(upbeat music) >> Hey everyone. Welcome back to the show floor in Las Vegas Snowflake Summit 22 with 7,000 plus folks here, Lisa Martin with Dave Vellante. Great to be back in person. We're excited to welcome a couple of guests that join us next. Persona Christian is here. The director of product for collaboration and Snowflake marketplace. Katie Laughlin joins us as well. The Global Head Offerings, Human Data Science Cloud at Customer IQVIA. Ladies, welcome to the program. >> Thank you. >> Thank you for having us. >> Dave: All right. Thanks for coming on. >> Katie, let's go ahead and start with you. Give the audience an overview of IQVIA. What you guys do, your mission, what you deliver? >> Yeah, sure. So, IQVIA is a healthcare focused data analytics and clinical research organization. We have 82,000 employees. We operate in a hundred countries and we have tens of thousands of data deliverables that we curate for our customers and deliver to them on a monthly basis. So, we're 100% healthcare focused, whether it's clinical research, helping our customers support their clinical trials, real world evidence, how are medicines operating in the market or commercial aspects. You know, how is your company performing overall in the market? >> How long have you been a customer of Snowflake's? >> A few years. Yeah. >> A few years, okay. Persona, tremendous growth going on right now. There's a rocket ship. You could even feel kind of like the whiplash from the keynote and all the announcements going on, but looking at the first quarter 23, fiscal 23 results, product revenue, 384 million, 85% growth tremendous momentum going on, big growth in customers. Talk to us about IQVIA, its partnership with Snowflake and the data driver award program. They, they just won. >> Yeah, absolutely. I'll start with a little bit about the Snowflake collaboration capabilities, which enable these thousands of customers to really collaborate on the data cloud to be able to break down silos between data and drive business decisions based on data and applications that live outside your own four walls as well. And this is where IQVIA, as a leader in healthcare data, bringing together data to enable healthcare organizations to be more data driven and to really drive insights. One, the data for good award, which we are really excited with for the partnership and really excited to have IQVIA be the winner of the award. >> And what does that mean? The data for good. We always love talking about that, Katie. >> Katie: Sure. What does that mean? How is that embodied at IQVIA? >> Can you say the last part? >> Yeah. How is that embodied at IQVIA? >> That's a great question. I think everyone that works at IQVIA believes in the mission, which is really to drive healthcare forward. We're really proud of a lot of the things that we do. So, with the advent of COVID, for example, we really had to pivot and help our customers. How do we keep executing on clinical trials? We supported a lot of the COVID trials that came forward and helped our customers understand how is this affecting patients in the real world? And how is it affecting your commercial operations? So, being in Vegas with tens of thousands of people around and almost nobody wearing masks, I think to myself, I'm part of the organization an organization that helped make that possible. >> So Frank Slootman today, Katie talked about compress. He talked about one pharmaceutical compressing from nine years to seven years, you guys have done a lot of obviously contract research over the years. So, what has that Snowflake journey been like? What's been the business impact of of working with that and the collaboration? >> Yeah. So my focus is really around our data as a service offering, which is where we're enabling our customers to ingest their data in modern ways. So if you imagine, you know, we've done everything from paper to big tapes of data for over 60 years of of our company being in business, now to VPN, SFTP, making multiple hops of data from one end to the other. I was just learning about one of our use cases where we're able to cut down processing time for our customers for two weeks. They data share some data with us. We do some additional processing on that. We serve it back to them and we're saving them two weeks of time to gain time to insights. >> Right. And Prasanna, collaboration transcends data sharing, right? It's almost like it's, that's, that's sort of the the first, the core of the concentric circle, right? >> Prasanna: Yeah. >> Talk about what else is embodied in collaboration. >> Yeah, that's a great question. So the first problem that we solved was getting access to data through our core sharing technology. And as you were talking about Katie, replacing FTPs and having to build APIs, which were cumbersome, and instead being able to access data on the data cloud without having to copy or move anything. That was the core sharing technology. But that solves the first problem, which is the access problem. The second problem is how do I discover what what's out there? How do I better understand it? How do I evaluate it? How do I try it and buy it? And those are all the problems that we're solving with the marketplace, which is now home to both data and applications that you can discover, try, and buy. >> Katie, talk to us about what IQVIA was doing before Snowflake? What was that life like before? How were you enabling customers to leverage data to make data driven decisions? >> Yeah, so we, as I said, we're a data and analytics company. So we provide some native analytics capabilities to our customers, but most customers, most of the large customers I would say, they're building their own data lakes. They have their own ecosystems. Some of them are adopting Snowflake and we really needed to partner with them on being able to get the data to them as quickly as possible. So like, I, I was just describing a minute ago we would have multiple hops where we deliver to a location, customer ingests it, customer does their QC. Then they process it and then it appears in their data warehouse. And now we're able to adopt their QC protocols within our own platform and deliver the data to them much more quickly. >> And what does that enable to your business from an outcomes perspective? If you look at overall Snowflake as an engine what is it enabling and empowering IQVIA to accomplish? >> So it helps us partner with our customers in modern ways. So I'm saying we've been in the data business for 60 years. So it's sometimes it's a legacy behemoth that you need to bring along to modern times. And I think for us, the shift has been night and day in terms of Snowflake's capabilities. >> So you will build data based apps in the Snowflake data cloud? Is that, is that where you're headed? >> Yes. So we have several applications that we built natively on Snowflake that we offer to our customers. >> And what will that bring you that you kind of couldn't do before? >> That we couldn't do before? I think the the ability to, we talk a lot about how you spend 80% of your time cooking the data, right? Getting it ready for insights and only 20% of your time being able to to bring those insights forward and Snowflake, it really helps us flip that ratio so that we don't have to worry so much about the scaling and the infrastructure and the data sourcing. We can focus more on driving those insights and innovations. >> So Prasanna, we talk a lot about, you have this application stack over here and it sends a database over here and then you have an analytics stack. It seems like you're enabling those worlds to come together. Is that, is that by design? Is that more organic? Can you talk about that? >> Yeah. I mean, that is essential to our our mission and our value prop is to bring it together. It's one product, it's seamless and lets you do more with your data. Benoit talked today in the opening keynote about running multiple workloads on your data and the way you do that is by having one product that allows you to to run your data, data queries but also build applications that can run against that data. >> Katie, can you share a little bit about the partnership? We'll say collaboration that IQVIA has with Snowflake in terms of your ability to influence the roadmap in the direction. We heard a lot of customer stories in the keynote and they talked a lot about Frank Slootman did, Benoit, Christian. We are listening to our customers. Do you feel that as a, a customer for the last few years? >> Yeah, absolutely. So we have a really broad partnership with Snowflake. We're a customer. We have OEM licensing where we're building applications on top of Snowflake. We're an SI partner where we're marrying our data healthcare expertise along with Snowflake technology expertise and helping customers build and utilize the data internally and as well as just, if nothing else, the Snowflake data share in order to deliver the data into their environment. >> Prasanna, what do you look for in a data driver winner? Like what stood out about IQVIA and others that aspire to that, what should they be focused on? >> Yeah, I mean, you know, we ultimately think that in every business you have business needs that you're trying to solve and business is inherently collaborative. You never solve problems with just what you have within your own four walls. And IQVIA is an example of someone that's really enabling outcomes for healthcare companies to be much faster through live access to data. Which is what we want to accomplish for the data cloud, help our company, help our customers solve business needs. >> Every company has to be a data company these days, right? There's no, you have no choice. We talked about, you know, software eating the world a few years. Now we're talking about data eating the world. For organizations, it's in any any vertical healthcare, life sciences, retail, finance. It's essential to not just have data, live data access to it, to be able to extract insights from it that you can act on. Talk about what you are doing at Snowflake as a differentiator? Is that goal of becoming the defacto standard data platform and what that enables partners like IQVIA to accomplish? >> Yeah. It starts with our fundamental architecture, which allows you to collaborate and access data without creating copies of it or sending around copies and built on top of that now, the ability to build applications and to monetize them really enables our customers to do more with their data and to monetize it and to be able to distribute it without having to deal with all the plumbing. >> That's nice. That saves you a lot of time. What do you think when you, Katie, if you talk to people that are your peers in either healthcare or other industries, what are like the top couple of recommendations that you would have for them? We have a data problem. It's all a data problem. How do we actually leverage value from this fast so we can be competitive? >> Yeah. So I think if I were to advise someone who is thinking about commercializing their data set, when if they haven't before, you know, you have to think about good data governance protocols, good data cataloging. Make sure you're, you know, conforming to all of the privacy rules that you need to and overseeing the management of that data, any changes in the data, you know, delivering that both to internal and external customers. But I think, just a quick plug for Snowflake, what I would say on a personal level is that their partner first mentality really is a pleasure, makes it a pleasure to work with them and makes it really easy for us to enable our services through, through Snowflake. >> Frank Slootman talked about mission alignment this morning, kind of a mission I thought of, of aligning on with the missions of their customers and partners. It sounds like that's what Katie's talking about from a cultural perspective. You've got that alignment here? >> Yes, absolutely. You know, we work with our partners to enable our customers to drive business value and solve the needs of their industry. >> What are some of the things that you are excited about? Fourth Annual Summit. We, I, I said 7,000 plus people we'll get numbers kind of later on. What are you excited about finally being back in person? >> Yes, of course. >> Being able to access this hugely growing population of customers and partners, what excites you about this Summit 22? >> What excites me most is the fact that we are now enabling our customers to do more, to build applications which has been a big theme at Summit, but also to be able to distribute and monetize this. So as Frank talked about this morning, helping customers drive value and more value from, from their data. >> Critical. Katie, last question for you. If we look at all the,it was a very technical keynote this morning. You talked about the great partnership, the synergies the alignment that IQVIA has with Snowflake. What are you excited about in terms of hearing and seeing and feeling and touching this week at Summit? >> Well, yesterday we won an award for Data Marketplace. Marketplace Partner of the year for healthcare and life sciences. That was really exciting for us. It was great recognition for us in terms of how we've been able to modernize on the cloud. But I'm really excited to see how much the Snowflake business has grown as well. Our General Manager for information management was telling me, he said, when I come to this conference a couple of years ago it was only a few thousand people and now it's really, it's really grown and really taken off. And it's really exciting to see how many of the different partnerships are interacting and and that we're able to take advantage of as well. >> Yeah, I think we heard earlier this morning that the first summit four years ago was a couple thousand people. Now here we are eight, eight to ten. We've also seen, Persona, I mentioned some of the product revenue numbers for fiscal 23 Q1. I also noticed that in the last four years, the number percentage of customers with a million plus ARR is grown over 1200%. Number of customers is growing, the high value customers are growing. It seems like you're on a rocket ship here with Snowflake. Would you agree? >> Yeah. We're excited with all the value that we're bringing to our customers and the growth we're seeing. >> Dave: Yeah. Way to amp it up. >> Yeah, absolutely. >> Excellent. Ladies, thank you so much for joining us talking about the partnership with IQVIA and Snowflake. Congratulations again. >> Katie: Thank you. >> Katie, on IQVIA winning the data driver award, Data for good >> Great to hear what you're doing together and how you're enabling organizations in the healthcare industry to maximize the value of data. We appreciate your insights. >> Thank you. >> Dave: Thank you guys. >> Thanks. >> For our guests, Dave Vellante, I'm Lisa Martin. You're watching the Cube's live coverage from Las Vegas of Snowflake Summit 22. Stick around, Dave and I will be right back with our next guest.

(upbeat music) >> Hello and welcome to today's session, theCUBE's presentation of the AWS Startup Showcase, New Breakthroughs in DevOps, Data Analytics, Cloud Management Tools, featuring Ascend.io for the data and analytics track. I'm your host, John Furrier with theCUBE. Today, we're proud joined by Sean Knapp, CEO and founder of Ascend.io and Jason Robinson who's the VP of Data Science and Engineering at Steady. Guys, thanks for coming on and congratulations, Sean, for the continued success, loves our cube conversation and Jason, nice to meet you. >> Great to meet you. >> Thanks for having us. >> So, the session today is really kind of looking at automating analytics workloads, right? So, and Steady as a customer. Sean, talk about the relationship with the customer Steady. What's the main product, what's the core relationship? >> Yeah, it's a really great question. when we work with a lot of companies like Steady we're working hand in hand with their data engineering teams, to help them onboard onto the Ascend platform, build these really powerful data pipelines, fueling their analytics and other workloads, and really helping to ensure that they can be successful at getting more leverage and building faster than ever before. So we tend to partner really closely with each other's teams and really think of them even as extensions of each other's own teams. I watch in slack oftentimes and our teams just go back and forth. And it's like, as if we were all just part of the same company. >> It's a really exciting time, Jason, great to have you on as a person cutting your teeth into this kind of what I call next gen data as intellectual property. Sean and I chat on theCUBE conversation previous to this event where every company is a data company, right? And we've heard that cliche. >> Right. >> But it's true, right? It's going to, it's getting more powerful with the edge. You seeing more diverse data, faster data, small, big, large, medium, all kinds of different aspects and patterns. And it's becoming a workflow kind of intellectual property paradigm for companies, not so much. >> That's right. >> Just the tech it's the database is you can, it's the data itself, data in flight, it's moving around, it's got value. What's your take-- >> Absolutely. >> On this trend? >> Basically, Steady helps our members and we have a community of members earn more income. So we want to help them steady their financial lives. And that's all based on data, so we have a web app, you could go to the iOS Store, you could go to the Google Play Store, you can download the app. And we have a large number of members, 3 million plus, who are actively using this. And we also have a very exciting new product called income passport. And this helps 1099 and mixed wage earners verify their income, which is very important for different government benefits. And then third, we help people with emergency cash grants as well as awards. So all of that is built on a bedrock of data, so if you're using our apps, it's all data powered. So what you were mentioning earlier from pipelines that are running it real time to yeah, anything, that's a kind of a small data aggregation, we do everything from small to real-time and large. >> You guys are like a multiple sided marketplace here, you've got it, you're a FinTech app, as well as the future of work and with virtual space-- >> That's right. >> Happening now, this is becoming, actually encapsulates kind of the critical problems that people trying to solve right now, you've got multiple stakeholders. >> That's right. >> In the data. >> Yes, we absolutely do. So we have our members, but we also, within the company, we have product, we have strategy, we have a growth team, we have operations. So data engineering and data science also work with a data analytics organization. So at Steady we're very much a data company. And we have a data organization led by our chief data officer and we have data engineering and data science, which are my teams, but also that business insights and analytics. So a lot of what we're building on the data engineering side is powering those insights and analytics that the business stakeholders use every day to run the organization. >> Sean, I want to get your thoughts on this because we heard from Emily Freeman in the keynote about how this revolution in DevOps or for premiering her talk around how, it's not just one persona anymore, I'm a release engineer, I'm this kind of engineer, you're seeing now all engineering, all developers are developers. You have some specialty, but for the most part, the team makeups are changing. We touched on this in our cube conversation. The journey of data is not just the data people, the data folks. It's like there's, they're developers too. So the confluence of data science, data management, developing, is changing the team and cultural makeup of companies. Could you share your thoughts on this dynamic and how it impacts customers? >> Absolutely, I think the, we're finding a similar trend to what we saw a number of years ago, when we talked about how software was eating the world and every company was now becoming a software company. And as a result, we saw this proliferation and expansion of what the software roles look like and thought of a company pulled through this entire new era of DevOps. We were finding that same pattern now emerging around data as not only is every company a software company, every company is a data company and data really is that field, that oil that fuels the business and in doing so, we're finding that as Jason describes it's pervasive across the team, it is no longer just one team that is creating some insights and reports around operational analytics, or maybe a team over here doing data science or machine learning. It is expensive. And I think the really interesting challenges that start to come with this too, are so many data teams are so over capacity. We did a recent study that highlighted that 96% of data teams are at, or over capacity, only 4% had spare capacity. But as a result, the net is being cast even wider to pull in people from even broader and more adjacent domains to all participate in the data future of their organization. >> Yeah, and I think I'd love to get your guys react to this conversation with Andy Jassy, who's now the CEO of Amazon, but when he was the CEO of AWS last year, I talked with him about how the old guard and new guard are thinking around team formations. Obviously team capacity is growing and challenged when you've got the right formula. So that's one thing, right? But what if you don't have the right formula? If you're in the skills gap, problem, or team formation side of it, where you maybe there was two years ago where the mandate came down? Well, we got to build a data team even in two years, if you're not inquisitive. And this is what Andy and I were talking about is the thinking and the mindset of that mission and being open to discovering and understanding the changes, because if you were deciding what your team was two, three years ago, that might have changed a lot. So team capacity, Sean, to your point, if you got it right, and that's a challenge in and of itself, but what if you don't have it, right? What do you guys think about this? >> Yeah, I think that's exactly right. Basically trying to see, look and gaze into the crystal ball and see what's going to happen in a year or two years, even six months is quite difficult. And if you don't have it right, you do spend a lot of time because of the technical debt that you've amassed. And we certainly spend quite a bit of time with technical debt for things we wanted to build. So, deconvolving that, getting those ETLs to a runnable state, getting performance there, that's what we spend a bit of time on. And yeah, it's something that it's really part of the package. >> What do you guys see as the big challenge on teams? The scaling challenge okay. Formation is one thing, Sean, but like, okay, getting it right, getting it formed properly and then scaling it, what are the big things you're seeing? >> One of the, I think the overarching management themes in general, it is the highest out by the highest performing teams are those where the individual with the context and the idea is able to execute as far and as fast and as efficiently as possible, and removing a lot of those encumbrances and put it a slightly different way. If DevOps was all basically boiled down to, how do we help more people write more software faster and safely data ops would be very similarly, how do we enable more people to do more things with data faster and safely? And to do that, I think the era of these massive multi-year efforts around data are gone and hopefully in the not too distant future, even these multi-quarter efforts around data are gone and we get into a much more agile, nimble methodology where smaller initiatives and smaller efforts are possible by more diverse skillsets across the business. And really what we should be doing is leveraging technology and automation to ensure that people are able to be productive and efficient and that we can trust our data and that systems are automated. And these are problems that technology is good at. And so in many ways, how in the early days Amazon would described as getting people out of the muck of DevOps. I think we're going to do the same thing around getting people out of the muck of the data and get them really focused on the higher level aspects. >> Yeah, we're going to get into that complexity, heavy lifting side muck, and then the heavy lifting taking away from the customers. But I want to go back to real quick with Jason while we're on this topic. Jason, I was just curious, how much has your team grown in the recent year and how much could've, should've grown, what's the status and how has Ascend helped you guys? What's the dynamic there? ' Cause that's their value proposition. So, take us through that. >> Absolutely, so, since the beginning of the year data engineering has doubled. So, we're a lean team, we certainly use the agile mindset and methodologies, but we have gone from, yeah, we've essentially doubled. So a lot of that is there's just so much to do and the capacity problem is certainly there. So we also spend a lot of time figuring out exactly what the right tooling is. And I was mentioning the technical debt. So you have those, there's the big O notation of whatever that involves technical debt. And when you're building new things, you're fixing old things. And then you're trying to maintain everything. That scaling starts to hit hard. So even if we continue to double, I mean, we could easily add more data engineers. And a lot of that is, I mean, you know about the hiring cycles, like, a lot of of great talent, but it's difficult to make all of those hires. So, we do spend quite a bit of time thinking about exactly what tools data engineering is using day-to-day. And what I mentioned, were technologies on the streaming side all the way to like the small batch things, but, like something that starts as a small batch getting grow and grow and grow and take, say 15 hours, it's possible, I've seen it. But, and getting that back down and managing that complexity while not overburdening people who probably don't want to spend all their waking hours building ETLs, maintaining ETL, putting in monitoring, putting in alerting, that I think is quite a challenge. >> It's so funny because you mentioned 18 hours, you got to kind of being, you didn't roll your eyes, but you almost did, but this is, but people want it yesterday, they want real time, so there's a lot of demand-- >> Yes. >> On the minds of the business outcome side of it. So, I got to ask you, because this comes up a lot with technical debt, and now we're starting to see that come into the data conversation. And so I always curious, is there a different kind of technical debt with data? Because again, data is like software, but it's a little bit of more elusive in the sense it's always changing. So is there, what kind of technical debt do you see in the data side that's different than say software side? >> Absolutely, now that's a great question. So a lot of thinking about your data and structuring your data and how you want to use that data going into a particular project might be different from what happens after stakeholders have a new considerations and new products and new items that need to be built. So thinking about how that, let's say you have a document store, or you have something that you thought was going to be nice and structured, how that can evolve and support those particular products can essentially, unless you take the time and go through and say, well, let's architect it perfectly so that we can handle that. You're going to make trade-offs and choices, and essentially that debt builds up. So you start cutting corners, you start changing your normalization. You start essentially taking those implicit schema that then tend to build into big things, big implicit schema. And then of course, with implicit schema, you're going to have a lot of null values, you're going to have a lot of items to deal with. So, how do you deal with that? And then you also have the opportunity to create keys and values and oops, do we take out those keys that were slightly misspelled? So, I could go on for hours, but basically the technical debt certainly is there with on data. I see a lot of this as just a spectrum of technical debt, because it's all trade-offs that you made to build a product, and the efficiency has start to hit you. So, the 15 hour ETL, I was mentioning, basically you start with something and you were building things for stakeholders and essentially you have so much complex logic within that. So for the transforms that you're doing from if you're thinking of the bronze, silver, gold, kind of a framework, going from that bronze to a silver, you may have a massive number of transformations or just a few, just to lightly dust it. But you could also go to gold with many more transformations and managing that, managing the complexity, managing what you're spending for servers day after day after day. That's another real challenge of that technical debt stuff. >> That's a great lead into my next question, for Sean, this is the disparate system complexity, technical debt and software was always kind of the belief was, oh yeah, I'll take some technical debt on and work it off once I get visibility and say, unit economics or some sort of platform or tool feature, and then you work it off as fast as possible. I was, this becomes the art and science of technical debt. Jason, what you're saying is that this can be unwieldy pretty quickly. You got state and you got a lot of different inter moving parts. This is a huge issue, Sean, this is where it's, technical debt in the data world is much different architecturally. If you don't get it right, this is a huge, huge issue. Could you aluminate why that is and what you guys are doing to help unify and change some of those conditions? >> Yeah, absolutely. When we think about technical debt and I'll keep drawing some parallels between DevOps and data ops, 'cause I think there's a tremendous number of similarities in these worlds. We used to always have the saying that "Your tech debt grows manually across microservices, "but exponentially within services." And so you want that right level of architecture and composibility if you will, of your systems where you can deploy changes, you can test, you can have high degrees of competence and the roll-outs. And I think the interesting part in the data side, as Jason highlighted, the big O-notation for tech debt in the data ecosystem, is still fairly exponential or polynomial in nature. As right now, we don't have great decomposition of the components. We have different systems. We have a streaming system, we have a databases, we have documents, doors and so on, but how the whole data pipeline data engineering part works generally tends to be pretty monolithic in nature. You take your whole data pipeline and you deploy the whole thing and you basically just cross your fingers, and hopefully it's not 15 hours, but if it is 15 hours, you go to sleep, you wake up the next morning, grab a coffee and then maybe it worked. And that iteration cycle is really slow. And so when we think about how we can improve these things, right? This is combinations of intelligent systems that do instantaneous schema detection, and validation, excuse me, it's combinations of things that do instantaneous schema detection and validation. It's things like automated lineage and dependency tracking. So you know that when you deploy code, what piece of data it affects, it's things like automated testing on individual core parts of your data pipelines to validate that you're getting the expected output that you need. So it's pulling a lot of these same DevOps style principles into the data world, which is really designed to going back to how do you help more people build more things faster and safely really rapid iterations for rapid feedback. So you know if there's breaks in the system much earlier on. >> Well, I think Sean, you're onto something really big there. And I think this is something that's emerging pretty quickly in the cloud scale that I called, 2.0, whatever, what version we're in, is the systems thinking mindset. 'Cause you mentioned the model that that was essentially a silo or subsystem. It was cohesive in it's own way, but now it's been monolithic. Now you have a broken down set of decomposed sets of data pieces that have to work together. So Jason, this is the big challenge that everyone, not really people are talking about, I think most these guys are, and you're using them. What are you unifying? Because this is a systems operating systems thinking, this is not like a database problem. It's a systems problem applied to data where databases are just pieces of it, what's your thoughts? >> That's absolutely right. And I would, so Sean touched on composibility of ETL and thinking about reusable components, thinking about pieces that all fit together, because as you're building something as complex as some of these ETS are, we do think about the platform itself and how that lends to the overarching output. So one thing, being able to actually see the different components of an ETL and blend those in and you as the dry principal, don't repeat yourself. So you essentially are able to take pieces that one person built, maybe John builds a couple of our connectors coming in, Sean also has a bunch of transforms and I just want this stuff out, so I can use a lot of what you guys have already built. I think that's key, because a lot of engineering and data engineering is about managing complexity. So taking that complexity and essentially getting it out fast and getting out error free, is where we're going with all of the data products we're building. >> What are some of the complexity that you guys have that you're dealing with? Can you be specific and share what these guys are doing to solve that problem for you? That's, this is a big problem everyone's having, I'm seeing that all over the place. >> Absolutely, so I could start at a couple of places. So I don't know if you guys are on the three Vs, four Vs or five Vs, but we have all of those. And if you go to that five, four or five V model, there is the veracity piece, which you have to ask yourself, is it true? Is it accurate when? So change happens throughout the pipeline, change can come from web hooks, change can come from users. You have to make sure that you're managing that complexity and what we we're building, I mentioned that we are paying down a lot of tech debt, but we're also building new products. And one pretty challenging, quite challenging ETL that we're building is something going from a document store to an analytical application. So in that document store, we talked about flexible schema. Basically, you don't really know exactly what you're going to get day to day, and you need to be able to manage that change through the whole process in a way that the ultimate business users find value. So, that's one of the key applications that we're using right now. And that's one that the team at Ascend and my team are working hand in hand going through a lot of those challenges. And it's, I also watch the slack just as Sean does, and it's a very active discussion board. So it is essentially like they're just partnering together. It's fabulous, but yeah-- >> And you're seeing kind of a value on this too, I mean, in terms of output what's the business results? >> Yes, absolutely. So essentially this is all, so yes, the fifth V value. So, getting to that value is essentially, there were a few pieces of the, to the value. So there's some data products that we're building within that product and their data science, data analytics based products that essentially do things with the data that help the user. There's also the question of exactly the usage and those kinds of metrics that people in ops want to understand as well as our growth team. So we have internal and external stakeholders for that. >> Jason, this is a great use case, a great customer, Sean, you guys are automating. For the folks watching, who were seeing their peer living the dream here and the data journey, as we say, things are happening. What's the message to customers that you guys want to send because you guys are really cutting your teeth into a whole another level of data engineering, data platform. That's really about the systems view and about cloud. What's the pitch, Sean? What should people know about the company? >> Absolutely, yeah, well, so one, I'd say even before the pitch, I would encourage people to not accept the status quo. And in particular, in data engineering today, the status quo is an incredibly high degree of pain and discomfort. And I think the important part of why Ascend exists and why we're so helpful for our customers, there is a much more automated future of how we build data products, how we optimize those and how we can get a larger cohort of builders into the data ecosystem. And that helps us get out of the muck as we talked about before and put really advanced technology to work for more people inside of our companies to build these data products, leveraging the latest and greatest technologies to drive increased business value faster. >> Jason, what's your assessment of these guys, as people are watching might say, hey, you know what, I'm going to contact them, I need this. How would you talk about Ascend into your peers? >> Absolutely, so I think just thinking about the whole process has been a great partnership. We started with a POC, I think Ascend likes to start with three use cases, I think we came out with four and we went through the ones that we really cared about and really wanted to bring value to the company with. So we have roadmaps for some, as we're paying down technical debt and transitioning, others we can go directly to. And I think that thinking about just like you're saying, John, that systems view of everything you're building, where that makes sense, you can actually take a lot of that complexity and encapsulate it in a way that you can essentially manage it all in that platform. So the Ascend platform has the composibility piece that we touched on. It also, not only can you compose it, but you can drill into it. And my team is super talented and is going to drill into it. So basically loves to open up each of those data flows each of the components therein and has the control there with the combination of Spark Sequel, PI Spark SQL Scala and so on. And I think that the variety of connections is also quite helpful. So thinking about the dry principle from a systems perspective is extremely useful because it's dry, you often get that in a code review, right? I think you can be a little bit more dry here. >> Yeah. >> But you can really do that in the way that you're composing your systems as well. >> That's a great, great point. One quick thing for the folks that they're watching that are trying to figure this out, and a lot of architecture is going on. A lot of people are looking at different solutions. What things have you learned that you could give them a tip like to avoid like maybe some scar tissue or tips of the trade, where you can say, hey, this way, be careful, what's some of the learnings? Could you give a few pointers to folks out there, if they're kicking tires on the direction, what's the wrong direction? What's the right direction look like? >> Absolutely, I think that, I think it through, and I don't know how much time we have that, that feels like a few days conversation as far as ways to go wrong. But absolutely, I think that thinking through exactly where want to be is the key. Otherwise it's kind of like when you're writing a ticket on Jarrah, if you don't have clear success criteria, if you don't know where you going to go, then you'll end up somewhere building something and it might work. But if you think through your exact destination that you want to be at, that will drive a lot of the decisions as you think backwards to where you started. And also I think that, so Sean also mentioned challenging the status quo. I think that you really have to be ready to challenge the status quo at every step of that journey. So if you start with some particular service that you had and its legacy, if it's not essentially performing what you need, then it's okay to just take a step back and say, well, maybe that's not the one. So I think that thinking through the system, just like you were saying, John, and also I think that having a visual representation of where you want to go is critical. So hopefully that encapsulates a lot of it, but yes, the destination is key. >> Yeah, and having an engineering platform that also unifies the multiple components and it's agile. >> That's right. >> It gets you out of the muck and on the last day and differentiate heavy lifting is a cloud plan. >> Absolutely. >> Sean, wrap it up for us here. What's the bumper sticker for your vision, share your founding principles of the company. >> Absolutely, for us, we started the company as a former in recovery and CTO. The last company I founded, we had nearly 60 people on our data team alone and had invested tremendous amounts of effort over the course of eight years. And one of the things that I've learned is that over time innovation comes just as much from deciding what you're no longer going to do as what you're going to do. And focusing heavily around, how do you get out of that muck? How do you continue to climb up that technology stack? Is incredibly important. And so really we are excited to be a part of it and taking the industry is continuing to climb higher and higher level. We're building more and more advanced levels of automation and what we call our data awareness into the automated engine of the Ascend platform that takes us across the entire data ecosystem, connecting and automating all data movement. And so we have a very exciting vision for this fabric that's emerging over time. >> Awesome, Sean, thank you so much for that insight, Jason, thanks for coming on customer of Ascend.io. >> Thank you. >> I appreciate it, gentlemen, thank you. This is the track on automating analytic workloads. We here at the end of us showcase, startup showcase, the hottest companies here at Ascend.io, I'm John Furrier, with theCUBE, thanks for watching. (upbeat music)

(Upbeat intro music) >> Thanks guys. Hi everybody. Welcome back to The Spheres. My name is Dave Vellante and you're watching theCubes continuous coverage of AWS storage day. I'm really excited to bring on Wayne Duso. Wayne is the vice-president of AWS Storage Edge and Data Governance Services. Wayne, two Boston boys got to come to Seattle to see each other. You know. Good to see you, man. >> Good to see you too. >> I mean, I'm not really from Boston. The guys from East Boston give me crap for saying that. [Wayne laughs] That my city, right? You're a city too. >> It's my city as well I'm from Charlestown so right across the ocean. >> Charlestown is actually legit Boston, you know I grew up in a town outside, but that's my city. So all the sports fan. So, hey great keynote today. We're going to unpack the keynote and, and really try to dig into it a little bit. You know, last 18 months has been a pretty bizarre, you know, who could have predicted this. We were just talking to my line about, you know, some of the permanent changes and, and even now it's like day to day, you're trying to figure out, okay, you know, what's next, you know, our business, your business. But, but clearly this has been an interesting time to say the least and the tailwind for the Cloud, but let's face it. How are customers responding? How are they changing their strategies as a result? >> Yeah. Well, first off, let me say it's good to see you. It's been years since we've been in chairs across from one another. >> Yeah. A couple of years ago in Boston, >> A couple of years ago in Boston. I'm glad to see you're doing well. >> Yeah. Thanks. You too. >> You look great. (Wayne Laughs) >> We get the Sox going. >> We'll be all set. >> Mm Dave you know, the last 18 months have been challenging. There's been a lot of change, but it's also been inspiring. What we've seen is our customers engaging the agility of the Cloud and appreciating the cost benefits of the Cloud. You know, during this time we've had to be there for our partners, our clients, our customers, and our people, whether it's work from home, whether it's expanding your capability, because it's surging say a company like zoom, where they're surging and they need more capability. Our cloud capabilities have allowed them to function, grow and thrive. In these challenging times. It's really a privilege that we have the services and we have the capability to enable people to behave and, execute and operate as normally as you possibly can in something that's never happened before in our lifetimes. It's unprecedented. It's a privilege. >> Yeah. I mean, I agree. You think about it. There's a lot of negative narrative, in the press about, about big tech and, and, and, you know, the reality is, is big tech has, has stood and small tech has stepped up big time and we were really think about it, Wayne, where would we be without, without tech? And I know it sounds bizarre, but we're kind of lucky. This pandemic actually occurred when it did, because had it occurred, you know, 10 years ago it would have been a lot tougher. I mean, who knows the state of vaccines, but certainly from a tech standpoint, the Cloud has been a savior. You've mentioned Zoom. I mean, you know, we, productivity continues. So that's been, been pretty key. I want to ask you, in you keynote, you talked about two paths to, to move to the Cloud, you know, Vector one was go and kind of lift and shift if I got it right. And then vector two was modernized first and then go, first of all, did I get that right? And >> Super close and >> So help me course correct. And what are those, what are those two paths mean for customers? How should we think about that? >> Yeah. So we want to make sure that customers can appreciate the value of the Cloud as quickly as they need to. And so there's, there's two paths and with not launches and, we'll talk about them in a minute, like our FSX for NetApp ONTAP, it allows customers to quickly move from like to like, so they can move from on-prem and what they're using in terms of the storage services, the processes they use to administer the data and manage the data straight onto AWS, without any conversion, without any change to their application. So I don't change to anything. So storage administrators can be really confident that they can move. Application Administrators know it will work as well, if not better with the Cloud. So moving onto AWS quickly to value that's one path. Now, once they move on to AWS, some customers will choose to modernize. So they will, they will modernize by containerizing their applications, or they will modernize by moving to server-less using Lambda, right? So that gives them the opportunity at the pace they want as quickly or as cautiously as they need to modernize their application, because they're already executing, they're already operating already getting value. Now within that context, then they can continue that modernization process by integrating with even more capabilities, whether it's ML capabilities or IOT capabilities, depending on their needs. So it's really about speed agility, the ability to innovate, and then the ability to get that flywheel going with cost optimization, feed those savings back into betterment for their customers. >> So how did the launches that you guys have made today and even, even previously, do they map into those two paths? >> Yeah, they do very well. >> How so? Help us understand that. >> So if we look, let's just run down through some of the launches today, >> Great. >> And we can, we can map those two, those two paths. So like we talked about FSX for NetApp ONTAP, or we just like to say FSX for ONTAP because it's so much easier to say. [Dave laughs] >> So FSX for ONTAP is a clear case of move. >> Right >> EBS io2 Block Express for Sand, a clear case of move. It allows customers to quickly move their sand workloads to AWS, with the launch of EBS direct API, supporting 64 terabyte volumes. Now you can snapshot your 64 terabyte volumes on-prem to already be in AWS, and you can restore them to an EBS io2 Block Express volume, allowing you to quickly move an ERP application or an Oracle application. Some enterprise application that requires the speed, the durability and the capability of VBS super quickly. So that's, those are good examples of, of that. In terms of the modernization path, our launch of AWS transfer managed workflows is a good example of that. Manage workflows have been around forever. >> Dave: Yeah. >> And, and customers rely on those workflows to run their business, but they really want to be able to take advantage of cloud capabilities. They want to be able to, for instance, apply ML to those workflows because it really kind of makes sense that their workloads are people related. You can apply artificial intelligence to them, >> Right >> This is an example of a service that allows them to modify those workflows, to modernize them and to build additional value into them. >> Well. I like that example. I got a couple of followup questions, if I may. Sticking on the machine learning and machine intelligence for a minute. That to me is a big one because when I was talking to my line about this is this, it's not just you sticking storage in a bucket anymore, right? You're invoking other services: machine intelligence, machine learning, might be database services, whatever it is, you know, streaming services. And it's a service, you know, there it is. It's not a real complicated integration. So that to me is big. I want to ask you about the block side of things >> Wayne: Sure >> You built in your day, a lot of boxes. >> Wayne: I've built a lot of boxes. >> And you know, the Sand space really well. >> Yeah. >> And you know, a lot of people probably more than I do storage admins that say you're not touching my Sand, right? And they just build a brick wall around it. Okay. And now eventually it ages out. And I think, you know, that whole cumbersome model it's understood, but nonetheless, their workloads and our apps are running on that. How do you see that movement from those and they're the toughest ones to move. The Oracle, the SAP they're really, you know, mission critical Microsoft apps, the database apps, hardcore stuff. How do you see that moving into the Cloud? Give us a sense as to what customers are telling you. >> Storage administrators have a hard job >> Dave: Yeah >> And trying to navigate how they move from on-prem to in Cloud is challenging. So we listened to the storage administrators, even when they tell us, No. we want to understand why no. And when you look at EBS io2 Block Express, this is in part our initial response to moving their saying into the Cloud super easily. Right? Because what do they need? They need performance. They need their ability. They need availability. They need the services to be able to snap and to be able to replicate their Capa- their storage. They need to know that they can move their applications without having to redo all they know to re-plan all they work on each and every day. They want to be able to move quickly and confidently. EBS io2 Block Express is the beginning of that. They can move confidently to sand in the Cloud using EBS. >> Well, so why do they say 'no'? Is it just like the inherent fear? Like a lawyer would say, don't do that, you know, don't or is it just, is it, is it a technical issue? Is it a cultural issue? And what are you seeing there? >> It's a cultural issue. It's a mindset issue, but it's a responsibility. I mean, these folks are responsible for the, one of the most important assets that you have. Most important asset for any company is people. Second most important asset is data. These folks are responsible for a very important asset. And if they don't get it right, if they don't get security, right. They don't get performance right. They don't get durability right. They don't get availability right. It's on them. So it's on us to make sure they're okay. >> Do you see it similar to the security discussion? Because early on, I was just talking to Sandy Carter about this and we were saying, you remember the CIA deal? Right? So I remember talking to the financial services people said, we'll never put any data in the Cloud. Okay they got to be one of your biggest industries, if not your biggest, you know customer base today. But there was fear and, and the CIA deal changed that. They're like, wow CIA is going to the Cloud They're really security conscious. And that was an example of maybe public sector informing commercial. Do you see it as similar? I mean there's obviously differences, but is it a sort of similar dynamic? >> I do. I do. You know, all of these ilities right. Whether it's, you know, durability, availability, security, we'll put ility at the end of that somehow. All of these are not jargon words. They mean something to each persona, to each customer. So we have to make sure that we address each of them. So like security. And we've been addressing the security concern since the beginning of AWS, because security is job number one. And operational excellence job number two. So, a lot of things we're talking about here is operational excellence, durability, availability, likeness are all operational concerns. And we have to make sure we deliver against those for our customers. >> I get it. I mean, the storage admins job is thankless, but the same time, you know, if your main expertise is managing LUNs, your growth path is limited. So they, they want to transform. They want to modernize their own careers. >> I love that. >> It's true. Right? I mean it's- >> Yeah. Yeah. So, you know, if you're a storage administrator today, understanding the storage portfolio that AWS delivers will allow you, and it will enable you empower you to be a cloud storage administrator. So you have no worry because you're, let's take FSX for ONTAP. You will take the skills that you've developed and honed over years and directly apply them to the workloads that you will bring to the Cloud. Using the same CLIs, The same APIs, the same consoles, the same capabilities. >> Plus you mentioned you guys announced, you talked about AWS backup services today, announced some stuff there. I see security governance, backup, identity access management, and governance. These are all adjacency. So if you're a, if you're a cloud storage administrator, you now are going to expand your scope of operations. You, you know, you're not going to be a security, Wiz overnight by any means, but you're now part of that, that rubric. And you're going to participate in that opportunity and learn some things and advance your career. I want to ask you, before we run out of time, you talked about agility and cost optimization, and it's kind of the yin and the yang of Cloud, if you will. But how are these seemingly conflicting forces in sync in your view. >> Like many things in life, right? [Wayne Laughs] >> We're going to get a little spiritually. >> We might get a little philosophical here. [Dave Laughs] >> You know, cloud announced, we've talked about two paths and in part of the two paths is enabling you to move quickly and be agile in how you move to the Cloud. Once you are on the Cloud, we have the ability through all of the service integrations that we have. In your ability to see exactly what's happening at every moment, to then cost optimize, to modernize, to cost optimize, to improve on the applications and workloads and data sets that you've brought. So this becomes a flywheel cost optimization allows you to reinvest, reinvest, be more agile, more innovative, which again, returns a value to your business and value to your customers. It's a flywheel effect. >> Yeah. It's kind of that gain sharing. Right? >> It is. >> And, you know, it's harder to do that in a, in an on-prem world, which everything is kind of, okay, it's working. Now boom, make it static. Oh, I want to bring in this capability or this, you know, AI. And then there's an integration challenge >> That's true. >> Going on. Not, not that there's, you know, there's differences in, APIs. But that's, to me is the opportunity to build on top of it. I just, again, talking to my line, I remember Andy Jassy saying, Hey, we purposefully have created our services at a really atomic level so that we can get down to the primitives and change as the market changes. To me, that's an opportunity for builders to create abstraction layers on top of that, you know, you've kind of, Amazon has kind of resisted that over the years, but, but almost on purpose. There's some of that now going on specialization and maybe certain industry solutions, but in general, your philosophy is to maintain that agility at the really granular level. >> It is, you know, we go back a long way. And as you said, I've built a lot of boxes and I'm proud of a lot of the boxes I've built, but a box is still a box, right? You have constraints. And when you innovate and build on the Cloud, when you move to the Cloud, you do not have those constraints, right? You have the agility, you can stand up a file system in three seconds, you can grow it and shrink it whenever you want. And you can delete it, get rid of it whenever you want back it up and then delete it. You don't have to worry about your infrastructure. You don't have to worry about is it going to be there in three months? It will be there in three seconds. So the agility of each of these services, the unique elements of all of these services allow you to capitalize on their value, use what you need and stop using it when you don't, and you don't have the same capabilities when you use more traditional products. >> So when you're designing a box, how is your mindset different than when you're designing a service? >> Well. You have physical constraints. You have to worry about the physical resources on that device for the life of that device, which is years. Think about what changes in three or five years. Think about the last two years alone and what's changed. Can you imagine having been constrained by only having boxes available to you during this last two years versus having the Cloud and being able to expand or contract based on your business needs, that would be really tough, right? And it has been tough. And that's why we've seen customers for every industry accelerate their use of the Cloud during these last two years. >> So I get that. So what's your mindset when you're building storage services and data services. >> So. Each of the surfaces that we have in object block file, movement services, data services, each of them provides very specific customer value and each are deeply integrated with the rest of AWS, so that when you need object services, you start using them. The integrations come along with you. When, if you're using traditional block, we talked about EBS io2 Block Express. When you're using file, just the example alone today with ONTAP, you know, you get to use what you need when you need it, and the way that you're used to using it without any concerns. >> (Dave mumbles) So your mindset is how do I exploit all these other services? You're like the chef and these are ingredients that you can tap and give a path to your customers to explore it over time. >> Yeah. Traditionally, for instance, if you were to have a filer, you would run multiple applications on that filer you're worried about. Cause you should, as a storage administrator, will each of those applications have the right amount of resources to run at peak. When you're on the Cloud, each of those applications will just spin up in seconds, their own file system. And those file systems can grow and shrink at whatever, however they need to do so. And you don't have to worry about one application interfering with the other application. It's not your concern anymore. And it's not really that fun to do. Anyway. It's kind of the hard work that nobody really you know, really wants to reward you for. So you can take your time and apply it to more business generate, you know, value for your business. >> That's great. Thank you for that. Okay. I'll I'll give you the last word. Give us the bumper sticker on AWS Storage day. Exciting day. The third AWS storage day. You guys keep getting bigger, raising the bar. >> And we're happy to keep doing it with you. >> Awesome. >> So thank you for flying out from Boston to see me. >> Pleasure, >> As they say. >> So, you know, this is a great opportunity for us to talk to customers, to thank them. It's a privilege to build what we build for customers. You know, our customers are leaders in their organizations and their businesses for their customers. And what we want to do is help them continue to be leaders and help them to continue to build and deliver we're here for them. >> Wayne. It's great to see you again. Thanks so much. >> Thanks. >> Maybe see you back at home. >> All right. Go Sox. All right. Yeah, go Sox. [Wayne Laughs] All right. Thank you for watching everybody. Back to Jenna Canal and Darko in the studio. Its Dave Volante. You're watching theCube. [Outro Music]

>>from around the globe. It's the Cube with coverage of Yukon and Cloud. Native Con North America. 2020. Virtual Brought to You by Red Hat, The Cloud, Native Computing Foundation and Ecosystem Partners Welcome back to the Cube. Virtual coverage of KUB Con Cloud native 2020. It's virtual this year. We're not face to face. Were normally in person where we have great interviews. Everyone's kind of jamming in the hallways, having a good time talking tech, identifying the new projects and knew where So we're not. There were remote. I'm John for your host. We've got two great gas, both Cuba alumni's Chris. And is it chief technology officer of the C and C F Chris, Welcome back. Great to see you. Thanks for coming on. Appreciate it. >>Awesome. Glad to be here. >>And, of course, another Cube alumni who is in studio. But we haven't had him at a Show Jr store meant executive director of the Fin Ops Foundation. And that's the purpose of this session. A interesting data point we're going to dig into how cloud has been enabling Mawr communities, more networks of practitioners who are still working together, and it's also a success point Chris on the C N C F vision, which has been playing out beautifully. So we're looking forward to digging. Jr. Thanks for coming on. Great to see you. >>Yeah, great to be here. Thanks, John. >>So, first of all, I want to get the facts out there. I think this is really important story that people should pay attention to the Finn Ops Foundation. That J. R. That you're running is really an interesting success point because it's it's not the c n c f. Okay. It's a practitioner that builds on cloud. Your experience in community you had is doing specific things that they're I won't say narrow but specific toe a certain fintech things. But it's really about the success of Cloud. Can you explain and and layout for take a minute to explain What is the fin Ops foundation and has it relate to see NCF? >>Yeah, definitely. So you know, if you think about this, the shift that we've had to companies deploying primarily in cloud, whether it be containers a ciencia focuses on or traditional infrastructure. The thing that typically people focus on right is the technology and innovation and speed to market in all those areas. But invariably companies hit this. We'd like to call the spend panic moment where they realize they're They're initially spending much more than they expected. But more importantly, they don't really have the processes in place or the people or the tools to do things like fully, you know, understand where their costs are going to look at how to optimize those to operate that in their organizations. And so the foundation pinups foundation eyes really focused on, uh, the people in practitioners who are in organizations doing cloud financial management, which is, you know, being those who drive this accountability of this variable spin model that's existed. So we were partnering very closely with, uh, see NCF. And we're now actually part of the Linux Foundation as of a few months ago, Uh, and you know, just to kind of put into context how that you kind of Iraq together, whereas, you know, CNC s very focused on open source coordinative projects, you know, For example, Spotify just launched their backstage cloud called Management Tool into CFCF Spotify folks, in our end, are working on the best practices around the cloud financial management that standards to go along with that. So we're there to help, you know, define this sort of cultural transformation, which is a shift to now. Engineers happen to think about costs as they never did before. On finance, people happen to partner with technology teams at the speed of cloud, and, you know executives happen to make trade off decisions and really change the way that they operate the business. With this variable page ago, engineers have all the access to spend the money in Cloud Model. >>Hey, blank check for engineers who doesn't like that rain that in its like shift left for security. And now you've got to deal with the financial Finn ops. It's really important. It's super point, Chris. In all seriousness. Putting kidding aside, this is exactly the kind of thing you see with open sores. You're seeing things like shift left, where you wanna have security baked in. You know what Jr is done in a fabulous job with his community now part of Linux Foundation scaling up, there's important things to nail down that is specific to that domain that are related to cloud. What's your thoughts on this? Because you're seeing it play out. >>Yeah, no, I mean, you know, I talked to a lot of our end user members and companies that have been adopting Cloud Native and I have lots of friends that run, you know, cloud infrastructure at companies. And Justus Jr said, You know, eventually there's been a lot of success and cognitive and want to start using a lot of things. Your bills are a little bit more higher than you expect. You actually have trouble figuring out, you know, kind of who's using what because, you know, let's be honest. A lot of the clouds have built amazing services. But let's say the financial management and cost management accounting tools charge back is not really built in well. And so I kind of noticed this this issue where it's like, great everyone's using all these services. Everything is great, But costs are a little bit confusing, hard to manage and, you know, you know, scientifically, you know, I ran into, you know, Jr and his community out there because my community was having a need of like, you know, there's just not good tools, standards, no practices out there. And, you know, the Finau Foundation was working on these kind of great things. So we started definitely found a way to kind of work together and be under the same umbrella foundation, you know, under the under Linux Foundation. In my personal opinion, I see more and more standards and tools to be created in this space. You know, there's, you know, very few specifications or standards and trying to get cost, you know, data out of different clouds and tools out there, I predict, Ah, lot more work is going to be done. Um, in this space, whether it's done and defendants foundation itself, CNC f, I think will probably be, uh, collaboration amongst communities. Can I truly figure this out? So, uh, engineers have any easier understanding of, you know, if I spent up the service or experiment? How much is this actually going to potentially impact the cost of things and and for a while, You know, uh, engineers just don't think about this. When I was at Twitter, we spot up services all time without really care about cost on, and that's happening a lot of small companies now, which don't necessarily have as a big bucket. So I'm excited about the space. I think you're gonna see a huge amount of focus on cloud financial management drops in the near future. >>Chris, thanks for that great insight. I think you've got a great perspective. You know, in some cases, it's a fast and loose environment. Like Twitter. You mentioned you've got kind of a blank check and the rocket ships going. But, Jr, this brings up to kind of points. This kind of like the whole code side of it. The software piece where people are building code, but also this the human error. I mean, we were playing with clubs, so we have a big media cloud and Amazon and we left there. One of the buckets open on the switches and elemental. We're getting charged. Massive amounts for us cash were like, Wait a minute, not even using this thing. We used it once, and it left it open. It was like the water was flowing through the pipes and charging us. So you know, this human error is throwing the wrong switch. I mean, it was simply one configuration error, in some cases, just more about planning and thinking about prototypes. >>Yeah. I mean, so take what your experience there. Waas and multiply by 1000 development teams in a big organization who all have access to cloud. And then, you know, it's it's and this isn't really about a set of new technologies. It's about a new set of processes and a cultural change, as Chris mentioned, you know, engineers now thinking about cost and this being a whole new efficiency metric for them to manage, right? You know, finance teams now see this world where it's like tomorrow. The cost could go three x the next day they could go down. You've got, you know, things spending up by the second. So there's a whole set of cross functional, and that's the majority of the work that are members do is really around. How do we get these cross functional teams working together? How do we get you know, each team up leveled on what they need, understand with cloud? Because not only is it, you know, highly variable, but it's highly decentralized now, and we're seeing, you know, cloud hit. These sort of material spend levels where you know, the big, big cloud spenders out there spending, you know, high nine figures in some cases you know, in cloud and it's this material for their for their businesses. >>And let's just let's be honest. Here is like Clouds, for the most part, don't really have a huge incentive in offering limits and so on. It's just, you know, like, hey, the more usage that the better And hopefully getting a group of practitioners in real figures. Well, holy put pressure to build better tools and services in this area. I think actually it is happening. I think Jared could correct me if wrong. I think AWS recently announced a feature where I think it's finally like quotas, you know, enabled, you know, you have introducing quotas now for and building limits at some level, which, you know, I think it's 2020 Thank you know, >>just to push back a little bit in support of our friends, you ask Google this company, you know, for a long time doing this work, we were worried that the cloud would be like, What are you doing? Are you trying to get our trying to minimize commitments and you know the dirty secret of this type of work? And I were just talking a bunch of practitioners today is that cloud spend never really goes down. When you do this work, you actually end up spending more because you know you're more comfortable with the efficiency that you're getting, and your CEO is like, let's move more workloads over. But let's accelerate. Let's let's do Maurin Cloud goes out more data centers. And so the cloud providers air actually largely incentivized to say, Yeah, we want people to be officially don't understand this And so it's been a great collaboration with those companies. As you said, you know, aws, Google, that you're certainly really focused in this area and ship more features and more data for you. It's >>really about getting smart. I mean, you know, they no, >>you could >>do it. I mean, remember the old browser days you could switch the default search engine through 10 menus. You could certainly find the way if you really wanted to dig in and make policy a simple abstraction layer feature, which is really a no brainer thing. So I think getting smarter is the right message. I want to get into the synergy Chris, between this this trend, because I think this points to, um kind of what actually happened here if you look at it at least from my perspective and correct me if I'm wrong. But you had jr had a community of practitioners who was sharing information. Sounds like open source. They're talking and sharing, you know? Hey, don't throw that switch. Do This is the best practice. Um, that's what open communities do. But now you're getting into software. You have to embed cost management into everything, just like security I mentioned earlier. So this trend, I think if you kind of connect the dots is gonna happen in other areas on this is really the synergy. Um, I getting that right with CNC >>f eso The way I see it is, and I dream of a future where developers, as they develop software, will be able to have some insight almost immediately off how much potential, you know, cost or impact. They'll have, you know, on maybe a new service or spinning up or potentially earlier in the development cycle saying, Hey, maybe you're not doing this in a way that is efficient. Maybe you something else. Just having that feedback loop. Ah lot. You know, closer to Deb time than you know a couple weeks out. Something crazy happens all of a sudden you notice, You know, based on you know, your phase or financial folks reaching out to you saying, Hey, what's going on here? This is a little bit insane. So I think what we'll see is, as you know, practitioners and you know, Jr spinoffs, foundation community, you know, get together share practices. A lot of them, you know, just as we saw on sense. Yeah, kind of build their own tools, models, abstractions. And, you know, they're starting to share these things. And once you start sharing these things, you end up with a you know, a dozen tools. Eventually, you know, sharing, you know, knowledge sharing, code sharing, you know, specifications. Sharing happens Eventually, things kind of, you know, become de facto tools and standards. And I think we'll see that, you know, transition in the thin ops community over the next 12 to 4 months. You know, very soon in my thing. I think that's kind of where I see things going, >>Jr. This really kind of also puts a riel, you know, spotlight and illustrates the whole developer. First cliche. I mean, it's really not a cliche. It's It's happening. Developers first, when you start getting into the calculations of our oi, which is the number one C level question is Hey, what's the are aware of this problem Project or I won't say cover your ass. But I mean, if someone kind of does a project that it breaks the bank or causes a, you know, financial problem, you know, someone gets pulled out to the back would shed. So, you know, here you're you're balancing both ends of the spectrum, you know, risk management on one side, and you've got return on investment on the other. Is that coming out from the conversation where you guys just in the early stages, I could almost imagine that this is a beautiful tailwind for you? These thes trends, >>Yeah. I mean, if you think about the work that we're doing in our practice you're doing, it's not about saving money. It's about making money because you actually want empower those engineers to be the innovation engines in the organization to deliver faster to ship faster. At the same time, they now can have, you know, tangible financial roo impacts on the business. So it's a new up leveling skill for them. But then it's also, I think, to Christmas point of, you know, people seeing this stuff more quickly. You know what the model looks like when it's really great is that engineers get near real time visibility into the impact of their change is on the business, and they can start to have conversations with the business or with their finance partners about Okay, you know, if you want me to move fast, I could move fast, But it's gonna cost this if you want me to optimize the cost. I could do that or I can optimize performance. And there's actually, you know, deeper are like conversation the candidate up. >>Now I know a lot of people who watch the Cube always share with me privately and Chris, you got great vision on this. We talked many times about it. We're learning a lot, and the developers are on the front lines and, you know, a lot of them don't have MBAs and, you know they're not in the business, but they can learn quick. If you can code, you can learn business. So, you know, I want you to take a minute Jr and share some, um, educational knowledge to developers were out there who have to sit in these meetings and have to say, Hey, I got to justify this project. Buy versus build. I need to learn all that in business school when I had to see s degree and got my MBA, so I kind of blended it together. But could you share what the community is doing and saying, How does that engineer sit in the meeting and defend or justify, or you some of the best practices what's coming out of the foundation? >>Yeah, I mean, and we're looking at first what a core principles that the whole organization used to line around. And then for each persona, like engineers, what they need to know. So I mean, first and foremost, it's It's about collaboration, you know, with their partners andan starting to get to that world where you're thinking about your use of cloud from a business value driver, right? Like, what is the impact of this? The critical part of that? Those early decentralization where you know, now you've got everybody basically taking ownership for their cloud usage. So for engineers, it's yes, we get that information in front of us quickly. But now we have a new efficiency metric. And engineers don't like inefficiency, right? They want to write fishing code. They wanna have efficient outcomes. Um, at the same time, those engineers need to now, you know, have ah, we call it, call it a common lexicon. Or for Hitchhiker's Guide to the Galaxy, folks. Ah, Babel fish that needs to be developed between these teams. So a lot of the conversations with engineers right now is in the foundation is okay. What What financial terms do I need to understand? To have meaningful conversations about Op X and Capex? And what I'm going to make a commitment to a cloud provider like a committed use discount, Google or reserved instance or savings Planet AWS. You know, Is it okay for me to make that? What? How does that impact our, you know, cost of capital. And then and then once I make that, how do I ensure that I could work with those teams to get that allocated and accounted? The right area is not just for charge back purposes, but also so that my teams can see my portion of the estate, right? And they were having the flip side of that conversation with all the finance folks of like, You need to understand how the variable cloud, you know, model works. And you need to understand what these things mean and how they impact the business. And then all that's coming together. And to the point of like, how we're working with C and C f you know, into best practices White papers, you know, training Siri's etcetera, sets of KP eyes and capabilities. Onda. All these problems have been around for years, and I wouldn't say they're solved. But the knowledge is out there were pulling it together. The new level that we're trying to talk with the NCF is okay. In the old world of Cloud, you had 1 to 1 use of a resource. You're running a thing on an instance in the new world, you're running in containers and that, you know, cluster may have lots of pods and name spaces, things inside of it that may be doing lots of different workloads, and you can no longer allocate. I've got this easy to instance and this storage to this thing it's now split up and very ephemeral. And it is a whole new layer of virtualization on top of virtual ization that we didn't have to deal with before. >>And you've got multiple cloud. I'll throw that in there, just make another dimension on it. Chris, tie this together cause this is nice energy to scale up what he's built with the community now, part of the Linux Foundation. This fits nicely into your vision, you know, perfectly. >>Yeah, no, 100% like, you know, so little foundation. You know, as you're well, well aware, is just a federation of open source foundations of groups working together to share knowledge. So it definitely fits in kind of the little foundation mission of, you know, building the largest share technology investment for, you know, humankind. So definitely good there with my kind of C and C f c T o hat, you know, on is, you know, I want to make sure that you know, you know my community and and, you know, the community of cloud native has access and, you know, knowledge about modern. You know, cloud financial management practices out there. If you look at some of the new and upcoming projects in ciencia things like, you know, you know, backstage, which came out of Spotify. They're starting to add functionality that, you know, you know, originally backstage kind of started out as this, you know, everyone builds their own service catalog to go catalog, and you know who owns what and, you know and all that goodness and developers used it. And eventually what happened is they started to add cost, you know, metrics to each of these services and so on. So it surfaces things a little bit closer, you know, a depth time. So my whole goal is to, you know, take some of these great, you know, practices and potential tools that were being built by this wonderful spinoffs community and trying to bring it into the project. You know, front inside of CNC F. So having more projects either exposed, you know, useful. You know, Finn, ops related metrics or, you know, be able to, you know, uh, you know, tool themselves to quickly be able to get useful metrics that could be used by thin ox practitioners out there. That's my kind of goal. And, you know, I just love seeing two communities, uh, come together to improve, improve the state of the world. >>It's just a great vision, and it's needed so and again. It's not about saving money. Certainly does that if you play it right, but it's about growth and people. You need better instrumentation. You need better data. You've got cloud scale. Why not do something there, right? >>Absolutely. It's just maturity after the day because, you know, a lot of engineers, you know, they just love this whole like, you know, rental model just uses many Resource is they want, you know, without even thinking about just basic, you know, metrics in terms of, you know, how many idle instances do I have out there and so, like, people just don't think about that. They think about getting the work done, getting the job done. And if they anything we do to kind of make them think a little bit earlier about costs and impact efficiency, charge back, you know, I think the better the world isn't Honestly, you know, I do see this to me. It's It's almost like, you know, with my hippie hat on. It's like Stephen Green or for the more efficient we are. You know, the better the world off cloud is coming. Can you grow? But we need to be more efficient and careful about the resource is that we use in sentencing >>and certainly with the pandemic, people are virtually you wanted mental health, too. I mean, if people gonna be pulling their hair out, worrying about dollars and cents at scale, I mean, people are gonna be freaking out and you're in meetings justifying why you did things. I mean, that's a time waster, right? I mean, you know, talking about wasting time. >>I have a lot of friends who, you know, run infrastructure at companies. And there's a lot of you know, some companies have been, you know, blessed during this, you know, crazy time with usage. But there is a kind of laser focused on understanding costs and so on and you not be. Do not believe how difficult it is sometimes even just to get, you know, reporting out of these systems, especially if you're using, you know, multiple clouds and multiple services across them. It's not. It's non trivial. And, you know, Jared could speak to this, But, you know, a lot of this world runs in like terrible spreadsheets, right and in versus kind of, you know, nice automated tools with potential, a p I. So there's a lot of this stuff. It's just done sadly in spreadsheets. >>Yeah, salute the flag toe. One standard to rally around us. We see this all the time Jr and emerging inflection points. No de facto kind of things develop. Kubernetes took that track. That was great. What's your take on what he just said? I mean, this is a critical path item for people from all around. >>Yeah, and it's It's really like becoming this bigger and bigger data problem is well, because if you look at the way the clouds are building, they're building per seconds and and down to the very fine grain detail, you know, or functions and and service. And that's amazing for being able to have accountability. But also you get people with at the end of the month of 300 gigabyte billing files, with hundreds of millions of rows and columns attached. So, you know, that's where we do see you companies come together. So yeah, it is a spreadsheet problem, but you can now no longer open your bill in a spreadsheet because it's too big. Eso you know, there's the native tools are doing a lot of work, you know, as you mentioned, you know, AWS and Azure Google shipping a lot. There's there's great, you know, management platforms out there. They're doing work in this area, you know, there's there's people trying to build their own open source the things like Chris was talking about as well. But really, at the end of the day like this, this is This is not a technology. Changes is sort of a cultural shift internally, and it's It's a lot like the like, you know, move from data center to cloud or like waterfall to Dev ops. It's It's a shift in how we're managing, you know, the finances of the money in the business and bringing these groups together. So it it takes time and it takes involvement. I'm also amazed I look like the job titles of the people who are plugged into the Phenoms Foundation and they range from like principal engineers to tech procurement. Thio you know, product leaders to C. T. O. S. And these people are now coming together in the classic to get a seat at the table right toe, Have these conversations and talk about not How do we reduce, you know, cost in the old eighties world. But how do we work together to be more quickly to innovate, to take advantage of these cognitive technologies so that we could be more competitive? Especially now >>it's automation. I mean, all these things are at play. It's about software. I mean, software defined operations is clearly the trend we've been covering. You guys been riding the wave cloud Native actually is so important in all these modern APS, and it applies to almost every aspect of stacks, so makes total sense. Great vision. Um, Chris props to you for that, Jr. Congratulations on a great community, Jerry. I'll give you the final word. Put a plug in for the folks watching on the fin ops Foundation where you're at. What are you looking to do? You adding people, What's your objectives? Take a minute to give the plug? >>Yeah, definitely. We were in open source community, which means we thrive on people contributing inputs. You know, we've got now almost 3000 practitioner members, which is up from 1500 just this this summer on You know, we're looking for those who have either an interesting need to plug into are checked advisory council to help define standards as part of this event, The cognitive gone we're launching Ah, white paper on kubernetes. Uh, and how to do confidential management for it, which was a collaborative effort of a few dozen of our practitioners, as well as our vendor members from VM Ware and Google and APP Thio and a bunch of others who have come together to basically defined how to do this. Well, and, you know, we're looking for folks to plug into that, you know, because at the end of the day, this is about everybody sort of up leveling their skills and knowledge and, you know, the knowledge is out there, nobody's head, and we're focused on how toe drive. Ah, you know, a central collection of that be the central community for it. You enable the people doing this work to get better their jobs and, you know, contribute more of their companies. So I invite you to join us. You know, if your practitioner ITT's Frito, get in there and plug into all the bits and there's great slack interaction channels where people are talking about kubernetes or pinups kubernetes or I need to be asked Google or where we want to go. So I hope you consider joining in the community and join the conversation. >>Thanks for doing that, Chris. Good vision. Thanks for being part of the segment. And, as always, C N C F. This is an enablement model. You throw out the soil, but the 1000 flowers bloom. You don't know what's going to come out of it. You know, new standards, new communities, new vendors, new companies, some entrepreneur Mike jump in this thing and say, Hey, I'm gonna build a better tool. >>Love it. >>You never know. Right? So thanks so much for you guys for coming in. Thanks for the insight. Appreciate. >>Thanks so much, John. >>Thank you for having us. >>Okay. I'm John Furry, the host of the Cube covering Coop Con Cloud, Native Con 2020 with virtual This year, we wish we could be there face to face, but it's cute. Virtual. Thanks for watching

>>Studios in Palo Alto in Boston, connecting with thought leaders all around the world. This is a cute conversation. >>Hello everyone. This is Dave Volante. Welcome to this cube conversation. This is a company that we've been following now for the last couple of years in a trend in robotic process automation, and then automation specifically, uh, it's a, it's a company in an area that we really like. Uh, we've been researching this and publishing and Daniel Dienes is here. He's the CEO of UI path. Yeah, it was great to see you again. Thanks for coming on. >>Thank you for inviting me, David. >>That's our pleasure. So let's, let's get an update in your business. You know, we covert obviously you sent everybody for a loop. We had been and have been following you guys quite, quite closely. How's how are things going for UI path? How has the pandemic affected your business? >>We we've designed this company from day one to work in a hybrid mode, local and under, obviously working from anywhere. And the transition to working from anywhere model was a really fast to implement for us. So COVID-19 itself. In the fact of the way we work on the business side, I would say that we are seeing, you know, mixed of events, some, uh, industries that was, that were mostly affected by COVID we're putting their budgets one hole while other industries were increasing 10 times. What I can tell you that in, um, in a nutshell, the numbers for us were really good. We are able to keep eating and beating the thought gets that we set pretty COVID and we focused quite a lot on helping our customers, not the gating through these murky waters. We have quite a lot of, um, involvement in healthcare and federal business. We worked with a few hospitals to help with accelerating the COVID test. In one case, we were able to save two hours a day for every nurse. So instead of filling up paperwork, they are able to focus on the patient. And that's not one isolated instance. We've done tremendous work across the, across the globe. And, uh, you know, we, uh, you know, that we raised our last round in, uh, June end of June. And that was a recognition of our accelerated business, >>Right? Yes. I mean, you raised it in the, of the pandemic, you know, I I've been saying that. I mean, everybody of course says the covert has accelerated a number of trends and I've been saying there's a, that there's now an increased mandate for automation, I think there was before, but yeah, maybe there was some complacency, although you didn't see it in your numbers, you guys obviously growing very right fast. You mentioned healthcare. I would think of banking and financial as well, which of course was a stronghold. But when you think about in the U S anyway, that the payroll protection act and the number of loans that had to be processed, you know, bank bankers would talk to me and say, we are volume, increased two orders of magnitude. We had, we had no way to do it. And they turn to automation to do that. So, so I've said that there is an automation mandate, and I think there, there, there has been because of the productivity gap, particularly in the U S in Europe, you don't see it so much in, in of course in China. Uh, but, but certainly the U S in the last couple of decades has declined in terms of productivity. So, you know, people are not going to be able to solve the world's biggest problems without automation. How are you thinking about that? Um, in, in this post COVID world, >>As you said, the awareness that we have to automate has increased 10 times compared to pre COVID the days. I would not say that yet automation is number one priority on the company's leader's agenda, not in the same way as conferencing and video conferencing, and all this directly affected, positively affected the software industries. But I believe that, uh, while automation is slower to adopt, and it requires a lot more investment to adopt it's, uh, it's gone, uh, dominate the agenda post COVID in the, in the sense that people will have to recoup, you know, all the losses that they had in the COVID, they learn their lessons. And, uh, you know, for instance, I talked to the few CEOs of watch, you know, fortune 500 businesses, and they are telling me, Daniel, I wish that we have started earlier. So now we are seeing, you know, an adoption that is more top down and adoption that is starting from the C level suite, even the CEO of large enterprises. >>Yeah. I mean, it seems to me that if a customer has tasted, you know, the benefits of, of RPA and automation, uh, and as realizes what it can do for their business, they're gonna maybe double down on it, especially in a time when revenues might be under pressure. Uh, and, and you're not hiring a, a no, a lot of people have put, you know, freezes on number of head layoffs. You've got to do more with less you guys. I wonder if you could bring up this, this chart, I want to share this and get Daniel's reaction. So we all were talking about land and expand. So what this is ETR data, and what it does is it asks customers where they're at. Do they know about a vendor in this case? It's it's UI path is on the left and automation anywhere, and then some others, but do you know about the vendor? >>And, and are you planning on, you know, are you evaluating it? Are you planning to implement it? Uh, and this chart shows those respondents that said, yes, we, we, we are a customer. And we, we plan to expand our usage and you can see over the last three surveys that the yellow is even an uptick. And so people, this essentially the takeaway here is that once people taste it, that you land, and then they expand and find new use cases, are you seeing that in your business? And maybe you can give us some, some high level examples we've seen quite the look >>We have today more than 60 customers with, uh, over a million dollars in spending with us, uh, more than, uh, like 800 customers that spend more than a hundred K we've lost. And our net expansion rate is more than 140% consistent over many past quarters that shows a very solid, uh, expansion desire from our customers. And it shows that our technology is very well suitable for large case automation, deployments, enterprise wide, especially with our, uh, program or robot for every person. We are seeing huge interest and way bigger deals. We are able to lend upfront work to upscale our existing customers. You know, in a way I don't believe that in five years from now on, we will ever have people just to mindlessly move data from one screen to the other. I think this is a thing of the past, as much as plowing the fields is a thing of the past. >>So I wonder if he could talk a little bit about the, where you've come from as a company. So, I mean, you started in 2005. So, I mean, I think of you as a startup, but you've been around for a long time. Uh, and, and my sense is you started as a product company, but, you know, recently you guys announced this end to end platform for what you call or maybe Gardner calls. I don't know their term of hyper automation, but, but you've gone from a product company to a platform company. I wonder if you could talk about how you think about that transition and, and, and the platform generally >>To become a platform requires of certain level. And it's in a way, a harder business to promote to one enterprise customer. They, uh, they are very likely to test water with the product, but when, uh, you know, bad thing, everything, automation, why don't a platform, it's a different game. So this is why we, uh, we had to go from the steps of products, you know, product then like a couple of products, and then putting everything together into a platform. The power of the platform in, uh, in this particular instance comes from, uh, the integration of all pieces in a platform and an automation, white platform will have a different sets of products that play from the discovery of the processes that you automated, the implementation and maintenance of the process into the analytics that helps you track your progress. And also you have technologies that addresses two different persona in an enterprise from, you know, software engineers, RPA developers into the citizen developers. >>So it's a, it's a, it's a huge offering. And, um, the, what is really important for us is that we give full fledged platform. So an enterprise customer knows we will be able to build everything on the top of this platform, and they will offer best in class where it matters. And we believe that best-in-class matters in few important areas like RPA, like process mining, like analytics, while they will offer good enough where they will offer integration with best-in-class products where, uh, it's, uh, it's not so important in the, in the grand scheme of deploying automation, but the integration is tremendously important piece, put yourself in the shoes of a big enterprise instead of buying 20 different products, different, a licensing agreement, different maintainers stuff, different teams to support them. You just have one and they, and you have the guarantee. They work very well together. It's a very big proposition. I did requires maturity of the platform when they are making, you know, big strides into having the credibility that you know is required to have such a big investment. >>Well, I have to bring you bring that up. I have to ask you, so you guys are obviously a RPA and automation specialist building out a platform, very focused on that. And we always talk about this best of breed versus, uh, versus integrated suites. And you're sort of talking about integration. Of course, we saw Microsoft come out and as, as well as others, IBM, I think SAP have announced sort of what I would consider one dot products, you know, not nearly as robust as you and some of your, your leading competition, but how do you think about that in terms of staying ahead of that? I mean, you know, we all know Microsoft, you used to work there, they come out with a one Datto and, you know, then the two dot O and it's just still, and then eventually they get it right? So you have to move fast. >>Yes, absolutely. And we, we proved that we can move fast. We've built this company from zero five years ago to, you know, we are almost half a billion dollar in era today. So wait, we are fast. This is one of the four tenants of our culture be fast. But speaking about what the strategy in, uh, I believe that the space of low code, no code business application development, and the hyper automation space will, uh, converge into one single space, a company like Microsoft started with, uh, a simple product like, uh, if TTT and, uh, that was dedicated only to citizen developer to build very, you know, small and quick integrations. Like if you look at, uh, if you look at the power automate use cases, you'll see one of the most common use cases to set on a lot for myself. Well, I understand the value of such use case, but it's a far cry from setting an alarm and to automating, you know, end to end, procure to pay or order to cash for a big enterprise or COVID testing. >>And basically where we are coming from two different angles. We are coming from the RPA angle that is putting computer vision at the center of the technology. And they are coming from weak API integration. And we are making, we are making progress, you know, towards each other. My belief, I believe that, um, we have an advantage here in a sense that, uh, RPA is a technology that can produce immediate returns, but the labs K Y LA while the anther type of technologies, first of all, traditional automation, and then all this new type of API economy, API integrations kind of largely failed to show scalability within big enterprises. They are nice to have, but they are not essential when you are choosing a platform. My, uh, take is that you are choosing a platform based on what you need the most. This is where you choose the best in class. And you need the certainty that you partner with a vendor that invest the most. Well, this is our bread and butter. This is where we start. And of course we are offering every piece that the other are doing while they are also getting into, into our world. But our advantage being cloud agnostic, being ERP, agnostic, being CRM agnostic, and having started from the most sensitive technology that offer you, you know, the most, uh, the most savings center, best productivity increases. It's a tremendous advantage. >>And of course, you know, I'm excited about this opportunity and I've talked to a number of your customers. And so, you know, to me, that's the proof in the pudding, but you mentioned your annual recurring revenue, you know, approaching half a billion. So I got add, and, you know, as well that in my breaking analysis, we took a look at the total available market for RPA. And then I think, well, we've extended that I think we kinda missed the broader automation agenda in the platform thinking, and we've, we've updated those figures. I mean, it's, uh, it's hundreds of billions of dollars of an opportunity at least. And so the reason I bring this up is of course, last week we saw the hottest software IPO in history, and snowflake is a company with $400 million ARR growing at 120%. The company went from, you know, early this year, $15 billion valuation went up to 20, went up to 30. >>They, they launched a 33 billion within five minutes. It was worth 80 billion. You know, of course it's settling down now in the 60 billion, but unbelievable. And I would argue that your total available market is perhaps even even larger. I would say it is larger because it has a deeper business impact, uh, than, than say a snowflake. And of course, people watching my programs know that I'm a very, very high on that company. So my question is, what do you think about that, that IPO? How are you thinking about your, your own IPO? It would seem that that UI path is in a great position to at some point become a public company. >>We, first of all, if you are speaking about the time way, nobody would argue that our team is not higher than a snowflake. Pam, I, we can argue that their market is maybe more consolidated. Everybody understands data market in a way, and our market might be way more scattered across different use cases, but in a way, it's the market of data versus the market of all data versus old processes in the world. It's way, way more people are tasked today with processes then to analyzing and working with data in the way we are going after a very large problem that we have to solve. And we have to empower people of doing what they are naturally built to do, like, you know, talking to other people, socially interact, being creative, making decision, instead of doing this numbing part of their daily jobs that aren't required by this state of the industry. >>So our time we talked with different bankers and I've seen various figures from like 200 BD, one, two way into like two, three years for something that it's happy with. So time is the problem. It's the way, the way we are. I think, uh, we, uh, what we want to build, it's a durable business and it's a, it's a durable growth. Why in the same time being a cashflow positive, and we are very close to achieve this goals. And that will look, I believe that will be a very compelling proposition for our own IPO. I don't know if we can get snowflake multiples or not, but this is the feeling not the more, the biggest thing when my agenda, my, my agenda is to build a longterm sustainable, durable business. I am looking to next five to 10 years of this business. And IPO is just the fundraising event in, in, you know, after all. >>Great. So yeah, that's good. I wanted to ask you kind of what the, what the parameters are and, you know, I think you answered it is you're not rushing to get in, to draft off of some event that you had no control over that that notion of cashflow positive is really interesting to me. I said about the snowflake. I feel they have plenty of Tam just like you guys. And I agreed somewhere between 200 billion and 3 trillion. That's about right. And so, and, but, but I think that the, what I said about Snowflake's IPO is that I'm not worried about their lack of profitability right now. At some point I'm really going to be focused on their operating cash flow. And if you can, if you can come out with the large Tam, your, your growth that you're at the large ARR and cashflow positive, I can't wait to see that IPO Daniel. That's going to be super exciting. So we'll, we'll, uh, we'll be patient, but Daniel Dienes thank you so much for coming back into QBR. I was a great guest. Really appreciate the update on your business. >>Thank you so much. I really appreciate the invitation. Thanks. You're welcome. And >>Keep it right there. Everybody we'll be back with our next guest. Run up to this short break. This is Dave Volante.

>>Fly from Barcelona, Spain. It's the cube covering Cisco live 2020 route to you by Cisco and its ecosystem partners. >>Hello everyone. Welcome back to the cubes live coverage. Four days here in Barcelona, Spain for Cisco live 2020 kicking off the year. Great event. I'm Jafar way my co Stu Miniman, our next guest, Danny Winokur, general manager of app dynamics, part of Cisco and special keynoter headlining the event, the networking show headlining by the app development story. Any welcome to the cube. Thanks for joining us. Thank you. It's good to be here. So one of the big signals, I think it was a shot across the bow to the industry, but also internally within Cisco has been the multi-year movement around getting API APIs built into the products. You start to see dev ops become network ops. Now with app dynamics, digging down into the infrastructure provide great value, but in a DevOps way. This is, this is the top story in my mind. You've led the keynote, which was very unusual for Cisco. Was it planned that way? Tell us some of the background. >>Well, it was planned that way. And I think part of what we're recognizing is that in the world that we're now living in where applications have moved to become the center of the business, you have business initiatives encoded in applications and that's what actually drives the use of technology in the organization. So it really starts with the application. And Cisco of course recognizes that and that has implications for the way we think about the entire technology stack. And so we see it as an opportunity to actually make the infrastructure and the people that actually buy and work with the infrastructure, the infrastructure engineers and operations teams, network engineers and network operations teams, they become much more relevant by actually looking at how the technologies and the work that they do are actually placed within the context of the application and how that application and the experiences within it are delivering a business result to the larger organization. >>Yeah, and one of the big trends, how she's doing, I were early days in the cloud, watch Amazon rise up. No one kind of saw that common. Most of the insiders did, but API APIs were key, but the word dev ops was started around that time. Infrastructure as code makes a lot of sense. Programmable infrastructure. That's right. You guys picked up on that. We've been covering that for now for a four years around programmable networking and you guys have been just goes, been shifting the products, but during the keynote you mentioned biz dev ops, which I thought was a very fascinating stake in the ground. Could you explain what you meant by that? Because if I think what you're saying is true, this is now another layer of opportunity that takes advantage of all the scale. The agility. Efficiency. >>Yeah, that's right. That's right. So I mean, what's what's going on is companies that now say, okay, my business is now in my app. The app has become the business. They have to now figure out how do they iterate very, very quickly on that application. And in order to iterate very quickly, the business team, the development team and the operations teams need to work together in a closed loop operating model. Because if they don't work together closely, they have two big problems, right? One is the business initiatives which move very quickly, can't get encoded quickly enough in the application and the application falls behind and the business suffers. Number two, they can't produce winning experiences because executives like us sitting in a conference room with an idea for an experience are almost always wrong about what's going to work for the end-users. The way it works in the modern world and what we know from digital native organizations that have pioneered this is that you actually have to form user research and a hypothesis from it and it in your application and get it quickly in front of your users with real time measurement and telemetry and then you use that to inform yourself in real time around what's working and what's not. >>You reform your hypothesis, make that adjustment reimplementing code, get it back out and iterate and iterate and the more shots on goal that you're able to take with the velocity of iteration, the more likely you are to get to the winning experience. So biz dev ops is really around getting those three teams, the business team, the development team, and the operations team working together with velocity in a new operating model that allows you to actually gain the competitive advantage that's necessary in an experience driven application centric world where the infrastructure and development team and the business are now all working together in tandem, in lockstep. >>The antennae had really interesting stuff as we all know that the organizational construct in the silos often slow down that that innovation and growth we watched for years developers, they find their tools, they do their thing, but as you said, it's got to be connected with the business. I want to make sure I understand. We've seen somewhat places where some of the tooling actually is getting people together because they have common data. You give the business people things in their colors and languages as opposed to the developers. They need different things out of it, but it's a, it's a backbone or back plane buildings together. So are those business product owners or business leaders actually coming into and seeing things? Is it at that level, the >>really important point, right? The problem that we've seen in the traditional operating model is not only are the teams siloed, but the technologies and the data that they rely upon keep them siloed. And so as the changes in the market are pushing them to work together for the reasons I said, they need common tooling and common data sets. And so what we're actually doing at Cisco is connecting app dynamics to the tools that are beneath it in the stack. Like what we announced yesterday with the inner site inner site workload optimizer and what we've previously done with ACI for the software defined data center networking fabric so that you can actually have each team, each persona use a tool that they're comfortable with that's specialized for their domain, but the datasets are now connected. So it gives them a single source of truth that allows them, instead of finger-pointing when something goes wrong or they need to optimize, they're able to actually have a shared source of truth and they can say, okay, I understand my domain here, I understand my domain here, but they're telling me the same thing, and that makes it easier for them to collaborate in this closed loop, the operating model. >>Whereas it was harder to do that when they were looking at their separate tools and separate data. One of the things I want to get your thoughts on, Danny, is as coming from the app dynamics side now at Cisco, you've seen a lot of modern used the word modern applications. The modern architecture is evolving. People see the picture, they know what to do. Most enterprises outside of the the pioneers, they're like, okay, I love the idea of biz dev ops, but Hey, I'm just trying to figure out which cloud I'm going to use. Right. Okay. So take me through how you engage with that because you're kind of, you might be ahead of the curve on the thought process, but I'm just trying to crop the cloud and it's impacted me as a notarized. What do you say to that? What's that? What's your answer to that? >>I mean, what we see going on right now is that in almost every single organization that has their business now running in the apps, those apps are hybrid multi-cloud apps, right? They recognize that in order to iterate quickly on the front end of the application, they probably need to use some of the latest cloud based technologies, either in the public cloud or in a private cloud on premise us. But they also have other components of their architecture that are going to be using something more like web technologies or client server technologies or in some organizations still mainframe technologies for backend data access. And so you end up with this sort of diverse array of layered tech stacks across different deployment environments in a multi-cloud world. And they have to work together seamlessly. And so part of what we've done is innovate lenses within app dynamics that actually give you a view through that complexity so that you can focus on what really matters most. >>And that was yesterday's announcement of the experience journey map that we have from app dynamics, right? It compliments what we've done before with business transactions and business IQ and adds a new lens that is focused on the screens that the end users are actually seeing in their browser or on their mobile device. And it automatically uses AI and ML technology to map a screen by screen journey flow through the application that the user's actually seen and experienced seeing and experiencing. And within that screen-based view, it gives you business data like abandonment rate and it correlates it down to the technical performance of what's actually being served to the user on that screen so that you can quickly determine where are the technology issues across this broad hybrid multi-cloud estate. Where are they actually surfacing issues or not on the screens that your users are seeing. >>So you can now prioritize the warnings on the back end based on what your users really need you to address right away. So if I hear you correctly, what you're saying is essentially cause instrumentation. You mentioned that earlier data is critical. So what you're saying is you could have abandonment rates say's an app or whatever and say maybe there's a DDoS attack on, on a switch or a firewall. So I might want to scale that up with policy. So you're seeing, you're coordinating technical remedies or architectural changes based upon what you know, the business logic, is that what you're kind of getting at? That's exactly right. So we know from data that we have from our app attention index, that 50% of users are willing to pay more for a competitor's product if it performs and gives them a better experience. And worst yet 63% of users and the app potential index have told us that if they get a subpar digital experience, they're going to go out and actually not only leave but bad mouth, the experience that they had and spread ill will about your application. >>So what has to happen in that world is you have to actually relate your business performance data to the user experience within the screen, through the experience journey map into the backend application components, which is the business transaction and then down through intersite into the layers of the infrastructure where you can actually get into the chassies, the blades, the fans, the Dems and the network. So essentially it's like auto scale and concept that will, you know, in cloud that's right. Fly to the app level and a feature by feature basis. That's right. And you can do it exactly. You can do it within the context of the key experiences that have been prioritized as the ones that contribute the greatest impact of your business results. And you can work load optimize and scale infrastructure dynamically and automatically. Final, final point on this cause a good thread here. >>So final question is, okay, now prove it to me. How much money did I make? Can you guys tie that to actual dollars? Because then on the client's side, do they have to program then? No. So you can within app dynamics, through our business IQ capabilities, tell us through the interface of their product, what are the pieces of business data that are the key measures of your business success. It could be dollars, it could be cents, it could be skews, it could be a product ID, it could be an abandonment rate or a funnel conversion through your funnel. You tell us what are those metrics that you need and we will actually introspect, pull them out and give you a real time ROI. It is. That's what it is. So, so Denny, the thing I've been trying to chomp at the bid here, I'm agreeing with a lot of what you're saying. >>There was a trend that was all over everywhere that we went in 2019 that I heard and haven't heard you use a certain word. It's observability. Certain people are like one of the biggest trends of 2020 help us understand your viewpoint on observability what you're hearing from customers because much of the language you're talking about of that systems view resonates as what we're talking about. Observability so just not fond of the word or none. Not trying to jump on that bandwagon. It's a buzzword. What I'm talking about is full stack observability. That's exactly what it is. You can go from the business to the end user experience, the application, the compute infrastructure, the network infrastructure and the security domain that wraps at all and you can actually now see with telemetry that we're pulling in from each of those layers whether it's using app dynamics or using some of the instrumentation that Cisco has across those other infrastructure layers and security layers of the stack. >>We pull that all together with AI and ML produce insights and then provide an API that allows integration with systems for automation and action that is not only full stack observability it's full stack observability paired with the ability to implement an AI ops operating model that then supports a biz dev ops way of working for the company. You might want to throw in horizontal observability too because you know with cloud you've got horizontal scalable across deployment. Exactly across your deployment environment and from an application standpoint, everything from kind of traditional model is through microservices. Do things with serverless do are absolutely we have, we have agent technologies that take care of the very latest serverless technologies. We have things for Kubernetes cluster monitoring, we have support for CloudWatch and then going all the way back to the other side. Of course, traditional job applications.net applications back to mainframes IIB. >>We monitor and support all of that. It's the broadest array of visibility of what you're going to cabbage in the company working for you, the all the cool stuff. Cloud native Coobernetti's we've tried, we let, we like to be the cool kids. Magic questions. So I got to ask you, since you've got a good view up and down the stack and across multiple domains and workloads and clouds, what do you think, going into 2020 with this show and beyond, what is the most important story you think that people are talking about and what's the most important story that you think people should be talking about? I think the most important thing that's going on right now is figuring out how to connect across the different technologies and the different layers, right? We're coming from a place where there's naturally been a specialization within each of the domains. >>The whole point now is about multi domain and actually connecting the different layers of the technology stack to produce insights that allow for movement in this lock step higher velocity model. Because what we know from all of the data and all of the experience with customers is that the winners and an experience driven world are those that can actually implement with velocity, not break things and deliver well-designed, beautiful experiences. And in order to do that, you need to be able to connect these different technologies and get the teams that traditionally run them working together in a much more collaborative, what are people missing? What should we be people be focused on. Outside of that, what other areas that either the media or customers, what are the, what are some of the hidden gems out there that people should really pay attention to? Well, I think, I mean I think there's a lot of exciting innovation that is going on in some of the new cloud native technologies in the cloud native architectures. >>The other thing that I think is a little bit of a hidden thing that a lot of people haven't realized is that the cloud is great for some of the really high velocity, fast moving things, but it's not always the most efficient or the least, sorry, the most cost effective way, least least costly way of running everything and so we actually do see some recoil back to these hybrid environments where people are actually now running some cloud technologies on premise us and so I think that's an area to watch as we see some of the public cloud players, obviously out of the traditional players bringing cloud innovation, but running that on premises in a way that connects seamlessly to elastic scalable public cloud resources that work together in tandem. I guess last >>question I had for you, I think it was in your keynote, I heard you talk about customers using app D as being agents of transformation. Just what advice do you give them? You know, where are some of the stumbling blocks that if they don't have a conversation or understand a certain architecture that they're going to run into some issues? >>Yeah. So for us, an agent of transformation is the sort of notion of a change agent in the organization that recognizes the things we've been talking about where the world is going and is seeking to be that disruptive force of change inside the company. And in order to do that, what we have found is they're most successful when they get their hands on hard cold data, right? That's how you convince an organization. You show them the data and you connect the data and the technology to a business result. And so the most effective change agents have been able to go into the depths of the technology. They've been able to correlate data sets up and down the stack and then walk into the board room at the executive level and show in an undeniable evidence based way that these layers of technology are producing this business result and the organization needs to invest to accelerate that. And that's >>jail model too. You just get the data and iterate. Double down on absolutely what you want. It brings it all the way up to the boardroom. Danny, thanks so much for taking the time to share that. Great insights. I'll give you a minute to get a plug in for app dynamics. What do you guys got going on? Which shows you're going to be at the coming year, actually Cisco live in America. Any other event you going to be there? Any investment areas? Give a quick plug for what's going on. >>Yeah, no, I appreciate it. The next big one for us is on February the 20th we're running a global virtual event called app dynamics transform 2020 which is our annual showcase where we bring together all of the latest and greatest innovations that app dynamics has across what we're doing with AI and ML. Everything that we're doing around new experiences, cloud native technologies, the AI ops operating model, our vision for the central nervous system for it, and we're going to showcase all of that demo and talk about our roadmap. So it's a global live virtual event. Come to our website, aptdynamics.com and please tune it. Right. Well, congratulations for your success and thank you. Love to have you come into our studio. Talk about what you're doing with video because that's a hard, hard problem. We talked to Sri about that. Thanks for coming. I really appreciate it. Thank you guys. Yeah, appreciate it. We're here in the cube AptDynamics headlining the keynote at Cisco systems. A networking company turned into a data company, a video company, an instrumentation company. Application can be all now in one. Just the cube bringing you all the data here in Barcelona. I'm John. We'll be back with more live coverage after this short break.

>> From Washington DC, it's theCUBE. Covering ScienceLogic Symposium 2019. Brought to you by ScienceLogic. >> I'm Stu Miniman and this is theCUBE's coverage of ScienceLogic Symposium 2019 here at The Ritz-Carlton in Washington DC. Really excited to welcome back to the program. It's the co-founder CEO and the Headmaster of Wizarding school, >> Wizarding school, yes. >> Dave Link, thank you so much for joining us. Great to be here Steve. >> All right, so Dave first of all congratulations, really been enjoying the event you know you you kicked it off in the keynote this morning great energy, really I think capturing you know where we are in you know IT in business today. We understand how things are changing so much and it's a complex world and ScienceLogic is trying to do Its part to help simplify and make it easier for IT to you know run at the speed of business and machines. >> That's exactly right. What's happening in the world right now is you've got a confluence of cloud apps, traditional legacy apps and they're colliding together and as they collide together you need new tools to manage that in a way that's different than what we've seen in the past. You're looking at lots of sources coming together to contextualize, not just seeing what's happening, understanding how systems relate to one another but acting upon them. Machine at machine speed means that automation is king and the wizard hat actually relates to a storyline we had earlier today when we think about how to educate the marketplace and the customers we realized that we needed a very new way of communicating. So videos E-Learning The Wizard of learning has been a theme of the show to help our customers to get up to speed and actually take full advantage of the application that we provide to help them deliver great service quality. >> Yeah well and we appreciate you bringing theCUBE to help with that video education of the community overall. >> That's right >> Yeah so you know look Dave you know wanted... let's step back for a second and you know we want to going to get to the business update but first you know the company is founded in 2003. You know cloud wasn't a term, some of the underlying foundations of what became cloud, you know existed back there. Those of us in the industry understand some of the waves that have happened there but you know to talk about cloud and micro services and all of these changes that have... So give us a little bit about that evolution about the original premise of the company, as we move now to you know the world of today and how you manage to keep the company moving and relevant >> I love telling this story Stu because it never gets old >> Yeah >> A lot of the original feces that we had about where service business service analysis was going, the application analysis connected to the infrastructure. Our belief was we were going to move to a world where it wasn't based on devices or nodes or systems. It was really based on this service and what we're seeing with cloud has accentuated that tenfold because services now are made up of compound things, technologies, service delivery mechanisms as a service platforms and they all have to work with one another The platform we built had an architecture that was very open that could take data streams from lots of different sources, create a common information model contextualize that and then act upon it. So now more than ever before, we really built the right platform with multi-tenancy, with role based access control with all the things that were really hard problems to solve code day one and now the thesis that we had that it was more about the service view is as important as it as it's ever been with ephemeral systems that are coming and going, with really containerized systems on top of virtual machines on top of their metal. All these abstraction layers require a different mindset but an open architecture is really at the heart of pulling lots of data streams together contextualizing it and then acting upon it >> Yeah, so I'm a sucker for Venn diagrams. so you heard that the analyst in the keynote this morning talked about AI ops and he said to the inner structure intersection of IT operations data science and machine learning >> Yes >> Data at the center of everything, it's something we've had a couple of waves of trying on intelligence and automation, are things we've been talking about for decades in IT. Give us a little bit as why some of those waves are coming together so that now and what you're doing is the right moment to really help accelerate. You've been having great growth for a number of years and project out some really strong growth for the next few. >> We have over the last five years the company has grown over five hundred and forty percent from a revenue perspective and I think that's the underpinnings of that relates to do we have the right market fit. Are we solving a problem that's material to customers that it's hard for them to solve without our product. But I really envision a future we've been working on this for a couple decades, right? The future is one I hope where from a artificial intelligence at machine speed where we're getting so predictive and understanding, through really smart scalable algorithms the future faults that may occur for you know we've both been at this for a long time. we've been talking about event correlation for many years. I envision a world where you're not doing event correlation when you've had an event, it's actually too late. Usually that's caused by a system telling you that there is a problem. So what we're really working on what we've talked a lot about here at the show is not just predictive analytics but really understanding what's abnormal and getting in front of a problem before there is a problem with the system with really super smart algorithms that help customers understand, many different data sets converge together and what they really mean so that you can get ahead of a service outage rather than have the fault that you're then working on correlating to infrastructure to application layers. >> You know the other thing that's been interesting for me to watch is, the core of where you started was really working with the service Fighters. I've had a chance to talk to a number of your service fighters >> Yes and Hughes has been with you since the early days up to you know one that just bought a couple of weeks ago and you know they're happy very. Talked about kind of the compare/contrast of the service riders and the enterprise because you know cloud is impacting you know the big hybrid hyper scale clouds are impacting both of those and the rate of change is affecting both of those in a lot of ways. So I'm curious as you see you know what what what's similar and what's different between going into those markets. >> When we thought about the problem for service providers there were two axes that we were looking at. Number one was from one instance of our platform you had to serve many customers that all had their own tenancy. But on top of that, you had to layer in a role based access control who could see what the customer had their view, the internal ops teams had their view. So building out a really complicated foundational model and an architecture that would support tenancy on steroids with one instance of our product was a really important linchpin of what's now, incredibly important to enterprises, because enterprises are getting into a moment where they're having to really act as service bureau's, service brokers and that means that all the different teams that support different technology silos, really have to work together as one and... but yet they still need their own views.So a lot of the foundational highly differentiated capabilities we built for service providers, for large scale globally distributed enterprises, actually meets a need profile that is very hard to find solutions that fit that profile and can give them that consolidated view but yet the deep dive view for the practitioner and we're finding that more and more enterprises, have follow-the-sun operations, follow-the-sun architecture teams, follow-the-sun engineering teams that need different views that is really hard to get most products that were built in this space were built for a single tenant enterprise view and that never gives you the granularity for each consumer and each persona to get the view that they need. So it's interesting that although we kind of over engineered those capabilities for the service provider needs it's becoming involved with the enterprises as they're looking at how do they need to do things as really a converged team, working as one team across many silo disciplines and that requires a very different way of thinking, a different tool space a different solution to the problem that we built kind of from the ground up. It's now really appropriate for the DevOps teams the teams that are really having to break down the silos and work as one team. >> Yeah the, the the the term that often gets misused and misunderstood is scale. But if you truly can build something that's distributed architecture for scale, It really opens up a lot of opportunities. One of the things you highlight it also is that, ScienceLogic puts a lot of investment into you know R&D and keep working on things big announcement of Big Ben, seemed I've had a chance to hear what everybody likes and the best. Talk a little bit about you know how you keep the development efforts going how you put that strong and effort on it and you know boy you know you said you worked on the UI for three years and now it sounds you know it's a bold statement to be like okay and everybody you're using this, you know you can't have the safety blanket of old away in new way for a while, >> You're constantly reinventing and refactoring code base to get to new outcomes for customers. we're spending between 35 and 40 percent of revenues on R&D. That's generally almost twice as much as many of our competitors and we're doing that because there is so much still to do. At times we have really thought carefully, could we scale back should we scale back our R&D spend but fortunately we've had a very supportive board of directors that believes in our vision. Believes in the vision that this is a unique moment in time the whole market is transitioning to a new tool set, because of all of the crosswinds of public cloud refactoring of applications containerization abstraction of the network, a storage, compute. All of these things combining together require a very different way of solving this problem We've, we've actually seen this play out in the past which again is why we're over investing in engineering. When you look at the mainframes and the compute architecture of mainframes and then we went to client-server, the tools that managed the mainframe really didn't manage the client-server. we've now gone from client-server to cloud the same things happening again. Because the needs are so different and we're going to see a very different generation of tools rule this next gen of requirements the customers have when they have a multitude of clouds that all work together to deliver an outcome to an application that you as a user are benefiting from. >> Alright so talked about the growth, talked about the investment, it's a strong industry validation today also. Gartner up on stage talked about the definition of AI ops they might not be fully in sync as to how mature the market is but it's still important that they are you know this is a trend and something to watch and it's on their hype cycle and Forrester released the wave which had congratulations ScienceLogic as the the top scorer up in the leaders category. So congratulations on that and what does that mean. >> Well we're thrilled about that because that external validation is what customers look at. It helps them with their analysis and that the talk tracks that everybody's on in our industry sometimes it's hard to discern who does what and how well each company does it to some degree from a marketing perspective many people use the same words so the good words are already used up. So sometimes it's hard to understand how each product is differentiated in the marketplace the Forrester wave report was so thorough so comprehensive, put us through over 30 use case scenarios where we had to demonstrate to get the qualifications for that ranking. So it wasn't just us responding in writing and waving our arms and throwing out a few powerpoints to get to that result we had to prove it and it feels the satisfaction of actually proving it for our team for our engineering team for everybody here at the company I'm so proud of everybody because that's really from a product perspective. We love those product recognition awards are actually sometimes more enjoyable than the growth recognition awards because that means you're really delivering a value to the customer where they're going to when they deploy the product they're going to have a good outcome. So that's what we're focused on and having Forrester put us at the top of the wave report is a special moment in the history of the company. >> Alright so Dave this is your user conference, so what I want to end on... Let's talk about the customers and here's here's my observation as you know, my first time coming to your event and I've talked to a number of seen some of the interactions there. There are certain products that customers love the relationship is an interesting and I would say a really good one the customers are really engaged and enjoying and liking it and it's almost like that friend that you can be like I really like you and your friends in their car I can be like this is how I want you to get better in ScienceLogic this is what you've done and I'm excited once on the roadmap and this is where I want you to go even more. So it's it's like you know that that friend that you can kind of hang out with and joke with and I've seen some of those relationships it's a good robust relationship and strong partnerships. It seems that you build with your customers am I getting the right vibe how do you look at your relationships with your customers. >> From a simple business perspective, I look at a couple things this is just as a run the business metric. On average our customers buy about twenty four, twenty five percent more capacity each year. On average our customers stay with us for 7-10 years. On average our customers pay us within 59 days. So I look at are we getting paid on time, do our customers buy more capacity each and every year and do we retain our customers. We retain about ninety five percent of our customers. So those metrics are really best-in-class, net subscription retention, DSO. All of those things are really good foundational indicators of we're doing a great job for our customers but what I love is this interaction that we have with them where they're they're never ending pressure on us to do better to strive for something that makes a day in their life a better day. I love that pressure it's uncomfortable many days of the week as I mentioned in my opening presentation but it makes us a better company and everybody in the company embodies this sense of how do we capture that synthesize it and then deliver against their needs and wants as quick as we can. So our innovation rates now are as high as they've ever been the throughput our of our development team this last quarter was the best we've ever seen in the history of the company, not just because we have more people but we're getting more done in the same amount of time. So all the KPIs that I look at are pointing in a really positive direction of great momentum for the business and really good alignment with customer needs and wants. We have probably the best market fit I've ever seen with the needs and wants of a net new customer and how our product fits against that. The Forrester wave report was yet another independent validation of how good our market fit in our strategy is right now to solve real problems that are very painful for customers to solve without our product >> Alright, Dave I can't let the head wizard gone without looking a little bit into the future. So as you look down the road what should we be looking as industry watchers to seem from ScienceLogic, seen from the industry you know I asked customers if they had a magic wand you know what would they do to make things better. You had a magic wand up on stage what will you be doing to make the industry better for all of us. >> There's so many things that when we think about making the industry better, it's a community and that means that among the key things that everybody's focused on right now for AI OPS is automation. So sharing those lessons learned cauterizing, validating the automation opportunities whether it's with provisioning systems, with end devices for capacity planning. All the things that we're doing we're starting to work with our customers to publish that broadly so that they can benefit from one another as quick as possible to take those best practices and throughout our community put them into production. If we do that each and every day and really focus on delivering that value across the customer base even for competitive customers. They compete with one another what we've seen is the spirit of cooperation and that to me is among the most satisfying parts of our customer and user community that it's a community that wants to help each other get better every day of the week and that's really hard mission as well. So from a trend line for the entire industry, I think we're all moving towards a moment in time where we have this autonomic capability where we know the applications are infrastructure, we're the tools that help us keep those applications running are getting smarter and smarter by the day and basically move us away from a fault and event correlation storyline to a predictive automation storyline >> Alright well Dave actually I said it on theCUBE a couple of years ago data holds the potential be that flywheel of growth for many years to come. Really appreciate you sharing the story and thanks again for having theCUBE at the event. >> Thanks too great to be here with you. Alright we'll be back with more coverage here from ScienceLogic Symposium 2019, I'm Stu Miniman and thank you for watching theCUBE.

(upbeat music) >> Live from Barcelona, Spain it's the Cube! Covering Cisco Live Europe. Brought to you by Cisco and it's ecosystem partners. >> Okay, welcome back everyone and we're live here at Cisco Live, 2019 in Europe. It's the Cube's three days of wall-to-wall coverage, day two. I'm John Furrier, your host, with Dave Vellante co-hosting with me as well as Stu Miniman who's been in and out on interviews. Our next guest is Alex Henthorn-Iwane, vice president of marketing for company Thousand Eyes. welcome back to the cube, welcome to the show. >> Thanks great to be here. >> So talk about what you guys do first, you guys do a very interesting business, a rapidly growing business. What is Thousand Eyes, what do you guys do, What's your product, who is your customer? >> OK, so the vision of thousand eyes was really to help organizations deal with all the connected experiences that they have to deliver. So we're giving visibility into those connected experiences but not just how there, you know if they're working or not but all the external dependencies that they rely on. So we developed a ton of expertise on how the internet works how the networks work, how routing works and all that. And we can give that insight so that all the things that IT now no longer controls and owns, but has to own the outcome for, we're giving that visibility. >> And when you guys sell a Saas Solutions, the software, what's the product? >> Yeah >> Who's the buyer? >> So we're Saas Platform and the way that we gather this data is we're primarily doing active monitoring at a few different layers; so we're monitoring the app layer things like HTTP and page loads and things like that you would think of that as synthetics classically but we've paired that with some patented ways of understanding how everything connects from a user out in the internet or from a branch office or from a data-center out to somewhere else typically across the internet all those networks the cloud networks going through things like Z-scaler all those complex pieces that again you don't control. We can trace all that and then map it down even to internet routing. One other kind of cool thing that we added to all that we do that on an agent basis so we have agents around the world that you can put them in your data-centers your VPC's and your branches. >> And the value proposition is what; visibility in the patterns; optimization; what's the outcome for the customer? >> The outcome is ultimately that we're going to help IT deliver the digital experiences for their employees for their customers that could be e-commerce, e-banking, it could be open banking or PSD2 here in Europe and UK. >> So full knowledge of what's going on >> Right >> But the name talks to that >> Yeah >> It talks to the problem you're solving >> Right, and it's really, the focus is and our specialty is all the external things, right. You've always had a lot of data, maybe too much data on the stuff that you did own, right, in IT. Okay, you could collect packets and flows and device status and all that sort of this and sort of, the challenge was always to know what does that mean, but whether or not that's perfect it exsited, but you simply can't get that from outside, you've got your four walls >> Yeah >> So you just have this big drop off in visibility once you get to the edge of your data-center etcetera >> Now, lets talk about the dynamics in IT; we were talking before we came on camera here about ya know, our lives in IT and going back and look at the history and how it's changed but there are new realities now >> Right >> Certainly Cisco here talking about intent based network ACI anywhere, Hyperflex anywhere, the ecosystem is growing the worlds changed. >> Right >> Security challenges, IOT, the whole things completely going high scale, more complexity. >> Right, Yeah. >> IT? What's the impact to IT? What's the structural change of IT from your prospective? >> Well, the way we see it what's happening with IT is the move from owning and controlling all the stuff, you know and managing that granting access to that. To a world where you really don't own a lot of the stuff anymore. You don't own the software, you don't own the networks. You don't own the infrastructure increasingly. Right? So how do you operate in that role? Changes. What the role of IT is in that role, really changes. And then out of that comes a big question. How does IT retain relevance? In that role? And a lot of that role is shifting away from being the proprietor, to being more of like a manager of an ecosystem. Right? And you need data to do that. So I think that's a really big step. >> So this is now, an actual job description kind of thing? >> Yeah. The roles and make up of the personnel in IT is changing. Because of the SAAS cloud, Hybrid cloud, Multi cloud? >> Right. It's more of like a product management role, than it is the classic operations role. You know? And we observed some really big changes in just operations. So, when you own all the stuff you can find a fix. Right? That's a classic statement of IT operations. But when all the stuff is outside, You can't fix it directly. So you go to what we call an evidence in escalation. You have to actually persuade someone else to fix it for you And if you can't persuade them, you don't have governance you don't have accountability and you don't have the outcome that you're supposed to deliver. >> So the infrastructure is to serve it's players; Google, Amazon, Microsoft, more SAAS All of this is taking data away from your control? >> Right >> And obviously network visibility? >> Sure >> So how are you guys dealing with that? What are some of the nuances of whether it's SAAS, or different infrastructures of service providers? >> And I would add to that SUN, Shift to the internet I would add to that just the increasing number of digital experiences that companies offer to customers. Right? >> Right. So the way that we deal with that is, that we believe that you need a highly correlated way of understanding things. Because at the top layer, if the outcome that IT is supposed to deliver is a digital experience. Right? The customers at the center now, not the infrastructure. Right? So I have to start with experience. So we need to look at, how is the app preforming? How is it delivering to that end user? And now you have to think about it from a persona basis. To who? Where? Right? So that's why we have all these agents floating around the world in different cities. Because if you're offering a let's say e-banking portal, and your surveying 100 cities as markets. You need to see from those cities, right? You also then need to be able to understand the why. When something is not working well, whose fault is it? Right? Is it us? >> Its the network guys! (laughing) >> What you don't to get is the everlasting war room circular firing squad kind of scenario. Where nobody actually knows, right? This is what happens, because the issue is that often times you suspect its not you. Maybe. Right? That search for innocents. >> Yeah. >> But again that's not enough because, the whole point is to deliver the experience. So, now who could it be? Say you're offering e-banking or e-commerce. Is it your CDM provider? Is it that your DMS manage provider is not responsive? Or somethings down? Are you under a D DOS attack? Or some of your ecosystem is. Is one of your back end providers, like your Braintree payments not working right. Right? There is so many pieces, is there an ISP in the middle there? That's being effected? >> There's so many moving parts now. >> If from each persona or location just to get to 1 URL. Could be traversing several ISP networks. Dozens of HOPS across the internet. How on earth are you supposed to isolate, and go an even find who to ask for help? That's a really sticky problem. >> So this will expose all those external credits? >> So we expose all those things. We expose all these multiple layers, and we have some patenting correlation, visual correlation. So you can say alright I see a drop in the responsiveness of a critical internal application or of .. I mean, we never have. Butt lets say like if SAAS like sails course, or something like that. And it may not be their fault by the way, its not them being a problem. But the users having a problem. So you see this drop and say well where's it happening? You can now say is it a network issue? Is it an app issue? Now if it is a network issue I can look at all the paths, from every where and say aha there's a commonality here. For example, we could surface through our collective intelligence that there's an ISP outage in the middle of the internet that's causing this. Or we could say, hey you know your ISP is having an issue. Or guess what? Sales force is maybe, you know things happen. People have problems in data centers sometimes. It's nothing you know, it's not.. >> So there's two things there's the post mortem view, and there's the reactive policy based intention. >> Right >> To say okay hey we've got an outage, go here do somethings take some action. >> Right. So some of those things you can automate. But the fact of the matter is that, automation requires learning. And machines need to be taught, and humans have to teach them. I mean that's one of the sort of sticky parts of automation. (laughing) Right, its not auto-magic its automation. >> So you guys are in the data business basically? >> Right, visibility, data. Right. >> Big data, its about data. You're servicing data. Insights, actionable insights, all this stuffs coming together. So the question is on AI. Cause AI plays a role here. IT OPS and machine learning you've got deterministic and non deterministic behavior. >> Sure. >> How do you solve the AI OPS problem here? Because this is a great opportunity for customers, to automate all this complexity and moving parts. To get faster time to data or insight. >> Okay so I would say that the prime place where you could do AI and ML is where you have a relatively closed system. Lets say an infrastructure that you do control. And you have a ton of data. You know like a high volumetric set of data-streams. That you can then train a machine to interpret. The problem with externalities is that One, you have sparse data. For example we have to use agents, cause you can't get all that traditional data from it. Right? So that means that that's why we built this in a visually correlated way. It's the only way to figure it out. But the other aspect to that is that, when your dealing with external providers you have an essential human part of this. There's no way as far as I know to automate an escalation process with your service providers. Which now we have so many, right? First of all, we have to figure out who. And then you have to have enough evidence, to get an escalation to happen to the right people. Empowered people. So they don't go through the three D's of provider response. Which is Deny, Deflect and Defer. (laughing) Right? You know you have to overcome plausible deniability, and that's very human interaction. So the way we deal with that. All this interactive correlated data we make it ridiculously easy, To share that. in an interactive way, with a deep link that you send to your provider and say "just look and see" and you can see that it's having issues. >> So get the evidence escalated, that's the goal as fast as possible? >> Right so then your time, like your mean time to repair now in the cloud is dependent on mean time to effective escalation. Right? >> Who are some of your customers? >> So, we have our kind of foundational customers. We have 20 of the top 25 SAAS companies in the world, as our customers. We have five of the top six US banks, four of the five top UK banks. 100 plus of global two thousand and growing fast. A lot of verticals, I would say enterprise I started with financials not surprisingly. But now we see heavy manufacturing, and telecom and oil and gas and all that. >> What's going on here at Cisco Live? What's your relationship with Cisco? >> So with Cisco we have a number of integration points, we have our enterprise agents. We have these could agents pre deployed, same software as what we call the enterprise agent. That's been certified as an VNF or as container deployments, on a variety of Cisco Adriatic platforms. So that's kind of our integration point. where we can add value and visibility from those you know, branch or data center or other places you know out to the cloud or outside in as well. >> And who's your buyer, typically? >> So I would say a couple of years ago we would be very network central. But now because of the change in IT, and our crossover into the largest enterprises we find that now it's the app owners. It's the folks who are rolling out sales force to forty thousand people and their adopting lighting. Right? You know or they're putting Office 365 out, and they're dealing with the complexities of a CDM based service or a centralized service like SharePoint. So we're seeing those kind of buyers emerge, along with the classic IT operations and network buyers. >> So it only gets better for you, as more API centric systems get out there. Because as its more moving parts, its basically an operating system. And you look at it wholistically, and you got to understand the IO if you will? >> Right. The microservices way of doing everything, means that when you click something or you interact with something as a user. There are probably 20 things happening at a back end, at least half of which are going off across the internet. And all of them have to work flawlessly. Right? For me to get that experience that I'm expecting. Whether I'm trying to buy something or, just get something done. >> What's your secret sauce in the application? >> So I'd say our secret sauce comes down to a couple really key things. One is the data that we generate. We have a unique data center from all these vantage points that we have now. That's what allows us to do this collective intelligence. No body else has that data. And an example we did a study, a couple studies last year. Major resource studies using our platform to look at public cloud performance from the internet within regions. Inter regions, and between clouds. And we found some really interesting phenomenon. And no body else had ever published that before. A lot of assumptions, a lot of inter-claims, we where actually able to show with data, exactly how this stuff performs. >> I'm sorry, you guys have published that? Where can we find that? >> Yeah, so we have that published, we also did another major report on DNS. >> Is that on your website? >> It's on our website, so definitely something to check out. >> Alright, Alex well thanks for coming on, give the quick plug, what's up for you guys? Hiring? What's new? Give the quick two cents. >> So here in Europe we're scaling up, hiring a lot and expanding across Europe. We have major offices in London and Dublin, so that's a big deal. And I think in this next year you'll see some bigger topped out ways that we can help folks understand. Not just how the internet is effecting them, but more of like the unknown of unknowns of internet behavior. So there's going to be some exciting things coming down the pipe. >> Well we need a thousand eyes on all the instrumentation as things become more instrumented having that data centric data. is it going to help feed machine learning? And again its just the beginning of more and more complexity being abstracted away by software on network Programmability. theCUBE bringing you The Data Here from Barcelona, for Cisco Live! Europe 2019 stay with us for more day 2 coverage after the short break. I'm Jeff Furrier here with Dave Vellante, thanks for watching. ( upbeat music )

>> From Yorktown Heights, New York, it's theCUBE, covering IBM Cloud Innovation Day, brought to you by IBM. >> I'm Peter Burris of Wikibon. Welcome back to IBM Innovation Day, covered by theCUBE, from beautiful Yorktown Heights, New York, Thomas J. Watson Research Center. A lot of great conversations about the journey to the cloud and what it means, and we're going to have another one here with Denis Kennelly, who is the General Manager of Cloud Integration in IBM. Denis, welcome to theCUBE. >> Thank you, Peter, and welcome to Yorktown also. >> I love it here. So, very quickly, what does the GM of Cloud Integration do? >> Yeah, so, I suppose we start from the beginning, right? So I am responsible for a lot of what we call the traditional IBM middleware. So these are brands that are known to the industry and to our customers, things like WebSphere, Message Queue, or MQ, as we know it, which is kind of the core foundation stones for a lot of IT today that's out there in the industry. And it's not just about, you know, sometimes people talk about this legacy, but this is what all the systems run on today. And also, I'm involved in the whole journey of moving that middleware to the cloud and enabling customers to get on that journey to cloud. And it's not just to a cloud, because your typical enterprise today has probably on average about five different clouds, and clouds, as we know them as the IS players of the past, but also when we talk about cloud, we also think about things like SaaS properties and applications of that regard. So it's helping customers go from that traditional IT infrastructure and on their journey to the cloud. That's what I do. >> So utilizing these enterprise-ready technologies that have driven the enterprise, bringing them to the cloud as services, but also making sure that the stuff that's currently installed can engage and integrate the cloud from a management service standpoint as well. >> Absolutely, because customers have made a huge investment in this middleware, and a lot of the transactions, and a lot of the security, and a lot of the risks set in these systems, and they have served us very well for many decades. Now, as we start to move to the cloud, it isn't a binary switch. It's going to be a transition over time, and today, I think we're about 20% into that journey. I would say we've done some of the easier parts. Now we're getting into some of the more complex and some of the more difficult problems. And kind of one of the underlying pieces of technology we're using to enable customers to do that is container technology. So we've made the decision to use containers right across our middleware, our software. So what I mean by that is we've taken all our software and it's running on containers today, and that's a key enabler to make this happen, because containers give you that flexibility and that openness to run on different targeted environments and be able to run on different clouds at the end of the day. >> The model by which developers thought about integration would be through a transaction. Generally pretty stateful. So, I'll put something in a queue, I'll wait for a response, guaranteed delivery. Now we're moving to a world, containers, a lot more reliance on stateless interactions. It means we're being driven mainly by events. I'm thinking in terms of events. Talk about how that is changing the way we think about the role of middleware or the role of integration amongst all these different possible services. >> Yeah, it's a great point. I mean, so if you think about containers, we think about stateless, and we think about microservices, and we talk about event-based applications, so a lot of those front ends are on that today and building on those technologies. So you've got to enable the new developers to build in that way. Now, how do you integrate that with that backend, right? Because at the end of the day, these transactions are running in the backend, and you really want to enable, as part of the transformation, you want to open up those backends to those new developers and to those new customer insights, because what is digital transformation? It's about putting the customer at the middle and enable insights on those customers, and enable rapid development of those applications. So at the core of that is integration, and integration is not just message-based integration. It's being able to take those backend transactions and surface them up through APIs, not just the standard APIs as we think of maybe as web services, but event-based probability models, and event-based APIs also, and doing that in a consistent and a secure manner, because if you have all these complex transactional systems, who has access to that data? Who has access to make those transactions? Who can, at certain levels, et cetera, and we really have to do that in a secure and a consistent manner across these environments is critical to what we do. >> So, can you give us some examples of some customers that are successfully transitioning their backend systems to these new technologies in a way that protects the backend system, makes it economical to do so, in other words, doesn't force change, but can utilize some of these new integration technologies to make both the new investments more valuable but also the backends more valuable too. >> Yeah, I mean, if you think of, I'll give you an example of a customer, American Airlines, in the airline industry, right? So, if you think about travel and airline travel in times past, you know, you made a reservation maybe through an agent and you booked the flight from A to B. Today, you have your cellphone, you get regular updates on your flights. If you're delayed, you're possibly offered re-routing options, et cetera, right, so there's a classic example of how digital has transformed the airline industry and the airline booking industry. If your flight, you know, if there's weather patterns, et cetera, how you can get real time updates on your flights. So, okay, that's all happening on the front end, on your cellphone, or your tablet, or whatever, but the backend booking system is still a transactional-based system that says, Peter is on this flight going from A to B at this time, et cetera. So, that's an example of how we have modernized an application and we have worked with American Airlines to make that happen, to give you that kind of 360 view as a customer, where you bring in together flight information, weather information, rating information, because we'll offer you different alternatives in terms of if you need to rebook in the event of something going on, and at the backend, there's still a transaction that says, book Peter on this flight from A to B, and that's a real life example of a transformation, how we've integrated those two worlds there. >> So if we go back five or six, or more than that, say 10, 15 years, in the days of MQ, for example, the people who were developing, and setting up those systems, and administering and managing those systems were a relatively specialized group. Today, the whole concept of DevOps in many respects is borrowing from much of the stuff that those folks did many, many years ago as infrastructure builders, or developers, as I call them. How does that group move into this new world of integration in the cloud? >> Yeah, so, I think first of all, the rate and pace has multiplied, right, so the rate and pace of which we make changes to the system has multiplied. I mean, maybe traditionally, we run in changes maybe once a month. We have things like change control windows. Things were very well controlled, et cetera, right? But at the end of the day, it doesn't meet the needs of today and what we need to do in a digital world. So today, we're running in changes on the hour. So now, you're faced with a challenge, right? So when you make changes, how do you know that the system is still performing, is still operating at the level you need it to operate on? You start to think about security and you start to think about, okay, I've made a change, have I introduced vulnerabilities into the system? You've got to, you know, in the past, these were all separate groups and almost islands within the operation center, where you have the developer, who kind of over to all the code, and then operations looked at it and see how it's performed, and security checked for compliance, et cetera, and they were kind of three different islands of personas or groups within the organization. Today, that's really collapsing into one organization. The developer is responsible for making sure the change gets in, for making sure the change performs, and is also security compliant. And we call this the role of the SRE, or the systems reliability engineer, and really bringing those two worlds together into one persona, and it's not just one persona but having the systems on the inside to make that happen. And that's critical in how management is changing and the management of these systems is changing, and how the skill level is needed in this new world. >> So Denis, one more question. In a few months, IBM Think is going to take over San Francisco, February 2019, >> Looking forward to it. >> 3,000 people. Talk to us a little bit about what gets you excited about Think, and what kind of conversations you hope to be having while you're there. >> Yeah, well, you know, this is the one time of the year where all of IBM comes together, and it's new this year that we're going to San Francisco, and in particular, in our cloud business, which I'll talk about, which really encompasses everything we're talking about here, which is our middleware business and also how we move customers to the cloud, and really engaging with customers in those conversations. And this is the one time of the year where all of IBM comes together, and where you can see the full breadth of our capabilities all the ways from our systems, and the hardware, down at that level, at the chip level, right through to the middleware and the software to our cloud, and actually engaging with customers, and really understanding what the customer needs are, and making sure that what we are working on is meeting those customer needs, and of course, if we need to adapt or change, and take that feedback back into the organization, so we do that in real time. It's a very exciting time for us. It's a week in the year that I really look forward to, because that's where all of IBM comes together, including our services, et cetera, and where we actually have conversations with key customers and partners and really understanding what's going on in the industry and how we can help people on this journey to the cloud that I talked about. >> Denis Kennelly, IBM General Manager of Cloud Integration, thanks very much for being on theCUBE. >> Thank you, Peter. And once again, this is Peter Burris. We're signing off from the IBM Innovation Day, here at the Thomas J. Watson Research Center in Yorktown Heights. Thank you very much for watching. Let's carry on these conversations about cloud and the future of computing.

>> Live from Las Vegas, it's theCUBE. Covering Boomi World 2018. Brought to you by Dell Boomi. >> Welcome back to theCUBE. We are live at the Encore in Las Vegas, I'm Lisa Martin with John Furrier. We're at Dell Boomi World 2018, second annual Dell Boomi World, and we're here with one of Dell Boomi and Dell's biggest GSIs. We've got Jason Cook, the Global Client Account Lead at Accenture serving Dell. Jason, thanks for joining John and me today. >> Thank you. >> So, second annual Dell Boomi World, bigger than last year. They were talking today, a lot of interesting numbers. 7,500 plus customers to date. They're adding five new customers everyday. I saw the Gartner Magic Quadrant from earlier this year and iPaaS, they are right up there in that strong leader category. Talk to us about the relationship that you have with Dell Technologies and the business heat of Dell Boomi. >> Yeah, yeah, it's an interesting one. So, Accenture has become very big. I think we now have 470,000 global employees, and our brand and presence is technology advisory and delivery, it predominates what we did. What's interesting about Dell and, specifically, Boomi is being so central to the technology ecosystem, there's much opportunity for partnership. Where Dell is present with enterprise clients, we're present too. And we tend to have long-running relationships with those clients. Most of our clients are tenured over 15 years. So it gives us an opportunity to have the type of longstanding relationship that Dell has with clients and advise on technology trends, and change, and break into the best thinking of the marketplace in their clients as they look to solve problems, of course, Dell is central to that solution set, as Boomi is too. >> And yesterday, they announced a new technology partner program. Dell Boomi has a broad partner ecosystem that it partners, implementation, GSIs, talk to us about that and the maybe new business opportunities that it will give to Accenture. >> Yeah, so we've enjoyed a relationship over the past several years in Europe working with Boomi. And we incubated a program over there called Eccentric Growth Partnerships, where with emerging companies such as Boomi, we've gone to market, leveraged the Accenture channel, and then brought scale to those technologies to deliver at enterprise level for their expectations. It's been very successful, you know, seen on both sides is a real win. And we're now transferring that into the North American market, so we're based on the heels of that success. We're looking to formalize some of the things we've been doing internationally in North America. A larger market for both of us, and so it's expanded opportunity in both places. >> Jason, talk about Accenture's own transformation. We've been following you guys for, I've been following Accenture when they changed their name. But recently you guys have invested, in the past decade, really early in data science. You guys have been on the public cloud very early. You've been partnering with your customers. And so that's all great, you guys do a good job with that. But what's interesting is you're actually helping them change their business model. >> Yes. >> So how has your own transformation within Accenture dealing with Dell, he's been doing a trillion dollars in business. Millions and millions of servers sold. His customers are changing. You guys are in that business model, enablement business, you're helping customers. What's the big business model impact that's happening in the market right now. >> Well, I think you know, as it pertains to Accenture, yeah, we've grown. I would say one of the hallmarks of the growth has been around digital, and I think 60% of our revenues are now digitally oriented, which are in the areas you described. So that's become our brand and presence, and the majority of what we do in the marketplace. I think the things that we're doing to serve clients, which are several of the things we've done internally, have been around all sorts of digitally-enabled journeys, whether it's the intelligent enterprise, the connected customer, the adoption of platforms, and the expanded use as a service within enterprises. There are plays within all those spaces where we end up bringing enablement to those clients. You know, examples would be, in the retail space, you know, growth and expansion of omnichannel techniques, so that the same customer experience exists across anywhere in retail. Programs around single views of customer are very, very common for us globally. Traditionally, less technical areas of the business, like a supply chain operating that's dominated by manufacturing and fulfillment and brick and mortar in the retail space. The real time visibility challenges that have historically been there are only now being able to be solved by technologies, and so there's several different. >> And the cloud certainly is horizontally scaled, so it impacts all industries that you play in, so, good for business. But the challenge that the CIOs have that we talked to, we hear and want to get your reaction to is, okay, I loved technology scale. I need to have proof points. I got to have mile markers that are going to be attainable with time-to-value. But the number one thing they say is I got to bring a competitive advantage into I.T., in a cloud construct that's horizontally scalable and work with partners in areas that aren't core. So, leverage supplier relationships, but build a core intellectual property or competitive advantage with I.T. How do you guys help them? What are some trends? What are those I.P. moments for your large and medium-sized customers? >> Yeah, I think that because we have the heritage of both advising on and delivering technology, where we tend to work closely with CIOs is around the speed-to-value, delivering on programs. We represent a wealth of experience and work in the marketplace, and those learnings can be brought to different clients, and fundamentally that's what's valuable to them. So I think that when we talk about cloud enablement, it's often a matter, too of thinking through, what are the specific business outcomes that can be delivered from the use of technology. And so, clients for example, I can think of some clients, that one company that has 1,400 legacy applications in a cloud footprint. And yet the business initiatives that come into the IT-- >> They must use containers a lot. >> Yeah, well exactly. The questions that come into the I.T. organization are often ones around how can we improve our visibility to product line profitability, as an example. And so, the use of cloud, the use of integration technologies like Boomi accelerates the ability to connect information from that disparate environment and deliver outcomes. >> And specifically more tactical, to get those outcomes, what specific things do you see? Is the cloud native? Is it the role of data? How are CIOs getting down and dirty, saying okay, I'm going to lock in on this as territory, we're going to build around and build on top of. Data, cloud, and IoT's new, and everyone knows what IoT is, it's going to be part of, either physical and/or low-hanging fruit. But what are they building on from an I.T. standpoint? Is it the data, is it the network? Is it the storage? So what do you see there? >> Yeah, I think it is the data. I think that's where we see, data-led seems to be the thinking in most of these cases around getting information consistently consumed throughout. 'Cause the world has become so data intensive that access to data is not the problem. It's the integration, and the derivation of value from it that's-- >> And scale, too, I mean. >> And scale, right, yeah. >> Hello cloud, so cloud and data seem to be. >> And it's become more distributed, too. And so dealing with distributed data sources and normalizing has been a-- >> That's where Boomi comes in, integrating all that stuff in, so cloud and data seem to be the pattern across the board generically speaking. I mean, obviously certain industries financial, service, oil, and gas have unique requirements. >> They all have their own cases for it, whether you're a distributed bank, or whether you're a distributed retailer, or whether you're dealing with oil wells in distributed locations, you run into common problems across all industries. >> And integration is so much more, as the iPaaS market has evolved, it's so much more than integrating applications. It's integrating applications, data from existing sources, from new sources, the API economy is essential for that. To enable an organization to create a customer experience that's going to allow them to use that data, and continue to get more customers, more data, and evolve faster than their competition. But transformation is a big challenge, right? And here, well, and even Dell Technologies were, the theme was about making it real, making it real for digital transformation, security transformation, huge priority, workforce. How, when Accenture is going in to integrate at, whether it's a retailer or an oil and gas company, how do you help them start? What's that start of a transformation? >> Well, it often is the transformations you were just referring to. Our typical engagement profile ranges from how do I engage my workforce in a new way? Or how do I improve visibility across a distributed network of retail stores, or banks, or what have you? And so those are the transformations, and then inevitably, the connection of information across those things become the enabling source. If you take, as an example, a customer experience program where, let's talk about a government example where they want a single view of a citizen, a tax payer, whatever it may be. There's so much information on that person in so many disparate places that has to be brought together in a cohesive way. Not only that, but brought together and then used effectively in serving that person. And that's where you see a lot of value. >> Jason, I want to pick your brain while you're here, 'cause Accenture's always got the smart people who know what's going on. And you got big customers, big examples. There's a dynamic right now between two kind of personas. Kind of making it generic for the conversation now. Persona one is the business executive who is responsible and chartered to drive the digital transformation with new and improved applications. Taking advantage of the legacy, bringing in the new, managing them either on their own schedule. And the second persona is the person deploying cloud. So how are companies organizing around these personas? One's got to be under the hood, I got to do multicloud I got to do Kubernetes, I got to do all these things. Stateless applications, stateful applications, integrate them all together. I'm deploying it. And then the business persona, hey, take that hill, more apps, more outcomes. So how are companies organizing around these dynamics? What's the best practice? >> Yeah, along the lines you describe. So, specifically, the business functions are becoming aligned with application domains, and those tend to be programmatically managed. And so we see structures around that programmatic management. To be very responsive to business needs, and particularly as clock speeds accelerate on delivery, maintaining that partnership is very, very important. Likewise, on the infrastructural side, we see alignment there too to take advantage of creating platforms, and enablement, and infrastructure, and delivery capabilities that can deliver on that promise. >> So they're working together on pizza teams, or like agile teams? >> So it's a customer-focused model for the programmatic work and it's an industrialization and an acceleration on the infrastructural side. And that's, again, where there's a strong fit with some of these-- >> Do you have a favorite example, speaking of that? So many departments, lines of business, need to have access to the same data to be able to develop new products and services, tune things, make things better, faster than their competition. So there's this sort of democratization and this need to be able to share the information so that the entire business can grow together. Do you have a favorite example of an organization of any industry that you've worked with that you've seen really do that well, so that business, at the end of the day, everyone's playing well together because they have to. The business now is connecting customers, vendors, partners, and delivering experiences that are truly differentiating. >> Integration programs, data programs, data lake programs, data science programs often have a governance mechanism out in front of them to prioritize the needs of their business. Both in the back, in terms of enablement of different sources of information being accessed, but also the uses on the front end. And so that is a practice that we're seeing grow exponentially. The other thing that's interesting, I think, in terms of best practice is that as intelligence accelerates and companies become more analytically driven, the traditional process of continuous improvement which used to be defined in terms of Six Sigma events and other things, where once in a while a function would be evaluated for efficiencies becomes a continuous capability. So in this governance model, the ability to refine, and tune, and improve things like integration, AI, analytics on a continuous cycle as opposed to having it be event-driven is certainly an emerging trend and a best practice that we see a lot of. >> Well, Jason, thanks so much for joining the program with John and me today, and sharing with us what's new with Accenture and Dell Boomi and how you're helping customers globally truly transform. >> It's a pleasure, thank you for having me. >> And for John Furrier, I'm Lisa Martin. You're watching theCUBE live from Boomi World 2018 in Las Vegas. John and I will be right back with our next guest. (electronic music)

>> Announcer: From Berlin, Germany, it's theCUBE covering Dataworks Summit Europe 2018 brought to you by Hortonworks. (upbeat music) >> Well hello, I'm James Kobielus and welcome to theCUBE. We are here at Dataworks Summit 2018, in Berlin, Germany. It's a great event, Hortonworks is the host, they made some great announcements. They've had partners doing the keynotes and the sessions, breakouts, and IBM is one of their big partners. Speaking of IBM, from IBM we have a program manager, Piotr, I'll get this right, Piotr Mierzejewski, your focus is on data science machine learning and data science experience which is one of the IBM Products for working data scientists to build and to train models in team data science enterprise operational environments, so Piotr, welcome to theCUBE. I don't think we've had you before. >> Thank you. >> You're a program manager. I'd like you to discuss what you do for IBM, I'd like you to discuss Data Science Experience. I know that Hortonworks is a reseller of Data Science Experience, so I'd like you to discuss the partnership going forward and how you and Hortonworks are serving your customers, data scientists and others in those teams who are building and training and deploying machine learning and deep learning, AI, into operational applications. So Piotr, I give it to you now. >> Thank you. Thank you for inviting me here, very excited. This is a very loaded question, and I would like to begin, before I get actually to why the partnership makes sense, I would like to begin with two things. First, there is no machine learning about data. And second, machine learning is not easy. Especially, especially-- >> James: I never said it was! (Piotr laughs) >> Well there is this kind of perception, like you can have a data scientist working on their Mac, working on some machine learning algorithms and they can create a recommendation engine, let's say in a two, three days' time. This is because of the explosion of open-source in that space. You have thousands of libraries, from Python, from R, from Scala, you have access to Spark. All these various open-source offerings that are enabling data scientists to actually do this wonderful work. However, when you start talking about bringing machine learning to the enterprise, this is not an easy thing to do. You have to think about governance, resiliency, the data access, actual model deployments, which are not trivial. When you have to expose this in a uniform fashion to actually various business units. Now all this has to actually work in a private cloud, public clouds environment, on a variety of hardware, a variety of different operating systems. Now that is not trivial. (laughs) Now when you deploy a model, as the data scientist is going to deploy the model, he needs to be able to actually explain how the model was created. He has to be able to explain what the data was used. He needs to ensure-- >> Explicable AI, or explicable machine learning, yeah, that's a hot focus of our concern, of enterprises everywhere, especially in a world where governance and tracking and lineage GDPR and so forth, so hot. >> Yes, you've mentioned all the right things. Now, so given those two things, there's no ML web data, and ML is not easy, why the partnership between Hortonworks and IBM makes sense, well, you're looking at the number one industry leading big data plot from Hortonworks. Then, you look at a DSX local, which, I'm proud to say, I've been there since the first line of code, and I'm feeling very passionate about the product, is the merger between the two, ability to integrate them tightly together gives your data scientists secure access to data, ability to leverage the spark that runs inside a Hortonworks cluster, ability to actually work in a platform like DSX that doesn't limit you to just one kind of technology but allows you to work with multiple technologies, ability to actually work on not only-- >> When you say technologies here, you're referring to frameworks like TensorFlow, and-- >> Precisely. Very good, now that part I'm going to get into very shortly, (laughs) so please don't steal my thunder. >> James: Okay. >> Now, what I was saying is that not only DSX and Hortonworks integrated to the point that you can actually manage your Hadoop clusters, Hadoop environments within a DSX, you can actually work on your Python models and your analytics within DSX and then push it remotely to be executed where your data is. Now, why is this important? If you work with the data that's megabytes, gigabytes, maybe you know you can pull it in, but in truly what you want to do when you move to the terabytes and the petabytes of data, what happens is that you actually have to push the analytics to where your data resides, and leverage for example YARN, a resource manager, to distribute your workloads and actually train your models on your actually HDP cluster. That's one of the huge volume propositions. Now, mind you to say this is all done in a secure fashion, with ability to actually install DSX on the edge notes of the HDP clusters. >> James: Hmm... >> As of HDP 264, DSX has been certified to actually work with HDP. Now, this partnership embarked, we embarked on this partnership about 10 months ago. Now, often happens that there is announcements, but there is not much materializing after such announcement. This is not true in case of DSX and HDP. We have had, just recently we have had a release of the DSX 1.2 which I'm super excited about. Now, let's talk about those open-source toolings in the various platforms. Now, you don't want to force your data scientists to actually work with just one environment. Some of them might prefer to work on Spark, some of them like their RStudio, they're statisticians, they like R, others like Python, with Zeppelin, say Jupyter Notebook. Now, how about Tensorflow? What are you going to do when actually, you know, you have to do the deep learning workloads, when you want to use neural nets? Well, DSX does support ability to actually bring in GPU notes and do the Tensorflow training. As a sidecar approach, you can append the note, you can scale the platform horizontally and vertically, and train your deep learning workloads, and actually remove the sidecar out. So you should put it towards the cluster and remove it at will. Now, DSX also actually not only satisfies the needs of your programmer data scientists, that actually code in Python and Scala or R, but actually allows your business analysts to work and create models in a visual fashion. As of DSX 1.2, you can actually, we have embedded, integrated, an SPSS modeler, redesigned, rebranded, this is an amazing technology from IBM that's been on for a while, very well established, but now with the new interface, embedded inside a DSX platform, allows your business analysts to actually train and create the model in a visual fashion and, what is beautiful-- >> Business analysts, not traditional data scientists. >> Not traditional data scientists. >> That sounds equivalent to how IBM, a few years back, was able to bring more of a visual experience to SPSS proper to enable the business analysts of the world to build and do data-mining and so forth with structured data. Go ahead, I don't want to steal your thunder here. >> No, no, precisely. (laughs) >> But I see it's the same phenomenon, you bring the same capability to greatly expand the range of data professionals who can do, in this case, do machine learning hopefully as well as professional, dedicated data scientists. >> Certainly, now what we have to also understand is that data science is actually a team sport. It involves various stakeholders from the organization. From executive, that actually gives you the business use case to your data engineers that actually understand where your data is and can grant the access-- >> James: They manage the Hadoop clusters, many of them, yeah. >> Precisely. So they manage the Hadoop clusters, they actually manage your relational databases, because we have to realize that not all the data is in the datalinks yet, you have legacy systems, which DSX allows you to actually connect to and integrate to get data from. It also allows you to actually consume data from streaming sources, so if you actually have a Kafka message cob and actually were streaming data from your applications or IoT devices, you can actually integrate all those various data sources and federate them within the DSX to use for machine training models. Now, this is all around predictive analytics. But what if I tell you that right now with the DSX you can actually do prescriptive analytics as well? With the 1.2, again I'm going to be coming back to this 1.2 DSX with the most recent release we have actually added decision optimization, an industry-leading solution from IBM-- >> Prescriptive analytics, gotcha-- >> Yes, for prescriptive analysis. So now if you have warehouses, or you have a fleet of trucks, or you want to optimize the flow in let's say, a utility company, whether it be for power or could it be for, let's say for water, you can actually create and train prescriptive models within DSX and deploy them the same fashion as you will deploy and manage your SPSS streams as well as the machine learning models from Spark, from Python, so with XGBoost, Tensorflow, Keras, all those various aspects. >> James: Mmmhmm. >> Now what's going to get really exciting in the next two months, DSX will actually bring in natural learning language processing and text analysis and sentiment analysis by Vio X. So Watson Explorer, it's another offering from IBM... >> James: It's called, what is the name of it? >> Watson Explorer. >> Oh Watson Explorer, yes. >> Watson Explorer, yes. >> So now you're going to have this collaborative message platform, extendable! Extendable collaborative platform that can actually install and run in your data centers without the need to access internet. That's actually critical. Yes, we can deploy an IWS. Yes we can deploy an Azure. On Google Cloud, definitely we can deploy in Softlayer and we're very good at that, however in the majority of cases we find that the customers have challenges for bringing the data out to the cloud environments. Hence, with DSX, we designed it to actually deploy and run and scale everywhere. Now, how we have done it, we've embraced open source. This was a huge shift within IBM to realize that yes we do have 350,000 employees, yes we could develop container technologies, but why? Why not embrace what is actually industry standards with the Docker and equivalent as they became industry standards? Bring in RStudio, the Jupyter, the Zeppelin Notebooks, bring in the ability for a data scientist to choose the environments they want to work with and actually extend them and make the deployments of web services, applications, the models, and those are actually full releases, I'm not only talking about the model, I'm talking about the scripts that can go with that ability to actually pull the data in and allow the models to be re-trained, evaluated and actually re-deployed without taking them down. Now that's what actually becomes, that's what is the true differentiator when it comes to DSX, and all done in either your public or private cloud environments. >> So that's coming in the next version of DSX? >> Outside of DSX-- >> James: We're almost out of time, so-- >> Oh, I'm so sorry! >> No, no, no. It's my job as the host to let you know that. >> Of course. (laughs) >> So if you could summarize where DSX is going in 30 seconds or less as a product, the next version is, what is it? >> It's going to be the 1.2.1. >> James: Okay. >> 1.2.1 and we're expecting to release at the end of June. What's going to be unique in the 1.2.1 is infusing the text and sentiment analysis, so natural language processing with predictive and prescriptive analysis for both developers and your business analysts. >> James: Yes. >> So essentially a platform not only for your data scientist but pretty much every single persona inside the organization >> Including your marketing professionals who are baking sentiment analysis into what they do. Thank you very much. This has been Piotr Mierzejewski of IBM. He's a Program Manager for DSX and for ML, AI, and data science solutions and of course a strong partnership is with Hortonworks. We're here at Dataworks Summit in Berlin. We've had two excellent days of conversations with industry experts including Piotr. We want to thank everyone, we want to thank the host of this event, Hortonworks for having us here. We want to thank all of our guests, all these experts, for sharing their time out of their busy schedules. We want to thank everybody at this event for all the fascinating conversations, the breakouts have been great, the whole buzz here is exciting. GDPR's coming down and everybody's gearing up and getting ready for that, but everybody's also focused on innovative and disruptive uses of AI and machine learning and business, and using tools like DSX. I'm James Kobielus for the entire CUBE team, SiliconANGLE Media, wishing you all, wherever you are, whenever you watch this, have a good day and thank you for watching theCUBE. (upbeat music)

>> Welcome back, everyone. We we are here live in our Palo Alto studios with theCUBE. I'm John Furrier, the host of today's special digital event, hybrid, cloud and IT analytics for digital business. This is our one-on-one segment with Umair Khan, principal product marketing manager at CA Technologies. Where we get to do a drill-down. He's got a special product, UIM. We're going to talk about unified management. Umair, great to see you. Nice shirt, looking good, same as mine. I got the cuff links. >> I know, we think alike and have the same shirt. >> Got the cloud cufflinks. >> You got to get me one of those. (laughs) >> Good to see you. >> Good to see you. >> Hey, I want to just drill down. We had the two keynote presenters, Peter Burris, we'll keep on the research perspective and then kind of, where you guys tie in with your VP of Product Management, Sudip Datta, and interesting connection. Peter laid out the future of digital business, matches perfectly with the story of CA, so interesting. More importantly, it's got to be easy, though. How are you guys doing? I want to drill down to your product, UIM. Unified management, what is that? Unified infrastructure management. What's making it so easy? So, like you said, it's unified infrastructure management. It's a single product to monitor your cloud, your on-prem, your traditional and your entire stack, be it compute layer, storage layer, application services layer. It's a single product to monitor it all, so a) you get a single view to resolve problems, and at the back end, people tend to underestimate the time it takes to configure different tools, right? Imagine a different tool for cloud, different tool for public cloud too that you use, I'm not going to name vendors. Traditional environment you have, or maybe one silo group is using hybrid infrastructure, right? So configuring those, managing those, it's tough. And having a single console to deploy monitoring configuration in the same time monitor that infrastructure makes it easy. >> You and I were talking yesterday, before we came here and were doing a dry run, about cars. >> Yeah. >> And we were talking about the Tesla is so cool compared to an older car, but it's got everything in there. It's got analytics, it's got data, but it's a car. The whole purpose is to drive. It has nothing to do with IT, yet it's got a ton of IT analytics in it. How is business related to that? Because you could almost say that the single pane of glass is analytics. It's almost like Tesla for the business. The business is the car. How do you view that, because you have an interesting perspective. I want to get your take on that. >> Absolutely. So I've seen a lot of people giving examples as well, but I think cars of today is a great example of how monitoring should be, right? Cars, yes, it's still about the look and feel and the brand, but when you're sitting in the car now you expect a unified view. You want blind spot detector, you want collision detector, everything there. Even your fuel gauge, it shouldn't tell you how much is left, it should tell you how much mileage is left, right? Everything is becoming more intelligent. And you know Peter talked about the importance of expedience in the digital business, so IT team needs that visibility, that end-to-end unified view, just like in a modern car, to avoid any blind spots and resolve issues faster, and at the same time, it has to be more proactive and predictive in nature. So that collision detection, all the car companies these days have a commercial on safety features, collision detection, and same with IT. They need to have that ability to use intelligent monitoring tools to be able to resolve issues before the customer experience suffers. And one of our customers says, if someone opened a service desk ticket, that means everyone knows about the issue. I need to be resolving that issue before the service desk ticket is issued, right? >> You don't see Tesla opening up issues, "Hey, you're on the freeway, slow down." But this is important. I mean, Tesla was disruptive because they didn't just build a car and say "bolt on analytics." They took holistic, proactive view of the car experience with technology and analytics in mind to bring that tech to the table. That's similar to the message that we heard from Peter and Sudip about analytics. It's not just a thing you bolt on anymore. You got to think about the outcome of what you're trying to do. >> Exactly. >> That really is the key. And how does that unified infrastructure management do that? >> So it's all about unifying all different, today's digital businesses are adopting a lot of technologies. Every developer has their own stacks. As an IT ops person, you don't want to be someone who says, "you cannot adopt this cloud" or, "you do not adopt this technology." You should be flexible enough to whatever stack they have. You should be able to monitor that infrastructure for them, get yourself a unified view to resolve issues faster. But at the same time, provide your dev teams the flexibility of choosing the stack they like. >> A lot of IT ops guys are impacted and energized, quite frankly, by the future that's upon us with all these opportunities, but the realities of having uptime is a for opsis key and also enabling new (mutters) like IOT. The question for you is, who is most impacted in the enterprise organization or in IT operations, by your modern analytics products and visions? >> So I think there are two groups, right? One is the traditional VP of IT infrastructure, IT operations, so he has a lot of concerns about his infrastructure is becoming more and more dynamic, more complex, clouds are being adopted, businesses talking about expedience, right? So he needs a modern approach to get that end-to-end picture and make sure there are no blame games happening between different groups, and resolve issues really proactively. And at the same time, his tool and his analytics approach need to support modern infrastructures, right? If businesses wants to adopt cloud-based technologies, he needs to be, or she needs to be, able to provide that monitoring, needs to cover that approach as well. >> Is there one that pops out that you see growing faster in terms of the persona within IT? Because we hear Sudip talk about network, which we all want the network to go faster. I mean, you can't go to to Levi's Stadium or any kind of place and people complain about wifi. My kids are like, "Dad, the network's too slow." But in IT, network's critical. But only up to the app, so it's a bigger picture than that. Is there one persona that's rising up that you see that really hones in on this message of this holistic view of looking at modern analytics? >> I think rules are changing overall in IT, right? The system admin is becoming cloud admin, or the dev ops guy, so I think it's getting more and more collaborative. Roles will be redefined, reengineered a bit to meet the needs of modern technologies, modern companies, and so on. And we're also seeing the rise of a site reliability engineer, right? Because he's more concerned about reliability versus individual component. To him an app might be bad because of the network, because of the application itself, or the infrastructure that runs it. >> Okay, what does the UIM stand for and how does that impact in the overall stack? >> So UIM is our unified, as I mentioned before, unified infrastructure management product that's the most comprehensive solution on the market. If you look at technology support from your public-private cloud-based infrastructures like Amazon, Azure, or your hyperconverge. You can also call them private cloud, like mechanics, and being variable stack, or your traditional IT as well, from your (mutters) environments or from your Cisco environments, Cisco UCS, or anything. So it really gives that comprehensive solution set, and at the same time it provides an open architecture if you wanted to monitor some technology that we don't provide support for, it allows you to monitor that. And again, because of that, people are able to resolve issues faster, they're able to improve mean time to repair, and at the same time, I'll reemphasize the configuration part, right? Imagine you have multiple tools for each silos, then you need to configure that. In a dev ops world, you have to release applications faster, but you cannot deploy an application without configuring the monitoring for it, right? But if the infrastructure monitoring guys are taking three or four days to configure monitoring, then the entire concept of dev ops falls apart. So that's where UIM helps too. It really helps ops deploy configurations a lot faster through out-of-the-box templates in a unified approach across hybrid stacks. >> And developers want infrastructure as code, that's clear as day, and now they want great analytics. Okay, so I got to ask you the use case. I got to drill down on use cases, specifically, for the folks watching, whether they're maybe a CA customer in the past or one now, or not yet a customer. Where are you winning? Where is CA actually winning right now? How would talk about the specific use cases where it's a perfect fit and where you've got beachhead and where you can go. >> No, I think the places we typically win really well is as companies become more hybrid, if they're starting up in cloud-based infrastructures, they all of a sudden realize that the monitoring approach for traditional infrastructure is really not for cloud. The more technology that (mutters), you started with cloud and you want to adopt containers, and you start adding these monitoring tools. All of a sudden you realize this approach cannot work. I'm creating more silos, I don't the internal visibility and these infrastructures are more dynamic, going up and down all the time. I need a modern tool, modern approach. So typically, when you have hybrid infrastructures, we typically win there. And I think of a large insurance company as well, where initially we started working with them, and initially they had a lot of different tools that they worked on-- >> I think we actually have a slide for this. Can you pull that up on the thing here, the slide. Before you get to the insurance company, I want to get the graphic up. There it is. So we had the global 500 company, go ahead, continue. >> So basically worked with a global 500 insurance company. They had the same kind of issue, right? A lot of different technologies being adopted, cloud being adopted by a lot of the application team, and they wanted to really scale the business, digitize the business, but they didn't want the monitoring to get in the way. Right, so they implemented UIM, and they significantly improved mean time to repair and the time they spent in monitoring tools, right? That's the biggest thing. IT while monitoring may sound cool, but it's, the IT wants to work in modern innovative stuff. They want to stare at a screen, spending time and creating scripts and monitoring. So it really gave them the ability to get you the single tool to monitor increasingly complex and hybrid infrastructure. >> So you guys also ran a survey, also validated by Tech Validate, which is a third party firm which surveys top IT folks, on the three important ITOA, IT analytics solutions, correlation of data across apps, infrastructure, and network, 78%. Full stack visibility with in-context log monitoring and analysis, 65%. Ability to scale in high volume environments. So interesting how those are the top three. Kind of speaks to the conversation Peter Burris and I had. Lot of data (laughs), okay, multiple stack issues, so you're talking about a holistic view. What's the importance of these top three trends? >> I think a lot of companies miss out when they only monitor a silo, right? Even when I talk about our unified product, it's unified infrastructure. Even within infrastructure, there's so many components. You have to unify them, and that's the UIM work. But as Sudip mentioned, we have one of the biggest portfolio in the market. We're not only good at unified infrastructure, but also the network that connects that infrastructure to the application, and the application itself, right? The mobile application, the user experience of it, and the code-level visibility that you need. So as the survey mentioned, one of the biggest issues that companies have is they want to aggregate this data from app, network, and infrastructure. And at CA we are uniquely positioned because we have products in all three areas. I think typically no vendor covers all three areas and we're tying these together with more contextual analytics, which includes log which we released a while back, and I love to give the example of logs as well, right? People even monitor logs in a silo. But the value of using log together with performance is performance tells you a system is slow, okay, but logs tell you why. So it's using context together with your performance across app, infra, and network, really helps you solve these problems. >> Well, the Internet of Things and the car example we use also takes advantage of potential log data because data exhaust could be sitting around, but with realtime it could be very relevant. Okay, so let's move on to some of the kudos you're getting. Customers recognize CA as a leader in ITOA, IT analytics, operational analytics. 82% of organizations agree with the following, little thumb-up there. "CA has the breadth and depth of monitoring expertise to deliver the cross-correlation of IT operation analytics data from app to infrastructure to network. I buy the vision. I'm going to challenge you on this. What's the most important thing you got that this survey says? Because that's a huge number. Some might challenge that number. So I'm going to challenge that. Why is that number so important, and describe how it's reached. >> So I think it's some of our customers that have bought the belief of this, right, because we have in the portfolio an application performance like I mentioned, infrastructure performance with UIM, our net ops product portfolio, we are the only vendor in the market with that holistic set of products and experience in all three areas. So that really positions us uniquely. If you pick up any vendor out there, they either started on the app side, just started going on the infrastructure side, or they're a pure network player, starting to go infra and trying to get into app. But we are the vendor that has all three, and now we are bringing all of these three areas together through our operation intelligence platform that Sudip mentioned. >> Okay, so go to the next slide here. This one here is kind of chopped down, so move to the next one, you can come to that, look at that, later. This is the one I want to talk about, because retail is huge. We cover retail as a retail analyst firm, but retail does have a lot of edge components to it. It's heavily data-driven, evolving realtime from wearables to whatever. I mean, it's just going crazy. So it's turbulent from a change standpoint, but it's heavily IT operations driven. Why is this important? It says "Global 500 retail company was spending too much time in issue resolution. They lacked end-to-end visibility across cloud, traditional, and applications. After implementing CA UIM, they improved their mean time to repair by 35-50%. I'll translate that. Basically, it's broken, they got to repair it. Things aren't working. Retail can't be down. Why did you guys provide this kind of performance? Give a specific example of how this all plays out. >> So actually this tech firm named the customer, but in a typical scenario in retail, everyone is getting these mobile apps, right? So you need to monitor performance of the mobile app, the application running on it, we have tools for that, and the infrastructure behind it. So typically these mobile apps are on the cloud, right? IT ops have a traditional infrastructure, but this is Amazon-based or Azure-based. They come to us, we are adopting these mobile applications, but at the same time, we don't want to set up a separate IT ops team for these mobile applications as well. So retail organizations are proactively implementing an analytics-based approach for their unified end-to-end view. So even though the mobile app might be siloed, but it's multi-channel in retail, right? So they might order from their application but they might pick up in the store, and the store might be running on a physical Windows machine, versus some cloud-based boss. >> So you're saying they get to the cloud real fast, then realize, "oh, damn, I got to fix this. "I need analytics." So either way the customer use case is they can work with you on the front end to design that reimagined infrastructure, or bring you in at the right time. >> And our monitoring tool helps that, gives that end-to-end view, right, from the user's genie all the way from logging in, to all the way to the transaction being updated on the inventory software, being updated on the store, all the back-end SOP system. So we monitor all these technologies, give them end-to-end views. And we give them proactive (mutters). That's what analytics is, right? If their experience is slow, again, a user shouldn't be telling them on social media, "I can't order this," right? That IT team should be proactively testing, proactively-- >> Agility, speed and agility. >> Right, and without a unified view, it's not possible. >> All right, I'm at a bottom line here for you, and get your personal perspective. Take your CA hat off and your personal industry tech hat on. What should IT guys, what should they think of when working with CA? Why is CA good for them, and why should they look at you, and why should they continue to use you if they're an existing customer? >> So I think CA, like I said before, they're experienced in this space, right? And the investment we are making in analytics and cloud, we have a large customer base, so pretty much every customer, every enterprise, every industry you name, we have a customer there. And we have a huge portfolio already. So we have the basis from application to network to infrastructure, and are building this analytics layer that our customers have been asking us, that you're one of the rare vendors that have the most depth of information already available, right? So if aggregating that into an operational intelligence platform really helps puts us in a unique position by giving them the broadest set of data through a single platform. Right, and our experience for 30 years in monitoring, like Peter mentioned as well, and the investment we are bringing in cloud, UIM is a example. We were recently applauded by industry analysts as well that it's one of the best tools for single pane of glass for hybrid cloud environments. That shows how heavily we are investing in new, modern infrastructures like Amazon and Azure and even Utanics, right? >> Well, certainly you've got a lot of props. We just shared some of those stats and from independent firms like Tech Validate. But more, I think, impressive is that Peter Burroughs is on the cutting edge of digital business. You guys are aligned really with some of the cutting-edge research, where we see the market going, so congratulations. This digital event's been great. I want to ask you one final question. We see you guys out a lot at all the events we go to with TheCUBE, we go to all the cloud events. So you guys are going to be going to all the cloud events this year. So is that how customers can get ahold of you in the field? Which events will you be at? Where should they look for CA out in the field? >> So I think we're pretty much everywhere, on all the key events that you mentioned. Amazon Reinvent and C-World is coming as well. Customers should come to us and see how CA is helping people better manage the modern software factory, what we call it, every customer is in a digital economy, is trying to build software to deliver unique experiences, and at CA we talked about our IT operations, from dev to test to ops, we provide all the solutions. So C-World, Amazon Reinvent, you know, come find us there, or online at ca.com as well. >> All right, Umair, thanks for coming here and sharing your thoughts as part of our one-on-one drill downs from the digital event here at Silicon Angle Media's Cube Studios in Palo Alto, where we discuss the cloud and IT analytics for digital business, sponsored by CA Technologies. I'm John Furrier. I've been the host and moderator for today. I want to thank Peter Burris, head of research at wikibon.com for the opening keynote and Sudip Datta, who's the vice president of product management for CA for the second keynote. And all the conversation will be online, and thanks for watching, everyone. And check out CA. We'll see you at all the different cloud events with TheCUBE, thanks for watching.