so that in every cell you can then split it into, let's say for example, four more cells. So you now have like an organization. You split the world in a grid that you can have multiple resolutions think like Google maps when you see the entire world, but you can zoom in and you end up seeing, you know, like one particular place, so that's one thing. So what a spatial indexes allows you is to technically put, you know like your location, not based coordinate, but actually on one grid place on an index. And we use that then later to correlate, let's say your data with someone else data, as we can use what we call this spatial indexes to do joints very, very fast and we can do a lot of operations with it. So it is a new way to do spatial computing based on this type of indexes, but for more than anything for an organization, what spatial index allows is that you don't need to work on zip codes or in boundaries on artificial boundaries. I mean, your customer doesn't change because he goes from this place to the road, to the other side of the road, this is the same place. It's an arbitrary in location. It's a spatial index break out all of that. You're like you break with your zip codes, you break. And you essentially have a continuous geography, that actually is a much closer look up to the reality. >> It's like the forest and the trees and the bark of the tree. (Javier laughing) You can see everything. >> That's it, you can get a look at everything. >> Javi, great to have you on. In real quick closing give a quick plug for the company, summarize what you do, what you're looking into, how many people you got, when you're hiring, what's the key goals for the company? >> Yeah, sure. So Carto is a company, now we are around 200 people. Our vision is that spatial analytics is something that every organization should do. So we really try to enable organizations with the best data and analysis around spatial. And we do all that cloud native on top of your data warehouse. So what we are really in enabling these organizations is to take that cloud native approach that they're already embracing it also to spatial analysis. >> Javi, founder, chief strategy officer for Carto. Great to have you on data as code, all data's real, all data has impact, operational impact with data is the new big trend. Thanks for coming on and sharing the company story and all your key innovations. Thank you. >> Thanks to you. >> Okay. This is the startup showcase. Data as code, season two episode two of the ongoing series. Every episode will explore new topics and new exciting companies pioneering this next cloud native wave of innovation. I'm John Furrier, your host of theCUBE. Thanks for watching. (upbeat music)

(upbeat music) >> Welcome to AnsibleFest, 2021, the virtual version. This is The Cube and my name is Dave Volante. We're going to dig into automation and its continuing evolution. Tom Anderson is here. He's the vice president of Red Hat Ansible, the automation platform. And Richard Henshall is also here, Senior Manager of Ansible Product Management, of course, at Red Hat. Guys, welcome to the cube. Good to see you. >> Thanks for having us. >> Thank you for having us Dave. You're welcome, so Rich with this latest release of the Ansible Automation Platform, AAP, we'll get the acronyms out of the way. The focus seems to be an expanding the reach of automation and its potential use cases. I mean, I'll say automation everywhere, not to be confused with the RPA vendor, but the point is, you're trying to make it easier to automate things like provisioning, configuration management, application deployment, throw in orchestration and all these other IT processes. Now, you've talked about this theme in previous releases of AAP. So what's new in this release? What can customers do now that they couldn't do before? >> Yeah, it's a good question thank you. So, we look at this in two dimensions. So, the first dimension we have is like where automation can happen, right? So, you know, we always have traditional data center, clouds being been very prevalent for us for the last, you know, sort of five, 10 years in most people's view. But now we have the Edge, right? So now we have Edge computing, which is sometimes a lot more of the same, but also it comes with a different dynamic of how it has to be sort of used and utilized by different use cases, different industry segments. But then, while you expand the use cases to make sure that people can do automation where they need to do it and make sure if we don't close to the Edge or close to the data center, based on where the technology needs to be run, you also have to think about who's now using automation. So, the second dimension is making sure that different users can take access. You mentioned like application deployment, or infrastructure, or network configuration. We expand the number of different users we have that are starting to take advantage of Ansible. So how do we get more developers? How do we get into the developer workflow, into the development workflow, for how Ansible is created, as well as how we help with the operational, the posts deployment stage that people do operating automation, as well as then the running of Ansible Automation Platform itself. >> Excellent, okay. So, in thinking about some of those various roles or personas, I mean, I think about product leads. I would see developers, obviously you're going to be in there. Managers I would think want that view. You know the thrust seems to be, you're trying to continue to enhance the experience, for these personas and others, I suppose, with new tooling. Maybe you could add some color to that and what's happening in the market Tom if you take this and Rich chime in, what's happening in the market that makes this so important? Who are the key roles and personas that you're targeting? >> Yeah. So, there's a couple of things happening here. I mean, traditionally the people that had been using Ansible to automate their subsystems were the domain expert for that subsystem, right? I'm the storage operations team. I'm the network operations team. I'm using this tool to automate the tasks that I do day to day to operate my piece of the sub system. Now, what they're being asked to do is to expose that subsystem to other constituencies in the organization, right? So they had not, they're not waiting for a call to come in to say, can I have a network segment? Can I have this storage allocated to me? Can I deploy these servers so I can start testing or building or deploying my application. Those subsystems need to be exposed to those different audiences. And so the type of automation that is required is different. Now, we need to expose those subsystems in a way that makes those domain owners comfortable. So they're okay with another audience having access to their subsystem. But at the same time, they're able to ensure the governance and compliance around that, and then give that third-party that developer, that QE person, that man, that business, that line of business manager, whoever it might be, that's accessing that resource, a interface that is friendly and easy enough for them to do. It's kind of the democratization. I know it's a cliche, but the democratization of automated automation within organizations, giving them roles, specific experiences, of how they can access these different subsystems and speed their access to these systems and deploy applications. >> So if we could stay on that for a second, cause that's a complicated situation. You're now opening this up. You Richard mentioned the Edge. So you got to make sure that the person that's getting access has access, but then you also have to make sure that that individual can't screw it up, do things that you don't want that individual to do. And it's probably a whole other set of compliance issues and policy things that you have to bake in. Is that, am I getting that right? >> Yeah. And then that's the aspect of it. When you start to think, you know, Tom listed off there, you know, 10, you can just keep adding different sort of personas that individuals that work in roles, identify with as themselves. I'm a network person, I'm a storage person. To us they're all just Ansible users, right? There may be using a slightly different way, maybe using it slightly different places, but they're just an Ansible user, right? And so as you have, like those people that just like become organically, you've now got thousands potentially of Ansible users inside a large enterprise organization, or if you know, a couple of hundred if your smaller. But you're then go, well, what do I do with Ansible, right? And so at that point, you then start to say, now we try to look at it as what's their use of Ansible itself, because it's not just a command line tool. It's got a management interface, it's got analytics, we've got content management, we've got operational runtime, we've got responsiveness to, you know, disaster recovery scenarios for when, you know, when you need to be able to do certain actions, you may use it in different ways at different places. So we start, try and break out, what is the person doing with Ansible Automation Platform at this part of their workflow? Are they creating content, right? Are they consuming content, or are they operating that automation content for those other constituent users that Tom referred to. >> Yeah, that's really helpful because there's context, there are different roles, different personas need different contexts, you know, trying to do different things. Sometimes somebody just wants to see the analytics to make sure it's, you know, hey, everything's green, Oh, we got a yellow, versus, hey actually want to make some changes and I'm authorized to do so. Let's shift gears a little bit and talk about containers. I want to understand how containers are driving change for customers. Maybe what new tools you're providing to support this space? What about the Edge? Yeah, how real is that in terms of tangible pockets or patterns that you can identify that require new types of capabilities that you're delivering? Maybe you can help us unpack that a little bit. >> Okay so, I think there's two ways to look at containers, right? So the first is how are we utilizing the container technology itself, right? So containers are a package, right? So the amount of work we've been doing as Ansible's become more successful in the last couple of years, separating content out with Ansible collections. The ability to bring back manage, control a containerized runtime of Ansible so that you can lifecycle it, you can deploy it, it becomes portable. Edge is important there. How do I make sure I have the same automation running in the data center as the same automation running out on the Edge, if I'm looking at something that needs to be identical. The portability that the packaging of the container gives us, is a fantastic advantage, given you need to bring together just that automation you want. Smaller footprint, more refined footprint, lifecycle manage footprint. But at the same time, containers are also a very useful way of scaling the operation, right? And so as red hat puts things like Open Shift out in all these different locations, how can we leverage those platforms, to push the runtime of Ansible, the execution component, the execution plane of Ansible. How into anywhere that's hospitable for it to run? And as you move out towards Edge, as you move further away from the data center, you need a more ubiquitous sort of like run-time plane that you can put these things on. So they can just spin up when as, and when you need to. Potentially even at the end, actually being on the device, because at the same time with Edge, you also have different limits around how Edge works. It's not just about, hey I'm wifi points in an NFL stadium, actually, you're talking about I'm at the end of a 2000 mile, you know, piece of cable on an oil pipeline or potentially I'm a refinery out in the Gulf of Mexico. You know, you've got a very different dynamic to how you interact with that end point, than you do when it's a nice big controlled network, you know, powered location, which is well-governed and well-orchestrated. >> That's good. Thank you Rich. So Tom, think about automation, you know, back in the day, seems like a long time ago, but it really wasn't, automation used to scare some IT folks, because you know, sometimes it created unintended consequences or maybe it was a cultural thing and that you didn't want to automate themselves out of a job, but regardless. The cloud has changed that mindset, you know, showing us what's possible. You guys obviously had a big role in that, and the pandemic and digital initiatives, they really have made I call it the automation mandate. It was like the fourth March to digital, at least that's how I see it. I wonder if you could talk about, how you see your users approaching automation in as it relates to their business goals. Do you think automation is still being treated sometimes with trepidation or as a side project for some organizations or is it really continuing to evolve as a mainstream business imperative? >> Yes, so Dave we see it continuing to evolve as a strategic imperative for our customers. I mean, you'll, hear some of the keynote folks that are speaking here today. I've done an interview or doing an interview with Joe Mills from Discover, talking about extreme automation throughout Discovers organization. You'll hear representatives from JPMC talk about 22,000 JPMC employees contributing automation content in their environment, across 20 or 22 countries. I mean, just think about that scale, and the number of people that are involved in automation now and their tasks. So I think it's, I think we are, we have moved beyond or are moving beyond that idea that automation is just there to replace people's jobs. And it's much more about automation replacing the mundane, increasing consistency, increasing security, increasing agility, and giving people an opportunity to do more and more interesting stuff. So that's what we hear from our customers, this idea of them building. And it's not just the technology piece, but it's the cultural piece inside organizations where they're building these guilds or communities of practice, bringing people together to share best practices and experience with automation, so that they can feel comfortable learning from others and sharing with others and driving the organization forward. So we see a lot of that, and you'll hear a lot of that, at some of the Ansible Fest sessions this week. >> Well, I mean though I think that's a really important point. The last point you made about the skills, because I think you're right. I think we have moved beyond it's just job replacement. I don't know anybody who loves provisioning LUNs and say, oh, I'm the best in the world at that. It's just kind of something that was maybe important 10, 15, 20 years ago, but today, he should let the machines do that. So that's the whole skills transformation, is obviously a big part of digital transformation. Isn't it? >> It absolutely is. And frankly, we still hear, it's an impediment, that skills shortages are still an impediment to our customer success. They are still skilling up. I mean, honestly, that's one of the differentiators, for Ansible, as a language, a human readable language, that is easy to learn, easy to use, easy to share across an organization. So that's why you see job boards, and whatnot with so many opportunities that require or, or ask for Ansible skills out there. It's just a, it's become sort of a ubiquitous automation language in organizations, because it can be shared across lots of different roles. You don't have to be a Ruby software developer or a Python software developer to create automation with Ansible. You can be Tom Anderson or Rich Henshall. You don't have to, you don't have to be the, you know, the, the sharpest software developer in the world to take advantage of it. So anyway, that's one of the things that kind of overcoming some of the skills apprehension and bringing people into this, into the kind of new environment, of thinking about automation as code, not software code, but thinking of it like code. >> Got it. Guys we've got to leave it there, but Rich, how about you bring us home. We'll give you the last word. >> I mean, I think, you know what Tom just said there I think, about the skills side of things, is I think that the part that made it resonates the most. I mean I was a customer before I joined Red Hat, and trying to get large numbers of people, onto a same path, to try and achieve that outbound objective, that an organization has. The objective of an organization is not to automate, it's to achieve what is needed by what the automation facilitates. So how do we get those different groups to go from, Hey, this is about me, to this is actually about what we're trying to achieve as a business what we're trying to facilitate as a business, and how do we get those people easier access, a reduced barrier of entry to the skills they need to help make that successful, that compliments what they do, in their primary role, with a really strong secondary skill set that helps them do all the bits and pieces they need to do to make that job work. >> That's great, I mean you guys have done a great job, I mean it wasn't clear, you know, decade ago, or maybe half a decade ago, who was going to win this battle. Ansible clearly has market momentum and has become the leader. So guys congratulations on that and good job. Keep it going. I really appreciate your time. >> Thank you. >> Thank you. Thanks. >> Okay. This is the cubes, continuous coverage of Ansible Fest, 2021. Keep it right there for more content that educates and inspires. Thanks for watching. (upbeat music)

(cheerful music) >> Hello everyone, welcome to theCUBE's coverage of AnsibleFest 2021, with Red Hat. Topic of this power panel is the future of automation, we've got a great lineup of CUBE alumni, Joe Fitzgerald, vice president, general manager of the Red Hat business unit, thanks for coming on, Tom Anderson, vice president, product manager of Red Hat, and Alessandro Perilli, the senior director of product market at Red, all good CUBE alumni. Distinct power panel, Joe we'll start out with you, what have you seen in automation game right now, 'cause it continues to evolve. I mean you can't go to an event, a virtual event, or read anything online without hearing AI automation, automation hybrid, automation hybrid hybrid hybrid hybrid, I mean automation is the top conversation in almost all verticals. What do you see happening right now? >> Yeah, it's sort of amazing, you know? Automation is quite fashionable these days, as you pointed out. Automation's always been on the radar of a lot of enterprises, and I think it was always perceived as sort of like that, an efficiency, a task model thing, that people did. Now automation is, if you believe some of the analysts, it's up to a board room imperative in some cases. So we are seeing with our customers that the level of complexity they're dealing with, particularly exaggerated by what's gone past year and a half in the world, is putting a tremendous amount of pressure, attention and importance on automation. So automation's definitely one of the busiest places to be right now. >> What's the big change this year, though? I mean we love the automation conversation, we had it last year a lot too, as well. What's the change, what's the trend right now that's driving this next level automation conversation with customers? >> Well, I'll ask my colleagues to comment on that in a second, but, the challenges here with automation, is people are constrained now, they can't access facilities as easy as they used to be able to. They still need to go fast, some businesses have had to expand dramatically, and introduce new services to handle all sorts of new scenarios, they've had to deploy things faster. Security, not a week goes by you don't read about something going on regarding security and breaches and hacking and things like that, so they're trying to secure things as fast as possible, right, and deploy critical fixes and patches and things like that. So there's just tremendous amount of activity, that's really been exaggerated by what's gone on over the past year. >> And all of this is being compounded with a nature of increasing complexity, that we're seeing in the architecture, explosion microservices, the adoption en masse of containers, and the adoption of multiple clouds for most customers around the world. So really, the extension of the IT environment, especially for large enterprises, enormous for any team, no matter how big it is, so how scale it is, to really go after and look for all the systems, and then the complexity of the architectures, is enormous within that IT environment. It is impossible to scale the applications and to scale the infrastructure, and not scale the IT operations. And so automation becomes really a way to scale IT operations, rather than just keep repeating the same steps over and over, in an attempt to simplify, or to reduce costs. It's well beyond that at this point. >> That's a great point. Tom, what's your reaction to this, because Alessandro brings up a good point, developers are going faster than ever before. The changes of speed and complexity have gone up, so the demand for the IT and/or security groups, or anyone, to be faster, not weeks, minutes. We're talking about a complete time shift here. >> Yeah, so I talk to a lot of customers, and what I keep hearing again and again from them is kind of two things, which is, a need for skills, and reskilling existing staff. When Alessandro talks about the complexity and the scale, think about all the different new tools, new environments, new platforms that these employees and these associates are being exposed to and expected to be able to handle. So, a real, not a skill shortage, but a stress on the skills of the organization. And then secondly, really, our customers are talking to us about the culture in the environment itself, the culture of collaboration, the culture of automation, and the kind of impact that has in our organization, the way teams are now expected to work together, to share information, to share automation, to push, you know, we talk about shifting left in a lot of things now in IT, automation is now shifting left, pushing automation and access to subsystems, IT subsystems and resources, into the hands of people who traditionally haven't had direct access to those resources. So really kind of shift in skills, and a shift in culture I see. >> Ah, the culture. (indistinct), I want to come back to that culture thing, but I want to ask you specifically on that point, do you think automation users still view automation as just repeating and simplifying processes that they already are doing? You've heard the term, "Done it three times, automate it." Is that definition changing and evolving, what's your thoughts? >> Yeah, IT is really changing, going from the traditional, "I'm a network engineer and I use a command line to update my devices I'm responsible for, the config devices, and then I decide to write a playbook using a really cool product like Ansible to drive automation into my daily tasks." And then it comes up to exposing, again, exposing that subsystem I'm responsible for, whatever it is, storage, network, compute, whatever it is, exposing that op so other people can consume it without me being involved, right? So that's a real change in a mindset, and tooling, and approach, that I'm going to expose that op to a set of workflows, business workflows, that drive automation throughout an organization. So that's a real kind of evolution of automation, (indistinct) first, and that's usually focused mostly on day zero, provisioning of a new service. Now we see a lot more focus, or a lot of additional focus on day two operations. How do I automate my day two operations to make them a lot more efficient, as my scale and complexity grows? How do I take the human element out of operating this on a day to day basis? >> So you're saying basically, if I understand you correctly, the system's architecture view, or mindset, around automation, it moves from "Hey, I'm going to use," and Ansible by the way is great for "Hey, I want to automate something, I'm doing a lot," that's cool. But you're looking at it differently. If I understand you correctly, you're saying the automation has to be a system view, meaning you create the rules of the road so that automation can happen at the front lines of the CICD pipeline. You mentioned shift left, is that the difference, is that kind of what's happening here, that's beyond just doing automation, because you can automate it, so you've done that, this is like the next level, is that what you're getting at? >> It is, and we joke about it a little bit, crushing silos, right? Breaking down silos, and again, I keep talking about culture, it really is important, tools are important and technology's important, but the culture's super important, and trying to think of that thing from a systems mindset, of sort of workflows and orchestration of a business process that touches IT components, and how do I automate that and expose that to that workflow, without a human having to touch it, right? Yet still enforce my security protocols, my performance expectations, my compliance stuff, all of that stuff still needs to be enforced, and that's where repeatable automation comes in, of being able to expose this stuff up into these system-level workflows. >> And then there is another element to this (indistinct), I think it's really important to attach to this, the element of speed. We talk about complexity, we talk about scale, but then there is this emerging third dimension, as I call it, that is the speed. And the speed has a number of different articulation, it's the speed when you're thinking about how quickly you need to deliver the application. If you're in a very competitive environment, think about web scale startups for example, or companies in an emerging market, and then you have the speed in terms of reacting to a cybersecurity attack, which Tom just mentioned. And then you have the third kind of speed I'm thinking about right now, which is the increasing amount of artificial intelligence, so an algorithmic kind of operation that is taking place in the organization. For now it's still very limited, but it's not unthinkable that going forward, the operations will be driven, or at least assisted by artificial intelligence. This speed, just like the scale and the complexity we mentioned before, are impossible to be addressed by a single team, and so automation becomes indispensable. >> Yeah, that's a great point, I want to just double click on that, I mean both Tom and Joe were just talking about system, they used the word system. In a subsystem, if one is going faster than the other, to your point, there's a bottleneck there. So if the IT group or security groups are going to take time to approve things, they're not putting rules to the road together to automate and help developers be faster, because look, it's clear, we've been reporting on this in theCUBE, cloud developers are fast. They're moving really fast with code. And so what happens is, if they're going to shift left, that means they're going to be at the point of coding to set policies on security. So, that's going to put pressure on the other subsystems to go faster, so they have to then expose rules of the road, or I'm just making that up, but policy base, or have some systems thinking. They can't just be the old way of saying "No, slow it down." So this is a cultural thing, I think Joe, you brought up culture, Alessandro, you brought up culture. Is that still there? That speed, fast team here and a slow team here? Is that still around, or people getting faster on both sides? And I'm kind of talking about IT, generally speaking, they tend to be slower than the developers. >> Well, just a couple comments, first of all, you heard silos, you heard complexity, you heard speed, talked about shift left. Let me sort of maybe tie those together, right? What's happened to date is every silo has their own set of tooling, right? And so one silo might move very fast, with a very private set of tools, or network management, or security, or whatever, right? And if you think about it, one of the number one skills gaps right now is for automation people. But if an automation person has to learn 17 different tools, 'cause I'm running on three public clouds, I'm on-premise, edge, and I'm doing things to move network storage, compute, security, all sorts of different systems, the tooling is so complicated, right, that I end up with a bunch of specialists. Which can only do one or two things, because they don't know the other domains and they don't know the skills. One of the things we've seen from our customers, I think this is a fundamental shift in automation, is that what we've done with Ansible in particular is, we actually adopted Ansible because of its simplicity. It's actually human-readable, you don't have to be a hardcore programmer to write automation. So that allows the emergence of citizen creators of automation. There's not like a group in some ivory tower that now can make automation and they do it for the masses. Individuals can now use Ansible to create automation. Going cross-domain, Ansible automation touches networks, security, storage, compute, cloud, edge, Linux, Windows, containers, traditional, ITSM, it touches so many systems, that basically what you have is you have a set of power tooling, in Ansible, that allows you now to share automation across teams, 'cause they speak the same language, right? And that's how you go faster. If every silo is fast, but when you have to go inter-silo you slow down, or have to open a ticket, or have some (indistinct) mismatch, it causes delays, errors, and exposures. >> I think that is a very key point, I mean that delay of opening up tickets, not being responsive, Alessandro, you put up machine learning and AI, I mean if you think about what that could do from an automation standpoint, if you can publish the HIPAA rules for your healthcare, you can just traverse that with a bot, right? I mean this is the new... This just saves so much time, why even open up a ticket? So if you can shift left and do the security, and there's kind of rules there, this is a trend, how do you make that happen, how do you bust the silos, and I guess that's the question I'd love to get everyone to react to, because that implies some sort of horizontally scalable control plane. How does someone do that in an architectural way, that doesn't really kind of, maybe break everything, or make the (indistinct) go into a cultural sideways situation? >> Maybe I can jump in, and grab this one, and then maybe ask Alessandro to weigh in afterwards, but, what we've seen and what you'll see some of the speakers at AnsibleFest this year talk about, from a cultural perspective is bringing teams together across automation guilds, JPMC calls it a community of practice, where they're bringing hundreds and thousands of individuals in the organization together virtually, into these teams that share best practices, and processes and automation that they've created. Secondly, and this is a little bit of a shameless plug for Ansible, which is having a common language, a common automation language across these teams, so that sharing becomes obviously a lot easier when you're using the same language. And then thirdly, what we see a lot now is people treating automation as code. Storing that, and get version managing and version controlling and checking in, checking out, really thinking of automation differently from an individual writing a script, to this being infrastructure or whatever my subsystem is, managed it and automated it as code, and thinking of themselves as people responsible for code. >> These are all great points. I think that on top of all these things, there is an additional element which is change management. You cannot count on technology alone to change something that is purely cultural, as we kept saying during this video right now. So, I believe that a key element to win, to succeed in an automation project, is to couple the technology, great technology, easy to understand, able to become the common language as Tom just said, with an effort in change management that starts from the top. It's something you don't see very often because a technology vendor rarely works with a more consulting firm, but it's definitely an area that I think would be very interesting to explore for our customers. >> That's a great point on the change management, but let me ask you, what do you think it needs to make automation more frictionless for users, what do you see that needs to happen, Alessandro? >> I think there are at least a couple of elements that need to change. The first one is that, the effort that we're seeing right now in the industry, to further democratize the capability to automate has to go one notch further. And by that I mean, implementing cell service provisioning portals and ways for automatically execute an automation workflow that already exists, so that an end user, somebody that works in the line of business, and doesn't understand necessarily what the automation workflow, the script is doing, still able to use it, to consume it when it, she or he needs to use it. This is the first element, and then the second element that is definitely more ambitious, is about the language, about how do I actually write the automation workflow? This is a key problem. It's true that some automation engines and some workflows have done, historically speaking, a better job than others, in simplifying the way we write automation workflows, and definitely this is much simpler than writing code with a programming language, and it's simpler than writing automation compared to a tool that we use 10, 15 years ago. But still, there is a certain amount of complexity, because you need to understand how to write in a way that the automation framework understands, and you need even before that, you need to express what you want to achieve, and in a way that the automation engine understands. So, I'm thinking that going forward we'll start to see artificial intelligence being applied to this problem, in a way that's very similar to what OpenAI Microsoft are doing with Codex, the capability that is a model that allows a person to write in plain English through a comment in code, to translate that comment into actual code, taken from GitHub or through the machine learning process that's been done. I'm really thinking that going forward, we will start to see some effort in the same direction, but applied to automation. What if the AI could assist us, not replace us, in writing the automation workflow so that more people are capable to translating what they want to achieve, in a way that is automatable? >> So you're saying the language, making it easy to program, or write, or create. Being a creator of automation. And then having that be available as code, with other code, so there's kind of this new paradigm of automating the automation. >> In a sense, this is absolutely true, yes. >> In addition to that, John, I think there's another dimension here which is often overlooked, which we do spend a lot of time on. It's one thing to have things like Alessandro mentioned, that are front edge in terms of helping you write code, but you want to know something? In big organizations, a lot of times what we find is, someone's already written the code that you need. You know what the problem is? You don't know about it, you can't find it, you can't share it and you can't collaborate on it, so the best code is something that somebody's already invested the time to write, test, burn in, certify, what if they could share it, and what if people could find it, and then reuse it? Right, everybody's talking about low code, no code, well, reuse is the best, right? Because you've already invested expertise into doing it. So we've spent a lot of time working with our customers based on their feedback, on building the tools necessary for them to share automation, to collaborate on it, certify it, and also to create that supply chain from partners who create integrations and interfaces to their systems, and to be able to share that content through the supply chain out to our customers and have them be able to share automation across very large globally distributed organizations. Very powerful. >> That's a powerful point, I mean reuse, leverage there, is phenomenal. Discovery engine's got to be built. You got to know, I mean someone's got to build a search engine for the code. "Hey code, who's written some code?" But just a whole 'nother mindset, so this brings up my next question for you guys, 'cause this is really, we're teasing out the biggest things coming next in automation. These are all great points, they're all about the future, where will the puck be, let's skate to where the puck will be, but it's computer science and automation that's being democratized and opened up more, so it's, what do you guys think is the biggest thing coming next for automation? >> Joe, you want to go next? >> Sure. Sure. Yeah, I'll take it. So we're getting a glimpse of that with a number of customers right now that we're working with that are doing things around concepts like self-healing infrastructures. Well what the heck is that? Basically, it's tying event systems, and AI, which is looking at what's going on in an environment, and deciding that something is broken, sub-optimal, spending too much, there's some issue that needs to be dealt with. In the old days, it was, that system would stop with opening a ticket, dispatch some people who were either manually or semi-automated go fix their whatever. Now people are connecting these systems and saying "Wait a minute. I've got all this rich data coming through my eventing systems. I can make some sense out of it with AI or machine learning. Then I can drive automation, I just eliminated a whole bunch of people, time, exposure, cost, everything else." So I think that, sort of a ventureman automation is going to be huge. I'm going to argue that every single system in the world that uses AI, the result of that's going to be, I want to go do something, I want to change, optimize my move, secure, stop, start, relocate, how's that going to get done? It's going to get done with automation. >> And what Joe just said is really highly successful in the consumer space. If you think about solutions like If This Then That, or Zapier for example, those are examples of event-driven automation. They've been in the consumer space for a long time, and they are wildly popular to the point that there are dozens of clones and competitors. The enterprise space, it didn't adopt the same approach so far, but we start to see event bridges, and event hubs that can really help with this. And this really connects to the previous point, at this point I'm a broken record, which is about the speed and the complexity. If the environment is so spread out, so complex, and it goes all the way to the edge, and all these events take place at a neck-breaking pace, the only way for you is to tie the automation workflows that you have written, to a trigger, an event that takes place at some point, according to your logic. >> Tom, what are your thoughts? >> Yeah, last but not least on that kind of thread, which is sort of the architectures as we get out to the edge, what does it take to automate things at the edge? We thought there was a big jump from data center to cloud, and now when you start extending that out to the edge, am I going to need a new automation platform to handle those edge devices? Will I need a new language, will I need a new team, or can I connect these things together using a common platform to develop the automate at the edge? And I think that's where we see some of our customers moving now, which is automating those edge environments which have become critical to their business. >> Awesome, I want to ask one final question while I've got you guys here in this power panel, great insights here. Operational complexity was mentioned, skills gap was mentioned earlier, I want to ask you guys about the organizational behavior and dynamic going on with this change. Automation, hybrid, multi-cloud, all happening. When you start getting into speed of application development for the modern app, opensource where things are opening up and things are going to be democratized with automation and code and writing automation, and scaling that, you're going to have a cultural battle that's happening, and we're kind of seeing it play out in real time. DevOps has kind of gone and been successful, and we're seeing cloud-native bring new innovation, people are refactoring their business models with cloud technologies, now the edge is here, so this idea of speed, shifting left, from a developer standpoint, is putting pressure on the old, incumbent systems, like the security group, or the IT group that's still holding onto their ticketing system, and they're slower, they're getting requests, and the developer's like "Okay, go faster, I want this done faster." So we're seeing departments reorganizing. What do you guys see, 'cause Red Hat, you guys have been in there, all these big accounts for the generation of this modern era. What's the cultural dynamics happening, and what can companies do to be successful, to get to the next level? >> So I think for us, John, we certainly see it and we experience it, across thousands of customers, and what we've done as an organization is put together adoption journeys, a consulting engagement for our customers around an automation adoption journey, and that isn't just about the technology, it's all throughout that technology, it's about those cultural things, thinking differently about the way I automate and the way I share, and the way I do these tasks. So it's as much about cultural and process as it is about technology. And our customers are asking us for that help. Red Hat, you have thousands of customers that are using this product, surely you can come and tell us how we can achieve more with automation, how can we break down these silos, how can we move faster, and so we've put together these offerings, both directly as well as with our partners, to try and help these customers kind of get over that cultural hump. >> Awesome. Anyone else want to react to the cultural shift and dynamics and how it can play out in a positive way? >> Yeah, I think that it's a huge issue. We always talk about people, processes, and technology. Well the people issue's a really big deal here. We're seeing customers, huge organizations, with really capable teams building apps and services and infrastructures, saying "Help me think about automation in a new way." The old days, it was "Hey, I'm thinking about it as a cost savings thing." Yeah, there's still cost savings in there. To your point, John, now they're talking about speed, and security, and things like that. How fast, zero day exploits, now it's like zero hour exploits. How fast can I think about securing something? You know, time to heal, time to secure, time to optimize, so people are asking us, "What are the best practices? What is the best way to look at what I've got, my automation deficits," used to have tech deficits, now you got automation deficits, right? "What do I need to do culturally?" It's very similar to what happened with DevOps, right? Getting teams to get together and think about it differently and holistically, that same sort of transition is happening, and we're helping customers do that, 'cause we're talking to a lot of them where you've got the scholars have been through it. >> Awesome. Alessandro, your thoughts on this issue. >> I think that what Tom and Joe just said is going to further aggravate, it's going to happen more and more going forward, and there is a reason for that. And this connects back with the skill problem, that we discussed before. In the last 10 years, I've seen growing demand for developers to become experts in a lot of areas that have nothing to do with development, code development. They had to become experts in cloud infrastructures, they had to become experts in security because, you've probably heard this many times, security's everybody's responsibility. Now they've been asked to become experts in artificial intelligence, transforming their title into something like ML engineer. The amount of skills and disciplines that they need to master, alone, by themself, would require a lifetime of work. And we're asking human beings to get better and better at all of these things, and all of the best practice. It's absolutely impossible. And so the only way for them, yeah, five jobs in one, six jobs in one, right? Probably for the same seller, and the only way that these people can execute the best practice, enforce the best practice, if the best practices are encoded in automation workflow, not necessarily written by them, but by somebody else, and execute them at the right time, the right context, and for the right reason. >> It's like the five tool player in baseball, you got to do five different things, I mean this is, you got to do AI, you got to do machine learning, you got to have access to all the data, you got to do all these different things. This is the future of automation, and automation's critical. I've never heard that term, automation deficit or automation debt, we used to talk about tech debt, but I think automation is so important because the only way to go fast is to have automation, kind of at the center of it. This is a huge, huge topic. Thank you very much for coming on, power panel on the future of automation, Joe, Tom, Alessandro from Red Hat, thanks for coming on, everyone, really appreciate the insight, great conversation. >> Thanks, John. >> 'Kay, this is theCUBE's coverage of AnsibleFest 2021 virtual. This is theCUBE, I'm John Furrier, your host, thanks for watching. (calm music)

(soft electronic music) >> Well, it's good to have you here as we continue our series of CUBE Conversations in the AWS Startup Showcase. Today, our focus is on HackerOne and the CEO of HackerOne, Mårten Mickos joins us. Mårten, thanks for being with us, we appreciate the time. Good to see you here, on theCUBE, today. >> Thanks for inviting me, John. >> Let's talk about HackerOne, the global, digital security leader. You are taking care of everybody's worst digital nightmares these days and so congratulations on that front, but I know you've got your hands full. Let's go back for those who are watching that don't know a lot about your history and just tell us about the origination, about how you gathered this stable of hackers, if you will, for good, ethical hacking, we might call that, and how that began and where that path has led you. >> Yes, thank you, John. You mentioned it already, you said the worst nightmare. The worst nightmare we all now have is that we get hacked. We all have to worry as consumers, companies, governments that criminals will break into our system. And then when you start thinking rational think, okay, if the worst nightmare is a cyber crime and getting breached, what is then a medication potent enough to rise to that same level? What can stop your software vulnerabilities from being exploited by criminals? And the world has built a lot of testing software, procedures, scanners, all kinds of things to get there, but none have risen to the level of true criminal activity. But then this movement of ethical hacking has people with the same skill and same passion, and same ability to come from the outside and break in except one difference, they have good intent. So we have a collection, a community of all the ethical hackers in the world, over a million of them, who are all ready to go in and in a way, think the bad and do the good. So they approach your system as if they were attacking you and when they find a hole, they tell you and you can fix it. And it turns out that there's no other way of finding all the ways in which a bad guy could break in. You could do all the other things and you should do all the testing and scanning and whatnot, but it won't rise to that same level, it won't find all the vulnerabilities, it won't think as expansively as a criminal will think. But the ethical hackers do and they are unstoppable. And there are many more ethical hackers than are bad hackers in the world. We have 1.2 million in our community, that's more than there are black hats or criminal hackers in the whole world. >> Yeah, that's an incredible number. I mean, 1.2 million-- >> And growing. >> Ethical hackers. >> And growing. >> How did you go about building that community and vetting that community, right? Because there has to be some kind of credential that you bring to the table, some kind of expertise. So how do you know that everybody in that 1.2 million, which again, just a phenomenal number is of the same cloth, if you will, of good intent and willing to help? >> They would never sign up if they didn't have good intent because we know about them, we can see where they came from. So if you're a criminal, you would never voluntarily give away such information about yourself. So we know their intent. They're, of course, varying in terms of skill and drive and passion and abilities, so we have a ranking system where we can learn about their skills and we test them, so we can, out of that giant community, find the ones who are truly outstanding. Because like in any endeavor in life, some are just natural talent, some work hard to become the top talent, and most of us are just regular, mediocre players in whatever sports we are in, like, like I am. But we have, we managed to find the most talented hackers in the whole world and through sort of a social competition we cause them to learn more, get better, and just better and better. And, and here's the other dimension. So the first dimension is that we have to have a cure that is as strong, as potent as the risk so we have to find vulnerabilities at the same level as criminals will find. Well our hackers will do that. The second thing is it's a moving target. Whatever you learned in cybersecurity yesterday may already be outdated. Whatever technology you are, you are catching up with may already be different than it was yesterday. But thanks to our giant community, we have this sort of evolution inside of the community where new talent is always coming in with new skill and replacing the old ones. So as a hacker, of course, you compete with all your other friendly hackers to be the best, but one day you'll get beaten by a new guy, a new person, a new hacker who has figured out the new technology. And that's how we stay current. Like we, there's no risk of the knowledge being outdated or stagnated because the people revolve in this community and it's always the freshest, most accurate, current talent that's being deployed in our programs. >> Yeah, we've had a lot of conversations with cybersecurity experts over the years here, on theCUBE and generally there's been a theme of, I wouldn't say resignation, that's too strong. I'd say almost acceptance that there are going to be challenges and sometimes bad guys win. Sometimes vulnerabilities are, do yield results, you know, will ill intent. So how do you match the skill level on your side with the skill level and the motivation of the criminal actors on the other side and keep up with that? Because there's great financial motivation on that, on the bad side, you know, in order to, ransomware, you know, a great example of that. But how do you continue to fortify the hackers on your side to match that motivation that is so deeply embedded on the ill side? >> You brought up many good points, so let me start from the backend of them. So first of all, when we say that it's very lucrative to do cyber crime, I don't think it is lucrative for the actual doers. Like in ransomware, a lot of monies is changing hands, but I think it ends up in, ends up in very few hands. So a lot of the technical cyber criminals who are conducting it are probably not making much money. In opposition of this, in our ethical hacking community, we already have 14 hackers who have earned more than a million dollars by working on our programs. That is a lot of money. It's a lot of money even for criminals. If you are enlisted by a nefarious government or other nefarious organization to work for them, they don't necessarily pay you well, but working as a white hat, you can earn much, much more. So I do think the economics is rigged the right way, especially as human beings inherently want to do good. And they are ready to do good even if their pay is much lower. Now, the pay isn't lower, but even if it were, the propensity to do good, it overpowers the likelihood of somebody becoming a criminal. So, so as we, as long as we work together and pool our defenses, we'll be much stronger than any criminals. >> So, so let, if you would, let's turn the page then to you've established the talent pool, very deep, great bench. You've got a lot of people doing really good work. So let's talk about the work they are doing in terms of vulnerabilities that they're sighting, whether it's app security, cloud security, whatever the case may be. What, generally, what are you finding? What are you seeing, like where are the mistakes being made generally in your client base? What kinds of things are you pinpointing to them that you're finding through your work that they can shore up and build those defenses a little stronger? >> Broadly speaking, when you look at the industry today, every organization is undergoing digital transformation, and some do it from a primitive standpoint, some are already running on software. But there's a digital transformation going on, most organizations are moving workloads to the cloud, to a public cloud. When that happens, the nature of your application workload changes, the nature of the threat changes, and the possibilities for mistakes will be different. When you deploy workloads on a public cloud, you may have configuration issues, you may leave secrets in public repositories, there are new threats that come to you. But at the same time, it's a more uniform space because everybody's running on the same cloud and the cloud, itself, is secure. So we have devised specific services for those who run on cloud, where we go in and say, we know AWS, we know Google Cloud, we know Microsoft Azure. We will find the specific, typical vulnerabilities that you have there and we'll tell you about them so you can fix them. And then you get a much stronger cyber defense because the, the world of vulnerabilities is known to us, we've trained our hackers in identifying them. When we find them with one company, we learn, and we can look for the same in some other company. So the pace of learning is much faster in our system and that's how we can bring companies to a higher level of security when they're on the public cloud than they were before. So actually, like when you said many are resigned in front of the situation, the ship is already turning. It's important to look the threats in the eye and be unafraid of it, and just meet it, but we don't have to be resigned anymore. We have the powers in the cloud vendors, in the ethical hacking community, in software automation to now build proper systems that are broadly speaking, very secure. >> So, so how do you? >> Yes. >> How, how do we, when you look at the ransomware incidents that continue to occur, and yet I, and, and that, you know, it frightens a lot of people in the corporate world, municipal, public sector and private citizens even, right? But, but you sound, if I hearing you right, a little more optimistic, that we're getting to be a little more adept at security, if you will, and of sighting vulnerabilities and finding these loopholes and whatever. So you're not as pessimistic as, as some might be. You're thinking that perhaps we are starting to turn the corner a little bit and maybe some of these things that have been big threats are being somewhat more mitigated now? >> Well, I believe that whether you think you can fight cyber crime or not, you are correct, meaning you must have a belief of the power that you have with your other defenders. And today, we can create a defense that's strong enough. Nobody's 100% safe, ever. You can take any vaccinations you like, you may still get the, the virus. So like, as a metaphor, it's the same with software. You can never get 100% safety, but you can get much better than you were before. And you do it step by step with boring, small steps. It's not, there's no silver bullet. There's nothing that in one change will make you secure. But if you, every day fix one little thing, soon, you are more secure than your competitors and soon you are among the most secure in the industry. >> So, you know, Mårten, it is almost, I think about the old saying, "If you can't beat 'em, join 'em." This is like, if you can't beat them, have them join you. Right? >> No, it is if you can't beat them, keep beating them, keep beating at them. Like, criminal activity is very bad. The nefarious actors that are out there, there's nothing good with them. And whether they are operating voluntarily or mandated by somebody who has power over them, it's really, really bad. But, but in terms of numbers of people, they are already in a minority. They have vast resources, they have as technical resources and skills, but we have more people lined up on the defense and pooled defense will always overpower an asymmetric threat. >> Well, it's a great story what HackerOne has done in just a very short period of time over the past seven, eight years. It's important work, it's vital work and you're doing it very well. And so thanks for being with us here, on theCUBE and we wish you all the best down the road, too. >> We want the companies to do well, that's when we do well and they are very secure. So thank you very much, John. This was a wonderful conversation. >> I appreciate the time. Mårten Mickos joining us, the CEO of HackerOne. You've been watching a CUBE Conversation part of the AWS Startup Showcase. (soft electronic music)

>>Okay. Thanks Adam. In the studio of Jeffery David Lonnie, back on the cube set here in the middle of all the action of mobile world Congress is cloud city telco DRG, digital revolution, Michael Speranza CEO of separate tells here with us. Great to see you. Thanks for coming on the queue. Thanks for having me, Dave and John. Appreciate it. All right. So we're in the middle of the act we were just talking about while we're waiting to come on camera, uh, east coast, I'm from California days in Boston, you're from the New York area. We're back to real life. This is what's happening here. It's a hybrid event, so not fully packed, but good showing a lot of action. So let's get to it. Yeah. What do you guys do? Take a minute to explain what you guys do real quick. And I got some specific public cloud. The questions >>Were excellent. So Zephyr tells a global provider of telecommunication solutions, uh, to everyone across the stack, tier one, tier two and tier three CSPs throughout the world. We've got 300 customers. And I think the, probably the most interesting part about us as a company is that we actually probably started our lineage in the traditional sense in terms of these on-premise legacy, large systems. And we are a provider that has jumped in feet first. So the cloud revolution, and we're taking the effort now and the investment to move all of our solutions to the public cloud. And we're super excited to be here and showcase that to everybody >>I ask you, because one of the things that we've been documenting, Dave and I for the past 10 years is the enterprise transformation. I mean the digital digital transformation has been going on for quite some time on the enterprise side. Telco now is, seems like it's getting tuned up nicely to be disrupted and transformed. You're in the middle of it. You're on bolt. You've been on both sides of the table. Now you're on the full throttle, transport transformation. What's, what's your take? What, where are we at? >>Yeah, it feels as though we're at an inflection point. I think the, you know, the last call it 12 or 18 months, maybe that's been the industry pause where we kind of look back and reflect around what their long-term strategies are and more and more just over the last 12 or 18 months, we've seen more announcements, more big names, jump in feet first, be a little bit more public. Uh, no pun intended about what their, what their intentions are, what their investments, um, that they're making into the efforts that they're trying to deploy and their networks. And we feel like we're at an inflection point and we're ready for it. >>It's interesting that Zephyr tell, had a legacy business on-prem business and you're transitioning. What was the catalyst for that transition? And what does it take into would that journey look like? >>Yeah, I think the catalyst is really figuring out how to enable our customers to compete on a ladder that has more rungs to it. Right? If you look at these legacy on premise solutions, we could pour mountains and mountains of dollars into R and D. And at the end of the day, we would still be struggling to offer them something that really creates a true competitive disadvantage or a competitive advantage for their business. And really when you make the move to the public cloud and you start leveraging some of the platforms that are out there, it changes your PR your profile dynamically in terms of what you can offer to the customer. So for us, that realization is what happened. And we jumped in feet first and just the ability to get products to market, to give them a different cost structure is something that we would have never been able to do with our traditional legacy. >>So how did you do it? Because you have a balancing act, there you go to your existing business. Did you sort of fence that off and sort of start with the cloud native approach? And what did you find was the sort of before and after, and some of the benefits that you see? >>Yeah, that's a great question. So it's, it's a difficult conversation with the customer, right? I think you have to go and engage the customer and be our, our approach has been to be very open and transparent with them. Um, we're not telling them that what they're using is doesn't have a future. We're just telling them that we believe the future is bright or somewhere else. And that's what we're choosing to invest. Yeah. >>And they won't, they shouldn't even know it. I mean, if they get, cause the cup of cloud is bringing in new things, one of the things we've been riffing on in the past year is the three RS reset, reboot phase one, you gotta reboot things. Then you replatform with the cloud. And then all the winners, once they replatform to the cloud, they refactored their operations. And so this seems to be like the secret recipe. Once you get to the refactoring, then you're introducing net new opportunities. So you do some cost recovery, you'd be platform. You get some things going and then boom, new things >>Are happening. Yes, absolutely. Yet you have to have a long-term horizon. You can't, if you're trying to make these types of transitions over quarters or even years, it, it's not something that's easy to do with the business. >>Did you move the whole house into the cloud? We're working on it. Okay. Okay. But, so when you think about the telco industry, this is to me anyway, there's clear workloads that can and should go into the cloud. Like immediately. I mean, I, I think about it like mainframe downsizing in the day. I mean, anything that could go did go fast, you know? And so is it a similar parallel here? >>we've got maybe 10 products in the portfolio. We probably have half of them really in the public cloud already. Uh, it doesn't mean every customer is using them in that dimension, but we have half of them already in the public cloud as an option. Yeah, absolutely. As an option. And then the other is it's really, I think I'm understanding this paradigm of kind of, um, when we get into more detail, I got of this no feature left behind mindset that you have to challenge where you really have to convince the customer that no industry disruption ever started by making the new, innovative product, do everything the old one did great. And they have to kind of take that journey with you and lean into the change and understand what the long-term benefits >>And, and talk about those, the business. I mean, you remember John, when we got started, John was driving to the data center and, you know, racking cabling. And then when we entered the public cloud was like, wow, we saw the light. So until you do it, sometimes you can't experience it. What are they seeing in terms of the benefits? >>Yeah. I mean the two major benefits are obviously cost and agility, right. And you know, you talk about agility and just what it enables them to do from a subscriber experience, perspective, deploying new services, adapting those services to, uh, the new business paradigms and really improving the customer experience. Right. There's no mistake. You look at this industry, it's probably got one of the most depressing statistics around customer experience and NPS of any industry in the world. >>Right. So cables up there. Yes. >>It's hard not to improve that. And the other is cost, right. And that's an undeniable discussion that you can have if you get to the right level of the customer about what the long-term benefits of the cloud are. >>Yeah. You know, it's funny as you usually hear NPS in the context of how great the MPS is, but you hear it a lot in this industry and the different contexts. >>One of the things we've been talking about, and this is the big theme we're going to get to tomorrow the next day, the open, open side of this open arrangement. And rather than ran alliances got more and more members were reporting on that. As you look at the stack, the tech stack, you got a lot of OT and it is coming in with, because it's digital a lot more IP based systems. So you have this OT legacy culture of just, okay, we have sometimes regulation drives it, but sometimes it's just old ways of doing things. Is that going to be encapsulated with, with like say containers or is it does have a di does it have to like be, let go, or could you, can you, can you nurture it like something, we still have mainframes. I mean, big banks have mainframes. You don't go away. They do one thing. So is there a coexistence between that old legacy on top and abstracting away that >>Nonsense? I think certainly there, there can be. And we've, we've employed that approach with customers where if they're looking at deploying new services or tackling a new emergent market or rolling out a new kind of tiered service offering that might be under a different brand or label from their core brand, we've certainly approached it that way. Um, the big thing for us is really approaching that discussion with the customer and really talking about what we can do, not what we can do and can't emphasize that enough. And the other piece is really having the right decision makers in the conversation. Right. And understanding that you're talking to, um, someone who understands the impact on the P and L not just on the making kind of virtuous technical decisions about the way things are >>Michael. I got to get your thoughts on the agile because obviously cloud speed agility. We just heard from Microsoft on the interview, I did with him around, um, high density chips and, you know, low power and that's going to enable more stuff. So there's more stuff coming in the hyperscale of more cloud. And you start to see them. It's like snowflake built on top of AWS, hugely successful. So this is an enablement market. And she'll be these key, how, as the CEO, you're looking at this, okay, you're refactoring, replatforming, agility is a benefit. How does that change? How you run the business, how you serve customers. >>Yeah. So really changes where our investment dollars go. Uh, that's probably the number one impact. So if you look at AWS as a platform, you know, it started 15 years ago, it had one service right today, it's got over 200. So understanding that these platforms receive billions and billions of dollars of investment every single year, regardless of which one you're, you're aligned to and leveraging that to power, the services that we provide, we don't have to build everything right. We can leverage the capabilities that they provide to us, really focus our energy and our dollars and the things that make our application unique to our customer. So that was really one major tradition as leading an organization that you have to make. And then you have to convince the organization to go in that direction. It's different and you have to be very overt and transparent with the customer. >>Well, the early days of financial service cloud was an evil word. And now every financial service organizations leaning in big time, there's obviously reticence among public telcos moved to the public cloud. There's a lot of discussion about openness. It's hard to replicate the reliability of the network, et cetera, et cetera, maybe some of that, you know, rational and founded, but w what do you see in terms of the reticence and the risks and how are you helping mitigate those? >>Yes, my perspective it's really just been this kind of cascade of excuses and explanations as to why not to go, right. And it started with, you know, things that could have been done, legitimate issues at the time of data sovereignty or security they've been solved, right? You move on and now it's, uh, you know, that, that notion of no feature, no man left behind there. We have to have feature parody. We can't go. Um, we've, you know, every other industry has debunk that myth. And now the one that I think is most important to challenge is, is organizational dysfunction, where it's really about accessing the right levels in the customer to have that conversation, to neutralize the power or importance of any one dimension of decision-making and have an overt business discussion about what are the numbers behind, um, the solution and how you're providing it. You have to include the cost discussion and it, and you have to actually get the finance person to really understand the technology and not just outsource that decision to somebody else. Um, cause that person is often faced with making a decision that, um, you know, it could be deleterious to their role, their business, uh, their own organizations. And that's something that needs to be arbitrated at a senior level. I think >>So, no, we've seen this before. I love it. It's protecting turf. Exactly. No. And just internal politics. It does that. And that's what we saw in the financial services business, and then forget it. >>It's just, well, the thing too, though, with, with the skill set is not only is it a skill retraining going on, new roles are emerging. So for instance, the SRE was, we've been covering in a lot of these big companies. You've got site reliability, engineers pioneer by Google. That's a new role dealing with infrastructure. And as infrastructure as code comes out, it gets more fuzzy. What's under the hood because now you've got Lambda, you've got serverless. So that entire program ability is going to put pressure on these old OT stacks. Yep. What's your forecast. >>And that's what I'm saying. That's what broke the barrier in a lot of these regulated industries was infrastructure go all the developers to your point, want it in. And that was like the penguins off the iceberg, >>The pressure on the OT stacks, or does the abstraction at the top driving innovation? Is it going to be disrupted down here or is it going to be at the top of the stack? What's >>Your team? Yeah. So I think, you know, I've seen that traditional financial services, et cetera, a front row seat and financial services in a different role. And the way I saw it actually unfold was having some sort of role that sits between the CFO and the CTO. Someone who's responsible for the, not only the technical evolution of the architecture, but the financial evolution of the architecture that can actually be a compatriot to the CFL, making those decisions. That's how I've seen it really traditional, unless there's buy-in from the top down, it will just be kind of pressure from the bottom up. And we'll never see it take hold >>If bottom up, bottom up will not work and less top-down. It's got it. It's gotta >>Be the top down, bottom up, as you were saying. Yeah. >>I mean, what drives that? There's the legal come in and drive that a little bit now because you've got a lot of pressure with cyber. So you've got cyber, you've got regulatory pressure with telcos. I mean, let me carry as they get more. Are they still >>Blockers? Is that the legal still a blockage? >>Yeah. Uh, w we haven't seen legal really enter the decision making process for us and, you know, for us, we probably wouldn't involve them unless we needed to. Quite honestly, I think it could be more of a blocker than then someone, uh, you know, they can always say, no, they can't really say yes. Um, is our view of that. So they're an important person to be, uh, on the journey, but probably not someone that we were, we would seek to include >>In the decision. All right, Michael, I want you to take the last minute to just take a minute to explain what you guys do. What's your vision of the company, obviously you're on the right wave, the wave big, you get your surfboards out there. You're gonna ride the wave. What are you going to do? What's the big goals. What's your plans have to put a plug in. Yeah, >>Look, we see the future as a marketplace of cloud-based solutions, right? We feel that we're, um, someone that's trying to lead that innovation. I think it's fantastic to see what's happening here right now with cloud city and giving all the disruptive vendors, a voice in the industry that they probably did not have before. And we really see the vision as that open marketplace, where things are leveraging API APIs. All these systems can communicate across the stack, and we're going to be part of that journey. Uh, we're showcasing solutions here today, around subscriber management to drive ARPU. We've got, um, solutions for managing the customer experience in the home that will increase retention. And really everybody has to just open their minds to learn about what's possible with the public cloud, and then translate it to how it can actually benefit their business. >>Are you guys looking to hire and he think you want to share plugged for >>Look, we're growing we're we we've got solutions here that we're looking at positions to customers across the SAC globally, tier one tier twos and tier threes. And please come by the booth and visit us and learn about us. >>No, John, the big difference between the cloud this decade and cloud last decade is such an ecosystem. Now that's built in that's forming that you can leverage, right? That, you know, it was kind of really immature last decade. And it's not just one company going after this. It is the ecosystem. >>Well, not the problem. The opportunity is, is that the telcos are going to get punched in the face with the edge now, driving with 5g, because now they cannot ignore the fact that you now have a cloud edge that it's explosive and functionality low costs, Silicon identity. It's just a game changer that consumer technology is coming to the edge and it's going a forcing function so that they, if they don't, if they blink, who blinks first, yeah, telcos are cloud. So public clouds, great train Blake. The freight train of public cloud is coming into the telco. And if people don't adapt to it, they're going to be toast. And I think the opportunity is for entrepreneurs and founders and CEOs to drive, drive that innovation, okay. Innovations here. And we're going to continue bringing the coverage and we're going to go right to the studio where Adam and the team are there.

>>Hi, Felix. Great to be here. >>Likewise. Um, so when I started reading about data mesh, I think about a year ago, I found myself the more I read about it, the more I find myself agreeing with other principles behind data mesh, it actually took me back to almost the starting of Colibra 13 years ago, based on the research we were doing on semantic technologies, even personally my own master thesis, which was about domain driven ontologies. And we'll talk about domain-driven as it's a key principle behind data mesh, but before we get into that, let's not assume that everybody knows what data measures about. Although we've seen a lot of traction and momentum, which is fantastic to see, but maybe if you could start by talking about some of the key principles and, and a brief overview of what data mesh, uh, Isabella of >>Course, well, they're happy to, uh, so Dana mesh is an approach is a new approach. It's a decentralized, decentralized approach to managing and accessing data and particularly analytical data at scale. So we can break that down a little bit. What is analytical data? Well, analytical data is the data that fuels our reporting as a business intelligence. Most importantly, the machine learning training, right? So it's the data, that's, it's an aggregate view of historical events that happens across organizations, many domains within organizations, or even beyond one organization, right? Um, and today we manage, uh, this analytical data through very centralized solutions. So whether it's a data lake or data warehouse or combinations of the two, and, uh, to be honest, we have kind of outsource the accountability for it, to the data team, right? It doesn't happen within the domains. Uh, what we have found ourselves with is, uh, central button next. >>So as we see the growth in the scale of organizations, in terms of the origins of the data and in terms of the great expectations for the data, all of these wonderful use cases that are, that requires access to that, unless we're data, uh, we find ourselves kind of constraints and limited in agility to respond, you know, because we have a centralized bottleneck from team to technology, to architecture. So there's a mesh kind of is that looks at the past what we've done, accidental complexity that we've kind of created and tries to reimagine a different way of, uh, managing and accessing data that can truly scale as this origins of the data grows. As they become available within one organization, we didn't want a cloud or another, and it links down really the approach based on four principles. Uh, so I so far, I haven't tried to be prescriptive as exactly how you implement it. >>I leave that to Elizabeth, to the imaginations of the users. Um, of course I have my opinions, but, but without being prescriptive, I think there are full shifts that needs to happen. One is, uh, we need to start breaking down the, kind of this complex problem of accessing to data around boundaries that can allow this to scale out a solution. So boundaries that are, that naturally fits into that model or domains, right. Our business domain. So, so there's a first principle is the domain ownership of the data. So analytical data will be shared and served and accountable, uh, by the domains where they come from. And then the second dimension of that is, okay. So once we break down this, the ownership of the database on domains, how can we prevent this data siloing? So the second principle is really treating data as a product. >>So considering the success of that data based on the access and usability and the lifelong experience of data analysts, data scientists. So we talk about data as a product and that the third principle is to really make it possible feasible. We need to really rethink our data platforms, our infrastructure capabilities, and create a new set ourselves of capabilities that allows domain in fact, to own their data in fact, to manage the life cycle of their analytical data. So then self-serve daytime frustration and platform is the fourth principle. And the last principle is really around governance because we have to think about governance. In fact, when I first wrote it down, this was like a little kind of concern in, in embedded in what some of my texts and I thought about, okay, now to make this real, we need to think about securing and quality of the data accessibility of the data at scale, in a fashion that embraces this autonomous domain ownership. So we have to think about how can we make this real with competition of governance? How can we make those domains be part of the governance, federated governance, federally, the competition of governance is the fourth principle. So at insurance it's a organizational shift, it's an architectural change. And of course technology needs to change to get us to decentralize access and management of Emily's school data. >>Yeah, I think that makes a ton of sense. If you want to scale, typically you have to think much more distributed versus centralized at we've seen it in other practices as well, that domain-driven thinking as well. I think, especially around engineering, right? We've seen a lot of the same principles and best practices in order to scale engineering teams and not make the same mistakes again, but maybe we can start there with kind of the core principles around that domain driven thinking. Can you elaborate a little bit on that? Why that is so important than the kind of data organizations, data functions as well? >>Absolutely. I mean, if you look at your organizations, organizations are complex systems, right? There are eight made of parts, which are basically domains functions of the business, your automation and your customer management, yourselves marketing. And then the behavior of the organization is the result of an intuitive, you know, network of dependencies and interactions with these domains. So if we just overlay data on this complex system, it does make sense to really, to scale, to bring the ownership and, um, really access to data right at the domain where it originates, right. But to the people who know that data best and most capable of providing that data. So to optimize response, to change, to optimize creating new features, new services, new machine learning models, we've got to kind of think about your call optimization, but not that the cost of global good. Right. Uh, so the domain ownership really talks about giving autonomy to the domains and accountability to provide their data and model the data, um, in a responsible way, be accountable for its quality. >>So no collect some of the empower them and localize some of those responsibilities, but at the same time, you know, thinking about the global goods, so what are they, how that domain needs to be accountable against the other domains on the mission? That's the governance piece covers that. And that leads to some interesting kind of architectural shifts, because when you think about not submission of the data, then you think about, okay, if I have a machine learning model that needs, you know, three pieces of the data from the different domains, I ended up actually distributing the computer also back to those domains. So it actually starts shifting kind of architectural as well. We start with ownership. Yeah, >>No, I think that makes a ton of sense, but I can imagine people thinking, well, if you're organizing, according to these domains, aren't gonna be going to grades different silos, even more silos. And I think that's where it second principle that's, um, think of data as a product and it comes in, I think that's incredibly powerful in my mind. It's powerful because it helps us think about usability. It helps us think about the consumer of that data and really packaging it in the right way. And as one sentence that I've heard you use that I think is incredibly powerful, it's less collecting, more connecting. Um, and can you elaborate on that a little bit? >>Absolutely. I mean the power and the value of the data is not enhanced, which we have got and stored on this, right. It's really about connecting that data to other data sets to aluminate new insights. The higher order information is connecting that data to the users, right. Then they want to use it. So that's why I think, uh, if we shift that thinking from just collecting more in one place, like whatever, and ability to connect datasets, then, then arrive at a different solution. So, uh, I think data as a product, as you said, exactly, was a kind of a response to the challenges that domain-driven siloing could create. And the idea is that the data that now these domains own needs to be shared with some accountability and incentive structure as a product. So if you bring product thinking to data, what does that mean? >>That means delighting the experience that there are users who are they, they're the data analysts, data scientists. So, you know, how can we delight their experience of their journey starts with a hypothesis. I have a question. Do I have right data to answer this question with a particular model? Let me discover it, let me find it if it's useful. Do I trust it? So really fascinated in that journey? I think we have two choices in that we have the choice of source of that data. The people who are really shouldn't be accountable for it, shrug off the responsibility and say, you know, I dumped this data on some event streaming and somebody downstream, the governance or data team will take care of a terror again. So it usable piece of information. And that's what we have done for, you know, half century almost. And, or let's say let's bring intention of providing quality data back to the source and make the folks both empower them and make them accountable for providing that data right at the source as a product. And I think by being intentional about that, um, w we're going to remove a lot of accidental complexity that we have created with, you know, labyrinth pipelines of moving data from one place to another, and try to build quality back into it. Um, and that requires, you know, architectural shifts, organizational shifts, incentive models, and the whole package, >>The hope is absolutely. And we'll talk about that. Federated computational governance is going to be a really an important aspect, but the other part of kind of data as a product next to usability is whole trust. Right? If you, if you want to use it, why is also trusts so important if you think about data as a product? >>Well, uh, I mean, maybe we turn this question back to you. Would you buy the shiniest product if you don't trust it, if you, if you don't trust where it comes from, can I use it? Is it, does it have integrity? I wouldn't. I think, I think it's almost irresponsible to use the data that you can trust, right. And the, really the meaning of the trust is that, do I know enough about this data to, to, for it, to be useful for the purpose that I'm using it for? So, um, I think trust is absolutely fundamental to, as a fundamental characteristics of a data as a product. And again, it comes back to breaching the gap between what the data user knows needs to know to really trust them, use that data, to find it, whether it's suitable and what they know today. So we can bridge that gap with, uh, you know, adding documentation, adding SLRs, adding lineage, like all of these additional information, but not only that, but also having people that are accountable for providing that integrity and those silos and guaranteeing. So it's really those product owners. So I think, um, it's just, for me, it's a non trust is a non-negotiable characteristic of the data as a product, like any other consumer product. >>Exactly. Like you said, if you think about consumer product, consumer marketplace is almost Uber of Amazon, of Airbnb. You have the simple rating as a very simple way of showing trust and those two and those different stakeholders and that almost. And we also say, okay, how do we actually get there? And I think data measure also talks a little bit about the roles responsibilities. And I think the importance overall of a, of a data product owner probably is aligned with that, that importance and trust. Yeah, >>Absolutely. I think we can't just wish for these good things happens without putting the accountability and the right roles in place. And the data product owner is just the starting point for us to stop playing hot potato. When it comes to, you know, who owns the data will be accountable for not so much. Who's the actual owner of that data because the owner of the data is you and me where the data comes really from, but it's the data product owner who's going to be responsible for the life cycle of this. They know when the data gets changed with consumers, meaning you feel as a new information, make sure that that gets carried out and maybe one day retire that data. So that long term ownership with intimate understanding of the needs of the user for that data, as well as the data itself and the domain itself and managing the life cycle of that, uh, I think that's a, that's a necessary role. >>Um, and then we have to think about why would anybody want to be a data product owner, right? What are the incentives we have to set up in the infrastructure, you know, in the organization. Um, and it really comes down to, I think, adopting prior art that exists in the product ownership landscape and bring it really to the data and assume the data users as the, as the customers, right. To make them happy. So our incentives on KPIs for these people before they get product on it needs to be aligned with the happiness of their data users. >>Yep. I love that. The alignment again, to the consumer using things like we know from product management, product owner of these roles and reusing that for data, I think that makes it makes a ton of sense. And it's a good leeway to talk a little about governance, right? We mentioned already federated governance, computational governance at we seeing that challenge often with our customers centralizing versus decentralizing. How do we find the right balance? Can you talk a little bit about that in the context of data mesh? How do we, how do we do this? >>Yeah, absolutely. I think the, I was hoping to pack three concepts in the title of the governance, but I thought that would be quite mouthful. So, uh, as you mentioned, uh, the kind of that federated aspects, the competition aspects, and I think embedded governance, I would, if I could add another kind of phrasing there and really it's about, um, as we talked about to how to make it happen. So I think the Federation matters because the people who are really in a position listed this, their product owners in a position to provide data in a trustworthy, with integrity and secure way, they have to have a stake in doing that, right. They have to be accountable, not just for their little domain or a big domain, but also they have to have an accountability for the mesh. So some of the concerns that are applied to all of the data front, I've seen fluid, how we secure them are consistently really secure them. >>How do we model the data or the schema language or the SLO metrics, or that allows this, uh, data to be interoperable so we can join multiple data products. So we have to have, I think, a set of policies that are really minimum set of policies that we have to apply globally to all the data products and then in a federated fashion, incentivize the data product owners. So have a stake in that and make that happen because there's always going to be a challenge in prioritizing. Would I add another few attributes? So my data sets to make my customers happy, or would I adopt that this standardized modeling language, right? They have to make that kind of continuous, um, kind of prioritization. Um, and they have to be incentivized to do both. Right. Uh, and then the other piece of it is okay, if we want to apply these consistent policies, across many data products and the mesh, how would it be physically possible? >>And the only way I can see, and I have seen it done in service mesh would be possible is by embedding those policies as competition, as code into every single data product. And how do we do that again, platform has a big part of it. So be able to have this embedded policy engines and whatever those things are into the data products, uh, and to, to be able to competition. So by default, when you become a data product, as part of the scaffolding of that data product, you get all of these, um, kind of computational capabilities to configure your, your policies according to the global policies. >>No, that makes sense. That makes, that makes it on a sense. That makes sense. >>I'm just curious. Really. So you've been at this for a while. You've built this system for the 13 years came from kind of academic background. So, uh, to be honest, we run into your products, lots of our clients, and there's always like a chat conversation within ThoughtWorks that, uh, do you guys know about this product then? So and so, oh, I should have curious, well, how do you think data governance tehcnology then skip and you need to shift with data mesh, right. And, and if, if I would ask, how would your roadmap changes with database? >>Yeah, I think it's a really good question. Um, what I don't want to do is to make, make the mistake that Venice often make and think of data mesh as a product. I think it's a much more holistic mindset change, right? That that's organization. Yes. It needs to be a kind of a platform enablement component there. And we've actually, I think authentically what, how we think about governance, that's very aligned with some of the principles and data measures that federate their thinking or customers know about going to communities domains or operating model. We really support that flexibility. I think from a roadmap perspective, I think making that even easier, uh, as always kind of a, a focus focus area for us, um, specifically around data measures are a few things that come to mind. Uh, one, I think is connectivity, right? If you, if you give different teams more ownership and accountability, we're not going to live in a world where all of the data is going to be stored on one location, right? >>You want to give people themes the opportunity and the accountability to make their own technology decisions so that they are fit for purpose. So I think whatever platform being able to really provide out of the box connectivity to a very wide, um, area or a range of technologies, I think is absolutely critical, um, on the, on the product as a or data as a product, thinking that usability, I think that's top of mind, uh, that's part of our roadmap. You're going to hear us, uh, stock about that tomorrow as well. Um, that data consumer, how do we make it as easy as possible for people to discover data that they can trust that they can access? Um, and in that thinking is a big part of our roadmap. So again, making that as easy as possible, uh, is a, is a big part of it. >>And, and also on the, I think the computation aspect that you mentioned, I think we believe in as well, if, if it's just documentation is going to be really hard to keep that alive, right? And so you have to make an active, we have to get close to the actual data. So if you think about a policy enforcement, for example, some things we're talking about, it's not just definition is the enforcement data quality. That's why we are so excited about our or data quality, um, acquisition as well. Um, so these are a couple of the things that we're thinking of, again, your, your, um, your, your, uh, message around from collecting to connecting. We talk about unity. I think that that works really, really well with our mission and vision as well. So mark, thank you so much. I wish we had more time to continue the conversation, uh, but it's been great to have a conversation here. Thank you so much for being here today and, uh, let's continue to work on that on data. Hello. I'm excited >>To see it. Just come to like.

(upbeat music) >> Hello, welcome to this CUBE conversation. I'm John Furrier your host of theCUBE here in our Palo Alto studio. We got a great conversation with the CEO of Lacework, David Hatfield. Who's in on theCUBE remote. David great to see you guys, a security platform at Lacework, you're at the helm as CEO. Welcome to theCUBE conversation. >> Thank you, John. Great to see you congrats to you and the team and all the success. I think what you guys are doing is really important so happy to be part of it. >> Great to have you in the community and you guys are doing great work. I know about Lacework I've done some due diligence on you guys. I love your business model, but for the folks who don't know what you guys do, take a minute to explain who is Lacework? What do you guys do? What's your positioning? And what's your focus? >> Yeah, well, we're a modern data security platform for the cloud. And so I think data science meets cloud security ultimately. The company has been around since 2015. We received one of the largest financing rounds that we're aware of I think in history in security business, $525 million in January. Led by Sutter Hill Ventures which many people may know about they founded PureStorage with the notion that we're going to go fundamentally change and revamp the ownership model for a high speed data storage using flash versus using spinning disc drives. I spent eight years with that company. Love with what we built there. Then Mike Speiser considered an investment in a company called Snowflake computing. I think you're aware of what Snowflake does which is bringing data warehousing into the cloud. And the third big investment that Sutter Hill made is really to help disrupt security, and that's in Lacework. So north of a billion dollar valuation a 300% year over year growth and have a ton of momentum. So at the core of what we do, it's really trying to merge, when we look at we look at security as a data problem, security and compliance the data problem. And when you apply that to the cloud, it's a massive data problem. you literally have trillions of data points across shared infrastructure that we need to be able to ingest and capture and then you need to be able to process efficiently and provide context back to the end-user. And so we approached it very differently than how legacy approaches have been in place, you know largely rules-based engines that are written to be able to try and stop the bad guys. And they miss a lot of things. And so our data-driven approach that we patented is called a polygraph. It's a, it's a security architecture and there are three primary benefits. It does a lot of things, but the three things that we think are most profound first is it eliminates the need for, you know dozens of point solutions. I was shocked when I, you know kind of learned about security. I was at Symantec back in the day. And just to see how fragmented this market is, it's one of the biggest markets in tech. $124 billion in annual spend growing at, to $300 billion in the next three years. And it's massively fragmented. And the average number of point solutions that customers have to deal with is dozens. Like literally 75 is the average number. And so we wanted to take a platform approach to solve this problem where the larger the attack surface that you put in the more data that you put into our machine learning algorithms the smarter it gets and the higher, the efficacy. So eliminating point solutions is his value proposition one. Point two is that we have to be 10 X better than everybody else in the business. Otherwise the merchant companies don't get a breakout and become long and during companies. And so there's a number of different dimensions. The first dimension that I think is probably the most important is efficacy, you know in anomaly detection or in, you know threat detection where you're trying to identify what risks we have in the business. It's, it's generally a very noisy activity. And so rules-based approaches on average will produce a hundred alerts to our one or two. Those, the signal to noise ratio, is, is, you know is a massive a 100x, but call it 10x a reduction. And so we're actually delivering the needle versus the haystack for security administrators and dev developers to actually solve the problem. So it's 10x, higher efficacy it's 10x faster to be able to resolve the problems. And obviously the ROI is, is a no-brainer because you're eliminating all these points which is in having to manage it. And the third, and probably the thing that I'm most excited about what we're doing and what our customers are already realizing is that we're transforming security and compliance teams from kind of compliance into business enablers. when you automate all these processes and you build it into, you know the CICD platforms for the developers you actually enable the developers to write code to differentiate their business, you know to create new customer experiences to get competitive advantage and drive revenue for their businesses. And, and you know that's not what security has done up to this point. We oftentimes, they're the ones we're the ones having to say, no, you know we're slow down or it's too risky, etc. But when you automate that and you increase the efficacy you can enable the developers to do their thing. And it allows the CSOs and allows the security professionals to up level their responsibility into selling and driving revenue. And that is increasingly going to become more and more important for supply chains and partners of these cloud native businesses of how secure am I working with you, etc. And so we think that that transformation of the role of security is going to be as, as meaningful as the technology that we're providing the business. So we're super excited about it. >> I could tell you have so much going on this investment team Sutter Hill, you mentioned big time players huge success track record. Just saw them written up in the wall street journal as one of the best venture capital firms and returns. It's just that the bets are all coming home, but their bet strategy is simple. Disrupting the market that's growing and changing PureStorage, you mentioned company you've worked for, you know people were saying, oh, they'll never get escape velocity. They disrupted an existing, boring storage market changed the game there, security, right for change. A lot of tools, a lot of people have buying tools off the shelf, you know and everyone fighting for the platform. That seems to be the conversation. So I have to ask you, you guys want to be the player that that platform you are, that platform what's different in this platform where everyone's trying to be a security platform, what's makes you different. >> Yeah. So I mean, I think the platform wars are, are clearly, upon us, you know I think what's different about our approach is that we were built on the cloud, for the cloud so we're a cloud native business that, you know runs our business on AWS and everything that we do. We don't have hardware, we don't own data centers. we don't have any of the legacy elements that are there. we use software run on the cloud to enable this. So that's point number one point number two is we did the hard work of mapping the data elements that are out there and adjusting them in and then have this polygraph, you know behavioral anomaly detection, that is it can be applied to today. It's being applied to vulnerability and discovery management and containers and Kubernetes. But over time we believe it extends very naturally to a larger part of the attack server. So we don't have to rewrite the data engine to develop solutions across broader attack services. We already have that, you know so I think our time to develop and innovate will be profound. And I think the third thing that we're seeing companies do and largely the legacy bigger companies is that they're just acquiring their way there. And, it's very, very difficult to acquire 8 to 10 to 20, 30 companies, 30 different CTOs 30 different code bases and try and integrate them to provide a delightful customer experience. And, the parallels, you know in the storage business are, are are pretty similar actually, Dell bought EMC, EMC bought a hundred companies. And, we went after a platform approach to be able to go attack them with a unified file system in a in a unified customer experience that was native for the media that we're working with. We're doing the same playbook here, you know which is you have to have the hard work of the foundation elements in place to be cloud native to deliver great outcomes, great efficacy and and a really great customer experience. So when we get head to head with any of these points coming out and trying to solve something for containers or Kubernetes, or just vulnerability discovery and management, etc, or we're competing with the legacy companies that have, a hodgepodge of acquisitions that they're trying to pull together we went North of 95% of the time. our POC win rates are phenomenal better than anything I've ever seen. We had a pretty good one to appear too. And the, the product and the experience and the efficacy kind of stand on their own once we're in those fights. So part of why we enjoy working with AWS and are really focused on building the partnership together is that it creates awareness of what could be and what possibilities all we want is a shot. And, our approach is such that you can be up and running in minutes, you know and every single one of our customers does a POC. So we'll stand behind our technology as our real differentiator compared to anybody else that's out there. >> Great. You guys had great traction going on with the company certainly saw the investment news that you mentioned earlier at the top. Why did you come on as CEO? And when did you come on and join the team? And what was the reason? What, what, what attracted you to join as the CEO of Lacework? >> Well, I've been involved in the company for since the beginning actually I invested in the early rounds participated on the board and I've always bought into this. The thesis that security is fundamentally a data problem. And if we can get the data problem and the data processing right, you know you can fundamentally change the industry but you need to have a major inflection. And that inflection is people moving to the cloud. And we all have seen it during the pandemic. things are accelerating. AWS just did their earnings yesterday. I think they increased their top-line guidance from 46 billion to 56 billion this year. I mean, it's a machine that is continuing to move forward. They have 30% market share. Azure's investing at 20% GCP still investing people are moving their businesses online aggressively. And as they shift to the cloud the rules-based approach just doesn't work. It doesn't scale. And so a new approach needs to be done. And so by being cloud native and best of breed and solving the thorny problem of this data processing problem first, you know it gives us an opportunity to use that to then extend and build a business, you know at an enduring level over the next 10 to 20 years. And that's Sutter's model, that's their playbook. They don't invest in 400 companies and kind of spray and pray, which is what most venture funds do. And I love them. They're great. And we appreciate the investment in tech, but Sutter's focus is find a really big market find a catalyst for change. In our case, it's moving to the cloud and then build a modern approach. that is 10x better in every dimension. And that attracted to me. I mean, it's, it's a, it's one of the biggest markets in tech and it's one of the most important things that we can do is a digital business is to ensure that we're secure and we're safe and the threats are becoming much more skilled much more deliberate, much better funded. And so the importance for us to ensure that company's security is really tight is, is increasingly critical. So the combination of those factors, and then as I dove back into it and talked to a bunch of customers and talk to partners and seeing the outcomes and enthusiasm that they had and the, the team is phenomenal. And so talking to them, and I just kind of got energized by the opportunity to go build a really important company that really delivers great outcomes. So I'm having a ball great to be back into it. >> Yeah. It's great to have leadership that has experienced that you have and go to the next level because this is classic next level. When you talk about Amazon's earnings and cloud scale and hybrid and edge right around the corner at scale as well. So you start to see that transformation really hit the tipping point, which is changing the landscape on the developer side, which I think is super valuable. I think you hit that. You mentioned core problem. You guys look at that through the lens of data problem. How does this trend of everything going hybrid and soon to be, you know edge core to edge impact your businesses of tailwind? How do you see you capturing that next level of scale from a business perspective for lease work? >> Well, I think that the trend, you know from core to edge, you know, hybrid and, you know ultimately cloud a hundred percent, there we've started with the cloud native businesses. Like, we've been focusing on those companies that are already there, you know and so now we're we just had finished a phenomenal record-breaking Q1 and multiple seven figure deals, you know with very complex global environments where they do have a hybrid environment and they are leveraging the edge. And we're perfect for that. I mean, as you think about what we deliver in its most simplistic context, you know we're effectively delivering a security solution from the container to control plane, right. You know we want to be able to have a granular understanding of operated trillions of data points coming in and those can be collected in the core. They can be collected on-prem. They can be collected in the cloud. Ultimately they need to be collected and then contextualized so, you know and this is where our behavioral polygraph technology transitions data into information that's useful via the polygraph. And so we think that, the complexity that's added with environments that are hybrid environments that are leveraging the edge environments that are leveraging the cloud native all need a control plane to run across that to deliver efficacy, you know, for our customers. And, we work with, you know AWS has their own security tools. Azure has some security tools UCPs security tools, but ultimately, our, our challenge and opportunity is to be best of breed to deliver incremental value on top of that and that horizontal value across it. so customers have choice but they know that their security posture is, is, is secure. And so we, we see it as a tailwind for our businesses as we go forward. >> I always said the companies that have the horizontal scalability with cloud and then have that vertical AI kind of vibe where you can get in the context of the data is there to win it all. And I think that you guys have a great solution potentially there. I want to get more information if you don't mind double clicking on that with me, this is kind of a different take on cloud security because you've got the scalability, which gives you the observation space. And then you got to get the context to get the right patterns or whatever magic you guys have in the, in the secret sauce. But you doing that on top of massive exponential velocity. >> Yeah. >> Where's that secret sauce? Is it in the compute? Is it in the software? What's different about what you guys have in security to give us a- >> It's all in the, it's all in the software. Ultimately, it's the intelligence of how you capture it how you ingest it, how you, you process it but then ultimately how you, how you contextualize it and then how you apply it to different problems. and so the attack surface area and security is a very broad, that's why there's so many point solutions that are out there. And so the breadth of solutions, you know we just want to continue to add solutions and capabilities on top of this polygraph security architecture that allows for the same kind of simple experience, the same kind of 10x value proposition, but, but, but wider. And so we can eliminate more and more of those of those point solutions. So, our, our thinking on it is that, you know we can participate once we have a customer the land and expand motion of what we have. We want to make it really really frictionless for customers to try our technology. And so that's why we do POC. That's why it only takes a couple of minutes and you can do it for just Kubernetes or just containers or just vulnerability discovery and managed like wherever your specific pain point is. We want to help identify what that is, you know give you a chance to try it. And then once we prove ourselves it's very easy to extend that across the board. So we get natural growth in velocity from people moving to cloud and just, you know more usage of, of compute and storage and sort of etc, but breadth of actually the security or posture or a tax service that they have as well. So, you know so I think we have an opportunity to benefit from, from both the depth and the breadth, you know but the value that we're delivering is ultimately the software that we're running on top of the infrastructure. And you mentioned observability, there's a number of companies that are leveraging the data and insights collected in different ways to converge security and observability over time. And, we see that, you know that ultimately there's a very very big security company that needs to be built. That really is best of breed, but the data and the insights that we're providing to our primary customer, which is really DevOps. I mean, it's really the development communities and the builders or who we're changing security for and enabling, in addition to the security teams, you know we think that we're going to continue to drive software that adds value on that data set and it can be applied to multiple problems in the future. So today security is a massive market. We're going to focus there, but it does. It does extend pretty naturally to other markets >> It's a hot market security. Everyone needs to have the latest and greatest and also has to be effective. I got to ask you specifically around startup transition to a rapidly growing company to now you're going to the next level where you're starting to having to get into some serious, big complex enterprise go to market sales motions. So what's in it for the customer. What's the, what's the pain point? What's the customer orientation. What do you marketing into as a solution? Is it the developer? Is it the CSO? Is it the CXO, what's in it for the enterprise? Why Lacework, why are they engaging? You guys get record numbers. What's the, what's in it for them. What's the, if I'm the customer what's in it for me? >> Ultimately efficacy, which is your security posture is it goes up significantly, simplicity, which is makes it easier for you to do your other jobs, you know and I'll have to look for those needles in a haystack and ROI, you know which is it's just compelling, and much, much more efficient than what, what you're doing today. So that that's a pretty universal value proposition and applies to cloud native businesses that are high growth that applies to government agencies. It applies to a large complex enterprises. We have a wonderful kind of go to market motion right now. I think Andy Byron and the team who've been here have really done a wonderful job of really making the customer buying experience and the journey really efficient, you know and help them quantify the impact and the risks and then deliver value. And I think, that that applies in sort of the commercial mid-market and cloud native space. And like I mentioned, we had, a number of deals in the quarter that were seven figure deals, you know in very complex organizations with massive demands. And, you know it ultimately selling is a team sport and, you know and still having the process and the rigor, that's there fine tuning that to make sure you have the people and the partnerships, you know, that deliver solutions in the way that customers want to buy them and then ultimately deliver a value proposition that is just unquestionably better. And I think we have all of those elements, you know we'll be entering the, the large enterprise very aggressively in the quarters to come. I that's where I've come from, you know running a multi-tool, you know, kind of go to market engines where you've got mid-market commercial enterprise large enterprise government across all geographies is, is really fun to expand. And, we're we're hiring as fast as we can maintain quality, you know? And so we're out of that startup phase now and entering into real scale. And, I think that, you know in the AWS marketplace I think we're the number one startup vendor. If I, if I got my facts, right. for, for private offers, we're one of the top security players and top 50 ISBs in the marketplace overall. And so in order for us to get the motion we need to make sure that we're delivering our value in the context of how companies want to buy it. And people want to use AWS credits, you know to apply to their solutions. And so it's really important for us to make that frictionless buying experience occur. And so we're excited about it. I think we've got a really nice start and it's the fun part of building companies, which is how do you attune things to make sure you're making it really really easy for the market to absorb your technology. And then once you're there, delight the hell out of them and just make sure that, that there's that they're excited in our, our net retention rates are the best I've seen in the marketplace. Our net promoter scores, you know, are in the high fifties low sixties, which, which is fantastic in this space. I think it's best in class by order of magnitude some players, big SIM players that are out there, you know have a customer in net promoter score of four. You know that means 96% of the people or 96 boats that says they wouldn't recommend the solution to their, to their peers. So, at pure, we've got this at scale. So from 70 to, in the, in the low eighties I think we have the opportunity to do the same thing here. So, combination of tailoring the motion that we have making it really easy for the buyer to buy what they want with whom they want from whom they want, you know and then just spreading a value proposition. That is a no brainer is, is I think the secret recipe >> If anything, it's interesting, you know you're so much experience in the enterprise and tech with cloud native you're basically laying out the success formula, which is if you have a value proposition you should be able to get it in quickly. You don't need the top down. win everything you can have a value proposition that can be enabled for usage and then grow rapidly when it's successful and that's cloud, that's the cloud business model. So it's not so much about organic versus this. It's really what the preferred motion is. >> It's speed, and I think developers in particular it's why the cloud happened, right? I.T wasn't delivering services in, in the speed and the efficacy that, that, that the developers wanted. And so in order to appeal to the developer community you need to deliver something that's frictionless and easy and fits into JIRA and fits into their workflow processes and speaks their language. And so we built our platform and our solutions for builders because that's where the money is. That's where the pain point is and that's and they want to build secure code. They just don't want to be told no. And so, we want to automate that process and make code secure and do that, you know in the build phase and then do it in the runtime. And then across the CICD pipeline we want to continuously be adding value across that. And, and the developers, candidly when pure bought the solution, many years ago and I introduced him to the company, it was it was the general manager of our software business unit that bought it not the security team. And I think that's a trend that is continuing that we're going to focus on. >> A lot of people realize that security and compliance and automation kind of all go together where you don't want to disrupt developers to kind of engineer something just to do an integration, for instance. So there's a real business model impact that you're hitting on here. That's not just a technical solution. It's really how the business is operating. And I think that to me is super interesting use case. What's your reaction to that? Do you see this as a, as a- >> No it's, that's that's that third part that I was talking about, you know which is that's most exciting is that, you know people are calling shift left, right. so moving, you know security into the development pipeline as it's happening and in integrating security architects as value added into the development organizations themselves and leveraging automated machine learning tools like ours to be able to simplify and automate the process versus slowing it down. So we think that shift left is, is super exciting and, and will continue. And we actually think we're the leaders in that space. We want to continue to be the leaders in that. >> Congratulations, great insight. Awesome to have you on and to hear from your experience and also the great venture that your scaling up and to the next level. Lacework, David thanks for coming on, but I'll give you the last minute to close us out. Give us a quick plug for the company vitals, what you're working on now, what you're looking for, you're obviously hiring give a quick plug for Lacework. What you, what are you working on? >> So, number one, we love our partnership with AWS. And so we're going to continue to invest, invest there. Two the businesses growing North of 300% year over year. That means that we've got record breaking growth and lots of hiring. So we're hiring across all functions. And three give us an opportunity. I, I think that, you know, you can fundamentally we want to be the bar of what you define all other security companies and all the technology companies. So it's a high bar. We want to make it frictionless, frictionless to try give us a shot, give us some feedback. And I'm grateful and privileged to be part of this, this wonderful team. So look forward to spending more time with you, John, in the future. >> Man, looking forward to a lot lots of talk about David Hatfield CEO of Lacework great company scaling up again. Another success story in cloud, cloud native as Po, COVID comes to a close, if you will for this phase and people get back to real life. The scale of cloud is going to be leading it and a new technology is going to be powering it. This is theCube conversation. I'm John Furrier. Thanks for watching. (soft music playing) (music fades)

>> Announcer: From around the globe, it's theCUBE! With digital coverage of IBM Think 2021. Brought to you by IBM. >> Well welcome everyone as theCUBE continues our IBM Think series. It's a pleasure to have you with us here on theCUBE. I'm John Walls, and we're joined today by Brian Loveys who is the Director of Offering Management for Customer and Employee Care Applications at IBM in the Data and AI Division. So, Brian, thanks for joining us from Ottawa, Canada. Good to see you today. >> Yeah, great to be here, John. And looking forward to the session today. >> Which, by the way, I've learned Ottawa are the home of the world's largest ice skating rink. I doubt we get into that today, but it is interesting food for thought. So, Brian, first off, let's just talk about the AI landscape right now. I know IBM obviously very heavily invested in that. Just in terms of how you see this currently in terms of enterprise adoption, what people are doing with it, and just how you would talk about the state of the industry right now. >> You know, it's a really interesting one, right? I think if you look at it, you know, different companies, different industries, frankly, are at different stages of their AI journey, right? I think for me personally, what was really interesting was, and we're all going through the pandemic right now, but last year with COVID-19 in the March timeframe, it was really interesting to see the impact, frankly, in the space that I play predominantly in around customer care, right? When the pandemic hit, immediately call centers, contact centers got flooded with calls, right? And so it created a lot of problems for organizations. But what was interesting to me is it accelerated a lot of adoption of AI to organizations that typically lag in technology, right? So if you think about public sector, right, that was one area that got hit very, very hard with questions and those types of things, and trying to, you know, communicate out information. So it was really interesting to see those organizations, frankly, accelerate really, really quickly, right? And if you actually, you know, talk to those organizations now, I think one of the most interesting things to me in thinking about it and talking to them now is like, hey, you know, we can do this, right? AI is really not that complicated. It can be simplified, we can take advantage of it and all of those types of things, right? So I think for me, you know, I kind of see different industries at sort of different levels, but I think with COVID in particularly, you know, and frankly not just COVID, but even digital transformation alongside COVID is really driving a lot of AI in an accelerated manner. The other thing that I'll kind of talk to a little bit here is I still think we're very much in the early innings of this, right? There's a tremendous opportunity to innovate in this space. And I think we all know that, you know, data is continually being created every single day. And as more people become even more digitalized, there's more and more data being created. Like it's how do you start to harness that data more effectively, right, in your business every day. And frankly, I think we're just scratching the surface on it. And I think tremendous amount of opportunity as we move forward. >> Yeah, you really raised an interesting point which I hadn't thought about in terms of, we think about disruptors, we think about technology being a disruptor, right, but in this case it was purely, or really largely environment, you know, that was driving this disruption, right, forcing people to make these adoption moves and transitions maybe a little quicker than they expected. Well, so because of that, because maybe somebody had to speed up their timetable for deployments and what have you, what kind of challenges have they run into then, where, because as you describe it, it's not been the more organic kind of decision-making that might be made sometimes, situation dictated it. So what have you seen in terms of challenges, you know, barriers, or just a little more complexity, perhaps, for some people who're just now getting into the space because of the environment you were talking about? >> I think a lot of this is like, you know, people don't know where to get started, right, a lot of the time, or how AI can be applied. So a lot of this is going to be about education in terms of what it can and cannot do. And then it all depends on the use cases you're talking about, right? So if I think about, you know, building out machine learning models and those types of things, right, you know, the set of challenges that people will typically face in these types of things are, you know, how do I, you know, collect all the data that I need to go build these models, right? How do I organize that data? You know, how do I get the skillsets needed to ultimately, you know, take advantage of all of that data to actually then apply to where I need it in my business, right? So a lot of this is, you know, people need to understand those concepts or those pieces to ultimately be successful with AI. And you know, what IBM is doing right here, and I'll kind of, this will be a key theme throughout this conversation today is, you know, how do you sort of lower the time to value to get there across that spectrum, but also, you know, frankly, the skills required along the way as well? But a lot of it is like, people don't know what they don't know at the end of the day. >> Well, let me ask you about your AI play then. A lot of people involved in this space, as you well know, competition's pretty fierce and pretty widespread. There's a deep bench here. In terms of IBM though, what do you see as kind of your market differentiator then? You know, what do you think sets you apart in terms of what you're offering in terms of AI deployments and solutions? >> No, that's a great question. I think it's a multifaceted answer, frankly. The first thing I'll kind of talk through a little bit, right, is really around our platform and our framework, right? We kind of refer to as our AI ladder, but it's really an integrated, you know, sort of cohesive platform for companies around the journey to AI, right? So kind of what I was mentioning a bit earlier, right? If you think about, you know, AI is really about supplying the right data into AI, and then being able to infuse it to where you need it to go, right? So to do that, you need a lot of the underlying information architecture to do that, right? So you need the ability to collect the data. You need the ability to organize the data. You need the ability to build out these models or analyze the data, right? And then of course you need to be able to infuse that AI wherever you need it to be, right? And so we have a really nice integrated platform that frankly can be deployed on any cloud, right, so we get the flexibility of that deployment model with that integrated platform. And if you think about it, we also have built, right, you know, sort of these industry-leading AI applications that sit on top of that platform and that underlying infrastructure, right? So Watson Assistant, right, our conversational AI which we'll talk probably a little bit more on this conversation, right? Watson Discovery focused on, you know, intelligent document processing, right, AI search type applications. We've got these sort of market-leading applications that sit on top, but there's also other things, right? Like we have a very, very strong research arm, right, that continues to invest and funnel innovations into our product platform and into our product portfolio, right? I think many people are aware of Project Debater we took on some of the top debaters in the world, right? But research ultimately is very much tied, right, and even, you know, some of the teams that I work with on the ground, we've got them tied directly into the squads that build these products, right? So we have this really big strong research arm that continues to bring innovation around AI and around other aspects into that product portfolio. But it's not just- >> I'm sorry go ahead, please. >> Go ahead, sorry. >> No, no, you go, (laughs) I interrupted, you go ahead. >> Don't worry, I was just going to say, the other two things I'll say like, you know, I'm saying this right, but we've got a lot of sort of proof points in around it, right, so if you talk about the scale, right, the number of customers, the number of case studies, the number of references across the board, right, in around AI at IBM it is significant, right? And not only that, but we've got a lot of, sort of I'll say industry and third-party industry recognition, right? So think about most people are aware of sort of Gartner Magic Quadrants, right, and we're the leader almost across the board, right, or a leader across the board. So, you know, cloud AI developer service, insight engines, machine learning, go down the line. So, you know, if you don't trust me, there's certainly a lot of third party validation around that as well, if that makes sense. >> Yeah, sure does. You know, we hear a lot about conversational AI and, you know, with online chat bots and voice assistance, and a myriad applications in that respect. Let's talk about conversational right now. Some people think is a little narrow, but yet there appears to be a pretty broad opportunity at the same time. So let's talk about that conversational AI element to what you're talking about at IBM and how that is coming into play. And perhaps is a pretty big growth sector in this space. >> Yeah, I think, again, I talk about scratching the surface, early innings, you'll see that theme a lot too. And I think this is another area around that, right? So, listen, let's talk about the broader side. Let's first talk about where conversational AI is typically applied, right? So you see it in customer service. That's the obvious place where I've seen the most deployments in. But if you think about, it's not just really around customer service, right? There's use cases around sales and marketing. You can think about, you know, lead qualification for example, right. You know, I'm on a website, how can I get information about a product or service? How can I automate some of that information collection, answering questions, how can I schedule console? All those things can be automated using, right, conversational AI, but organizations don't want these sort of points solutions across the customer journey. What they're ultimately looking for is a single assistant to kind of, you know, front that particular customer. So what if I do come on from a lead qual perspective, but really I'm not there for lead qual, I'm actually a customer, and I want to get a question answered, right? You don't want to have these awkward starts and stops with organizations, right? So on the customer side where we see the conversational AI going is really sort of covering that whole gambit in terms of that customer journey, right? And it's not just the customer journey, but you also want to be across channels, right? So you can imagine not just, you know, the website and the chat on the website, but also, right, across your messaging channels, across your phone, right? And not just that, but you also want to be able to have a really nice experience around, hey maybe I'm on a phone call with some automation, but I need to be able to hand them off to a digital play, right? Maybe that's easier to sign up for a particular offer, or do some authentication, or whatever it might be, right? So to sort of be able to switch between the channels is really, really going to become more important in terms of a seamless experience as you do kind of go through it, right- >> So let's talk about customers- >> Oh, go ahead sir. >> Yeah, you talked about customers a little bit, and you mentioned case studies, but I hope we can get into some specifics, if you can give us some examples about people, companies with whom you've worked and some success that you've had in that respect. And I think maybe the usual suspects come to mind. I think about finance, I think about healthcare, but you said, "Hey buddy, but customer call issues, you know, service centers, that kind of thing would certainly come into play," but can you give us an idea or some examples of deployments and how this is actually working today? >> Oh, absolutely, right? So I think you were kind of mentioning, you were talking about sort of industries that are relevant, right? So, you know, the ones that I think are most relevant that we've seen are the ones with the biggest sort of consumer side of it, right? So clearly in financial services, banks, insurance are clearly obvious ones. Telecommunication, retail, healthcare, these are all sort of big industries with a lot of sort of customers coming in, right? And so you'll see different use cases in those industries as well, right? So the obvious one, we've got a really good client, Royal Bank of Scotland, they've now changed their name to NatWest in Scotland. So they started out with customer service, right? So dealing with personal banking questions through their website. What's interesting, and you'll see this with a lot of these use cases is they will start small, right, with a single use case, but they'll start to expand from there. So for example, NatWest, right, they're starting with personal banking, but they're now expanding to other areas of the business across that customer journey, right? So that's a great example of where we've seen it. Cardinal Health, right, because we're not dealing with customers in terms of external customers, but dealing with internal customers, right, from an IT help desk standpoint. So it's not always external customers. Oftentimes, frankly, it can be employees, right? So they are using it through an IDR system, right? So through over the phone, right, so I can call, instead of getting that 1-800 number, I'm going to get a nice natural language experience over the phone to help employees with common problems that they have with their help desk. So, and they started really, really small, right? They started with, you know, simple things like password resets, but that represented a tremendous amount of volume that ultimately hit at their call centers. So NatWest is a great example. CIBC, another bank in Canada, Toronto, is a great example. And the nice thing about what CIBC is doing and they're a big, you know, we have four big banks here in Canada. What CIBC do is really focusing a lot on the transactional side. So making it really easy to do interact transfers or send money, or all those types of things, or check your balance or whatever it might be. So putting a nice, simple interface on some of those common, transactional things that you would do with a bank as well. >> You know, before I let you go, I'd like to hit just a buzzword we hear a lot of these days, natural language processing, NLP. All right, so NLP, define that in terms of how you see it and how is it being applied today? Why does NLP matter, and what kind of differences is it making? >> Wow, natural language processing is a loaded term as a buzzword, I completely agree. I mean, listen, at the 50,000 foot level, natural language processing is really about understanding language, right? So what do I mean by that? So let's use the simple conversational example we just talked about. If somebody's asking about, you know, "I'd like to reset my password," right? You have to be able to understand, well what is the intent behind what that user is trying to do, right? They're trying to reset a password, right? So being able to understand that inquiry that user has that's coming in and being able to understand what the intent is behind it. That's sort of one key aspect of natural language processing, right? What is the intent or the topic around that paragraph or whatever it might be. The other sort of key thing around natural language processing, the importance of extracting certain things that you need to know. And again, using the conversational AI side, just for a minute, to give a simple example. If I said, "You know what, I need to reset my password." I know what the intent is, I want to reset a password, but, right, I don't know which password I'm trying to reset. Right, and so this is where sort of you have to be able to extract objects, and we call them entities a lot of the time and sort of the (indistinct) or lingo. But you got to be able to extract those elements. So, you know, I want to reset my ATM password. Great, right, so I know what they're trying to do, but I also need to extract that it's the ATM password that I'm trying to do. So that's one sort of key angle, natural language processing, and there's a lot of different AI techniques to be able to do those types of things. I'll also tell you though, there's a lot around the content side of the fence as well. So you can imagine how like a contract, right, and there were thousands of these contracts, and some of your terms may change. You know, how do you know, out of those thousands of contracts where the problems are, where I need to start looking, right? So another sort of key area of natural language processing is looking at the content itself, right? Can I look at these contracts and automatically understand that this is an indemnity clause, right? Or this is an obligation, right? Or those types of things, right, and being able to sort of pick those things out, so that I can help deal with those sort of contract-processing things. So that's sort of a second dimension. The third dimension I'll kind of give around this is really around, you can think about extracting things like sentiment, right? So we talked about, you know, extracting objects and nouns, and those types of things, but maybe I want to know in an analytics use case with customers, you know, what is the sentiment and, you know, analyzing social media posts or whatever it might be, what's the sentiment that people have around my product or service. So natural language process, if you think about it at the real high level is really about how do I understand language, but there's a variety of sort of ways to do that, if that makes sense. >> Yeah, no sure, and I think there are a lot of people out there saying, "Yeah, the sooner we can identify exasperation (laughs) the better off we're going to be, right, in handling the problems." So, it's hard work, but it's to make our lives easier, and congratulations for your fine work in that space. And thanks for joining us here on theCUBE. We appreciate the time today, Brian. >> Thank you very much. >> You bet, Brian Loveys, he's talking to us from IBM, talking about conversational AI and what it can do for you. I'm John Walls, thanks for joining us here on theCUBE. (upbeat music) ♪ Dah, deeah ♪ ♪ Dah, dee ♪ (chimes ringing)

>>from >>Around the globe. It's the cube with digital coverage of IBM think 2021 brought to you by IBM >>Well welcome everyone is the cube continues or IBM Thanks series. It's a pleasure to have you with us here on the cube. I'm john walls and we're joined today by brian loves who is the director of offering management for customer and employee care applications in the at IBM in the data and AI division. So brian, thanks for joining us from Ottawa Canada, good to see you today. >>Yeah, great to be here john I'm looking forward to the session today >>which by the way I've learned Ottawa is the home of the world's largest ice skating rink. I doubt we'll get into that today, but it is interesting food for thought. Uh so brian first off, let's just talk about um the Ai landscape right now. I know IBM obviously very heavily invested in that uh just in terms of how you see this currently as in terms of enterprise adoption, what people are doing with it and and just how you would talk about the state of the industry right now, >>you know, it's a really interesting one, right? I think if you look at it, you know different companies, different industries frankly are at different stages of their Ai journey, right? Um I think for me personally what was really interesting was, and we're all going through the pandemic right now, but last year with covid 19 in the March timeframe, it was really interesting to see the impact, frankly in the space that I played predominantly in around customer care, right? When the pandemic hit immediately call centers, contact centres got flooded with calls, right? And so it created a lot of problems for organizations. But it was interesting to me is it accelerated a lot of adoption of ai to organizations that typically lag and technology. Right? So if you think about public sector, right, that was one area that got hit very, very hard with questions and those types of things and trying to communicate and communicate out information. So it was really interesting to see those organizations frankly accelerate really, really quickly, right? And if you actually talk to those organizations now, I think one of the most interesting things to me and thinking about it and talking to them now is like, hey, you know, we can do this right, AI is really not that complicated, it can be simplified, we can take advantage of it and all of those types of things. Right? So I think for me, you know, I kind of see different industries that sort of different levels, but I think with Covid in particularly, you know, and frankly not just Covid, but even digital transformation alongside Covid is really driving a lot of ai in an accelerated manner. The other thing I'll kind of I'll kind of talk to a little bit here is I still think we're very much in the early innings of this, right, there is a tremendous opportunity innovating in the space and I think we all know that you know data is continually being created every single day and as more people become even more digitalized, there's more and more data being created. Like how do you start to harness that data more effectively, right in your business every day? And frankly I think we're just scratching scratching the surface on it and I think tremendous amount of opportunity as we move forward. >>Yeah, he really is really raised an interesting point which I hadn't thought about in terms of, we think about disruptors, we think about technology being a disrupter, right? But in this case it was purely really, largely environment that was driving this disruption, right, forcing people to to make these adoption moves and transitions maybe a little quicker than they expected. So because of that, because maybe somebody had to speed up their timetable for deployments and what have you what what kind of challenges have they run into them? Where because, as you describe it, it's not been the more organic kind of decision making that might be made, sometimes situation dictated it. So what have you seen in terms of challenges, barriers or just a little more complexity perhaps for some people who are just not getting into the space because of the environment you were talking about? >>I think a lot of this is like people don't know where to get started, right, a lot of the time or how ai can be applied. So a lot of this is going to be a bad education in terms of what it can and cannot do, and then it all depends on the use cases you're talking about, right? So if I think about, you know, building a machine learning models and those types of things right? You know, this set of challenges that people will typically face in these types of things are, you know, how do I collect all the data that I need to go build these models? Right? How do I organize that data? Um you know, how do I get the skill sets needed to ultimately, you know, take advantage of all that data to actually then apply to where I needed in my business? Right, So a lot of this is, you know, people need to understand, you know, those concepts are those pieces um to ultimately be successful with AI and you know what IBM is doing right here and I'll kind of this will be a key theme through this conversation today, is how do you sort of lower the time to value, to get there across that spectrum, but also, you know, frankly the skills >>required along the way as >>well, but a lot of it is like people don't know what they don't know at the end of the day. Mhm. >>Well, let me ask you about about your AI play then, a lot of people involved in this space, as you well know, you know, competitions pretty fierce and pretty widespread, there's a deep bench here um in terms of IBM know, what do you see is kind of your market different differentiator then, you know, what what do you think set you apart in terms of what you're offering in terms of AI deployments and solutions? >>No, that's a great question. I think it's a multifaceted answer, frankly. Um the first thing I'll kind of talk through a little bit right, is really around our platform and our our framework, right? We could refer to as our air ladder, um but it's really an integrated, you know, sort of cohesive platform for companies around the journey to AI, right? So kind of what I was mentioning earlier, right? If you think about, you know, AI is really about supplying the right data into A I. And then being able to infuse it to where you needed to go. Right? So to do that, you need a lot of the underlying information architecture to do that, Right? So you need the ability to collect the data, you need the ability to organize the data, you need the ability to to build out these models, right? Or analyze the data and then of course you need to be able to infuse that ai wherever you need it to be. Right. And so we have a really nice integrated platform that frankly can be deployed on any cloud. Right? So we got the flexibility that deployment model with that in greater platform. And you think about it? We also have built right, you know, sort of these industry leading Ai applications that sit on top of that platform and that underlying infrastructure. Right? So Watson assistant, Right. Our conversational AI, which we'll talk probably a little bit more on this conversation. Right, Watson discovery focus on, you know, intelligent document processing, right. AI search type applications. We've got these sort of market leading applications that sit on top, but there's also other things, right? Like we have a very, very strong research arm right, that continues to invest and funnel innovations into our product platform and into our product portfolio. Right? I think many people are aware of project debater, we took on some of the top debaters in the world, right? But research ultimately is very much tied, right? And even some of the teams that I work with on the ground, we've got them tied directly into the squads that build these products, Right? So we have this really big strong research arm that continues to bring innovation around AI and around other aspects into that product portfolio. But it's not just go ahead, >>Please go ahead. three. No, no. You know, I interrupted you. Go ahead. >>No, I was just gonna say that the other two things, I'll say it like, you know, I'm saying this right, but we've got a lot of sort of proof points and around it. Right? So, if you talk about the scale right? The number of customers, the number of case studies, a number of references across the board, right? In around AI AT IBM It is significant, Right? Um, and not only that, but we've got a lot of sort of, I'll say industry and third party industry recognition. Right? So think about most people are aware of sort of Gartner magic quadrants, right? And we're the leader almost across the board, Right? Or a leader across the board. So cloudy I developer service inside engines, machine learning go down the line. So, you know, if you don't trust me, there's certainly a lot of third party validation around that as well. That makes sense. >>Yeah, it sure does. You know, we're hearing a lot about conversational AI and, you know, with online chat bots and voice assistance and a myriad applications in that respect. Let's talk about conversational right now. Some people think it's little narrow, but, but yet there appears to be a pretty broad opportunity at the same time. So let's talk about that conversational AI um, uh, element um, to what you're talking about at IBM and how that is coming into play and, and perhaps is a pretty big growth sector in this space. >>Yeah, I think again, I talked about scratching the surface early innings. You'll see that theme a lot too. And I think this is another area around that. So listen, let's talk about the broader side. Let's first talk about where conversation always typically applied. Right? So you see it in customer service, that's the obvious place we're seeing the most appointments in. But if you think about, it's not just really around customer service, right? There's use cases around sales and marketing. If you think about, you know, lead qualification, for example, right? How can, you know, I'm on a website, how can I get information about a product or service? How can I automate some of that information collection, answering questions? How can I schedule console? All those things can be automated using great conversationally. I, the organizations don't want these sort of point solutions across the customer journey. What we're ultimately looking for is a single assistant to kind of, you know, front right, that particular customer. So what if I do come on from a legal perspective, but really I'm not here for legal. I'm actually a customer and I want to get a question answered, right? You don't want to have these awkward starts and stops with organizations, Right? So on the customer side where we see the conversation like, hey, I going and it's really kind of covering that full gambit in terms of that customer journey, right? And it's not just the customer journey, but you also want to be across channels, right? So you can imagine right now, not just, you know, the website and the chat on the website, but also right across their messaging channels, right across your phone. Right. And not just that, but you also want to be a really nice experience around, hey, maybe I'm on a phone call with some automation, but I need to be able to hand them off to a digital play. Right? Maybe that's easier to sign up for a particular offer or do some authentication or whatever might be, right. So to sort of be able to sort of switch between the channels, it's really, really going to become more important in this sort of sort of seamless experience as you just kind of go through it. Right? >>So you're coming by customers. Yeah. >>You talked about customers a little bit and you mentioned case studies, but can we get, I hope we can get into some specifics. You can give us some examples about people, companies with whom you've worked and and some success that you've had that respect. And I think maybe the usual suspects come to mind about finance. I might health care, but you said anybody with customer call issues, service centers, that kind of thing would certainly come into play. But can you give us an idea or some examples of deployments and how this is actually working today? >>Oh, absolutely. Right. So I think you kind of mentioned you become sort of industries that are relevant. Right? So, you know, the ones that I think are most relevant that we've seen are the ones with the biggest sort of consumer sort of side to it. Right? So clearly in financial services, banks, insurance, and clearly obvious ones telecommunications, retail, healthcare, these are all sort of big industries with a lot of sort of customers coming in. Right? So you'll see different use cases in those industries as well. Right. So the obvious one, we've got a really good client, Royal Bank of Scotland, they've now changed their name to natwest Open Scotland. Um So they started out with customer service. Right? So dealing with personal banking questions through their website, what's interesting and you'll see this with a lot of these use cases is they will start small, right with a single use case that they'll start to expand from there. So, for example, >>natwest right there, starting with they started with personal banking, but they're not expanding to other areas of the business across that customer journey. Right. So it's a great example of where we've seen it. Cardinal Health Right. We're not dealing with customers in terms of external customers but dealing with internal customers right from the help that standpoint. So it's not always external customers. Oftentimes frankly it can be employees. Right? So they are using it right through an I. V. R. System. Right? So through over the phone. Right. So I can call instead of getting that 1 800 number. I'm going to get a nice natural language experience over the phone to help employees with common problems that they have with their health does so. And they started really, really small, right? They started with simple things like password resets but that represented a tremendous amount of volume but ultimately headed their cost cost centers. So not West is a great example. C I B C. Another bank in Canada Toronto is a great example and the nice thing about what CNBC is doing and there are big, you know, we have four big banks here in Canada, what have you seen do is really focusing a lot on the transactional side. So making it really easy to do interact transfers or send money or over those types of things or check your balance or whatever it might be. So putting a nice simple interface on some of those common transactional things that you >>would do with the bank as well, >>you know, before I let you go, uh I'd like to hit this of buzz where we hear a lot of these days natural language processing. NLP Alright, so, so NLP define that in terms of how you see it and and how is it being applied today? Why why does NLP matter? And what kind of difference is it making? >>Wow, that's a loaded natural language processing. There's a loaded term in a buzzword. I completely agree. I mean listen, at the 50,000 ft level, natural language processing is really about understanding length, Right? So what do I mean by that? So let's use the simple conversational example. We just talked about if somebody is asking about, I'd like to reset my password right? You have to be able to understand what is the intent behind what that user is trying to do right there? Trying to reset a password, right? So being able to understand that inquiry that the user has that's coming in and being able to understand what the intent is behind it. >>That's sort of one, you know, aspect of natural language processing, right? What is the intent or the topic around that paragraph or whatever it might be. The other sort of key thing around natural language processing the importance, extracting certain things that you need to know. And again using the conversational ai side, just for a minute to give a simple example if I said you know what I need to reset my password, I know what the intent is. I want to reset a password but Right I don't know which password I'm trying to reset. Right? So this is where you have to be able to extract objects and we call them entities a lot of time in sort of the ice bake or lingo but you've got to be able to extract those elements. So you know I want to reset my A. T. M. Password. Great. Right so I know what they're trying to do but I also need to extract that it's the A. T. M. Password that I'm trying to do. So that's one sort of key angle of natural language processing and there's a lot of different techniques to be able to do those types of things. I'll also tell you though there's a lot around the content side of the fence as well, right? So you can imagine having a contract, right? And there are thousands of these contracts and some of your terms may change. How do you know, out of those thousands of contracts where the problems are, where I need to start looking, Right? So another sort of keep key area of natural language processing is looking at the content itself. Can I look at these contracts and automatically understand that this is an indemnity clause, Right? And this is an obligation, right? Or those types of things, right? And be able to sort of pick pick those things out so that I can help deal with those sort of contract processing things. That's sort of a second dimension. The third dimensional kind of kind of give around this is really around. You can think about extracting things like sentiment, right? So we talked about, you know, extracting objects and downs and those types of things. But maybe I want to know and analytics use case with customers. Um you know, what is the sentiment and you know, analyzing social media posts or whatever it might be. What's the sentiment that people have around my product or service? So naturally this process, if you think about it, the real high level is really about how do I understand language? But there's a variety of sort of ways to do that if that makes sense? >>Yeah, sure. And I think there's a lot of people out there saying, yeah, the sooner we can identify exasperation, the better off we're going to be right and handling the problems. But it's hard work but it's to make our lives easier and congratulations for your fine work in that space. And thanks for joining us here on the cube. We appreciate the time. Today, brian, >>thank very much. >>You bet BRian Levine is talking to us from IBM talking about conversational Ai and what it can do for you. I'm john Walsh, thanks for joining us here on the cube. Mhm. >>Mhm.

(upbeat music) >> In 2010 Wikibon predicted that the all flash data center was coming. The forecast at the time was that flash memory consumer volumes, would drive prices of enterprise flash down faster than those of high spin speed, hard disks. And by mid decade, buyers would opt for flash over 15K HDD for virtually all active data. That call was pretty much dead on and the percentage of flash in the data center continues to accelerate faster than that, of spinning disk. Now, the analyst that made this forecast was David FLoyer and he's with me today, along with Renen Hallak who is the founder and CEO of Vast Data. And they're going to discuss these trends and what it means for the future of data and the data center. Gentlemen, welcome to the program. Thanks for coming on. >> Great to be here. >> Thank you for having me. >> You're very welcome. Now David, let's start with you. You've been looking at this for over a decade and you know, frankly, your predictions have caused some friction, in the marketplace but where do you see things today? >> Well, what I was forecasting was based on the fact that the key driver in any technology is volume, volume reduces the cost over time and the volume comes from the consumers. So flash has been driven over the years by initially by the iPod in 2006 the Nano where Steve Jobs did a great job with Samsung and introducing large volumes of flash. And then the iPhone in 2008. And since then, all of mobile has been flash and mobile has been taking in a greater and greater percentage share. To begin with the PC dropped. But now the PCs are over 90% are using flash when there delivered. So flash has taken over the consumer market, very aggressively and that has driven down the cost of flash much much faster than the declining market of HDD. >> Okay and now, so Renen I wonder if we could come to you, we've got I want you to talk about the innovations that you're doing, but before we get there, talk about why you started Vast. >> Sure, so it was five years ago and it was basically the kill of the hard drive. I think what David is saying resonates very, very well. In fact, if you look at our original presentation for Vast Data. It showed flash and tape. There was no hard drive in the middle. And we said 10 years from now, and this was five years ago. So even the dates match up pretty well. We're not going to have hard drives anymore. Any piece of information that needs to be accessible at all will be on flash and anything that is dormant and never gets read will be on tape. >> So, okay. So we're entering this kind of new phase now, with which is being driven by QLC. David maybe you could give us a quick what is QLC? Just give us a bumper sticker there. >> There's 3D NAND, which is the thing that's growing, very very fast and it's growing on several dimensions. One dimension is the number of layers. Another dimension is the size of each of those pieces. And the third dimension is the number of bits which a QLC is five bits per cell. So those three dimensions have all been improving. And the result of that is that on a wafer of, that you create, more and more data can be stored on the whole wafer on the chip that comes from that wafer. And so QLC is the latest, set of 3D NAND flash NAND flash. That's coming off the lines at the moment. >> Okay, so my understanding is that there's new architectures that are entering the data center space, that could take advantage of QLC enter Vast. Someone said they've rented this, a nice set up for you and maybe before we get into the architecture, can you talk a little bit more about the company? I mean, maybe not everybody's familiar with with Vast, you share why you started it but what can you tell us about the business performance and any metrics you can share would be great? >> Sure, so the company as I said is five years old, about 170, 180 people today. We started selling product just around two years ago and have just hit $150 million in run rate. That's with eight sales people. And so, as you can imagine, there's a lot of demand for flash all the way down the stack in the way that David predicted. >> Wow, okay. So you got pretty comfortable. I think you've got product market fit, right? And now you're going to scale. I would imagine you're going to go after escape velocity and you're going to build your moat. Now part of that, I mean a lot of that is product, right? Product is sales. Those are the cool two golden pillars, but, and David when you think back to your early forecast last decade it was really about block storage. That was really what was under attack. You know, kind of fusion IO got it started with Facebook. They were trying to solve their SQL database performance problems. And then we saw pure storage. They hit escape velocity. They drove a truck through EMC sym metrics HDD based install base which precipitated the acquisition of XtremeIO by EMC. Something Renan knows a little bit about having led development, of the product but flash was late to the NAS party guys, Renan let me start with you. Why is that? And what is the relevance of QLC in that regard? >> The way storage has been always, it looks like a pyramid and you have your block devices up at the top and then your NAS underneath. And today you have object down at the bottom of that pyramid. And the pyramid basically represents capacity and the Y axis is price performance. And so if you could only serve a small subset of the capacity, you would go for block. And that is the subset that needed high performance. But as you go to QLC and PLC will soon follow the price of all flash systems goes down to a point where it can compete on the lower ends of that pyramid. And the capacity grows to a point where there's enough flash to support those workloads. And so now with QLC and a lot of innovation that goes with it it makes sense to build an all flash, NAS and object store. >> Yeah, okay. And David, you and I have talked about the volumes and Renan sort of just alluded to that, the higher volumes of NAS, not to mention the fact that NAS is hard, you know files difficult, but that's another piece of the equation here, isn't it? >> Absolutely, NAS is difficult. It's a large, very large scale. We're talking about petabytes of data. You're talking about very important data. And you're talking about data, which is at the moment very difficult to manage. It takes a lot of people to manage it, takes a lot of resources and it takes up a lot, a lot of space as well. So all of those issues with NAS and complexity is probably the biggest single problem. >> So maybe we could geek out a little bit here. You guys go at it, but Renan talk about the Vast architecture. I presume it was built from the ground up for flash since you were trying to kill HTD. What else do we need to know? >> It was built for flash. It was also built for Crosspoint which is a new technology that came out from Intel and micron about three years ago. Cross point is basically another level of persistent media above flash and below Ram. But what we really set out to do is, as I said to kill the hard drive, and for that what you need is to get the price parity. And of course, flash and hard drives are not at price parity today. As David said, they probably will be in a few years from now. And so we wanted to, jumpstart that, to accelerate that. And so we spent a lot of time in building a new type of architecture with a lot of new metadata structures and algorithms on top to bring that effective price down to a point where it's competitive today. And in fact, two years ago the way we did it was by going out to talk to these vendors Intel with 3D Crosspoint and QLC flash Mellanox with NVMe over fabrics, and very fast ethernet networks. And we took those building blocks and we thought how can we use this to build a completely different type of architecture, that doesn't just take flash one level down the stack but actually allows us to break that pyramid, to collapse it down and to build a single system that is as fast as your fastest all flash block device or faster but as affordable as your hard drive based archives. And once that happens you don't need to think about storage anymore. You have a single system that's big enough and cheap enough to throw everything at it. And it's fast enough such that everything is accessible as sub-millisecond latencies. The way the architecture is built is pretty much the opposite of the way scale-out storage has been done. It's not based on shared nothing. The way XtremIO was the way Isilon is the way Hadoop and the Google file system are. We're basing it on a concept called Dis-aggregated Shared Everything. And what that means is that we have the media on one set of devices, the logic running in containers, just software and you can scale each of those independently. So you can scale capacity independently from performance and you have this shared metadata space, that all of the containers can see. So the containers don't actually have to talk to each other in the synchronous path. That means that it's much more scalable. You can go up to hundreds of thousands of nodes rather than just a few dozen. It's much more resilient. You can have all of them fail and you still didn't lose any data. And it's much more easy to use to David's point about complexity. >> Thank you for that. And then you, you mentioned up front that you not only built for flash, but built for Crosspoint. So you're using Crosspoint today. It's interesting. There was always been this sort of debate about Crosspoint It's less expensive than Ram, or maybe I got that wrong but it's persistent, >> It is. >> Okay, but it's more expensive than flash. And it was sort of thought it was a fence sitter cause it didn't have the volume but you're using it today successfully. That's interesting. >> We're using it both to offset the deficiencies of the low cost flash. And the nice thing about QLC and PLC is that you get the same levels of read performance as you would from high-end flash. The only difference between high cost and low cost flash today is in right cycles and in right performance. And so Crosspoint helps us offset both of those. We use it as a large right buffer and we use it as a large metadata store. And that allows us not just to arrange the information in a very large persistent right buffer before we need to place it on the low cost flash. But it also allows us to develop new types of metadata structures and algorithms that allow us to make better use of the low cost flash and reduce the effective price down even lower than the rock capacity. >> Very cool. David, what are your thoughts on the architecture? give us kind of the independent perspective >> I think it's brilliant architecture. I'd like to just go one step down on the network side of things. The whole use of NBME over fabric allows the users all of the servers to get any data across this whole network directly to it. So you've got great performance right away across the stack. And then the other thing is that by using RDMA for NASS, you're able, if you need to, to get down in microseconds to the data. So overall that's a thousand times faster than any HDD system could manage. So this architecture really allows an any to any simple, single level of storage which is so much easier to think about, architect use or manage is just so much simpler. >> If you had I mean, I said I don't know if there's an answer to this question but if you had to pick one thing Renan that you really were dogmatic about and you bet on from an architectural standpoint, what would that be? >> I think what we bet on in the early days is the fact that the pyramid doesn't work anymore and that tiering doesn't work anymore. In fact, we stole Johnson and Johnson's tagline No More Tears. Only, It's not spelled the same way. The reason for that is not because of storage. It's because of the applications as we move to applications more and more that are machine-based and machines are now not just generating the data. They're also reading the data and analyzing it and providing insights for humans to consume. Then the workloads changed dramatically. And the one thing that we saw is that you can't choose which pieces of information need to be accessible anymore. These new algorithms, especially around AI and machine learning and deep learning they need fast access to the entirety of the dataset and they want to read it over and over and over again in order to generate those insights. And so that was the driving force behind us building this new type of architecture. And we're seeing every single day when we talk to customers how the old architecture is simply break down in the face of these new applications. >> Very cool speaking of customers. I wonder if you could talk about use cases, customers you know, and this NASS arena maybe you could add some color there. >> Sure, our customers are large in data. We started half a petabyte and we grow into the exabyte range. The system likes to be big as, as it grows it grows super linearly. If you have a 100 nodes or a 1000 nodes you get more than 10X in performance, in capacity efficiency and resilience, et cetera. And so that's where we thrive. And those workloads are today. Mainly analytics workloads, although not entirely. If you look at it geographically we have a lot of life science in Boston research institutes medical imaging, genomics universities pharmaceutical companies here in New York. We have a lot of financials, hedge funds, Analyzing everything from satellite imagery to trade data to Twitter feeds out in California. A lot of AI, autonomous driving vehicles as well as media and entertainment both generation of films like animation, as well as content distribution are being done on top of best. >> Great thank you and David, when you look at the forecast that you've made over the years and when I imagine that they match nicely with your assumptions. And so, okay, I get that, but that doesn't, not everybody agrees, David. I mean, certainly the HDD guys don't agree but they, they're obviously fighting to hang on to their awesome run for 50 years, but as well there's others to do in hybrids and the like, and they kind of challenge your assumptions and you don't have a dog in this fight. We just want the truth and try to do our best to report it. But let me start with this. One of the things I've seen is that you're comparing deduped and compressed flash with raw HDD. Is that true or false? >> It's in terms of the fundamentals of the forecast, et cetera, it's false. What I'm taking is the new egg price. And I did it this morning and I looked up a two terabyte disc drive, NAS disc drive. I think it was $54. And if you look at the cost of a a NAND for two terabytes, it's about $200. So it's a four to one ratio. >> So, >> So and that's coming down from what people saw last year, which was five or six and every year has been, that ratio has been coming down. >> The ratio between the cost Delta, between HDD is still cheaper. So Renan I wonder one of the other things that Floyer has said is that because of the advantages of flash, not only performance but also data sharing, et cetera, which really drives other factors like TCO. That it doesn't have to be at parody in order for customers to consume that. I certainly saw that on my laptop, I could have got more storage and it could have been cheaper for per bit for my laptop. I took the flash. I mean, no problem. That that was an intelligence test but what are you seeing from customers? And by the way Floyer I think is forecasting by what, 2026 there will be actually a raw to raw crossover. So then it's game over. But what are you seeing in terms of what customers are telling you or any evidence you have that it doesn't have to be, even that customers actually get more value even if it's more expensive from flash, what are you seeing? >> Yeah in the enterprise space customers aren't buying raw flash they're buying storage systems. And so even if the raw numbers flash versus hard drive are still not there there is a lot of things that can be done at the system level to equalize those two. In fact, a lot of our IP is based on that we are taking flash today is, as David said more expensive than hard drives, but at the system level it doesn't remain more expensive. And the reason for that is storage systems waste space. They waste it on metadata, they waste it on redundancy. We built our new metadata structures, such that they everything lives in Crosspoint and is so much smaller because of the way Crosspoint is accessible at byte level granularity, we built our erasure codes in a way where you can sustain 10, 20, 30 drive failures but you only pay two or 1% in overhead. We built our data reduction mechanisms such that they can reduce down data even if the application has already compressed it and already de-duplicated it. And so there's a lot of innovation that can happen at the software level as part of this new direct dis-aggregated shared everything architecture that allows us to bridge that cost gap today without having customers do fancy TCO calculations. And of course, as prices of flash over the next few years continue declining, all of those advantages remain and it will just widen the gap between hard drives and flash. And there really is no advantage to hard drives once the price thing is solved. >> So thank you. So David, the other thing I've seen around these forecasts is that the comments that you can't really data reduce effectively hard disk. And I understand why the overhead and of course you can in flash you can use all kinds of data reduction techniques and not affect performance, or it's not even noticeable like put the cloud guys, do it upstream. Others do it upstream. What's your comment on that? >> Yes, if you take sequential data and you do a lot of work upfront you can write out in very lot big blocks and that's a perfect sequentially, good way of doing it. The challenge for the HDD people is if they go for that for that sort of sequential type of application that the cheapest way of doing that is to use tape which comes back to the discussion that the two things that are going to remain are tape and flash. So that part of the HDD market in my assertion will go towards tape and tape libraries. And those are serving very well at the moment. >> Yeah I mean, It's just the economics of tape are really attractive. I just feel like I've said this many times that the marketing of tape is lacking. Like I'd like to see, better thinking around how it could play. Cause I think customers have this perception tape, but there's actually a lot of value there. I want to carry on, >> Small point there. Yeah, I mean, there's an opportunity in the same way that Vast have created an architecture for flash. There's an opportunity out there for the tech people with flash to make an architecture that allows you to take that workload and really lower the price, enormously. >> You've called it Flape >> Flape yes. >> There's some interesting metadata opportunities there but we won't go into that. And then David, I want to ask you about NAND shortages. We saw this in 2016 and 2017. A lot of people saying there's an NAND shortage again. So that's a flaw in your forecast prices of you're assuming prices of flash continue to come down faster than those of HDD but the shortages of NAND could be problematic. What do you say to that? >> Well, I've looked at that in some detail and one of the big, important things is what's happening in the flash market and the Chinese, YMTC Chinese company has introduced a lot more volume into the market. They're making 100,000 wafers a month for this year. That's around six to 8% of market of NAND at this year, as a result, Samsung, micron, Intel, Hynix they're all increasing their volumes of NAND so that they're all investing. So I don't see that NAND itself is going to be a problem. There is certainly a shortage of processor chips which drive the intelligence in the NAND itself. But that's a problem for everybody. That's a problem for cars. It's a problem for disk drives. >> You could argue that's going to create an oversupply, potentially. Let's not go there, but you know what at the end of the day it comes back to the customer and all this stuff. It's interesting. I love talking about the architecture but it's really all about customer value. And so, so Renan, I want you to sort of close there. What should customers be paying attention to? And what should observers of Vast Data really watch as indicators for progress for you guys milestones and things in the market that we should be paying attention to but start with the customers. What's your advice to them? >> Sure, for any customer that I talked to I always ask the same thing. Imagine where you'll be five years from now because you're making an investment now that is at least five years long. In our case, we guaranteed the lifespan of the devices for a decade, such that you know that it's going to be there for you and imagine what is going to happen over those next five years. What we're seeing in most customers is that they have a lot of doormen data and with the advances in analytics and AI they want to make use of that data. They want to turn it from a cost center to a profit center and to gain insight from that data and to improve their business based on that information that they have the same way the hyperscalers are doing in order to do that, you need one thing you need fast access to all of that information. Once you have that, you have the foundation to step into this next generation type world where you can actually make money off of your information. And the best way to get very, very fast access to all of your information is to put it on Vast media like flash and Crosspoint. If I can give one example, Hedge Funds. Hedge funds do a lot of back-testing on Vast. And what makes sense for them is to test as much information back as they possibly can but because of storage limitations, they can't do that. And the other thing that's important to them is to have a real-time experience to be able to run those simulations in a few minutes and not as a batch process overnight, but because of storage limitations, they can't do that either. The third thing is if you have many different applications and many different users on the same system they usually step on each other's toes. And so the Vast architecture is solves those three problems. It allows you a lot of information very fast access and fast processing an amazing quality of service where different users of the system don't even notice that somebody else is accessing the same piece of information. And so Hedge Funds is one example. Any one of these verticals that make use of a lot of information will benefit from this architecture in this system. And if it doesn't cost any more, there's really no real reason delay this transition into all flash. >> Excellent very clear thinking. Thanks for laying that out. And what about, you know, things that we should how should we judge you? What are the things that we should watch? >> I think the most important way to judge us is to look at customer adoption and what we're seeing and what we're showing investors is a very high net dollar retention number. What that means is basically a customer buys a piece of kit today, how much more will they buy over the next year, over the next two years? And we're seeing them buy more than three times more, within a year of the initial purchase. And we see more than 90% of them buying more within that first year. And that to me indicates that we're solving a real problem and that they're making strategic decisions to stop buying any other type of storage system. And to just put everything on Vast over the next few years we're going to expand beyond just storage services and provide a full stack for these AI applications. We'll expand into other areas of infrastructure and develop the best possible vertically integrated system to allow those new applications to thrive. >> Nice, yeah. Think investors love that lifetime value story. If you can get above 3X of the customer acquisition cost is to IPO in the way. Guys hey, thanks so much for coming to the Cube. We had a great conversation and really appreciate your time. >> Thank you. >> Thank you. >> All right, Thanks for watching everybody. This is Dave Volante for the Cube. We'll see you next time. (gentle music)

>>From around the globe. It's the cube with digital coverage, a BizOps manifesto unveiled brought to you by biz ops coalition. >>Hey, welcome back everybody. Jeff Frick here with the cube. Welcome back to our ongoing coverage of the biz ops manifesto. Unveil. Something has been in the works for a little while. Today's the formal unveiling, and we're excited to have three of the core of founding members of the manifesto authors of the manifesto. If you will, uh, joining us again, we've had them all on individually. Now we're going to have a great power panel first up. We're gab Mitt, Kirsten returning he's the founder and CEO of Tasktop mic. Good to see you again. Where are you dialing in from? >>Great to see you again, Jeff I'm dialing from Vancouver, >>We're Canada, Vancouver, Canada. One of my favorite cities in the whole wide world. Also we've got Tom Davenport come in from across the country. He's a distinguished professor and author from Babson college, Tom. Great to see you. And I think you said you're at a fun, exotic place on the East coast >>Realm of Memphis shoe sits on Cape Cod. >>Great to see you again and also joining surge Lucio. He is the VP and general manager enterprise software division at Broadcom surge. Great to see you again, where are you coming in from? >>Uh, from Boston right next to kickoff. >>Terrific. So welcome back, everybody again. Congratulations on this day. I know it's, it's been a lot of work to get here for this unveil, but let's just jump into it. The biz ops manifesto, what was the initial reason to do this? And how did you decide to do it in a kind of a coalition, a way bringing together a group of people versus just making it an internal company, uh, initiative that, you know, you can do better stuff within your own company, surge, why don't we start with you? >>Yeah, so, so I think we were at a really critical juncture, right? Many, um, large enterprises are basically struggling with their digital transformation. Um, in fact, um, many recognize that, uh, the, the business side, it collaboration has been, uh, one of the major impediments, uh, to drive that kind of transformation. And if we look at the industry today, many people are, whether we're talking about vendors or, um, you know, system integrators, consulting firms are talking about the same kind of concepts, but using very different language. And so we believe that bringing all these different players together, um, as part of the coalition and formalizing, uh, basically the core principles and values in a BizOps manifesto, we can really start to F could have a much bigger movement where we can all talk about kind of the same concepts and we can really start to provide, could have a much better support for large organizations to transform. Uh, so whether it is technology or services or, um, we're training, I think that that's really the value of bringing all of these players together, right. >>And Nick to you, why did you get involved in this, in this effort? >>So Ben close and follow the agile movement since it started two decades ago with that manifesto. >>And I think we got a lot of improvement at the team level, and I think as satisfies noted, uh, we really need to improve at the business level. Every company is trying to become a software innovator, uh, trying to make sure that they can adapt quickly and the changing market economy and what everyone's dealing with in terms of needing to deliver the customer sooner. However, agile practices have really focused on these metrics, these measures and understanding processes that help teams be productive. Those things now need to be elevated to the business as a whole. And that just hasn't happened. Uh, organizations are actually failing because they're measuring activities and how they're becoming more agile, how teams are functioning, not how much quickly they're delivering value to the customer. So we need to now move past that. And that's exactly what the that's manifested provides. Right, >>Right, right. And Tom, to you, you've been covering tech for a very long time. You've been looking at really hard challenges and a lot of work around analytics and data and data evolution. So there's a definitely a data angle here. I wonder if you could kind of share your perspective of what you got excited to, uh, to sign onto this manifesto. >>Sure. Well, I have, you know, for the past 15 or 20 years, I've been focusing on data and analytics and AI, but before that I was a process management guy and a knowledge management guy. And in general, I think, you know, we've just kind of optimized that to narrow a level, whether you're talking about agile or dev ops or ML ops, any of these kinds of ops oriented movements, we're making individual project, um, performance and productivity better, but we're not changing the business, uh, effectively enough. And that's the thing that appealed to me about the biz ops idea that we're finally creating a closer connection between what we do with technology and how it changes the business and provides value to it. >>Great. Uh, surge back to you, right? I mean, people have been talking about digital transformation for a long time and it's been, you know, kind of trucking along and then covert hit and it was instant lights, which everyone's working from home. You've got a lot more reliance on your digital tools, digital communication, uh, both within your customer base and your partner base, but also then your employees when you're, if you could share how that really pushed this all along. Right? Because now suddenly the acceleration of digital transformation is higher. Even more importantly, you got much more critical decisions to make into what you do next. So kind of your portfolio management of projects has been elevated significantly when maybe revenues are down, uh, and you really have to, uh, to prioritize and get it right. >>Yeah. Maybe I'll just start by quoting Satina Nello basically recently said that they're speeding the two years of digital preservation just last two months in any many ways. That's true. Um, but, but yet when we look at large enterprises, they're >>Still struggling with the kind of a changes in culture that they really need to drive to be able to disrupt themselves. And not surprisingly, you know, when we look at certain parts of the industry, you know, we see some things which are very disturbing, right? So about 40% of the personal loans today, or being, uh, origin data it's by fintechs, uh, of a like of Sophie or, uh, or a lending club, right? Not to a traditional brick and mortar for BEC. And so the, well, there is kind of a much more of an appetite and it's a, it's more of a survival type of driver these days. Uh, the reality is that's in order for these large enterprises to truly transform and engage with this digital transformation, they need to start to really align the business. And it, you know, in many ways, uh, make covered that agile really emerged from the core desire to truly improve software predictability between which we've really missed is all that we, we start to aligning the software predictability to business predictability and to be able to have continual sleep continuous improvement and measurement of business outcomes. So by aligning kind of these, uh, kind of inward metrics, that's, it is typically being using to business outcomes. We think we can start to really ELP different stakeholders within the organization to collaborate. So I think there is more than ever. There's an imperative to act now. Um, and, and resolves, I think is kind of the right approach to drive that transformation. Right. >>I want to follow up on the culture comment, uh, with Utah, because you've talked before about kind of process flow and process flow throughout a whore and an organization. And, you know, we talk about people process and tech all the time. And I think the tech is the easy part compared to actually changing the people the way they think. And then the actual processes that they put in place. It's a much more difficult issue than just the tech issue to get this digital transformation in your organization. >>Yeah. You know, I've always found that the soft stuff about, you know, the culture of the behavior, the values is the hard stuff to change and more and more, we, we realized that to be successful with any kind of digital transformation you have to change people's behaviors and attitudes. Um, we haven't made as much progress in that area as we might have. I mean, I've done some surveys suggesting that, um, most organizations still don't have data-driven cultures. And in many cases there is a lower percentage of companies that say they have that then, um, did a few years ago. So we're kind of moving in the wrong direction, which means I think that we have to start explicitly addressing that, um, cultural, behavioral dimension and not just assuming that it will happen if we, if we build a system, >>If we build it, they won't necessarily come. Right. >>Right. So I want to go to, to you Nick cause you know, we're talking about workflows and flow, um, and, and you've written about flow both in terms of, um, you know, moving things along a process and trying to find bottlenecks, identify bottlenecks, which is now even more important again, when these decisions are much more critical. Cause you have a lot less, uh, wiggle room in tough times, but you also talked about flow from the culture side and the people side. So I wonder if you can just share your thoughts on, you know, using flow as a way to think about things, to get the answers better. >>Yeah, absolutely. And I'll refer back to what Tom has said. If you're optimized, you need to optimize your system. You need to optimize how you innovate and how you deliver value to the business and the customer. Now, what we've noticed in the data, since that we've learned from customers, value streams, enterprise organizations, value streams, is that when it's taking six months at the end to deliver that value with the flow is that slow. You've got a bunch of unhappy developers, unhappy customers when you're innovating house. So high performing organizations we can measure at antenna flow time and dates. All of a sudden that feedback loop, the satisfaction, your developers measurably, it goes up. So not only do you have people context, switching glass, you're delivering so much more value to customers at a lower cost because you've optimized for flow rather than optimizing for these, these other approximate tricks that we use, which is how efficient is my adult team. How quickly can we deploy software? Those are important, but they do not provide the value of agility of fast learning of adaptability to the business. And that's exactly what the biz ops manifesto pushes your organization to do. You need to put in place this new operating model that's based on flow on the delivery of business value and on bringing value to market much more quickly than you were before. Right. >>I love that. And I'm gonna back to you Tom, on that to follow up. Cause I think, I don't think people think enough about how they prioritize what they're optimizing for, because you know, if you're optimizing for a versus B, you know, you can have a very different product that, that you kick out. And, you know, my favorite example is with Clayton Christianson and innovator's dilemma talking about the three inch hard drive, if you optimize it for power, you know, is one thing, if you optimize it for vibration is another thing and sure enough, you know, they missed it on the poem because it was the, it was the game console, which, which drove that whole business. So when you're talking to customers and we think we hear it with cloud all the time, people optimizing for a cost efficiency, instead of thinking about it as an innovation tool, how do you help them kind of rethink and really, you know, force them to, to look at the, at the prioritization and make sure they're prioritizing on the right thing is make just that, what are you optimizing for? >>Oh yeah. Um, you have one of the most important aspects of any decision or attempt to resolve a problem in an organization is the framing process. And, um, you know, it's, it's a difficult aspect to have the decision to confirm it correctly in the first place. Um, there, it's not a technology issue. In many cases, it's largely a human issue, but if you frame >>That decision or that problem incorrectly to narrowly say, or you frame it as an either or situation where you could actually have some of both, um, it, it's very difficult for the, um, process to work out correctly. So in many cases, I think we need to think more at the beginning about how we bring this issue or this decision in the best way possible before we charge off and build a system to support it. You know, um, it's worth that extra time to think, think carefully about how the decision has been structured. Right, >>Sir, I want to go back to you and talk about the human factors because as we just discussed, you can put it in great technology, but if the culture doesn't adopt it and people don't feel good about it, you know, it's not going to be successful and that's going to reflect poorly on the technology, even if that had nothing to do with it. And you know, when you look at the, the, the, the core values, uh, of the Bezos manifesto, you know, a big one is trust and collaboration, you know, learn, respond, and pivot. Wonder if you can share your thoughts on, on trying to get that cultural shift, uh, so that you can have success with the people, or excuse me, with the technology in the process and helping customers, you know, take this more trustworthy and kind of proactive, uh, position. >>So I think, I think at the ground level, it truly starts with the realization that we're all different. We come from different backgrounds. Uh, oftentimes we tend to blame the data. It's not uncommon my experiments that we spend the first 30 minutes of any kind of one hour conversation to debate the validity of the data. Um, and so, um, one of the first kind of, uh, probably manifestations that we've had or revelations as we start to engage with our customers is spoke just exposing, uh, high-fidelity data sets to different stakeholders from their different lens. We start to enable these different stakeholders to not debate the data. That's really collaborate to find a solution. So in many ways, when, when, when we think about kind of the types of changes we're trying to, to truly affect around data driven decision making, he told about bringing the data in context and the context that is relevant and understandable for, for different stakeholders, whether we're talking about an operator or develop for a business analyst. >>So that's, that's the first thing. The second layer I think, is really to provide context to what people are doing in their specific silo. And so I think one of the best examples I have is if you start to be able to align business KPI, whether you are counting, you know, sales per hour, or the engagements of your users on your mobile applications, whatever it is, you can start to connect that PKI to business KPI, to the KPIs that developers might be looking at, whether it is all the number of defects or velocity or whatever over your metrics that you're used to, to actually track you start to be able to actually contextualize in what we are, the effecting, basically a metric of that that is really relevant. And then what we see is that this is a much more systematic way to approach the transformation than say, you know, some organizations kind of creating some of these new products or services or initiatives, um, to, to drive engagements, right? >>So if you look at zoom, for instance, zoom giving away a it service to, uh, to education, he's all about, I mean, there's obviously a marketing aspect in there, but it's, it's fundamentally about trying to drive also the engagement of their own teams. And because now they're doing something for good and many organizations are trying to do that, but you only can do this kind of things in the limited way. And so you really want to start to rethink how you connect to, everybody's kind of a business objective fruit data, and now you start to get people to stare at the same data from their own lens and collaborate on all the data. Right, >>Right. That's a good, uh, Tom, I want to go back to you. You've been studying it for a long time, writing lots of books and getting into it. Um, why now, you know, what, why, why now are we finally aligning business objectives with, with it objectives? You know, why didn't this happen before? And, you know, what are the factors that are making now the time for this, this, this move with the, uh, with the biz ops? >>Well, and much of a past, it was sort of a back office related activity. And, you know, it was important for, um, uh, producing your paychecks and, uh, capturing the customer orders, but the business wasn't built around it now, every organization needs to be a software business, a data business, a digital business, the auntie has been raised considerably. And if you aren't making that connection between your business objectives and the technology that supports it, you run a pretty big risk of, you know, going out of business or losing out to competitors. Totally. So, um, and even if you're in, uh, an industry that hasn't historically been terribly, um, technology oriented customer expectations flow from, uh, you know, the digital native, um, companies that they work with to basically every industry. So you're compared against the best in the world. So we don't really have the luxury anymore of screwing up our it projects or building things that don't really work for the business. Um, it's mission critical that we do that well. Um, almost every time, I just want to fall by that, Tom, >>In terms of the, you've talked extensively about kind of these evolutions of data and analytics from artismal stage to the big data stage, the data economy stage, the AI driven stage and what I find diff interesting that all those stages, you always put a start date, you never put an end date. Um, so you know, is the, is the big data I'm just going to use that generically a moment in time finally here where we're, you know, off mahogany row with the data scientists, but actually can start to see the promise of delivering the right insight to the right person at the right time to make that decision. >>Well, I think it is true that in general, these previous stages never seemed to go away. The, um, the artisinal stuff is still being done, but we would like for less and less of it to be artisinal, we can't really afford for everything to be artisinal anymore. It's too labor and, and time consuming to do things that way. So we shift more and more of it to be done through automation and B to be done with a higher level of productivity. And, um, you know, at some point maybe we reached the stage where we don't do anything artisanally anymore. I'm not sure we're there yet, but we are, we are making progress. Right. >>Right. And Mick, back to you in terms of looking at agile, cause you're, you're such a student of agile. When, when you look at the opportunity with biz ops and taking the lessons from agile, you know, what's been the inhibitor to stop this in the past. And what are you so excited about? You know, taking this approach will enable. >>Yeah. I think both search and Tom hit on this is that in agile what's happened is that we've been measuring tiny subsets of the value stream, right? We need to elevate the data's there. Developers are working on these tools that delivering features that the foundations for for great culture are there. I spent two decades as a developer. And when I was really happy is when I was able to deliver value to customers, the quicker I was able to do that the fewer impediments are in my way, that quicker was deployed and running in the cloud, the happier I was, and that's exactly what's happening. If we can just get the right data, uh, elevated to the business, not just to the agile teams, but really this, these values of ours are to make sure that you've got these data driven decisions with meaningful data that's oriented around delivering value to customers. Not only these legacies that Tom touched on, which has cost center metrics. So when, from where for it being a cost center and something that provided email and then back office systems. So we need to rapidly shift to those new, meaningful metrics that are customized business centric and make sure that every development the organization is focused on those as well as the business itself, that we're measuring value. And that will help you that value flow without interruptions. >>I love that mic. Cause if you don't measure it, you can't improve on it and you gotta, but you gotta be measuring the right thing. So gentlemen, uh, thank you again for, for your time. Uh, congratulations on the, uh, on the unveil of the biz ops manifesto and bringing together this coalition, uh, of, of, uh, industry experts to get behind this. And, you know, there's probably never been a more important time than now to make sure that your prioritization is in the right spot and you're not wasting resources where you're not going to get the ROI. So, uh, congratulations again. And thank you for sharing your thoughts with us here on the cube. >>Thank you. >>Alright, so we had surge Tom and Mick I'm. Jeff, you're watching the cube. It's a biz ops manifesto unveil. Thanks for watching. We'll see you next time >>From around the globe. It's the cube with digital coverage of biz ops manifesto unveiled brought to you by biz ops coalition. >>Hey, welcome back. Variety. Jeff Frick here with the cube. We're in our Palo Alto studios, and we'd like to welcome you back to our continuing coverage of biz ops manifesto unveil some exciting day to really, uh, kind of bring this out into public. There's been a little bit of conversation, but today's really the official unveiling and we're excited to have our next guest is share a little bit more information on it. He's Patrick tickle. He's a chief product officer for planned view. Patrick. Great to see you. >>Yeah, it's great to be here. Thanks for the invite. So why >>The biz ops manifesto, why the biz ops coalition now when you guys have been at it, it's relatively mature marketplace businesses. Good. What was missing? Why, why this, why this coalition? >>Yeah. So, you know, again, why is, why is biz ops important and why is this something that I'm, you know, I'm so excited about, but I think companies as well, right? Well, no, in some ways or another, this is a topic that I've been talking to the market and our customers about for a long time. And it's, you know, I really applaud this whole movement. Right. And, um, it resonates with me because I think one of the fundamental flaws, frankly, of the way we have talked about technology and business literally for decades, uh, has been this idea of, uh, alignment. Those who know me, I occasionally get off on this little rant about the word alignment, right. But to me, the word alignment is, is actually indicative of the, of the, of the flaw in a lot of our organizations and biz ops is really, I think now trying to catalyze and expose that flaw. >>Right. Because, you know, I always say that, you know, you know, alignment implies silos, right. Instantaneously, as soon as you say there's alignment, there's, there's obviously somebody who's got a direction and other people that have to line up and that kind of siloed, uh, nature of organizations then frankly, the passive nature of it. Right. I think so many technology organizations are like, look, the business has the strategy you guys need to align. Right. And, and, you know, as a product leader, right. That's where I've been my whole career. Right. I can tell you that I never sit around. I almost never use the word alignment. Right. I mean, whether, you know, I never sit down and say, you know, the product management team has to get aligned with dev, right. Or the dev team has to get aligned with the delivery and ops teams. I mean, what I say is, you know, are we on strategy, right? >>Like we've, we have a strategy as a, as a full end to end value stream. Right. And that there's no silos. And I mean, look, every on any given day we got to get better. Right. But the context, the context we operate is not about alignment. Right. It's about being on strategy. And I think I've talked to customers a lot about that, but when I first read the manifesto, I was like, Oh yeah, this is exactly. This is breaking down. Maybe trying to eliminate the word alignment, you know, from a lot of our organizations, because we literally start thinking about one strategy and how we go from strategy to delivery and have it be our strategy, not someone else's that we're all aligning to. And I, and it's a great way to catalyze that conversation that I've, it's been in my mind for years, to be honest. Right. >>So, so much to unpack there. One of the things obviously, uh, stealing a lot from, from dev ops and the dev ops manifesto from 20 years ago. And, and as I look through some of the principles and I looked through some of the values, which are, you know, really nicely laid out here, you know, satisfy customer, do continuous delivery, uh, measure, output against real results. Um, the ones that, that jumps out though is really about, you know, change, change, right? Requirements should change frequently. They do change frequently, but I'm curious to get your take from a, from a software development point, it's easy to kind of understand, right. We're making this widget and our competitors, beta widget plus X, and now we need to change our plans and make sure that the plus X gets added to the plan. Maybe it wasn't in the plan, but you talked a lot about product strategy. So in this kind of continuous delivery world, how does that meld with, I'm actually trying to set a strategy, which implies the direction for a little bit further out on the horizon and to stay on that while at the same time, you're kind of doing this real time continual adjustments because you're not working off a giant PRD or MRD anymore. >>Yeah, yeah, totally. Yeah. You know, one of the terms, you know, that we use internally a lot and even with my customers, our customers is we talk about this idea of rewiring, right. And I think, you know, it's kind of a, now an analogy for transformation. And I think a lot of us have to rewire the way we think about things. Right. And I think at Planview where we have a lot of customers who live in that, you know, who operationalize that traditional PPM world. Right. And are shifting to agile and transforming that rewire is super important. And, and to your point, right, it's, you've just, you've got to embrace this idea of, you know, just iterative getting better every day and iterating, iterating, iterating as opposed to building annual plans or, you know, I get customers occasionally who asked me for two or three year roadmap. >>Right. And I literally looked at them and I go, there's no, there's no scenario where I can build a two or three year roadmap. Right. You, you, you think you want that, but that's not, that's not the way we run. Right. And I will tell you the biggest thing that for us, you know, that I think is matched the planning, uh, you know, patents is a word I like to use a lot. So the thing that we've like, uh, that we've done from a planning perspective, I think is matched impedance to continuous delivery is instituting the whole program, implement, you know, the program, increment planning, capabilities, and methodologies, um, in the scaled agile world. Right. And over the last 18 months to two years, we really have now, you know, instrumented our company across three value streams. You know, we do quarterly PI program increment 10 week planning, you know, and that becomes, that becomes the Terra firma of how we plan. >>Right. And it's, what are we doing for the next 10 weeks? And we iterate within those 10 weeks, but we also know that 10 weeks from now, we're gonna, we're gonna adjust iterate again. Right. And that shifting of that planning model to, you know, to being as cross-functional is that as that big room planning kind of model is, um, and also, uh, you know, on that shorter increment, when you get those two things in place, also the impedance really starts to match up, uh, with continuous delivery and it changes, it changes the way you plan and it changes the way you work. Right? >>Yeah. Their thing. Right. So obviously a lot of these things are kind of process driven, both within the values, as well as the principles, but there's a whole lot, really about culture. And I just want to highlight a couple of the values, right? We already talked about business outcomes, um, trust and collaboration, uh, data driven decisions, and then learn, respond and pivot. Right. A lot of those are cultural as much as they are process. So again, is it the, is it the need to really kind of just put them down on paper and, you know, I can't help, but think of, you know, the hammer and up the, a, the thing in the Lutheran church with it, with their manifesto, is it just good to get it down on paper? Because when you read these things, you're like, well, of course we should trust people. And of course we need an environment of collaboration and of course we want data driven decisions, but as we all know saying it and living, it are two very, very different things. >>Yeah. Good question. I mean, I think there's a lot of ways to bring that to life you're right. And just hanging up, you know, I think we've all been through the hanging up posters around your office, which these days, right. Unless you're going to hang a poster in everybody's home office. Right. You can't even, you can't even fake it that you think that might work. Right. So, um, you know, you really, I think we've attacked that in a variety of ways. Right. And you definitely have to, you know, you've got to make the shift to a team centric culture, right. Empowered teams, you know, that's a big deal. Right. You know, a lot of, a lot of the people that, you know, we lived in a world of quote, unquote work. We lived in a deep resource management world for a long, long time, and right. >>A lot of our customers still do that, but, you know, kind of moving to that team centric world is, uh, is really important and core to the trust. Um, I think training is super important, right. I mean, we've, you know, we've internally, right. We've trained hundreds employees over the last a year and a half on the fundamentals really of safe. Right. Not necessarily, you know, we've had, we've had teams delivering in scrum and the continuous delivery for, you know, for years, but the scaling aspect of it, uh, is where we've done a lot of training investment. Um, and then, you know, I think a leadership has to be bought in. Right. You know? And so when we pie plan, you know, myself and Cameron and the other members of our leadership, you know, we're NPI planning, you know, for, for four days. Right. I mean, it's, it's, you've got to walk the walk, you know, from top to bottom and you've got to train on the context. Right. And then you, and then, and, and then once you get through a few cycles where you've done a pivot, right. Or you brought a new team in, and it just works, it becomes kind of this virtuous circle where he'll go, man, this really works so much better than what we used to do. Right. >>Right. The other really key principle to this whole thing is, is aligning, you know, the business leaders and the business prioritization, um, so that you can get to good outcomes with the development and the delivery. Right. And we know again, and kind of classic dev ops to get the dev and the production people together. So they can, you know, quickly ship code that works. Um, but adding the business person on there really puts, puts a little extra responsibility that they, they understand the value of a particular feature or particular priority. Uh, they, they can make the, the, the trade offs and that they kind of understand the effort involved too. So, you know, bringing them into this continuous again, kind of this continuous development process, um, to make sure that things are better aligned and really better prioritize. Cause ultimately, you know, we don't live in an infinite resources situation and people gotta make trade offs. They gotta make decisions as to what goes and what doesn't go in for everything that goes. Right. I always say you pick one thing. Okay. That's 99 other things that couldn't go. So it's really important to have, you know, this, you said alignment of the business priorities as well as, you know, the execution within, within the development. >>Yeah. I think that, you know, uh, you know, I think it was probably close to two years ago. Forester started talking about the age of the customer, right. That, that was like their big theme at the time. Right. And I think to me what that, the age of the customer actually translates to and Mick, Mick and I are both big fans of this whole idea of the project, the product shift, mixed book, you know, it was a great piece on a, you're talking to Mick, you know, as part of the manifesto is one of the authors as well, but this shift from project to product, right? Like the age of the customer, in my opinion, the, the, the embodiment of that is the shift to a product mentality. Right. And, and the product mentality in my opinion, is what brings the business and technology teams together, right? >>Once you, once you're focused on a customer experience, that's delivered through a product or a service that's when I that's, when I started to go with the alignment problem goes away, right. Because if you look at software companies, right, I mean, we run product management models, you know, with software development teams, customer success teams, right. That, you know, the software component of these products that people are building is obviously becoming bigger and bigger, you know, in an, in many ways, right. More and more organizations are trying to model themselves over as operationally like software companies. Right. Um, they obviously have lots of other components in their business than just software, but I think that whole model of customer experience equaling product, and then the software component of product, the product is the essence of what changes that alignment equation and brings business and teams together because all of a sudden, everyone knows what the customer's experiencing. Right. And, and that, that, that makes a lot of things very clear, very quickly. >>Right. I'm just curious how far along this was as a process before, before covert hit, right. Because serendipitous, whatever. Right. But th the sudden, you know, light switch moment, everybody had to go work from home and in March 15th compared to now, we're in October, and this is going to be going on for a while, and it is a new normal and whatever that whatever's going to look like a year from now, or two years from now is TBD, you know, had you guys already started on this journey cause again, to sit down and actually declare this coalition and declare this manifesto is a lot different than just trying to do better within your own organization. >>Yeah. So we had started, uh, you know, w we definitely had started independently, you know, some, some, you know, I think people in the community know that, uh, we, we came together with a company called lean kit a handful of years ago, and I give John Terry actually one of the founders leaned to immense credit for, you know, kind of spearheading our cultural change and not, and not because of, we were just going to be, you know, bringing agile solutions to our customers, but because, you know, he believed that it was going to be a fundamentally better way for us to work. Right. And we kind of, you know, when we started with John and built, you know, out of concentric circles of momentum and, and we've gotten to the place where now it's just part of who we are, but, but I do think that, you know, COVID has, you know, um, I think pre COVID a lot of companies, you know, would, would adopt, you know, the, you would adopt digital slash agile transformation. >>Um, traditional industries may have done it as a reaction to disruption. Right. You know, and in many cases, the disruption to these traditional industries was, I would say a product oriented company, right. That probably had a larger software component, and that disruption caused a competitive issue or a customer issue that caused companies and tried to respond by transforming. I think COVID, you know, all of a sudden flatten that out, right. We literally all got disrupted. Right. And, and so all of a sudden, every one of us is dealing with some degree of market uncertainty, customer uncertainty, uh, and also know none of us were insulated from the need to be able to pivot faster, deliver incrementally, you know, and operate in a different, completely more agile way, uh, you know, post COVID. Right. Yeah. That's great. >>So again, a very, very, very timely, you know, a little bit of serendipity, a little bit of, of planning. And, you know, as, as with all important things, there's always a little bit of luck and a lot of hard work involved. So a really interesting thank you for, for your leadership, Patrick. And, you know, it really makes a statement. I think when you have a bunch of leaderships across an industry coming together and putting their name on a piece of paper, uh, that's aligned around us some principles and some values, which again, if you read them who wouldn't want to get behind these, but if it takes, you know, something a little bit more formal, uh, to kind of move the ball down the field, and then I totally get it and a really great work. Thanks for, uh, thanks for doing it. >>Oh, absolutely. No. Like I said, the first time I read it, I was like, yeah, like you said, this is all, this all makes complete sense, but just documenting it and saying it and talking about it moves the needle. I'll tell you as a company, you gotta, we're pushing really hard on, uh, you know, on our own internal strategy on diversity inclusion. Right? And, and like, once we wrote the words down about what, you know, what we aspire to be from a diversity and inclusion perspective, it's the same thing. Everybody reads the words and goes, why wouldn't we do this? Right. But until you write it down and kind of have again, a manifesto or a Terrafirma of what you're trying to accomplish, you know, then you can rally behind it. Right. As opposed to it being something that's, everybody's got their own version of the flavor. Right. And I think it's a very analogous, you know, kind of, uh, initiative, right. And, uh, and this happening, both of those things, right. Are happening across the industry these days. Right. >>And measure it too. Right. And measure it, measure, measure, measure, get a baseline. Even if you don't like to measure, even if you don't like what the, even if you can argue against the math, behind the measurement, measure it, and at least you can measure it again and you can, and you've got some type of a comp and that is really the only way to, to move it forward. Well, Patrick really enjoyed the conversation. Thanks for, uh, for taking a few minutes out of your day. >>It's great to be here. It's an awesome movement and we're glad >>That'd be part of it. All right. Thanks. And if you want to check out the biz ops, Manifesta go to biz ops, manifesto.org, read it. You might want to sign it. It's there for you. And thanks for tuning in on this segment will continuing coverage of the biz op manifesto unveil here on the cube. I'm Jeff, thanks for watching >>From around the globe. It's the cube with digital coverage of biz ops manifesto unveiled brought to you by biz ops coalition. >>Hey, welcome back, everybody Jeffrey here with the cube. We're coming to you from our Palo Alto studios. And welcome back to this event is the biz ops manifesto unveiling. So the biz ops manifesto and the biz ops coalition had been around for a little while, but today's the big day. That's kind of the big public unveiling or excited to have some of the foundational people that, you know, have put their, put their name on the dotted, if you will, to support this initiative and talk about why that initiative is so important. And so the next guest we're excited to have is dr. Mick Kirsten. He is the founder and CEO of Tasktop mic. Great to see you coming in from Vancouver, Canada, I think, right? Yes. Thank you. Absolutely. I hope your air is a little better out there. I know you had some of the worst air of all of us, a couple, a couple of weeks back. So hopefully things are, uh, are getting a little better and we get those fires under control. Yeah. >>Things have cleared up now. So yeah, it's good. It's good to be close to the U S and it's going to have the Arabic cleaner as well. >>Absolutely. So let's, let's jump into it. So you you've been an innovation guy forever starting way back in the day and Xerox park. I was so excited to do an event at Xerox park for the first time last year. I mean, that, that to me represents along with bell labs and, and some other, you know, kind of foundational innovation and technology centers, that's gotta be one of the greatest ones. So I just wonder if you could share some perspective of getting your start there at Xerox park, you know, some of the lessons you learned and what you've been able to kind of carry forward from those days. >>Yeah. I was fortunate to join Xerox park in the computer science lab there at a very early point in my career, and to be working on open source programming languages. So back then in the computer science lab, where some of the inventions around programming around software development teams, such as object oriented programming, and a lot of what we had around really modern programming levels constructs, those were the teams I have the fortune of working with, and really our goal was. And of course there's as, as you know, uh, there's just this DNA of innovation and excitement and innovation in the water. And really it was the model back then was all about changing the way that we work, uh, was looking at for how we could make it 10 times easier to write code. But this is back in 99. And we were looking at new ways of expressing, especially business concerns, especially ways of enabling people who are, who want to innovate for their business to express those concerns in code and make that 10 times easier than what that would take. >>So we create a new open source programming language, and we saw some benefits, but not quite quite what we expected. I then went and actually joined Charles Stephanie, that former to fucking Microsoft who was responsible for, he actually got Microsoft word as a spark and into Microsoft and into the hands of bill Gates on that company. I was behind the whole office suite and his vision. And then when I was trying to execute with, working for him was to make PowerPoint like a programming language, make everything completely visual. And I realized none of this was really working in that there was something else, fundamentally wrong programming languages, or new ways of building software. Like let's try and do with Charles around intentional programming. That was not enough. >>That was not enough. So, you know, the agile movement got started about 20 years ago, and we've seen the rise of dev ops and really this kind of embracing of, of, of sprints and, you know, getting away from MRDs and PRDs and these massive definitions of what we're going to build and long build cycles to this iterative process. And this has been going on for a little while. So what was still wrong? What was still missing? Why the BizOps coalition, why the biz ops manifesto? >>Yeah, so I basically think we nailed some of the things that the program language levels of teams can have effective languages deployed soften to the cloud easily now, right? And at the kind of process and collaboration and planning level agile two decades, decades ago was formed. We were adopting and all the, all the teams I was involved with and it's really become a self problem. So agile tools, agile teams, agile ways of planning, uh, are now very mature. And the whole challenge is when organizations try to scale that. And so what I realized is that the way that agile was scaling across teams and really scaling from the technology part of organization to the business was just completely flawed. The agile teams had one set of doing things, one set of metrics, one set of tools. And the way that the business was working was planning was investing in technology was just completely disconnected and using a whole different set of advisors. >>Interesting. Cause I think it's pretty clear from the software development teams in terms of what they're trying to deliver. Cause they've got a feature set, right. And they've got bugs and it's easy to, it's easy to see what they deliver, but it sounds like what you're really honing in on is this disconnect on the business side, in terms of, you know, is it the right investment? You know, are we getting the right business ROI on this investment? Was that the right feature? Should we be building another feature or should we building a completely different product set? So it sounds like it's really a core piece of this is to get the right measurement tools, the right measurement data sets so that you can make the right decisions in terms of what you're investing, you know, limited resources. You can't, no one has unlimited resources and ultimately have to decide what to do, which means you're also deciding what not to do. And it sounds like that's a really big piece of this, of this whole effort. >>Yeah. Jeff, that's exactly it, which is the way that the agile team measures their own way of working is very different from the way that you measure business outcomes. The business outcomes are in terms of how happy your customers are, but are you innovating fast enough to keep up with the pace of a rapidly changing economy, rapidly changing market. And those are, those are all around the customer. And so what I learned on this long journey of supporting many organizations transformations and having them try to apply those principles of agile and dev ops, that those are not enough, those measures technical practices, those measured sort of technical excellence of bringing code to the market. They don't actually measure business outcomes. And so I realized that it really was much more around having these entwined flow metrics that are customer centric and business centric and market centric where we need it to go. Right. >>So I want to shift gears a little bit and talk about your book because you're also a bestselling author, a project, a product, and, and, and you, you brought up this concept in your book called the flow framework. And it's really interesting to me cause I know, you know, flow on one hand is kind of a workflow and a process flow and, and you know, that's how things get done and, and, and embrace the flow. On the other hand, you know, everyone now in, in a little higher level existential way is trying to get into the flow right into the workflow and, you know, not be interrupted and get into a state where you're kind of at your highest productivity, you know, kind of your highest comfort, which flow are you talking about in your book or is it a little bit about, >>Well, that's a great question. It's not what I get asked very often. Just to me, it's absolutely both. So that the thing that we want to get to, we've learned how to master individual flow. That is this beautiful book by me, how he teaches me how he does a beautiful Ted talk by him as well about how we can take control of our own flow. So my question with the book with project replies, how can we bring that to entire teams and really entire organizations? How can we have everyone contributing to a customer outcome? And this is really what if you go to the biz ops manifesto, it says, I focus on outcomes on using data to drive whether we're delivering those outcomes rather than a focus on proxy metrics, such as, how quickly did we implement this feature? No, it's really how much value did the customer go to the feature and how quickly did you learn and how quickly did you use that data to drive to that next outcome? >>Really that with companies like Netflix and Amazon have mastered, how do we get that to every large organization, every it organization and make everyone be a software innovator. So it's to bring that co that concept of flow to these entwined value streams. And the fascinating thing is we've actually seen the data. We've been able to study a lot of value streams. We see when flow increases, when organizations deliver value to a customer faster, developers actually become more happy. So things like the employee net promoter scores rise, and we've got empirical data for this. So the beautiful thing to me is that we've actually been able to combine these two things and see the results in the data that you increase flow to the customer. Your developers are more happy. >>I love it, right, because we're all more, we're all happier when we're in the flow and we're all more productive when we're in the flow. So I, that is a great melding of, of two concepts, but let's jump into the, into the manifesto itself a little bit. And, you know, I love that, you know, took this approach really of having kind of four key values and then he gets 12 key principles. And I just want to read a couple of these values because when you read them, it sounds pretty brain dead. Right? Of course. Right. Of course you should focus on business outcomes. Of course you should have trust and collaboration. Of course you should have database decision making processes and not just intuition or, you know, whoever's the loudest person in the room, uh, and to learn and respond and pivot. But what's the value of actually just putting them on a piece of paper, because again, this is not this, these are all good, positive things, right? When somebody reads these to you or tells you these are sticks it on the wall, of course. But unfortunately of course isn't always enough. >>No. And I think what's happened is some of these core principles originally from the agile manifesto two decades ago, uh, the whole dev ops movement of the last decade of flow feedback and continue learning has been key. But a lot of organizations, especially the ones that are undergoing digital transformations have actually gone a very different way, right? The way that they measure value in technology and innovation is through costs for many organizations. The way that they actually are looking at that they're moving to cloud is actually as a reduction in cost. Whereas the right way of looking at moving to cloud is how much more quickly can we get to the value to the customer? How quickly can we learn from that? And how quickly can we drive the next business outcome? So really the key thing is, is to move away from those old ways of doing things, a funny projects and cost centers, uh, to actually funding and investing in outcomes and measuring outcomes through these flow metrics, which in the end are your fast feedback and how quickly you're innovating for your customer. >>So these things do seem, you know, very obvious when you look at them. But the key thing is what you need to stop doing to focus on these. You need to actually have accurate realtime data of how much value your phone to the customer every week, every month, every quarter. And if you don't have that, your decisions are not driven on data. If you don't know what your boggling like is, and this is something that in decades of manufacturing, a car manufacturers, other manufacturers, master, they always know where the bottom back in their production processes. You ask a random CIO when a global 500 company where their bottleneck is, and you won't get a clear answer because there's not that level of understanding. So let's, you actually follow these principles. You need to know exactly where you fall. And I guess because that's, what's making your developers miserable and frustrated around having them context, which on thrash. So it, the approach here is important and we have to stop doing these other things, >>Right? There's so much there to unpack. I love it. You know, especially the cloud conversation, because so many people look at it wrong as, as, as a cost saving device, as opposed to an innovation driver and they get stuck, they get stuck in the literal and the, and you know, I think at the same thing, always about Moore's law, right? You know, there's a lot of interesting real tech around Moore's law and the increasing power of microprocessors, but the real power, I think in Moore's laws is the attitudinal change in terms of working in a world where you know that you've got all this power and what you build and design. I think it's funny to your, your comment on the flow and the bottleneck, right? Cause, cause we know manufacturing, as soon as you fix one bottleneck, you move to your next one, right? You always move to your next point of failure. So if you're not fixing those things, you know, you're not, you're not increasing that speed down the line, unless you can identify where that bottleneck is or no matter how many improvements you make to the rest of the process, it's still going to get hung up on that one spot. >>That's exactly it. And you also make it sound so simple, but again, if you don't have the data driven visibility of where that bottom line is, and these bottlenecks are adjusted to say defense just whack them. All right. So we need to understand is the bottleneck because our security reviews are taking too long and stopping us from getting value for the customer. If it's that automate that process. And then you move on to the next bottleneck, which might actually be that deploying yourself into the cloud. It's taking too long. But if you don't take that approach of going flow first, rather than again, that sort of cost reduction. First, you have to think of the approach of customer centricity and you only focused on optimizing costs. Your costs will increase and your flow will slow down. And this is just one of these fascinating things. >>Whereas if you focus on getting closer to the customer and reducing your cycles out on getting value, your flow time from six months to two weeks or two, one week or two event, as we see with the tech giants, you actually can both lower your costs and get much more value for us to get that learning loop going. So I think I've, I've seen all these cloud deployments and one of the things happened that delivered almost no value because there was such big bottlenecks upfront in the process and actually the hosting and the AP testing was not even possible with all of those inefficiencies. So that's why going float us rather than costs when we started our project versus silky. >>I love that. And, and, and, and it, it begs repeating to that right within the subscription economy, you know, you're on the hook to deliver value every single month because they're paying you every single month. So if you're not on top of how you're delivering value, you're going to get sideways because it's not like they pay a big down payment and a small maintenance fee every month. But once you're in a subscription relationship, you know, you have to constantly be delivering value and upgrading that value because you're constantly taking money from the customer. So it's such a different kind of relationship than kind of the classic, you know, big bang with a maintenance agreement on the back end really important. Yeah. >>And I think in terms of industry shifts that that's, it that's, what's catalyzed. This industry shift is in this SAS and subscription economy. If you're not delivering more and more value to your customers, someone else's, and they're winning the business, not you. So, one way we know is to delight our customers with great user experience as well. That really is based on how many features you delivered or how much, how much, how many quality improvements or scalar performance improvements we delivered. So the problem is, and this is what the business manifesto, as well as the flow frame of touch on is if you can't measure how much value you deliver to a customer, what are you measuring? You just backed again, measuring costs, and that's not a measure of value. So we have to shift quickly away from measuring costs to measuring value, to survive. And in the subscription economy, >>We could go for days and days and days. I want to shift gears a little bit into data and, and a data driven decision making a data driven organization cause right day has been talked about for a long time, the huge big data meme with, with Hadoop over, over several years and, and data warehouses and data lakes and data oceans and data swamps. And you can go on and on and on. It's not that easy to do, right? And at the same time, the proliferation of data is growing exponentially. We're just around the corner from, from IOT and five G. So now the accumulation of data at machine scale, again, is this gonna overwhelm? And one of the really interesting principles, uh, that I wanted to call out and get your take right, is today's organizations generate more data than humans can process. So informed decisions must be augmented by machine learning and artificial intelligence. I wonder if you can, again, you've got some great historical perspective, um, reflect on how hard it is to get the right data, to get the data in the right context, and then to deliver it to the decision makers and then trust the decision makers to actually make the data and move that down. You know, it's kind of this democratization process into more and more people and more and more frontline jobs making more and more of these little decisions every day. >>Yeah. I definitely think the front parts of what you said are where the promises of big data have completely fallen on their face into the swamps as, as you mentioned, because if you don't have the data in the right format, you've cannot connect, collected that the right way you want it, that way, the right way you can't use human or machine learning on it effectively. And there've been the number of data where, how has this in a typical enterprise organization and the sheer investment is tremendous, but the amount of intelligence being extracted from those is, is, is a very big problem. So the key thing that I've noticed is that if you can model your value streams, so you actually understand how you're innovating, how you're measuring the delivery of value and how long that takes, what is your time to value through these metrics like full time? >>You can actually use both the intelligence that you've got around the table and push that down as well, as far as getting to the organization, but you can actually start using that those models to understand and find patterns and detect bottlenecks that might be surprising, right? Well, you can detect interesting bottlenecks when you shift to work from home. We detected all sorts of interesting bottlenecks in our own organization that were not intuitive to me that have to do with, you know, more senior people being overloaded and creating bottlenecks where they didn't exist. Whereas we thought we were actually an organization that was very good at working from home because of our open source roots. So the data is highly complex. Software value streams are extremely complicated. And the only way to really get the proper analysts and data is to model it properly and then to leverage these machine learning and AI techniques that we have. But that front part of what you said is where organizations are just extremely immature in what I've seen, where they've got data from all their tools, but not modeled in the right way. Right, right. >>Right. Well, all right. So before I let you go, you know, let's say you get a business leader. He, he buys in, he reads the manifesto, he signs on the dotted line and he says, Mick, how do I get started? I want to be more aligned with the, with the development teams. I know I'm in a very competitive space. We need to be putting out new software features and engage with our customers. I want to be more data-driven how do I get started? Well, you know, what's the biggest inhibitor for most people to get started and get some early wins, which we know is always the key to success in any kind of a new initiative. >>Right? So I think you can reach out to us through the website, uh, for the manifesto. But the key thing is just, it's definitely set up it's to get started and to get the key wins. So take a product value stream. That's mission critical if it'd be on your mobile and web experiences or part of your cloud modernization platform where your analytics pipeline, but take that and actually apply these principles to it and measure the end to end flow of value. Make sure you have a value metric that everyone is on the same page on, but the people on the development teams that people in leadership all the way up to the CEO, and one of the, where I encourage you to start is actually that end to end flow time, right? That is the number one metric. That is how you measure it, whether you're getting the benefit of your cloud modernization, that is the one metric that when the people I respect tremendously put into his cloud for CEOs, the metric, the one, the one way to measure innovation. So basically take these principles, deploy them on one product value stream measure, Antonin flow time, uh, and then you'll actually be well on your path to transforming and to applying the concepts of agile and dev ops all the way to, to the, to the way >>You're offering model. >>Well, Mick really great tips, really fun to catch up. I look forward to a time when we can actually sit across the table and, and get into this. Cause I just, I just love the perspective and, you know, you're very fortunate to have that foundational, that foundational base coming from Xerox park and they get, you know, it's, it's a very magical place with a magical history. So to, to incorporate that into, continue to spread that well, uh, you know, good for you through the book and through your company. So thanks for sharing your insight with us today. >>Thanks so much for having me, Jeff. Absolutely. >>All right. And go to the biz ops manifesto.org, read it, check it out. If you want to sign it, sign it. They'd love to have you do it. Stay with us for continuing coverage of the unveiling of the business manifesto on the cube. I'm Jeff. Rick. Thanks for watching. See you next time >>From around the globe. It's the cube with digital coverage, a biz ops manifesto unveiled brought to you by biz ops coalition. >>Hey, welcome back. You're ready. Jeff Frick here with the cube for our ongoing coverage of the big unveil. It's the biz ops manifesto manifesto unveil. And we're going to start that again from the top three And a Festo >>Five, four, three, two. >>Hey, welcome back everybody. Jeff Frick here with the cube come to you from our Palo Alto studios today for a big, big reveal. We're excited to be here. It's the biz ops manifesto unveiling a thing's been in the works for a while and we're excited to have our next guest. One of the, really the powers behind this whole effort. And he's joining us from Boston it's surge, Lucio, the vice president, and general manager enterprise software division at Broadcom surge. Great to see you. >>Hi, good to see you, Jeff. Glad to be here. >>Absolutely. So you've been in this business for a very long time. You've seen a lot of changes in technology. What is the biz ops manifesto? What is this coalition all about? Why do we need this today and in 2020? >>Yeah. So, so I've been in this business for close to 25 years, right? So about 20 years ago, the agile manifesto was created. And the goal of the agile manifesto was really to address the uncertainty around software development and the inability to predict the efforts to build software. And, uh, if you, if you roll that kind of 20 years later, and if you look at the current state of the industry of the product, the project management Institute, estimates that we're wasting about a million dollars, every 20 seconds in digital transformation initiatives that do not deliver on business results. In fact, we were recently served a third of the, a, a number of executives in partnership with Harvard >>Business review and 77% of those executives think that one of the key challenges that they have is really the collaboration between business and it, and that that's been kind of a case for, uh, almost 20 years now. Um, so the, the, the key challenge that we're faced with is really that we need a new approach. And many of the players in the industry, including ourselves have been using different terms, right? Some are being, are talking about value stream management. Some are talking about software delivery management. If you look at the site, reliability engineering movement, in many ways, it embodies a lot of these kind of concepts and principles. So we believed that it became really imperative for us to crystallize around, could have one concept. And so in many ways, the, a, the BizOps concept and the BizOps manifesto are bringing together a number of ideas, which has been emerging in the last five years or so, and, and defining the key values and principles to finally help these organizations truly transform and become digital businesses. And so the hope is that by joining our forces and defining public key principles and values, we can help the industry, uh, not just, uh, by, you know, providing them with support, but also tools and consulting that is required for them to truly achieve the kind of transformation that everybody's taking. >>Right. Right. So COVID now we're six months into it, approximately seven months into it. Um, a lot of pain, a lot of bad stuff still happening. We've got a ways to go, but one of the things that on the positive side, right, and you've seen all the memes and social media is, is a driver of digital transformation and a driver of change. Cause we had this light switch moment in the middle of March, and there was no more planning. There was no more conversation. You've suddenly got remote workforces, everybody's working from home and you got to go, right. So the reliance on these tools increases dramatically, but I'm curious, you know, kind of short of, of the beginnings of this effort in short of kind of COVID, which, you know, came along unexpectedly. I mean, what were those inhibitors because we've been making software for a very long time, right? The software development community has, has adopted kind of rapid change and, and iterative, uh, delivery and, and sprints, what was holding back the connection with the business side to make sure that those investments were properly aligned with outcomes. >>Well, so, so you have to understand that it is, is kind of a its own silos. And traditionally it has been treated as a cost center within large organizations and not as a value center. And so as a result, kind of a, the traditional dynamic between it and the business is basically one of a kind of supplier up to kind of a business. Um, and you know, if you go back to, uh, I think you'll unmask a few years ago, um, basically at this concept of the machines to build the machines and you went as far as saying that, uh, the, the machines or the production line is actually the product. So, uh, meaning that the core of the innovation is really about, uh, building, could it be engine to deliver on the value? And so in many ways, you know, we, we have missed on this shift from, um, kind of it becoming this kind of value center within the enterprises and end. >>He talks about culture. Now, culture is a, is a sum total of behaviors. And the reality is that if you look at it, especially in the last decade, uh, we've agile with dev ops with, um, I bring infrastructures, uh, it's, it's way more volatile today than it was 10 years ago. And so the, when you start to look at the velocity of the data, the volume of data, the variety of data to analyze the system, um, it's, it's very challenging for it to actually even understand and optimize its own processes, let alone, um, to actually include business as sort of an integral part of kind of a delivery chain. And so it's both kind of a combination of, of culture, um, which is required, uh, as well as tools, right? To be able to start to bring together all these data together, and then given the volume of variety of philosophy of the data. Uh, we have to apply some core technologies, which have only really, truly emerged in the last five to 10 years around machine learning and analytics. And so it's really kind of a combination of those freaks, which are coming together today, truly out organizations kind of get to the next level. Right, >>Right. So let's talk about the manifesto. Let's talk about, uh, the coalition, uh, the BizOps coalition. I just liked that you put down these really simple, you know, kind of straightforward core values. You guys have four core values that you're highlighting, you know, business outcomes, over individual projects and outputs, trust, and collaboration, oversight, load teams, and organizations, data driven decisions, what you just talked about, uh, you know, over opinions and judgment and learned, respond and pivot. I mean, surgery sounds like pretty basic stuff, right? I mean, aren't, isn't everyone working to these values already. And I think he touched on it on culture, right? Trust and collaboration, data driven decisions. I mean, these are fundamental ways that people must run their business today, or the person that's across the street, that's doing it. It's going to knock them out right off their block. >>Yeah. So that's very true. But, uh, so I'll, I'll mention an hour survey. We did, uh, I think about six months ago and it was in partnership with, uh, with, uh, an industry analyst and we serve at a, again, a number of it executives to understand only we're tracking business outcomes. I'm going to get the software executives, it executives we're tracking business outcomes. And the, there were less than 15% of these executives were actually tracking the outcomes of the software delivery. And you see that every day. Right? So in my own teams, for instance, we've been adopting a lot of these core principles in the last year or so, and we've uncovered that 16% of our resources were basically aligned around initiatives, which are not strategic for us. Um, I take another example, for instance, one of our customers in the, uh, in the airline industry and Harvard, for instance, that a number of, uh, um, that they had software issues that led to people searching for flights and not returning any kind of availability. >>And yet, um, you know, the it teams, whether it's operation software environments were completely oblivious to that because they were completely blindsided to it. And so the connectivity between kind of the inwards metrics that RT is using, whether it's database time, cycle time, or whatever metric we use in it are typically completely divorced from the business metrics. And so at its core, it's really about starting to align the business metrics with the, the, the software delivery chain, right? This, uh, the system, which is really a core differentiator for these organizations. It's about connecting those two things and starting to, um, infuse some of the agile culture and principles. Um, that's emerged from the software side into the business side. Um, of course the lean movement and other movements have started to change some of these dynamics on the business side. And so I think this, this is the moment where we are starting to see kind of the imperative to transform. Now, you know, Covina obviously has been a key driver for that. The, um, the technology is right to start to be able to weave data together and really kind of, uh, also the cultural shifts, uh, Prue agile through dev ops through, uh, the SRE movement, uh frulein um, business transformation, all these things are coming together and that are really creating kind of the conditions for the BizOps manifestor to exist, >>Uh, Clayton Christianson, great, uh, Harvard professor innovator's dilemma might steal my all time. Favorite business books, you know, talks about how difficult it is for incumbents to react to, to disruptive change, right? Because they're always working on incremental change cause that's what their customers are asking for. And there's a good ROI when you talk about, you know, companies not measuring the right thing. I mean, clearly it has some portion of their budget that has to go to keeping the lights on, right. That that's always the case, but hopefully that's an ever decreasing percentage of their total activity. So, you know, what should people be measuring? I mean, what are kind of the new metrics, um, in, in biz ops that drive people to be looking at the right things, measuring the right things and subsequently making the right decisions, investment decisions on whether they should do, you know, move project a along or project B. >>So there, there are only two things, right? So, so I think what you're talking about is portfolio management, investment management, right. And, um, which, which is a key challenge, right? Um, in my own experience, right? Uh, driving strategy or a large scale kind of software organization for years, um, it's very difficult to even get kind of a base data as to who is doing what, uh, um, I mean, some of our largest customers we're engaged with right now are simply trying to get a very simple answer, which is how many people do I have and that specific initiative at any point in time and just tracking that information is extremely difficult. So, and, and again, back to a product project management Institute, um, they're, they've estimated that on average, it organizations have anywhere between 10 to 20% of their resources focused on initiatives, which are not strategically aligned. >>So that's one dimension on portfolio management. I think the key aspect though, that we are really keen on is really around kind of the alignment of a business metrics to the it metrics. Um, so I'll use kind of two simple examples, right? And my background is around quality. And so I've always believed that fitness for purpose is really kind of a key, um, uh, philosophy if you will. And so if you start to think about quality as fitness for purpose, you start to look at it from a customer point of view, right. And fitness for purpose for core banking application or mobile application are different, right? So the definition of a business value that you're trying to achieve is different. Um, and so the, and yet, if you look at our, it, operations are operating, they were using kind of a same type of, uh, kind of inward metrics, uh, like a database of time or a cycle time, or what is my point of velocity, right? >>And, uh, and so the challenge really is this inward facing metrics that it is using, which are divorced from ultimately the outcome. And so, you know, if I'm, if I'm trying to build a poor banking application, my core metric is likely going to be uptime, right? If I'm trying to build a mobile application or maybe your social mobile app, it's probably going to be engagement. And so what you want is for everybody across it, to look at these metric, and what's hard, the metrics within the software delivery chain, which ultimately contribute to that business metric and some cases cycle time may be completely irrelevant, right? Again, my core banking app, maybe I don't care about cycle time. And so it's really about aligning those metrics and be able to start to differentiate, um, the key challenges you mentioned, uh, around the, the, um, uh, around the disruption that we see is, or the investors is the dilemma now is really around the fact that many it organizations are essentially applying the same approaches of, for innovation, right, for basically scrap work, then they would apply to kind of over more traditional projects. And so, you know, there's been a lot of talk about two-speed it, and yes, it exists, but in reality are really organizations, um, truly differentiating, um, all of the operate, their, their projects and products based on the outcomes that they're trying to achieve. And this is really where BizOps is trying to affect. >>I love that, you know, again, it doesn't seem like brain surgery, but focus on the outcomes, right. And it's horses for courses, as you said, this project, you know, what you're measuring and how you define success, isn't necessarily the same as, as on this other project. So let's talk about some of the principles we've talked about the values, but, you know, I think it's interesting that, that, that the BizOps coalition, you know, just basically took the time to write these things down and they don't seem all that, uh, super insightful, but I guess you just gotta get them down and have them on paper and have them in front of your face. But I want to talk about, you know, one of the key ones, which you just talked about, which is changing requirements, right. And working in a dynamic situation, which is really what's driven, you know, this, the software to change in software development, because, you know, if you're in a game app and your competitor comes out with a new blue sword, you've got to come out with a new blue sword. >>So whether you had that on your Kanban wall or not. So it's, it's really this embracing of the speed of change and, and, and, and making that, you know, the rule, not the exception. I think that's a phenomenal one. And the other one you talked about is data, right? And that today's organizations generate more data than humans can process. So informed decisions must be generated by machine learning and AI, and, you know, in the, the big data thing with Hadoop, you know, started years ago, but we are seeing more and more that people are finally figuring it out, that it's not just big data, and it's not even generic machine learning or artificial intelligence, but it's applying those particular data sets and that particular types of algorithms to a specific problem, to your point, to try to actually reach an objective, whether that's, you know, increasing the, your average ticket or, you know, increasing your checkout rate with, with, with shopping carts that don't get left behind and these types of things. So it's a really different way to think about the world in the good old days, probably when you got started, when we had big, giant, you know, MRDs and PRDs and sat down and coded for two years and came out with a product release and hopefully not too many patches subsequently to that. >>It's interesting. Right. Um, again, back to one of these surveys that we did with, uh, with about 600, the ITA executives, and, uh, and, and we, we purposely designed those questions to be pretty open. Um, and, and one of them was really role requirements and, uh, and it was really a wrong kind of what do you, what is the best approach? What is your preferred approach towards requirements? And if I remember correctly over 80% of the it executives set that the best approach they'll prefer to approach is for requirements to be completely defined before software development starts. Let me pause there where 20 years after the agile manifesto, right? And for 80% of these idea executives to basically claim that the best approach is for requirements to be fully baked before salt, before software development starts, basically shows that we still have a very major issue. >>And again, our hypothesis in working with many organizations is that the key challenge is really the boundary between business and it, which is still very much contract based. If you look at the business side, they basically are expecting for it deliver on time on budget, right. But what is the incentive for it to actually delivering all the business outcomes, right? How often is it measured on the business outcomes and not on an SLA or on a budget type criteria. And so that, that's really the fundamental shift that we need to, we really need to drive up as an industry. Um, and you know, we, we talk about kind of this, this imperative for organizations to operate that's one, and back to the innovator's dilemma. The key difference between these larger organization is, is really kind of a, if you look at the amount of capital investment that they can put into pretty much anything, why are they losing compared to, um, you know, startups? What, why is it that, uh, more than 40% of, uh, personal loans today or issued not by your traditional brick and mortar banks, but by, um, startups? Well, the reason, yes, it's the traditional culture of doing incremental changes and not disrupting ourselves, which Christiansen covered at length, but it's also the inability to really fundamentally change kind of a dynamic picture. We can business it and, and, and partner right. To, to deliver on a specific business outcome. Right. >>I love that. That's a great, that's a great summary. And in fact, getting ready for this interview, I saw you mentioning another thing where, you know, the, the problem with the agile development is that you're actually now getting more silos because you have all these autonomous people working, you know, kind of independently. So it's even a harder challenge for, for the business leaders to, to, to, as you said, to know, what's actually going on, but, but certainly I w I want to close, um, and talk about the coalition. Um, so clearly these are all great concepts. These are concepts you want to apply to your business every day. Why the coalition, why, you know, take these concepts out to a broader audience, including your, your competition and, and the broader industry to say, Hey, we, as a group need to put a stamp of approval on these concepts, values, these principles. >>So, first I think we, we want, um, everybody to realize that we are all talking about the same things, the same concepts. I think we were all from our own different vantage point, realizing that, um, things after change, and again, back to, you know, whether it's value stream management or site reliability engineering, or biz ops, we're all kind of using slightly different languages. Um, and so I think one of the important aspects of BizOps is for us, all of us, whether we're talking about, you know, consulting agile transformation experts, uh, whether we're talking about vendors, right, provides kind of tools and technologies, or these large enterprises to transform for all of us to basically have kind of a reference that lets us speak around kind of, um, in a much more consistent way. The second aspect is for, to me is for, um, these concepts to start to be embraced, not just by us or trying, or, you know, vendors, um, system integrators, consulting firms, educators, thought leaders, but also for some of our old customers to start to become evangelists of their own in the industry. >>So we, our, our objective with the coalition needs to be pretty, pretty broad. Um, and our hope is by, by starting to basically educate, um, our, our joint customers or partners, that we can start to really foster these behaviors and start to really change, uh, some of dynamics. So we're very pleased at if you look at, uh, some of the companies which have joined the, the, the, the manifesto. Um, so we have vendors and suggest desktop or advance, or, um, uh, PagerDuty for instance, or even planned view, uh, one of my direct competitors, um, but also thought leaders like Tom Davenport or, uh, or cap Gemini or, um, um, smaller firms like, uh, business agility, institutes, or agility elf. Um, and so our, our goal really is to start to bring together, uh, thought leaders, people who have been LP, larger organizations do digital transformation vendors, were providing the technologies that many of these organizations use to deliver on these digital preservation and for all of us to start to provide the kind of, uh, education support and tools that the industry needs. Yeah, >>That's great surge. And, uh, you know, congratulations to you and the team. I know this has been going on for a while, putting all this together, getting people to sign onto the manifesto, putting the coalition together, and finally today getting to unveil it to the world in a little bit more of a public, uh, opportunity. So again, you know, really good values, really simple principles, something that, that, uh, shouldn't have to be written down, but it's nice cause it is, and now you can print it out and stick it on your wall. So thank you for, uh, for sharing this story. And again, congrats to you and the team. Thank you. Appreciate it. My pleasure. Alrighty, surge. If you want to learn more about the biz ops, Manifesta go to biz ops manifesto.org, read it, and you can sign it and you can stay here for more coverage. I'm the cube of the biz ops manifesto unveiled. Thanks for watching. See you next time >>From around the globe. It's the cube with digital coverage of this ops manifesto unveiled and brought to you by >>This obstacle volition. Hey, welcome back, everybody Jeffrey here with the cube. Welcome back to our ongoing coverage of the biz ops manifesto unveiling. It's been in the works for awhile, but today's the day that it actually kind of come out to the, to the public. And we're excited to have a real industry luminary here to talk about what's going on, why this is important and share his perspective. And we're happy to have from Cape Cod, I believe is Tom Davenport. He's a distinguished author and professor at Babson college. We could go on, he's got a lot of great titles and, and really illuminary in the area of big data and analytics Thomas. Great to see you. >>Thanks Jeff. Happy to be here with you. >>Great. So let's just jump into it, you know, and getting ready for this. I came across your LinkedIn posts. I think you did earlier this summer in June and right off the bat, the first sentence just grabbed my attention. I'm always interested in new attempts to address longterm issues, uh, in how technology works within businesses, biz ops. What did you see in biz ops, uh, that, that kind of addresses one of these really big longterm problems? >>Well, yeah, but the longterm problem is that we've had a poor connection between business people and it people between business objectives and the, it solutions that address them. This has been going on, I think since the beginning of information technology and sadly it hasn't gone away. And so biz ops is a new attempt to deal with that issue with a, you know, a new framework, eventually a broad set of solutions that increase the likelihood that will actually solve a business problem with an it capability. >>Right. You know, it's interesting to compare it with like dev ops, which I think a lot of people are probably familiar with, which was, you know, built around, uh, agile software development and a theory that we want to embrace change that that changes. Okay. And we want to be able to iterate quickly and incorporate that. And that's been happening in the software world for, for 20 plus years. What's taken so long to get that to the business side, because as the pace of change has changed on the software side, you know, that's a strategic issue in terms of execution, the business side that they need now to change priorities. And, you know, there's no PRDs and MRDs and big, giant strategic plans that sit on the shelf for five years. That's just not the way business works anymore. It took a long time to get here. >>Yeah, it did. And, you know, there had been previous attempts to make a better connection between business and it, there was the so called strategic alignment framework that a couple of friends of mine from Boston university developed, I think more than 20 years ago, but you know, now we have better technology for creating that linkage. And the, you know, the idea of kind of ops oriented frameworks is pretty pervasive now. So I think it's time for another serious attempt at it. >>And do you think doing it this way, right. With the, with the BizOps coalition, you know, getting a collection of, of, of kind of likeminded individuals and companies together, and actually even having a manifesto, which we're making this declarative statement of, of principles and values, you think that's what it takes to kind of drive this kind of beyond the experiment and actually, you know, get it done and really start to see some results in, in, uh, in production in the field. >>I think certainly no one vendor organization can pull this off single handedly. It does require a number of organizations collaborating and working together. So I think our coalition is a good idea and a manifesto is just a good way to kind of lay out what you see as the key principles of the idea. And that makes it much easier for everybody to understand and act on. >>I, I think it's just, it's really interesting having, you know, having them written down on paper and having it just be so clearly articulated both in terms of the, of the values as well as, as the, uh, the principles and the values, you know, business outcomes matter trust and collaboration, data-driven decisions, which is the number three of four, and then learn, respond and pivot. It doesn't seem like those should have to be spelled out so clearly, but, but obviously it helps to have them there. You can stick them on the wall and kind of remember what your priorities are, but you're the data guy. You're the analytics guy, uh, and a big piece of this is data and analytics and moving to data driven decisions. And principle number seven says, you know, today's organizations generate more data than humans can process and informed decisions can be augmented by machine learning and artificial intelligence right up your alley. You know, you've talked a number of times on kind of the mini stages of analytics. Um, and how has that evolved over over time, you know, as you think of analytics and machine learning, driving decisions beyond supporting decisions, but actually starting to make decisions in machine time. What's that, what's that thing for you? What does that make you, you know, start to think, wow, this is this going to be pretty significant. >>Yeah. Well, you know, this has been a longterm interest of mine. Um, the last generation of AI, I was very interested in expert systems. And then, um, I think, uh, more than 10 years ago, I wrote an article about automated decision-making using what was available then, which was rule-based approaches. Um, but you know, this addresses an issue that we've always had with analytics and AI. Um, you know, we, we tended to refer to those things as providing decision support, but the problem is that if the decision maker didn't want their support, didn't want to use them in order to make a decision, they didn't provide any value. And so the nice thing about automating decisions, um, with now contemporary AI tools is that we can ensure that data and analytics get brought into the decision without any possible disconnection. Now, I think humans still have something to add here, and we often will need to examine how that decision is being made and maybe even have the ability to override it. But in general, I think at least for, you know, repetitive tactical decisions, um, involving a lot of data, we want most of those, I think to be at least, um, recommended if not totally made by an algorithm or an AI based system. And that I believe would add to, um, the quality and the precision and the accuracy of decisions and in most organizations, >>No, I think, I think you just answered my next question before I, before I asked it, you know, we had dr. Robert Gates on the former secretary of defense on a few years back, and we were talking about machines and machines making decisions. And he said at that time, you know, the only weapon systems, uh, that actually had an automated trigger on it were on the North Korea and South Korea border. Um, everything else, as you said, had to go through a sub person before the final decision was made. And my question is, you know, what are kind of the attributes of the decision that enable us to more easily automated? And then how do you see that kind of morphing over time, both as the data to support that as well as our comfort level, um, enables us to turn more and more actual decisions over to the machine? >>Well, yeah, as I suggested we need, um, data and the data that we have to kind of train our models has to be high quality and current, and we need to know the outcomes of that data. You know, um, most machine learning models, at least in business are supervised. And that means we need to have labeled outcomes in the, in the training data. But I, you know, um, the pandemic that we're living through is a good illustration of the fact that, that the data also have to be reflective of current reality. And, you know, one of the things that we're finding out quite frequently these days is that, um, the data that we have do not reflect, you know, what it's like to do business in a pandemic. Um, I wrote a little piece about this recently with Jeff cam at wake forest university, we call it data science quarantined, and we interviewed with somebody who said, you know, it's amazing what eight weeks of zeros will do to your demand forecast. We just don't really know what happens in a pandemic. Um, our models maybe have to be put on the shelf for a little while and until we can develop some new ones or we can get some other guidelines into making decisions. So I think that's one of the key things with automated decision making. We have to make sure that the data from the past and that's all we have of course, is a good guide to, you know, what's happening in the present and the future as far as we understand it. >>Yeah. I used to joke when we started this calendar year 2020, it was finally the year that we know everything with the benefit of hindsight, but I turned down 20, 20 a year. We found out we actually know nothing and everything and thought we knew, but I want to, I want to follow up on that because you know, it did suddenly change everything, right? We've got this light switch moment. Everybody's working from home now we're many, many months into it, and it's going to continue for a while. I saw your interview with Bernard Marr and you had a really interesting comment that now we have to deal with this change. We don't have a lot of data and you talked about hold fold or double down. And, and I can't think of a more, you know, kind of appropriate metaphor for driving the value of the biz ops when now your whole portfolio strategy, um, these to really be questioned and, and, you know, you have to be really, uh, well, uh, executing on what you are, holding, what you're folding and what you're doubling down with this completely new environment. >>Well, yeah, and I hope I did this in the interview. I would like to say that I came up with that term, but it actually came from a friend of mine. Who's a senior executive at Genpact. And, um, I, um, used it mostly to talk about AI and AI applications, but I think you could, you could use it much more broadly to talk about your entire sort of portfolio of digital projects. You need to think about, well, um, given some constraints on resources and a difficult economy for a while, which of our projects do we want to keep going on pretty much the way we were and which ones are not that necessary anymore? You see a lot of that in AI, because we had so many pilots, somebody told me, you know, we've got more pilots around here than O'Hare airport and, and AI. Um, and then, but the ones that involve doubled down, they're even more important to you. They are, you know, a lot of organizations have found this out, um, in the pandemic on digital projects, it's more and more important for customers to be able to interact with you, um, digitally. And so you certainly wouldn't want to cancel those projects or put them on hold. So you double down on them and get them done faster and better. Right, >>Right. Uh, another, another thing that came up in my research that, that you quoted, um, was, was from Jeff Bezos, talking about the great bulk of what we do is quietly, but meaningfully improving core operations. You know, I think that is so core to this concept of not AI and machine learning and kind of the general sense, which, which gets way too much buzz, but really applied right. Applied to a specific problem. And that's where you start to see the value. And, you know, the, the BizOps, uh, manifesto is, is, is calling it out in this particular process. But I'd love to get your perspective as you know, you speak generally about this topic all the time, but how people should really be thinking about where are the applications where I can apply this technology to get direct business value. >>Yeah, well, you know, even talking about automated decisions, um, uh, the kind of once in a lifetime decisions, uh, the ones that, um, ag Lafley, the former CEO of Procter and gamble used to call the big swing decisions. You only get a few of those. He said in your tenure as CEO, those are probably not going to be the ones that you're automating in part because, um, you don't have much data about them. You're only making them a few times and in part, because, um, they really require that big picture thinking and the ability to kind of anticipate the future, that the best human decision makers, um, have. Um, but, um, in general, I think where they, I, the projects that are working well are, you know, what I call the low hanging fruit ones, the, some people even report to it referred to it as boring AI. >>So, you know, sucking data out of a contract in order to compare it to a bill of lading for what arrived at your supply chain companies can save or make a lot of money with that kind of comparison. It's not the most exciting thing, but AI, as you suggested is really good at those narrow kinds of tasks. It's not so good at the, at the really big moonshots, like curing cancer or, you know, figuring out well what's the best stock or bond under all or even autonomous vehicles. Um, we, we made some great progress in that area, but everybody seems to agree that they're not going to be perfect for quite a while, and we really don't want to be driving around on, um, and then very much unless they're, you know, good and all kinds of weather and with all kinds of pedestrian traffic and you know, that sort of thing, right? >>That's funny you bring up contract management. I had a buddy years ago, they had a startup around contract management and I've like, and this was way before we had the compute power today and cloud proliferation. I said, you know, how can you possibly build software around contract management? It's language, it's legal, ease. It's very specific. And he's like, Jeff, we just need to know where's the contract. And when does it expire? And who's the signatory. And he built a business on those, you know, very simple little facts that weren't being covered because their contracts are in people's drawers and files and homes. And Lord only knows. So it's really interesting, as you said, these kind of low hanging fruit opportunities where you can extract a lot of business value without trying to, you know, boil the ocean. >>Yeah. I mean, if you're Amazon, um, uh, Jeff Bezos thinks it's important to have some kind of billion dollar project. And he even says it's important to have a billion dollar failure or two every year. But I think most organizations probably are better off being a little less aggressive and, you know, sticking to, um, what AI has been doing for a long time, which is, you know, making smarter decisions based on, based on data. >>Right? So Tom, I want to shift gears one more time before, before we let you go on, on kind of a new topic for you, not really new, but you know, not, not a, the vast majority of, of your publications and that's the new way to work, you know, as, as the pandemic hit in mid March, right. And we had this light switch moment, everybody had to work from home and it was, you know, kind of crisis and get everybody set up. Well, you know, now we're five months, six months, seven months. A number of companies have said that people are not going to be going back to work for a while. And so we're going to continue on this for a while. And then even when it's not what it is now, it's not going to be what it was before. So, you know, I wonder, and I know you, you, uh, you teased, you're working on a new book, you know, some of your thoughts on, you know, kind of this new way to work and, and, and the human factors in this new, this new kind of reality that we're kind of evolving into, I guess. >>Yeah. I missed was an interest of mine. I think, um, back in the nineties, I wrote an article called, um, a coauthored, an article called two cheers for the virtual office. And, you know, it was just starting to emerge. Then some people were very excited about it. Some people were skeptical and, uh, we said two cheers rather than three cheers because clearly there's some shortcomings. And, you know, I keep seeing these pop up. It's great that we can work from our homes. It's great that we can, most of what we need to do with a digital interface, but, um, you know, things like innovation and creativity, and certainly, um, uh, a good, um, happy social life kind of requires some face to face contact every now and then. And so I, you know, I think we'll go back to an environment where there is some of that. >>Um, we'll have, um, times when people convene in one place so they can get to know each other face to face and learn from each other that way. And most of the time, I think it's a huge waste of people's time to commute into the office every day and to jump on airplanes, to, to, um, give every little, um, uh, sales call or give every little presentation. Uh, we just have to really narrow down what are the circumstances where face to face contact really matters. And when can we get by with digital? You know, I think one of the things in my current work I'm finding is that even when you have AI based decision making, you really need a good platform in which that all takes place. So in addition to these virtual platforms, we need to develop platforms that kind of structure the workflow for us and tell us what we should be doing next, then make automated decisions when necessary. And I think that ultimately is a big part of biz ops as well. It's not just the intelligence of an AI system, but it's the flow of work that kind of keeps things moving smoothly throughout your organization. >>I think such, such a huge opportunity as you just said, cause I forget the stats on how often we're interrupted with notifications between email texts, Slack, a sauna, Salesforce, the list goes on and on. So, you know, to put an AI layer between the person and all these systems that are begging for attention, you've written a book on the attention economy, which is a whole nother topic, we'll say for another day, you know, it, it really begs, it really begs for some assistance because you know, you just can't get him picked, you know, every two minutes and really get quality work done. It's just not, it's just not realistic. And you know what? I don't think that's a feature that we're looking for. >>I agree. Totally >>Tom. Well, thank you so much for your time. Really enjoyed the conversation. I got to dig into the library. It's very long. So I might start at the attention economy. I haven't read that one. And to me, I think that's the fascinating thing in which we're living. So thank you for your time and, uh, great to see you. >>My pleasure, Jeff. Great to be here. >>All right. He's Tom I'm Jeff. You are watching the continuing coverage of the biz ops manifesto and Vail. Thanks for watching the cube. We'll see you next time.

>> Announcer: From around the globe, it's theCUBE with digital coverage of BizOps Manifesto Unveiled, brought to you by BizOps Coalition. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE for our ongoing coverage of the big unveil. It's the BizOps Manifesto Unveil and we're going to start that again. >> From the top. >> Three. >> Crew Member: Yeah, from the top. Little bleep bleep bleep, there we go. >> Manifesto. >> Crew Member: Second time's the charm, coming to you in five, four, three, two. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE coming to you from our Palo Alto studios today for a big, big reveal. We're excited to be here. It's the BizOps Manifesto Unveiling. Things have been in the works for a while and we're excited to have our next guest, one of the really the powers behind this whole effort and he's joining us from Boston. It's Serge Lucio, the Vice President and General Manager, Enterprise Software Division at Broadcom. Serge, great to see you. >> Good to see you, Jeff, Glad to be here. >> Absolutely. So, you've been in this business for a very long time, you've seen a lot of changes in technology. What is the BizOps Manifesto? What is this coalition all about? Why do we need this today in 2020? >> Yeah, so I've been in this business for close to 25 years, right? So, about 20 years ago, the Agile Manifesto was created. And the goal of the Agile Manifesto was really to address the uncertainty around software development and the inability to predict the effort to build software. And if you roll back kind of 20 years later and if you look at the current state of the industry, the Project Management Institute estimates that we're wasting about a million dollars every 20 seconds in digital transformation initiatives that do not deliver on business results. In fact, we recently surveyed a number of executives in partnership with Harvard Business Review and 77% of those executives think that one of the key challenges that they have is really at the collaboration between business and IT. And that's been kind of the case for almost 20 years now. So, the key challenge we're faced with is really that we need a new approach. And many of the players in the industry, including ourselves, have been using different terms, right? Some are talking about value stream management, some are talking about software delivery management. If you look at the Site Reliability Engineering movement, in many ways, it embodies a lot of these kind of concepts and principles. So, we believe that it became really imperative for us to crystallize around that one concept. And so, in many ways, the BizOps concept and the BizOps Manifesto are around bringing together a number of ideas which have been emerging in the last five years or so and defining the key values and principles to finally help these organizations truly transform and become digital businesses. And so, the hope is that by joining our forces and defining the key principles and values, we can help the industry, not just by providing them with support, but also the tools and consulting that is required for them to truly achieve the kind of transformation that everybody is seeking. >> Right, right. So, COVID, now, we're six months into it approximately, seven months into it, a lot of pain, a lot of bad stuff still happening, we've got two ways to go. But one of the things that on the positive side, right, and you seen all the memes in social media is a driver of digital transformation and a driver of change 'cause we had this light switch moment in the middle of March and there was no more planning, there was no more conversation, you suddenly got remote workforces, everybody's working from home and you got to go, right? So, the reliance on these tools increases dramatically. But I'm curious kind of short of the beginnings of this effort and short of kind of COVID which came along unexpectedly, I mean, what were those inhibitors 'cause we've been making software for a very long time, right? The software development community has adopted kind of rapid change and iterative delivery and sprints, what was holding back the connection with the business side to make sure that those investments were properly aligned with outcomes? >> Well, you have to understand that IT is kind of its own silos and traditionally, IT has been treated as a cost center within large organizations and not as a value center. And so as a result, kind of the traditional dynamic between IT and the business is basically one of kind of supplier up to kind of a business. And if you go back to I think Elon Musk a few years ago basically had these concepts of the machines to build the machines and he went as far as saying that the machines or the production line is actually the product. So, meaning that the core of the innovation is really about building kind of the engine to deliver on the value. And so, in many ways, we have missed on this shift from kind of IT becoming this kind of value center within the enterprises. And it's all about culture. Now, culture is the sum total of behaviors and the reality is that if you look at IT, especially in the last decade, with Agile, with DevOps, with hybrid infrastructures, it's way more volatile today than it was 10 years ago. And so, when you start to look at the velocity of the data, the volume of data, the variety of data to analyze the system, it's very challenging for IT to actually even understand and optimize its own processes, let alone to actually include business as kind of an integral part of a delivery chain. And so, it's both kind of a combination of culture, which is required, as well as tools, right? To be able to start to bring together all these data together. And then, given the volume, variety, velocity of the data, we have to apply some core technologies, which have only really truly emerged in the last five to 10 years around machine learning and analytics. And so, it's really kind of a combination of those things, which are coming together today to really help organizations kind of get to the next level. >> Right, right. So, let's talk about the manifesto. Let's talk about the coalition, the BizOps Coalition. I just like that you put down these really simple kind of straightforward core values. You guys have four core values that you're highlighting, business outcomes over individual projects and outputs, trust and collaboration over siloed teams and organizations, data driven decisions, what you just talked about, over opinions and judgment and learn to respond and pivot. I mean, Serge, these sounds like pretty basic stuff, right? I mean, isn't everyone working to these values already? And I think you touched on it, on culture, right? Trust and collaboration, data driven decisions. I mean, these are fundamental ways that people must run their business today or the person that's across the street that's doing it is going to knock them right off their block. >> Yeah, so that's very true. So, I'll mention another survey we did I think about six months ago. It was in partnership with an industry analyst. And we surveyed, again, a number of IT executives to understand how many were tracking business outcomes, how many of these software executives, IT executives were tracking business outcomes. And there were less than 15% of these executives who were actually tracking the outcomes of the software delivery. And you see that every day, right? So, in my own teams, for instance, we've been adopting a lot of these core principles in the last year or so. And we've uncovered that 16% of our resources were basically aligned around initiatives which were not strategic for us. I take another example. For instance, one of our customers in the airline industry uncovered, for instance, that a number of... That they had software issues that led to people searching for flights and not returning any kind of availability. And yet, the IT teams, whether it's operations or software development, were completely oblivious to that because they were completely blindsided to it. And so, the connectivity between the inwards metrics that IT is using, whether it's database uptime, cycle time or whatever metric we use in IT, are typically completely divorced from the business metrics. And so, at its core, it's really about starting to align the business metrics with the software delivery chain, right? This system which is really a core differentiator for these organizations. It's about connecting those two things and starting to infuse some of the Agile culture and principles that emerge from the software side into the business side. Of course, the Lean movement and other movements have started to change some of these dynamic on the business side. And so, I think this is the moment where we are starting to see kind of the imperative to transform now, COVID obviously has been a key driver for that. The technology is right to start to be able to weave data together and really kind of also the cultural shifts through Agile, through DevOps, through the SRE movement, through Lean business transformation. All these things are coming together and are really creating kind of conditions for the BizOps Manifesto to exist. So, Clayton Christensen, great Harvard Professor, "Innovator's Dilemma", still my all-time favorite business book, talks about how difficult it is for incumbents to react to disruptive change, right? Because they're always working on incremental change 'cause that's what their customers are asking for and there's a good ROI.' When you talk about companies not measuring the right thing, I mean, clearly, IT has some portion of their budget that has to go to keeping the lights on, right? That's always the case, but hopefully, that's an ever decreasing percentage of their total activity. So, what should people be measuring? I mean, what are kind of the new metrics in BizOps that drive people to be looking at the right things, measuring the right things and subsequently making the right decisions, investment decisions, on whether they should move project A along or project B? >> So, there are really two things, right? So, I think what you were talking about is portfolio management, investment management, right? And which is a key challenge, right? In my own experience, right? Driving strategy or a large scale kind of software organization for years, it's very difficult to even get kind of a base data as to who's doing what. I mean, some of our largest customers we're engaged with right now are simply trying to get a very simple answer, which is, how many people do I have in that specific initiative at any point in time and just tracking down information is extremely difficult. And again, back to the Project Management Institute, they have estimated that on average, IT organizations have anywhere between 10 to 20% of their resources focused on initiatives which are not strategically aligned. So, that's one dimension on portfolio management. I think the key aspect though, that's we're really keen on is really around kind of the alignment of a business metrics to the IT metrics. So, I'll use kind of two simple examples, right? And my background is around quality and I've always believed that fitness for purpose is really kind of a key philosophy, if you will. And so, if you start to think about quality as fitness for purpose, you start to look at it from a customer point of view, right? And fitness for purpose for a core banking application or mobile application are different, right? So, the definition of a business value that you're trying to achieve is different. And yet, if you look at our IT operations are operating, they were using kind of a same type of inward metrics, like a database uptime or a cycle time or what is my point velocity, right? And so, the challenge really is this inward facing metrics that the IT is using which are divorced from ultimately the outcome. And so, if I'm trying to build a core banking application, my core metric is likely going to be uptime, right? If I'm trying to build a mobile application or maybe a social mobile app, it's probably going to be engagement. And so, what you want is for everybody across IT to look at these metric and what are the metrics within the software delivery chain which ultimately contribute to that business metric? In some cases, cycle time may be completely irrelevant, right? Again, my core banking app, maybe I don't care about cycle time. And so, it's really about aligning those metrics and be able to start to differentiate. The key challenge you mentioned around the disruption that we see is or the investor's dilemma is really around the fact that many IT organizations are essentially applying the same approaches for innovation, right? For basically scrap work than they would apply to kind of other more traditional projects. And so, there's been a lot of talk about two-speed IT. And yes, it exists, but in reality, are really organizations truly differentiating how they operate their projects and products based on the outcomes that they're trying to achieve? And this is really where BizOps is trying to affect. >> I love that. Again, it doesn't seem like brain surgery, but focus on the outcomes, right? And it's horses for courses, as you said. This project, what you're measuring and how you define success isn't necessarily the same as on this other project. So, let's talk about some of the principles. We talked about the values, but I think it's interesting that the BizOps coalition just basically took the time to write these things down and they don't seem all that super insightful, but I guess you just got to get them down and have them on paper and have them in front of your face. But I want to talk about one of the key ones, which you just talked about, which is changing requirements, right? And working in a dynamic situation, which is really what's driven the software to change in software development because if you're in a game app and your competitor comes out with a new blue sword, you got to come out with a new blue sword. So, whether you had that on your Kanban wall or not. So, it's really this embracing of the speed of change and making that the rule, not the exception. I think that's a phenomenal one. And the other one you talked about is data, right? And that today's organizations generate more data than humans can process. So, informed decisions must be generated by machine learning and AI. And the big data thing with Hadoop started years ago, but we are seeing more and more that people are finally figuring it out, that it's not just big data and it's not even generic machine learning or artificial intelligence, but it's applying those particular data sets and that particular types of algorithms to a specific problem to your point, to try to actually reach an objective, whether that's increasing your average ticket or increasing your checkout rate with shopping carts that don't get left behind and these types of things. So, it's a really different way to think about the world in the good old days, probably when you guys started when we had big giant MRDs and PRDS and sat down and coded for two years and came out with a product release and hopefully, not too many patches subsequently to that. >> It's interesting, right? Again, back to one of these surveys that we did with about 600 IT executives. And we purposely designed those questions to be pretty open. And one of them was really around requirements. And it was really around kind of what is the best approach? What is your preferred approach towards requirements? And if I remember correctly, over 80% of the IT executives said that the best approach, their preferred approach, is for requirements to be completely defined before software development starts. So, let me pause there. We're 20 years after the Agile Manifesto, right? And for 80% of these IT executives to basically claim that the best approach is for requirements to be fully baked before software development starts, basically shows that we still have a very major issue. And again, our hypothesis in working with many organizations is that the key challenge is really the boundary between business and IT, which is still very much contract-based. If you look at the business side, they basically are expecting for IT to deliver on time on budget, right? But what is the incentive for IT to actually deliver on the business outcomes, right? How often is IT measured on the business outcomes and not on an SLA or on a budget type criteria. And so, that's really the fundamental shift that we really need to drive out as an industry. And, we talk about kind of this imperative for organizations to operate as one. And back to the the "Innovator's Dilemma", the key difference between these larger organization is really kind of a... If you look at the amount of capital investment that they can put into pretty much anything, why are they losing compared to startups? Why is it that more than 40% of personal loans today are issued, not by your traditional brick and mortar banks, but by startups? Well, the reason, yes, it's the traditional culture of doing incremental changes and not disrupting ourselves, which Christensen covered at length, but it's also the inability to really fundamentally change kind of the dynamic between business and IT and partner, right? To deliver on a specific business outcome. >> Right, I love that. That's a great summary and in fact, getting ready for this interview, I saw you mentioning another thing where the problem with the Agile development is that you're actually now getting more silos 'cause you have all these autonomous people working kind of independently. So, it's even a harder challenge for the business leaders, as you said, to know what's actually going on. But Serge, I want to close and talk about the coalition. So clearly, these are all great concepts. These are concepts you want to apply to your business every day. Why the coalition? Why take these concepts out to a broader audience, including your competition and the broader industry to say, "Hey, we as a group need to put a stamp of approval on these concepts, these values, these principles?" >> So first, I think we want everybody to realize that we are all talking about the same things, the same concepts. I think we're all from our own different vantage point realizing that things have to change. And again, back to whether it's value stream management or Site Reliability Engineering or BizOps, we're all kind of using slightly different languages. And so, I think one of the important aspects of BizOps is for us, all of us, whether we're talking about consulting, Agile transformation experts, whether we're talking about vendors, right? To provides kind of tools and technologies or these large enterprises to transform for all of us to basically have kind of a reference that lets us speak around kind of in a much more consistent way. The second aspect, to me, is for these concepts to start to be embraced, not just by us or vendors, system integrators, consulting firms, educators, thought leaders, but also for some of our own customers to start to become evangelists of their own in the industry. So, our objective with the coalition is to be pretty, pretty broad. And our hope is by starting to basically educate our joint customers or partners, that we can start to really foster these behaviors and start to really change some of dynamics. So, we're very pleased that if you look at some of the companies which have joined the manifesto, so we have vendors, such as Tasktop, or Appvance or PagerDuty, for instance, or even Planview, one of my direct competitors, but also thought leaders like Tom Davenport or Capgemini or smaller firms like Business Agility Institute or AgilityHealth. And so, our goal really is to start to bring together thought leaders, people who've been helping large organizations do digital transformation, vendors who are providing the technologies that many of these organizations use to deliver on this digital transformation and for all of us to start to provide the kind of education, support and tools that the industry needs. >> Yeah, that's great, Serge, and congratulations to you and the team. I know this has been going on for a while, putting all this together, getting people to sign on to the manifesto, putting the coalition together and finally today, getting to unveil it to the world in a little bit more of a public opportunity. So again, really good values, really simple principles, something that shouldn't have to be written down, but it's nice 'cause it is and now you can print it out and stick it on your wall. So, thank you for sharing the story and again, congrats to you and the team. >> Thank you, thanks, Jeff, appreciate it. >> My pleasure, all righty, Serge. If you want to learn more about the BizOps Manifesto, go to bizopsmanifesto.org, read it and you can sign it and you can stay here for more coverage on theCUBE of the BizOps Manifesto Unveiled. Thanks for watching, see you next time. (upbeat music)