>>the Cube presents H P E discovered 2022. Brought to you by H P E. >>Hi, buddy Dave Balon and Jon Ferrier Wrapping up the cubes. Coverage of day two, hp Discover 2022. We're live from Las Vegas. Vishal Lall is here. He's the senior vice president and general manager for HP ES Green Lake Cloud Services Solutions. Michelle, good to see you again. >>Likewise. David, good to see you. It was about a year ago that we met here. Or maybe nine months >>ago. That's right. Uh, September of last year. A new role >>for you. Is that right? I was starting that new role when I last met you. Yeah, but it's been nine months. Three quarters? What have you learned so far? I mean, it's been quite a right, right? I mean, when I was starting off, I had, you know, about three priorities we've executed on on all of them. So, I mean, if you remember back then they we talked about, you know, improving a cloud experience. We talked about data and analytics being a focus area and then building on the marketplace. I think you heard a lot of that over the last couple of days here. Right? So we've enhanced our cloud experience. We added a private cloud, which was the big announcement yesterday or day before yesterday that Antonio made so that's been I mean, we've been testing that with customers. Great feedback so far. Right? And we're super excited about that. And, uh, you know, uh, down there, the test drive section people are testing that. So we're getting really, really good feedback. Really good acceptance from customers on the data and Analytics side. We you know, we launched the S three connector. We also had the analytics platform. And then we launched data fabric as a service a couple of days ago, right, which is kind of like back into that hybrid world. And then on the marketplace side, we've added a tonne of partners going deep with them about 80 plus partners now different SVS. So again, I think, uh, great. I think we've accomplished a lot over the last three quarters or so lot more to be done. Though >>the marketplace is really interesting to us because it's a hallmark of cloud. You've got to have a market price. Talk about how that's evolving and what your vision is for market. Yes, >>you're exactly right. I mean, having a broad marketplace provides a full for the platform, right? It's a chicken and egg. You need both. You need a good platform on which a good marketplace can set, but the vice versa as well. And what we're doing two things there, Right? One Is we expanding coverage of the marketplace. So we're adding more SVS into the marketplace. But at the same time, we're adding more capabilities into the marketplace. So, for example, we just demoed earlier today quickly deploy capabilities, right? So we have an I S p in the marketplace, they're tested. They are, uh, the work with the solution. But now you can you can collect to deploy directly on our infrastructure over time, the lad, commerce capabilities, licencing capabilities, etcetera. But again, we are super excited about that capability because I think it's important from a customer perspective. >>I want to ask you about that, because that's again the marketplace will be the ultimate arbiter of value creation, ecosystem and marketplace. Go hand in hand. What's your vision for what a successful ecosystem looks like? What's your expectation now that Green Lake is up and running. I stay up and running, but like we've been following the announcement, it just gets better. It's up to the right. So we're anticipating an ecosystem surge. Yeah. What are you expecting? And what's your vision for? How the ecosystem is going to develop out? Yeah. I >>mean, I've been meeting with a lot of our partners over the last couple of days, and you're right, right? I mean, I think of them in three or four buckets right there. I s V s and the I S P is coming to two forms right there. Bigger solutions, right? I think of being Nutanix, right, Home wall, big, bigger solutions. And then they are smaller software packages. I think Mom would think about open source, right? So again, one of them is targeted to developers, the other to the I t. Tops. But that's kind of one bucket, right? I s P s, uh, the second is around the channel partners who take this to market and they're asking us, Hey, this is fantastic. Help us understand how we can help you take this to market. And I think the other bucket system indicators right. I met with a few today and they're all excited about. They're like, Hey, we have some tooling. We have the manage services capabilities. How can we take your cloud? Because they build great practise around extent around. Sorry. Aws around? Uh, sure. So they're like, how can we build a similar practise around Green Lake? So again, those are the big buckets. I would say. Yeah, >>that's a great answer. Great commentary. I want to just follow up on that real quick. You don't mind? So a couple things we're seeing observing I want to get your reaction to is with a i machine learning. And the promise of that vertical specialisation is creating unique opportunities on with these platforms. And the other one is the rise of the managed service provider because expertise are hard to come by. You want kubernetes? Good luck finding talent. So managed services seem to be exploding. How does that fit into the buckets? Or is it all three buckets or you guys enable that? How do you see that coming? And then the vertical piece? >>A really good question. What we're doing is through our software, we're trying to abstract a lot of the complexity of take communities, right? So we are actually off. We have actually automated a whole bunch of communities functionality in our software, and then we provide managed services around it with very little. I would say human labour associated with it is is software manage? But at the same time we are. What we are trying to do is make sure that we enable that same functionality to our partners. So a lot of it is software automation, but then they can wrap their services around it, and that way we can scale the business right. So again, our first principle is automated as much as we can to software right abstract complexity and then as needed, uh, at the Manus Services. >>So you get some functionality for HP to have it and then encourage the ecosystem to fill it in or replicated >>or replicated, right? I mean, I don't think it's either or it should be both right. We can provide many services or we should have our our partners provide manage services. That's how we scale the business. We are the end of the day. We are product and product company, right, and it can manifest itself and services. That discussion was consumed, but it's still I p based. So >>let's quantify, you know, some of that momentum. I think the last time you call your over $800 million now in a are are you gotta You're growing at triple digits. Uh, you got a big backlog. Forget the exact number. Uh, give us a I >>mean, the momentum is fantastic Day. Right. So we have about $7 billion in total contract value, Right? Significant. We have 1600 customers now. Unique customers are running Green Lake. We have, um, your triple dip growth year over year. So the last quarter, we had 100% growth year over year. So again, fantastic momentum. I mean, the other couple, like one other metric I would like to talk about is the, um the stickiness factor associated tension in our retention, right? As renewal's is running in, like, high nineties, right? So if you think about it, that's a reflection of the value proposition of, like, >>that's that's kind of on a unit basis, if you will. That's the number >>on the revenue basis on >>revenue basis. Okay? >>And the 1600 customers. He's talking about the size and actually big numbers. Must be large companies that are. They're >>both right. So I'll give you some examples, right? So I mean, there are large companies. They come from different industries. Different geography is we're seeing, like, the momentum across every single geo, every single industry. I mean, just to take some examples. BMW, for example. Uh, I mean, they're running the entire electrical electric car fleet data collection on data fabric on Green Lake, right? Texas Children's Health on the on the healthcare side. Right On the public sector side, I was with with Carl Hunt yesterday. He's the CEO of County of Essex, New Jersey. So they are running the entire operations on Green Lake. So just if you look at it, Barclays the financial sector, right? I mean, they're running 100,000 workloads of three legs. So if you just look at the scale large companies, small companies, public sector in India, we have Steel Authority of India, which is the largest steel producer there. So, you know, we're seeing it across multiple industries. Multiple geography is great. Great uptake. >>Yeah. We were talking yesterday on our wrap up kind of dissecting through the news. I want to ask you the question that we were riffing on and see if we can get some clarity on it. If I'm a customer, CI or C so or buyer HP have been working with you or your team for for years. What's the value proposition? Finish this sentence. I work with HPV because blank because green like, brings new value proposition. What is that? Fill in that blank for >>me. So I mean, as we, uh, talked with us speaking with customers, customers are looking at alternatives at all times, right? Sometimes there's other providers on premises, sometimes as public cloud. And, uh, as we look at it, uh, I mean, we have value propositions across both. Right. So from a public cloud perspective, some of the challenges that our customers cr around latency around, uh, post predictability, right? That variability cost is really kind of like a challenge. It's around compliance, right? Uh, things of that nature is not open systems, right? I mean, sometimes, you know, they feel locked into a cloud provider, especially when they're using proprietary services. So those are some of the things that we have solved for them as compared to kind of like, you know, the other on premises vendors. I would say the marketplace that we spoke about earlier is huge differentiator. We have this huge marketplace. Now that's developing. Uh, we have high levels of automation that we have built, right, which is, uh, you know, which tells you about the TCO that we can drive for the customers. What? The other thing that is really cool that be introduced in the public in the private cloud is fungible itty across infrastructure. Right? So basically on the same infrastructure you can run. Um, virtual machines, containers, bare metals, any application he wants, you can decommission and commission the infrastructure on the fly. So what it does, is it no matter where it is? Uh, on premises, right? Yeah, earlier. I mean, if you think about it, the infrastructure was dedicated for a certain application. Now we're basically we have basically made it compose herbal, right? And that way, what? Really? Uh, that doesnt increases utilisation so you can get increased utilisation. High automation. What drives lower tco. So you've got a >>horizontal basically platform now that handle a variety of work and >>and these were close. Can sit anywhere to your point, right? I mean, we could have a four node workload out in a manufacturing setting multiple racks in a data centre, and it's all run by the same cloud prints, same software train. So it's really extensive. >>And you can call on the resources that you need for that particular workload. >>Exactly what you need them exactly. Right. >>Excellent. Give you the last word kind of takeaways from Discover. And where when we talk, when we sit down and talk next year, it's about where do you want to be? >>I mean, you know, I think, as you probably saw from discovered, this is, like, very different. Antonio did a live demo of our product, right? Uh, visual school, right? I mean, we haven't done that in a while, so I mean, you started. It >>didn't die like Bill Gates and demos. No, >>no, no, no. I think, uh, so I think you'll see more of that from us. I mean, I'm focused on three things, right? I'm focused on the cloud experience we spoke about. So what we are doing now is making sure that we increase the time for that, uh, make it very, you know, um, attractive to different industries to certifications like HIPAA, etcetera. So that's kind of one focus. So I just drive harder at that adoption of that of the private out, right across different industries and different customer segments. The second is more on the data and analytics I spoke about. You will have more and more analytic capabilities that you'll see, um, building upon data fabric as a service. And this is a marketplace. So that's like it's very specific is the three focus areas were driving hard. All right, we'll be watching >>number two. Instrumentation is really keen >>in the marketplace to I mean, you mentioned Mongo. Some other data platforms that we're going to see here. That's going to be, I think. Critical for Monetisation on the on on Green Lake. Absolutely. Uh, Michelle, thanks so much for coming back in the Cube. >>Thank you. Thanks for coming. All >>right, keep it right. There will be John, and I'll be back up to wrap up the day with a couple of heavies from I d. C. You're watching the cube. Mhm. Mm mm. Mhm.

(upbeat music) >> Live from Boston, Massachusetts, it's theCube, covering AWS reInforce, 2019. Brought to you by Amazon Web Services and its ecosystem partners. >> Okay, welcome back, everyone. We are here live in Boston with theCube's coverage of AWS, Amazon Web Services, reInforce their inaugural conference, getting into the security event business because the customers are here and it's growing like crazy. I'm John Furrier, Dave Vellante. We are two guests of a hot startup called Valtix, Vishal Jain CEO, and Brian Lazear, Chief Product Officer. Valtix, you guys just launched out of stealth, congratulations. >> Thank you. >> You guys got some good pedigree I here, in the company. >> Yeah. >> Welcome to the cube. >> Thank you so much. >> Thank you John. >> Okay, so first of all, before we get to the conference, which I think is very relevant, you guys are are getting out there. What do you guys do? What is Valtix all about? What is the core problem you solve? Why start this company? What's the value proposition? >> Yeah, so Valtix is building the first cloud native network security platform. So before you start a company, you talk to lot of customers, and you talk to customers, and we saw the cloud is real. You can see here, cloud is real. And we saw that network security, have challenges in how to scale in the cloud, that mainly because of three things to look at that main thing is that the cloud is crawling. The data center used to be like three and four. Now the customer says is hard in the morning in the keynote, they have suddenly one than 10, hundred and 30 PCs. So the new logical perimeter you're seeing. Second thing we saw was that the apps are agile. And the third thing is security is always falling behind DevOps. So if you want to make security to be scaled with apps. >> So, you're saying level up the security apps piece to the DevOps pace. So DevOps is kind of pushing things really fast. You mentioned cloud come the new way. I mean, I remember the conversations around Software Defined data center, Brian, that was the holy grail for the on premises activity, was going to put some software on the storage and you got virtualization, we're done. In comes the cloud, changed the game on the Hadoop ecosystem, change the game on the on premises ecosystem. So what has it actually done differently? Where's it going? Where's the game happening now for security with kind of, because software is key to it? Where do you see it? >> Yeah, we definitely see that, I mean, DevOps is doing such a great job in the public cloud. I mean, DevOps is just, they're really doing a great job with the tooling, the teamwork, you know, automation aspects, and traditionally, security is always had a little bit of a lag to that. And in the cloud, that distance is much greater than ever has before so the security teams, particularly we do, which is network security, they are struggling. And so we focus on providing them a really good platform for that. And that platform includes the firewall. So we are building a cloud based firewall, that goes to the customer's premise, it's all structured around a controller, we have a cloud based controller that manages the firewall is in their central place to configure things. And also that controller is very aware of the applications. So we're keen on giving them that cloud-like experience with a vendor like us that comes over the top, and it can provide that capability as they grow. >> And the status of the product is what, shipping? It's a service? >> Yep. >> Explain the product. >> So last week, we did launch. We announced our funding, and we launched the the availability of the product, and it is built as a SAS. So the controller is a SAS model. The customer does own the firewall, we're a software company, so the software goes into their cloud premise, and it has all the services that they need for protecting their network edge. >> So what are the finer aspects, what are the real differences of network security in the cloud relative to traditional network security? >> Yeah, so what we saw was that the enterprises try to bring the our on prem vendor to the cloud, based as boxes, and as you said, a software defined environment, you need to bring up something more. So what we do is, we bring the whole lifecycle and three core elements of that is the visibility that we do the inventory of the apps, across your accounts, across your regions, across the cloud even. And second thing is how to plumb yours in the path and how to build an unified enforcement solution, which is what we call a firewall. So and built on three principles, cloud native, unification, and performance. >> And the the purpose of the company, when was the origination? When would you get the idea? Was it like, you decided to start a company? What was the motivation? >> Yeah, the big motivation was that, again, we talked to our customers, and we saw the cloud is real. But security is a big impediment to the public adoption and that's why we have this conference here, as well. And then we noticed the network security is not scaling the cloud. We like the problem, we found a team. Our team has the networking background, security background, and the cloud background. And we like the problem. We like a team and he said, okay, let's attack this problem and go after the market. >> So the blocker is scale, right? >> Scale and agility. Okay, so it's a company like Cisco is not solving this problem? Yeah, so what they did was they tried to bring the appliances to the cloud, in a virtual form factor. But in this new world of the cloud, getting sprawl. Agile's... You need kind of centralized control model to secure this new logical perimeter. You can't be appliance by appliance to secure the perimeter. You need to have a more data. >> You can't throw boxes at them. >> Yeah. >> Right, whether whether it's physical or virtual Yeah, exactly. I mean, what Vishal's pointing out too is that we want one aspect of what we do is that there's this super elegance to that day zero. You can just click a button and we deploy the gateway through the controller. That gateway is your firewall. Its right there. I mean, its almost instantaneous. So, even that level reflects the cloud native capabilities. That really gets people excited because the alternative is they grudgingly have to go and get the license and build it and build their functions to scale it and we handle all that. >> And I get why the hardware box model doesn't scale. Why doesn't the software defined virtual appliance scale? >> Yeah. Well, the background is that we see a couple competitors. We see the classic NG firewall players and we see the cloud native capabilities. On the cloud native side, they've made efforts to get into a virtual form factor, but its still basically a box. Its a VM form factor. The instrumentation for it, in a cloud environment, its sub-par and there's still a lot of manual effort to get these things up and running. The plumbing, its not... The user experience is very poor. >> So, its really bring your own box as opposed to here's a... >> Yeah and it has to be a solid form factor. >> So, network security, we heard yesterday at the partner event I attended, and I heard the folks from Amazon up there and they're getting serious about this cause they see the big enterprise opportunity. They want channel marketing, all kinds of new things. But, network security kind of has that same vibe that DevOps had. Which was, you have different consumption mechanisms, the customers are buying services, the pricing's different, the scale is different, you have policy, APIs too, its very cloud native. Are customers ready for that or is your controller, Valtix controller the gateway drug to the cloud so to speak cause, certainly if all those things are changing, that means the old just can be retrofitted for the new. You got to have something from scratch. And not a lot of people are lifting and shifting beyond infrastructure as a service. That's easy to replicate with the cloud, but when you get into some of the nuances with the apps that you're mentioning, these new dynamics have to be pure play features. >> Correct. >> Are you a solution to that? Or are you a gateway to that? Its the controller right? >> Yeah, we are a solution. For example, as I said, we do the full lifecycle. We have a controller will discover all your apps, so, an enterprise can have apps that cross your accounts and cross your cloud even and we discover all the apps. Second thing is once we discover the apps, put yourself in the path of security and we do that automatically. Third thing is enforcement. For that, we have two core engines, as I said. Provide re-development, which we call a cloud firewall from Valtix and secondly the cloud controller, which sees everything. So, its a global view of the entire enterprise infrastructure. >> In your marketing documentation, you talk about the trade-offs that people have to make between security and agility. That's always been a trade-off. Do you solve that problems and if so, how? >> So, again when we saw the customer we talked to and they bring their workshop appliances, or appliances to the cloud, then there are two choices they have. One is that are apps agile, but then you cannot secure using the client's model, so you kind of insecure, or naked we call it. The other option is that you must have heard, security slows me down. So you kind of become a secure and rigid. So every time you have a new app, a new EPC, you open a ticket and you install the new firewall. So, what we are giving a third option because both options I gave are bad choices, so we give a third option, which is agile and secure. That's what a centralized controller and a Valtix file will give you that option. >> Vishal and Brian, I want to get your thoughts on why you guys, so be the devil's advocate. You guys are just a startup, although your startups actually doing well in the cloud environment, I'm being a skeptic, I'm trying to shoot my own narrative here. But the reality is you guys are young company, you want to get the attention of the enterprise or customers, what's the pitch? Why you guys? What's your backgrounds, pedigrees, the backgrounds you guys bring to the table with software, talk about why you guys? What's the differentiator? >> In terms of the team, I would say, there are three core pillars, networking, security, and cloud, right? So, this team has built up billions of parkline and deployed in thousands of enterprises and there were two core expertise initially the team was, building fast performance by plans. Second thing is decoupling the control development. I mentioned some of that. So, those are some of the aspects and then you build your team around network expertise, security expertise, and a cloud expertise. >> Have they done it before? >> Yes, multiple times. >> How big's the team? >> The team is right now twenty people. >> Twenty people? And you just raised 14 million or over 14 million? >> Yeah, over 14 million we raised and we announced it last week. >> Yeah, great. Congratulations. >> What are some of the backgrounds of the team members? >> I mean they're Cisco, Juniper, Palo Alto, Google Cloud... >> Fortinet. >> Yeah, Fortinet. Its kind of that bench strength of security in a networking cloud and then I think the other component to that is that we all come from a common denominator of building, hands on building, shipping and marketing products that are transformative. That's also exciting. So, we see this and say, this is clearly transformative or this big market opportunity to help customers and we're like, ecstatic. >> Yeah, the cloud really... It sounds like to me you guys have a real holistic systems view of the world. Because the cloud is essentially an operating system or large, distributed computer and decentralized with crypto and blockchain. Its the system thinking that's interesting. Right, you guys have that... To know the network, you got to know the system. And you get into the apps, you got to understand that middle layer that's developing with Kubernetes and containers. With cloud native, that's developing really fast. So, to see that end to end is more of a systems kind of mindset. A lot of companies are lacking that because they've outsourced everything to global SI's and now they got to rebuild. Capital One's Sie So said, we're investing everything building. We're building more. So, they're builders, they're systems guys. What's your reaction to that? >> Yeah, so basically we also know this, that all of the enterprise we talk to were told that a lot of wine products, what we're building the platform. So, we'll be starting off with the food services, but its a platform, so a wholistic platform could do the full network security in the public cloud. That's what we are working towards. >> What's the differentiator? Why you guys? What's the main value proposition that you guys bring to the table? What's in it for the customer? >> Correct, the main value proposition is the team can build it and second thing is taking a cloud related approach to this problem. We are building for the cloud and we are building using the cloud are the principles. >> So you just went through your raise, so all these answers to the questions are fresh in your mind. But, Brian you talked about a large market. Help us understand that because the market is enormous, its like a hundred billion dollars or whatever it is, but its so fragmented, there's so many different segments. How do you guys look at the TAM and then the served market for you guys, that you go after? >> Our goal is to protect their data center, this new data center, basically everything that's going in or out of the data center on the network side, that's our focus. We didn't mention some of these services, but in the product we're shipping right now, it does decryption of TLS traffic, it does firewall, it does intrusion prevention, it does WAF, so it has this, and more, so there's this set of things that when we talk to the customers, they'll say, my blueprint for the cloud is like the prep, I have to stack all these things together, risk in security says you have to emulate that environment, its worked well here, make it happen out there. And so that's where you see people getting a little bit amped up. Its hard to do that. We have a platform that can consolidates that really well and knows the system level things that John was mentioning, but it is covering a lot of space, but we are very optimistic. We're making good grounds with that. >> So its a platform approach versus five, six products? >> Exactly, so the consolidation story connects really well. >> What's the most important story that needs to be told in the security industry today in your opinion? What do you think that customers should know about, that the media and or the industry should be discussing? >> The main thing is that we talk about DevOps. DevOps is very agile. So one thing is the current security is slowing me down. Security has to be agile, especially network security, we have heard in the past, slows you down. So that's, in the cloud world, the main reason people are going to cloud is because of the agility and network security should not stop that. >> So, security's slowing down... >> Yeah and we don't want that. >> Its a deep bottleneck for mass adoption, we're seeing that more and more and that problem statement, there's a lot of Ops angles to this. Its understanding, like multi-AZ deploys and the Transit Gateway, the new Transit Gateway from Amazon and how does this all work together and we're on top of that in the network security perspective. >> What do you think about the show here? Amazon's inaugural re:Inforce. Its not a summit, summits are regional re-invents. This is its own name, just like re-invent's different for the customer. Re-invent isn't re:Inforce. Pretty important, pretty strategic for Amazon Web Services. What do you guys think? >> I think its great. I mean, we have been using all alternatives like Transit, their mutilated support, the ST bucket. We use all the infrastructure they provide. Its always good to know what they are doing because in the reinvent around Transit Gateway and we incorporate that into our product. So, we want to be ahead of what they announcing, incorporate that and giving our customer what they need as a whole solution. >> So, Brian you're running the product, Chief Product Officer. What's on the roadmap? (laughter) >> Lots of good stuff. >> C'mon! >> We're very busy. >> Feed your request coming in. Give you their services, you could just bang them out, no big deal. (talking over each other) >> Just so easy, 2,000 a year. Amazon does it, you could do a couple hundred a year, no problem. >> There's probably a couple things. One is that we will continue to expand to other clouds because our customers want that. But its also just about more capabilities. So, they're seeing what we could do today. There's a lot that it could do and they're with us, they're on the journey with us and saying we want more help and this show is an example of that. The cloud is becoming more than a thing and security's getting emphasized, literally, its emphasized here. So, we're happy to help our customers along. >> Well you guys are launched, what's the priority? You're obviously hiring, what kind of culture do you have? What are some of your needs here? Put a plug for the company real quick. >> In terms of hiring, initially I'm also hiring more engineering, building the product. They're the core of the engine. But, now we are expanding the go to market team, we have sales, marketing and we are going to expand on both the sides, like sell and build more and sell more. >> Yeah, get the revenue in. Congratulations, hot startup. Good job, well done. Thanks for coming on theCube. >> Thanks John. >> Valtix launching with new product out of stealth with funding, getting off the runway, here at Amazon Websters Re:Invent theCube coverage. I'm John Furrier, Dave Vellante. Stay with us for more after this short break. (upbeat music)

>> From Tavern On The Green, in Central Park, New York, it's theCUBE. Covering Veritas Vision Solution Day. Brought to you by Veritas. >> Hello everybody welcome back to the Tavern On The Green. We're here in the heart of Central Park in New York City you're watching theCUBE the leader in live tech coverage. We go out to the events, we extract the signal from the noise, big events, small events. We're here at the Veritas Solution Days, #VtasVision. Veritas Vision used to be a very large, big tent conference. They've changed the format now and they go out, they're going out to 20 cities this year belly to belly with the customers and we've got one here. Vishal Kadakia who is the data protection manager at NBC Universal. Vishal thanks so much for coming on theCUBE. >> No problem thank you for having me. >> So as I say we love to get the customer perspectives, but let me start with this event. Why, you're a busy person, you're managing a lot of data, why do you take time out to come to event like this? What do you learn? >> You always get to learn new stuff, new products that you don't necessarily get to learn, 'cause you're always just zoned into your day-to-day work that you're doing so you don't always get to see what the new features may be or you miss it. These type of events are generally good to come see that. >> So what's the day in the life like these days for data protection manager and really I'm interested in how it's changed over the last five or six years, as you see things like, the buzzwords, digital transformation, big data, cloud, multi cloud, all the vendor buzzwords, but you actually have to live that. So how has that changed the role of data protection and data protection managers specifically? >> It's definitely a lot more complicated. Before you were just backing up om prem, you had tape, pretty much made it simple. Now you have all these different workloads, you're sending out to clouds, multi tenant as they keep calling it, the hybrid, which is another buzzword. Trying to manage the different workloads is a lot more complex than it was five years ago. You have various cloud vendors, you have various storage vendors, so managing all of that, obviously the data growth from the smaller backups to now, big data which could be terabytes, petabytes, to try to back that up has been a bit of a challenge. >> But that's a challenge for someone like you who's, you know, RPO and RTO is not getting relaxed. >> Right. >> Right. And you know people always talk about getting my weekends back so, but now you have to keep up with all of these other technologies so what is it? Is it a lot of reading, is it just going to sessions like this, having vendors come in, how do you keep up with it all? >> I think it's a big mix of both. It's going out to these events, but also having vendors come to you. Doing your own research, so it's a combination of just constantly keeping up. So, I would say it's a combination of all. >> One of the things that I would be concerned about in your roll is to have just more stove pipes. Are you able to just conceptually, not technical, deep technical anyway, I love tech, but are you to create, let's call it a abstraction layer for your data protection. Is that kind of your vision or where you're headed, so that you don't have to have 10 different formats and methodologies and processes around data protection? >> Yeah, I think that's the goal that I think every company's trying to go to, is consolidate, simplify. Whether that's vendor, whether that's hardware. I think that's really the goal of any organization now. And that's kind of where we're headed also. >> So if it's a baseball game analogy, and you're nine inning game, where are you in terms of that journey? Is it early days, kind of first inning, are you kind of warmin' up in the bullpen, are you sort of well into the game? >> I think we're well into the game. We're probably into the middle innings I would say. >> Okay. So you can see sort of that vision becoming a reality. And what are the priorities then in terms of getting to that point? Is it skill sets, is it technology, is it people? >> I would say it's technology. I would say that consolidation is probably the big word. We're all trying to consolidate while trying back up the large data sets. And I think that's where we are right now. That's where we're starting to get to, and see the plan forming, seeing where our methodologies, our strategies on how we're going to go forward. >> As you move toward the cloud, Vishal, whether or not it's even pushing data to the cloud, a lot of times you just can't. But it seems like that cloud operating model is something that's alluring to folks. Simplifying, agility, self service, are those initiatives that you guys have enacted? >> In terms of that, yeah we're I think in that phase, I think we're in our beginning to form that plan, because once you get to a cloud, you have to really have a good plan. Otherwise, your data is going to be all over the place. You're not going to know where it is, and managing that's just going to become that much harder. So I think in terms of that, we're trying to really come out with a good plan of how you migrate to the cloud. 'Cause once you get to the cloud, there's a whole different set of complexities that you have in managing it. >> Like what? Maybe tick off a few, so we can paint a picture. >> So once you get to the cloud, migrating, so you've formulated your plan how to get to, what cloud to use, what vendor you're using. How do you migrate from your on prem to the cloud is I think one of the big complexities, which I think kind of stumps a lot of people. You know you want to go to the cloud, just don't know how to get there. >> Is that just because the volume of data and you got to move data and it just takes so long? I mean to back up your iPhone takes forever and it fails left and right. >> Yeah, absolutely. >> So okay. It's the amount of data and the time it takes? >> Right, and you also have legacy applications, which may not be cloud ready and how do you deal with that? So you have that hybrid model you still want to keep some stuff om prem but you want to go the cloud. What goes to the cloud, which cloud do you go to? All of that is where I think we're really at and I don't think it's any different than any other organization, so that's kind of where. >> And how about this notion of multi cloud? I mean is that something that is real in your business? >> Yeah, I think it definitely is. I think our end users are trying to take advantage of where to go best? Some places Azure might work best. Some places AWS might work. There's also Google now that's coming up, so I think you have to kind of consider where the workload would be best to go to. >> Is Shadow sort of IT and cloud creep problematic for you and in other words, you know, lines of businesses saying, it's easy, I can swipe a credit card and I'm up and running in minutes. And then, oh I got to protect this data, it's got to be compliant. Has that been a challenge for you, do you feel like you have that under control? >> No, that has definitely been a challenge area. Different groups that have kind of tried to do their own thing and then found out, oh wait, this is way harder than we thought. Let us go back to our central team. But by then it's kind of all over the place, right so that's definitely been interesting. >> Yeah it's hard, because thinking about that you probably might have done it differently. You might have put in processes and procedures in place and now you've got to clean up the mess so to speak. But okay, so I want to get into Veritas, and you're a Veritas customer? >> I am. >> So how does Veritas help you with all these solutions? I mean a lot of the things I've just asked you, I think are part of either their road map or they're making claims that they can currently help solve some of these problems. Can they, what do you do with Veritas, and how legitimate is their ability in terms of being able to solve some of these problems? >> So we've been able to use Veritas to kind of, as a central location, management of everything. One of their tools as such is CloudPoint. So our biggest thing is if you don't have a central management tool like CloudPoint, which can manage your various cloud backups, then you're left with managing each cloud on its own. So as an operations standpoint, that's like a nightmare. So having a tool such as CloudPoint, right, and then that getting integrated back into NetBackup, which now gives us a central location for all my backups, for reporting, for audit purposes, any of that has been great. And I've been using Veritas since 3.1 so I've been a Veritas customer for a long time. I've seen the evolution of when it was 3.1, a lot of it was manually operated, a lot of scripts, where now a lot of it is automated. So that's helped a lot. We're automating VM policies, we're automating SQL backup policies, all of that has been great. >> Where are you today in terms of these. >> I'm sorry? >> Where are you at today in terms of the release? >> We're, I know they just released eight one two, we're on eight one one. >> Okay so close to current. Yeah I've seen some videos on eight one two. It looks like they've really put a lot of time and effort in to refreshing it. It looks like a microservices architecture, they're talking about containers and certainly you know, saying all the right things. From your perspective have you dug into it yet or is it still early? >> It's still early. I did deploy it on a test environment. Haven't fully played around with it but some of the cool concepts obviously are, you're going away from that Java console eventually, getting to that web based, able to access it from anywhere, the manageability, like a central tool to manage all of that. That I think they're finally gearing towards that and. >> And you guys are a VMware shop? >> We are a VMware shop. >> So when we were at VM World last August, this past year, and even the year before. Data protection was one of the hottest topics, you know, on the show floor. Were you there, I don't know if you were there. >> I was not there. >> I mean it was really a lot of buzz there, sort of a lot of new entrance in that space, and would I imagine a lot of people coming after you for your business, because that's a very large install base. So when you look at the vendor landscape, how do you look at it? Where do you position Veritas, relative to some of the other upstarts? Your thoughts on the competitive landscape, why Veritas? >> Well, my point of view has always been, if it's not broke you don't fix it. There may be other that may be doing something better, but at the end of the day if it's not drastically different, it's a lot of work to move away from one product to another. They'll always come to you and say, hey, we do this better, we do this better. But then when you compare it, to me, Veritas is that all encompassing. It doesn't only do virtual, it does physical well also. It doesn't only do big data, it does all the traditional databases as well. They're always constantly evolving and adding new workloads that it can also be compatible with. >> Yeah so, I would imagine it would be a little difficult to go to your CFO and try to justify a huge migration project given the other priorities that you have. Give me some insight there. I mean what kinds of things do you want to focus on, I mean obviously nobody wants to migrate anything, it's like moving a house. >> Yeah. >> You really don't want to do it, I mean sometimes you get a bigger house or a nicer house or a smaller house, but it's, moving is always a pain. So you'd rather put your effort in your shop somewhere else. Where are you putting that effort? What are some of the priorities that you have either personally or professionally? >> I would say in this sense I think it's I don't want to work the weekends, right. So how do we automate? How do we make operations easier for everybody, the engineering, the solution, the operations. I want to make it simple. I think Veritas allows us to do that 'cause they're an open source, they work with many vendors which makes it nice. So you can, such as VMware, it works with vRealize. All those plugins with VMware and you can eventually just automate and make it simple. >> And kind of get rid of a lot of the scripts which tend to be fragile, they take a lot of maintenance, they tend to be error prone, so if you can through a set of APIs automate programmatically move towards sort of infrastructurous code or a DevOps environment. I'm sure you guys do that internally. And what a difference it makes, from the sort of classic waterfall in terms of speed, agility, quality. I presume that you're seeing that in your shop? >> Yeah, we definitely are and something like a flex appliance would allow us to move towards that. It simplifies, gets us to where we are, but also helps us with our goals of simplifying, reducing our footprint, but still being able to be agile enough to go to cloud, to keep a hybrid model. So something like that is I think where we're seeing. >> Well Vishal, we love the customer perspective, Thank you for coming on. We like to hear the truth, Vertias, truth in Latin, of course. And really appreciate your time. >> Thank you very much. >> You're welcome. All right keep it right there everybody. We're here at Vtas Vision, #VtasVision, Veritas Vision Days in New York City, Central Park, Tavern on the Green, beautiful location. My name's Dave Vallante. We'll be right back, right after this short break. (upbeat music)

>> Announcer: From the Corinium Chief Analytics Officer Conference. Spring, San Francisco, it's theCUBE! >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're in downtown San Francisco at the Corinium Chief Analytics Officer Spring event 2018. About 100 people, really intimate, a lot of practitioners sharing best practices about how they got started, how are they really leveraging data and becoming digitally transformed, analytically driven, data driven. We're excited to have Vishal Morde. He's the VP of Data Science at Barclays, welcome. >> Glad to be here, yeah. >> Absolutely. So we were just talking about Philly, you're back in Delaware, and you actually had a session yesterday talking about Barclays journey. So I was wondering if you could share some of the highlights of that story with us. >> Absolutely, so I had a talk, I opened the conference with data science journey at Barclays. And, we have been on this journey for five years now where we transform our data and analytics practices and really harness the power of Big Data, Machine Learning, and advanced analytics. And the whole idea was to use this power of, newly found power that we have, to make the customer journey better. Better through predictive models, better through deeper and richer consumer insights and better through more personalized customer experience. So that is the sole bet. >> Now it's interesting because we think of financial services as being a data driven, organization already. You guys are way ahead Obviously Wall Street's trading on microseconds. What was different about this digital transformation than what you've been doing for the past? >> I think the key was, we do have all the data in the world. If you think about it, banks know everything about you, right? We have our demographic data, behaviors data. From very granular credit card transactions data, we have your attitudal data, but what we quickly found out that we did not have a strategy to use that data well. To improve our our productivity, profitability of a business and make the customer experience better. So what we did was step one was developing a comprehensive data strategy and that was all about organizing, democratizing, and monetizing our data assets. And step towards, then we went about the monetization part in a very disciplined way. We built a data science lab where we can quickly do a lot of rapid prototyping, look at any idea in machine learning data science, incubate it, validate it, and finally, it was ready for production. >> So I'm curious on that first stage, so you've got all this data, you've been collecting it forever, suddenly now you're going to take an organized approach to it. What'd you find in that first step when you actually tried to put a little synthesis and process around what you already had? >> Well the biggest challenge was, the data came from different sources. So we do have a lot of internal data assets, but we are in the business where we do have to get a lot of external data. Think about credit bureau's, right? Also we have a co-brand business, where we work with partners like Uber, imagine the kind of data we get from them, we have data from American Airlines. So our idea was to create a data governance structure of, we formed a Chief Data Office, the officer forum, we got all the people across our organization to understand the value of data. We are a data driven company as you said but, it took us a while to take that approach and importance of data, and then, data analytics need to be embedded in the organizational DNA, and that's what we're going to focus on first. Data awareness of importance of data, importance of governance as well, and then we could think about democratizing and monetizing, organization's the key for us. >> Right, right, well so how did you organize, how has the Chief Data Officer, what did he or she, who did he or she report to, how did you organize? >> Right, so it was directly reporting to our CEO. >> Jeff: Into the CEO, not into the CIO? >> Not into the CIO. We had a technology office, we do kind of, have a line-of-sight or adopted line with technology, and we made sure that that office has a lot of high-level organization buy-in, they are given budgets to make sure the data governance was in place, key was to get data ownership going. We were using a lot of data, but there was no data ownership. And that was the key, once we know that, who actually owned this data, then you can establish a governance framework, then you can establish how you use this data, and then, how to be monetized. >> So who owned it before you went through this exercise, just kind of, it was just kind of there? >> Yeah, there wasn't a clear ownership, and that's the key for us. Once you establish ownership, then it becomes an asset, we were not treating data as an asset, so there was a change in, kind of mindset, that we had to go through, that data is an asset, and it was used as a means to an end, rather than an asset. >> Right, well what about the conflict with the governance people, I'm sure there was a lot of wait, wait, wait, we just can't open this up to anybody, I'm sure it's a pretty interesting discussion because you have to open it up to more people, but you still have to obviously follow the regs. >> Right, and that's where there are a lot of interesting advancement in data science, where, in the area of data governance, there are new tools out there which lets you track who's actually accessing your data. Once we had that infrastructure, then you can start figuring out okay, how do we allow access, how do we actually proliferate that data across different levels of the organization? Because data needs to be in the hands of decision makers, no matter who they are, could be our CEO, to somebody who's taking our phone calls. So that democratization piece became so important, then we can think about how do you-- you can't directly jump into monetization phase before you get your, all the ducks in order. >> So what was the hardest part, the biggest challenge, of that first phase in organizing the data? >> Creating that 360 degree view on our customers, we had a lot of interesting internal data assets, but we were missing big pieces of the puzzles, where we're looking at, you're trying to create a 360 degree view on a customer, it does take a while to get that right, and that's where the data, setting up the data governance piece, setting up the CDO office, those are the more painful, more difficult challenges, but they lay the foundation for all the the work that we wanted to do, and it allowed to us to kind of think through more methodically about our problems and establish a foundation that we can now, we can take any idea and use it, and monetize it for you. >> So it's interesting you, you said you've been on this journey for five years, so, from zero to a hundred, where are you on your journey do you think? >> Right, I think we're just barely scratching the surface, (both laughing) - I knew you were going to say that >> Because I do feel that, the data science field itself is evolving, I look at data science as like ever-evolving, ever-mutating kind of beast, right? And we just started our journey, I think we are off to a good start, we have really good use-cases, we have starting using the data well, we have established importance of data, and now we are operationalized on the machine learning data science projects as well. So that's been great, but I do feel there's a lot of untapped potential in this, and I think it'll only get better. >> What about on the democratization, we just, in the keynote today there was a very large retailer, I think he said he had 50 PhDs on staff and 150 data centers this is a multi-billion dollar retailer. How do you guys deal with resource constraints of your own data science team versus PhDs, and trying to democratize the decision making out to a much broader set of people? >> So I think the way we've thought about this is think big, but start small. And what we did was, created a data science lab, so what it allowed is to kind of, and it was the cross-functional team of data scientists, data engineers, software developers kind of working together, and that is a primary group. And they were equally supported by your info-sec guys, or data governance folks, so, they're a good support group as well. And with that cross-functional team, now we are able to move from generating an idea, to incubating it, making sure it has a true commercial value and once we establish that, then we'll even move forward operationalization, so it was more surgical approach rather than spending millions and millions of dollars on something that we're not really sure about. So that did help us to manage a resource constraint now, only the successful concepts were actually taken through operationalization, and we before, we truly knew the bottom line impact, we could know that, here's what it means for us, and for consumers, so that's the approach that we took. >> So, we're going to leave it there, but I want to give you the last word, what advice would give for a peer, not in the financial services industry, they're not watching this. (both laugh) But you know, in terms of doing this journey, 'cause it's obviously, it's a big investment, you've been at it for five years, you're saying you barely are getting started, you're in financial services, which is at it's base, basically an information technology industry. What advice do you give your peers, how do they get started, what do they do in the dark days, what's the biggest challenge? >> Yeah, I feel like my strong belief is, data science is a team sport, right? A lot of people come and ask me: how do we find these unicorn data scientist, and my answer always being that, they don't exist, they're figments of imagination. So it's much better to take cross-functional team, with a complimentary kind of skill set, and get them work together, how do you fit different pieces of the puzzle together, will determine the success of the program. Rather than trying to go really big into something, so that's, the team sport is the key concept here, and if I can get the word out across, that'll be really valuable. >> Alright, well thanks for sharin' that, very useful piece of insight! >> Vishal: Absolutely! >> Alright thanks Vishal, I'm Jeff Frick, you are watching theCUBE, from the Corinium Chief Analytic Officer summit, San Francisco, 2018, at the Parc 55, thanks for watching! (bubbly music plays)

(upbeat music) >> Welcome back to theCUBE's coverage of HPE GreenLake announcements. We're seeing the transition of Hewlett Packard Enterprise as a company, yes they're going all in for as a service, but we're also seeing a transition from a hardware company to what I look at increasingly as a data management company. We're going to talk today to Vishal Lall who's GreenLake cloud services solutions at HPE and Matt Maccaux who's a global field CTO, Ezmeral Software at HPE. Gents welcome back to theCube. Good to see you again. >> Thank you for having us here. >> Thanks Dave. >> So Vishal let's start with you. What are the big mega trends that you're seeing in data? When you talk to customers, when you talk to partners, what are they telling you? What's your optic say? >> Yeah, I mean, I would say the first thing is data is getting even more important. It's not that data hasn't been important for enterprises, but as you look at the last, I would say 24 to 36 months has become really important, right? And it's become important because customers look at data and they're trying to stitch data together across different sources, whether it's marketing data, it's supply chain data, it's financial data. And they're looking at that as a source of competitive advantage. So, customers were able to make sense out of the data, enterprises that are able to make sense out of that data, really do have a competitive advantage, right? And they actually get better business outcomes. So that's really important, right? If you start looking at, where we are from an analytics perspective, I would argue we are in maybe the third generation of data analytics. Kind of the first one was in the 80's and 90's with data warehousing kind of EDW. A lot of companies still have that, but think of Teradata, right? The second generation more in the 2000's was around data lakes, right? And that was all about Hadoop and others, and really the difference between the first and the second generation was the first generation was more around structured data, right? Second became more about unstructured data, but you really couldn't run transactions on that data. And I would say, now we are entering this third generation, which is about data lake houses, right? Customers what they want really is, or enterprises, what they want really is they want structured data. They want unstructured data altogether. They want to run transactions on them, right? They want to use the data to mine it for machine learning purposes, right? Use it for SQL as well as non-SQL, right? And that's kind of where we are today. So, that's really what we are hearing from our customers in terms of at least the top trends. And that's how we are thinking about our strategy in context of those trends. >> So lake house use that term. It's an increasing popular term. It connotes, "Okay, I've got the best of data warehouse "and I've got the best of data lake. "I'm going to try to simplify the data warehouse. "And I'm going to try to clean up the data swamp "if you will." Matt, so, talk a little bit more about what you guys are doing specifically and what that means for your customers. >> Well, what we think is important is that there has to be a hybrid solution, that organizations are going to build their analytics. They're going to deploy algorithms, where the data either is being produced or where it's going to be stored. And that could be anywhere. That could be in the trunk of a vehicle. It could be in a public cloud or in many cases, it's on-premises in the data center. And where organizations struggle is they feel like they have to make a choice and a trade-off going from one to the other. And so what HPE is offering is a way to unify the experiences of these different applications, workloads, and algorithms, while connecting them together through a fabric so that the experience is tied together with consistent, security policies, not having to refactor your applications and deploying tools like Delta lake to ensure that the organization that needs to build a data product in one cloud or deploy another data product in the trunk of an automobile can do so. >> So, Vishal I wonder if we could talk about some of the patterns that you're seeing with customers as you go to deploy solutions. Are there other industry patterns? Are there any sort of things you can share that you're discerning? >> Yeah, no, absolutely. As we kind of hear back from our customers across industries, I think the problem sets are very similar, right? Whether you look at healthcare customers. You look at telco customers, you look at consumer goods, financial services, they're all quite similar. I mean, what are they looking for? They're looking for making sense, making business value from the data, breaking down the silos that I think Matt spoke about just now, right? How do I stitch intelligence across my data silos to get more business intelligence out of it. They're looking for openness. I think the problem that's happened is over time, people have realized that they are locked in with certain vendors or certain technologies. So, they're looking for openness and choice. So that's an important one that we've at least heard back from our customers. The other one is just being able to run machine learning on algorithms on the data. I think that's another important one for them as well. And I think the last one I would say is, TCO is important as customers over the last few years have realized going to public cloud is starting to become quite expensive, to run really large workloads on public cloud, especially as they want to egress data. So, cost performance, trade offs are starting to become really important and starting to enter into the conversation now. So, I would say those are some of the key things and themes that we are hearing from customers cutting across industries. >> And you talked to Matt about basically being able to essentially leave the data where it belongs, bring the compute to data. We talk about that all the time. And so that has to include on-prem, it's got to include the cloud. And I'm kind of curious on the edge, where you see that 'cause that's... Is that an eventual piece? Is that something that's actually moving in parallel? There's lot of fuzziness as an observer in the edge. >> I think the edge is driving the most interesting use cases. The challenge up until recently has been, well, I think it's always been connectivity, right? Whether we have poor connection, little connection or no connection, being able to asynchronously deploy machine learning jobs into some sort of remote location. Whether it's a very tiny edge or it's a very large edge, like a factory floor, the challenge as Vishal mentioned is that if we're going to deploy machine learning, we need some sort of consistency of runtime to be able to execute those machine learning models. Yes, we need consistent access to data, but consistent access in terms of runtime is so important. And I think Hadoop got us started down this path, the ability to very efficiently and cost-effectively run large data jobs against large data sets. And it attempted to work into the source ecosystem, but because of the monolithic deployment, the tightly coupling of the compute and the data, it never achieved that cloud native vision. And so what as role in HPE through GreenLake services is delivering with open source-based Kubernetes, open source Apache Spark, open source Delta lake libraries, those same cloud native services that you can develop on your workstation, deploy in your data center in the same way you deploy through automation out at the edge. And I think that is what's so critical about what we're going to see over the next couple of years. The edge is driving these use cases, but it's consistency to build and deploy those machine learning models and connect it consistently with data that's what's going to drive organizations to success. >> So you're saying you're able to decouple, to compute from the storage. >> Absolutely. You wouldn't have a cloud if you didn't decouple compute from storage. And I think this is sort of the demise of Hadoop was forcing that coupling. We have high-speed networks now. Whether I'm in a cloud or in my data center, even at the edge, I have high-performance networks, I can now do distributed computing and separate compute from storage. And so if I want to, I can have high-performance compute for my really data intensive applications and I can have cost-effective storage where I need to. And by separating that off, I can now innovate at the pace of those individual tools in that opensource ecosystem. >> So, can I stay on this for a second 'cause you certainly saw Snowflake popularize that, they were kind of early on. I don't know if they're the first, but they certainly one of the most successful. And you saw Amazon Redshift copied it. And Redshift was kind of a bolt on. What essentially they did is they teared off. You could never turn off the compute. You still had to pay for a little bit compute, that's kind of interesting. Snowflakes at the t-shirt sizes, so there's trade offs there. There's a lot of ways to skin the cat. How did you guys skin the cat? >> What we believe we're doing is we're taking the best of those worlds. Through GreenLake cloud services, the ability to pay for and provision on demand the computational services you need. So, if someone needs to spin up a Delta lake job to execute a machine learning model, you spin up that. We're of course spinning that up behind the scenes. The job executes, it spins down, and you only pay for what you need. And we've got reserve capacity there. So you, of course, just like you would in the public cloud. But more importantly, being able to then extend that through a fabric across clouds and edge locations, so that if a customer wants to deploy in some public cloud service, like we know we're going to, again, we're giving that consistency across that, and exposing it through an S3 API. >> So, Vishal at the end of the day, I mean, I love to talk about the plumbing and the tech, but the customer doesn't care, right? They want the lowest cost. They want the fastest outcome. They want the greatest value. My question is, how are you seeing data organizations evolve to sort of accommodate this third era of this next generation? >> Yeah. I mean, the way at least, kind of look at, from a customer perspective, what they're trying to do is first of all, I think Matt addressed it somewhat. They're looking at a consistent experience across the different groups of people within the company that do something to data, right? It could be a SQL users. People who's just writing a SQL code. It could be people who are writing machine learning models and running them. It could be people who are writing code in Spark. Right now they are, you know the experience is completely disjointed across them, across the three types of users or more. And so that's one thing that they trying to do, is just try to get that consistency. We spoke about performance. I mean the disjointedness between compute and storage does provide the agility, because there customers are looking for elasticity. How can I have an elastic environment? So, that's kind of the other thing they're looking at. And performance and DCU, I think a big deal now. So, I think that that's definitely on a customer's mind. So, as enterprises are looking at their data journey, those are the at least the attributes that they are trying to hit as they organize themselves to make the most out of the data. >> Matt, you and I have talked about this sort of trend to the decentralized future. We're sort of hitting on that. And whether it's in a first gen data warehouse, second gen data lake, data hub, bucket, whatever, that essentially should ideally stay where it is, wherever it should be from a performance standpoint, from a governance standpoint and a cost perspective, and just be a node on this, I like the term data mesh, but be a node on that, and essentially allow the business owners, those with domain context to you've mentioned data products before to actually build data products, maybe air quotes, but a data product is something that can be monetized. Maybe it cuts costs. Maybe it adds value in other ways. How do you see HPE fitting into that long-term vision which we know is going to take some time to play out? >> I think what's important for organizations to realize is that they don't have to go to the public cloud to get that experience they're looking for. Many organizations are still reluctant to push all of their data, their critical data, that is going to be the next way to monetize business into the public cloud. And so what HPE is doing is bringing the cloud to them. Bringing that cloud from the infrastructure, the virtualization, the containerization, and most importantly, those cloud native services. So, they can do that development rapidly, test it, using those open source tools and frameworks we spoke about. And if that model ends up being deployed on a factory floor, on some common X86 infrastructure, that's okay, because the lingua franca is Kubernetes. And as Vishal mentioned, Apache Spark, these are the common tools and frameworks. And so I want organizations to think about this unified analytics experience, where they don't have to trade off security for cost, efficiency for reliability. HPE through GreenLake cloud services is delivering all of that where they need to do it. >> And what about the speed to quality trade-off? Have you seen that pop up in customer conversations, and how are organizations dealing with that? >> Like I said, it depends on what you mean by speed. Do you mean a computational speed? >> No, accelerating the time to insights, if you will. We've got to go faster, faster, agile to the data. And it's like, "Whoa, move fast break things. "Whoa, whoa. "What about data quality and governance and, right?" They seem to be at odds. >> Yeah, well, because the processes are fundamentally broken. You've got a developer who maybe is able to spin up an instance in the public cloud to do their development, but then to actually do model training, they bring it back on-premises, but they're waiting for a data engineer to get them the data available. And then the tools to be provisioned, which is some esoteric stack. And then runtime is somewhere else. The entire process is broken. So again, by using consistent frameworks and tools, and bringing that computation to where the data is, and sort of blowing this construct of pipelines out of the water, I think is what is going to drive that success in the future. A lot of organizations are not there yet, but that's I think aspirationally where they want to be. >> Yeah, I think you're right. I think that is potentially an answer as to how you, not incrementally, but revolutionized sort of the data business. Last question, is talking about GreenLake, how this all fits in. Why GreenLake? Why do you guys feel as though it's differentiable in the market place? >> So, I mean, something that you asked earlier as well, time to value, right? I think that's a very important attribute and kind of a design factor as we look at GreenLake. If you look at GreenLake overall, kind of what does it stand for? It stands for experience. How do we make sure that we have the right experience for the users, right? We spoke about it in context of data. How do we have a similar experience for different users of data, but just broadly across an enterprise? So, it's all about experience. How do you automate it, right? How do you automate the workloads? How do you provision fast? How do you give folks a cloud... An experience that they have been used to in the public cloud, on using an Apple iPhone? So it's all about experience, I think that's number one. Number two is about choice and openness. I mean, as we look at GreenLake is not a proprietary platform. We are very, very clear that the design, one of the important design principles is about choice and openness. And that's the reason we are, you hear us talk about Kubernetes, about Apaches Spark, about Delta lake et cetera, et cetera, right? We're using kind of those open source models where customers have a choice. If they don't want to be on GreenLake, they can go to public cloud tomorrow. Or they can run in our Holos if they want to do it that way or in their Holos, if they want to do it. So they should have the choice. Third is about performance. I mean, what we've done is it's not just about the software, but we as a company know how to configure infrastructure for that workload. And that's an important part of it. I mean if you think about the machine learning workloads, we have the right Nvidia chips that accelerate those transactions. So, that's kind of the last, the third one, and the last one, I think, as I spoke about earlier is cost. We are very focused on TCO, but from a customer perspective, we want to make sure that we are giving a value proposition, which is just not about experience and performance and openness, but also about costs. So if you think about GreenLake, that's kind of the value proposition that we bring to our customers across those four dimensions. >> Guys, great conversation. Thanks so much, really appreciate your time and insights. >> Matt: Thanks for having us here, David. >> All right, you're welcome. And thank you for watching everybody. Keep it right there for more great content from HPE GreenLake announcements. You're watching theCUBE. (upbeat music)

>>Yeah, >>yeah. Hello and welcome to this track to creating engaging analytics experiences for all. I'm Hannah Sinden Thought spots Omiya director of marketing on. I'm delighted to have you here today. A boy Have we got to show for you now? I might be a little bit biased as the host of this track, but in my humble opinion, you've come to a great place to start because this track is all about everything. Thought spot. We'll be talking about embedded search in a I thought spot one spot I. Q. We've got great speakers from both thoughts about andare customers as well as some cool product demos. But it's not all product talk. We'll be looking at how to leverage the tech to give your users a great experience. So first up is our thoughts about one deep dive. This session will be showing you how we've built on our already superb search experience to make it even easier for users across your company to get insight. We've got some great speakers who are going to be telling you about the cool stuff they've been working on to make it really fantastic and easy for non technical people to get the answers they need. So I'm really delighted to introduce Bob Baxley s VP of design and experience That thought spot on Vishal Kyocera Thought spots director of product management. So without further ado, I'll hand it over to Bob. Thanks, >>Hannah. It's great to be here with everybody today and really excited to be able to present to you thought spot one. We've been working on this for months and months and are super excited to share it before we get to the demo with Shawl, though, I just want to set things up a little bit to help people understand how we think about design here. A thought spot. The first thing is that we really try to think in terms of thought. Spot is a consumer grade product, terms what we wanted. Consumer grade you x for an analytics. And that means that for reference points rather than looking at other enterprise software companies, we tend to look at well known consumer brands like Google, YouTube and WhatsApp. We firmly believe that people are people, and it doesn't matter if they're using software for their own usage or thought are they're using software at work We wanted to have a great experience. The second piece that we were considering with thoughts about one is really what we call the desegregation of bundles. So instead of having all of your insights wraps strictly into dashboards, we want to allow users to get directly to individual answers. This is similar to what we saw in music. Were instead of you having to buy the entire album, of course, you could just buy individual songs. You see this in iTunes, Spotify and others course. Another key idea was really getting rid of gate keepers and curators and kind of changing people from owning the information, helping enable users to gather together the most important and interesting insights So you can follow curator rather than feeling like you're limited in the types of information you can get. And finally, we wanted to make search the primary way, for people are thinking about thought spot. As you'll see, we've extended search from beyond simply searching for your data toe, also searching to be able to find pin boards and answers that have been created by other people. So with that, I'll turn it over to my good friend Rachel Thio introduce more of thought, spot one and to show you a demo of the product. >>Thank you, Bob. It's a pleasure to be here to Hello, everyone. My name is Michelle and Andy, product management for Search. And I'm really, really excited to be here talking about thoughts about one our Consumer analytics experience in the Cloud. Now, for my part of the talk, we're gonna first to a high level overview of thoughts about one. Then we're going to dive into a demo, and then we're gonna close with just a few thoughts about what's coming next. So, without any today, let's get started now at thought spot. Our mission is to empower every user regardless of their expertise, to easily engage with data on make better data driven decisions. We want every user, the nurse, the neighborhood barista, the teacher, the sales person, everyone to be able to do their jobs better by using data now with thoughts about one. We've made it even more intuitive for all these business users to easily connect with the insights that are most relevant for them, and we've made it even easier for analysts to do their jobs more effectively and more efficiently. So what does thoughts about one have? There's a lot off cool new features, but they all fall into three main categories. The first main category is enhanced search capabilities. The second is a brand new homepage that's built entirely for you, and the third is powerful tools for the analysts that make them completely self service and boost their productivity. So let's see how these work Thought Spot is the pioneer for search driven analytics. We invented search so that business users can ask questions of data and create new insights. But over the years we realized that there was one key piece off functionality that was missing from our search, and that was the ability to discover insights and content that had already been created. So to clarify, our search did allow users to create new content, but we until now did not have the ability to search existing content. Now, why does that matter? Let's take an example. I am a product manager and I am always in thought spot, asking questions to better understand how are users are using the product so we can improve it now. Like me, A lot of my colleagues are doing the same thing. Ah, lot of questions that I asked have already been answered either completely are almost completely by many of my colleagues, but until now there's been no easy way for me to benefit from their work. And so I end up recreating insights that already exists, leading to redundant work that is not good for the productivity off the organization. In addition, even though our search technology is really intuitive, it does require a little bit of familiarity with the underlying data. You do need to know what metric you care about and what grouping you care about so that you can articulate your questions and create new insights. Now, if I consider in New employees product manager who joins Hotspot today and wants to ask questions, then the first time they use thought spot, they may not have that data familiarity. So we went back to the drawing board and asked ourselves, Well, how can we augment our search so that we get rid off or reduced the redundant work that I described? And in addition, empower users, even new users with very little expertise, maybe with no data familiarity, to succeed in getting answers to their questions the first time they used Hot Spot, and we're really proud and excited to announce search answers. Search answers allows users to search across existing content to get answers to their questions, and its a great compliment to search data, which allows them to search the underlying data directly to create new content. Now, with search answers were shipping in number of cool features like Answer Explainer, Personalized search Results, Answer Explorer, etcetera that make it really intuitive and powerful. And we'll see how all of these work in action in the demo. Our brand new homepage makes it easier than ever for all these business users to connect with the insights that are most relevant to them. These insights could be insights that these users already know about and want to track regularly. For example, as you can see, the monitor section at the top center of the screen thes air, the KP eyes that I may care most about, and I may want to look at them every day, and I can see them every day right here on my home page. By the way, there's a monitoring these metrics in the bankrupt these insights that I want to connect with could also be insights that I want to know more about the search experience that I just spoke about ISS seamlessly integrated into the home page. So right here from the home page, I can fire my searchers and ask whatever questions I want. Finally, and most interestingly, the homepage also allows me to connect with insights that I should know about, even if I didn't explicitly ask for them. So what's an example? If you look at the panel on the right, I can discover insights that are trending in my organization. If I look at the panel on the left, I can discover insights based on my social graph based on the people that I'm following. Now you might wonder, How do we create this personalized home page? Well, our brand new, personalized on boarding experience makes it a piece of cake as a new business user. The very first time I log into thought spot, I pay three people I want to follow and three metrics that I want to follow, and I picked these from a pool of suggestions that Ai has generated. And just like that, the new home page gets created. And let's not forget about analysts. We have a personalized on boarding experience specifically for analysts that's optimized for their needs. Now, speaking of analysts, I do want to talk about the tools that I spoke off earlier that made the analysts completely self service and greatly boost their productivity's. We want analysts to go from zero to search in less than 30 minutes, and with our with our new augmented data modeling features and thoughts about one, they can do just that. They get a guided experience where they can connect, model and visualize their data. With just a few clicks, our AI engine takes care off a number of tasks, including figuring out joints and, you know, cleaning up column names. In fact, our AI engine also helps them create a number of answers to get started quickly so that these analysts can spend their time and energy on what matters most answering the most complicated and challenging and impactful questions for the business. So I spoke about a number of different capabilities off thoughts about one, but let's not forget that they are all packaged in a delightful user experience designed by Bob and his team, and it powers really, really intuitive and powerful user flows, from personalized on boarding to searching to discover insights that already exist on that are ranked based on personalized algorithms to making refinements to these insights with a assistance to searching, to create brand new insights from scratch. And finally sharing all the insights that you find interesting with your colleagues so that it drives conversations, decisions and, most importantly, actions so that your business can improve. With that said, let's drive right into the demo for this demo. We're going to use sales data set for a company that runs a chain off retail stores selling apparel. Our user is a business user. Her name is Charlotte. She's a merchandiser, She's new to this company, and she is going to be leading the genes broader category. She's really excited about job. She wants to use data to make better decisions, so she comes to thought spot, and this is what she sees. There are three main sections on the home page that she comes to. The central section allows you to browse through items that she has access to and filter them in various ways. Based for example, on author or on tags or based on what she has favorited. The second section is this panel on the right hand side, which allows her to discover insights that are trending within her company. This is based on what other people within her company are viewing and also personalized to her. Finally, there's this search box that seamlessly integrated into the home page. Now Charlotte is really curious to learn how the business is doing. She wants to learn more about sales for the business, so she goes to the search box and searches for sales, and you can see that she's taken to a page with search results. Charlotte start scanning the search results, and she sees the first result is very relevant. It shows her what the quarterly results were for the last year, but the result that really catches her attention is regional sales. She'd love to better understand how sales are broken down by regions. Now she's interested in the search result, but she doesn't yet want to commit to clicking on it and going to that result. She wants to learn more about this result before she does that, and she could do that very easily simply by clicking anywhere on the search result card. Doing that reveals our answer. Explain our technology and you can see this information panel on the right side. It shows more details about the search results that she selected, and it also gives her an easy to understand explanation off the data that it contains. You can see that it tells her that the metrics sales it's grouped by region and splitter on last year. She can also click on this preview button to see a preview off the chart that she would see if she went to that result. It shows her that region is going to be on the X axis and sales on the Y axis. All of this seems interesting to her, and she wants to learn more. So she clicks on this result, and she's brought to this chart now. This contains the most up to date data, and she can interact with this data. Now, as she's looking at this data, she learns that Midwest is the region with the highest sales, and it has a little over $23 million in sales, and South is the region with the lowest sales, and it has about $4.24 million in sales. Now, as Charlotte is looking at this chart, she's reminded off a conversation she had with Suresh, another new hire at the company who she met at orientation just that morning. Suresh is responsible for leading a few different product categories for the Western region off the business, and she thinks that he would find this chart really useful Now she can share this chart with Suresh really easily from right here by clicking the share button. As Charlotte continues to look at this chart and understand the data, she thinks, uh, that would be great for her to understand. How do these sales numbers across regions look for just the genes product category, since that's the product category that she is going to be leading? And she can easily narrow this data to just the genes category by using her answer Explorer technology. This panel on the right hand side allows her to make the necessary refinements. Now she can do that simply by typing in the search box, or she can pick from one off the AI generated suggestions that are personalized for her now. In this case, the AI has already suggested genes as a prototype for her. So with just a single click, she can narrow the data to show sales data for just jeans broken down by region. And she can see that Midwest is still the region with the highest sales for jeans, with $1.35 million in sales. Now let's spend a minute thinking about what we just saw. This is the first time that Charlotte is using Thought spot. She does not know anything about the data sources. She doesn't know anything about measures or attributes. She doesn't know the names of the columns. And yet she could get to insights that are relevant for her really easily using a search interface that's very much like Google. And as she started interacting with search results, she started building a slightly better understanding off the underlying data. When she found an insight that she thought would be useful to a colleague offers, it was really seamless for her to share it with that colleague from where she Waas. Also, even though she's searching over content that has already been created by her colleagues in search answers. She was in no way restricted to exactly that data as we just saw. She could refine the data in an insight that she found by narrowing it. And there's other things you can do so she could interact with the data for the inside that she finds using search answers. Let's take a slightly more complex question that Charlotte may have. Let's assume she wanted to learn about sales broken down by, um, by category so that she can compare her vertical, which is jeans toe other verticals within the company. Again, she can see that the very first result that she gets is very relevant. It shows her search Sorry, sales by category for last year. But what really catches her attention about this result is the name of the author. She's thrilled to note that John, who is the author of this result, was also an instructor for one off for orientation sessions and clearly someone who has a lot of insight into the sales data at this company. Now she would love to see mawr results by John, and to do that, all she has to do is to click on his name now all of the search results are only those that have been authored by John. In fact, this whole panel at the top of the results allow her to filter her search results or sort them in different ways. By clicking on these authors filter, she can discover other authors who are reputed for the topic that she's searching for. She can also filter by tags, and she can sort these results in different ways. This whole experience off doing a search and then filtering search results easily is similar to how we use e commerce search engines in the consumer world. For example, Amazon, where you may search for a product and then filter by price range or filter by brand. For example, Let's also spend a minute talking about how do we determine relevance for these results and how they're ranked. Um, when considering relevance for these results, we consider three main categories of things. We want to first make sure that the result is in fact relevant to the question that the user is asking, and for that we look at various fields within the result. We look at the title, the author, the description, but also the technical query underpinning that result. We also want to make sure that the results are trustworthy, because we want users to be able to make business decisions based on the results that they find. And for that we look at a number of signals as well. For example, how popular that result is is one of those signals. And finally, we want to make sure the results are relevant to the users themselves. So we look at signals to personalize the result for that user. So those are all the different categories of signals that we used to determine overall ranking for a search result. You may be wondering what happens if if Charlotte asks a question for which nobody has created any answer, so no answers exist. Let's say she wants to know what the total sales of genes for last year and no one's created that well. It's really easy for her to switch from searching for answers, which is searching for content that has already been created to searching the data directly so she can create a new insight from scratch. Let's see how that works. She could just click here, and now she's in the search data in her face and for the question that I just talked about. She can just type genes sales last year. And just like that, she could get an answer to her question. The total sales for jeans last year were almost $4.6 million. As you can see, the two modes off search searching for answers and searching, the data are complementary, and it's really easy to switch from one to the other. Now we understand that some business users may not be motivated to create their own insights from scratch. Or sometimes some of these business users may have questions that are too complicated, and so they may struggle to create their own inside from scratch. Now what happens usually in these circumstances is that these users will open a ticket, which would go to the analyst team. The analyst team is usually overrun with these tickets and have trouble prioritizing them. And so we started thinking, How can we make that entire feedback loop really efficient so that analysts can have a massive impact with as little work as possible? Let me show you what we came up with. Search answers comes with this system generated dashboard that analysts can see to see analytics on the queries that business users are asking in search answers so it contains high level K P. I is like, You know how many searches there are and how many users there are. It also contains one of the most popular queries that users are asking. But most importantly, it contains information about what are popular queries where users are failing. So the number on the top right tells you that about 10% off queries in this case ended with no results. So the user clearly failed because there were no results on the table. Right below it shows you here are the top search queries for original results exist. So, for example, the highlighted row there says jean sales with the number three, which tells the analysts that last week there were three searches for the query jean sales and the resulted in no results on search answers. Now, when an analyst sees a report like this, they can use it to prioritize what kind of content they could be creating or optimizing. Now, in addition to giving them inside into queries which led to no results or zero results. This dashboard also contains reports on creatives that lead to poor results because the user did get some results but didn't click on anything, meaning that they didn't get the answer that they were looking for. Taking all these insights, analysts can better prioritize and either create or optimize their content to have maximum impact for their business users with the least amount of for. So that was the demo. As you can see with search answers, we've created a very consumer search interface that any business user can use to get the answers to their questions by leveraging data or answers that have already been created in the system by other users in their organization. In addition, we're creating tools that allow analysts toe create or optimized content that can have the highest impact for these business users. All right, so that was the demo or thoughts about one and hope you guys liked it. We're really excited about it. Now Let me just spend a minute talking about what's coming next. As I've mentioned before, we want to connect every business user with the insights that are most relevant for them, and for that we will continue to invest in Advanced AI and personalization, and some of the ways you will see it is improved relevance in ranking in recommendations in how we understand your questions across the product within search within the home page everywhere. The second team that will continue to invest in is powerful analyst tools. We talked about tools and, I assure you, tools that make the analysts more self service. We are committed to improving the analyst experience so that they can make the most off their time. An example of a tool that we're really excited about is one that allows them to bridge the vocabulary difference that this even business user asks questions. A user asked a question like revenue, but the column name for the metric in the data set its sales. Now analysts can get insights into what are the words that users air using in their questions that aren't matching anything in the data set and easily create synonyms so that that vocabulary difference gets breached. But that's just one example of how we're thinking about empowering the analysts so that with minimal work, they can amplify their impact and help their business users succeed. So there's a lot coming, and we're really excited about how we're planning to evolve thoughts about one. With all that said, Um, there's just, well, one more thing that my friend Bob wants to talk to you guys about. So back to you, Bob. >>Thanks, Michelle. It's such a great demo and so fun to see all the new work that's going on with thought. Spot one. All the happenings for the new features coming out that will be under the hood. But of course, on the design side, we're going to continue to evolve the front end as well, and this is what we're hoping to move towards. So here you'll see a new log in screen and then the new homepage. So compared to the material that you saw just a few minutes ago, you'll notice this look is much lighter. A little bit nicer use of color up in the top bar with search the features over here to allow you to switch between searching against answers at versus creating new answers, the settings and user profile controls down here and then on the search results page itself also lighter look and feel again. Mork color up in the search bar up the top. A little bit nicer treatments here. We'll continue to evolve the look and feel the product in coming months and quarters and look forward to continue to constantly improving thoughts about one Hannah back to you. >>Thanks, Bob, and thank you both for showing us the next generation of thought spot. I'd love to go a bit deeper on some of the points you touched on there. I've got a couple of questions here. Bob, how do you think about designing for consumer experience versus designing for enterprise solutions? >>Yes, I mentioned Hannah. We don't >>really try to distinguish so much between enterprise users and consumer users. It's really kind of two different context of use. But we still always think that users want some product and feature and experience that's easy to use and makes sense to them. So instead of trying to think about those is two completely different design processes I think about it may be the way Frank Lloyd Wright would approached architecture. >>Er I >>mean, in his career, he fluidly moved between residential architecture like falling water and the Robie House. But he also designed marquis buildings like the Johnson wax building. In each case, he simply looked at the requirements, thought about what was necessary for those users and designed accordingly. And that's really what we do. A thought spot. We spend time talking to customers. We spend time talking to users, and we spent a lot of time thinking through the problem and trying to solve it holistically. And it's simply a possible >>thanks, Bob. That's a beautiful analogy on one last question for you. Bischel. How frequently will you be adding features to this new experience, >>But I'm glad you asked that, Hannah, because this is something that we are really really excited about with thoughts about one being in the cloud. We want to go really, really fast. So we expect to eventually get to releasing new innovations every day. We expect that in the near future, we'll get to, you know, every month and every week, and we hope to get to everyday eventually fingers crossed on housing. That can happen. Great. Thanks, >>Michelle. And thank you, Bob. I'm so glad you could all join us this morning to hear more about thoughts about one. Stay close and get ready for the next session. which will be beginning in a few minutes. In it will be introduced to thoughts for >>everywhere are >>embedded analytics product on. We'll be hearing directly from our customers at Hayes about how they're using embedded analytics to help healthcare providers across billing compliance on revenue integrity functions. To make more informed decisions on make effective actions to avoid risk and maximize revenue. See you there.

>> Voiceover: Live, from Orlando, Florida, it's The Cube, covering Sapphire Now, headlining sponsored by SAP HANA Cloud, the leader in platform as a service, with support from Consolink, the cloud internet company. Now, here are your hosts, John Furrier and Peter Burse. >> Welcome back everyone, we are here live in Orlando, Florida for Sapphire Now, SiliconeANGLE Media's exclusive coverage of Sapphire. I'm John Furrier with Peter Burse. This is our flagship program, we go out to the events, and extract the citizen noise, you're watching The Cube. I want to do a shout-out to our sponsors. Without their help, we would not be here. SAP HANA Cloud Platform, Consolink at CONSOL Cloud, hot start up in Silicone Valley, and also we have Cap Gemini, we have EMC. Thanks so much for your support. Our next guest is Gaby Corin, who's the EVP of the Americas for Panaya, accompanied about a year ago by Infosys, now a part of Infosys. Welcome to the Cube. >> Thank you so much. >> Congratulations on the acquisition over a yeah ago, but you guys are to a part of the big machinery of Infosys, which is tier one systems integrated part of SAP's global channel, as they call it, but essentially, you're out serving customers all over the world. >> Gaby: That is correct, yes. >> At Infosys, what's your role in the Infosys organization, and what does your company do? >> Okay, so, I'll start with the company. Panaya was founded ten years ago. Our quest is to help customers to perform all their changes in their ERP environment. We basically analyze the environment, create that mapping, that baseline that helps them understand exactly what they're dealing with, then we support them in scoping out the changes, and then, we work with them throughout the journey of executing on all the testing cycles associated with all the changes. We serve about two thousand customers, and we are a hundred percent cloud-based solution. My role as EVP for the Americas is to support all customers in the region, and we're working very closely with Infosys into bringing Panaya as part of their offering to accelerate the processes, to bring innovation, and to bring much more efficiency to all the SAP projects and activities that they perform with our customers. >> We had the global partner person on earlier, and that was the big point, innovation's now at the center, not just delivery, which Infosys has been great at, but also other things, innovation, time is very important. >> Exactly. >> Your solution speeds things up, so share with us what it is, is it a SAS space? Is it code analyzers? Is it for QA? Is it for testing? What specifically do you guys solve? What problem do you solve? >> Great question. First of all, we are a SAS-based solution, so we do everything in the cloud. This helps, as you said, perform all the tasks faster and more efficiently. The pain that we're coming to address is the fact that change is constant in the ERP. The ERP is never an island, never an isolated solution. It's always in changes, the core of a lot of the businesses that we meet here, so change is their reality, they need to change all the time. They are highly customized, so every change that come from the vendor or from the business requires a lot of preparation and very fast execution, and this is where Panaya plays. We simulate the change virtually in the cloud, and we tell customers in advance what is going to happen to their environment all the way to the code line level what exactly is going to break, how to fix it, what to test, and we support them, again, throughout all the testing cycles from the unit test or the technical test all the way to user-acceptance test, UATs, that is a big pain to organization because of the collaboration. >> It's faster is the point. So, you guys speed up the process. >> Absolutely, we speed up the process, we reduce costs, we bring customers faster to market by about fifty percent, and we allow them to do their projects at the budget that they establish or lower. >> Give me an example of someone who has the problem, and what their environment looks like. Because everyone's trying to get to the cloud, and your solution is tailor-made perfectly for the cloud because it's very dev-ops-like. It makes things go faster, it's part of that whole agile iteration speed game, which we love, but the people trying to get there that are figuring it out, what's their environment, people who have the problem? What's their environment look like? Paint the picture. >> Virtually any SAP customer needs Panaya. >> John: That's a good plug. It's complicated. >> Yes. Their environment can have one instance, or multiple instances of SAP ECCs. They all have the need for testing because they perform testing all the way. They are trying to bring some of the applications to the cloud, but not necessarily. Most of our customers still are heavily on-premise based, so what we do is that we do all the analysis in the cloud, and this is how we help them do things much faster. >> So I got to ask you the Infosys question, because I'm a big fan of Vishal Sikka. For many years, I've watched his work at SAP, certainly. He was very, very early on and very right on a lot of technical decisions around how things played out. I watched him during the SOA days, going back to the web services days, which is the late 90's, early 2000s, he had the right call and vision on web services, and then service-oriented architectures. >> Yes. >> He brought a lot of great mojo to SAP and has always been very open-source driven. >> Right. >> John: And he's just a cool guy, so what's it like working there? I mean, is he always on top of the employees? Do you talk to him? What's it like inside the company at Infosys, and specifically Vishal, what's he up to? >> First of all, he's such a visionary. You listen to him and his vision. His vision is people and software. And he wants to make a difference when it comes to supporting customers, being an SI, being at a company that creates and makes a difference. He's also very personal, so he's very approachable. He loves ideas as innovation, and he believes that the innovations come from within, so he's a huge supporter of Panaya and bringing Panaya to every single Infosys customer and opportunity, but he has that vision that you don't replace a thing, you don't replace stuff. You take something, and you bring, but you learn to collaborate, and you understand that the environments needs to be flexible, and the only way to bring that flexibility is to take the existing environment and continue to bring innovation, even if it's in small steps, you bring that innovation to the table. And this is what makes it so unique to work for a guy like him. >> The traditional systems integrator relationship, there's always been tension, a lot of tension between customers and systems integrators. >> Gaby: Yes. >> Customers say they want something. Systems integrators have the expertise to do it. Customers want it fast, systems integrators sometimes use their experience to inflate billings, but the customer increasingly is in charge in almost all global markets. The question is are you helping your customers stay more in control of Infosys engagements? And if the answer is yes, how does that improve the value proposition of Infosys? >> Okay, that's a great question. One of the reasons that Panaya remains an independent and contained organization within Infosys is, besides commitment to support that, we sell direct a lot to our customers, and we support, we remain objective, whoever the customer chooses to work with, whether it's to do it in house or to use system integrators. And we have more and more projects that there are three, four, or five system integrators that are involved, and each one does a piece of the solution, and Panaya gives that control because of their analysis, because of the support on the planning stage. We paint the right picture of where you are today, where do you want to go, and in the journey of doing that. This is one of the claims of victory of Panaya is that we bring that control back to the hands of the customers exactly as they want to, because they want to understand what are they dealing with, what are the pricing, and SIs on the other hand, also understand that prices cannot continue to be cut forever and ever. But if you don't bring that innovation, that people plus software, it will be impossible to continue to compete in this market. >> They get more net contract value on the sales as they deliver value. >> Gaby: Exactly, to the customers. >> So if they're helping their customers drive more cash and revenue-- >> Well, I would presume that it actually starts with the contracting process for a lot of these efforts is itself very, very expensive and often leads to not a lot of value, and so I presume that in response to what you just mentioned, John, that you're generating artifacts to make it easy for the customer, the SAP customer, to envision where they need to go, and those artifacts then help the SAP customer manage the integrator and the company doing it, which then dramatically reduces the contracting process. >> Gaby: Exactly. >> Because it's a lot clearer, which means I can focus more on the management of the partner-- >> You release resources, correct. >> As a set of capabilities because because it always changes along the way. >> That is correct. >> As I change, I can envision that using some of the technologies you're bringing to bear. >> That is correct, we create these assets that can be reused time and again, and then we free up resources so they can focus on innovation and additional activities. That is exactly our value proposition, you got it absolutely right. >> So, are you a consultant management system in the SAP world? >> We don't claim to be, no, we bring solutions. We're not in the consulting business at all. >> Peter: No, managing the consulting business. >> Oh, absolutely, we help to manage that process. >> Helping the customer manage those consultants. >> That is correct, that is correct. Yes, you're absolutely right. >> My final question for you, thanks for coming on The Cube, by the way, I know it's short notice. >> Thank you, thank you for having me. >> Great to have the insight. What's the biggest change in the ecosystem are you seeing today? Because you're close to the code, so you're close to all the action at Panaya and certainly Infosys is massive and global. What is the biggest change that's happening in the ecosystem, with SI's and generally across the board? >> That's a great question. One thing that we're seeing is much more competition. The customer is much more educated, exactly as you, Peter, said. The customers are much more educated, they know what they want, and they're coming in with much more control and knowledge, so we're seeing this. Customers are looking for much more long-term activities. This is why HANA is becoming such a strong, we're seeing this also here in this show how everybody's talking HANA, because it's not something that you do for the next year. It's something that is going to be with these customers for a long term. They are looking for long-term type of engagements. >> They don't have to buy a lot of HANA. They can actually put their toe in the water, if you will. The old days it was you buy SAP, and you hired the SI's, project management, delivery over a long period of time. They don't have to do that today. They can still have a long view with HANA, right? I mean, are you seeing that, too? >> Yes, and what we're seeing is, a move on this regard, we're seeing a move from best of suite into best of breed. We want on each area the best solution possible. >> Without ballooning integration and training costs. >> Correct, correct, and we fit perfectly into that story. >> Well, thanks so much. Real quick question for you. You guys have a big end-user event like Sapphire. >> Gaby: Yes. >> Didn't you just have one in San Francisco recently? Or do you have one coming up? What's going on with the events for Infosys? >> We participated in Confluence, which is a very large event of Infosys, just a couple of weeks ago. Very, very well-attended, and we-- >> John: Is that a global conference in San Francisco or is it in other areas? >> It's a global event in which the largest, the biggest customers of Infosys attend, once a year, they get together. It's all about thought leadership and sharing ideas, design thinking, which Vishal is leading very strongly. That was the main theme of the event, so we had the chance to meet a lot of our customers and prospects. Now, of course, Sapphire. >> Thank you so much for coming on, Gaby. Great to have you on The Cube, and welcome to the Cube alumni now that you're on The Cube. We are live here in Orlando for SAP Sapphire Now. I'm John Furrier with Peter Burse with the Cube. You're watching SiliconANGLE' The Cube. (futuristic music)