(lively music) >> Hello, everyone. Welcome back to the Supercloud presentation here. I'm theCUBE, I'm John Furrier, your host. What a great segment here. We're going to unpack the networking aspect of the cloud, how that translates into what Supercloud architecture and platform deployment scenarios look like. And demystify multi-cloud, hybridcloud. We've got two great experts. Amir Khan, the Co-Founder and CEO of Alkira, Atif Khan, Co-Founder and CTO of Alkira. These guys been around since 2018 with the startup, but before that story, history in the tech industry. I mean, routing early days, multiple waves, multiple cycles. >> Welcome three decades. >> Welcome to Supercloud. >> Thanks. >> Thanks for coming on. >> Thank you so much for having us. >> So, let's get your take on Supercloud because it's been one of those conversations that really galvanized the industry because it kind of highlights almost this next wave, this next side of the street that everyone's going to be on that's going to be successful. The laggards on the legacy seem to be stuck on the old model. SaaS is growing up, it's ISVs, it's ecosystems, hyperscale, full hybrid. And then multi-cloud around the corners cause all this confusion, everyone's hand waving. You know, this is a solution, that solution, where are we? What do you guys see as this supercloud dynamic? >> So where we start from is always focusing on the customer problem. And in 2018 when we identified the problem, we saw that there were multiple clouds with many diverse ways of doing things from the network perspective, and customers were struggling with that. So we delved deeper into that and looked at each one of the cloud architectures completely independent. And there was no common solution and customers were struggling with that from the perspective. They wanted to be in multiple clouds, either through mergers and acquisitions or running an application which may be more cost effective to run in something or maybe optimized for certain reasons to run in a different cloud. But from the networking perspective, everything needed to come together. So that's, we are starting to define it as a supercloud now, but basically, it's a common infrastructure across all clouds. And then integration of high lift services like, you know, security or IPAM services or many other types of services like inter-partner routing and stuff like that. So, Amir, you agree then that multi-cloud is simply a default result of having whatever outcomes, either M&A, some productivity software, maybe Azure. >> Yes. >> Amazon has this and then I've got on-premise application, so it's kinds mishmash. >> So, I would qualify it with hybrid multi-cloud because everything is going to be interconnected. >> John: Got it. >> Whether it's on-premise, remote users or clouds. >> But have CTO perspective, obviously, you got developers, multiple stacks, got AWS, Azure and GCP, other. Not everyone wants to kind of like go all in, but yet they don't want to hedge too much because it's a resource issue. And I got to learn this stack, I got to learn that stack. So then now, you have this default multi-cloud, hybrid multi-cloud, then it's like, okay, what do I do? How do you spread that around? Is it dangerous? What's the the approach technically? What's some of the challenges there? >> Yeah, certainly. John, first, thanks for having us here. So, before I get to that, I'll just add a little bit to what Amir was saying, like how we started, what we were seeing and how it, you know, correlates with the supercloud. So, as you know, before this company, Alkira, we were doing, we did the SD-WAN company, which was Viptela. So there, we started seeing when people started deploying SD-WAN at like a larger scale. We started like, you know, customers coming to us and saying they needed connectivity into the cloud from the SD-WAN. They wanted to extend the SD-WAN fabric to the cloud. So we came up with an architecture, which was like later we started calling them Cloud onRamps, where we built, you know, a transit VPC and put like the virtual instances of SD-WAN appliances extended from there to the cloud. But before we knew, like it started becoming very complicated for the customers because it wasn't just connectivity, it also required, you know, other use cases. You had to instantiate or bring in security appliances in there. You had to secure all of that stuff. There were requirements for, you know, different regions. So you had to bring up the same thing in different regions. Then multiple clouds, what did you do? You had to replicate the same thing in multiple clouds. And now if there was was requirement between clouds, how were you going to do it? You had to route traffic from somewhere, and come up with all those routing controls and stuff. So, it was very complicated. >> Like spaghetti code, but on network. >> The games begin, in fact, one of our customers called it spaghetti mess. And so, that's where like we thought about where was the industry going and which direction the industry was going into? And we came up with the Alkira where what we are doing is building a common infrastructure across multiple clouds, across in, you know, on-prem locations, be it data centers or physical sites, branches sites, et cetera, with integrated security and network networking services inside. And, you know, nowadays, networking is not only about connectivity, you have to secure everything. So, security has to be built in. Redundancy, high availability, disaster recovery. So all of that needs to be built in. So that's like, you know, kind of a definition of like what we thought at that time, what is turning into supercloud now. >> Yeah. It's interesting too, you mentioned, you know, VPCs is not, configuration of loans a hassle. Nevermind the manual mistakes could be made, but as you decide to do something you got to, "Oh, we got to get these other things." A lot of the hyper scales and a lot of the alpha cloud players now, and cloud native folks, they're kind of in that mode of, "Wow, look at what we've built." Now, they're got to maintain, how do I refresh it? Like, how do I keep the talent? So they got this similar chaotic environment where it's like, okay, now they're already already through, so I think they're going to be okay. But then some people want to bypass it completely. So there's a lot of customers that we see out there that fit the makeup of, I'm cloud first, I've lifted and shifted, I move some stuff to the cloud. But I want to bypass all that learnings from all the people that are gone through the past three years. Can I just skip that and go to a multi-cloud or coherent infrastructure? What do you think about that? What's your view? >> So yeah, so if you look at these enterprises, you know, many of them just to find like the talent, which for one cloud as far as the IT staff is concerned, it's hard enough. And now, when you have multiple clouds, it's hard to find people the talent which is, you know, which has expertise across different clouds. So that's where we come into the picture. So our vision was always to simplify all of this stuff. And simplification, it cannot be just simplification because you cannot just automate the workflows of the cloud providers underneath. So you have to, you know, provide your full data plane on top of it, fed full control plane, management plane, policy and management on top of it. And coming back to like your question, so these nowadays, those people who are working on networking, you know, before it used to be like CLI. You used to learn about Cisco CLI or Juniper CLI, and you used to work on it. Nowadays, it's very different. So automation, programmability, all of that stuff is the key. So now, you know, Ops guys, the DevOps guys, so these are the people who are in high demand. >> So what do you think about the folks out there that are saying, okay, you got a lot of fragmentation. I got the stacks, I got a lot of stove pipes, if you will, out there on the stack. I got to learn this from Azure. Can you guys have with your product abstract the way that's so developers don't need to know the ins and outs of stack's, almost like a gateway, if you will, the old days. But like I'm a developer or team develop, why should I have to learn the management layer of Azure? >> That's exactly what we started, you know, out with to solve. So it's, what we have built is a platform and the platform sits inside the cloud. And customers are able to build their own network or a virtual network on top using that platform. So the platform has its own data plane, own control plane and management plane with a policy layer on top of it. So now, it's the platform which is sitting in different clouds, but from a customer's point of view, it's one way of doing networking. One way of instantiating or bringing in services or security services in the middle. Whether those are our security services or whether those are like services from our partners, like Palo Alto or Checkpoint or Cisco. >> So you guys brought the SD-WAN mojo and refactored it for the cloud it sounds like. >> No. >> No? (chuckles) >> We cannot said. >> All right, explain. >> It's way more than that. >> I mean, SD-WAN was wan. I mean, you're talking about wide area networks, talking about connected, so explain the difference. >> SD-WAN was primarily done for one major reason. MPLS was expensive, very strong SLAs, but very low speed. Internet, on the other hand, you sat at home and you could access your applications much faster. No SLA, very low cost, right? So we wanted to marry the two together so you could have a purely private infrastructure and a public infrastructure and secure both of them by creating a common secure fabric across all those environments. And then seamlessly tying it into your internal branch and data center and cloud network. So, it merely brought you to the edge of the cloud. It didn't do anything inside the cloud. Now, the major problem resides inside the clouds where you have to optimize the clouds themselves. Take a step back. How were the clouds built? Basically, the cloud providers went to the Ciscos and Junipers and the rest of the world, built the network in the data centers or across wide area infrastructure, and brought it all together and tried to create a virtualized layer on top of that. But there were many limitations of this underlying infrastructure that they had built. So number of routes per region, how inter region connectivity worked, or how many routes you could carry to the VPCs of V nets? That all those were becoming no common policy across, you know, these environments, no segmentation across these environments, right? So the networking constructs that the enterprise customers were used to as enterprise class carry class capabilities, they did not exist in the cloud. So what did the customer do? They ended up stitching it together all manually. And that's why Atif was alluding to earlier that it became a spaghetti mess for the customers. And then what happens is, as a result, day two operations, you know, troubleshooting, everything becomes a nightmare. So what do you do? You have to build an infrastructure inside the cloud. Cloud has enough raw capabilities to build the solutions inside there. Netflix's of the world. And many different companies have been born in the cloud and evolved from there. So why could we not take the raw capabilities of the clouds and build a network cloud or a supercloud on top of these clouds to optimize the whole infrastructure and seamlessly connecting it into the on-premise and remote user locations, right? So that's your, you know, hybrid multi-cloud solution. >> Well, great call out on the SD-WAN in common versus cloud. 'Cause I think this is important because you're building a network layer in the cloud that spans out so the customers don't have to get into the, there's a gap in the system that I'm used to, my operating environment, of having lockdown security and network. >> So yeah. So what you do is you use the raw capabilities like bandwidth or virtual machines, or you know, containers, or, you know, different types of serverless capabilities. And you bring it all together in a way to solve the networking problems, thereby creating a supercloud, which is an abstraction layer which hides all the complexity of the underlying clouds from the customer, right? And it provides a common infrastructure across all environments to that customer, right? That's the beauty of it. And it does it in a way that it looks like, if they have the networking knowledge, they can apply it to this new environment and carry it forward. One way of doing security across all clouds and hybrid environments. One way of doing routing. One way of doing large-scale network address translation. One way of doing IPAM services. So people are tired of doing individual things and individual clouds and on-premise locations, right? So now they're getting something common. >> You guys brought that, you brought all that to bear and flexible for the customer to essentially self-serve their network cloud. >> Yes, yeah. Is that the wave? >> And nowadays, from business perspective, agility is the key, right? You have to move at the pace of the business. If you don't, you are losing. >> So, would it be safe to say that you guys have a network supercloud? >> Absolutely, yeah. >> We, pretty much, yeah. Absolutely. >> What does that mean to our customer? What's in it for them? What's the benefit to the customer? I got a network supercloud, it connects, provides SLA, all the capabilities I need. What do they get? What's the end point for them? What's the end? >> Atif, maybe you can talk some examples. >> The IT infrastructure is all like distributed now, right? So you have applications running in data centers. You have applications running in one cloud. Other cloud, public clouds, enterprises are depending on so many SaaS applications. So now, these are, you can call these endpoints. So a supercloud or a network cloud, from our perspective, it's a cloud in the middle or a network in the middle, which provides connectivity from any endpoint to any endpoint. So, you are able to connect to the supercloud or network cloud in one way no matter where you are. So now, whichever cloud you are in, whichever cloud you need to connect to. And also, it's not just connecting to the cloud. So you need to do a lot of stuff, a lot of networking inside the cloud also. So now, as Amir was saying, every cloud has its own from a networking, you know, the concept perspective or the construct, they are different. There are limitations in there also. So this supercloud, which is sitting on top, basically, your platform is sitting into the cloud, but the supercloud is built on top of using your platform. So that abstracts all those complexities, all those limitations. So now your limitations are whatever the limitations of that platform are. So now your platform, that platform is in our control. So we can keep building it, we can keep scaling it horizontally. Because one of the things is that, you know, in this cloud era, one of the things is autoscaling these services. So why can't the network now autoscale also, just like your other services. >> Network autoscaling is a genius idea, and I think that's a killer. I want to ask the the follow on question because I think, first of all, I love what you guys are doing. So, I think it's a great example of this new innovation. It's not obvious until you see it, right? Geographical is huge. So, you know, single instance, global instances, multiple instances, you're seeing global. How do you guys look at that global equation? Because as companies expand their clouds into geos, and then ultimately, you know, it's obviously continent, region and locales. You're going to have geographic issues. So, this is an extension of your network cloud? >> Amir: It is the extension of the network cloud because if you look at this hyperscalers, they're sitting pretty much everywhere in the globe. So, wherever their regions are, the beauty of building a supercloud is that you can by definition, be available in those regions. It literally takes a day or two of testing for our stack to run in those regions, to make sure there are no nuances that we run into, you know, for that region. The moment we bring it up in that region, all customers can onboard into that solution. So literally, what used to take months or years to build a global infrastructure, now, you can configure it in 10 minutes basically, and bring it up in less than one hour. Since when did we see any solution- >> And by the way, >> that can come up with. >> when the edge comes out too, you're going to start to see more clouds get bolted on. >> Exactly. And you can expand to the edge of the network. That's why we call cloud the new edge, right? >> John: Yeah, it is. Now, I think you guys got a good solutions, network clouds, superclouds, good. So the question on the premise side, so I get the cloud play. It's very cool. You can expand out. It's a nice layer. I'm sure you manage the SLAs between latency and all kinds of things. Knowing when not to do things. Physics or physics. Okay. Now, you've got the on-premise. What's the on-premise equation look like? >> So on-premise, the kind of customers, we are working with large enterprises, mid-size enterprises. So they have on-prem networks, they have deployed, in many cases, they have deployed SD-WAN. In many cases, they have MPLS. They have data centers also. And a lot of these companies are, you know, moving the applications from the data center into the cloud. But we still have large enterprise- >> But for you guys, you can sit there too with non server or is it a box or what is it? >> It's a software stack, right? So, we are a software company. >> Okay, so no box. >> No box. >> Okay, got it. >> No box. >> It's even better. So, we can connect any, as I mentioned, any endpoint, whether it's data centers. So, what happens is usually these enterprises from the data centers- >> John: It's a cloud endpoint for you. >> Cloud endpoint for us. And they need highspeed connectivity into the cloud. And our network cloud is sitting inside the or supercloud is sitting inside the cloud. So we need highspeed connectivity from the data centers. This is like multi-gig type of connectivity. So we enable that connectivity as a service. And as Amir was saying, you are able to bring it up in minutes, pretty much. >> John: Well, you guys have a great handle on supercloud. I really appreciate you guys coming on. I have to ask you guys, since you have so much experience in the industry, multiple inflection points you've guys lived through and we're all old, and we can remember those glory days. What's the big deal going on right now? Because you can connect the dots and you can imagine, okay, like a Lambda function spinning up some connectivity. I need instant access to a new route, throw some, I need to send compute to an edge point for process data. A lot of these kind of ad hoc services are going to start flying around, which used to be manually configured as you guys remember. >> Amir: And that's been the problem, right? The shadow IT, that was the biggest problem in the enterprise environment. So that's what we are trying to get the customers away from. Cloud teams came in, individuals or small groups of people spun up instances in the cloud. It was completely disconnected from the on-premise environment or the existing IT environment that the customer had. So, how do you bring it together? And that's what we are trying to solve for, right? At a large scale, in a carrier cloud center (indistinct). >> What do you call that? Shift right or shift left? Shift left is in the cloud native world security. >> Amir: Yes. >> Networking and security, the two hottest areas. What are you shifting? Up or down? I mean, the network's moving up the stack. I mean, you're seeing the run times at Kubernetes later' >> Amir: Right, right. It's true we're end-to-end virtualization. So you have plumbing, which is the physical infrastructure. Then on top of that, now for the first time, you have true end-to-end virtualization, which the cloud-like constructs are providing to us. We tried to virtualize the routers, we try to virtualize instances at the server level. Now, we are bringing it all together in a truly end-to-end virtualized manner to connect any endpoint anywhere across the globe. Whether it's on-premise, home, multiple clouds, or SaaS type environments. >> Yeah. If you talk about the technical benefits beyond virtualizations, you kind of see in virtualization be abstracted away. So you got end-to-end virtualization, but you don't need to know virtualization to take advantage of it. >> Exactly. Exactly. >> What are some of the tech involved where, what's the trend around on top of virtual? What's the easy button for that? >> So there are many, many use cases from the customers and they're, you know, some of those use cases, they used to deliver out of their data centers before. So now, because you, know, it takes a long time to spend something up in the data center and stuff. So the trend is and what enterprises are looking for is agility. And to achieve that agility, they are moving those services or those use cases into the cloud. So another technical benefit of like something like a supercloud and what we are doing is we allow customers to, you know, move their services from existing data centers into the cloud as well. And I'll give you some examples. You know, these enterprises have, you know, tons of partners. They provide connectivity to their partners, to select resources. It used to happen inside the data center. You would bring in connectivity into the data center and apply like tons of ACLs and whatnot to make sure that you are able to only connect. And now those use cases are, they need to be enabled inside the cloud. And the customer's customers are also, it's not just coming from the on-prem, they're coming from the cloud as well. So, if they're coming from the cloud as well as from on-prem, so you need like an infrastructure like supercloud, which is sitting inside the cloud and is able to handle all these use cases. So all of these use cases have to be, so that requires like moving those services from the data center into the cloud or into the supercloud. So, they're, oh, as we started building this service over the last four years, we have come across so many use cases. And to deliver those use cases, you have to have a platform. So you have to have your own platform because otherwise you are depending on somebody else's, you know, capabilities. And every time their capabilities change, you have to change. >> John: I'm glad you brought up the platform 'cause I want to get your both reaction to this. So Bob Muglia just said on theCUBE here at Supercloud, that supercloud is a platform that provides programmatically consistent services hosted on heterogeneous cloud providers. So the question is, is supercloud a platform or an architecture in your view? >> That's an interesting view on things, you know? I mean, if you think of it, you have to design or architect a solution before we turn it into a platform. >> John: It's a trick question actually. >> So it's a, you know, so we look at it as that you have to have an architectural approach end to end, right? And then you build a solution based on that approach. So, I don't think that they are mutually exclusive. I think they go hand in hand. It's an architecture that you turn into a solution and provide that agility and high availability and disaster recovery capability that it built into that. >> It's interesting that these definitions might be actually redefined with this new configuration. >> Amir: Yes. >> Because architecture and platform used to mean something, like, aight here's a platform, you buy this platform. >> And then you architecture solution. >> Architect it via vendor. >> Right, right, right. >> Okay. And they have to deal with that architecture in the place of multiple superclouds. If you have too many stove pipes, then what's the purpose of supercloud? >> Right, right, right. And because, you know, historically, you built a router and you sold it to the customer. And the poor customer was supposed to install it all, you know, and interconnect all those things. And if you have 40, 50,000 router network, which we saw in our lifetime, 'cause there used to be many more branches when we were growing up in the networking industry, right? You had to create hierarchy and all kinds of things to figure out how to solve that problem. We are no longer living in that world anymore. You cannot deploy individual virtual instances. And that's what approach a lot of people are taking, which is a pure overly network. You cannot take that approach anymore. You have to evolve the architecture and then build the solution based on that architecture so that it becomes a platform which is readily available, highly scalable, and available. And at the same time, it's very, very easy to deploy. It's a SaaS type solution, right? >> So you're saying, do the architecture to get the solution for the platform that the customer has. >> Amir: Yes. >> They're not buying a platform, they end up with a platform- >> With the platform. >> as a result of Supercloud path. All right. So that's what's, so you mentioned, that's a great point. I want to double click on what you just said. 'Cause I like that what you said. What's the deployment strategy in your mind for supercloud? I'm an architect. I'm at an enterprise in the Midwest. I'm an insurance company, got some cloud action going on. I'm mostly on-premise. I've got the mandate to transform the company. We have apps. We'll be fully transformed in five years. What's my strategy? What do I do? >> Amir: The resources. >> What's the deployment strategy? Single global instance, code in every region, on every cloud? >> It needs to be a solution which is available as a SaaS service, right? So from the customer's perspective, they are onboarding into the supercloud. And then the supercloud is allowing them to do whatever they used to do, you know, historically and in the new world, right? That needs to come together. And that's what we have built is that, we have brought everything together in a way that what used to take months or years, and now taking an hour or two hours, and then people test it for a week or so and deploy it in production. >> I want to bring up something we were talking about before we were on camera about the TCP/IP, the OSI model. That was a concept that destroyed the proprietary narcissist. Work operating systems of the mini computers, which brought in an era of tech prosperity for generations. TCP/IP was kind of the magical moment that allowed for that kind of super networking connection. Inter networking is what's called as a category. It feels like something's going on here with supercloud. The way you describe it, it feels like there's this unification idea. Like the reality is we've got multiple stuff sitting around by default, you either clean it up or get rid of it, right? Or it's almost a, it's either a nuance, a new nuisance or chaos. >> Yeah. And we live in the new world now. We don't have the luxury of time. So we need to move as fast as possible to solve the business problems. And that's what we are running into. If we don't have automated solutions which scale, which solve our problems, then it's going to be a problem. And that's why SaaS is so important in today's world. Why should we have to deploy the network piecemeal? Why can't we have a solution? We solve our problem as we move forward and we accomplish what we need to accomplish and move forward. >> And we don't really need standards here, dude. It's not that we need a standards body if you have unification. >> So because things move so fast, there's no time to create a standards body. And that's why you see companies like ours popping up, which are trying to create a common infrastructure across all clouds. Otherwise if we vent the standardization path may take long. Eventually, we should be going in that direction. But we don't have the luxury of time. That's what I was trying to get to. >> Well, what's interesting is, is that to your point about standards and ratification, what ratifies a defacto anything? In the old days there was some technical bodies involved, but here, I think developers drive everything. So if you look at the developers and how they're voting with their code. They're instantly, organically defining everything as a collective intelligence. >> And just like you're putting out the paper and making it available, everybody's contributing to that. That's why you need to have APIs and terra form type constructs, which are available so that the customers can continue to improve upon that. And that's the Net DevOps, right? So that you need to have. >> What was once sacrilege, just sayin', in business school, back in the days when I got my business degree after my CS degree was, you know, no one wants to have a better mousetrap, a bad business model to have a better mouse trap. In this case, the better mouse trap, the better solution actually could be that thing. >> It is that thing. >> I mean, that can trigger, tips over the industry. >> And that that's where we are seeing our customers. You know, I mean, we have some publicly referenceable customers like Coke or Warner Music Group or, you know, multiple others and chart industries. The way we are solving the problem. They have some of the largest environments in the industry from the cloud perspective. And their whole network infrastructure is running on the Alkira infrastructure. And they're able to adopt new clouds within days rather than waiting for months to architect and then deploy and then figure out how to manage it and operate it. It's available as a service. >> John: And we've heard from your customer, Warner, they were just on the program. >> Amir: Yes. Okay, okay. >> So they're building a supercloud. So superclouds aren't just for tech companies. >> Amir: No. >> You guys build a supercloud for networking. >> Amir: It is. >> But people are building their own superclouds on top of all this new stuff. Talk about that dynamic. >> Healthcare providers, financials, high-tech companies, even startups. One of our startup customers, Tekion, right? They have these dealerships that they provide sales and support services to across the globe. And for them to be able to onboard those dealerships, it is 80% less time to production. That is real money, right? So, maybe Atif can give you a lot more examples of customers who are deploying. >> Talk about some of the customer activity. What are they like? Are they laggards, they innovators? Are they trying to hit the easy button? Are they coming in late or are you got some high customers? >> Actually most of our customers, all of our customers or customers in general. I don't think they have a choice but to move in this direction because, you know, the cloud has, like everything is quick now. So the cloud teams are moving faster in these enterprises. So now that they cannot afford the network nor to keep up pace with the cloud teams. So, they don't have a choice but to go with something similar where you can, you know, build your network on demand and bring up your network as quickly as possible to meet all those use cases. So, I'll give you an example. >> John: So the demand's high for what you guys do. >> Demand is very high because the cloud teams have- >> John: Yeah. They're going fast. >> They're going fast and there's no stopping. And then network teams, they have to keep up with them. And you cannot keep deploying, you know, networks the way you used to deploy back in the day. And as far as the use cases are concerned, there are so many use cases which our customers are using our platform for. One of the use cases, I'll give you an example of these financial customers. Some of the financial customers, they have their customers who they provide data, like stock exchanges, that provide like market data information to their customers out of data centers part. But now, their customers are moving into the cloud as well. So they need to come in from the cloud. So when they're coming in from the cloud, you cannot be giving them data from your data center because that takes time, and your hair pinning everything back. >> Moving data is like moving, moving money, someone said. >> Exactly. >> Exactly. And the other thing is like you have to optimize your traffic flows in the cloud as well because every time you leave the cloud, you get charged a lot. So, you don't want to leave the cloud unless you have to leave the cloud, your traffic. So, you have to come up or use a service which allows you to optimize all those traffic flows as well, you know? >> My final question to you guys, first of all, thanks for coming on Supercloud Program. Really appreciate it. Congratulations on your success. And you guys have a great positioning and I'm a big fan. And I have to ask, you guys are agile, nimble startup, smart on the cutting edge. Supercloud concept seems to resonate with people who are kind of on the front range of this major wave. While all the incumbents like Cisco, Microsoft, even AWS, they're like, I think they're looking at it, like what is that? I think it's coming up really fast, this trend. Because I know people talk about multi-cloud, I get that. But like, this whole supercloud is not just SaaS, it's more going on there. What do you think is going on between the folks who get it, supercloud, get the concept, and some are who are scratching their heads, whether it's the Ciscos or someone, like I don't get it. Why is supercloud important for the folks that aren't really seeing it? >> So first of all, I mean, the customers, what we saw about six months, 12 months ago, were a little slower to adopt the supercloud kind of concept. And there were leading edge customers who were coming and adopting it. Now, all of a sudden, over the last six to nine months, we've seen a flurry of customers coming in and they are from all disciplines or all very diverse set of customers. And they're starting to see the value of that because of the practical implications of what they're doing. You know, these shadow IT type environments are no longer working and there's a lot of pressure from the management to move faster. And then that's where they're coming in. And perhaps, Atif, if you can give a few examples of. >> Yeah. And I'll also just add to your point earlier about the network needing to be there 'cause the cloud teams are like, let's go faster. And the network's always been slow because, but now, it's been almost turbocharged. >> Atif: Yeah. Yeah, exactly. And as I said, like there was no choice here. You had to move in this industry. And the other thing I would add a little bit is now if you look at all these enterprises, most of their traffic is from, even from which is coming from the on-prem, it's going to the cloud SaaS applications or public clouds. And it's more than 50% of traffic, which is leaving your, you know, what you used to call, your network or the private network. So now it's like, you know, before it used to just connect sites to data centers and sites together. Now, it's a cloud as well as the SaaS application. So it's either internet bound or the public cloud bound. So now you have to build a network quickly, which caters to all these use cases. And that's where like something- >> And you guys, your solution to me is you eliminate all that work for the customer. Now, they can treat the cloud like a bag of Legos. And do their thing. Well, I oversimplify. Well, you know I'm talking about. >> Atif: Right, exactly. >> And to answer your question earlier about what about the big companies coming in and, you know, now they slow to adopt? And, you know, what normally happens is when Cisco came up, right? There used to be 16 different protocols suites. And then we finally settled on TCP/IP and DECnet or AppleTalk or X&S or, you know, you name it, right? Those companies did not adapt to the networking the way it was supposed to be done. And guess what happened, right? So if the companies in the networking space do not adopt this new concept or new way of doing things, I think some of them will become extinct over time. >> Well, I think the force and function too is the cloud teams as well. So you got two evolutions. You got architectural relevance. That's real as impact. >> It's very important. >> Cost, speed. >> And I look at it as a very similar disruption to what Cisco's the world, very early days did to, you know, bring the networking out, right? And it became the internet. But now we are going through the cloud. It's the cloud era, right? How does the cloud evolve over the next 10, 15, 20 years? Everything's is going to be offered as a service, right? So slowly data centers go away, the network becomes a plumbing thing. Very, you know, simple to deploy. And everything on top of that is virtualized in the cloud-like manners. >> And that makes the networks hardened and more secure. >> More secure. >> It's a great way to be secure. You remember the glory days, we'll go back 15 years. The Cisco conversation was, we got to move up to stack. All the manager would fight each other. Now, what does that actually mean? Stay where we are. Stay in your lane. This is kind of like the network's version of moving up the stack because not so much up the stack, but the cloud is everywhere. It's almost horizontally scaled. >> It's extending into the on-premise. It is already moving towards the edge, right? So, you will see a lot- >> So, programmability is a big program. So you guys are hitting programmability, compatibility, getting people into an environment they're comfortable operating. So the Ops people love it. >> Exactly. >> Spans the clouds to a level of SLA management. It might not be perfectly spanning applications, but you can actually know latencies between clouds, measure that. And then so you're basically managing your network now as the overall infrastructure. >> Right. And it needs to be a very intelligent infrastructure going forward, right? Because customers do not want to wait to be able to troubleshoot. They don't want to be able to wait to deploy something, right? So, it needs to be a level of automation. >> Okay. So the question for you guys both on we'll end on is what is the enablement that, because you guys are a disruptive enabler, right? You create this fabric. You're going to enable companies to do stuff. What are some of the things that you see and your customers might be seeing as things that they're going to do as a result of having this enablement? So what are some of those things? >> Amir: Atif, perhaps you can talk through the some of the customer experience on that. >> It's agility. And we are allowing these customers to move very, very quickly and build these networks which meet all these requirements inside the cloud. Because as Amir was saying, in the cloud era, networking is changing. And if you look at, you know, going back to your comment about the existing networking vendors. Some of them still think that, you know, just connecting to the cloud using some concepts like Cloud OnRamp is cloud networking, but it's changing now. >> John: 'Cause there's apps that are depending upon. >> Exactly. And it's all distributed. Like IT infrastructure, as I said earlier, is all distributed. And at the end of the day, you have to make sure that wherever your user is, wherever your app is, you are able to connect them securely. >> Historically, it used to be about building a router bigger and bigger and bigger and bigger, you know, and then interconnecting those routers. Now, it's all about horizontal scale. You don't need to build big, you need to scale it, right? And that's what cloud brings to the customer. >> It's a cultural change for Cisco and Juniper because they have to understand that they're still could be in the game and still win. >> Exactly. >> The question I have for you, what are your customers telling you that, what's some of the anecdotal, like, 'cause you guys have a good solution, is it, "Oh my god, you guys saved my butt." Or what are some of the commentary that you hear from the customers in terms of praise and and glory from your solution? >> Oh, some even say, when we do our demo and stuff, they say it's too hard to believe. >> Believe. >> Like, too hard. It's hard, you know, it's >> I dont believe you. They're skeptics. >> I don't believe you that because now you're able to bring up a global network within minutes. With networking services, like let's say you have APAC, you know, on-prem users, cloud also there, cloud here, users here, you can bring up a global network with full routed connectivity between all these endpoints with security services. You can bring up like a firewall from a third party or our services in the middle. This is a matter of minutes now. And this is all high speed connectivity with SLAs. Imagine like before connecting, you know, Singapore to U.S. East or Hong Kong to Frankfurt, you know, if you were putting your infrastructure in columns like E-connects, you would have to go, you know, figure out like, how am I going to- >> Seal line In, connect to it? Yeah. A lot of hassles, >> If you had to put like firewalls in the middle, segmentation, you had to, you know, isolate different entities. >> That's called heavy lifting. >> So what you're seeing is, you know, it's like customer comes in, there's a disbelief, can you really do that? And then they try it out, they go, "Wow, this works." Right? It's deployed in a small environment. And then all of a sudden they start taking off, right? And literally we have seen customers go from few thousand dollars a month or year type deployments to multi-million dollars a year type deployments in very, very short amount of time, in a few months. >> And you guys are pay as you go? >> Pay as you go. >> Pay as go usage cloud-based compatibility. >> Exactly. And it's amazing once they get to deploy the solution. >> What's the variable on the cost? >> On the cost? >> Is it traffic or is it. >> It's multiple different things. It's packaged into the overall solution. And as a matter of fact, we end up saving a lot of money to the customers. And not only in one way, in multiple different ways. And we do a complete TOI analysis for the customers. So it's bandwidth, it's number of connections, it's the amount of compute power that we are using. >> John: Similar things that they're used to. >> Just like the cloud constructs. Yeah. >> All right. Networking supercloud. Great. Congratulations. >> Thank you so much. >> Thanks for coming on Supercloud. >> Atif: Thank you. >> And looking forward to seeing more of the demand. Translate, instant networking. I'm sure it's going to be huge with the edge exploding. >> Oh yeah, yeah, yeah, yeah. >> Congratulations. >> Thank you so much. >> Thank you so much. >> Okay. So this is Supercloud 2 event here in Palo Alto. I'm John Furrier. The network Supercloud is here. Checkout Alkira. I'm John Furry, the host. Thanks for watching. (lively music)

(upbeat music) >> Hey, everyone. Welcome back to New York City. Lisa Martin and John Furrier with theCUBE here live covering the AWS Summit NYC 2022. There's about 15 different summits going on this year, John, globally. We're here with about 10,000 attendees. Just finished the keynote and two guests from SoftwareONE. Please welcome David Torres, the director of cloud services and Asim Khan, a North American AWS services delivery lead at SoftwareONE. Welcome, guys. >> Thank you for having us. >> Thank you for having us. >> Talk to us, David, kick us off. Give the audience an overview of SoftwareONE. What do you guys do? And then tell us a little bit about the AWS partnership. >> Sure, so SoftwareONE, we are one of Microsoft and VMware's largest resellers. We help customers with our IT asset management services, managing their on-premises license real estate, but we're definitely a company that's undergoing a transformation. And when I say that, essentially we're focused on three key pillars with our go to market, supporting the hyperscalers. So we do support AWS, Azure, GCP at modernization because we do see this with a lot of our customers, you know, they're moving from on premises to AWS. They have a lot of technical debt and they're looking at options to modernize that, and mission critical workloads like SAP, Windows, Oracle, and we offer, you know, a suite of professional services, managed services, migrations, quite quite a bit of services. >> Asim, can you kind of double click on the services that SoftwareONE delivers to customers? Maybe some key use cases? >> Yeah, sure. I think in the Amazon space, I would say we're currently focusing in the area of funding programs that Amazon currently has, for example, the Migration Acceleration Program, which is a map with supporting customers basically with the entire cloud journey that they might have, or helping them define that cloud journey. And then we can help the customer in any phase of that journey as well to basically take them a step step above. So that's what our area of focus is right now to basically help enable customers. >> So on the Microsoft, AWS, you mentioned Microsoft, I mean, they've had the enterprise business for years and, you know, developers was their, you know, ecosystem. Back in the day, "Developers, developers, developers" as Steve Ballmer once said, and that was their crown jewel. But then, you know, .NET now has Linux. They got a lot more open source. So those enterprises, their customers are changing. A lot of them are on AWS. So talk about that dynamic of the shift to AWS. And now that Azure's out there, what's the relationship of those hyperscale? How do you guys navigate those waters? >> Sure, I mean, it's always the concept of work backwards from the customer, right? What are the business outcomes they're trying to drive and, you know, define a strategy from that. And it's still a function of change management for a lot of customers, people, process and tools. So, you know, in a lot of cases, our customers are evaluating what's a skillset of our people, do we need to upskill them, the tools that we're using, how do we use those on the multiple clouds, right? And then the processes. So for us, you know, we have some customers that prefer one cloud over another. We have customers that run cross multiple clouds. They deploy different workloads. And then we have some customers that transformation and modernization are really big top of stack for them. So in some cases, those customers are going to AWS and, you know, we're helping them kind of with that journey. It's interesting, Amazon literally won the developer cloud market early on, going back 15 years. >> Absolutely. >> But not all developers, enterprise developers who, you know, in the enterprises, they're stuck in their ways, but are changing. This is a digital transformation moment 'cause cloud native applications, the modernization piece, is developer centric. >> Absolutely. >> That's key, the developers. So I'm interested in your perspective and reaction to what's going on in that developer market right now with DevOps exploding in a great way, the goodness of the cloud coming more and more to the table. >> Sure, no, absolutely, great question. So I think with enterprise developers, you know, we see just the businesses driving a lot of the outcomes, right? So the modernization aspect of needing to get to market faster, needing to deploy applications faster, having a more efficient operating model, more automation. And for your point on the .NET modernization, you know, we work with customers too as well. We made an acquisition a couple years ago, a company, InterGrupo. They actually specialize in this in .NET modernization. So we know we're seeing some customers that are moving to Linux, right? And they want to go .NET Core and, you know, they're kind of standardizing on Linux. So we kind of see a, you know, wide spectrum, but yeah, maybe. >> Where are your customer conversations as things have changed so much in accelerated dramatically in the last couple of years? >> Sure. >> Obviously we've talked about the developers, but talk to me about, you know, business imperatives for businesses in every industry to digitally transform, number one, to survive the last couple of years, but, two, to be at a competitive advantage. >> Sure, no, so I think with businesses, you know, obviously, 2017, innovation, 2022, it's a little bit different, right? There's obviously macro conditions, you have COVID. So, you know, we're seeing where customers are essentially really doing their due diligence, right, when they make their choices more than ever before. And they're trying to maximize, right, their spend and their ROI when they move to cloud and that involves, you know, the licensing advisory, what they can move, what they can modernize, migrations, and just the roadmap and what strategy. But what I see is, it's the business outcomes, what they're trying to drive, and, you know, we're seeing some trends too with maybe a more conservative segments like healthcare, public sector, right, utilities that they are really investing and moving towards the cloud. >> Asim, I got a question from Twitter, a DM, I want to ask. You guys are on the front line. So you see the customers, which is really great 'cause it's primary data. You guys are right there. And you're not biased. You work with whatever hyperscaler. So it's really good. So the question that came up was, "Can you ask them the following, 'What's going on in the data warehouse front, cloud warehouse front, you got Redshift competing with Synapse, Azure Synapse, Google BigQuery, and then you got Snowflake and Databricks out there?'" So you got this new data provider, but it's not a data warehouse. And you got data refactoring on AWS, for instance as well. So, you know, this whole new level of data analytics with how you're doing cloud data. And you call it a data warehouse, I guess for categorically, but it's really not a warehouse. It's a data lake and you got lake front foundation. What are you guys seeing on the front lines with customers as they try to squint through how to deal with the data and which cloud to work with? >> That is a good question. I mean, I've been in the industry a long time. I've worked for some major financial institutions as well and data or big data was big for that industry. (John chuckles) So I've seen how the trends have changed, but from our perspective, because we are an agnostic services company, as you mentioned, we basically can work with any hyperscaler, we initially see what the business needs are for the customer. If the customer is already, for example, using Amazon, we initially want to have the customer use native tooling available within that hyperscaler space. If the customer is open for us to give them any recommendations, of course, we look at the business needs. We look at what type of data is going to be stored. What the industry is. Based on all of those inputs is when we basically give the right recommendation, it could be a third party data warehousing solution. It could be an area one. It all depends on what the business needs of the customer are. >> So for example, and most companies do this they build on say AWS, who is one of the first big clouds. And then they go, "Hey, we got customers over there at Azure, that's Microsoft they got thousands and thousands of customers. Snowflake's done, and they have marketplaces as well." So you guys are kind of agnostic it sounds like. Whatever the architecture is on the stack that they choose. >> Correct, so that's what makes us special. I think we are one of those services companies which is quite unique in the industry. And I don't say that just because I work for SoftwareONE, (John chuckle) that is a fact that gives us a very unique perspective of giving the customer the right piece of advice because we've seen it all and we've done it all. So that's, I think what puts us unique and regarding technology, all the different hyperscalers, they might have a very similar backend technology stack, but what the front end services each hyperscaler is building are very unique. Amazon being the leader in this space, they've been ahead of the curb by a few years, they will always have certain solutions which are above the rest. So I mean, I've always been an Amazon person, so I'm slightly biased, but, hey, I mean, I'm not complaining about that. >> The good news is the customer has choices. >> Right, absolutely. And we do see customers that want to be agnostic, right, >> Yeah. >> With their technology choices. Actually, that's a good segue about our partnership with AWS. We recently signed a strategic collaboration agreement between both parties. So there's going to continue to be big investment from us, scaling out our professional services, our practice areas, and then also key focus area for a fin ops. >> Is that your number one area? >> It's one of the areas, yeah. >> Okay, what your top three practice areas? >> Top three, mission critical workloads. So enterprise workloads like SAP, Microsoft, Oracle, two, app modernization, and, three, definitely fin ops and the hyperscalers, right? Because we see a lot of customers that have already heavily adopted cloud, they're struggling with that cloud financial management aspect. >> So if they're struggling, what are some of the key business outcomes that they come to you, to SoftwareONE, and say, "Help us figure this out. We have to achieve A, B, C." >> Sure, so depending on the maturity of the customer and where they are in the journey, if they're already very heavily adopting cloud, you know, AWS or Azure, we see in a lot of cases that the customers are unsure if they're getting the most out of their cloud spend, and they're looking at their operations, and their governance, and, you know, they're coming to us and basically asking us, "Hey, we feel like our cloud spend is a little bit out of control. Can you help us?" And that's where we can come in, you know, provide the advice, the guidance, the advisory but also give them the tooling, right, to have visibility into their cloud spend and make those conditions. And we also offer a managed fin op service that will end to end do this for the customers to help to manage their resale, their invoicing, their marketplace buy, as well as their cloud spend. >> So obviously the engagement varies customer to customer. What's a typical timeframe? Like how long does it take you to really get in there with a customer, understand the direction they need to go, and create the right plan? >> Sure, again, comes back to the cloud journey. You know, if the customer is still, you know, very much on prem and maybe more, you know, conservative, it may start with licensing assessments just to give them an idea of what it would cost to move those workloads, right? Then it turns into migration modernization, you know, it can be an anywhere from one to six months, you know, of just consulting, right, to get the customer ready. And then we help 'em, you know, obviously with their migration plan. But if they're already heavily adopting cloud, you know, we do remediation work, we do optimization. Obviously, SAP, that's a longer cycle, so. (chuckles) >> So I got to ask you guys, what is the PyraCloud? SoftwareONE as a platform PyraCloud. What is that? >> I might want to answer that. >> Sure. (chuckles) >> It's pronounced PyraCloud. >> How do you pronounce it? >> PyraCloud. >> PyraCloud, okay. I like PyraCloud better. (chuckles) >> With the Y in there. It's basically our spend insight platform. It gives customers an a truly agnostic single pane of glass view into their entire cloud enterprise spend. What I mean by that is with a single login, the customer has access to looking at their enterprise spend on AWS, on Azure, as well as GCP. And in the future, of course, we're going to add other hyperscalers in there as well. Because of the single pin of glass view, the customer has a true or the customer leadership, or, for example, the CTO has a single pane of glass view into the entire spend. We allow the customer to basically have an enterprise level tagging strategy, which is across all the hyperscalers as well as then allowing a certain amount of automated cost management as well, which is again agnostic and enterprisewide. >> Can you share an example of a customer for whom you've given them this single pane of glass through PyraCloud, and by how much they've been able to reduce costs or optimize costs? >> Yes, mostly the customers who would be a very good fit for PyraCloud would be a slightly more mature customer who already has a large amount of spend, or who is already very mature in their different hyperscalers. And usually what we've seen once a customer is mature in the cloud over a certain period of time, controlling costs does become difficult, even though you might have automation in place, but to get to that automation, you have to go through a certain amount of time of basically things breaking and you fixing them. So this is where per cloud becomes very helpful to help control that. And building a strategy, which once in place is repetitive and helps you manage costs and spend in the cloud year after year then. >> One of the things I want to get your guys reaction before we wrap up is this show here has got 10,000 people which is a big number, post COVID, events are coming back but in the past five years, or six years, or seven years, since like 2015, a lot's changed. What's changed the most? Shared to the audience what you think is the biggest step function change that's happening right now? Is it that data's now prime time? Everyone's got a lot of data, hasn't figured out the consequences with it. Is it scale? Is it super cloud? Is it the ecosystem because this is not stopping ,the growth in the enterprise on the digital transformation is expanding, even though GDPs down, and gas prices are high, and inflation, this isn't stopping. Now, some of the unicorns might be impacted by the headwinds, the big overfunded valuations but not the ecosystem. What's changed? What's the big change? >> Well, I think what I see is this cloud is becoming the defacto operating model and customers are working backwards from that as their primary goal, right, to operate in the cloud. And as I mentioned before, they really are doing due diligence, right, to really understand the best approach for seeing kind of maybe some of the challenges other customers have had when they first moved to AWS, so. And I'm, you know, seeing industries that maybe five years ago, you know, were not about moving to cloud, like healthcare. I can tell you a lot of our healthcare customers, they're trying to get to cloud as fast as possible. >> It's a wake up call. >> It's a wake up call. >> Absolutely. Absolutely. >> Asim, what's your reaction? >> In my point of view with what's happened these last few years with a lot of companies having their employees work from home and being remotely, I think end user compute was one of the big booms which happened about two years ago. We support a lot of customer in that space as well. And then overall, I think we actually saw that there was much more business focus with employees working for home for some reason. And we saw that internally in our own organization as well. And with that focus, the whole area of being more lean and agile in the cloud space, I think became much more prevalent for all the enterprises. Everybody wanted to be spend conscious, availing the different tools available in the cloud arena, like autoscaling like using, for example, containerization, using such solutions to basically be more resilient and more lean to basic control costs. >> So necessity is the mother of all inventions >> It is. >> That got forced. So you got wake up call and then a forcing function to like, okay, but exposes the consequences of a modern application, modern environment because they didn't, they're out of business. So then it's like, okay, this is actually working, (chuckles) why don't we like kill that project that we've doubled down on, move it over here." So I see that same pattern. What do you guys see? >> Yeah, no, I mean, we see that pattern as well. Just modernization, efficiency. You could just move faster, more elasticity, you know, and, again, the wake up call, you know, for organizations that people couldn't go to data centers, right? (chuckles) >> Yeah. (chuckles) >> We actually have a customer, that was literally the reason they made the move, right, to AWS. >> And I would add one more thing to that particular point. With the time available, I think customers were able to actually now re-architect their applications slightly better to be able to avail, for example, no server type of solutions or using certain design principles which were much more cost lean in the cloud. That's what we saw. I think customers spent that time available over the past couple of years to be much more cloud centric, I would say. >> Yeah, the forced March was really an accelerant and a catalyst in a lot of ways for good, and there's definitely some silver linings there. Guys, we're out of time. But thank you so much for joining John >> Oh, awesome. >> And me talking about SoftwareONE, what you guys are doing, helping customers, what you're doing with AWS and the hyperscalers. We appreciate your time and your insights. >> Thank you. >> Awesome. Thank you for having us. >> Thanks for having us. >> Really appreciate it. >> All right, for our guests and John Furrier, I'm Lisa Martin. You're watching theCUBE live from New York City at AWS Summit at NYC. Stick around, John and I will be right back with our next guest. (upbeat music) (upbeat music continues)

>>Hey everyone. Welcome back to the Cube's live coverage of snowflake summit 22 live from Las Vegas. Caesar's forum. Lisa Martin here with Dave Valante. We've got a couple of guests here. We're gonna be talking about every day. AI. You wanna know what that means? You're in the right spot. Kurt UL joins us, the chief customer officer at data ICU and the mod Conn, the head of AI and ML strategy at snowflake guys. Great to have you on the program. >>It's wonderful to be here. Thank you so much. >>So we wanna understand Kurt what everyday AI means, but before we do that for the audience who might not be familiar with data, I could give them a little bit of an overview. What about what you guys do your mission and maybe a little bit about the partnership? >>Yeah, great. Uh, very happy to do so. And thanks so much for this opportunity. Um, well, data IKU, we are a collaborative platform, uh, for enterprise AI. And what that means is it's a software, you know, that sits on top of incredible infrastructure, notably snowflake that allows people from different backgrounds of data, analysts, data, scientists, data, engineers, all to come together, to work together, to build out machine learning models and ultimately the AI that's gonna be the future, uh, of their business. Um, and so we're very excited to, uh, to be here, uh, and you know, very proud to be a, a, a very close partner of snowflake. >>So Amad, what is Snowflake's AI strategy? Is it to, is it to partner? Where do, where do you pick up? And Frank said today, we, we're not doing it all. Yeah. The ecosystem by design. >>Yeah. Yeah, absolutely. So we believe in the best of breed look. Um, I think, um, we, we think that we're the best data platform and for data science and machine learning, we want our customers to really use the best tool for their use cases. Right. And, you know, data ICU is, is our leading partner in that space. And so, you know, when, when you talk about, uh, machine learning and data science, people talk about training a model, but it's really the difficult part and challenges are really, before you train the model, how do you get access to the right data? And then after you train the model, how do you then run the model? And then how do you manage the model? Uh, that's very, very important. And that's where our partnership with, with data, uh, IKU comes in place. Snowflake provides the platform that can process data at scale for the pre-processing bit and, and data IKU comes in and really, uh, simplifies the process for deploying the models and managing the model. >>Got it. Thank >>You. You talk about KD data. Aico talks about everyday AI. I wanna break that down. What do you mean by that? And how is this partnership with snowflake empowering you to deliver that to companies? >>Yeah, absolutely. So everyday AI for us is, uh, you know, kind of a future state that we are building towards where we believe that AI will become so pervasive in all of the business processes, all the decision making that organizations have to go through that it's no longer this special thing that we talk about. It's just the, the day to day life of, uh, of our businesses. And we can't do that without partners like snowflake and, uh, because they're bringing together all of that data and ensuring that there is the, uh, the computational horsepower behind that to drive that we heard that this morning in some of the keynote talking about that broad democratization and the, um, let's call it the, uh, you know, the pressure that that's going to put on the underlying infrastructure. Um, and so ultimately everyday AI for us is where companies own that AI capability. They're building it themselves very broad, uh, participation in the development of that. And all that work then is being pushed down into best of breed, uh, infrastructure, notably of course, snowflake. Well, >>You said push down, you, you guys, you there's a term in the industry push down optimization. What does that mean? How is it evolving? Why is it so important? >>So Amma, do you want to take a first step at that? >>Yeah, absolutely. So, I mean, when, when you're, you know, processing data, so saying data, um, before you train a, uh, a model, you have to do it at scale, that that, that data is, is coming from all different sources. It's human generated machine generated data, we're talking millions and billions of rows of data. Uh, and you have to make sense of it. You have to transform that data into the right kind of features into the right kind of signals that inform the machine learning model that you're trying to, uh, train. Uh, and so that's where, you know, any kind of large scale data processing is automatically pushed down by data IQ, into snowflakes, scalable infrastructure. Um, so you don't get into like memory issues. You don't get into, um, uh, situations where you're where your pipeline is running overnight, and it doesn't finish in time. Right? And so, uh, you can really take advantage of the scalable nature of cloud computing, uh, using Snowflake's infrastructure. So a lot of that processing is actually getting pushed down from data I could down into the scalable snowflake compute engine. How >>Does this affect the life of a data scientist? You always hear a data scientist spend 80% of the time wrangling data. Uh, I presume there's an infrastructure component around that you trying, we heard this morning, you're making infrastructure, my words, infrastructure, self serve, uh, does this directly address that problem and, and talk about that. And what else are you doing to address that 80% problem? >>It, it certainly does, right? Uh, that's how you solve for, uh, data scientists needing to have on demand access to computing resources, or of course, to the, uh, to the underlying data, um, is by ensuring that that work doesn't have to run on their laptop, doesn't have to run on some, you know, constrained, uh, physical machines, uh, in, in a data center somewhere. Instead it gets pushed down into snowflake and can be executed at scale with incredible parallelization. Now what's really, uh, I important is the ongoing development, uh, between the two products, uh, and within that technology. And so today snowflake, uh, announced the introduction of Python within snow park, um, which is really, really exciting, uh, because that really opens up this capability to a much wider audience. Now DataCo provides that both through a visual interface, um, in historically, uh, since last year through Java UDFs, but that's kind of the, the two extremes, right? You have people who don't code on one side, you know, very no code or a low code, uh, population, and then a very high code population. On the other side, this Python, uh, integration really allows us to, to touch really kind the, the fat center of the data science population, who, uh, who, for whom, you know, Python really is the lingua franca that they've been learning for, uh, for decades now. Sure. So >>Talking about the data scientist, I wanna elevate that a little bit because you both are enterprise customers, data ICO, and snowflake Kurt as the chief customer officer, obviously you're with customers all the time. If we look at the macro environment of all the challenges, companies have to be a data company these days, if you're not, you're not gonna be successful. It's how do we do that? Extract insights, value, action, take it. But I'm just curious if your customer conversations are elevating up to the C-suite or, or the board in terms of being able to get democratize access to data, to be competitive, new products, new services, we've seen tremendous momentum, um, on, on the, the part of customer's growth on the snowflake side. But what are you hearing from customers as they're dealing with some of these current macro pains? >>Yeah, no, I, I think it is the conversation today, uh, at that sea level is not only how do we, you know, leverage, uh, new infrastructure, right. You know, they they're, you know, most of them now are starting to have snowflake. I think Frank said, uh, you know, 50% of the, uh, fortune 500, so we can say most, um, have that in place. Um, but now the question is, how do we, how do we ensure that we're getting access to that data, to that, to that computational horsepower, to a broader group of people so that it becomes truly a transformational initiative and not just an it initiative, not just a technology initiative, but really a core business initiative. And that, that really has been a pivot. You know, I've been, you know, with my company now for almost eight years, right. Uh, and we've really seen a change in that discussion going from, you know, much more niche discussions at the team or departmental level now to truly corporate strategic level. How do we build AI into our corporate strategy? How do we really do that in practice? And >>We hear a lot about, Hey, I want to inject data into apps, AI, and machine intelligence into applications. And we've talked about, those are separate stacks. You got the data stack and analytics stack over here. You got the application development, stack the databases off in the corner. And so we see you guys bringing those worlds together. And my question is, what does that stack look like? I took a snapshot. I think it was Frank's presentation today. He had infrastructure at the lowest level live data. So infrastructure's cloud live data. That's multiple data sources coming in workload execution. You made some announcements there. Mm-hmm, <affirmative>, uh, to expend expand that application development. That's the tooling that is needed. Uh, and then marketplace, that's how you bring together this ecosystem. Yes. Monetization is how you turn data into data products and make money. Is that the stack, is that the new stack that's emerging here? Are you guys defining that? >>Absolutely. Absolutely. You talked about like the 80% of the time being spent by data scientists and part of that is actually discovering the right data. Right. Um, being able to give the right access to the right people and being able to go and discover that data. And so you, you, you go from that angle all the way to processing, training a model. And then all those predictions that are insights that are coming out of the model are being consumed downstream by data applications. And so the two major announcements I'm super excited about today is, is the ability to run Python, which is snow park, uh, in, in snowflake. Um, that will do, you know, you can now as a Python developer come and bring the processing to where the data lives rather than move the data out to where the processing lives. Right. Um, so both SQL developers, Python developers, fully enabled. Um, and then the predictions that are coming out of models that are being trained by data ICU are then being used downstream by these data applications for most of our customers. And so that's where number, the second announcement with streamlet is super exciting. I can write a complete data application without writing a single line of JavaScript CSS or HTML. I can write it completely in Python. It's it makes me super excited as, as a Python developer, myself >>And you guys have joint customers that are headed in this direction, doing this today. Where, where can you talk about >>That? Yeah, we do. Uh, you know, there's a few that we're very proud of. Um, you know, company, well known companies like, uh, like REI or emeritus. Um, but one that was mentioned today, uh, this morning by Frank again, uh, Novartis, uh, pharmaceutical company, you know, they have been extremely successful, uh, in accelerating their AI and ML development by expanding access to their data. And that's a combination of, uh, both the data ICU, uh, layer, you know, allowing for that work to be developed in that, uh, in that workspace. Um, but of course, without, you know, the, the underlying, uh, uh, platform of snowflake, right, they, they would not have been able to, to have re realized those, uh, those gains. And they were talking about, you know, very, very significant increases in inefficiency everything from data access to the actual model development to the deployment. Um, it's just really, really honestly inspiring to see. >>And it was great to see Novartis mentioned on the main stage, massive time to value there. We've actually got them on the program later this week. So that was great. Another joint customer, you mentioned re I we'll let you go, cuz you're off to do a, a session with re I, is that right? >>Yes, that's exactly right. So, uh, so we're going to be doing a fireside chat, uh, talking about, in fact, you know, much of the same, all of the success that they've had in accelerating their, uh, analytics, workflow development, uh, the actual development of AI capabilities within, uh, of course that, uh, that beloved brand. >>Excellent guys, thank you so much for joining Dave and me talking about everyday AI, what you're doing together, data ICO, and snowflake to empower organizations to actually achieve that and live it. We appreciate your insights. Thank you both. You guys. Thank you for having us for our guests and Dave ante. I'm Lisa Martin. You're watching the Cube's live coverage of snowflake summit 22 from Las Vegas. Stick around our next guest joins us momentarily.

>>The cube presents, Coon and cloud native con Europe, 2022, brought to you by red hat, the cloud native computing foundation and its ecosystem partners. >>Welcome to Valencia Spain in Coon cloud native con Europe, 2022. I'm your host Keith Townsend. And we're continuing the conversation with community, with startups, with people building cloud native, a cube alum joint by a CTO. And not as the CTO advisor. I really appreciate talking to CTOs Capel. Th Lou don't forgive me if I murder the name, that's a tough one. I'm I'm, I'm getting warmed up to the cubey, but don't worry. When we get to the technical parts, it's gonna be fun. And then a cube alum, Umer K director of marketing Capel. You're the CTO. So we we'll start out with you. What's the problem statement? What, what, what are you guys doing? >>So, uh, we're building on top of an open source project podcast, custodian, uh, that is in CNCF. And that I built when I was at capital one and just as they were going, they're taking those first few steps. It's a large regulated enterprise into the cloud. And the challenge that I saw was, you know, how do we enable developers to pick whatever tools and technologies they want, if they wanna use Terraform or cloud formation or Ansible? I mean, the cloud gives us APIs and we wanna be able to enable people to use those APIs through innovative ways. Uh, but at the same time, we wanna make sure that the, regardless of what choices those developers make, that the organization is being is being well managed, that all those resources, all that infrastructure is complying to the organizational's policies. And what we saw at the time was that what we were getting impediments around our velocity into the cloud, because we had to cover off on all of the compliance and regulation aspects. >>And we were doing that them as one offs. And so, uh, taking a step back, I realized that what we really needed was a way to go faster on the compliance side and clock custodian was born out of that effort side of desk that we took through enterprise wide. And it was really about, um, accelerating the velocity around compliance, but doing it in the same way that we do application and infrastructure is code. So doing policy as code in a very simple readable YAML DSL, um, because, you know, PO you have, we, anytime we write code, we're gonna more people are gonna read that code than, than are going to need to be able to write it. And so being able to make it really easy to understand from both the developers that are in the environment from the compliance folks or auditors or security folks that might wanna review it, um, it was super important. And then instead of being at the time, we saw lots of very under products and they were all just big walls of red in somebody's corner office and getting that to actually back the information back in the hands of developers so that they can fix things, um, was problematic. So being able to do time remediation and real time collaboration and communication back to developers, Hey, you put a database on the internet. It's okay. We fixed it for you. And here's the corporate policy on how to do it better in the future. >>So this is a area of focus of mind that people, I think don't get right. A lot, the technology hard enough by itself. The transformation cloud is not just about adopting new technologies, but adopting new processes, the data, and information's there automatically. But when I go to an auditor or, or, uh, compliance and say, Hey, we've changed the process for how do we do change control for our software stack? I get a blank stare. It's what do you mean we've been doing it this way for the past 15, 20 years, that's resistance, it's a pain point and projects fail due to this issue. So talk to me about that initial customer engagement. What's what's that conversation like? >>So we start off by deploying our, our platform on top of buck custodian. Um, and as far as our customers, and we give them a view of all the things that are in their cloud, what is their baseline, so to speak. Um, but I think it's really important. Like I think you bring up a good point, like communication, the challenge, larger challenge for enterprises in the cloud, and especially with grocery compliance is understanding that it is not a steady state. It's always, there's always something new in the backlog. And so being able, and the, one of the challenges for larger orgs is just being able to communicate out what that is. I remember changing a tag policy and spending the next two years, explaining it to people what the actual tag policy was. Um, and so being able to actually inform them, you know, via email, via slack, via, you know, any communication mechanism, uh, as they're doing things is, is so powerful to be able to, to help the organization grow together and move and get an alignment about what, what the, what the new things are. >>And then additionally, you know, from a perspective of, uh, tooling that is built for the real world, like being able to, as those new policies come into play, being able to say, okay, we're going to segment into stopping the bleeding on the net new and being able to then take action on what's already deployed that now needs to become into compliance is, is really important. But coming back to your question on customer engagements, so we'll go in and we'll deploy, uh, a SAC platform for them. We'll basically show them all of the things that are there already and extent. Um, we provide a real time SQL interface that customers can use, um, that is an asset inventory of all their cloud assets. Uh, and then we provide, uh, policy packs that sort of cover off on compliance, security, cost, optimizations, and opportunities for them. Uh, and then we help them through, uh, get ops around those policies, help deploy remediation activities and capabilities for their environment. >>So walk me through some of the detail of, of, of the process and where the software helps and where people need to step in. I'm making I'm, I'm talking to my security auditor, and he's saying, you know what, Keith, I understand that the Aw, that the, uh, VM talking to the application, VM talking to the Oracle database, there is a firewall rule that says that that can happen. Show me that rule in cloud custodian. And you're trying to explain, well, well, there's no longer a firewall. There's a service. And the service is talking to that. And it, it is here and clouds, custodian and St is whether Stant help come to either help with the conversation, or where do I inject more of my experience and my ability to negotiate with the auditor. >>So stalet from the perspective, uh, and if we take a step back, we, we talk about governances code and, and the four pillars around compliance, security, cost, optimization operations, uh, that we help organizations do. But if we take a step back, what is cloud custodian? Cloud custodian is really a cloud orchestrator, a resource orchestrator. What <inaudible> provides on top of that is UI UX, um, policy packs at scale execution, across thousands of accounts, but in the context of an auditor, what we're really providing is here's the policy that we're enforcing. And here's the evidence, the attestation over time. And here's the resource database with history that shows how we, how we got here, where we compliant last year to this policy that we just wrote today. >>So shifting the conversation, you just mentioned operations. One of the larger conversations that I have with CIOs and CTOs is where do I put my people? Like this is a really tough challenge. When you look at moving to something like a SRE model, or, uh, let's say, even focus on the SRE, like what, where does the SRE sit in an organization? How does stack, like if at all, help me make those types of strategic decisions if I'm talking about governance overall. So, >>So I think in terms of personas, if you look at there's a cloud engineer, then SRE, I think that what at its core Stackler and cloud custodian does is a centralized engine, right? So your cost policies, your compliance policies, your security policies are not in a silo anymore. It's one tool. It's one repository that everyone can collaborate on as well. And even engineering, a lot of engineering teams run custodian and, and adopt custodian as well. So in terms of persona stack, it really helps bring it together. All teams have the same simple YAML DSL file that they can write their policies, share their policies and communicate and collaborate better as well. >>Yeah. So I mean, cloud transformation for an enterprise is a deeper topic. Like I think, you know, there's a lot of good breast practices establishing a cloud center of excellence. Um, I, I think, you know, investing in training for people, uh, getting certification so everyone can speak the same language when it comes to cloud is a key aspect. When it comes to the operations aspect, I very much believe that you should have, you know, try to devolve and get the developers writing, uh, some of the DevOps. And so having SREs around for the actual application teams is, is valuable, but you still have a core cloud infrastructure engineering group that's doing potentially any of your core networking, any of your, you know, IM authentication aspects. And so, uh, what we found is that, you know, SLA and cloud custodian get PR primarily get deployed by one of three groups. >>The, uh, you know, you've got the, the CIO buyer within that cloud infrastructure engineering team. And what we found is that group is because they're working with the application teams in a read right way. Uh, they're very much more, um, uh, used to doing and open to doing remediation in real time. Um, and so, and then we also have the CISO teams that want to get to a secure compliance state, be able to do audit and, and validate that all the environments are, um, you know, secure, frankly. And then we get to the CFO groups. Uh, and so, and this sometimes is part of the cloud center of excellence. And so it, it has to be this cross team collaboration. And they're really focused on the, that, that cost optimization, finding the over provision, underutilized things, establishing workloads for dev environments to turn them off at night. Um, and of course, respective of time zones, cause we're all global these days. Uh, and so those are sort of the three groups that we see that sort of really want to engage with us because we can provide value for them to help their accelerate their business goals. >>So that's an expansive view, cost compliance, security operations. That's a lot, I'm thinking about all the tools, all the information that feeds into that, where does cloud custodians start and stop? Like, am I putting cloud custodian agents on servers or, uh, pods, like how, how am I interacting with this? >>So the core clock suiting is just to see lot it's stateless, it's designed to be operationally simple. Um, and so you can run it in Kubernetes, in Jenkins. We've seen people use GitLab. We've seen people run just as a query interactive tool just from, um, investigations perspective on their laptop. But when you write a policy, a policy really consists of, you know, a couple of core elements. Uh, you identify a resource you want to target say an S3 bucket or, uh, a Google cloud VM. And then you say establishes that a filters. I want to look for all the C two instances that are on public subnets with an IM roll attached that has the ability to, uh, create another IM user. And so that, you know, you filter down, you ask the arbitrary questions to filter to the interesting set of things you want, and then you take a set of actions on them. >>So you might take an action, like stop an C two instance, and you might use it as an incident response. Um, you might, uh, use it for off hours in a, in that type of policy. So you get this library of filters and actions that you can combine to form, you know, millions of different types of policies. Now, we also have this notion of an execution mode. So you might say, uh, let's operate in real time. Whenever someone launches this instance, whenever there's an API call, we want to introspect what that API I call is doing and make sure that it's compliant to policy. Now, when you do that, custo will, when you, and you run it with the COI, cause you will actually provision a Lambda function and hook up the event sources to it. Uh, and sorry, Lambda really the serverless we bind into the serverless native capabilities of the underlying cloud provider. So Google cloud function, Azure serverless functions, uh, and native AWS Lambda native us. And so now that policy is effectively hermetically sealed, running, uh, in the Seus runtime of that cloud and responding to API calls in real time, all with, you know, structured outputs and logs and metrics to the native cloud provider capabilities around those. Um, and that really ensures that, uh, you know, it's effectively becomes operation free from the perspective of the user of having to maintain infrastructure >>For it. So let's talk about >>Agent agent list and API based. >>Let's talk about like the a non-developer use case specifically finance. Absolutely. We, you have to deploy the ability to deploy, uh, um, uh, SAP in a, uh, E C two instance, but it's very expensive. Do it only when you absolutely need to do it, but you have the rights to do it. And I wanna run a, uh, a check to see if anyone's doing it like this is this isn't a colder developer, what is their experience? So, >>So primarily we focus on the infrastructure. So low balancers, VMs, you know, encryption and address on discs. Um, when we get into the application workloads running on those instances, we spend, we don't spend that that's on our target focus area. Mm-hmm <affirmative>, we can do it. Uh, and it really depends on the underlying cloud provider's capabilities. So in Amazon, there's a system called systems manager and it runs, and it's basically running an agent on the box. We're not running the agent, but we can communicate with that agent. We can, I inspect the, the inventory that's running on that box. We can send commands to that box, through those serverless functions and through those policies. And so we see it commonly used for like incident response and a security perspective where you might wanna take a memory snapshot of, of, of the instance before, uh, um, yeah, putting it into a forensic cloud and adding >>To that, like these days we're seeing the emerging personas of a fops engineer or a fops director as well, because cost in cloud is totally different. So what custodian and Stackler allows to do is again, using the simple policy files. Even if they have a non-developer background, they can understand this DSL, they can create policies, they can better, uh, target developers, better get them to take actions on policy as well. If they're overspending in the cloud or underspending in the cloud, uh, especially with St. You get, they get a lot of, out of the box dashboards and policy packs too. So say they can really understand how the cost has been consumed. They can have the developers take actions because a lot of the fops finance people complain like my developers does not understand it. Right. How do we get them to take action and make sure we are not over spending? Right. So with custodian policies, they're able to send them, uh, educational messages on slack or open a J ticket and really enforce them to take action as well and start saving cost. Like >>If you, uh, if you imagine cloud custodian as, um, you know, cleaning staff for, for the, your, your cloud environment, like it, it's, uh, you know, if you go to a typical, you know, cloud account, you're gonna see chairs that are 10 feet tall sitting at the table. You're gonna, because it's been over provision and obviously, you know, one can use it. Um, you're gonna find like the trash is overflowing because no one set up a log retention policy on the log group or set up S3, uh, life cycle rules on their buckets. And so you just have this, um, sort of this, uh, this explosion of things that people now, you know, beyond application functioning, like beyond, you know, getting to, you know, high performance, Dr. Capable, uh, SLAs around your application model, you now have to worry about the life cycle of all those resources and helping people manage that life cycle and making sure that they're using the, the, just the resources and consumption that they need, because we're all utilization based, uh, in the cloud. And so getting that to be more in line with what the application actually needs is really where we can help organizations and the CFO cost context. >>So, Emil, you got 10 seconds to tell me why you brought me a comic book. >><laugh> we created this comic book, uh, to explain the concept of governance scored in a simplified fashion. I know Keith, you like comic books, I believe. Uh, so it's a simple way of describing what we do, why it's important for pH ops for SecOps teams. And it talks about custodian and St. It as well. >>Well, I'm more of an Ironman type of guy or Batman cloud governance or governance cloud native governance is a very tough problem. I can't under emphasize how many projects get stalled or fail from a perception perspective, even if you're technically delivered what you've asked to deliver. That's where a lot of these conversations are going. We're gonna talk to a bunch of startups that are solving these tough problems here from Licia Spain, I'm Keith Townsend, and you're watching the cube, the leader in high tech coverage.

>> Announcer: theCUBE presents "Kubecon and Cloudnativecon Europe 2022" brought to you by Red Hat, the Cloud Native Computing Foundation and its ecosystem partners. >> Welcome to theCUBE coverage of Kubecon 2022, E.U. I'm here with my cohost, Paul Gillin. >> Pleased to work with you, Keith. >> Nice to work with you, Paul. And we have our first two guests. "theCUBE" is hot. I'm telling you we are having interviews before the start of even the show floor. I have with me, we got to start with the customers first. Enterprise Architect Adnan Khan, welcome to the show. >> Thank you so much. >> Keith: CUBE time first, now you're at CUBE-alumni. >> Yup. >> And Haseeb Budhani, CEO Arathi, welcome back. >> Nice to talk to you again today. >> So, we're talking all things Kubernetes and we're super excited to talk to MoneyGram about their journey to Kubernetes. First question I have for Adnan. Talk to us about what your pre-Kubernetes landscape looked like? >> Yeah. Certainly, Keith. So, we had a traditional mix of legacy applications and modern applications. A few years ago we made the decision to move to a microservices architecture, and this was all happening while we were still on-prem. So, your traditional VMs. And we started 20, 30 microservices but with the microservices packing. You quickly expand to hundreds of microservices. And we started getting to that stage where managing them without sort of an orchestration platform, and just as traditional VMs, was getting to be really challenging, especially from a day two operational. You can manage 10, 15 microservices, but when you start having 50, and so forth, all those concerns around high availability, operational performance. So, we started looking at some open-source projects. Spring cloud, we are predominantly a Java shop. So, we looked at the spring cloud projects. They give you a number of initiatives for doing some of those management. And what we realized again, to manage those components without sort of a platform, was really challenging. So, that kind of led us to sort of Kubernetes where along with our journey new cloud, it was the platform that could help us with a lot of those management operational concerns. >> So, as you talk about some of those challenges, pre-Kubernetes, what were some of the operational issues that you folks experienced? >> Yeah, certain things like auto scaling is number one. I mean, that's a fundamental concept of cloud native, right? Is how do you auto scale VMs, right? You can put in some old methods and stuff, but it was really hard to do that automatically. So, Kubernetes with like HPA gives you those out of the box. Provided you set the right policies, you can have auto scaling where it can scale up and scale back, so we were doing that manually. So, before, you know, MoneyGram, obviously, holiday season, people are sending more money, Mother's Day. Our Ops team would go and basically manually scale VMs. So, we'd go from four instances to maybe eight instances, but that entailed outages. And just to plan around doing that manually, and then sort of scale them back was a lot of overhead, a lot of administration overhead. So, we wanted something that could help us do that automatically in an efficient and intrusive way. That was one of the things, monitoring and and management operations, just kind of visibility into how those applications were during what were the status of your workloads, was also a challenge to do that. >> So, Haseeb, I got to ask the question. If someone would've came to me with that problem, I'd just say, "You know what? Go to the plug to cloud." How does your group help solve some of these challenges? What do you guys do? >> Yeah. What do we do? Here's my perspective on the market as it's playing out. So, I see a bifurcation happening in the Kubernetes space. But there's the Kubernetes run time, so Amazon has EKS, Azure as AKS. There's enough of these available, they're not managed services, they're actually really good, frankly. In fact, retail customers, if you're an Amazon why would you spin up your own? Just use EKS, it's awesome. But then, there's an operational layer that is needed to run Kubernetes. My perspective is that, 50,000 enterprises are adopting Kubernetes over the next 5 to 10 years. And they're all going to go through the same exact journey, and they're all going to end up potentially making the same mistake, which is, they're going to assume that Kubernetes is easy. They're going to say, "Well, this is not hard. I got this up and running on my laptop. This is so easy, no worries. I can do EKS." But then, okay, can you consistently spin up these things? Can you scale them consistently? Do you have the right blueprints in place? Do you have the right access management in place? Do you have the right policies in place? Can you deploy applications consistently? Do you have monitoring and visibility into those things? Do your developers have access when they need it? Do you have the right networking layer in place? Do you have the right chargebacks in place? Remember you have multiple teams. And by the way, nobody has a single cluster, so you got to do this across multiple clusters. And some of them have multiple clouds. Not because they want to be multiple clouds, because, but sometimes you buy a company, and they happen to be in Azure. How many dashboards do you have now across all the open-source technologies that you have identified to solve these problems? This is where pain lies. So, I think that Kubernetes is fundamentally a solve problem. Like our friends at AWS and Azure, they've solved this problem. It's like a AKS, EKS, et cetera, EGK for that matter. They're great, and you should use them, and don't even think about spinning up QB best clusters. Don't do it, use the platforms that exist. And commensurately on-premises, OpenShift is pretty awesome. If you like it, use it. But then when it comes to the operations layer, that's where today, we end up investing in a DevOps team, and then an SRE organization that need to become experts in Kubernetes, and that is not tenable. Can you, let's say unlimited capital, unlimited budgets. Can you hire 20 people to do Kubernetes today? >> If you could find them. >> If you can find 'em, right? So, even if you could, the point is that, see five years ago when your competitors were not doing Kubernetes, it was a competitive advantage to go build a team to do Kubernetes so you could move faster. Today, you know, there's a high chance that your competitors are already buying from a Rafay or somebody like Rafay. So, now, it's better to take these really, really sharp engineers and have them work on things that make the company money. Writing operations for Kubernetes, this is a commodity now. >> How confident are you that the cloud providers won't get in and do what you do and put you out of business? >> Yeah, I mean, absolutely. In fact, I had a conversation with somebody from HBS this morning and I was telling them, I don't think you have a choice, you have to do this. Competition is not a bad thing. If we are the only company in a space, this is not a space, right? The bet we are making is that every enterprise, they have an on-prem strategy, they have at least a handful of, everybody's got at least two clouds that they're thinking about. Everybody starts with one cloud, and then they have some other cloud that they're also thinking about. For them to only rely on one cloud's tools to solve for on-prem, plus that second cloud, they potentially they may have, that's a tough thing to do. And at the same time, we as a vendor, I mean, the only real reason why startups survive, is because you have technology that is truly differentiator. Otherwise, I mean, you got to build something that is materially interesting, right? We seem to have- >> Keith: Now. Sorry, go ahead. >> No, I was going to, you actually have me thinking about something. Adnan? >> Yes. >> MoneyGram, big, well known company. a startup, adding, working in a space with Google, VMware, all the biggest names. What brought you to Rafay to solve this operational challenge? >> Yeah. A good question. So, when we started out sort of in our Kubernetes, we had heard about EKS and we are an AWS shop, so that was the most natural path. And we looked at EKS and used that to create our clusters. But then we realized very quickly, that, yes, to Haseeb's point, AWS manages the control plane for you, it gives you the high availability. So, you're not managing those components which is some really heavy lifting. But then what about all the other things like centralized dashboard? What about, we need to provision Kubernetes clusters on multicloud, right? We have other clouds that we use, or also on-prem, right? How do you do some of that stuff? We also, at that time were looking at other tools also. And I had, I remember come up with an MVP list that we needed to have in place for day one or day two operations before we even launch any single applications into production. And my Ops team looked at that list and literally, there was only one or two items that they could check off with EKS. They've got the control plane, they've got the cluster provision, but what about all those other components? And some of that kind of led us down the path of, you know, looking at, "Hey, what's out there in this space?" And we realized pretty quickly that there weren't too many. There were some large providers and capabilities like Antos, but we felt that it was a little too much for what we were trying to do at that point in time. We wanted to scale slowly. We wanted to minimize our footprint, and Rafay seemed to sort of, was a nice mix from all those different angles. >> How was the situation affecting your developer experience? >> So, that's a really good question also. So, operations was one aspect to it. The other part is the application development. We've got MoneyGram is when a lot of organizations have a plethora of technologies from Java, to .net, to node.js, what have you, right? Now, as you start saying, okay, now we're going cloud native and we're going to start deploying to Kubernetes. There's a fair amount of overhead because a tech stack, all of a sudden goes from, just being Java or just being .net, to things like Docker. All these container orchestration and deployment concerns, Kubernetes deployment artifacts, (chuckles) I got to write all this YAML as my developer say, "YAML hell." (panel laughing) I got to learn Docker files. I need to figure out a package manager like HELM on top of learning all the Kubernetes artifacts. So, initially, we went with sort of, okay, you know, we can just train our developers. And that was wrong. I mean, you can't assume that everyone is going to sort of learn all these deployment concerns and we'll adopt them. There's a lot of stuff that's outside of their sort of core dev domain, that you're putting all this burden on them. So, we could not rely on them in to be sort of CUBE cuddle experts, right? That's a fair amount overhead learning curve there. So, Rafay again, from their dashboard perspective, saw the managed CUBE cuddle, gives you that easy access for devs, where they can go and monitor the status of their workloads. They don't have to figure out, configuring all these tools locally, just to get it to work. We did some things from a DevOps perspective to basically streamline and automate that process. But then, also Rafay came in and helped us out on kind of that providing that dashboard. They don't have to break, they can basically get on through single sign on and have visibility into the status of their deployment. They can do troubleshooting diagnostics all through a single pane of glass, which was a key key item. Initially, before Rafay, we were doing that command line. And again, just getting some of the tools configured was huge, it took us days just to get that. And then the learning curve for development teams "Oh, now you got the tools, now you got to figure out how to use it." >> So, Haseeb talk to me about the cloud native infrastructure. When I look at that entire landscape number, I'm just overwhelmed by it. As a customer, I look at it, I'm like, "I don't know where to start." I'm sure, Adnan, you folks looked at it and said, "Wow, there's so many solutions." How do you engage with the ecosystem? You have to be at some level opinionated but flexible enough to meet every customer's needs. How do you approach that? >> So, it's a really tough problem to solve because... So, the thing about abstraction layers, we all know how that plays out, right? So, abstraction layers are fundamentally never the right answer because they will never catch up, because you're trying to write a layer on top. So, then we had to solve the problem, which was, well, we can't be an abstraction layer, but then at the same time, we need to provide some, sort of like centralization standardization. So, we sort of have this the following dissonance in our platform, which is actually really important to solve the problem. So, we think of a stack as floor things. There's the Kubernetes layer, infrastructure layer, and EKS is different from AKS, and it's okay. If we try to now bring them all together and make them behave as one, our customers are going to suffer. Because there are features in EKS that I really want, but then if you write an abstraction then I'm not going to get 'em so not okay. So, treat them as individual things that we logic that we now curate. So, every time EKS, for example, goes from 1.22 to 1.23, we write a new product, just so my customer can press a button and upgrade these clusters. Similarly, we do this for AKS, we do this for GK. It's a really, really hard job, but that's the job, we got to do it. On top of that, you have these things called add-ons, like my network policy, my access management policy, my et cetera. These things are all actually the same. So, whether I'm EKS or AKS, I want the same access for Keith versus Adnan, right? So, then those components are sort of the same across, doesn't matter how many clusters, doesn't matter how many clouds. On top of that, you have applications. And when it comes to the developer, in fact I do the following demo a lot of times. Because people ask the question. People say things like, "I want to run the same Kubernetes distribution everywhere because this is like Linux." Actually, it's not. So, I do a demo where I spin up access to an OpenShift cluster, and an EKS cluster, and then AKS cluster. And I say, "Log in, show me which one is which?" They're all the same. >> So, Adnan, make that real for me. I'm sure after this amount of time, developers groups have come to you with things that are snowflakes. And as a enterprise architect, you have to make it work within your framework. How has working with Rafay made that possible? >> Yeah, so I think one of the very common concerns is the whole deployment to Haseeb's point, is you are from a deployment perspective, it's still using HELM, it's still using some of the same tooling. How do you? Rafay gives us some tools. You know, they have a command line Add Cuddle API that essentially we use. We wanted parity across all our different environments, different clusters, it doesn't matter where you're running. So, that gives us basically a consistent API for deployment. We've also had challenges with just some of the tooling in general that we worked with Rafay actually, to actually extend their, Add Cuddle API for us so that we have a better deployment experience for our developers. >> Haseeb, how long does this opportunity exist for you? At some point, do the cloud providers figure this out, or does the open-source community figure out how to do what you've done and this opportunity is gone? >> So, I think back to a platform that I think very highly of, which has been around a long time and continues to live, vCenter. I think vCenter is awesome. And it's beautiful, VMware did an incredible job. What is the job? It's job is to manage VMs, right? But then it's for access, it's also storage. It's also networking in a sec, right? All these things got done because to solve a real problem, you have to think about all the things that come together to help you solve that problem from an operations perspective. My view is that this market needs essentially a vCenter, but for Kubernetes, right? And that is a very broad problem. And it's going to spend, it's not about a cloud. I mean, every cloud should build this. I mean, why would they not? It makes sense. Anto exist, right? Everybody should have one. But then, the clarity in thinking that the Rafay team seems to have exhibited, till date, seems to merit an independent company, in my opinion, I think like, I mean, from a technical perspective, this product's awesome, right? I mean, we seem to have no real competition when it comes to this broad breadth of capabilities. Will it last? We'll see, right? I mean, I keep doing "CUBE" shows, right? So, every year you can ask me that question again, and we'll see. >> You make a good point though. I mean, you're up against VMware, You're up against Google. They're both trying to do sort of the same thing you're doing. Why are you succeeding? >> Maybe it's focused. Maybe it's because of the right experience. I think startups, only in hindsight, can one tell why a startup was successful. In all honesty, I've been in a one or two startups in the past, and there's a lot of luck to this, there's a lot of timing to this. I think this timing for a product like this is perfect. Like three, four years ago, nobody would've cared. Like honesty, nobody would've cared. This is the right time to have a product like this in the market because so many enterprises are now thinking of modernization. And because everybody's doing this, this is like the boots strong problem in HCI. Everybody's doing it, but there's only so many people in the industry who actually understand this problem, so they can't even hire the people. And the CTO said, "I got to go. I don't have the people, I can't fill the seats." And then they look for solutions, and via that solution, that we're going to get embedded. And when you have infrastructure software like this embedded in your solution, we're going to be around with the... Assuming, obviously, we don't score up, right? We're going to be around with these companies for some time. We're going to have strong partners for the long term. >> Well, vCenter for Kubernetes I love to end on that note. Intriguing conversation, we could go on forever on this topic, 'cause there's a lot of work to do. I don't think this will over be a solved problem for the Kubernetes as cloud native solutions, so I think there's a lot of opportunities in that space. Haseeb Budhani, thank you for rejoining "theCUBE." Adnan Khan, welcome becoming a CUBE-alum. >> (laughs) Awesome. Thank you so much. >> Check your own profile on the sound's website, it's really cool. From Valencia, Spain, I'm Keith Townsend, along with my Host Paul Gillin . And you're watching "theCUBE," the leader in high tech coverage. (bright upbeat music)

>>The cube presents, Coon and cloud native con Europe 22, brought to you by the cloud native computing foundation. >>Welcome to the cube coverage of CubeCon 2022 EU. I'm here with my cohost Paul Gill. Please work with you, Keith. Nice to work with you, Paul. And we have our first two guests. The cube is hot. I'm telling you we are having interviews before the start of even the show floor I have with me. We gotta start with the customers first enterprise architect, a non-con Aon con. Welcome to the show. >>Thank you so >>Much. Cube time cube time. First now you're at cube alumni. Yep. <laugh> and, and, uh, has Havani CEO. Arai welcome back. Nice to, >>Uh, >>Talk to you again today. So we're talking all things Kubernetes and we're super excited to talk to MoneyGram about their journey to Kubernetes. First question I have for Anon. Talk to us about what your pre Kubernetes landscape looked like. >>Yeah, certainly. Uh, Keith, so, um, we had a, uh, you know, a traditional mix of legacy applications and modern applications. Uh, you know, a few years ago we made the decision to move to a microservices architecture. Um, and this was all happening while we were still on prem. Right? So your traditional VMs, um, and you know, we started 20, 30 microservices, but with the microservices packing, you know, you quickly expand to hundreds of microservices. Um, and we started getting to that stage where managing them without sort of an orchestration platform, uh, and just as traditional VMs was getting to be really challenging, right. Uh, especially from a day two operational, uh, you know, you can manage 10, 15 microservices, but when you start having 50 and so forth, um, all those concerns around, uh, you know, high availability, operational performance. Um, so we started looking at some open source projects, you know, spring cloud. Uh, we are predominantly a Java, um, shop. So we looked at the spring cloud projects. Uh, they give you a number, uh, you know, of initiatives, um, for doing some of those, um, management and what we realized again, to manage those components, um, without sort of a platform was really challenging. So that, that kind of led us to sort of Kubernetes where, um, along with our journey cloud, uh, it was the platform that could help us with a lot of those management operational concerns. >>So as you talk about some of those challenges, pre Kubernetes, what were some of the operational issues that you folks experienced? >>Yeah. You know, uh, certain things like auto scaling is, is number one, right? I mean, that's a fundamental concept of cloud native, right. Is, um, how do you auto scale VMs? Right. Uh, you can put in some old methods and stuff, but, uh, it was really hard to do that automatically. Right. So, uh, Kubernetes with like HPA gives you those out of the box, right? Provided you set the right policies. Uh, you can have auto scaling, uh, where it can scale up and scale back. So we were doing that manually. Right. So before, uh, you know, MoneyGram, obviously, you know, holiday season, people are sending more money mother's day. Um, our ops team would go in basically manually scale, uh, VMs. Right. So we'd go from four instances to maybe eight instances. Right. Uh, but, but that entailed outages. Right. Um, and just to plan around doing that manually and then sort of scale them back was a lot of overhead, a lot of administration overhead. Right. So, uh, we wanted something that could help us do that automatically right. In a, in an efficient, uh, unintrusive way. So, so, you know, that was one of the things, uh, monitoring, um, and, and management, uh, operations, you know, just kind of visibility into how those applications were during, what were the status of your, um, workloads was also a challenge, right. Uh, to do that. >>So, cause see, I gotta ask the question. If someone would've came to me with that problem, I'd just say, you know, what, go to the plug, the cloud, what, how does, uh, your group help solve some of these challenges? What do you guys do? >>Yeah. What, what do we do? So here's my perspective on the market as it's playing out. So I see a bifurcation happening in the Kubernetes space, but there's the Kubernetes run time. So Amazon is EKS Azure as EKS, you know, there's enough of these available. They're not managed services. They're actually really good, frankly. Right? In fact, retail customers, if you're an Amazon, why would you spin up your own? Just use EK. It's awesome. But then there's an operational layer that is needed to run Kubernetes. Uh, my perspective is that, you know, 50,000 enterprises are adopting Kubernetes over the next five to 10 years. And they're all gonna go through the same exact journey and they're all gonna end up, you know, potentially making the same mistake, which is, they're gonna assume that Kubernetes is easy. <laugh> they're gonna say, well, this is not hard. I got this up and running on my laptop. >>This is so easy. No worries. Right. I can do key gas, but then, okay. Can you consistently spin up these things? Can you scale them consistently? Do you have the right blueprints in place? Do you have the right access management in place? Do you have the right policies in place? Can you deploy applications consistently? Do you have monitoring and visibility into those things? Do your developers have access to when they need it? Do you have the right networking layer in place? Do you have the right chargebacks in place? Remember you have multiple teams and by the way, nobody has a single cluster. So you gotta do this across multiple clusters. And some of them have multiple clouds, not because they wanna be multiple clouds because, but sometimes you buy a company and they happen to be in Azure. How many dashboards do you have now across all the open source technologies that you have identified to solve these problems? >>This is where pain lies. So I think that Kubernetes is fundamentally a solve problem. Like our friends at AWS and Azure they've solved this problem. It's like a KSKS et cetera, GK for that matter. They're they're great. And you should use them and don't even think about spinning up Q B and a best clusters. Don't do it. Use the platforms that exist and commensurately on premises. OpenShift is pretty awesome, right? If you like it, use it. But then when it comes to the operations layer, right, that's where today we end up investing in a DevOps team and then an SRE organization that need to become experts in Kubernetes. And that is not tenable, right? Can you let's say unlimited capital unlimited budgets. Can you hire 20 people to do Kubernetes today? >>If you could find them, if >>You can find 'em right. So even if you could, the point is that see, five years ago, when your competitors were not doing Kubernetes, it was a competitive advantage to go build a team to do Kubernetes. So you could move faster today. You know, there's a high chance that your competitors are already buying from a Rafa or somebody like Rafa. So now it's better to take these really, really sharp engineers and have them work on things that make the company money, writing operations for Kubernetes. This is a commodity. Now >>How confident are you that the cloud providers won't get in and do what you do and put you out of business? >>Yeah, I mean, absolutely. I think, I mean, in fact, I, I had a conversation with somebody from HBS this morning and I was telling them, I don't think you have a choice. You have to do this right. Competition is not a bad thing. Right? This, the, >>If we are the only company in a space, this is not a space, right. The bet we are making is that every enterprise has, you know, they have an on-prem strategy. They have at least a handful of, everybody's got at least two clouds that they're thinking about. Everybody starts with one cloud and then they have some other cloud that they're also thinking about, um, for them to only rely on one cloud's tools to solve for on-prem plus that second cloud, they potentially, they may have, that's a tough thing to do. Um, and at the same time we as a vendor, I mean the only real reason why startups survive is because you have technology that is truly differentiated, right. Otherwise, right. I mean, you gotta build something that is materially. Interesting. Right. We seem to have, sorry, go ahead. >>No, I was gonna ask you, you actually had me thinking about something, a non yes. MoneyGram big, well known company, a startup, adding, working in a space with Google, VMware, all the biggest names. What brought you to Rafi to solve this operational challenge? >>Yeah. Good question. So when we started out sort of in our Kubernetes, um, you know, we had heard about EKS, uh, and, and we are an AWS shop. So, uh, that was the most natural path. And, and we looked at, um, EKS and, and used that to, you know, create our clusters. Um, but then we realized very quickly that yes, toe's point AWS manages the control plane for you. It gives you the high availability. So you're not managing those components, which is some really heavy lifting. Right. Uh, but then what about all the other things like, you know, centralized dashboard, what about, we need to provision, uh, Kubernetes clusters on multi-cloud right. We have other clouds that we use, uh, or also on prem. Right. Um, how do you do some of that stuff? Right. Um, we, we also, at that time were looking at, uh, other, uh, tools also. >>And I had, I remember come up with an MVP list that we needed to have in place for day one or day two, uh, operations, right. To before we even launch any single applications into production. Um, and my ops team looked at that list. Um, and literally there was only one or two items that they could check, check off with S you know, they they've got the control plane, they've got the cluster provision, but what about all those other components? Uh, and some of that kind of led us down the path of, uh, you know, looking at, Hey, what's out there in this space. And, and we realized pretty quickly that there weren't too many, there were some large providers and capabilities like Antos, but we felt that it was, uh, a little too much for what we were trying to do. You know, at that point in time, we wanted to scale slowly. We wanted to minimize our footprint. Um, and, and Rafa seemed to sort of, uh, was, was a nice mix, uh, you know, uh, from all those different angles, how >>Was, how was the situation affecting your developer experience? >>So, um, so that's a really good question also. So operations was one aspect of, to it, right? The other part is the application development, right? We've got, uh, you know, Moneygrams when a lot of organizations have a plethora of technologies, right? From, from Java to.net to no GS, what have you, right. Um, now as you start saying, okay, now we're going cloud native, and we're gonna start deploying to Kubernetes. Um, there's a fair amount of overhead because a tech stack, all of a sudden goes from, you know, just being Java or just being.net to things like Docker, right? All these container orchestration and deployment concerns, Kubernetes, uh, deployment artifacts, right. I gotta write all this YAML, uh, as my developer say, YAML, hell right. <laugh>, uh, I gotta learn Docker files. I need to figure out, um, a package manager like helm, uh, on top of learning all the Kubernetes artifacts. >>Right. So, um, initially we went with sort of, okay, you know, we can just train our developers. Right. Um, and that was wrong. Right. I mean, you can't assume that everyone is gonna sort of learn all these deployment concerns, uh, and we'll adopt them. Right. Um, uh, there's a lot of stuff that's outside of their sort of core dev domain, uh, that you're putting all this burden on them. Right. So, um, we could not rely on them and to be sort of cube cuddle experts, right. That that's a fair amount, overhead learning curve there. Um, so Rafa again, from their dashboard perspective, right? So the managed cube cuddle gives you that easy access for devs, right. Where they can go and monitor the status of their workloads. Um, they can, they don't have to figure out, you know, configuring all these tools locally just to get it to work. >>Uh, we did some things from a DevOps perspective to basically streamline and automate that process. But then also office order came in and helped us out, uh, on kind of that providing that dashboard. They don't have to worry. They can basically get on through single sign on and have visibility into the status of their deployment. Uh, they can do troubleshooting diagnostics all through a single pane of glass. Right. Which was a key key item. Uh, initially before Rafa, we were doing that command line. Right. And again, just getting some of the tools configured was, was huge. Right. Took us days just to get that. And then the learning curve for development teams, right? Oh, now you gotta, you got the tools now you gotta figure out how to use it. Right. Um, so >>See, talk to me about the, the cloud native infrastructure. When I look at that entire landscaping number, I'm just overwhelmed by it. As a customer, I look at it, I'm like, I, I don't know where to start I'm sure. Or not, you, you folks looked at it and said, wow, there's so many solutions. How do you engage with the ecosystem? You have to be at some level opinionated, but flexible enough to, uh, meet every customer's needs. How, how do you approach that? >>Yeah. So it's a, it's a really tough problem to solve because, so, so the thing about abstraction layers, you know, we all know how that plays out, right? So abstraction layers are fundamentally never the right answer because they will never catch up. Right. Because you're trying to write and layer on top. So then we had to solve the problem, which was, well, we can't be an abstraction layer, but then at the same time, we need to provide some sort of, sort of like centralization standardization. Right. So, so we sort of have this, the following dissonance in our platform, which is actually really important to solve the problem. So we think of a, of a stack as sort of four things. There's the, there's the Kubernetes layer infrastructure layer, um, and EKS is different from ES and it's okay. Mm-hmm <affirmative>, if we try to now bring them all together and make them behave as one, our customers are gonna suffer because there are features in ESS that I really want. >>But then if you write an AB obsession layer, I'm not gonna get 'em so not. Okay. So treat them as individual things. And we logic that we now curate. So every time S for example, goes from 1 22 to 1 23, rewrite a new product, just so my customer can press a button and upgrade these clusters. Similarly, we do this fors, we do this for GK. We it's a really, really hard job, but that's the job. We gotta do it on top of that, you have these things called. Add-ons like my network policy, my access management policy, my et cetera. Right. These things are all actually the same. So whether I'm Anek or a Ks, I want the same access for Keith versus a none. Right. So then those components are sort of the same across doesn't matter how many clusters does money clouds on top of that? You have applications. And when it comes to the developer, in fact, I do the following demo a lot of times because people ask the question, right? Mean, I, I, I, people say things like, I wanna run the same Kubernetes distribution everywhere, because this is like Linux, actually, it's not. So I, I do a demo where I spin up a access to an OpenShift cluster and an EKS cluster and an AKs cluster. And I say, log in, show me which one is, which they're all the same. >>So Anan get, put, make that real for me, I'm sure after this amount of time, developers groups have come to you with things that are snowflakes and you, and as a enterprise architect, you have to make it work within your framework. How has working with RAI made that possible? >>Yeah. So, um, you know, I think one of the very common concerns is right. The whole deployment, right. Uh, toe's point, right. Is you are from an, from a deployment perspective. Uh, it's still using helm. It's still using some of the same tooling, um, right. But, um, how do you Rafa gives us, uh, some tools, you know, they have a, a command line, art cuddle API that essentially we use. Um, we wanted parody, um, across all our different environments, different clusters, you know, it doesn't matter where you're running. Um, so that gives us basically a consistent API for deployment. Um, we've also had, um, challenges, uh, with just some of the tooling in general, that we worked with RA actually to actually extend their, our cuddle API for us, so that we have a better deployment experience for our developers. So, >>Uh Huie how long does this opportunity exist for you? At some point, do the cloud providers figure this out or does the open source community figure out how to do what you've done and, and this opportunity is gone. >>So, so I think back to a platform that I, I think very highly of, which is a highly off, which has been around a long time and continues to live vCenter, I think vCenter is awesome. And it's, it's beautiful. VMware did an incredible job. Uh, what is the job? Its job is to manage VMs, right? But then it's for access. It's also storage. It's also networking and a sex, right? All these things got done because to solve a real problem, you have to think about all the things that come together to solve, help you solve that problem from an operations perspective. Right? My view is that this market needs essentially a vCenter, but for Kubernetes, right. Um, and that is a very broad problem, right. And it's gonna spend, it's not about a cloud, right? I mean, every cloud should build this. I mean, why would they not? It makes sense, Anto success, right. Everybody should have one. But then, you know, the clarity in thinking that the Rafa team seems to have exhibited till date seems to merit an independent company. In my opinion, I think like, I mean, from a technical perspective, this products awesome. Right? I mean, you know, we seem to have, you know, no real competition when it comes to this broad breadth of capabilities, will it last, we'll see, right. I mean, I keep doing Q shows, right? So every year you can ask me that question again. Well, you're >>You make a good point though. I mean, you're up against VMware, you're up against Google. They're both trying to do sort of the same thing you're doing. What's why are you succeeding? >>Maybe it's focus. Maybe it's because of the right experience. I think startups only in hindsight, can one tell why a startup was successful? In all honesty. I, I, I've been in a one or two service in the past. Um, and there's a lot of luck to this. There's a lot of timing to this. I think this timing for a com product like this is perfect. Like three, four years ago, nobody would've cared. Like honestly, nobody would've cared. This is the right time to have a product like this in the market because so many enterprises are now thinking of modernization. And because everybody's doing this, this is like the boots storm problem in HCI. Everybody's doing it. But there's only so many people in the industry who actually understand this problem. So they can't even hire the people. And the CTO said, I gotta go. I don't have the people. I can't fill the, the seats. And then they look for solutions and we are that solution that we're gonna get embedded. And when you have infrastructure software like this embedded in your solution, we're gonna be around with the assuming, obviously we don't score up, right. We're gonna be around with these companies for some time. We're gonna have strong partners for the long term. >>Well, vCenter for Kubernetes, I love to end on that note, intriguing conversation. We could go on forever on this topic, cuz there's a lot of work to do. I think, uh, I don't think this will over be a solve problem for the Kubernetes of cloud native solution. So I think there's a lot of opportunity in that space. Hi, thank you for rejoining the cube. I non con welcome becoming a cube alum. <laugh> I awesome. Thank you. Get your much your profile on the, on the Ken's. Website's really cool from Valencia Spain. I'm Keith Townsend, along with my whole Paul Gillon and you're watching the cube, the leader in high tech coverage.

(upbeat music) >> Welcome everyone to this CUBE coverage of AWS re:Invent 2021. We have a lot going on at this year's re:Invent with over 100 guests on the program, and I'm excited to welcome two of those guests here with me right now. We are joined by Ralph Munsen, the Chief Information Officer at Warner Music Group and Atif Khan, the CTO of Alkira and founder of Alkira as well. Gentlemen, welcome to the program. >> Thank you so much, Lisa. So glad to be here with you. >> Good to be here. >> Yeah. Good old fashioned Zoom is become our best friend in the last 22 months or so I'm losing count. Atif, I'd like to start with you. I know Alkira has been on the key before, but it's been a while and you guys are a relatively young company. Give the audience an overview of Alkira and what it is that you deliver. >> Absolutely, Lisa. So we started back in may of 2018, and the Cloud networking space, multicloud networking. And we came out of stealth mode back in April of 2020, and launched the company. In fact, one of our first events coming out of stealth mode was a Cuban interview back in April of 2020. So here at Telecare, what we are doing is we are building a Cloud platform, which allows customers to build a common network across multiple Clouds with built-in network and security services, with the policy and management layer on top full end to end visibility and governance capabilities. And all of this is delivered as a service and consumed as a service as well. And I'm very glad to be here with Ralph, who is from Warner Music Group and is one of our marquee customers. So I'll let Ralph introduce himself, and tell us a bit more about Alkira and WMTS Cloud journey. >> That sounds great. Ralph, why don't you start by giving the audience? I'm sure everyone knows Warner Music Group, but in case there's anyone out there that might not. Give us a little bit of a background. >> Yeah, so the Warner Music Group has been around since 1950 and 1940 even it had its roots at Hollywood and out of Warner Brothers Pictures, Today, say global company in 79 countries we operated. If the 100 employees and we have two major divisions, we have our era recorded music division, which has the labels people commonly turn to Atlantic records, Warner brothers records, and so forth. And then we have our publishing division, which is more a chapel, which is where our songwriters live. And of course we have some singer songwriters that are on both sides of our business. But now currently people may know our artists. We have ed Sheeran, Bruno Mars, Coldplay, Cardi B, Blake Shelton and I could go on and on. But exciting, great year, we're having one of our best years ever. And I'm so glad to be here and partnering with an Alkira. >> Excellent. I love all of those artists that you mentioned. Fantastic. So let's talk a little bit now Ralph about the backstory. Talk to me about the IT infrastructure at Warner Music Group, what you had there and some of the challenges that you had that you came to Alkira to solve. >> Yeah, well initially when I took over about five years ago now, we were very much a data center based business with traditional networking and IT functions. Additionally with our foreign affiliates, IT was sort of decentralized in the sense that a lot of the networking and data center components were left to regions. And so while we operated globally, we didn't really operate globally, at Warner among our affiliates. So one of the challenges was how do we get out of the data center? Cloud was new. One of the big things that were coming with big data, which is absolutely right for moving, going straight to the Cloud, especially if you don't have anything on-prem and how do we rationalize all of these different locations and conduct all the M&A work we've been doing? So it was quite a challenge, really. At the end, we wanted to have one view of the network, and Alkira. I looked at many a company and Alkira seemed the best to provide that to us. So. >> Well, talk to me a little bit more about why Alkira, because as Atif was saying, they're very young. What came out of stealth mode during the pandemic Warner Music Group, being around since the 40s and 50s, the legacy institution, a great brand. What made you take a risk on such an early stage startup? >> Quite frankly, there was nothing in the space (chuckles) at the time you loved, there were companies that had components of it, of what Alkira does, which is basically network orchestration allowing us to use existing components. And nobody has the whole package, especially incorporating security. So, we figured why not take, take a chance? There's no, it won't hurt you no harm. And if anything is successful, it will give us a great ability to manage our network, much more efficiently taking things that took days down to hours and being able to do it much more efficiently with much fewer staff, as opposed to hiring a lot more because when you orchestrate all the components that are underneath, obviously it requires more bodies, more resources. >> Right. That efficiency and cost optimization is key there. Atif I have to ask you, talk to me about, this is only a few years ago, the gap in the market that you and your brothers saw a few years ago, when you founded the company, because as Rob was saying, there was nobody else in the market at the time that could do what you're doing. >> Yeah, absolutely. So Lisa, as you know, myself and Amir, we were also a part of the founding team of Viptela, which was the SD-WAN Company. So back in the day when we did SD-WAN, the requirement was to connect sites together. So if you go back like 5, 10, 5, 7, 10 years ago, networking was done to connect sites together, which could be remote sites, data centers, sites to data centers, all of that together. But fast forward, a few more years with the adoption of Cloud, requirements changed from the networking perspective. So now your network is not just connecting sites together, but most of the traffic now is from sites or users, which could be sitting anywhere. If you look at, what's going on? in the pandemic people are working from all across the globe. They are not just sitting in campuses or sites. So traffic patterns are from sites or users mostly to the Cloud or SaaS applications. So now networks also need to evolve and they need to be built inside the Cloud rather than from outside or connecting into the Cloud. So Cloud access is one capability, but building a network inside the Cloud becomes a requirement. And secondly, now it's not just only about connectivity because security becomes even more important because your security perimeter is changing as well. So securing all these Cloud networks becomes very, very complicated. And now as Ralph can tell you, majority of the enterprises have a multicloud strategy and each Cloud is done differently. So the moment you bring in multiple Clouds, multiple regions across the globe, it becomes so complicated for enterprises to build and manage. They need something, or a platform which makes it easy, gives them one way of doing networking, building a common network across whether you're connecting multiple Clouds or Clouds to your on-prem locations or Clouds to internet or sites to internet. So that's where we saw this gap and we decided to build Alkira to tackle this problem. >> Got it. So Rob, let's talk now about what you've implemented as a team was saying we live in this, in this work from anywhere hybrid multicloud world. Talk to us about Warner, what you implemented and maybe a little bit about your multicloud strategy, if you've got one. >> Ralph: Yeah. So over the last five years, Warner has migrated entirely into Cloud. And to this point before it's multicloud, we're mainly in AWS, but we do have some pleasure and some Google Cloud. And with that, I was telling Atif and Amir. It was interesting and they built a Cloud on site. They totally forgot about the networking aspect. So (laughs), you have ease of use for services and servers inside (indistinct) cloud, but networking is not really present, not to mention when it was built out, it wasn't made to go to competing Clouds. So most companies are facing this problem. How do you treat these environments as a single holistic environment? How do you turn things up, turn things down? How do you secure it, When every single one is different habits, selling unique ways of doing things? So that really was, how we ended up looking for an out Alkira, because I just kept looking at the costs and the profit print grow and grow and grow. And the complexity to a (indistinct) before is growing exponential. One change in one thing would lead to two changes to another. If you add another Cloud or you add another point on the network, you've got exponential growth and complexity, complexity, you have to deal with. So one stop shop. (chuckles) >> One stop shop and reducing that complexity. Talk to me about reducing complexity, and what you're accomplishing there. Especially, in the last year and a half as things have been so dynamic, shall we say? (chuckles) >> Yeah, well, I will say this. It was turnkey for the most part. It took a matter of months as opposed to years, because out of the box, there was a lot of integrations with the major network of players. So as of right now, you can buy firewalls, routing, VPC, things like this, they all exist, but they're not orchestrated together. Right? And then you have policies and security, again not orchestrating a different set of tools. So it really only took us two to three months to get it up and running, I acts, I just had a conversation (chuckles) with them when we were going to finish. So I think we'll be finishing this up completely in January and sometime. So, I was pretty sure. >> LISA: That's fantastic. So really, >> Yeah. >> Sorry Relaph fast time to market there with getting things implemented. Talk to me about from a business outcome perspective, you are CIO, what are some of the outcomes? That this technology is enabling you to deliver back to the business? >> Yeah, it really, the number 1, 2 big ones come to mind. One being able to provide them a secure enterprise. I know when there is the change it's made uniforms for our network without, some of older something's being forgotten about. So that's number one, security is big. You can imagine a company like more ever marquee brands, all brands, any company of marquee brands are targets today. That's number one. Number two is our time to market for eminent. So when we buy a company the time it takes us to get them to be completely part of Warner and therefore start realizing the business case and benefits sort of reasonably bought. Bought the company to begin with. So, we're buying a lot more and we're turning them up and turning those business cases up faster. But usually those cases would say things like six months to a year to integrate with us, and then we can unlock the set of benefits. Now it's more like, two to three months and you start to be able to lock the benefits sooner. And of course, those are different than a case by case basis, but that's. >> Sure, but significantly faster there, you're looking at a two to three X multiplier there, as you talked about. >> Ralph: right. >> Now, you mentioned multicloud Ralph. So here we are at re:Invent. I imagine part of your AWS as part of your Cloud infrastructure and they're a technology partner of ALkira's. >> Ralph: Correct. Yeah. So AWS is actually our biggest Cloud provider of the three, and yeah (laugh) they're their partner without cure. So Good. >> And Atif then you, Alkira's technology partner of AWS, correct? >> Yeas. Alkira is a technology partner of AWS, we are also available on AWS marketplace. So customers can consume, AlKira's platform from AWS marketplace as well. >> But given the fact that so many businesses in every industry are multicloud, I assume that you work with all the Cloud vendors. Atif Yeah? >> Absolutely. So our platform runs inside of the Cloud and runs in AWS is a Cloud as well. And from there it connects to multiple Clouds. So if customers need to connect to Azure or AWS from there or Oracle Cloud or any other Cloud, for that matter, they can connect from our platform and our platform is it scales horizontally. So as customers needs scale, it scales as well. And one of the key advantages is, it's consumed as a service. So there's no software to download or hardware to run for or to acquire for any of the customers. It's a software solution and it's consumed as a service. >> Got it. Ralph one on one more question for you before we wrap things up here, want to get your recommendations for IT Executives, CEOs, who might be in a similar situation to you, whether or not they are with a legacy organization, what are some of your recommendations that you say you need to be looking at a, B and C? >> Yeah, I would primarily say really need to be looking at some of these newer technologies that can help speed up, people, especially in this case to transition to the Cloud and that planning ahead of time, especially goal-setting, I find to be it's any of these places, providers is absolutely Paramount, because you can, if you don't make your own (indistinct) take that step forward and you can end up with shelter. So I make sure that it's very important that when you commit to that, you commit fully, you plan it out and you make sure you actually use it to get the benefits. One of my tech key is software. So. (chuckles) (Lisa Laughing) I'm a bit of it so. >> Well, you've been there and It costs a lot of money and it doesn't do any good. It doesn't move the business forward. And in this day and age, there is a competitor right behind the rear view mirror who might be smaller, more nimble, and more agile, who can take your place easily. >> Absolutely. >> If the organization isn't willing to take the risks and commit, as you said, Atif last question over for you, where are the customers go to learn more? I know you are at re:Invent your booth 1628, but what do you recommend folks go attendees of the event, as well as just other prospects to go to learn more about what you guys are delivering for companies like Warner Music Group. >> So if you're at re:Invent, please stop by our booth. And one of our Cloud specialists will give you a demo as well. So it's a very quick demo and you'll see, how we are reinventing networking for the Cloud narrow. You can also go to our website and you'll find a lot of information on our website. You can request a demo there as well. So look forward to seeing most of you at our booth and those who are not attending in person, please go visit our website. >> Lisa: Reinventing Networking. I like your play on words. They are Atif very appropriate. Gentlemen, thank you for joining me today talking about Alkira, Warner Music Group, what you guys are doing together and how this new early stage technology is really quite transformative. We appreciate your insights. >> Thank you. >> Thank you so much. >> For Ralph Munsen and Atif Khan, I'm Lisa Martin, and you're watching theCUBE's continuous coverage of AWS re:Invent 2021. Thanks for watching. (soft techno music)

(upbeat music) >> Welcome everyone to this CUBE coverage of AWS re:Invent 2021. We have a lot going on at this year's re:Invent with over 100 guests on the program, and I'm excited to welcome two of those guests here with me right now. We are joined by Ralph Munsen, the Chief Information Officer at Warner Music Group and Atif Khan, the CTO of Alkira and founder of Alkira as well. Gentlemen, welcome to the program. >> Thank you so much, Lisa. So glad to be here with you. >> Good to be here. >> Yeah. Good old fashioned Zoom is become our best friend in the last 22 months or so I'm losing count. Atif, I'd like to start with you. I know Alkira has been on the key before, but it's been a while and you guys are a relatively young company. Give the audience an overview of Alkira and what it is that you deliver. >> Absolutely, Lisa. So we started back in may of 2018, and the Cloud networking space, multicloud networking. And we came out of stealth mode back in April of 2020, and launched the company. In fact, one of our first events coming out of stealth mode was a Cuban interview back in April of 2020. So here at Telecare, what we are doing is we are building a Cloud platform, which allows customers to build a common network across multiple Clouds with built-in network and security services, with the policy and management layer on top full end to end visibility and governance capabilities. And all of this is delivered as a service and consumed as a service as well. And I'm very glad to be here with Ralph, who is from Warner Music Group and is one of our marquee customers. So I'll let Ralph introduce himself, and tell us a bit more about Alkira and WMTS Cloud journey. >> That sounds great. Ralph, why don't you start by giving the audience? I'm sure everyone knows Warner Music Group, but in case there's anyone out there that might not. Give us a little bit of a background. >> Yeah, so the Warner Music Group has been around since 1950 and 1940 even it had its roots at Hollywood and out of Warner Brothers Pictures, Today, say global company in 79 countries we operated. If the 100 employees and we have two major divisions, we have our era recorded music division, which has the labels people commonly turn to Atlantic records, Warner brothers records, and so forth. And then we have our publishing division, which is more a chapel, which is where our songwriters live. And of course we have some singer songwriters that are on both sides of our business. But now currently people may know our artists. We have ed Sheeran, Bruno Mars, Coldplay, Cardi B, Blake Shelton and I could go on and on. But exciting, great year, we're having one of our best years ever. And I'm so glad to be here and partnering with an Alkira. >> Excellent. I love all of those artists that you mentioned. Fantastic. So let's talk a little bit now Ralph about the backstory. Talk to me about the IT infrastructure at Warner Music Group, what you had there and some of the challenges that you had that you came to Alkira to solve. >> Yeah, well initially when I took over about five years ago now, we were very much a data center based business with traditional networking and IT functions. Additionally with our foreign affiliates, IT was sort of decentralized in the sense that a lot of the networking and data center components were left to regions. And so while we operated globally, we didn't really operate globally, at Warner among our affiliates. So one of the challenges was how do we get out of the data center? Cloud was new. One of the big things that were coming with big data, which is absolutely right for moving, going straight to the Cloud, especially if you don't have anything on-prem and how do we rationalize all of these different locations and conduct all the M&A work we've been doing? So it was quite a challenge, really. At the end, we wanted to have one view of the network, and now Alkira. I looked at many of companies and I'm curious in the best to provide that to us. So. >> Well, talk to me a little bit more about why Alkira, because as Atif was saying, they're very young. What came out of stealth mode during the pandemic Warner Music Group, being around since the 40s and 50s, the legacy institution, a great brand. What made you take a risk on such an early stage startup? >> Quite frankly, there was nothing in the space (chuckles) at the time you loved, there were companies that had components of it, of what Alkira does, which is basically network orchestration allowing us to use existing components. And nobody has the whole package, especially incorporating security. So, we figured why not take, take a chance? There's no, it won't hurt you no harm. And if anything is successful, it will give us a great ability to manage our network, much more efficiently taking things that took days down to hours and being able to do it much more efficiently with much fewer staff, as opposed to hiring a lot more because when you orchestrate all the components that are underneath, obviously it requires more bodies, more resources. >> Right. That efficiency and cost optimization is key there. Atif I have to ask you, talk to me about, this is only a few years ago, the gap in the market that you and your brothers saw a few years ago, when you founded the company, because as Rob was saying, there was nobody else in the market at the time that could do what you're doing. >> Yeah, absolutely. So Lisa, as you know, myself and Amir, we were also a part of the founding team of Viptela, which was the SD-WAN Company. So back in the day when we did SD-WAN, the requirement was to connect sites together. So if you go back like 5, 10, 5, 7, 10 years ago, networking was done to connect sites together, which could be remote sites, data centers, sites to data centers, all of that together. But fast forward, a few more years with the adoption of Cloud, requirements changed from the networking perspective. So now your network is not just connecting sites together, but most of the traffic now is from sites or users, which could be sitting anywhere. If you look at, what's going on? in the pandemic people are working from all across the globe. They are not just sitting in campuses or sites. So traffic patterns are from sites or users mostly to the Cloud or SaaS applications. So now networks also need to evolve and they need to be built inside the Cloud rather than from outside or connecting into the Cloud. So Cloud access is one capability, but building a network inside the Cloud becomes a requirement. And secondly, now it's not just only about connectivity because security becomes even more important because your security perimeter is changing as well. So securing all these Cloud networks becomes very, very complicated. And now as Ralph can tell you, majority of the enterprises have a multicloud strategy and each Cloud is done differently. So the moment you bring in multiple Clouds, multiple regions across the globe, it becomes so complicated for enterprises to build and manage. They need something, or a platform which makes it easy, gives them one way of doing networking, building a common network across whether you're connecting multiple Clouds or Clouds to your on-prem locations or Clouds to internet or sites to internet. So that's where we saw this gap and we decided to build Alkira to tackle this problem. >> Got it. So Rob, let's talk now about what you've implemented as a team was saying we live in this, in this work from anywhere hybrid multicloud world. Talk to us about Warner, what you implemented and maybe a little bit about your multicloud strategy, if you've got one. >> Ralph: Yeah. So over the last five years, Warner has migrated entirely into Cloud. And to this point before it's multicloud, we're mainly in AWS, but we do have some pleasure and some Google Cloud. And with that, I was telling Atif and Amir. It was interesting and they built a Cloud on site. They totally forgot about the networking aspect. So (laughs), you have ease of use for services and servers inside (indistinct) cloud, but networking is not really present, not to mention when it was built out, it wasn't made to go to competing Clouds. So most companies are facing this problem. How do you treat these environments as a single holistic environment? How do you turn things up, turn things down? How do you secure it, When every single one is different habits, selling unique ways of doing things? So that really was, how we ended up looking for an out Alkira, because I just kept looking at the costs and the profit print grow and grow and grow. And the complexity to a (indistinct) before is growing exponential. One change in one thing would lead to two changes to another. If you add another Cloud or you add another point on the network, you've got exponential growth and complexity, complexity, you have to deal with. So one stop shop. (chuckles) >> One stop shop and reducing that complexity. Talk to me about reducing complexity, and what you're accomplishing there. Especially, in the last year and a half as things have been so dynamic, shall we say? (chuckles) >> Yeah, well, I will say this. It was turnkey for the most part. It took a matter of months as opposed to years, because out of the box, there was a lot of integrations with the major network of players. So as of right now, you can buy firewalls, routing, VPC, things like this, they all exist, but they're not orchestrated together. Right? And then you have policies and security, again not orchestrating a different set of tools. So it really only took us two to three months to get it up and running, I acts, I just had a conversation (chuckles) with them when we were going to finish. So I think we'll be finishing this up completely in January and sometime. So, I was pretty sure. >> LISA: That's fantastic. So really, >> Yeah. >> Sorry Relaph fast time to market there with getting things implemented. Talk to me about from a business outcome perspective, you are CIO, what are some of the outcomes? That this technology is enabling you to deliver back to the business? >> Yeah, it really, the number 1, 2 big ones come to mind. One being able to provide them a secure enterprise. I know when there is the change it's made uniforms for our network without, some of older something's being forgotten about. So that's number one, security is big. You can imagine a company like more ever marquee brands, all brands, any company of marquee brands are targets today. That's number one. Number two is our time to market for eminent. So when we buy a company the time it takes us to get them to be completely part of Warner and therefore start realizing the business case and benefits sort of reasonably bought. Bought the company to begin with. So, we're buying a lot more and we're turning them up and turning those business cases up faster. But usually those cases would say things like six months to a year to integrate with us, and then we can unlock the set of benefits. Now it's more like, two to three months and you start to be able to lock the benefits sooner. And of course, those are different than a case by case basis, but that's. >> Sure, but significantly faster there, you're looking at a two to three X multiplier there, as you talked about. >> Ralph: right. >> Now, you mentioned multicloud Ralph. So here we are at re:Invent. I imagine part of your AWS as part of your Cloud infrastructure and they're a technology partner of ALkira's. >> Ralph: Correct. Yeah. So AWS is actually our biggest Cloud provider of the three, and yeah (laugh) they're their partner without cure. So Good. >> And Atif then you, Alkira's technology partner of AWS, correct? >> Yeas. Alkira is a technology partner of AWS, we are also available on AWS marketplace. So customers can consume, AlKira's platform from AWS marketplace as well. >> But given the fact that so many businesses in every industry are multicloud, I assume that you work with all the Cloud vendors. Atif Yeah? >> Absolutely. So our platform runs inside of the Cloud and runs in AWS is a Cloud as well. And from there it connects to multiple Clouds. So if customers need to connect to Azure or AWS from there or Oracle Cloud or any other Cloud, for that matter, they can connect from our platform and our platform is it scales horizontally. So as customers needs scale, it scales as well. And one of the key advantages is, it's consumed as a service. So there's no software to download or hardware to run for or to acquire for any of the customers. It's a software solution and it's consumed as a service. >> Got it. Ralph one on one more question for you before we wrap things up here, want to get your recommendations for IT Executives, CEOs, who might be in a similar situation to you, whether or not they are with a legacy organization, what are some of your recommendations that you say you need to be looking at a, B and C? >> Yeah, I would primarily say really need to be looking at some of these newer technologies that can help speed up, people, especially in this case to transition to the Cloud and that planning ahead of time, especially goal-setting, I find to be it's any of these places, providers is absolutely Paramount, because you can, if you don't make your own (indistinct) take that step forward and you can end up with shelter. So I make sure that it's very important that when you commit to that, you commit fully, you plan it out and you make sure you actually use it to get the benefits. One of my tech key is software. So. (chuckles) (Lisa Laughing) I'm a bit of it so. >> Well, you've been there and It costs a lot of money and it doesn't do any good. It doesn't move the business forward. And in this day and age, there is a competitor right behind the rear view mirror who might be smaller, more nimble, and more agile, who can take your place easily. >> Absolutely. >> If the organization isn't willing to take the risks and commit, as you said, Atif last question over for you, where are the customers go to learn more? I know you are at re:Invent your booth 1628, but what do you recommend folks go attendees of the event, as well as just other prospects to go to learn more about what you guys are delivering for companies like Warner Music Group. >> So if you're at re:Invent, please stop by our booth. And one of our Cloud specialists will give you a demo as well. So it's a very quick demo and you'll see, how we are reinventing networking for the Cloud narrow. You can also go to our website and you'll find a lot of information on our website. You can request a demo there as well. So look forward to seeing most of you at our booth and those who are not attending in person, please go visit our website. >> Lisa: Reinventing Networking. I like your play on words. They are Atif very appropriate. Gentlemen, thank you for joining me today talking about Alkira, Warner Music Group, what you guys are doing together and how this new early stage technology is really quite transformative. We appreciate your insights. >> Thank you. >> Thank you so much. >> For Ralph Munsen and Atif Khan, I'm Lisa Martin, and you're watching theCUBE's continuous coverage of AWS re:Invent 2021. Thanks for watching. (soft techno music)

(gentle music) >> From around the globe, it's theCUBE, presenting Building Immersive Customer Experiences with Customer Data 360. Brought to you by Io-Tahoe. >> Hello everyone and welcome back to Io-Tahoe's seventh installment of their Data Automation series, Building Immersive Customer Experiences with Customer Data 360. Now in this first segment, we're to catch up with Yusef Khan, who is Io-Tahoe's Head of Data Services. Yusef, always great to see you. Welcome back to theCUBE. >> Thank you, Dave. It's great to be back. Thank you for having me. >> Our pleasure. So let's talk about Customer Data 360. What does that actually mean in terms of the data? Give us a little background here. >> Well, Dave, we're living in a world now, where customer expectations are really, really high. A world in which the customer ethos if you like, is almost, talk to me like you love me. And that attitude is pretty common. So it's a world in which if you've shared your data with an organization, you absolutely expect that organization, that company to optimize your experience using that data. And when it comes to data, these very high expectations can be challenging to meet and there are several reasons for that. I mean, to mention just a few, an enterprise can have many different diverse data sources. It can have customer records that are duplicated or incomplete, the data quality itself can be poor, and what Customer Data 360 does, is help enterprises understand their data states, get more insight on their customer base, improve data quality, and then ultimately improve their customer experience and bring it in line with the expectation of today's customers. >> Great. Thank you for that. Well, so maybe not love me, but at least know me, right? So, poor data quality, and I think we can all relate to this. Like, you call a service provider, they either have old data, or bad data, you sometimes get double billed and it's up to you to figure that out. So, can the 360 degree view help with this problem? How so? What data does it generate to address this? >> Yeah, absolutely. It can help. So Customer Data 360 allows organizations to produce a fundamentally more personalized experience for customers. It helps eliminate the often generic sales pitches people get on email or in social media ads. It helps curate recommendations that add genuine value to that specific customer. So for example, if you typically buy three products from a certain brand every month, that data is going to be tracked, saved for the future, and it will make the next month's shopping more convenient by suggesting the same products or complementary products. Not only that, Customer Data 360 will track purchases across all touch points, and understand the customer in the round. So across in store, online, mobile app, tracking all those patterns. Same time, all your data is kept secure and private, and it's only used in ways that you expect it to be used. >> Well, to me, this is really, really important. I mean, especially after this year, we've seen online purchases go through the roof. (chuckles) Every time I buy something, I get an ad for that something, then for the next week, until I turn it off. I mean, it's clear that the state of data still has a way to go based on the quality and so you're addressing that, but take us through the process of identifying for instance, incorrect data or duplicate customer data. How do you do that? >> Well, Dave customer data changes so frequently. So for example, people get married and there are name changes. People move homes, so the address changes. Emails change or get updated, people change phones or phone numbers. The list goes on. Customer Data 360 identifies records that probably belong to the same customer, and offers a unified view of the customer for insights and for campaigns. It also offers a single household view, hoping to link together data from customers based at the same address. And then finally, it gives a datum, a data target operating model, to help drive continuous improvement through the enterprise. This means it helps embed the right process and culture with the organization's people, as well as the technology. >> So Yusef, just a quick aside, if I may. So essentially, I presume you're using some kind of machine intelligence which we've talked about before, to infer from, triangulate different data points and identify the probability that this individual is the same person, right? And then making that call. >> Yeah. Using machine learning and algorithms, you're able to do this much more quickly, much more effectively, much more cost-effectively than doing it via manual methods. Sometimes using manual methods, it's not really possible to do this type of work. So absolutely, there is a technological core backend that enables this work. >> Yeah, the manual just doesn't scale and humans just frankly aren't that good at it. So besides incorrect customer data, what other kinds of challenges are companies facing, and how are you addressing those? >> There are lots of different challenges. The data quality itself may be poor, so you've got the classic, "I've got the wrong address for that customer or the wrong email address", and that can happen multiple times over if you've got multiple records for each customer as well. The customer age might not be there, can be quite critical for streaming and other online services, so who's really a child and who's an adult? That can be very, very key for consent and things like that. Data relationships and data lineage may be unclear. Updating one system may not flow through into another system. Marketing and other permissions may not be captured correctly, and even sensitive data, PII, Personally Identifiable Information may be spread through the enterprise with no real understanding of where it is. And finally, there are cultural factors, like individual functions may jealously guard their own database, they may not share data in a way that's collaborative or useful for the whole enterprise. >> Great. Thank you for that. So, the big picture is this is going to drop right to my bottom line. I mean, if I'm sending duplicate communications, physical flyers, snail mail to the same household, people are just tossing it, they get frustrated. Or if I'm unknowingly giving minors access to restricted information, we've seen horror shows like that before, if that happens, you're going to lose customers, you're going to lose money. We all know the cost of losing customers is much, much higher (chuckles) than getting them. You have to get them back, forget it. It's three, four times X, what it originally cost. Where is Io-Tahoe going, to address this and remediate these problems? >> Well, Customer Data 360 really starts by understanding and fixing the fundamentals. So it starts by helping the customer understand their data estate, mapping the data relationships and the data lineage, automatically populating a data catalog so the customer knows what they have in terms of data, automatically assessing data quality, and recommending how it can be improved, automatically analyzing data record duplication and data source redundancy, and the customer can then get to a single view of the customer and the household as we said, this is enabled by the data target operating model which embeds this process and drives continuous improvement. The enterprise can then deploy raw data for analytics, model building, data science, can then productionize those models and related pipelines, and use them to start pushing out relevant messages and offers to customers. Obviously then, you capture the results. You use those to refine the offering and continuously improve, win customers, win friends, influence people, and grow revenue times a thousand. >> So, I've got to ask you another aside, if I may. I mean, we've talked about this in previous episodes. A lot of this, correct me if I'm wrong, you've got data source issues as well. I mean, you may not know that the address has changed but there may be other data sources that you can ingest that where the address has changed and you can bring that into your platform, but oftentimes, organizations don't want to do that. They don't want to add the data source, it's too complex, it adds more data quality issues, so it's a challenge somewhat. So, I'm just kind of connecting the dots from previous conversations that we've had. You know, we're at number seven now, but I can start to see this coming together. Maybe you could comment on that data source challenge. >> Yeah, absolutely. Organizations often have, I suppose you could call it dark data or data that they don't know that they have. So it does partly start with going back to the fundamentals of what data do you hold, rationalizing that data, using automated processes and machine learning to do that so you can do it more rapidly and effectively, getting them to a single view of the customer, and then using that in all the ways that advanced analytics and data science give you these days to get to a better customer experience and a better customer outcome. But as you say, a lot of that starts with identifying your data sources and understanding your data sources in the first place. >> Well, I've been watching you guys, your progress since COVID began and you're making some good moves here, Yusef and always great to catch up. I really appreciate your time and insights. >> Thank you, Dave. Nice to speak to you. Thanks for having me. >> Our pleasure. Okay, don't go away folks. Up next, we've got Ajay Vohora. He's the CEO of Io-Tahoe, and he's going to be joined by Mongo DB's principal solutions architect, talking through how to build modern apps using data RPA. Keep it right there, be right back. (gentle music)

>>from around the globe. It's the >>cube presenting >>active de que intelligent automation for data quality brought to you by Iota Ho >>Welcome to the sixth episode of the I. O. Tahoe data automation series. On the cube. We're gonna start off with a segment on how to accelerate the adoption of snowflake with Glenn Grossman, who is the enterprise account executive from Snowflake and yusef khan, the head of data services from Iota. Gentlemen welcome. >>Good afternoon. Good morning, Good evening. Dave. >>Good to see you. Dave. Good to see you. >>Okay glenn uh let's start with you. I mean the Cube hosted the snowflake data cloud summit in November and we heard from customers and going from love the tagline zero to snowflake, you know, 90 minutes very quickly. And of course you want to make it simple and attractive for enterprises to move data and analytics into the snowflake platform but help us understand once the data is there, how is snowflake helping to achieve savings compared to the data lake? >>Absolutely. dave. It's a great question, you know, it starts off first with the notion and uh kind of, we coined it in the industry or t shirt size pricing. You know, you don't necessarily always need the performance of a high end sports car when you're just trying to go get some groceries and drive down the street 20 mph. The t shirt pricing really aligns to, depending on what your operational workload is to support the business and the value that you need from that business? Not every day. Do you need data? Every second of the moment? Might be once a day, once a week through that t shirt size price and we can align for the performance according to the environmental needs of the business. What those drivers are the key performance indicators to drive that insight to make better decisions, It allows us to control that cost. So to my point, not always do you need the performance of a Ferrari? Maybe you need the performance and gas mileage of the Honda Civic if you would just get and deliver the value of the business but knowing that you have that entire performance landscape at a moments notice and that's really what what allows us to hold and get away from. How much is it going to cost me in a data lake type of environment? >>Got it. Thank you for that yussef. Where does Io Tahoe fit into this equation? I mean what's, what's, what's unique about the approach that you're taking towards this notion of mobilizing data on snowflake? >>Well, Dave in the first instance we profile the data itself at the data level, so not just at the level of metadata and we do that wherever that data lives. So it could be structured data could be semi structured data could be unstructured data and that data could be on premise. It could be in the cloud or it could be on some kind of SAAS platform. And so we profile this data at the source system that is feeding snowflake within snowflake itself within the end applications and the reports that the snowflake environment is serving. So what we've done here is take our machine learning discovery technology and make snowflake itself the repository for knowledge and insights on data. And this is pretty unique. Uh automation in the form of our P. A. Is being applied to the data both before after and within snowflake. And so the ultimate outcome is that business users can have a much greater degree of confidence that the data they're using can be trusted. Um The other thing we do uh which is unique is employee data R. P. A. To proactively detect and recommend fixes the data quality so that removes the manual time and effort and cost it takes to fix those data quality issues. Uh If they're left unchecked and untouched >>so that's key to things their trust, nobody's gonna use the data. It's not trusted. But also context. If you think about it, we've contextualized are operational systems but not our analytic system. So there's a big step forward glen. I wonder if you can tell us how customers are managing data quality when they migrate to snowflake because there's a lot of baggage in in traditional data warehouses and data lakes and and data hubs. Maybe you can talk about why this is a challenge for customers. And like for instance can you proactively address some of those challenges that customers face >>that we certainly can. They have. You know, data quality. Legacy data sources are always inherent with D. Q. Issues whether it's been master data management and data stewardship programs over the last really almost two decades right now, you do have systemic data issues. You have siloed data, you have information operational, data stores data marks. It became a hodgepodge when organizations are starting their journey to migrate to the cloud. One of the things that were first doing is that inspection of data um you know first and foremost even looking to retire legacy data sources that aren't even used across the enterprise but because they were part of the systemic long running operational on premise technology, it stayed there when we start to look at data pipelines as we onboard a customer. You know we want to do that era. We want to do QA and quality assurance so that we can, And our ultimate goal eliminate the garbage in garbage out scenarios that we've been plagued with really over the last 40, 50 years of just data in general. So we have to take an inspection where traditionally it was E. T. L. Now in the world of snowflake, it's really lt we're extracting were loading or inspecting them. We're transforming out to the business so that these routines could be done once and again give great business value back to making decisions around the data instead of spending all this long time. Always re architect ng the data pipeline to serve the business. >>Got it. Thank you. Glenda yourself of course. Snowflakes renowned for customers. Tell me all the time. It's so easy. It's so easy to spin up a data warehouse. It helps with my security. Again it simplifies everything but so you know, getting started is one thing but then adoption is also a key. So I'm interested in the role that that I owe. Tahoe plays in accelerating adoption for new customers. >>Absolutely. David. I mean as Ben said, you know every every migration to Snowflake is going to have a business case. Um uh and that is going to be uh partly about reducing spending legacy I. T. Servers, storage licenses, support all those good things um that see I want to be able to turn off entirely ultimately. And what Ayatollah does is help discover all the legacy undocumented silos that have been built up, as Glenn says on the data estate across a period of time, build intelligence around those silos and help reduce those legacy costs sooner by accelerating that that whole process. Because obviously the quicker that I. T. Um and Cdos can turn off legacy data sources the more funding and resources going to be available to them to manage the new uh Snowflake based data estate on the cloud. And so turning off the old building, the new go hand in hand to make sure those those numbers stack up the program is delivered uh and the benefits are delivered. And so what we're doing here with a Tahoe is improving the customers are y by accelerating their ability to adopt Snowflake. >>Great. And I mean we're talking a lot about data quality here but in a lot of ways that's table stakes like I said, if you don't trust the data, nobody's going to use it. And glenn, I mean I look at Snowflake and I see obviously the ease of use the simplicity you guys are nailing that the data sharing capabilities I think are really exciting because you know everybody talks about sharing data but then we talked about data as an asset, Everyone so high I to hold it. And so sharing is is something that I see as a paradigm shift and you guys are enabling that. So one of the things beyond data quality that are notable that customers are excited about that, maybe you're excited about >>David, I think you just cleared it out. It's it's this massive data sharing play part of the data cloud platform. Uh you know, just as of last year we had a little over about 100 people, 100 vendors in our data marketplace. That number today is well over 450 it is all about democratizing and sharing data in a world that is no longer held back by FTp s and C. S. V. S and then the organization having to take that data and ingested into their systems. You're a snowflake customer. want to subscribe to an S and P data sources an example, go subscribe it to it. It's in your account there was no data engineering, there was no physical lift of data and that becomes the most important thing when we talk about getting broader insights, data quality. Well, the data has already been inspected from your vendor is just available in your account. It's obviously a very simplistic thing to describe behind the scenes is what our founders have created to make it very, very easy for us to democratize not only internal with private sharing of data, but this notion of marketplace ensuring across your customers um marketplace is certainly on the type of all of my customers minds and probably some other areas that might have heard out of a recent cloud summit is the introduction of snow park and being able to do where all this data is going towards us. Am I in an ale, you know, along with our partners at Io Tahoe and R. P. A. Automation is what do we do with all this data? How do we put the algorithms and targets now? We'll be able to run in the future R and python scripts and java libraries directly inside Snowflake, which allows you to even accelerate even faster, Which people found traditionally when we started off eight years ago just as a data warehousing platform. >>Yeah, I think we're on the cusp of just a new way of thinking about data. I mean obviously simplicity is a starting point but but data by its very nature is decentralized. You talk about democratizing data. I like this idea of the global mesh. I mean it's very powerful concept and again it's early days but you know, keep part of this is is automation and trust, yussef you've worked with Snowflake and you're bringing active D. Q. To the market what our customers telling you so far? >>Well David the feedback so far has been great. Which is brilliant. So I mean firstly there's a point about speed and acceleration. Um So that's the speed to incite really. So where you have inherent data quality issues uh whether that's with data that was on premise and being brought into snowflake or on snowflake itself, we're able to show the customer results and help them understand their data quality better Within Day one which is which is a fantastic acceleration. I'm related to that. There's the cost and effort to get that insight is it's a massive productivity gain versus where you're seeing customers who've been struggling sometimes too remediate legacy data and legacy decisions that they've made over the past couple of decades, so that that cost and effort is much lower than it would otherwise have been. Um 3rdly, there's confidence and trust, so you can see Cdos and see IOS got demonstrable results that they've been able to improve data quality across a whole bunch of use cases for business users in marketing and customer services, for commercial teams, for financial teams. So there's that very quick kind of growth in confidence and credibility as the projects get moving. And then finally, I mean really all the use cases for the snowflake depend on data quality, really whether it's data science, uh and and the kind of snow park applications that Glenn has talked about, all those use cases work better when we're able to accelerate the ri for our joint customers by very quickly pushing out these data quality um insights. Um And I think one of the one of the things that the snowflake have recognized is that in order for C. I. O. Is to really adopt enterprise wide, um It's also as well as the great technology with Snowflake offers, it's about cleaning up that legacy data state, freeing up the budget for CIA to spend it on the new modern day to a state that lets them mobilise their data with snowflake. >>So you're seeing the Senate progression. We're simplifying the the the analytics from a tech perspective. You bring in Federated governance which which brings more trust. Then then you bring in the automation of the data quality piece which is fundamental. And now you can really start to, as you guys are saying, democratized and scale uh and share data. Very powerful guys. Thanks so much for coming on the program. Really appreciate your time. >>Thank you. I appreciate as well. Yeah.

>>from around the globe. It's the Cube presenting enterprise, Digital resilience on hybrid and multi cloud Brought to You by Iota Ho. Okay, let's now get into the next segment where we'll explore data automation. But from the angle of digital resilience within and as a service consumption model, we're now joined by Yusuf Khan, who heads data services for Iota Ho and Shirish County. Up in Who's the vice president and head of U. S. Sales at happiest Minds. Gents, welcome to the program. Great to have you in the Cube. >>Thank you, David. >>Stretch. You guys talk about happiest minds. This notion of born digital, foreign agile. I like that. But talk about your mission at the company. >>Sure. A former in 2011 Happiest minds Up Born digital born a child company. >>The >>reason is that we are focused on customers. Our customer centric approach on delivering digitals and seamless solutions have helped us be in the race. Along with the Tier one providers, our mission, happiest people, happiest customers is focused to enable customer happiness through people happiness. We have Bean ranked among the top 25 I t services company in the great places to work serving hour glass to ratings off 4.1 against the rating off five is among the job in the Indian nineties services company that >>shows the >>mission on the culture. What we have built on the values, right sharing, mindful, integrity, learning and social on social responsibilities are the core values off our company on. That's where the entire culture of the company has been built. >>That's great. That sounds like a happy place to be. Now you have you head up data services for Iot Tahoe. We've talked in the past. Of course you're out of London. What do you what's your day to day focus with customers and partners? What you focused on? >>Well, David, my team work daily with customers and partners to help them better understand their data, improve their data quality, their data governance on help them make that data more accessible in a self service kind of way. To the stakeholders within those businesses on dis is all a key part of digital resilience that will will come on to talk about but later. You're >>right, e mean, that self service theme is something that we're gonna we're gonna really accelerate this decade, Yussef and so. But I wonder before we get into that, maybe you could talk about the nature of the partnership with happiest minds. You know, why do you guys choose toe work closely together? >>Very good question. Um, we see Io Tahoe on Happiest minds as a great mutual fit. A Suresh has said happiest minds are very agile organization. Um, I think that's one of the key things that attracts their customers on Io. Tahoe is all about automation. We're using machine learning algorithms to make data discovery data cataloging, understanding, data, redundancy, uh, much easier on. We're enabling customers and partners to do it much more quickly. So when you combine our emphasis on automation with the emphasis on agility, the happiest minds have that. That's a really nice combination. Work works very well together, very powerful. I think the other things that a key are both businesses, a serious have said are really innovative digital native type type companies. Um, very focused on newer technologies, the cloud etcetera, uh, on. Then finally, I think that both challenger brands Andi happiest minds have a really positive, fresh ethical approach to people and customers that really resonates with us that I have tied to its >>great thank you for that. So Russia, Let's get into the whole notion of digital resilience. I wanna I wanna sort of set it up with what I see. And maybe you can comment be prior to the pandemic. A lot of customers that kind of equated disaster recovery with their business continuance or business resilient strategy, and that's changed almost overnight. How have you seen your clients respond to that? What? I sometimes called the forced march to become a digital business. And maybe you could talk about some of the challenges that they faced along the way. >>Absolutely. So, uh, especially during this pandemic times when you see Dave customers have been having tough times managing their business. So happiest minds. Being a digital Brazilian company, we were able to react much faster in the industry, apart from the other services company. So one of the key things is the organizations trying to adopt onto the digital technologies right there has bean lot off data which has been to managed by these customers on. There have been lot off threats and risk, which has been to manage by the CEO Seo's so happiest minds digital resilient technology fight the where we're bringing the data complaints as a service, we were ableto manage the resilience much ahead off other competitors in the market. We were ableto bring in our business community processes from day one, where we were ableto deliver our services without any interruption to the services what we were delivering to our customers. >>So >>that is where the digital resilience with business community process enabled was very helpful for us who enable our customers continue there business without any interruptions during pandemics. >>So, I mean, some of the challenges that that customers tell me they obviously had to figure out how to get laptops to remote workers and that that whole remote, you know, work from home pivot figure out how to secure the end points. And, you know, those were kind of looking back there kind of table stakes, but it sounds like you've got a digital business means a data business putting data at the core, I like to say, but so I wonder if you could talk a little bit more about maybe the philosophy you have toward digital resilience in the specific approach you take with clients? >>Absolutely. They seen any organization data becomes. The key on this for the first step is to identify the critical data. Right. So we this is 1/6 process. What we following happiest minds. First of all, we take stock off the current state, though the customers think that they have a clear visibility off their data. How are we do more often assessment from an external point off view on See how critical their data is? Then we help the customers to strategies that right the most important thing is to identify the most important critical herself. Data being the most critical assault for any organization. Identification off the data's key for the customers. Then we help in building a viable operating model to ensure these identified critical assets are secure on monitor dearly so that they are consumed well as well as protected from external threats. Then, as 1/4 step, we try to bring in awareness, toe the people we train them at all levels in the organization. That is a P for people to understand the importance off the residual our cells. And then as 1/5 step, we work as a back up plan in terms of bringing in a very comprehensive and the holistic testing approach on people process as well as in technology. We'll see how the organization can withstand during a crisis time. And finally we do a continuous governance off this data, which is a key right. It is not just a one step process. We set up the environment. We do the initial analysis and set up the strategy on continuously govern this data to ensure that they are not only know managed will secure as well as they also have to meet the compliance requirements off the organization's right. That is where we help organizations toe secure on Meet the regulations off the organizations. As for the privacy laws, >>so >>this is a constant process. It's not on one time effort. We do a constant process because every organization goes towards the digital journey on. They have to face all these as part off the evolving environment on digital journey, and that's where they should be kept ready in terms off. No recovering, rebounding on moving forward if things goes wrong. >>So let's stick on that for a minute, and then I wanna bring yourself into the conversation. So you mentioned compliance and governance. When? When your digital business. Here, as you say, you're a data business. So that brings up issues. Data sovereignty. Uh, there's governance, this compliance. There's things like right to be forgotten. There's data privacy, so many things. These were often kind of afterthoughts for businesses that bolted on, if you will. I know a lot of executives are very much concerned that these air built in on, and it's not a one shot deal. So do you have solutions around compliance and governance? Can you deliver that as a service? Maybe you could talk about some of the specifics there, >>so some of way have offered multiple services. Tow our customers on digital race against. On one of the key service is the data complaints. As a service here we help organizations toe map the key data against the data compliance requirements. Some of the features includes in terms off the continuous discovery off data right, because organizations keep adding on data when they move more digital on helping the helping and understanding the actual data in terms off the residents of data, it could be a heterogeneous data sources. It could be on data basis or it could be even on the data lakes. Or it could be or no even on compromise, all the cloud environment. So identifying the data across the various no heterogeneous environment is very key. Feature off our solution. Once we identify, classify this sensitive data, the data privacy regulations on the traveling laws have to be map based on the business rules. So we define those rules on help map those data so that organizations know how critical their digital assets are. Then we work on a continuous marching off data for anomalies because that's one of the key teachers off the solution, which needs to be implemented on the day to day operational basis. So we're helping monitoring those anomalies off data for data quality management on an ongoing basis. And finally we also bringing the automatic data governance where we can manage the sensory data policies on their data relationships in terms off, mapping on manage their business rules on we drive reputations toe also suggest appropriate actions to the customers. Take on those specific data sets. >>Great. Thank you, Yousef. Thanks for being patient. I want to bring in Iota ho thio discussion and understand where your customers and happiest minds can leverage your data automation capability that you and I have talked about in the past. And I'm gonna be great if you had an example is well, but maybe you could pick it up from there. >>Sure. I mean, at a high level, assertions are clearly articulated. Really? Um, Iota, who delivers business agility. So that's by, um, accelerating the time to operationalize data, automating, putting in place controls and ultimately putting, helping put in place digital resilience. I mean, way if we step back a little bit in time, um, traditional resilience in relation to data are often met manually, making multiple copies of the same data. So you have a DB A. They would copy the data to various different places on business. Users would access it in those functional style owes. And of course, what happened was you ended up with lots of different copies off the same data around the enterprise. Very inefficient. Onda course ultimately, uh, increases your risk profile. Your risk of a data breach. Um, it's very hard to know where everything is, and I realized that expression they used David, the idea of the forced march to digital. So with enterprises that are going on this forced march, what they're finding is they don't have a single version of the truth, and almost nobody has an accurate view of where their critical data is. Then you have containers bond with containers that enables a big leap forward so you could break applications down into micro services. Updates are available via a P I s. And so you don't have the same need to build and to manage multiple copies of the data. So you have an opportunity to just have a single version of the truth. Then your challenge is, how do you deal with these large legacy data states that the service has been referring Thio, where you you have toe consolidate, and that's really where I Tahoe comes in. Um, we massively accelerate that process of putting in a single version of the truth into place. So by automatically discovering the data, um, discovering what's duplicate what's redundant, that means you can consolidate it down to a single trusted version much more quickly. We've seen many customers have tried to do this manually, and it's literally taken years using manual methods to cover even a small percentage of their I T estates with a tire. You could do it really very quickly on you can have tangible results within weeks and months. Um, and then you can apply controls to the data based on context. So who's the user? What's the content? What's the use case? Things like data quality validations or access permissions on. Then once you've done there, your applications and your enterprise are much more secure, much more resilient. As a result, you've got to do these things whilst retaining agility, though. So coming full circle. This is where the partnership with happiest minds really comes in as well. You've got to be agile. You've gotta have controls, um, on you've got a drug towards the business outcomes and it's doing those three things together that really deliver for the customer. Thank >>you. Use f. I mean you and I. In previous episodes, we've looked in detail at the business case. You were just talking about the manual labor involved. We know that you can't scale, but also there's that compression of time. Thio get to the next step in terms of ultimately getting to the outcome and we talked to a number of customers in the Cube. And the conclusion is really consistent that if you could accelerate the time to value, that's the key driver reducing complexity, automating and getting to insights faster. That's where you see telephone numbers in terms of business impact. So my question is, where should customers start? I mean, how can they take advantage of some of these opportunities that we've discussed >>today? Well, we've tried to make that easy for customers. So with our Tahoe and happiest minds, you can very quickly do what we call a data health check on. Dis is a is a 2 to 3 weeks process are two Really quickly start to understand and deliver value from your data. Um, so, iota, who deploys into the customer environment? Data doesn't go anywhere. Um, we would look at a few data sources on a sample of data Onda. We can very rapidly demonstrate how date discovery those catalog e understanding Jupiter data and redundant data can be done. Um, using machine learning, um, on how those problems can be solved. Um, and so what we tend to find is that we can very quickly as I say in a matter of a few weeks, show a customer how they could get toe, um, or Brazilian outcome on. Then how they can scale that up, take it into production on, then really understand their data state Better on build resilience into the enterprise. >>Excellent. There you have it. We'll leave it right there. Guys. Great conversation. Thanks so much for coming on the program. Best of luck to you in the partnership. Be well. >>Thank you, David. Sorry. Thank you. Thank >>you for watching everybody, This is Dave Volonte for the Cuban Are ongoing Siris on data Automation without Tahoe.

>> Announcer: From around the globe, It's theCUBE. Presenting Enterprise Digital Resilience on Hybrid and Multicloud. Brought to you by Io-Tahoe. >> Okay, Let's now get into the next segment where we'll explore data automation but from the angle of digital resilience within and as a service consumption model. We're now joined by Yusef Khan, who heads data services for Io-Tahoe and Suresh Kanniappan who's the vice president and head of US sales at Happiest Minds. Gents, welcome to the program, great to have you in theCUBE. >> Thank you, David. >> Suresh, you guys talk about at Happiest Minds this notion of born digital, foreign agile, I like that but talk about your mission at the company. >> Sure, far in 2011, Happiest minds is a born digital, born agile company. The reason is that, we are focused on customers. Our customer centric approach and delivering digital and seamless solutions, have helped us be in the race along with the Tier 1 providers. Our mission, Happiest People, Happiest Customers is focused to enable customer happiness through people happiness. We have been ranked among the top 25 ID services company in the great places to work in service. Our Glassdoor ratings, of four dot one against the rating of five, is among the top in the Indian ID services company, that shows the mission and the culture what we have built on the values, right? Is sharing, mindful, integrity, learning and social responsibilities, are the core values of our company. And that's where the entire culture of the company has been built. >> That's great, sounds like a happy place to be. Now Yusef, you had updated services for Io-Tahoe, we've talked in the past year, of course you're at London. What's your day to day focus with customers and partners? What are you focused on? >> Well David, my team worked daily with customers and partners to help them better understand their data, improve their data quality, their data governance, and help them make that data more accessible in a self-service kind of way to the stakeholders within those businesses. And this is a key part of digital resilience that we allow. We'll come on to talk about a bit later. >> You're right, I mean that self-service theme is something that we're going to really accelerate this decade Yusef. And so, but I wonder before we get into that, maybe you could talk about the nature of the partnership with Happiest Minds, why do you guys choose to work closely together? >> Very good question. We see Io-Tahoe and Happiest Minds as a great mutual fit. As Suresh said, Happiest Minds are a very agile organization. I think that's one of the key things that attracts the customers. And Io-Tahoe is all about automation. We're using machine learning algorithms to make data discovery, data cataloging, understanding data redundancy much easier and we're enabling customers and partners to do it much more quickly. So when you combine our emphasis on automation, with the emphasis on agility that Happiest Minds have. That's a really nice combination, works very well together, very powerful. I think the other things that are key, both businesses as Suresh have said, are really innovative, digital native type companies. Very focused on newer technologies, the cloud, et cetera. And then finally I think they're both challenge brands and Happiest Minds have a really positive, fresh, ethical approach to people and customers that really resonates with us at Io-Tahoe too. >> That's great, thank you for that. Suresh, let's get into the whole notion of digital resilience. I want to sort of set it up with what I see and maybe you can comment. Being prior to the pandemic, a lot of customers that kind of equated disaster recovery with their business continuance or business resilience strategy and that's changed almost overnight. How have you seen your clients respond to that? What I sometimes call the forced match to become a digital business and maybe you could talk about some of the challenges that they've faced along the way. >> Absolutely, So especially during this pandemic times when you see Dave, customers have been having tough times managing their business. So Happiest Minds being a digital resilient company, we were able to react much faster in the industry apart from the other services company. So, one of the key things is, the organizations are trying to adapt onto the digital technologies, right? There has been lot of data which has to be managed by these customers, and there've been a lot of threats and risk which has to be managed by the CIOs. So Happiest Minds Digital Resilient Technology, right? We're bringing the data complaints as a service. We were able to manage the resilience much ahead of other competitors in the market. We were able to bring in our business continuity processes from day one, where we were able to deliver our services without any interruption to the services what we are delivering to our customers. So that is where the digital, the resilience with business continuity process enabled was very helpful for us to enable our customers continue their business without any interruptions during pandemics. >> So, I mean some of the challenges that customers tell me if I may obviously had to figure out how to get laptops to remote workers, that whole remote, work from home pivot, figure out how to secure the end points, and those were kind of looking back they're kind of table stakes. And it sounds like, you got, I mean digital business means, a data business, putting data at the core, I like to say it. But so, I wonder if you could talk a little bit more about, maybe the philosophy you have toward digital resilience and the specific approach you take with clients. >> Absolutely Dave, see in any organization, data becomes the key. And thus for the first step, is to identify the critical data, right? So, this is a six step process plot we follow in Happiest Minds. First of all, we take stock of the current state, though the customers think that they have a clear visibility of their data. However, we do more often assessment from an external point of view and see how critical their data is. Then we help the customers to strategize that, right? The most important thing is to identify the most important critical asset. Data being the most critical asset for any organization, identification of the data are key for the customers. Then we help in building a viable operating model to ensure these identified critical assets are secure and monitored duly so that they are consumed well as well as protected from external threats. Then as a fourth step, now we try to bring in awareness to the people. We train them, at all levels in the organization. That is a key for people to understand the importance of the digital lessons. And then, as a fifth step, we work as a backup plan. In terms of bringing in a very comprehensive and a wholistic distinct approach on people, process, as well as in technology, to see how the organization can withstand during a crisis time. And finally, we do a continuous governance of these data. Which is a key, right? It is not just a one-step process. We set up the environment, we do the initial analysis, and set up the strategy and continuously govern these data to ensure that they are not only not managed well, secure, as well as they also have to meet the compliance requirements of the organizations, right? That is where we help organizations to secure and meet the regulations of the organizations as per the privacy laws. So this is a constant process. It's not a one time effort, we do a constant process because every organization grows towards their digital journey, and they have to face all these as part of the evolving environment on digital journey. And that's where they should be kept ready in terms of recovering, rebounding and moving forward if things goes wrong. >> So, let's stick on that for a minute and then I want to bring Yusef into the conversation. So, you mentioned compliance and governance. When you're in digital business here as you say you're a data business, so that brings up issues, data sovereignty, there's governance, there's compliance, there's things like right to be forgotten, there's data privacy, so many things. These were often kind of afterthoughts for businesses that bolted on, if you will. I know a lot of executives are very much concerned that these are built in and it's not a one-shot deal. So, do you have solutions around compliance and governance? Can you deliver that as a service? Maybe you could talk about some of the specifics there. >> Sure, we offer multiple services to our customers on digital residents. And one of the key service is the data compliance as a service. Here, we help organizations to map the key data against the data compliance requirements. Some of the features includes in terms of the continuous discovery of data, right? Because organizations keep adding on data when they move more digital. And helping and understanding the actual data in terms of the resilience of data, it could be an heterogeneous data sources, It could be on data basis, or it could be even on the data lakes, or it could be even on on-prem or on the cloud environment. So, identifying the data across the various heterogeneous environment is a very key feature of our solution. Once we identify and classify these sensitive data, the data privacy regulations and the prevalent laws have to be mapped based on the business rules. So we define those rules and help map those data so that organizations know how critical their digital assets are. Then we work on a continuous monitoring of data for anomalies. Because that's one of the key features of the solution, which needs to be implemented on the day-to-day operational basis. So, we help in monitoring those anomalies of data, for data quality management on an ongoing basis. And finally, we also bring in the automated data governance where we can manage the sensitive data policies and their data relationships in terms of mapping and manage that business rules. And we drive limitations and also suggest appropriate actions to the customers to take on those specific data assets. >> Great, thank you. Yusef thanks for being patient. I want to bring in Io-Tahoe to the discussion and understand where your customers and Happiest Minds can leverage your data automation capability that you and I have talked about in the past. And I mean it'd be great if you had an example as well, but maybe you could pick it up from there. >> Sure, I mean at a high level as Suresh articulated really, Io-Tahoe delivers business agility. So that's by accelerating the times operationalized data, automating, putting in place controls, and also helping put in place digital resilience. I mean, if we stepped back a little bit in time, traditional resilience in relation to data, often meant manually making multiple copies of the same data. So you'd have a DBA, they would copy the data to various different places, and then business users would access it in those functional silos. And of course, what happened was you ended up with lots of different copies of the same data around the enterprise. Very inefficient, and of course ultimately increases your risk profile, your risk of a data breach, It's very hard to know where everything is. And I realized that expression you used David, the idea of the forced match to digital. So, with enterprises that are going on this forced match, what they're finding is, they don't have a single version of the truth. And almost nobody has an accurate view of where their critical data is. Then you have containers, and with containers that enables a big leap forward. So you can break applications down into microservices, updates are available via APIs, and so you don't have the same need to to build and to manage multiple copies of the data. So, you have an opportunity to just have a single version of a truth. Then your challenge is, how do you deal with these large legacy data states that Suresh has been referring to? Where you have to consolidate. And that's really where Io-Tahoe comes in. We massively accelerate that process of putting in a single version of truth into place. So by automatically discovering the data, discovering what's duplicate, what's redundant, that means you can consolidate it down to a single trusted version, much more quickly. We've seen many customers who've tried to do this manually and it's literally taken years using manual methods to cover even a small percentage of their IT estates. With Io-Tahoe you can do it really very quickly and you can have tangible results within weeks and months. And then you can apply controls to the data based on context. So, who's the user? What's the content? What's the use case? Things like data quality validations or access permissions, and then once you've done that, your applications and your enterprise are much more secure, much more resilient as a result. You've got to do these things whilst retaining agility though. So, coming full circle, this is where the partnership with Happiest Minds really comes in as well. You've got to be agile, you've got to have controls and you've got to drive towards the business outcomes. And it's doing those three things together, we really deliver for the customer. >> Thank you, Yusef. I mean you and I in previous episodes we've looked in detail at the business case you were just talking about the manual labor involved. We know that you can't scale, but also there's that compression of time to get to the next step in terms of ultimately getting to the outcome and we've to a number of customers in theCUBE and the conclusion is, it's really consistent that if you can accelerate the time to value, that's the key driver, reducing complexity, automating and getting to insights faster. That's where you see telephone numbers in terms of business impact. So my question is, where should customers start? I mean how can they take advantage of some of these opportunities that we've discussed today? >> Well, we've tried to make that easy for customers. So, with Io-Tahoe and Happiest Minds you can very quickly do what we call a data health check. And this is a two to three week process to really quickly start to understand and deliver value from your data. So, Io-Tahoe deploys into the customer environment, data doesn't go anywhere, we would look at a few data sources, and a sample of data and we can very rapidly demonstrate how data discovery, data cataloging and understanding duplicate data or redundant data can be done, using machine learning, and how those problems can be solved. And so what we tend to find is that we can very quickly as I said in a matter of a few weeks, show a customer how they can get to a more resilient outcome and then how they can scale that up, take it into production, and then really understand their data state better, and build resilience into the enterprise. >> Excellent, there you have it. We'll leave it right there guys. Great conversation. Thanks so much for coming into the program. Best of luck to you in the partnership, be well. >> Thank you David, Suresh. >> Thank you Yusef. >> And thank you for watching everybody. This is Dave Vellante for theCUBE and our ongoing series on Data Automation with Io-Tahoe. (soft upbeat music)

>> Presenter: From around the globe, it's theCUBE. Presenting Adaptive Data Governance, brought to you by Io-Tahoe. >> Our next segment here is an interesting panel, you're going to hear from three gentlemen, about Adaptive Data Governance. We're going to talk a lot about that. Please welcome Yusef Khan, the global director of data services for Io-Tahoe. We also have Santiago Castor, the chief data officer at the First Bank of Nigeria, and Gudron Van Der Wal, Oracle's senior manager of digital transformation and industries. Gentlemen, it's great to have you joining us in this panel. (indistinct) >> All right, Santiago, we're going to start with you. Can you talk to the audience a little bit about the First Bank of Nigeria and its scale? This is beyond Nigeria, talk to us about that. >> Yes. So First Bank of Nigeria was created 125 years ago, it's one of the oldest, if not the oldest bank in Africa. And because of the history, it grew, everywhere in the region, and beyond the region. I'm currently based in London, where it's kind of the European headquarters. And it really promotes trade finance, institutional banking, corporate banking, private banking around the world, in particular in relationship to Africa. We are also in Asia, in the Middle East. And yes, and is a very kind of active bank in all these regions. >> So Santiago, talk to me about what adaptive data governance means to you, and how does it helps the First Bank of Nigeria to be able to innovate faster with the data that you have. >> Yes I like that concept of adaptive data governance, because it's kind of, I would say, an approach that can really happen today with the new technology before it was much more difficult to implement. So just to give you a little bit of context, I used to work in consulting for 16-17 years before joining the First Bank of Nigeria. And I saw many organizations trying to apply different type of approaches in data governance. And the beginning, early days was really kind of (indistinct), where you top down approach, where data governance was seen as implement a set of rules, policies and procedures, but really from the top down. And is important, it's important to have the battle of your sea level, of your director, whatever is, so just that way it fails, you really need to have a complimentary approach, I often say both amount, and actually, as a CEO I'm really trying to decentralized data governance, really instead of imposing a framework that some people in the business don't understand or don't care about it. It really needs to come from them. So what I'm trying to say is that, data basically support business objectives. And what you need to do is every business area needs information on particular decisions to actually be able to be more efficient, create value, et cetera. Now, depending on the business questions they have to show, they will need certain data sets. So they need actually to be able to have data quality for their own, 'çause now when they understand that, they become the stewards naturally of their own data sets. And that is where my bottom line is meeting my top down. You can guide them from the top, but they need themselves to be also in power and be actually in a way flexible to adapt the different questions that they have in order to be able to respond to the business needs. And I think that is where these adaptive data governance starts. Because if you want, I'll give you an example. In the bank, we work, imagine a Venn diagram. So we have information that is provided to finance, and all information to risk, or information for business development. And in this Venn diagram, there is going to be part of that every circle that are going to kind of intersect with each other. So what you want as a data governance is to help providing what is in common, and then let them do their own analysis to what is really related to their own area as an example, nationality. You will say in a bank that will open an account is the nationality of your customer, that's fine for final when they want to do a balance sheet an accounting or a P&L, but for risk, they want that type of analysis plus the net nationality of exposure, meaning where you are actually exposed as a risk, you can have a customer that are on hold in the UK, but then trade with Africa, and in Africa they're exposing their credit. So what I'm trying to say is they have these pieces in common and pieces that are different. Now I cannot impose a definition for everyone. I need them to adapt and to bring their answers to their own business questions. That is adaptive data governance. And all that is possible because we have and I was saying at the very beginning, just to finalize the point, we have new technologies that allow you to do these metadata classification in a very sophisticated way that you can actually create analytics of your metadata. You can understand your different data sources, in order to be able to create those classifications like nationalities and way of classifying your customers, your products, et cetera. But you will need to understand which areas need, what type nationality or classification, which others will mean that all the time. And the more you create that understanding, that intelligence about how your people are using your data you create in a way building blocks like a label, if you want. Where you provide them with those definitions, those catalogs you understand how they are used or you let them compose like Lego. They would play their way to build their analysis. And they will be adaptive. And I think the new technologies are allowing that. And this is a real game changer. I will say that over and over. >> So one of the things that you just said Santiago kind of struck me in to enable the users to be adaptive, they probably don't want to be logging in support ticket. So how do you support that sort of self service to meet the demand of the user so that they can be adaptive? >> Yeah, that's a really good question. And that goes along with that type of approach. I was saying in a way more and more business users want autonomy, and they want to basically be able to grab the data and answers their question. Now, when you have that, that's great, because then you have demand. The business is asking for data. They're asking for the insight. So how do you actually support that? I will say there is a changing culture that is happening more and more. I would say even the current pandemic has helped a lot into that because you have had, in a way, of course, technology is one of the biggest winners without technology we couldn't have been working remotely. Without this technology, where people can actually log in from their homes and still have a market data marketplaces where they self serve their information. But even beyond that, data is a big winner. Data because the pandemic has shown us that crisis happened, but we cannot predict everything and that we are actually facing a new kind of situation out of our comfort zone, where we need to explore and we need to adapt and we need to be flexible. How do we do that? With data. As a good example this, every country, every government, is publishing everyday data stats of what's happening in the countries with the COVID and the pandemic so they can understand how to react because this is new. So you need facts in order to learn and adapt. Now, the companies that are the same. Every single company either saw the revenue going down, or the revenue going very up for those companies that are very digital already now, it changed the reality. So they needed to adapt, but for that they needed information in order to think and innovate and try to create responses. So that type of self service of data, (indistinct) for data in order to be able to understand what's happening when the construct is changing, is something that is becoming more of the topic today because of the pandemic, because of the new capabilities of technologies that allow that. And then, you then are allowed to basically help, your data citizens, I call them in organization. People that know their business and can actually start playing and answer their own questions. So these technologies that gives more accessibility to the data, that gives some cataloging so we can understand where to go or where to find lineage and relationships. All this is basically the new type of platforms or tools that allow you to create what I call a data marketplace. So once you create that marketplace, they can play with it. And I was talking about new culture. And I'm going to finish with that idea. I think these new tools are really strong because they are now allowing for people that are not technology or IT people to be able to play with data because it comes in the digital world they are useful. I'll give you an example with all your stuff where you have a very interesting search functionality, where you want to find your data and you want to self serve, you go there in that search, and you actually go and look for your data. Everybody knows how to search in Google, everybody searching the internet. So this is part of the data culture, the digital culture, they know how to use those tools. Now similarly, that data marketplace is in Io-Tahoe, you can for example, see which data sources are mostly used. So when I'm doing an analysis, I see that police in my area are also using these sources so I trust those sources. We are a little bit like Amazon, when you might suggest you what next to buy, again this is the digital kind of culture where people very easily will understand. Similarly, you can actually like some type of data sets that are working, that's Facebook. So what I'm trying to say is you have some very easy user friendly technologies that allows you to understand how to interact with them. And then within the type of digital knowledge that you have, be able to self serve, play, collaborate with your peers, collaborate with the data query analysis. So its really enabling very easily that transition to become a data savvy without actually needing too much knowledge of IT, or coding, et cetera, et cetera. And I think that is a game changer as well. >> And enabling that speed that we're all demanding today during these unprecedented times. Gudron I wanted to go to you, as we talk about in the spirit of evolution, technology's changing. Talk to us a little bit about Oracle Digital. What are you guys doing there? >> Yeah, thank you. Well, Oracle Digital is a business unit at Oracle EMEA. And we focus on emerging countries, as well as low end enterprises in the mid market in more developed countries. And four years ago, they started with the idea to engage digital with our customers via central hubs across EMEA. That means engaging with video having conference calls, having a wall, agreeing wall, where we stand in front and engage with our customers. No one at that time could have foreseen how this is the situation today. And this helps us to engage with our customers in the way we're already doing. And then about my team. The focus of my team is to have early stage conversations with our customers on digital transformation and innovation. And we also have a team of industry experts who engage with our customers and share expertise across EMEA. And we we inspire our customers. The outcome of these conversations for Oracle is a deep understanding of our customer needs, which is very important. So we can help the customer and for the customer means that we will help them with our technology and our resources to achieve their goals. >> It's all about outcomes. Right Gudron? So in terms of automation, what are some of the things Oracle is doing there to help your clients leverage automation to improve agility so that they can innovate faster? Which on these interesting times it's demanding. >> Yeah. Thank you. Well, traditionally, Oracle is known for their databases, which has been innovated year over year since the first launch. And the latest innovation is the autonomous database and autonomous data warehouse. For our customers, this means a reduction in operational costs by 90%, with a multimodal converged database, and machine learning based automation for full lifecycle management. Our database is self driving. This means we automate database provisioning, tuning and scaling. The database is self securing. This means ultimate data protection and security and itself repairing the ultimate failure detection, failover and repair. And the question is for our customers, what does it mean? It means they can focus on their business instead of maintaining their infrastructure and their operations. >> That's absolutely critical. Yusef, I want to go over to you now. Some of the things that we've talked about, just the massive progression and technology, the evolution of that, but we know that whether we're talking about data management, or digital transformation. A one size fits all approach doesn't work to address the challenges that the business has. That the IT folks have. As you are looking to the industry, with what Santiago told us about First Bank of Nigeria, what are some of the changes that you're seeing that Io-Tahoe has seen throughout the industry? >> Well, Lisa, I think the first way I'd characterize it is to say, the traditional kind of top down approach to data, where you have almost a data policeman who tells you what you can and cannot do just doesn't work anymore. It's too slow, it's too result intensive. Data Management, data governance, digital transformation itself, it has to be collaborative. And it has to be an element of personalization today to users. In the environment we find ourselves in now, it has to be about enabling self service as well. A one size fits all model when it comes to those things around data doesn't work. As Santiago was saying, it needs to be adaptive to how the data is used and who is using it. And in order to do this, companies, enterprises, organizations really need to know their data. They need to understand what data they hold, where it is, and what the sensitivity of it is. They can then in a more agile way, apply appropriate controls and access so that people themselves are in groups within businesses are agile and can innovate. Otherwise, everything grinds to a halt, and you risk falling behind your competitors. >> Yet a one size fits all terms doesn't apply when you're talking about adaptive and agility. So we heard from Santiago about some of the impact that they're making with First Bank of Nigeria. Yusef, talk to us about some of the business outcomes that you're seeing other customers make leveraging automation that they could not do before. >> I guess one of the key ones is around. Just it's automatically being able to classify terabytes of data or even petabytes of data across different sources to find duplicates, which you can then remediate and delete. Now, with the capabilities that Io-Tahoe offers, and Oracle offers, you can do things not just with a five times or 10 times improvement, but it actually enables you to do project for stock that otherwise would fail, or you would just not be able to do. Classifying multi terabyte and multi petabyte estates across different sources, formats, very large volumes of data. In many scenarios, you just can't do that manually. We've worked with government departments. And the issues there as you'd expect are the result of fragmented data. There's a lot of different sources, there's a lot of different formats. And without these newer technologies to address it, with automation and machine learning, the project isn't doable. But now it is. And that could lead to a revolution in some of these businesses organizations. >> To enable that revolution now, there's got to be the right cultural mindset. And one, when Santiago was talking about those really kind of adopting that and I think, I always call that getting comfortably uncomfortable. But that's hard for organizations to do. The technology is here to enable that. But when you're talking with customers, how do you help them build the trust and the confidence that the new technologies and a new approaches can deliver what they need? How do you help drive that kind of attack in the culture? >> It's really good question, because it can be quite scary. I think the first thing we'd start with is to say, look, the technology is here, with businesses like Io-Tahoe, unlike Oracle, it's already arrived. What you need to be comfortable doing is experimenting, being agile around it and trying new ways of doing things. If you don't want to get left behind. And Santiago, and the team at FBN, are a great example of embracing it, testing it on a small scale and then scaling up. At Io-Tahoe we offer what we call a data health check, which can actually be done very quickly in a matter of a few weeks. So we'll work with the customer, pick a use case, install the application, analyze data, drive out some some quick wins. So we worked in the last few weeks of a large energy supplier. And in about 20 days, we were able to give them an accurate understanding of their critical data elements, help them apply data protection policies, minimize copies of the data, and work out what data they needed to delete to reduce their infrastructure spend. So it's about experimenting on that small scale, being agile, and then scaling up in a in a kind of very modern way. >> Great advice. Santiago, I'd like to go back to you. Is we kind of look at, again, that topic of culture, and the need to get that mindset there to facilitate these rapid changes. I want to understand kind of last question for you about how you're doing that. From a digital transformation perspective, we know everything is accelerating in 2020. So how are you building resilience into your data architecture and also driving that cultural change that can help everyone in this shift to remote working and a lot of the the digital challenges that we're all going through? >> That's a really interesting transition, I would say. I was mentioning, just going back to some of the points before to transition these I said that the new technologies allowed us to discover the data in a new way to blog and see very quickly information, to have new models of (indistinct) data, we are talking about data (indistinct), and giving autonomy to our different data units. Well, from that autonomy, they can then compose and innovate their own ways. So for me now we're talking about resilience. Because, in a way autonomy and flexibility in our organization, in our data structure, we platform gives you resilience. The organizations and the business units that I have experienced in the pandemic, are working well, are those that actually, because they're not physically present anymore in the office, you need to give them their autonomy and let them actually engage on their own side and do their own job and trust them in a away. And as you give them that they start innovating, and they start having a really interesting idea. So autonomy and flexibility, I think, is a key component of the new infrastructure where you get the new reality that pandemic shows that yes, we used to be very kind of structure, policies, procedures, as they're important, but now we learn flexibility and adaptability at the same site. Now, when you have that, a key other components of resiliency is speed, of course, people want to access the data and access it fast and decide fast, especially changes are changing so quickly nowadays, that you need to be able to, interact and iterate with your information to answer your questions quickly. And coming back maybe to where Yusef was saying, I completely agree is about experimenting, and iterate. You will not get it right the first time, especially that the world is changing too fast. And we don't have answers already set for everything. So we need to just go play and have ideas fail, fail fast, and then learn and then go for the next. So, technology that allows you to be flexible, iterate, and in a very fast agile way continue will allow you to actually be resilient in the way because you're flexible, you adapt, you are agile and you continue answering questions as they come without having everything said in a stroke that is too hard. Now coming back to your idea about the culture is changing in employees and in customers. Our employees, our customers are more and more digital service. And in a way the pandemic has accelerated that. We had many branches of the bank that people used to go to ask for things now they cannot go. You need to, here in Europe with the lockdown you physically cannot be going to the branches and the shops that have been closed. So they had to use our mobile apps. And we have to go into the internet banking, which is great, because that was the acceleration we wanted. Similarly, our employees needed to work remotely. So they needed to engage with a digital platform. Now what that means, and this is, I think the really strong point for the cultural change for resilience is that more and more we have two type of connectivity that is happening with data. And I call it employees connecting to data. The session we're talking about, employees connecting with each other, the collaboration that Yusef was talking about, which is allowing people to share ideas, learn and innovate. Because the more you have platforms where people can actually find themselves and play with the data, they can bring ideas to the analysis. And then employees actually connecting to algorithms. And this is the other part that is really interesting. We also are a partner of Oracle. And Oracle (indistinct) is great, they have embedded within the transactional system, many algorithms that are allowing us to calculate as the transactions happen. What happened there is that when our customers engage with algorithms, and again, with Io-Tahoe as well, the machine learning that is there for speeding the automation of how you find your data allows you to create an alliance with the machine. The machine is there to actually in a way be your best friend, to actually have more volume of data calculated faster in a way to discover more variety. And then, we couldn't cope without being connected to these algorithms. And then, we'll finally get to the last connection I was saying is, the customers themselves engaging with the connecting with the data. I was saying they're more and more engaging with our app and our website and they're digitally serving. The expectation of the customer has changed. I work in a bank where the industry is completely challenged. You used to have people going to a branch, as I was saying, they cannot not only not go there, but they're even going from branch to digital to ask to now even wanting to have business services actually in every single app that they are using. So the data becomes a service for them. The data they want to see how they spend their money and the data of their transactions will tell them what is actually their spending is going well with their lifestyle. For example, we talk about a normal healthy person. I want to see that I'm standing, eating good food and the right handle, healthy environment where I'm mentally engaged. Now all these is metadata is knowing how to classify your data according to my values, my lifestyle, is algorithms I'm coming back to my three connections, is the algorithms that allow me to very quickly analyze that metadata. And actually my staff in the background, creating those understanding of the customer journey to give them service that they expect on a digital channel, which is actually allowing them to understand how they are engaging with financial services. >> Engagement is absolutely critical Santiago. Thank you for sharing that. I do want to wrap really quickly. Gudron one last question for you. Santiago talked about Oracle, you've talked about it a little bit. As we look at digital resilience, talk to us a little bit in the last minute about the evolution of Oracle, what you guys are doing there to help your customers get the resilience that they have to have to be. To not just survive, but thrive. >> Yeah. Well, Oracle has a cloud offering for infrastructure, database, platform service, and the complete solutions offered at SaaS. And as Santiago also mentioned, we are using AI across our entire portfolio, and by this will help our customers to focus on their business innovation and capitalize on data by enabling your business models. And Oracle has a global coverage with our cloud regions. It's massively investing in innovating and expanding their cloud. And by offering cloud as public cloud in our data centers, and also as private clouds with clouded customer, we can meet every sovereignty and security requirement. And then this way, we help people to see data in new ways. We discover insights and unlock endless possibilities. And maybe one one of my takeaways is, if I speak with customers, I always tell them, you better start collecting your data now. We enable this by this like Io-Tahoe help us as well. If you collect your data now you are ready for tomorrow. You can never collect your data backwards. So that is my takeaway for today. >> You can't collect your data backwards. Excellent Gudron. Gentlemen, thank you for sharing all of your insights, very informative conversation. All right. This is theCUBE, the leader in live digital tech coverage. (upbeat music)

>>from around the globe. It's the Cube with digital coverage of enterprise data automation, an event Siri's brought to you by Iot. Tahoe, everybody, We're back. We're talking about enterprise data automation. The hashtag is data automated, and we're going to really dig into data migrations, data, migrations. They're risky. They're time consuming, and they're expensive. Yousef con is here. He's the head of partnerships and alliances at I o ta ho coming again from London. Hey, good to see you, Seth. Thanks very much. >>Thank you. >>So your role is is interesting. We're talking about data migrations. You're gonna head of partnerships. What is your role specifically? And how is it relevant to what we're gonna talk about today? >>Uh, I work with the various businesses such as cloud companies, systems integrators, companies that sell operating systems, middleware, all of whom are often quite well embedded within a company. I t infrastructures and have existing relationships. Because what we do fundamentally makes migrating to the cloud easier on data migration easier. A lot of businesses that are interested in partnering with us. Um, we're interested in parting with, So >>let's set up the problem a little bit. And then I want to get into some of the data. You know, I said that migration is a risky, time consuming, expensive. They're they're often times a blocker for organizations to really get value out of data. Why is that? >>Uh, I think I mean, all migrations have to start with knowing the facts about your data, and you can try and do this manually. But when that you have an organization that may have been going for decades or longer, they will probably have a pretty large legacy data estate so that I have everything from on premise mainframes. They may have stuff which is probably in the cloud, but they probably have hundreds, if not thousands of applications and potentially hundreds of different data stores. Um, now they're understanding of what they have. Ai's often quite limited because you can try and draw a manual maps, but they're outdated very quickly. Every time that data changes the manual that's out of date on people obviously leave organizations over time, so that kind of tribal knowledge gets built up is limited as well. So you can try a Mackel that manually you might need a db. Hey, thanks. Based analyst or ah, business analyst, and they won't go in and explore the data for you. But doing that manually is very, very time consuming this contract teams of people, months and months. Or you can use automation just like what's the bank with Iot? And they managed to do this with a relatively small team. Are in a timeframe of days. >>Yeah, we talked to Paul from Webster Bank. Awesome discussion. So I want to dig into this migration and let's let's pull up graphic it will talk about. We'll talk about what a typical migration project looks like. So what you see here it is. It's very detailed. I know it's a bit of an eye test, but let me call your attention to some of the key aspects of this Ah, and then use. If I want you to chime in. So at the top here, you see that area graph that's operational risk for a typical migration project, and you can see the timeline and the the milestones. That blue bar is the time to test so you can see the second step data analysis talking 24 weeks so, you know, very time consuming. And then Let's not get dig into the stuff in the middle of the fine print, but there's some real good detail there, but go down the bottom. That's labor intensity in the in the bottom and you can see high is that sort of brown and and you could see a number of data analysis, data staging data prep, the trial, the implementation post implementation fixtures, the transition toe B A B a year, which I think is business as usual. Those are all very labor intensive. So what do you take aways from this typical migration project? What do we need to know yourself? >>I mean, I think the key thing is, when you don't understand your data upfront, it's very difficult to scope to set up a project because you go to business stakeholders and decision makers and you say Okay, we want to migrate these data stores. We want to put them in the cloud most often, but actually, you probably don't know how much data is there. You don't necessarily know how many applications that relates to, you know, the relationships between the data. You don't know the flow of the data. So the direction in which the data is going between different data stores and tables, so you start from a position where you have pretty high risk and alleviate that risk. You could be stacking project team of lots and lots of people to do the next base, which is analysis. And so you set up a project which has got a pretty high cost. The big projects, more people, the heavy of governance, obviously on then there, then in the phase where they're trying to do lots and lots of manual analysis manage. That, in a sense, is, as we all know, on the idea of trying to relate data that's in different those stores relating individual tables and columns. Very, very time consuming, expensive. If you're hiring in resource from consultants or systems integrators externally, you might need to buy or to use party tools, Aziz said earlier. The people who understand some of those systems may have left a while ago. See you even high risks quite cost situation from the off on the same things that have developed through the project. Um, what are you doing with it, Ayatollah? Who is that? We're able to automate a lot of this process from the very beginning because we can do the initial data. Discovery run, for example, automatically you very quickly have an automated validator. A data map on the data flow has been generated automatically, much less time and effort and much less cars. Doctor Marley. >>Okay, so I want to bring back that that first chart, and I want to call your attention to the again that area graph the blue bars and then down below that labor intensity. And now let's bring up the the the same chart. But with a set of an automation injection in here and now. So you now see the So let's go Said Accelerated by Iot, Tom. Okay, great. And we're going to talk about this. But look, what happens to the operational risk. A dramatic reduction in that. That graph. And then look at the bars, the bars, those blue bars. You know, data analysis went from 24 weeks down to four weeks and then look at the labor intensity. The it was all these were high data analysis data staging data prep. Try a lot post implementation fixtures in transition to be a you. All of those went from high labor intensity. So we've now attack that and gone to low labor intensity. Explain how that magic happened. >>I think that the example off a data catalog. So every large enterprise wants to have some kind of repository where they put all their understanding about their data in its Price States catalog, if you like, um, imagine trying to do that manually. You need to go into every individual data store. You need a DB a business analyst, rich data store they need to do in extracted the data table was individually they need to cross reference that with other data school, it stores and schemers and tables. You probably were the mother of all lock Excel spreadsheets. It would be a very, very difficult exercise to do. I mean, in fact, one of our reflections as we automate lots of data lots of these things is, um it accelerates the ability to water may, But in some cases, it also makes it possible for enterprise customers with legacy systems um, take banks, for example. There quite often end up staying on mainframe systems that they've had in place for decades. Uh, no migrating away from them because they're not able to actually do the work of understanding the data g duplicating the data, deleting data isn't relevant and then confidently going forward to migrate. So they stay where they are with all the attendant problems assistance systems that are out of support. Go back to the data catalog example. Um, whatever you discover invades, discovery has to persist in a tool like a data catalog. And so we automate data catalog books, including Out Way Cannot be others, but we have our own. The only alternative to this kind of automation is to build out this very large project team or business analysts off db A's project managers processed analysts together with data to understand that the process of gathering data is correct. To put it in the repository to validate it except etcetera, we've got into organizations and we've seen them ramp up teams off 2030 people costs off £234 million a year on a time frame, 15 20 years just to try and get a data catalog done. And that's something that we can typically do in a timeframe of months, if not weeks. And the difference is using automation. And if you do what? I've just described it. In this manual situation, you make migrations to the cloud prohibitively expensive. Whatever saving you might make from shutting down your legacy data stores, we'll get eaten up by the cost of doing it. Unless you go with the more automated approach. >>Okay, so the automated approach reduces risk because you're not gonna, you know you're going to stay on project plan. Ideally, it's all these out of scope expectations that come up with the manual processes that kill you in the rework andan that data data catalog. People are afraid that their their family jewels data is not going to make it through to the other side. So So that's something that you're you're addressing and then you're also not boiling the ocean. You're really taking the pieces that are critical and stuff you don't need. You don't have to pay for >>process. It's a very good point. I mean, one of the other things that we do and we have specific features to do is to automatically and noise data for a duplication at a rover or record level and redundancy on a column level. So, as you say before you go into a migration process. You can then understand. Actually, this stuff it was replicated. We don't need it quite often. If you put data in the cloud you're paying, obviously, the storage based offer compute time. The more data you have in there that's duplicated, that is pure cost. You should take out before you migrate again if you're trying to do that process of understanding what's duplicated manually off tens or hundreds of bases stores. It was 20 months, if not years. Use machine learning to do that in an automatic way on it's much, much quicker. I mean, there's nothing I say. Well, then, that costs and benefits of guitar. Every organization we work with has a lot of money existing, sunk cost in their I t. So have your piece systems like Oracle or Data Lakes, which they've spent a good time and money investing in. But what we do by enabling them to transition everything to the strategic future repositories, is accelerate the value of that investment and the time to value that investment. So we're trying to help people get value out of their existing investments on data estate, close down the things that they don't need to enable them to go to a kind of brighter, more future well, >>and I think as well, you know, once you're able to and this is a journey, we know that. But once you're able to go live on, you're infusing sort of a data mindset, a data oriented culture. I know it's somewhat buzzword, but when you when you see it in organizations, you know it's really and what happens is you dramatically reduce that and cycle time of going from data to actually insights. Data's plentiful, but insights aren't, and that is what's going to drive competitive advantage over the next decade and beyond. >>Yeah, definitely. And you could only really do that if you get your data estate cleaned up in the first place. Um, I worked with the managed teams of data scientists, data engineers, business analysts, people who are pushing out dashboards and trying to build machine learning applications. You know, you know, the biggest frustration for lots of them and the thing that they spend far too much time doing is trying to work out what the right data is on cleaning data, which really you don't want a highly paid thanks to scientists doing with their time. But if you sort out your data stays in the first place, get rid of duplication. If that pans migrate to cloud store, where things are really accessible on its easy to build connections and to use native machine learning tools, you're well on the way up to date the maturity curve on you can start to use some of those more advanced applications. >>You said. What are some of the pre requisites? Maybe the top few that are two or three that I need to understand as a customer to really be successful here? Is it skill sets? Is it is it mindset leadership by in what I absolutely need to have to make this successful? >>Well, I think leadership is obviously key just to set the vision of people with spiky. One of the great things about Ayatollah, though, is you can use your existing staff to do this work. If you've used on automation, platform is no need to hire expensive people. Alright, I was a no code solution. It works out of the box. You just connect to force on your existing stuff can use. It's very intuitive that has these issues. User interface? >>Um, it >>was only to invest vast amounts with large consultants who may well charging the earth. Um, and you already had a bit of an advantage. If you've got existing staff who are close to the data subject matter experts or use it because they can very easily learn how to use a tool on, then they can go in and they can write their own data quality rules on. They can really make a contribution from day one, when we are go into organizations on way. Can I? It's one of the great things about the whole experience. Veritas is. We can get tangible results back within the day. Um, usually within an hour or two great ones to say Okay, we started to map relationships. Here's the data map of the data that we've analyzed. Harrison thoughts on where the sensitive data is because it's automated because it's running algorithms stater on. That's what they were really to expect. >>Um, >>and and you know this because you're dealing with the ecosystem. We're entering a new era of data and many organizations to your point, they just don't have the resources to do what Google and Amazon and Facebook and Microsoft did over the past decade To become data dominant trillion dollar market cap companies. Incumbents need to rely on technology companies to bring that automation that machine intelligence to them so they can apply it. They don't want to be AI inventors. They want to apply it to their businesses. So and that's what really was so difficult in the early days of so called big data. You have this just too much complexity out there, and now companies like Iot Tahoe or bringing your tooling and platforms that are allowing companies to really become data driven your your final thoughts. Please use it. >>That's a great point, Dave. In a way, it brings us back to where it began. In terms of partnerships and alliances. I completely agree with a really exciting point where we can take applications like Iot. Uh, we can go into enterprises and help them really leverage the value of these type of machine learning algorithms. And and I I we work with all the major cloud providers AWS, Microsoft Azure or Google Cloud Platform, IBM and Red Hat on others, and we we really I think for us. The key thing is that we want to be the best in the world of enterprise data automation. We don't aspire to be a cloud provider or even a workflow provider. But what we want to do is really help customers with their data without automated data functionality in partnership with some of those other businesses so we can leverage the great work they've done in the cloud. The great work they've done on work flows on virtual assistants in other areas. And we help customers leverage those investments as well. But our heart, we really targeted it just being the best, uh, enterprised data automation business in the world. >>Massive opportunities not only for technology companies, but for those organizations that can apply technology for business. Advantage yourself, count. Thanks so much for coming on the Cube. Appreciate. All right. And thank you for watching everybody. We'll be right back right after this short break. >>Yeah, yeah, yeah, yeah.

>> Commentator: From around the globe, it's theCUBE with digital coverage of Enterprise Data Automation. An event series brought to you by Io-Tahoe. >> Hi everybody, we're back, we're talking about Enterprise Data Automation. The hashtag is data automated, and we're going to really dig into data migrations. Data migrations are risky, they're time consuming and they're expensive. Yusef Khan is here, he's the head of partnerships and alliances at Io-Tahoe, coming again from London. Hey, good to see you, Yusef, thanks very much. >> Thank Dave, great guy. >> So your role is interesting. We're talking about data migrations, you're going to head of partnerships, what is your role specifically and how is it relevant to what we're going to talk about today? >> Well, I work with the various businesses, such as cloud companies, systems integrators, companies that sell operating systems, middleware, all of whom are often quite well embedded within a company IT infrastructures and have existing relationships, because what we do fundamentally makes migration to the cloud easier and data migration easier, there are lots of businesses that are interested in partnering with us some were interested in partnering with. >> So let's set up the problem a little bit and then I want to get into some of the data. You know, you said that migrations are risky, time consuming, expensive, they're often times a blocker for organizations to really get value out of data. Why is that? >> Ah, I think I mean, all migrations have to start with knowing the facts about your data and you can try and do this manually but when you have an organization that may have been going for decades or longer, they will probably have a pretty large legacy data estate. So they'll have everything from on premise mainframes, they may have stuff which is partly in the clouds but they probably have hundreds, if not thousands of applications and potentially hundreds of different data stores. Now their understanding of what they have, is often quite limited because you can try and draw manual maps but they're out-of-date very quickly, every time data changes, the manual map set a date and people obviously leave organizations all the time. So that kind of tribal knowledge gets built up is limited as well. So you can try and map all that manually, you might need a DBA, database analyst or a business analyst and they might go in and explore the data for you. But doing that manually is very very time consuming. This can take teams of people months and months or you can use automation, just like Webster Bank did with Io-Tahoe and they managed to do this with a relatively small team in a timeframe of days. >> Yeah, we talked to Paul from Webster Bank, awesome discussion. So I want to dig in to this migration, then let's pull up a graphic that we'll talk about, what a typical migration project looks like. So what you see here it's very detailed, I know, it's a bit of an eye test but let me call your attention to some of the key aspects of this and then Yusef, I want you to chime in. So at the top here, you see that area graph, that's operational risk for typical migration project and you can see the timeline and the milestones, that blue bar is the time to test, so you can see the second step data analysis it's taking 24 weeks, so you know, very time consuming and then let's not get dig into the stuff in the middle of the fine print but there's some real good detail there but go down the bottom, that's labor intensity in the bottom and you can see high is that sort of brown and you can see a number of data analysis, data staging, data prep, the trial, the implementation, post implementation fixtures, the transition to BAU, which I think is Business As Usual. Those are all very labor intensive. So what are your takeaways from this typical migration project? What do we need to know Yusef? >> I mean, I think the key thing is, when you don't understand your data upfront, it's very difficult to scope and to set up a project because you go to business stakeholders and decision makers and you say, "okay, we want to migrate these data stores, we want to put them into the cloud most often", but actually, you probably don't know how much data is there, you don't necessarily know how many applications it relates to, you don't know the relationships between the data, you don't know the flow of the data so the direction in which the data is going between different data stores and tables. So you start from a position where you have pretty high risk and alleviate that risk, you probably stack your project team with lots and lots of people to do the next phase, which is analysis and so you've set up a project which is got to pretty high cost. The bigger the project, the more people the heavier the governance obviously and then in the phase where they're trying to do lots and lots of manual analysis. Manual analysis, as we all know and the idea of trying to relate data that's in different data stores, relating individual tables and columns are very, very time consuming, expensive if you're hiring in resource from consultants or systems integrators externally, you might need to buy or to use third party tools. As I said earlier, the people who understand some of those systems may have left a while ago and so you are in a high risks, high cost situation from the off and the same thing sort of develops through the project. What you find with Io-Tahoe is that we're able to automate a lot of this process from the very beginning, because we can do the initial data discovery run for example automatically, so you very quickly have an automated view of the data, a data map and the data flow has been generated automatically, much less time and effort and much less cost of money. >> Okay, so I'm going to bring back that first chart and I want to call your attention to again, that area graph, the blue bars and then down below that labor intensity and now let's bring up the same chart, but with a sort of an automation injection in here and now so you now see the sort of essence celebrated by Io-Tahoe. Okay, great, we're going to talk about this but look what happens to the operational risk, a dramatic reduction in that graph and then look at the bars, the bars, those blue bars, you know, data analysis went from 24 weeks down to four weeks and then look at the labor intensity. All these were high, data analysis, data staging, Data Prep, trial, post implementation fixtures in transition to BAU. All those went from high labor intensity, so we've now attacked that and gone to low labor intensity, explain how that magic happened. >> Ah, let's take the example of a data catalog. So every large enterprise wants to have some kind of repository where they put all their understanding about that data and its price data catalog, if you like. Imagine trying to do that manually, you need to go into every individual data store, you need a DBA and the business analyst for each data store, they need to do an extract of the data, they need to put tables individually, they need to cross reference that with other data stores and schemas and tables, you've probably end up with the mother of all Excel spreadsheets and it would be a very, very difficult exercise to do. I mean, in fact, one of our reflections as we automate lots of these things is, it accelerates the ability to automate, but in some cases it also makes it possible for enterprise customers with legacy systems, take banks, for example, they quite often end up staying on mainframe systems that they've had in place for decades, and not migrating away from them because they're not able to actually do the work of understanding the data, duplicating the data, deleting data that isn't relevant and then confidently going forward to migrate. So they stay where they are with all the attendant problems or success systems that are out of their support. Go back to the data catalog example. Whatever you discover in data discovery has to persist in a tool like a data catalog and so we automate data catalogs including our own, we can also feed others but we have our own. The only alternative to this kind of automation is to build out this very large project team of business analysts, of DBAs, project managers, process analysts, to gather all the data, to understand that the process of gathering the data is correct, to put it in the repository, to validate it, etcetera, etcetera. We've got into organizations and we've seen them, ramp up teams of 20 30 people, cost of 2, 3, 4 million pounds a year and a timeframe of 15 to 20 years, just to try and get a data catalog done and that's something that we can typically do in a timeframe of months if not weeks and the differences is using automation and if you do what I've just described in this manual situation, you make migrations to the cloud prohibitively expensive, whatever saving you might make from shutting down your legacy data stores, will get eaten up by the cost of doing it unless you go with a more automated approach. >> Okay, so the automated approach reduces risk because you're not going to, you know, you're going to stay on project plan, ideally, you know, it's all these out of scope expectations that come up with the manual processes that kill you in the rework and then that data catalog, people are afraid that their family jewels data is not going to make it through to the other side. So, that's something that you're addressing and then you're also not boiling the ocean, you're really taking the pieces that are critical and the stuff that you don't need, you don't have to pay for as part of this process. >> It's a very good point. I mean, one of the other things that we do and we have specific features to do, is to automatically analyze data for duplication at a row-level or record level and redundancy at a column level. So as you say, before you go into migration process, you can then understand actually, this stuff here is duplicated, we don't need it. Quite often, if you put data in the cloud, you're paying obviously for storage space or for compute time, the more data you have in there is duplicated, that's pure cost you should take out before you migrate. Again, if you're trying to do that process of understanding was duplicated manually of 10s or 100s of data stores, it will take you months if not years, you use machine learning to do it in an automatic way and it's much much quicker. I mean, there's nothing I'd say about the net cost and benefit of Io-Tahoe. Every organization we work with has a lot of money existing sunk cost in there IT, so they'll have your IP systems like Oracle or data lakes which they've spent good time and money investing in. What we do by enabling them to transition everything to their strategic future repositories, is accelerate the value of investment and the time to value that investment. So we are trying to help people get value out of their existing investments and data estate, close down the things that they don't need and enable them to go to a kind of brighter and more present future. >> Well, I think as well, you know, once you're able to and this is a journey, we know that but once you're able to go live and you're infusing sort of a data mindset, a data oriented culture, I know it's somewhat buzzwordy, but when you when you see it in organizations, you know it's real and what happens is you dramatically reduce that and cycle time of going from data to actually insights, data is plentiful but insights aren't and that is what's going to drive competitive advantage over the next decade and beyond. >> Yeah, definitely and you can only really do that if you get your data state cleaned up in the first place. I've worked with and managed teams of data scientists, big data engineers, business analysts, people who are pushing out dashboards and are trying to build machine learning applications. You'll know you have the biggest frustration for lots of them and the thing that they spend far too much time doing is trying to work out what the right data is, and cleaning data, which really you don't want a highly paid data scientist doing with their time but if you sort out your data set in the first place, get rid of duplication, perhaps migrate to a cloud store where things are more readily accessible and it's easy to build connections and to use native machine learning tools, you're well on the way up the maturity curve and you can start to use some of those more advanced applications. >> Yusef, what are some of the prerequisites maybe the top, you know, few that are two or three that I need to understand as a customer to really be successful here? I mean, there's, is it skill sets? Is it, mindset, leadership buy-in? What do I absolutely need to have to make this successful? >> Well, I think leadership is obviously key, being able to sort of set the vision for people is obviously key. One of the great things about Io-Tahoe though, is you can use your existing staff to do this work if you use our automation platform, there's no need to hire expensive people. Io-Tahoe is a no code solution, it works out of the box, you just connect to source and then your existing staff can use it. It's very intuitive and easy to use, user interface is only to invest vast amounts with large consultancies, who may well charging the earth and you are actually a bit of an advantage if you've got existing staff who are close to the data, who are subject matter experts or use it because they can very easily learn how to use the tool and then they can go in and they can write their own data quality rules and they can really make a contribution from day one. When we go into organizations and we connect all of the great things about the whole experience via Io-Tahoe is we can get tangible results back within the day. Usually within an hour or two, were able to say, okay, we started to map the relationships here. Here's a data map of the data that we've analyzed and here are some thoughts on what your sensitive data is, because it's automated, because it's running algorithms across data and that's what people really should expect. >> And you know this because you're dealing with the ecosystem, we're entering a new era of data and many organizations to your point, they just don't have the resources to do what Google and Amazon and Facebook and Microsoft did over the past decade to become you know, data dominant, you know, trillion dollar market cap companies. Incumbents need to rely on technology companies to bring that automation, that machine intelligence to them so they can apply it. They don't want to be AI inventors, they want to apply it to their businesses. So and that's what really was so difficult in the early days of so called Big Data, you had this just too much complexity out there and now companies like Io-Tahoe are bringing you know, tooling and platforms that are allowing companies to really become data driven. Your final thoughts, please Yusef. >> But that's a great point, Dave. In a way it brings us back to where it began in terms of partnerships and alliances. I completely agree, a really exciting point where we can take applications like Io-Tahoe and we can go into enterprises and help them really leverage the value of these type of machine learning algorithms and AI. We work with all the major cloud providers, AWS, Microsoft Azure, Google Cloud Platform, IBM, Red Hat, and others and we really, I think, for us, the key thing is that we want to be the best in the world at Enterprise Data Automation. We don't aspire to be a cloud provider or even a workflow provider but what we want to do is really help customers with their data, with our automated data functionality in partnership with some of those other businesses so we can leverage the great work they've done in the cloud, the great work they've done on workflows, on virtual assistants and in other areas and we help customers leverage those investments as well but our heart we're really targeted at just being the best enterprise, data automation business in the world. >> Massive opportunities not only for technology companies but for those organizations that can apply technology for business advantage, Yusef Khan, thanks so much for coming on theCUBE. >> Pretty much appreciated. >> All right, and thank you for watching everybody. We'll be right back right after this short break. (upbeat music)