(light music) >> Announcer: TheCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (upbeat music) >> Welcome back to the Fira in Barcelona. Dave Vellante with Dave Nicholson. Lisa Martin's been here all week. John Furrier is in our Palo Alto studio, banging out all the news. Don't forget to check out siliconangle.com, thecube.net. This is day four, our last segment, winding down. MWC23, super excited to be here. Wayne Duso, friend of theCUBE, VP of engineering from products at AWS is here with Iyad Tarazi, who's the CEO of Federated Wireless. Gents, welcome. >> Good to be here. >> Nice to see you. >> I'm so stoked, Wayne, that we connected before the show. We texted, I'm like, "You're going to be there. I'm going to be there. You got to come on theCUBE." So thank you so much for making time, and thank you for bringing a customer partner, Federated Wireless. Everybody knows AWS. Iyad, tell us about Federated Wireless. >> We're a software and services company out of Arlington, Virginia, right outside of Washington, DC, and we're really focused on this new technology called Shared Spectrum and private wireless for 5G. Think of it as enterprises consuming 5G, the way they used to consume WiFi. >> Is that unrestricted spectrum, or? >> It is managed, organized, interference free, all through cloud platforms. That's how we got to know AWS. We went and got maybe about 300 products from AWS to make it work. Quite sophisticated, highly available, and pristine spectrum worth billions of dollars, but available for people like you and I, that want to build enterprises, that want to make things work. Also carriers, cable companies everybody else that needs it. It's really a new revolution for everyone. >> And that's how you, it got introduced to AWS. Was that through public sector, or just the coincidence that you're in DC >> No, I, well, yes. The center of gravity in the world for spectrum is literally Arlington. You have the DOD spectrum people, you have spectrum people from National Science Foundation, DARPA, and then you have commercial sector, and you have the FCC just an Uber ride away. So we went and found the scientists that are doing all this work, four or five of them, Virginia Tech has an office there too, for spectrum research for the Navy. Come together, let's have a party and make a new model. >> So I asked this, I'm super excited to have you on theCUBE. I sat through the keynotes on Monday. I saw Satya Nadella was in there, Thomas Kurian there was no AWS. I'm like, where's AWS? AWS is everywhere. I mean, you guys are all over the show. I'm like, "Hey, where's the number one cloud?" So you guys have made a bunch of announcements at the show. Everybody's talking about the cloud. What's going on for you guys? >> So we are everywhere, and you know, we've been coming to this show for years. But this is really a year that we can demonstrate that what we've been doing for the IT enterprise, IT people for 17 years, we're now bringing for telcos, you know? For years, we've been, 17 years to be exact, we've been bringing the cloud value proposition, whether it's, you know, cost efficiencies or innovation or scale, reliability, security and so on, to these enterprise IT folks. Now we're doing the same thing for telcos. And so whether they want to build in region, in a local zone, metro area, on-prem with an outpost, at the edge with Snow Family, or with our IoT devices. And no matter where they want to start, if they start in the cloud and they want to move to the edge, or they start in the edge and they want to bring the cloud value proposition, like, we're demonstrating all of that is happening this week. And, and very much so, we're also demonstrating that we're bringing the same type of ecosystem that we've built for enterprise IT. We're bringing that type of ecosystem to the telco companies, with CSPs, with the ISP vendors. We've seen plenty of announcements this week. You know, so on and so forth. >> So what's different, is it, the names are different? Is it really that simple, that you're just basically taking the cloud model into telco, and saying, "Hey, why do all this undifferentiated heavy lifting when we can do it for you? Don't worry about all the plumbing." Is it really that simple? I mean, that straightforward. >> Well, simple is probably not what I'd say, but we can make it straightforward. >> Conceptually. >> Conceptually, yes. Conceptually it is the same. Because if you think about, firstly, we'll just take 5G for a moment, right? The 5G folks, if you look at the architecture for 5G, it was designed to run on a cloud architecture. It was designed to be a set of services that you could partition, and run in different places, whether it's in the region or at the edge. So in many ways it is sort of that simple. And let me give you an example. Two things, the first one is we announced integrated private wireless on AWS, which allows enterprise customers to come to a portal and look at the industry solutions. They're not worried about their network, they're worried about solving a problem, right? And they can come to that portal, they can find a solution, they can find a service provider that will help them with that solution. And what they end up with is a fully validated offering that AWS telco SAS have actually put to its paces to make sure this is a real thing. And whether they get it from a telco, and, and quite frankly in that space, it's SIs such as Federated that actually help our customers deploy those in private environments. So that's an example. And then added to that, we had a second announcement, which was AWS telco network builder, which allows telcos to plan, deploy, and operate at scale telco network capabilities on the cloud, think about it this way- >> As a managed service? >> As a managed service. So think about it this way. And the same way that enterprise IT has been deploying, you know, infrastructure as code for years. Telco network builder allows the telco folks to deploy telco networks and their capabilities as code. So it's not simple, but it is pretty straightforward. We're making it more straightforward as we go. >> Jump in Dave, by the way. He can geek out if you want. >> Yeah, no, no, no, that's good, that's good, that's good. But actually, I'm going to ask an AWS question, but I'm going to ask Iyad the AWS question. So when we, when I hear the word cloud from Wayne, cloud, AWS, typically in people's minds that denotes off-premises. Out there, AWS data center. In the telecom space, yes, of course, in the private 5G space, we're talking about a little bit of a different dynamic than in the public 5G space, in terms of the physical infrastructure. But regardless at the edge, there are things that need to be physically at the edge. Do you feel that AWS is sufficiently, have they removed the H word, hybrid, from the list of bad words you're not allowed to say? 'Cause there was a point in time- >> Yeah, of course. >> Where AWS felt that their growth- >> They'll even say multicloud today, (indistinct). >> No, no, no, no, no. But there was a period of time where, rightfully so, AWS felt that the growth trajectory would be supported solely by net new things off premises. Now though, in this space, it seems like that hybrid model is critical. Do you see AWS being open to the hybrid nature of things? >> Yeah, they're, absolutely. I mean, just to explain from- we're a services company and a solutions company. So we put together solutions at the edge, a smart campus, smart agriculture, a deployment. One of our biggest deployment is a million square feet warehouse automation project with the Marine Corps. >> That's bigger than the Fira. >> Oh yeah, it's bigger, definitely bigger than, you know, a small section of here. It's actually three massive warehouses. So yes, that is the edge. What the cloud is about is that massive amount of efficiency has happened by concentrating applications in data centers. And that is programmability, that is APIs that is solutions, that is applications that can run on it, where people know how to do it. And so all that efficiency now is being ported in a box called the edge. What AWS is doing for us is bringing all the business and technical solutions they had into the edge. Some of the data may send back and forth, but that's actually a smaller piece of the value for us. By being able to bring an AWS package at the edge, we're bringing IoT applications, we're bringing high speed cameras, we're able to integrate with the 5G public network. We're able to bring in identity and devices, we're able to bring in solutions for students, embedded laptops. All of these things that you can do much much faster and cheaper if you are able to tap in the 4,000, 5,000 partners and all the applications and all the development and all the models that AWS team did. By being able to bring that efficiency to the edge why reinvent that? And then along with that, there are partners that you, that help do integration. There are development done to make it hardened, to make the data more secure, more isolated. All of these things will contribute to an edge that truly is a carbon copy of the data center. >> So Wayne, it's AWS, Regardless of where the compute, networking and storage physically live, it's AWS. Do you think that the term cloud will sort of drift away from usage? Because if, look, it's all IT, in this case it's AWS and federated IT working together. How, what's your, it's sort of a obscure question about cloud, because cloud is so integrated. >> You Got this thing about cloud, it's just IT. >> I got thing about cloud too, because- >> You and Larry Ellison. >> Because it's no, no, no, I'm, yeah, well actually there's- >> There's a lot of IT that's not cloud, just say that okay. >> Now, a lot of IT that isn't cloud, but I would say- >> But I'll (indistinct) cloud is an IT tool, and you see AWS obviously with the Snow fill in the blank line of products and outpost type stuff. Fair to say that you're, doesn't matter where it is, it could be AWS if it's on the edge, right? >> Well, you know, everybody wants to define the cloud as what it may have been when it started. But if you look at what it was when it started and what it is today, it is different. But the ability to bring the experience, the AWS experience, the services, the operational experience and all the things that Iyad had been talking about from the region all to all the way to, you know, the IoT device, if you would, that entire continuum. And it doesn't matter where you start. Like if you start in region and you need to bring your value to other places because your customers are asking you to do so, we're enabling that experience where you need to bring it. If you started at the edge, and- but you want to build cloud value, you know, whether it's again, cost efficiency, scalability, AI, ML or analytics into those capabilities, you can start at the edge with the same APIs, with the same service, the same capabilities, and you can build that value in right from the get go. You don't build this bifurcation or many separations and try to figure out how do I glue them together? There is no gluing together. So if you think of cloud as being elastic, scalable flexible, where you can drive innovation, it's the same exact model on the continuum. And you can start at either end, it's up to you as a customer. >> And I think if, the key to me is the ecosystem. I mean, if you can do for this industry what you've done for the technology- enterprise technology business from an ecosystem standpoint, you know everybody talks about flywheel, but that gives you like the massive flywheel. I don't know what the ratio is, but it used to be for every dollar spent on a VMware license, $15 is spent in the ecosystem. I've never heard similar ratios in the AWS ecosystem, but it's, I go to reinvent and I'm like, there's some dollars being- >> That's a massive ecosystem. >> (indistinct). >> And then, and another thing I'll add is Jose Maria Alvarez, who's the chairman of Telefonica, said there's three pillars of the future-ready telco, low latency, programmable networks, and he said cloud and edge. So they recognizing cloud and edge, you know, low latency means you got to put the compute and the data, the programmable infrastructure was invented by Amazon. So what's the strategy around the telco edge? >> So, you know, at the end, so those are all great points. And in fact, the programmability of the network was a big theme in the show. It was a huge theme. And if you think about the cloud, what is the cloud? It's a set of APIs against a set of resources that you use in whatever way is appropriate for what you're trying to accomplish. The network, the telco network becomes a resource. And it could be described as a resource. We, I talked about, you know, network as in code, right? It's same infrastructure in code, it's telco infrastructure as code. And that code, that infrastructure, is programmable. So this is really, really important. And in how you build the ecosystem around that is no different than how we built the ecosystem around traditional IT abstractions. In fact, we feel that really the ecosystem is the killer app for 5G. You know, the killer app for 4G, data of sorts, right? We started using data beyond simple SMS messages. So what's the killer app for 5G? It's building this ecosystem, which includes the CSPs, the ISVs, all of the partners that we bring to the table that can drive greater value. It's not just about cost efficiency. You know, you can't save your way to success, right? At some point you need to generate greater value for your customers, which gives you better business outcomes, 'cause you can monetize them, right? The ecosystem is going to allow everybody to monetize 5G. >> 5G is like the dot connector of all that. And then developers come in on top and create new capabilities >> And how different is that than, you know, the original smartphones? >> Yeah, you're right. So what do you guys think of ChatGPT? (indistinct) to Amazon? Amazon turned the data center into an API. It's like we're visioning this world, and I want to ask that technologist, like, where it's turning resources into human language interfaces. You know, when you see that, you play with ChatGPT at all, or I know you guys got your own. >> So I won't speak directly to ChatGPT. >> No, don't speak from- >> But if you think about- >> Generative AI. >> Yeah generative AI is important. And, and we are, and we have been for years, in this space. Now you've been talking to AWS for a long time, and we often don't talk about things we don't have yet. We don't talk about things that we haven't brought to market yet. And so, you know, you'll often hear us talk about something, you know, a year from now where others may have been talking about it three years earlier, right? We will be talking about this space when we feel it's appropriate for our customers and our partners. >> You have talked about it a little bit, Adam Selipsky went on an interview with myself and John Furrier in October said you watch, you know, large language models are going to be enormous and I know you guys have some stuff that you're working on there. >> It's, I'll say it's exciting. >> Yeah, I mean- >> Well proof point is, Siri is an idiot compared to Alexa. (group laughs) So I trust one entity to come up with something smart. >> I have conversations with Alexa and Siri, and I won't judge either one. >> You don't need, you could be objective on that one. I definitely have a preference. >> Are the problems you guys solving in this space, you know, what's unique about 'em? What are they, can we, sort of, take some examples here (indistinct). >> Sure, the main theme is that the enterprise is taking control. They want to have their own networks. They want to focus on specific applications, and they want to build them with a skeleton crew. The one IT person in a warehouse want to be able to do it all. So what's unique about them is that they're now are a lot of automation on robotics, especially in warehousing environment agriculture. There simply aren't enough people in these industries, and that required precision. And so you need all that integration to make it work. People also want to build these networks as they want to control it. They want to figure out how do we actually pick this team and migrate it. Maybe just do the front of the house first. Maybe it's a security team that monitor the building, maybe later on upgrade things that use to open doors and close doors and collect maintenance data. So that ability to pick what you want to do from a new processors is really important. And then you're also seeing a lot of public-private network interconnection. That's probably the undercurrent of this show that haven't been talked about. When people say private networks, they're also talking about something called neutral host, which means I'm going to build my own network, but I want it to work, my Verizon (indistinct) need to work. There's been so much progress, it's not done yet. So much progress about this bring my own network concept, and then make sure that I'm now interoperating with the public network, but it's my domain. I can create air gaps, I can create whatever security and policy around it. That is probably the power of 5G. Now take all of these tiny networks, big networks, put them all in one ecosystem. Call it the Amazon marketplace, call it the Amazon ecosystem, that's 5G. It's going to be tremendous future. >> What does the future look like? We're going to, we just determined we're going to be orchestrating the network through human language, okay? (group laughs) But seriously, what's your vision for the future here? You know, both connectivity and cloud are on on a continuum. It's, they've been on a continuum forever. They're going to continue to be on a continuum. That being said, those continuums are coming together, right? They're coming together to bring greater value to a greater set of customers, and frankly all of us. So, you know, the future is now like, you know, this conference is the future, and if you look at what's going on, it's about the acceleration of the future, right? What we announced this week is really the acceleration of listening to customers for the last handful of years. And, we're going to continue to do that. We're going to continue to bring greater value in the form of solutions. And that's what I want to pick up on from the prior question. It's not about the network, it's not about the cloud, it's about the solutions that we can provide the customers where they are, right? And if they're on their mobile phone or they're in their factory floor, you know, they're looking to accelerate their business. They're looking to accelerate their value. They're looking to create greater safety for their employees. That's what we can do with these technologies. So in fact, when we came out with, you know, our announcement for integrated private wireless, right? It really was about industry solutions. It really isn't about, you know, the cloud or the network. It's about how you can leverage those technologies, that continuum, to deliver you value. >> You know, it's interesting you say that, 'cause again, when we were interviewing Adam Selipsky, everybody, you know, all journalists analysts want to know, how's Adam Selipsky going to be different from Andy Jassy, what's the, what's he going to do to Amazon to change? And he said, listen, the real answer is Amazon has changed. If Andy Jassy were here, we'd be doing all, you know, pretty much the same things. Your point about 17 years ago, the cloud was S3, right, and EC2. Now it's got to evolve to be solutions. 'Cause if that's all you're selling, is the bespoke services, then you know, the future is not as bright as the past has been. And so I think it's key to look for what are those outcomes or solutions that customers require and how you're going to meet 'em. And there's a lot of challenges. >> You continue to build value on the value that you've brought, and you don't lose sight of why that value is important. You carry that value proposition up the stack, but the- what you're delivering, as you said, becomes maybe a bigger or or different. >> And you are getting more solution oriented. I mean, you're not hardcore solutions yet, but we're seeing more and more of that. And that seems to be a trend. We've even seen in the database world, making things easier, connecting things. Not really an abstraction layer, which is sort of antithetical to your philosophy, but it creates a similar outcome in terms of simplicity. Yeah, you're smiling 'cause you guys always have a different angle, you know? >> Yeah, we've had this conversation. >> It's right, it's, Jassy used to say it's okay to be misunderstood. >> That's Right. For a long time. >> Yeah, right, guys, thanks so much for coming to theCUBE. I'm so glad we could make this happen. >> It's always good. Thank you. >> Thank you so much. >> All right, Dave Nicholson, for Lisa Martin, Dave Vellante, John Furrier in the Palo Alto studio. We're here at the Fira, wrapping out MWC23. Keep it right there, thanks for watching. (upbeat music)

(gentle bright music) >> Okay, welcome back to our "Supercloud 2" event live coverage here at stage performance in Palo Alto syndicating around the world. I'm John Furrier with Dave Vellante. We've got exclusive news and a scoop here for SiliconANGLE and theCUBE. Zhamak Dehghani, creator of data mesh has formed a new company called NextData.com NextData, she's a cube alumni and contributor to our Supercloud initiative, as well as our coverage and breaking analysis with Dave Vellante on data, the killer app for Supercloud. Zhamak, great to see you. Thank you for coming into the studio and congratulations on your newly formed venture and continued success on the data mesh. >> Thank you so much. It's great to be here. Great to see you in person. >> Dave: Yeah, finally. >> John: Wonderful. Your contributions to the data conversation has been well-documented certainly by us and others in the industry. Data mesh taking the world by storm. Some people are debating it, throwing, you know, cold water on it. Some are, I think, it's the next big thing. Tell us about the data mesh super data apps that are emerging out of cloud. >> I mean, data mesh, as you said, it's, you know, the pain point that it surfaced were universal. Everybody said, "Oh, why didn't I think of that?" You know, it was just an obvious next step and people are approaching it, implementing it. I guess the last few years, I've been involved in many of those implementations, and I guess Supercloud is somewhat a prerequisite for it because it's data mesh and building applications using data mesh is about sharing data responsibly across boundaries. And those boundaries include boundaries, organizational boundaries cloud technology boundaries and trust boundaries. >> I want to bring that up because your venture, NextData which is new, just formed. Tell us about that. What wave is that riding? What specifically are you targeting? What's the pain point? >> Zhamak: Absolutely, yes. So next data is the result of, I suppose, the pains that I suffered from implementing a database for many of the organizations. Basically, a lot of organizations that I've worked with, they want decentralized data. So they really embrace this idea of decentralized ownership of the data, but yet they want interconnectivity through standard APIs, yet they want discoverability and governance. So they want to have policies implemented, they want to govern that data, they want to be able to discover that data and yet they want to decentralize it. And we do that with a developer experience that is easy and native to a generalist developer. So we try to find, I guess, the common denominator that solves those problems and enables that developer experience for data sharing. >> John: Since you just announced the news, what's been the reaction? >> Zhamak: I just announced the news right now, so what's the reaction? >> John: But people in the industry that know you, you did a lot of work in the area. What have been some of the feedback on the new venture in terms of the approach, the customers, problem? >> Yeah, so we've been in stealth modes, so we haven't publicly talked about it, but folks that have been close to us in fact have reached out. We already have implementations of our pilot platform with early customers, which is super exciting. And we're going to have multiple of those. Of course, we're a tiny, tiny company. We can have many of those where we are going to have multiple pilots, implementations of our platform in real world. We're real global large scale organizations that have real world problems. So we're not going to build our platform in vacuum. And that's what's happening right now. >> Zhamak: When I think about your role at ThoughtWorks, you had a very wide observation space with a number of clients helping them implement data mesh and other things as well prior to your data mesh initiative. But when I look at data mesh, at least the ones that I've seen, they're very narrow. I think of JPMC, I think of HelloFresh. They're generally obviously not surprising. They don't include the big vision of inclusivity across clouds across different data stores. But it seems like people are having to go through some gymnastics to get to, you know, the organizational reality of decentralizing data, and at least pushing data ownership to the line of business. How are you approaching or are you approaching, solving that problem? Are you taking a narrow slice? What can you tell us about Next Data? >> Zhamak: Sure, yeah, absolutely. Gymnastics, the cute word to describe what the organizations have to go through. And one of those problems is that, you know, the data, as you know, resides on different platforms. It's owned by different people, it's processed by pipelines that who owns them. So there's this very disparate and disconnected set of technologies that were very useful for when we thought about data and processing as a centralized problem. But when you think about data as a decentralized problem, the cost of integration of these technologies in a cohesive developer experience is what's missing. And we want to focus on that cohesive end-to-end developer experience to share data responsibly in this autonomous units, we call them data products, I guess in data mesh, right? That constitutes computation, that governs that data policies, discoverability. So I guess, I heard this expression in the last talks that you can have your cake and eat it too. So we want people have their cakes, which is, you know, data in different places, decentralization and eat it too, which is interconnected access to it. So we start with standardizing and codifying this idea of a data product container that encapsulates data computation, APIs to get to it in a technology agnostic way, in an open way. And then, sit on top and use existing existing tech, you know, Snowflake, Databricks, whatever exists, you know, the millions of dollars of investments that companies have made, sit on top of those but create this cohesive, integrated experience where data product is a first class primitive. And that's really key here, that the language, and the modeling that we use is really native to data mesh is that I will make a data product, I'm sharing a data product, and that encapsulates on providing metadata about this. I'm providing computation that's constantly changing the data. I'm providing the API for that. So we're trying to kind of codify and create a new developer experience based on that. And developer, both from provider side and user side connected to peer-to-peer data sharing with data product as a primitive first class concept. >> Okay, so the idea would be developers would build applications leveraging those data products which are discoverable and governed. Now, today you see some companies, you know, take a snowflake for example. >> Zhamak: Yeah. >> Attempting to do that within their own little walled garden. They even, at one point, used the term, "Mesh." I dunno if they pull back on that. And then they sort of became aware of some of your work. But a lot of the things that they're doing within their little insulated environment, you know, support that, that, you know, governance, they're building out an ecosystem. What's different in your vision? >> Exactly. So we realize that, you know, and this is a reality, like you go to organizations, they have a snowflake and half of the organization happily operates on Snowflake. And on the other half, oh, we are on, you know, bare infrastructure on AWS, or we are on Databricks. This is the realities, you know, this Supercloud that's written up here. It's about working across boundaries of technology. So we try to embrace that. And even for our own technology with the way we're building it, we say, "Okay, nobody's going to use next data mesh operating system. People will have different platforms." So you have to build with openness in mind, and in case of Snowflake, I think, you know, they have I'm sure very happy customers as long as customers can be on Snowflake. But once you cross that boundary of platforms then that becomes a problem. And we try to keep that in mind in our solution. >> So, it's worth reviewing that basically, the concept of data mesh is that, whether you're a data lake or a data warehouse, an S3 bucket, an Oracle database as well, they should be inclusive inside of the data. >> We did a session with AWS on the startup showcase, data as code. And remember, I wrote a blog post in 2007 called, "Data's the new developer kit." Back then, they used to call 'em developer kits, if you remember. And that we said at that time, whoever can code data >> Zhamak: Yes. >> Will have a competitive advantage. >> Aren't there machines going to be doing that? Didn't we just hear that? >> Well we have, and you know, Hey Siri, hey Cube. Find me that best video for data mesh. There it is. I mean, this is the point, like what's happening is that, now, data has to be addressable >> Zhamak: Yes. >> For machines and for coding. >> Zhamak: Yes. >> Because as you need to call the data. So the question is, how do you manage the complexity of big things as promiscuous as possible, making it available as well as then governing it because it's a trade off. The more you make open >> Zhamak: Definitely. >> The better the machine learning. >> Zhamak: Yes. >> But yet, the governance issue, so this is the, you need an OS to handle this maybe. >> Yes, well, we call our mental model for our platform is an OS operating system. Operating systems, you know, have shown us how you can kind of abstract what's complex and take care of, you know, a lot of complexities, but yet provide an open and, you know, dynamic enough interface. So we think about it that way. We try to solve the problem of policies live with the data. An enforcement of the policies happens at the most granular level which is, in this concept, the data product. And that would happen whether you read, write, or access a data product. But we can never imagine what are these policies could be. So our thinking is, okay, we should have a open policy framework that can allow organizations write their own policy drivers, and policy definitions, and encode it and encapsulated in this data product container. But I'm not going to fool myself to say that, you know, that's going to solve the problem that you just described. I think we are in this, I don't know, if I look into my crystal ball, what I think might happen is that right now, the primitives that we work with to train machine-learning model are still bits and bites in data. They're fields, rows, columns, right? And that creates quite a large surface area, an attack area for, you know, for privacy of the data. So perhaps, one of the trends that we might see is this evolution of data APIs to become more and more computational aware to bring the compute to the data to reduce that surface area so you can really leave the control of the data to the sovereign owners of that data, right? So that data product. So I think the evolution of our data APIs perhaps will become more and more computational. So you describe what you want, and the data owner decides, you know, how to manage the- >> John: That's interesting, Dave, 'cause it's almost like we just talked about ChatGPT in the last segment with you, who's a machine learning, could really been around the industry. It's almost as if you're starting to see reason come into the data, reasoning. It's like you starting to see not just metadata, using the data to reason so that you don't have to expose the raw data. It's almost like a, I won't say curation layer, but an intelligence layer. >> Zhamak: Exactly. >> Can you share your vision on that 'cause that seems to be where the dots are connecting. >> Zhamak: Yes, this is perhaps further into the future because just from where we stand, we have to create still that bridge of familiarity between that future and present. So we are still in that bridge-making mode, however, by just the basic notion of saying, "I'm going to put an API in front of my data, and that API today might be as primitive as a level of indirection as in you tell me what you want, tell me who you are, let me go process that, all the policies and lineage, and insert all of this intelligence that need to happen. And then I will, today, I will still give you a file. But by just defining that API and standardizing it, now we have this amazing extension point that we can say, "Well, the next revision of this API, you not just tell me who you are, but you actually tell me what intelligence you're after. What's a logic that I need to go and now compute on your API?" And you can kind of evolve that, right? Now you have a point of evolution to this very futuristic, I guess, future where you just describe the question that you're asking from the chat. >> Well, this is the Supercloud, Dave. >> I have a question from a fan, I got to get it in. It's George Gilbert. And so, his question is, you're blowing away the way we synchronize data from operational systems to the data stack to applications. So the concern that he has, and he wants your feedback on this, "Is the data product app devs get exposed to more complexity with respect to moving data between data products or maybe it's attributes between data products, how do you respond to that? How do you see, is that a problem or is that something that is overstated, or do you have an answer for that?" >> Zhamak: Absolutely. So I think there's a sweet spot in getting data developers, data product developers closer to the app, but yet not burdening them with the complexity of the application and application logic, and yet reducing their cognitive load by localizing what they need to know about which is that domain where they're operating within. Because what's happening right now? what's happening right now is that data engineers, a ton of empathy for them for their high threshold of pain that they can, you know, deal with, they have been centralized, they've put into the data team, and they have been given this unbelievable task of make meaning out of data, put semantic over it, curates it, cleans it, and so on. So what we are saying is that get those folks embedded into the domain closer to the application developers, these are still separately moving units. Your app and your data products are independent but yet tightly closed with each other, tightly coupled with each other based on the context of the domain, so reduce cognitive load by localizing what they need to know about to the domain, get them closer to the application but yet have them them separate from app because app provides a very different service. Transactional data for my e-commerce transaction, data product provides a very different service, longitudinal data for the, you know, variety of this intelligent analysis that I can do on the data. But yet, it's all within the domain of e-commerce or sales or whatnot. >> So a lot of decoupling and coupling create that cohesiveness. >> Zhamak: Absolutely. >> Architecture. So I have to ask you, this is an interesting question 'cause it came up on theCUBE all last year. Back on the old server, data center days and cloud, SRE, Google coined the term, "Site Reliability Engineer" for someone to look over the hundreds of thousands of servers. We asked a question to data engineering community who have been suffering, by the way, agree. Is there an SRE-like role for data? Because in a way, data engineering, that platform engineer, they are like the SRE for data. In other words, managing the large scale to enable automation and cell service. What's your thoughts and reaction to that? >> Zhamak: Yes, exactly. So, maybe we go through that history of how SRE came to be. So we had the first DevOps movement which was, remove the wall between dev and ops and bring them together. So you have one cross-functional units of the organization that's responsible for, you build it you run it, right? So then there is no, I'm going to just shoot my application over the wall for somebody else to manage it. So we did that, and then we said, "Okay, as we decentralized and had this many microservices running around, we had to create a layer that abstracted a lot of the complexity around running now a lot or monitoring, observing and running a lot while giving autonomy to this cross-functional team." And that's where the SRE, a new generation of engineers came to exist. So I think if I just look- >> Hence Borg, hence Kubernetes. >> Hence, hence, exactly. Hence chaos engineering, hence embracing the complexity and messiness, right? And putting engineering discipline to embrace that and yet give a cohesive and high integrity experience of those systems. So I think, if we look at that evolution, perhaps something like that is happening by bringing data and apps closer and make them these domain-oriented data product teams or domain oriented cross-functional teams, full stop, and still have a very advanced maybe at the platform infrastructure level kind of operational team that they're not busy doing two jobs which is taking care of domains and the infrastructure, but they're building infrastructure that is embracing that complexity, interconnectivity of this data process. >> John: So you see similarities. >> Absolutely, but I feel like we're probably in a more early days of that movement. >> So it's a data DevOps kind of thing happening where scales happening. It's good things are happening yet. Eh, a little bit fast and loose with some complexities to clean up. >> Yes, yes. This is a different restructure. As you said we, you know, the job of this industry as a whole on architects is decompose, recompose, decompose, recomposing a new way, and now we're like decomposing centralized team, recomposing them as domains and- >> John: So is data mesh the killer app for Supercloud? >> You had to do this for me. >> Dave: Sorry, I couldn't- (John and Dave laughing) >> Zhamak: What do you want me to say, Dave? >> John: Yes. >> Zhamak: Yes of course. >> I mean Supercloud, I think it's, really the terminology's Supercloud, Opencloud. But I think, in spirits of it, this embracing of diversity and giving autonomy for people to make decisions for what's right for them and not yet lock them in. I think just embracing that is baked into how data mesh assume the world would work. >> John: Well thank you so much for coming on Supercloud too, really appreciate it. Data has driven this conversation. Your success of data mesh has really opened up the conversation and exposed the slow moving data industry. >> Dave: Been a great catalyst. (John laughs) >> John: That's now going well. We can move faster, so thanks for coming on. >> Thank you for hosting me. It was wonderful. >> Okay, Supercloud 2 live here in Palo Alto. Our stage performance, I'm John Furrier with Dave Vellante. We're back with more after this short break, Stay with us all day for Supercloud 2. (gentle bright music)

(upbeat music) >> Welcome back to our Supercloud 2 event live coverage here of stage performance in Palo Alto syndicating around the world. I'm John Furrier with Dave Vellante. We got exclusive news and a scoop here for SiliconANGLE in theCUBE. Zhamak Dehghani, creator of data mesh has formed a new company called Nextdata.com, Nextdata. She's a cube alumni and contributor to our supercloud initiative, as well as our coverage and Breaking Analysis with Dave Vellante on data, the killer app for supercloud. Zhamak, great to see you. Thank you for coming into the studio and congratulations on your newly formed venture and continued success on the data mesh. >> Thank you so much. It's great to be here. Great to see you in person. >> Dave: Yeah, finally. >> Wonderful. Your contributions to the data conversation has been well documented certainly by us and others in the industry. Data mesh taking the world by storm. Some people are debating it, throwing cold water on it. Some are thinking it's the next big thing. Tell us about the data mesh, super data apps that are emerging out of cloud. >> I mean, data mesh, as you said, the pain point that it surface were universal. Everybody said, "Oh, why didn't I think of that?" It was just an obvious next step and people are approaching it, implementing it. I guess the last few years I've been involved in many of those implementations and I guess supercloud is somewhat a prerequisite for it because it's data mesh and building applications using data mesh is about sharing data responsibly across boundaries. And those boundaries include organizational boundaries, cloud technology boundaries, and trust boundaries. >> I want to bring that up because your venture, Nextdata, which is new just formed. Tell us about that. What wave is that riding? What specifically are you targeting? What's the pain point? >> Absolutely. Yes, so Nextdata is the result of, I suppose the pains that I suffered from implementing data mesh for many of the organizations. Basically a lot of organizations that I've worked with they want decentralized data. So they really embrace this idea of decentralized ownership of the data, but yet they want interconnectivity through standard APIs, yet they want discoverability and governance. So they want to have policies implemented, they want to govern that data, they want to be able to discover that data, and yet they want to decentralize it. And we do that with a developer experience that is easy and native to a generalist developer. So we try to find the, I guess the common denominator that solves those problems and enables that developer experience for data sharing. >> Since you just announced the news, what's been the reaction? >> I just announced the news right now, so what's the reaction? >> But people in the industry know you did a lot of work in the area. What have been some of the feedback on the new venture in terms of the approach, the customers, problem? >> Yeah, so we've been in stealth mode so we haven't publicly talked about it, but folks that have been close to us, in fact have reached that we already have implementations of our pilot platform with early customers, which is super exciting. And we going to have multiple of those. Of course, we're a tiny, tiny company. We can have many of those, but we are going to have multiple pilot implementations of our platform in real world where real global large scale organizations that have real world problems. So we're not going to build our platform in vacuum. And that's what's happening right now. >> Zhamak, when I think about your role at ThoughtWorks, you had a very wide observation space with a number of clients, helping them implement data mesh and other things as well prior to your data mesh initiative. But when I look at data mesh, at least the ones that I've seen, they're very narrow. I think of JPMC, I think of HelloFresh. They're generally, obviously not surprising, they don't include the big vision of inclusivity across clouds, across different data storage. But it seems like people are having to go through some gymnastics to get to the organizational reality of decentralizing data and at least pushing data ownership to the line of business. How are you approaching, or are you approaching solving that problem? Are you taking a narrow slice? What can you tell us about Nextdata? >> Yeah, absolutely. Gymnastics, the cute word to describe what the organizations have to go through. And one of those problems is that the data as you know resides on different platforms, it's owned by different people, is processed by pipelines that who knows who owns them. So there's this very disparate and disconnected set of technologies that were very useful for when we thought about data and processing as a centralized problem. But when you think about data as a decentralized problem the cost of integration of these technologies in a cohesive developer experience is what's missing. And we want to focus on that cohesive end-to-end developer experience to share data responsibly in these autonomous units. We call them data products, I guess in data mesh. That constitutes computation. That governs that data policies, discoverability. So I guess, I heard this expression in the last talks that you can have your cake and eat it too. So we want people have their cakes, which is data in different places, decentralization, and eat it too, which is interconnected access to it. So we start with standardizing and codifying this idea of a data product container that encapsulates data computation APIs to get to it in a technology agnostic way, in an open way. And then sit on top and use existing tech, Snowflake, Databricks, whatever exists, the millions of dollars of investments that companies have made, sit on top of those but create this cohesive, integrated experience where data product is a first class primitive. And that's really key here. The language and the modeling that we use is really native to data mesh, which is that I'm building a data product I'm sharing a data product, and that encapsulates I'm providing metadata about this. I'm providing computation that's constantly changing the data. I'm providing the API for that. So we we're trying to kind of codify and create a new developer experience based on that. And developer, both from provider side and user side, connected to peer-to-peer data sharing with data product as a primitive first class concept. >> So the idea would be developers would build applications leveraging those data products, which are discoverable and governed. Now today you see some companies, take a Snowflake for example, attempting to do that within their own little walled garden. They even at one point used the term mesh. I don't know if they pull back on that. And then they became aware of some of your work. But a lot of the things that they're doing within their little insulated environment support that governance, they're building out an ecosystem. What's different in your vision? >> Exactly. So we realized that, and this is a reality, like you go to organizations, they have a Snowflake and half of the organization happily operates on Snowflake. And on the other half, "oh, we are on Bare infrastructure on AWS or we are on Databricks." This is the reality. This supercloud that's written up here, it's about working across boundaries of technology. So we try to embrace that. And even for our own technology with the way we're building it, we say, "Okay, nobody's going to use Nextdata, data mesh operating system. People will have different platforms." So you have to build with openness in mind and in case of Snowflake, I think, they have very, I'm sure very happy customers as long as customers can be on Snowflake. But once you cross that boundary of platforms then that becomes a problem. And we try to keep that in mind in our solution. >> So it's worth reviewing that basically the concept of data mesh is that whether you're a data lake or a data warehouse, an S3 bucket, an Oracle database as well, they should be inclusive inside of the data. >> We did a session with AWS on the startup showcase, data as code. And remember I wrote a blog post in 2007 called "Data as the New Developer Kit" back then we used to call them developer kits if you remember. And that we said at that time, whoever can code data will have a competitive advantage. >> Aren't the machines going to be doing that? Didn't we just hear that? >> Well, we have. Hey, Siri. Hey, Cube, find me that best video for data mesh. There it is. But this is the point, like what's happening is that now data has to be addressable. for machines and for coding because as you need to call the data. So the question is how do you manage the complexity of big things as promiscuous as possible, making it available, as well as then governing it? Because it's a trade off. The more you make open, the better the machine learning. But yet the governance issue, so this is the, you need an OS to handle this maybe. >> Yes. So yes, well we call, our mental model for our platform is an OS operating system. Operating systems have shown us how you can abstract what's complex and take care of a lot of complexities, but yet provide an open and dynamic enough interface. So we think about it that way. Just, we try to solve the problem of policies live with the data, an enforcement of the policies happens at the most granular level, which is in this concept of the data product. And that would happen whether you read, write or access a data product. But we can never imagine what are these policies could be. So our thinking is we should have a policy, open policy framework that can allow organizations write their own policy drivers and policy definitions and encode it and encapsulated in this data product container. But I'm not going to fool myself to say that, that's going to solve the problem that you just described. I think we are in this, I don't know, if I look into my crystal ball, what I think might happen is that right now the primitives that we work with to train machine learning model are still bits and bytes and data. They're fields, rows, columns and that creates quite a large surface area and attack area for privacy of the data. So perhaps one of the trends that we might see is this evolution of data APIs to become more and more computational aware to bring the compute to the data to reduce that surface area. So you can really leave the control of the data to the sovereign owners of that data. So that data product. So I think that evolution of our data APIs perhaps will become more and more computational. So you describe what you want and the data owner decides how to manage. >> That's interesting, Dave, 'cause it's almost like we just talked about ChatGPT in the last segment we had with you. It was a machine learning have been around the industry. It's almost as if you're starting to see reason come into, the data reasoning is like starting to see not just metadata. Using the data to reason so that you don't have to expose the raw data. So almost like a, I won't say curation layer, but an intelligence layer. >> Zhamak: Exactly. >> Can you share your vision on that? 'Cause that seems to be where the dots are connecting. >> Yes, perhaps further into the future because just from where we stand, we have to create still that bridge of familiarity between that future and present. So we are still in that bridge making mode. However, by just the basic notion of saying, "I'm going to put an API in front of my data." And that API today might be as primitive as a level of indirection, as in you tell me what you want, tell me who you are, let me go process that, all the policies and lineage and insert all of this intelligence that need to happen. And then today, I will still give you a file. But by just defining that API and standardizing it now we have this amazing extension point that we can say, "Well, the next revision of this API, you not just tell me who you are, but you actually tell me what intelligence you're after. What's a logic that I need to go and now compute on your API?" And you can evolve that. Now you have a point of evolution to this very futuristic, I guess, future where you just described the question that you're asking from the ChatGPT. >> Well, this is the supercloud, go ahead, Dave. >> I have a question from a fan, I got to get it in. It's George Gilbert. And so his question is, you're blowing away the way we synchronize data from operational systems to the data stack to applications. So the concern that he has and he wants your feedback on this, is the data product app devs get exposed to more complexity with respect to moving data between data products or maybe it's attributes between data products? How do you respond to that? How do you see? Is that a problem? Is that something that is overstated or do you have an answer for that? >> Absolutely. So I think there's a sweet spot in getting data developers, data product developers closer to the app, but yet not overburdening them with the complexity of the application and application logic and yet reducing their cognitive load by localizing what they need to know about, which is that domain where they're operating within. Because what's happening right now? What's happening right now is that data engineers with, a ton of empathy for them for their high threshold of pain that they can deal with, they have been centralized, they've put into the data team, and they have been given this unbelievable task of make meaning out of data, put semantic over it, curate it, cleans it, and so on. So what we are saying is that get those folks embedded into the domain closer to the application developers. These are still separately moving units. Your app and your data products are independent, but yet tightly closed with each other, tightly coupled with each other based on the context of the domain. So reduce cognitive load by localizing what they need to know about to the domain, get them closer to the application, but yet have them separate from app because app provides a very different service. Transactional data for my e-commerce transaction. Data product provides a very different service. Longitudinal data for the variety of this intelligent analysis that I can do on the data. But yet it's all within the domain of e-commerce or sales or whatnot. >> It's a lot of decoupling and coupling create that cohesiveness architecture. So I have to ask you, this is an interesting question 'cause it came up on theCUBE all last year. Back on the old server data center days and cloud, SRE, Google coined the term, site reliability engineer, for someone to look over the hundreds of thousands of servers. We asked the question to data engineering community who have been suffering, by the way, I agree. Is there an SRE like role for data? Because in a way data engineering, that platform engineer, they are like the SRE for data. In other words managing the large scale to enable automation and cell service. What's your thoughts and reaction to that? >> Yes, exactly. So maybe we go through that history of how SRE came to be. So we had the first DevOps movement, which was remove the wall between dev and ops and bring them together. So you have one unit of one cross-functional units of the organization that's responsible for you build it, you run it. So then there is no, I'm going to just shoot my application over the wall for somebody else to manage it. So we did that and then we said, okay, there is a ton, as we decentralized and had these many microservices running around, we had to create a layer that abstracted a lot of the complexity around running now a lot or monitoring, observing, and running a lot while giving autonomy to this cross-functional team. And that's where the SRE, a new generation of engineers came to exist. So I think if I just look at. >> Hence, Kubernetes. >> Hence, hence, exactly. Hence, chaos engineering. Hence, embracing the complexity and messiness. And putting engineering discipline to embrace that and yet give a cohesive and high integrity experience of those systems. So I think if we look at that evolution, perhaps something like that is happening by bringing data and apps closer and make them these domain-oriented data product teams or domain-oriented cross-functional teams full stop and still have a very advanced maybe at the platform level, infrastructure level operational team that they're not busy doing two jobs, which is taking care of domains and the infrastructure, but they're building infrastructure that is embracing that complexity, interconnectivity of this data process. >> So you see similarities? >> I see, absolutely. But I feel like we're probably in a more early days of that movement. >> So it's a data DevOps kind of thing happening where scales happening. It's good things are happening, yet a little bit fast and loose with some complexities to clean up. >> Yes. This is a different restructure. As you said, the job of this industry as a whole, an architect, is decompose recompose, decompose recompose in new way and now we're like decomposing centralized team, recomposing them as domains. >> So is data mesh the killer app for supercloud? >> You had to do this to me. >> Sorry, I couldn't resist. >> I know. Of course you want me to say this. >> Yes. >> Yes, of course. I mean, supercloud, I think it's really, the terminology supercloud, open cloud, but I think in spirits of it this embracing of diversity and giving autonomy for people to make decisions for what's right for them and not yet lock them in. I think just embracing that is baked into how data mesh assume the world would work. >> Well, thank you so much for coming on Supercloud 2. We really appreciate it. Data has driven this conversation. Your success of data mesh has really opened up the conversation and exposed the slow moving data industry. >> Dave: Been a great catalyst. >> That's now going well. We can move faster. So thanks for coming on. >> Thank you for hosting me. It was wonderful. >> Supercloud 2 live here in Palo Alto, our stage performance. I'm John Furrier with Dave Vellante. We'll back with more after this short break. Stay with us all day for Supercloud 2. (upbeat music)

>> Good afternoon everyone, and welcome back to theCUBE where we're coming to you live from Detroit, Michigan at KubeCon and Cloud Native Con. We're going to keep theCUBE puns coming this afternoon because we have the pleasure of being joined by not one but two guests from Kubiya. John Furrier, my wonderful co-host. You're familiar with these guys. You just chatted with them last week. >> We broke the story of their launch and featured them on theCUBE in our studio conversation. This is a great segment. Real innovative company with lofty goals, and they're really good ones. Looking forward to it. >> If that's not a tease to keep watching I don't know what is. (John laughs) Without further ado, on that note, allow me to introduce Amit and Shaked who are here to tell us all about Kubiya. And I'm going to blow the pitch for you a little bit just because this gets me excited. (all laugh) They're essentially the Siri of DevOps, but that means you can, you can create using voice or chat or any medium. Am I right? Is this? Yeah? >> You're hired. >> Excellent. (all laugh) >> Okay. >> We'll take it. >> Who knows what I'll tell the chat to do or what I'll, what I will control with my voice, but I love where you're. >> Absolutely. I'll just give the high level. Conversational AI for the world of DevOps. Kind of redefining how self-service DevOps is supposed to be essentially accessed, right? As opposed to just having siloed information. You know, having different platforms that require an operator or somebody who's using it to know exactly how they're accessing what they're doing and so forth. Essentially, the ability to express your intent in natural language, English, or any language I use. >> It's quite literally the language barrier sometimes. >> Precisely. >> Both from the spoken as well as code language. And it sounds like you're eliminating that as an obstacle. >> We're essentially saying, turn simple, complex cast into simple conversations. That's, that's really what we're saying here. >> So let's get into the launch. You just launched a fresh startup. >> Yeah, yeah, yeah. >> Yeah. >> So you guys are going to take on the world. Lofty goals if that. I had the briefing. Where's the origination story come from? What, how did you guys get here? Was it a problem that you saw, you were experiencing, an itch you were scratching? What was the motivation and what's the origination story? >> Shaked: So. >> Amit: Okay, go first please. >> Essentially everything started with my experience as being an operator. I used to be a DevOps engineer for a few years for a large (indistinct) company. On later stages I even managed an SRE team. So all of these access requires Q and A staff is something that I experience nonstop on Slack or Teams, all of these communication channels. And usually I find out that everything happens from the chat. So essentially back then I created a chat bot. I connect this chat bot to the different organizational tools, and instead of the developers approaching to me or the team using the on call channel or directly they will just approach the bot. But essentially the bot was very naive, and they still needed to know what they, they want to do inside the bot. But it's still managed to solve 70% of the complexity and the toil on us as a team so we could focus on innovation. So Kubiya's a more advanced version of it. Basically with Kubiya you can define what we call workflows, and we convert all of these complexity of access request into simple conversations that the end users, which could be developers, but not only, are having with a DevOps team. So that's essentially how it works, and we're very excited about it. >> So you were up all night answering the same question over and over again. (all laugh) And you said, Hey, screw it. I'm going to just create a bot, bot myself up. (Shaked laughs) But it gets at something important. I mean, I'm not just joking. It probably happened, right? That was probably the case. You were up all night telling. >> Yeah, I mean it was usually stuff that we didn't need to maintain. It was training requests and questions that just keep on repeating themselves. And actually we were in Israel, but we sell three different time zones of developers. So all of these developers, as soon as the day finishes in Israel, the day in the US started. So they will approach us from the US. So we didn't really sleep. (all laugh) As with these requests non-stop. >> It's that 24 hour. >> Yeah, yeah. 24 hours for a single team. >> So the world clock global (indistinct) catches you a little sometimes. Yeah. >> Yeah, exactly. >> So you basically take all the things that you know that are common and then make a chat bot answering as if you're you. But this brings up the whole question of chat bot utilization. There's been a lot of debate in the AI circles that chat bots really haven't made it. They're not, they haven't been good enough. So 'cause NLP and other trivial, >> Amit: Sure. or things that haven't really clicked. What's different now? How do you guys see your approach cracking the code to go that kind of reasoning level? Bots can reason? Now we're in business. >> Yeah. Most of the chat bots are general purpose, right? We're coming with the domain expertise. We know the pain from the inside. We know how the operators want to define such conversations that users might have with the virtual assistant. So we combined all of the technical tools that are needed in order to get it going. So we have a a DSL, domain specific language, where the operators can define these easy conversations and combine all of the different organizational tools which can be done using DSDK. Besides this fact, we have a no code, for less technical people to create such workflows even with no code interface. And we have a CLI, which you could use to leverage the power of the virtual assist even right from your terminal. So that's how I see the domain expertise coming in that we have different communication channels for everyone that needs to be inside the loop. >> That's awesome. >> And I, and I can add to that. So that's one element, which is the domain expertise. The other one is really our huge differentiator, the ability to let the end users influence the system itself. So essentially. >> John: Like how? Give me an example. >> Sure. We call it teach me feature, but essentially if you have any type of a request and the system hasn't created an automation or hasn't, doesn't recognize it, you can go ahead and bind that into your intent and next time, and you can define the scope for yourself only, for the team, or even for the entire organization that actually has to have permission to access the request and control and so on. >> Savannah: That's something. Yeah, I love that as a knowledge base. I mean a custom tool kit. >> Absolutely. >> And I like that you just said for the individual. So let's say I have some crazy workflows that I don't need anybody else to know about. >> 100 percent. >> I can customize my experience. >> Mm hmm. >> Do you see your, this is really interesting, and I'm, it's surprising to me we haven't seen a lot of players in this space before because what you're doing makes a lot of sense to me, especially as someone who is less technical. >> Yeah. >> Do you view yourselves as a gateway tool for more folks to be involved in more complex technology? >> So, so I'll take that. It's not that we haven't seen advanced virtual assistants. They've existed in different worlds. >> Savannah: Right. >> Up until now they've existed more in CRM tools. >> Savannah: Right. >> Call centers, right? >> Shaked: Yeah. >> You go on to Ralph Lauren, Calvin Klein, you go and chat with. Now imagine you can bring that into a world of dev tools that has high domain expertise, high technical amplitude, and now you can go and combine the domain expertise with the accessibility of conversational AI. That's, that's a unique feature here. >> What's the biggest thing that's surprised you with the launch so far? The reaction to the name, Kubiya, which is Cube in Hebrew. >> Amit: Yes. >> Apparently. >> Savannah: Which I love. >> Which by the way, you know, we have a TM and R on our Cube. (all laugh) So we can talk, you know, license rights. >> Let's drop the trademark rules today, John, here. We're here to share information. Confuse the audience. Sorry about that, by the way. (all laugh) >> We're an open source, inclusive culture. We'll let it slide this time. >> The KubeCon, theCUBE, and Kubiya. (John laughs) In the Hebrew we have this saying, third time we all have ice cream. So. (all laugh) >> I think there's some ice cream over there actually. >> There is. >> Yeah, yeah. There you go. >> All kidding aside, all fun. What's, what's been the reaction? Got some press coverage. We had the launch. You guys launched with theCUBE in here, big reception. What's been the common feedback? >> And really, I think I expected this, but I didn't expect this much. Really, the fact that people really believe in our thesis, really expect great things from us, right? We've starting to working with. >> Savannah: Now the pressure's on. >> Rolling out dozens of POCs, but even that requires obviously a lot of attention to the detail, which we're rolling out. This is effectively what we're seeing. People love the fact that you have a unique and fresh way to approaching the self-service which really has been stalled for a while. And we've recognized that. I think our thesis is where we. >> Okay, so as a startup you have lofty goals, you have investors now. >> Amit: Yeah. >> Congratulations. >> Amit: Thank you. >> They're going to want to keep the traction going, but as a north star, what's your, what are you going to, what are you going to take? What territory are you going to take? Is it new territory? Are you eating someone's lunch? Who are you going to be competing with? What's the target? What's the, what's the? >> Sure, sure. >> I'm sure you guys have it. Who are you takin' over? >> I think the gateway, the entry point to every organization is a bottleneck. You solve the hard problem first. That's where you can go into other directions, and you can imagine where other operational workflows and pains that we can help solve once we have essentially the DevOps. >> John: So you see this as greenfield, new opportunity? >> I believe so. >> Is there any incumbent you see out there? An old stodgy? >> Today we're on internal developer platform service catalog type of, you know, use cases. >> John: Yeah. >> But that's kind of where we can grow from there and have the ecosystem essentially embrace us. >> John: How about the technology platform? >> Amit: Yeah. >> What's the vision for the innovation? >> Essentially want to be able to integrate with all of the different cloud providers, cloud solutions, SaaS platforms, and take the atlas approach that they were using right to the chats from everywhere to anywhere. So essentially we want in the end that users will be able to do anything that they need inside all of these complicated platforms, which some of them are totally complicated, with plain English. >> So what's the biggest challenge for you then on that front leading the technology side of the team? >> So I would say that the conversational AI part is truly complicated because it requires to extract many types of intentions from different types of users and also integrate with so many tools and solutions. >> Savannah: Yeah. So it requires a lot of thinking, a lot of architecture, but we are doing it just fine. >> Awesome. What do you guys think about KubeCon this week? What's, what's the top story that you see emerging out of this? Just generally as an industry observer, what's the most important? >> Savannah: Maybe it's them. Announcement halo. >> What's the cover story that you see? (all laugh) I mean you guys are in the innovation intent-based infrastructure. I get that. >> So obviously everyone's looking to diversify their engineering, diversify their platforms to make sure they're as decoupled from the main CSPs as possible. So being able to build their own, and we're really helping enable a lot of that in there. We're really helping improve upon that open source together with managed platforms can really play a very nice game together. So. >> Awesome. So are you guys hiring, recruiting? Tell us about the team DNA. Now you're in Tel Aviv. You're in the bay. >> Shaked: Check our openings on LinkedIn. (all laugh) >> We have a dozen job postings on our website. Obviously engineering and sales then go to market. >> So when theCUBE comes to Tel Aviv, and we have a location there. >> Yeah. >> Will you be, share some space? >> Savannah: Is this our Tel Aviv office happening right now? I love this. >> Amit: We will be hosting you. >> John: theCube with a C and Kube with a K over there. >> Yeah. >> All one happy family. >> Would love that. >> Get some ice cream. >> Would love that. >> All right, so last question for you all. You just had a very big exciting announcement. It's a bit of a coming out party for you. What do you hope to be able to say in a year that you can't currently say right now? When you join us on theCUBE next time? >> No, no, it's absolutely. I think our thesis that you can turn conversations into operations. It's, it sounds obvious to you when you think about it, but it's not trivial when you look into the workflows, into the operations. The fact that we can actually go a year from today and say we got hundreds of customers, happy customers who've proven the thesis or sharing knowledge between themselves, that would be euphoric for us. >> All right. >> You really are about helping people. >> Absolutely. >> It doesn't seem like it's a lip service from both of you. >> No. (all laugh) >> Is there going to be levels of bot, like level one bot level two, level three, and then finally the SRE gets on the phone? Is that like some point? >> Is there going to be bot singularity? Is that, is that what we're exploring right now? (overlapping chatter) >> Some kind of escalation bot. >> Enlightenment with bots. >> We actually planning a feature we want to call a handoff where a human in the loop is required, which often is needed. Machine cannot do it alone. We'll just. >> Yeah, I think it makes total sense for geos, ops at the same. >> Shaked: Yeah. >> But not exactly the same. Really good, good solution. I love the direction. Congratulations on the launch. >> Shaked: Thank you so much. >> Amit: Thank you very much. >> Yeah, that's very exciting. You can obviously look, check out that news on Silicon Angle since we had the pleasure of breaking it. >> Absolutely. >> If people would like to say hi, stalk you on the internet, where's the best place for them to do that? >> Be on our Twitter and LinkedIn handles of course. So we have kubiya.ai. We also have a free trial until the end of the year, and we also have free forever tier, that people can sign up, play, and come say hi. I mean, we'd love to chat. >> I love it. Well, Amit, Shaked, thank you so much for being with us. >> Shaked: Thank you so much. >> John, thanks for sitting to my left for the entire day. I sincerely appreciate it. >> Just glad I can help out. >> And thank you all for tuning in to this wonderful edition of theCUBE Live from Detroit at KubeCon. Who knows what my voice will be controlling next, but either way, I hope you are there to find out. >> Amit: Love it.

(upbeat music) >> Hello everyone, welcome to this special Cube conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE in theCUBE Studios. We've got a special video here. We love when we have startups that are launching. It's an exclusive video of a hot startup that's launching. Got great reviews so far. You know, word on the street is, they got something different and unique. We're going to' dig into it. Amit Govrin who's the CEO and co-founder of Kubiya, which stands for Cube in Hebrew, and they're headquartered in Bay Area and in Tel Aviv. Amit, congratulations on the startup launch and thanks for coming in and talk to us in theCUBE >> Thank you, John, very nice to be here. >> So, first of all, a little, 'cause we love the Cube, 'cause theCUBE's kind of an open brand. We've never seen the Cube in Hebrew, so is that true? Kubiya is? >> Kubiya literally means cube. You know, clearly there's some additional meanings that we can discuss. Obviously we're also launching a KubCon, so there's a dual meaning to this event. >> KubCon, not to be confused with CubeCon. Which is an event we might have someday and compete. No, I'm only kidding, good stuff. I want to get into the startup because I'm intrigued by your story. One, you know, conversational AI's been around, been a category. We've seen chat bots be all the rage and you know, I kind of don't mind chat bots on some sites. I can interact with some, you know, form based knowledge graph, whatever, knowledge database and get basic stuff self served. So I can see that, but it never really scaled or took off. And now with Cloud Native kind of going to the next level, we're starting to see a lot more open source and a lot more automation, in what I call AI as code or you know, AI as a service, machine learning, developer focused action. I think you guys might have an answer there. So if you don't mind, could you take a minute to explain what you guys are doing, what's different about Kubiya, what's happening? >> Certainly. So thank you for that. Kubiya is what we would consider the first, or one of the first, advanced virtual assitants with a domain specific expertise in DevOps. So, we respect all of the DevOps concepts, GitOps, workflow automation, of those categories you've mentioned, but also the added value of the conversational AI. That's really one of the few elements that we can really bring to the table to extract what we call intent based operations. And we can get into what that means in a little bit. I'll save that maybe for the next question. >> So the market you're going after is kind of, it's, I love to hear starters when they, they don't have a Gartner Magic quadrant, they can fit nicely, it means they're onto something. What is the market you're going after? Because you're seeing a lot of developers driving a lot of the key successes in DevOps. DevOps has evolved to the point where, and DevSecOps, where developers are driving the change. And so having something that's developer focused is key. Are you guys targeting the developers, IT buyers, cloud architects? Who are you looking to serve with this new opportunity? >> So essentially self-service in the world of DevOps, the end user typically would be a developer, but not only, and obviously the operators, those are the folks that we're actually looking to help augment a lot of their efforts, a lot of the toil that they're experiencing in a day to day. So there's subcategories within that. We can talk about the different internal developer tools, or platforms, shared services platforms, service catalogs are tangential categories that this kind of comes on. But on top of that, we're adding the element of conversational AI. Which, as I mentioned, that's really the "got you". >> I think you're starting to see a lot of autonomous stuff going on, autonomous pen testing. There's a company out there doing I've seen autonomous AI. Automation is a big theme of it. And I got to ask, are you guys on the business side purely in the cloud? Are you born in the cloud, is it a cloud service? What's the product choice there? It's a service, right? >> Software is a service. We have the classic, Multi-Tenancy SAAS, but we also have a hybrid SAAS solution, which allows our customers to run workflows using remote runners, essentially hosted at their own location. >> So primary cloud, but you're agnostic on where they could consume, how they want to' consume the product. >> Technology agnostic. >> Okay, so that's cool. So let's get into the problem you're solving. So take me through, this will drive a lot of value here, when you guys did the company, what problems did you hone in on and what are you guys seeing as the core problem that you solve? >> So we, this is a unique, I don't know how unique, but this is a interesting proposition because I come from the business side, so call it the top down. I've been in enterprise sales, I've been in a CRO, VP sales hat. My co-founder comes from the bottom up, right? He ran DevOps teams and SRE teams in his previous company. That's actually what he did. So, we met each other halfway, essentially with me seeing a lot of these problems of self-service not being so self-service after all, platforms hitting walls with adoption. And he actually created his own self-service platform, within his last company, to address his own personal pains. So we essentially kind of met with both perspectives. >> So you're absolutely hardcore on self-service. >> We're enabling self-service. >> And that basically is what everybody wants. I mean, the developers want self-service. I mean, that's kind of like, you know, that's the nirvana. So take us through what you guys are offering, give us an example of use cases and who's buying your product, why, and take us through that whole piece. >> Do you mind if I take a step back and say why we believe self-service has somewhat failed or not gotten off. >> Yeah, absolutely. >> So look, this is essentially how we're looking at it. All the analysts and the industry insiders are talking about self-service platforms as being what's going to' remove the dependency of the operator in the loop the entire time, right? Because the operator, that scarce resource, it's hard to hire, hard to train, hard to retain those folks, Developers are obviously dependent on them for productivity. So the operators in this case could be a DevOps, could be a SecOps, it could be a platform engineer. It comes in different flavors. But the common denominator, somebody needs an access request, provisioning a new environment, you name it, right? They go to somebody, that person is operator. The operator typically has a few things on their plate. It's not just attending and babysitting platforms, but it's also innovating, spinning up, and scaling services. So they see this typically as kind of, we don't really want to be here, we're going to' go and do this because we're on call. We have to take it on a chin, if you may, for this. >> It's their child, they got to' do it. >> Right, but it's KTLOs, right, keep the lights on, this is maintenance of a platform. It's not what they're born and bred to do, which is innovate. That's essentially what we're seeing, we're seeing that a lot of these platforms, once they finally hit the point of maturity, they're rolled out to the team. People come to serve themselves in platform, and low and behold, it's not as self-service as it may seem. >> We've seen that certainly with Kubernetes adoption being, I won't say slow, it's been fast, but it's been good. But I think this is kind of the promise of what SRE was supposed to be. You know, do it once and then babysit in the sense of it's working and automated. Nothing's broken yet. Don't call me unless you need something, I see that. So the question, you're trying to make it easier then, you're trying to free up the talent. >> Talent to operate and have essentially a human, like in the loop, essentially augment that person and give the end users all of the answers they require, as if they're talking to a person. >> I mean it's basically, you're taking the virtual assistant concept, or chat bot, to a level of expertise where there's intelligence, jargon, experience into the workflows that's known. Not just talking to chat bot, get a support number to rebook a hotel room. >> We're converting operational workflows into conversations. >> Give me an example, take me through an example. >> Sure, let's take a simple example. I mean, not everyone provisions EC2's with two days (indistinct). But let's say you want to go and provision new EC2 instances, okay? If you wanted to do it, you could go and talk to the assistant and say, "I want to spin up a new server". If it was a human in the loop, they would ask you the following questions: what type of environment? what are we attributing this to? what type of instance? security groups, machine images, you name it. So, these are the questions that typically somebody needs to be armed with before they can go and provision themselves, serve themselves. Now the problem is users don't always have these questions. So imagine the following scenario. Somebody comes in, they're in Jira ticket queue, they finally, their turn is up and the next question they don't have the answer to. So now they have to go and tap on a friend, or they have to go essentially and get that answer. By the time they get back, they lost their turn in queue. And then that happens again. So, they lose a context, they lose essentially the momentum. And a simple access request, or a simple provision request, can easily become a couple days of ping pong back and forth. This won't happen with the virtual assistant. >> You know, I think, you know, and you mentioned chat bots, but also RPA is out there, you've seen a lot of that growth. One of the hard things, and you brought this up, I want to get your reaction to, is contextualizing the workflow. It might not be apparent, but the answer might be there, it disrupts the entire experience at that point. RPA and chat bots don't have that contextualization. Is that what you guys do differently? Is that the unique flavor here? Is that difference between current chat bots and RPA? >> The way we see it, I alluded to the intent based operations. Let me give a tangible experience. Even not from our own world, this will be easy. It's a bidirectional feedback loop 'cause that's actually what feeds the context and the intent. We all know Waze, right, in the world of navigation. They didn't bring navigation systems to the world. What they did is they took the concept of navigation systems that are typically satellite guided and said it's not just enough to drive down the 280, which typically have no traffic, right, and to come across traffic and say, oh, why didn't my satellite pick that up? So they said, have the end users, the end nodes, feed that direction back, that feedback, right. There has to be a bidirectional feedback loop that the end nodes help educate the system, make the system be better, more customized. And that's essentially what we're allowing the end users. So the maintenance of the system isn't entirely in the hands of the operators, right? 'Cause that's the part that they dread. And the maintenance of the system is democratized across all the users that they can teach the system, give input to the system, hone in the system in order to make it more of the DNA of the organization. >> You and I were talking before you came on this camera interview, you said playfully that the Siri for DevOps, which kind of implies, hey infrastructure, do something for me. You know, we all know Siri, so we get that. So that kind of illustrates kind of where the direction is. Explain why you say that, what does that mean? Is that like a NorthStar vision that you guys are approaching? You want to' have a state where everything's automated in it's conversational deployments, that kind of thing. And take us through why that Siri for DevOps is. >> I think it helps anchor people to what a virtual assistant is. Because when you hear virtual assistant, that can mean any one of various connotations. So the Siri is actually a conversational assistant, but it's not necessarily a virtual assistant. So what we're saying is we're anchoring people to that thought and saying, we're actually allowing it to be operational, turning complex operations into simple conversations. >> I mean basically they take the automate with voice Google search or a query, what's the score of the game? And, it also, and talking to the guy who invented Siri, I actually interviewed on theCUBE, it's a learning system. It actually learns as it gets more usage, it learns. How do you guys see that evolving in DevOps? There's a lot of jargon in DevOps, a lot of configurations, a lot of different use cases, a lot of new technologies. What's the secret sauce behind what you guys do? Is it the conversational AI, is it the machine learning, is it the data, is it the model? Take us through the secret sauce. >> In fact, it's all the above. And I don't think we're bringing any one element to the table that hasn't been explored before, hasn't been done. It's a recipe, right? You give two people the same ingredients, they can have complete different results in terms of what they come out with. We, because of our domain expertise in DevOps, because of our familiarity with developer workflows with operators, we know how to give a very well suited recipe. Five course meal, hopefully with Michelin stars as part of that. So a few things, maybe a few of the secret sauce element, conversational AI, the ability to essentially go and extract the intent of the user, so that if we're missing context, the system is smart enough to go and to get that feedback and to essentially feed itself into that model. >> Someone might say, hey, you know, conversational AI, that was yesterday's trend, it never happened. It was kind of weak, chat bots were lame. What's different now and with you guys, and the market, that makes a redo or a second shot at this, a second bite at the apple, as they say. What do you guys see? 'Cause you know, I would argue that it's, you know, it's still early, real early. >> Certainly. >> How do you guys view that? How would you handle that objection? >> It's a fair question. I wasn't around the first time around to tell you what didn't work. I'm not afraid to share that the feedback that we're getting is phenomenal. People understand that we're actually customizing the workflows, the intent based operations to really help hone in on the dark spots. We call it last mile, you know, bottlenecks. And that's really where we're helping. We're helping in a way tribalize internal knowledge that typically hasn't been documented because it's painful enough to where people care about it but not painful enough to where you're going to' go and sit down an entire day and document it. And that's essentially what the virtual assistant can do. It can go and get into those crevices and help document, and operationalize all of those toils. And into workflows. >> Yeah, I mean some will call it grunt work, or low level work. And I think the automation is interesting. I think we're seeing this in a lot of these high scale situations where the talented hard to hire person is hired to do, say, things that were hard to do, but now harder things are coming around the corner. So, you know, serverless is great and all this is good, but it doesn't make the complexity go away. As these inflection points continue to drive more scale, the complexity kind of grows, but at the same time so is the ability to abstract away the complexity. So you're starting to see the smart, hired guns move to higher, bigger problems. And the automation seems to take the low level kind of like capabilities or the toil, or the grunt work, or the low level tasks that, you know, you don't want a high salaried person doing. Or I mean it's not so much that they don't want to' do it, they'll take one for the team, as you said, or take it on the chin, but there's other things to work on. >> I want to add one more thing, 'cause this goes into essentially what you just said. Think about it's not the virtual system, what it gives you is not just the intent and that's one element of it, is the ability to carry your operations with you to the place where you're not breaking your workflows, you're actually comfortable operating. So the virtual assistant lives inside of a command line interface, it lives inside of chat like Slack, and Teams, and Mattermost, and so forth. It also lives within a low-code editor. So we're not forcing anyone to use uncomfortable language or operations if they're not comfortable with. It's almost like Siri, it travels in your mobile phone, it's on your laptop, it's with you everywhere. >> It makes total sense. And the reason why I like this, and I want to' get your reaction on this because we've done a lot of interviews with DevOps, we've met at every CubeCon since it started, and Kubernetes kind of highlights the value of the containers at the orchestration level. But what's really going on is the DevOps developers, and the CICD pipeline, with infrastructure's code, they're basically have a infrastructure configuration at their disposal all the time. And all the ops challenges have been around that, the repetitive mundane tasks that most people do. There's like six or seven main use cases in DevOps. So the guardrails just need to be set. So it sounds like you guys are going down the road of saying, hey here's the use cases you can bounce around these use cases all day long. And just keep doing your jobs cause they're bolting on infrastructure to every application. >> There's one more element to this that we haven't really touched on. It's not just workflows and use cases, but it's also knowledge, right? Tribal knowledge, like you asked me for an example. You can type or talk to the assistant and ask, "How much am I spending on AWS, on US East 1, on so and so customer environment last week?", and it will know how to give you that information. >> Can I ask, should I buy a reserve instances or not? Can I ask that question? 'Cause there's always good trade offs between buying the reserve instances. I mean that's kind of the thing that. >> This is where our ecosystem actually comes in handy because we're not necessarily going to' go down every single domain and try to be the experts in here. We can tap into the partnerships, API, we have full extensibility in API and the software development kit that goes into. >> It's interesting, opinionated and declarative are buzzwords in developer language. So you started to get into this editorial thing. So I can bring up an example. Hey cube, implement the best service mesh. What answer does it give you? 'Cause there's different choices. >> Well this is actually where the operator, there's clearly guard rails. Like you can go and say, I want to' spin up a machine, and it will give you all of the machines on AWS. Doesn't mean you have to get the X one, that's good for a SAP environment. You could go and have guardrails in place where only the ones that are relevant to your team, ones that have resources and budgetary, you know, guidelines can be. So, the operator still has all the control. >> It was kind of tongue in cheek around the editorialized, but actually the answer seems to be as you're saying, whatever the customer decided their service mesh is. So I think this is where it gets into as an assistant to architecting and operating, that seems to be the real value. >> Now code snippets is a different story because that goes on to the web, that goes onto stock overflow, and that's actually one of the things. So inside the CLI, you could actually go and ask for code snippets and we could actually go and populate that, it's a smart CLI. So that's actually one of the things that are an added value of that. >> I was saying to a friend and we were talking about open source and how when I grew up, there was no open source. If you're a developer now, I mean there's so much code, it's not so much coding anymore as it is connecting and integrating. >> Certainly. >> And writing glue layers, if you will. I mean there's still code, but it's not, you don't have to build it from scratch. There's so much code out there. This low-code notion of a smart system is interesting 'cause it's very matrix like. It can build its own code. >> Yes, but I'm also a little wary with low-code and no code. I think part of the problem is we're so constantly focused on categories and categorizing ourselves, and different categories take on a life of their own. So low-code no code is not necessarily, even though we have the low-code editor, we're not necessarily considering ourselves low-code. >> Serverless, no code, low-code. I was so thrown on a term the other day, architecture-less. As a joke, no we don't need architecture. >> There's a use case around that by the way, yeah, we do. Show me my AWS architecture and it will build the architect diagram for you. >> Again, serverless architect, this is all part of infrastructure's code. At the end of the day, the developer has infrastructure with code. Again, how they deploy it is the neuron. That's what we've been striving for. >> But infrastructure is code. You can destroy, you know, terraform, you can go and create one. It's not necessarily going to' operate it for you. That's kind of where this comes in on top of that. So it's really complimentary to infrastructure. >> So final question, before we get into the origination story, data and security are two hot areas we're seeing fill the IT gap, that has moved into the developer role. IT is essentially provisioned by developers now, but the OP side shifted to large scale SRE like environments, security and data are critical. What's your opinion on those two things? >> I agree. Do you want me to give you the normal data as gravity? >> So you agree that IT is now, is kind of moved into the developer realm, but the new IT is data ops and security ops basically. >> A hundred percent, and the lines are so blurred. Like who's what in today's world. I mean, I can tell you, I have customers who call themselves five different roles in the same day. So it's, you know, at the end of the day I call 'em operators 'cause I don't want to offend anybody because that's just the way it is. >> Architectural-less, we're going to' come back to that. Well, I know we're going to' see you at CubeCon. >> Yes. >> We should catch up there and talk more. I'm looking forward to seeing how you guys get the feedback from the marketplace. It should be interesting to hear, the curious question I have for you is, what was the origination story? Why did you guys come together, was it a shared problem? Was it a big market opportunity? Was it an itch you guys were scratching? Did you feel like you needed to come together and start this company? What was the real vision behind the origination? Take a take a minute to explain the story. >> No, absolutely. So I've been living in Palo Alto for the last couple years. Previous, also a founder. So, you know, from my perspective, I always saw myself getting back in the game. Spent a few years in AWS essentially managing partnerships for tier one DevOps partners, you know, all of the known players. Some in public, some of them not. And really the itch was there, right. I saw what everyone's doing. I started seeing consistency in the pains that I was hearing back, in terms of what hasn't been solved. So I already had an opinion where I wanted to go. And when I was visiting actually Israel with the family, I was introduced by a mutual friend to Shaked, Shaked Askayo, my co-founder and CTO. Amazing guy, unbelievable technologists, probably one the most, you know, impressive folks I've had a chance to work with. And he actually solved a very similar problem, you know, in his own way in a previous company, BlueVine, a FinTech company where he was head of SRE, having to, essentially, oversee 200 developers in a very small team. The ratio was incongruent to what the SRE guideline would tell. >> That's more than 10 x rate developer. >> Oh, absolutely. Sure enough. And just imagine it's four different time zones. He finishes day shift and you already had the US team coming, asking for a question. He said, this is kind of a, >> Got to' clone himself, basically. >> Well, yes. He essentially said to me, I had no day, I had no life, but I had Corona, I had COVID, which meant I could work from home. And I essentially programed myself in the form of a bot. Essentially, when people came to him, he said, "Don't talk to me, talk to the bot". Now that was a different generation. >> Just a trivial example, but the idea was to automate the same queries all the time. There's an answer for that, go here. And that's the benefit of it. >> Yes, so he was able to see how easy it was to solve, I mean, how effective it was solving 70% of the toil in his organization. Scaling his team, froze the headcount and the developer team kept on going. So that meant that he was doing some right. >> When you have a problem, and you need to solve it, the creativity comes out of the woodwork, you know, invention is the mother of necessity. So final question for you, what's next? Got the launch, what are you guys hope to do over the next six months to a year, hiring? Put a plug in for the company. What are you guys looking to do? Take a minute to share the future vision and get a plug in. >> A hundred percent. So, Kubiya, as you can imagine, announcing ourselves at CubeCon, so in a couple weeks. Opening the gates towards the public beta and NGA in the next couple months. Essentially working with dozens of customers, Aston Martin, and business earn in. We have quite a few, our website's full of quotes. You can go ahead. But effectively we're looking to go and to bring the next operator, generation of operators, who value their time, who value the, essentially, the value of tribal knowledge that travels between organizations that could be essentially shared. >> How many customers do you guys have in your pre-launch? >> It's above a dozen. Without saying, because we're actually looking to onboard 10 more next week. So that's just an understatement. It changes from day to day. >> What's the number one thing people are saying about you? >> You got that right. I know it's, I'm trying to be a little bit more, you know. >> It's okay, you can be cocky, startups are good. But I mean they're obviously, they're using the product and you're getting good feedback. Saving time, are they saying this is a dream product? Got it right, what are some of the things? >> I think anybody who doesn't feel the pain won't know, but the folks who are in the trenches, or feeling the pain, or experiencing this toil, who know what this means, they said, "You're doing this different, you're doing this right. You architected it right. You know exactly what the developer workflows," you know, where all the areas, you know, where all the skeletons are hidden within that. And you're attending to that. So we're happy about that. >> Everybody wants to clone themselves, again, the tribal knowledge. I think this is a great example of where we see the world going. Make things autonomous, operationally automated for the use cases you know are lock solid. Why wouldn't you just deploy? >> Exactly, and we have a very generous free tier. People can, you know, there's a plugin, you can sign up for free until the end of the year. We have a generous free tier. Yeah, free forever tier, as well. So we're looking for people to try us out and to give us feedback. >> I think the self-service, I think the point is, we've talked about it on the Cube at our events, everyone says the same thing. Every developer wants self-service, period. Full stop, done. >> What they don't say is they need somebody to help them babysit to make sure they're doing it right. >> The old dashboard, green, yellow, red. >> I know it's an analogy that's not related, but have you been to Whole Foods? Have you gone through their self-service line? That's the beauty of it, right? Having someone in a loop helping you out throughout the time. You don't get confused, if something's not working, someone's helping you out, that's what people want. They want a human in the loop, or a human like in the loop. We're giving that next best thing. >> It's really the ratio, it's scale. It's a scaling. It's force multiplier, for sure. Amit, thanks for coming on, congratulations. >> Thank you so much. >> See you at KubeCon. Thanks for coming in, sharing the story. >> KubiyaCon. >> CubeCon. Cube in Hebrew, Kubiya. Founder, co-founder and CEO here, sharing the story in the launch. Conversational AI for DevOps, the theory of DevOps, really kind of changing the game, bringing efficiency, solving a lot of the pain points of large scale infrastructure. This is theCUBE, CUBE conversation, I'm John Furrier, thanks for watching. (upbeat electronic music)

(upbeat music) >> Hey, guys and girls, welcome back to New York City. Lisa Martin and John Furrier are live with theCUBE at AWS Summit 22, here in The Big Apple. We're excited to be talking about security next. James Arlen joins us, the CISO at Aiven. James, thanks so much for joining us on theCUBE today. >> Absolutely, it's good to be here. >> Tell the audience a little bit about Aiven, what you guys do, what you deliver, and what some of those differentiators are. >> Oh, Aiven. Aiven is a fantastic organization. I'm actually really lucky to work there. It's a database as a service, managed databases, all open source. And we're capital S, serious about open source. So 10 different open source database products delivered as a platform, all managed services, and the game is really about being the most performant, secure, and compliant database as a service on the market, friction free for your developers. You don't need people worrying about how to run databases. You just want to be able to say, here, take care of my data for me. And that's what we do. And that's actually the differentiator. We just take care of it for you. >> Take care of it for you, I like that. >> So they download the open source. They could do it on their own. So all the different projects are out there. >> Yeah, absolutely. >> What do you guys bringing to the table? You said the managed service, can you explain that. >> Yeah, the managed service aspect of it is, really, you could install the software yourself. You can use Postgres or Apache Kafka or any one of the products that we support. Absolutely you can do it yourself. But is that really what you do for a living, or do you develop software, or do you sell a product? So we take and do the hard work of running the systems, running the equipment. We take care of backups, high availability, all the security and compliance things around access and certifications, all of those things that are logging, all of that stuff that's actually difficult to do, well and consistently, that's all we do. >> Talk about the momentum, I see you guys were founded in what? 2016? >> Yes. >> Just in May of '22, raised $210 million in series D funding. >> Yes. >> Talk about the momentum and also from your perspective, all of the massive changes in security. >> It's very interesting to work for a company where you're building more than 100% growth year over year. It's a powers of two thing. Going from one to two, not so scary, two to four, not so scary. 512 to 1024, it's getting scary. (Lisa chuckles) 1024 to 2048, oh crap! I've been with Aiven for just almost two years now, and we are less than 70 when I started, and we're near 500 now. So, explosive growth is very interesting, but it's also that, you're growing within a reasonable burn rate boundary as well. And what that does from a security perspective, is it leaves you in the position that I had. I walked in and I was the first actual CISO. I had a team of four, I now have a team of 40. Because it turns out that like a lot of things in life, as you start unpacking problems, they're kind of fractal. You unpack the problem, you're like oh, well I did deal with that problem, but now I got another problem that I got to deal with. And so there's, it's not turtles all the way down. >> There's a lot of things going on and other authors, survive change. >> And there's fundamental problems that are still not fixed. And yet we treat them like they're fixed. And so we're doing a lot of hard work to make it so that we don't have to do hard work ongoing. >> And that's the value of the managed service. >> Yes. >> Okay, so talk about competition. Obviously, we had ETR on which is Enterprise Research Firm that we trust, we like. And we were looking at the data with the headwinds in the market, looking at the different players like got Amazon has Redshift, Snowflake, and you got Azure Sequence. I think it's called one of those products. The money that's being shifted from on premise data where the old school data warehouse like terra data and whatnot, is going first to Snowflake, then to Azure, then to AWS. Yes, so that points to snowflake being kind of like the bell of the ball if you will, in terms of from a data cloud. >> Absolutely. >> How do you compete with them? What's the pitch 'Cause that seemed to be a knee-jerk reaction from the industry. 'Cause snowflake is hot. They have a good value product. They have a smart team, Databrick is out there too. >> Yeah I mean... >> how do you guys compete against all that. >> So this is that point where you're balancing the value of a specific technology, or a specific technology vendor. And am I going to be stuck with them? So I'm tying my future to their future. With open source, I'm tying my future to the common good right. The internet runs on open source. It doesn't run on anything closed. And so I'm not hitching my wagon to something that I don't control. I'm hitching it to something where, any one of our customers could decide. I'm not getting the value I need from Aiven anymore. I need to go. And we provide you with the tools necessary, to move from our open source managed service to your own. Whether you go on-prem or you run it yourself, on a cloud service provider, move your data to you because it's your data. It's not ours. How can I hold your data? It's like weird extortion ransoming thing. >> Actually speaking, I mean enterprise, it's a big land grab 'cause with cloud you're horizontally scalable. It's a beautiful thing, open source is booming. It's going in Aiven, every day it's just escalating higher and higher. >> Absolutely. >> It is the software business. So open is open. Integration and scale seems to be the competitive advantage. >> Yeah. >> Right. So, how do you guys compete with that? Because now you got open source. How do you offer the same benefits without the lock in, or what's the switching costs? How do you guys maintain that position of not saying the same thing in Snowflake? >> Because all of the biggest data users and consumers tend to give away their data products. LinkedIn gave away their data product. Uber gave away their data product, Facebook gave away their data product. And we now use those as community solutions. So, if the product works for something the scale of LinkedIn, or something the scale of Uber. It will probably work for you too. And scale is just... >> Well Facebook and LinkedIn, they gave away the product to own the data to use against you. >> But it's the product that counts because you need to be able to manipulate data the way they manipulate data, but with yours. >> So low latency needs to work. So horizontally, scalable, fees, machine learning. That's what we're seeing. How do you make that available? Customers want on architecture? What do you recommend? Control plane, data plane, how do you think about that? >> It's interesting. There's architectural reasons to think about it in terms like that. And there's other good architectural reasons to not think about it. There's sort of this dividing line in the cloud, where your cloud service provider, takes over and provides you with the opportunity to say, I don't know. And I don't care >> As long as it's secure >> As long as it's secure absolutely. But there's sort of that water line idea, where if it's below the water line, let somebody else deal. >> What is in the table stakes? 'Cause I like that approach. I think that's a good value proposition. Store it, what boxes have to be checked? Compliance, secure, what are some of the boxes? >> You need to make sure that you've taken care of all of the same basics if you are still running it. Remember you can't absolve yourself of your duty to your customer. You're still on the hook. So, you have to have backups. You have to have access control. You have to understand who's administering it, and how and what they're doing. Good logging, good comprehension there. You have to have anomaly detection, secure operations. You have to have all those compliance check boxes. Especially if you're dealing with regulated data type like PCI data or HIPAA health data or you know what there's other countries besides the United States, there's other kinds of of compliance obligations there. So you have to make sure that you've got all that taken into account. And remember that, like I said, you can't absolve yourself with those things. You can share responsibilities. But you can't walk away from that responsibility. So you still have to make sure that you validate that your vendor knows what they're talking about. >> I wanted to ask you about the cybersecurity skills gap. So I'm kind of giving a little segue here, because you mentioned you've been with Aiven for about two years. >> Almost. >> Almost two years. You've started with a team of four. You've grown at 10X in less than two years. How have you accomplished that, considering we're seeing one of the biggest skills shortages in cyber in history. >> It's amazing, you see this show up in a lot of job Ads, where they ask for 10 years of experience in something that's existed for three years. (John Furrier laughs) And it's like okay, well if I just be logical about this I can hire somebody at less than the skill level that I need today, and bring them up to that skill level. Or I can spend the same amount of time, hoping that I'll find the magical person that has that set of skills that I need. So I can solve the problem of the skills gap by up-skilling the people that I hire. Which is strangely contrary to how this thing works. >> The other thing too, is the market's evolving so fast that, that carry up and pulling someone along, or building and growing your own so to speak is workable. >> It also really helps us with a bunch of sustainability goals. It really helps with anything that has to do with diversity and inclusion, because I can bring forward people who are never given a chance. And say, you know what? You don't have that magical ticket in life, but damn you know what you're talking about? >> It's a classic pedigree. I went to this school, I studied this degree. There's no degree if have to stop a hacker using state of the art malware. (John Furrier laughs) >> Exactly. What I do today as a job, didn't exist when I was in post-secondary at all. >> So when you hire, what do you look for? I mean obviously problem solving. What's your kind of algorithm for hiring? >> Oh, that's a really interesting question. The quickest sort of summary of it is, I'm looking for not a jerk. >> Not a jerk. >> Yeah. >> Okay. >> Because it turns out that the quality that I can't fix in a candidate, is I can't fix whether or not they're a jerk, but I can up-skill them, I can educate them. I can teach them of a part of the world that they've not had any interaction with. But if they're not going to work with the team, if they're going to be, look at me, look at me. If they're going to not have that moment of, I have this great job, and I get to work today. And that's awesome. (Lisa Martin laughs) That's what I'm trying to hire for. >> The essence of this teamwork is fundamental. >> Collaboration. >> Cooperation. >> Curiosity. >> That's the thing yeah, absolutely. >> And everybody? >> Those things, oh absolutely. Those things are really, really hard to interview for. And they're impossible to fix after the fact. So that's where you really want to put the effort. 'Cause I can teach you how to use a computer. I mean it's hard, but it's not that hard. >> Yeah, yeah, yeah. >> Well I love the current state of data management. Good overview, you guys are in the good position. We love open source. Been covering it for, since theCUBE started. It continues to redefine more and more the industry. It is the software industry. Now there's no debate about that. If people want to have that debate, that's kind of waste of time, but there are other ways that are happening. So I have to ask you. As things are going forward with innovation. Okay, if opensource is going to be the software industry. Where's the value? >> That's a fun question wow? >> Is it going to be in the community? Is it the integration? Is it the scale? If you're open and you have low switching costs... >> Yeah so, when you look at Aiven's commitment to open source, a huge part of that is our open source project office, where we contribute back to those core products, whether it's parts of the Apache Foundation, or Postgres, or whatever. We contribute to those, because we have staff who work on those products. They don't work on our stuff. They work on those. And it's like the opposite of a zero sum game. It's more like Nash equilibrium. If you ever watch that movie, "A beautiful mind." That great idea of, you don't have to have winners and losers. You can have everybody loses a little bit but everybody wins a little bit. >> Yeah and that's the open the ethos. >> And that's where it gets tied up. >> Another follow up on that. The other thing I want to get your reaction on is that, now in this modern era of open source, almost all corporations are part of projects. I mean if you're an entrepreneur and you want to get funding it's pretty simple. You start open source project. How many stars you get on GitHub guarantees it's a series C round, pretty much. So open source now has got this new thing going on, where it's not just open source folks who believe in it It's an operating model. What's the dynamic of corporations being part of the system. It used to be, oh what's the balance between corporate and influence, now it's standard. What's your reaction? >> They can do good and they can do harm. And it really comes down to why are you in it? So if you look at the example of open search, which is one of the data products that we operate in the Aiven system. That's a collaboration between Aiven. Hey we're an awesome company, but we're nowhere near the size of AWS. And AWS where we're working together on it. And I just had this conversation with one of the attendees here, where he said, "Well AWS is going to eat your story there. "You're contributing all of this "to the open search platform. "And then AWS is going to go and sell it "and they're going to make more money." And I'm like yep, they are. And I've got staff who work for the organization, who are more fulfilled because they got to deliver something that's used by millions of people. And you think about your jobs. That moment of, (sighs) I did a cool thing today. That's got a lot of value in it. >> And part of something. >> Exactly. >> As a group. >> 100%. >> Exactly. >> And we end up with a product that's used by millions. Some of it we'll capture, because we do a better job running than the AWS does, but everybody ends up winning out of the backend. Again, everybody lost a little, but everybody also won. And that's better than that whole, you have to lose so that I can win. At zero something, that doesn't work. >> I think the silo conversations are coming, what's the balance between siloing something and why that happens. And then what's going to be freely accessible for data. Because the real time information is based upon what you can access. "Hey Siri, what's the weather. "We had a guest on earlier." It says, oh that's a data query. Well, if the weather is, the data weathers stored in a database that's out here and it can't get to the response on the app. Yeah, that's not good, but the data is available. It just didn't get delivered. >> Yeah >> Exactly. >> This is an example of what people are realizing now the consequences of this data, collateral damage or economy value. >> Yeah, and it's understanding how data fits in your environment. And I don't want to get on the accountants too hard, but the accounting organizations, AICPA and ISAE and others, they haven't really done a good job of helping you understand data as an asset, or data as a liability. I hold a lot of customer data. That's a liability to me. It's going to blow up in my face. We don't talk about the income that we get from data, Google. We don't talk about the expense of regenerating that data. We talk about, well what happens if you lose it? I don't know. And we're circling the drain around fiduciary responsibility, and we know how to do this. If you own a manufacturing plant, or if you own a fleet of vehicles you understand the fiduciary duty of managing your asset. But because we can't touch it, we don't do a good job of it. >> How far do you think are people getting into the point where they actually see that asset? Because I think it's out of sight out of mind. Now there's consequences, there's now it's public companies might have to do filings. It's not like sustainability and data. Like, wait a minute, I got to deal with these things. >> It's interesting, we got this great benefit of the move to cloud computing, and the move to utility style computing. But we took away that. I got to walk around and pet my computers. Like oh! This is my good database. I'm very proud of you. Like we're missing that piece now. And when you think about the size of data centers, we become detached from that, you don't really think about, Aiven operates tens of thousands of machines. It would take entire buildings to hold them all. You don't think about it. So how do you recreate that visceral connection to your data? Well, you need to start actually thinking about it. And you need to do some of that tokenization. When was the last time you printed something out, like you get a report and happens to me all the time with security reports. Look at a security report and it's like 150 page PDF. Scroll, scroll, scroll, scroll. Print it out, stump it on the table in front of you. Oh, there's gravitas here. There's something here. Start thinking about those records, count them up, and then try to compare that to something in the real world. My wife is a school teacher, kindergarten to grade three, and tokenizing math is how they teach math to little kids. You want to count something? Here's 10 things, count them. Well, you've got 60,000 customer records, or you have 2 billion data points in your IOT database, tokenize that, what does 2 billion look like? What does $1 million look like in the form of $100 dollars bills on a pallet? >> Wow. >> Right. Tokenize that data, create that visceral connection with it, and then talk about it. >> So when you say tokenized, you mean like token as in decentralization token? >> No, I mean create like a totem or an icon of it. >> Okay, got it. >> A thing you can hold holy. If you're a token company. >> Not token as in Token economics and Crypto. >> If you're a mortgage company, take that customer record for one of your customers, print it out and hold the file. Like in a Manila folder, like it's 1963. Hold that file, and then say yes. And you're explaining to somebody and say yes, and we have 3 million of these. If we printed them all out, it would take up a room this size. >> It shows the scale. >> Right. >> Right. >> Exactly, create that connection back to the human level of interaction with data. How do you interact with a terabyte of data, but you do. >> Right. >> But once she hits upgrade from Google drive. (team laughs) >> What's a terabyte right? We don't hold that anymore. >> Right, right. >> Great conversation. >> Recreate that connection. Talk about data that way. >> The visceral connection with data. >> Follow up after this event. We'd love to dig more and love the approach. Love open source, love what you're doing there. That's a very unique approach. And it's also an alternative to some of the other vast growing plus your valuations are very high too. So you're not like a... You're not too far away from these big valuations. So congratulations. >> Absolutely. >> Yeah excellent, I'm sure there's lots of work to do, lots of strategic work to do with that round of funding. But also lots of opportunity, that it's going to open up, and we know you don't hire jerks. >> I don't >> You have a whole team of non jerks. That's pretty awesome. Especially 40 of 'em. That's impressive James.| >> It is. >> Congratulations to you on what you've accomplished in the course of the team. And thank you for sharing your insights with John and me today, we appreciate it. >> Awesome. >> Thanks very much, it's been great. >> Awesome, for John furrier, I'm Lisa Martin and you're watching theCube, live in New York city at AWS Summit NYC 22, John and I will be right back with our next segment, stick around. (upbeat music)

(upbeat music) >> Welcome, everyone, to theCUBE's presentation of the AWS Startup Showcase. And this episode two of season two is called "Data as Code," the ongoing series covering exciting new startups in the AWS ecosystem. I'm John Furrier, your host of theCUBE. Today, we're excited to be joined by Jon Dahl, who is the co-founder and CEO of MUX, a hot new startup building cloud video for developers, video with data. John, great to see you. We did an interview on theCube Conversation. Went into big detail of the awesomeness of your company and the trend that you're on. Welcome back. >> Thank you, glad to be here. >> So, video is everywhere, and video for pivot to video, you hear all these kind of terms in the industry, but now more than ever, video is everywhere and people are building with it, and it's becoming part of the developer experience in applications. So people have to stand up video into their code fast, and data is code, video is data. So you guys are specializing this. Take us through that dynamic. >> Yeah, so video clearly is a growing part of how people are building applications. We see a lot of trends of categories that did not involve video in the past making a major move towards video. I think what Peloton did five years ago to the world of fitness, that was not really a big category. Now video fitness is a huge thing. Video in education, video in business settings, video in a lot of places. I think Marc Andreessen famously said, "Software is eating the world" as a pretty, pretty good indicator of what the internet is actually doing to the economy. I think there's a lot of ways in which video right now is eating software. So categories that we're not video first are becoming video first. And that's what we help with. >> It's not obvious to like most software developers when they think about video, video industries, it's industry shows around video, NAB, others. People know, the video folks know what's going on in video, but when you start to bring it mainstream, it becomes an expectation in the apps. And it's not that easy, it's almost a provision video is hard for a developer 'cause you got to know the full, I guess, stack of video. That's like low level and then kind of just basic high level, just play something. So, in between, this is a media stack kind of dynamic. Can you talk about how hard it is to build video for developers? How is it going to become easier? >> Yeah, I mean, I've lived this story for too long, maybe 13 years now, when I first build my first video stack. And, you know, I'll sometimes say, I think it's kind of a miracle every time a video plays on the internet because the internet is not a medium designed for video. It's been hijacked by video, video is 70% of internet traffic today in an unreliable, sort of untrusted network space, which is totally different than how television used to work or cable or things like that. So yeah, so video is hard because there's so many problems from top to bottom that need to be solved to make video work. So you have to worry about video compression encoding, which is a complicated topic in itself. You have to worry about delivering video around the world at scale, delivering it at low cost, at low latency, with good performance, you have to worry about devices and how every device, Android, iOS, web, TVs, every device handles video differently and so there's a lot of work there. And at the end of the day, these are kind of unofficial standards that everyone's using. So one of the miracles is like, if you want to watch a video, somehow you have to get like Apple and Google to agree on things, which is not always easy. And so there's just so many layers of complexity that are behind it. I think one way to think about it is, if you want to put an image online, you just put an image online. And if you want to put video online, you build complex software, and that's the exact problem that MUX was started to help solve. >> It's interesting you guys have almost creating a whole new category around video infrastructure. And as you look at, you mentioned stack, video stack. I'm looking at a market where the notion of a media stack is developing, and you're seeing these verticals having similar dynamics with cloud. And if you go back to the early days of cloud computing, what was the developer experience or entrepreneurial experience, you had to actually do a lot of stuff before you even do anything, provision a server. And this has all kind of been covered in great detail in the glory of Agile and whatnot. It was expensive, and you had that actually engineer before you could even stand up any code. Now you got video that same thing's happening. So the developers have two choices, go do a bunch of stuff complex, building their own infrastructure, which is like building a data center, or lean in on MUX and say, "Hey, thank you for doing all that years of experience building out the stacks to take that hard part away," but using APIs that they have. This is a developer focused problem that you guys are solving. >> Yeah, that's right. my last company was a company called Zencoder, that was an API to video encoding. So it was kind of an API to a small part of what MUX does today, just one of those problems. And I think the thing that we got right at Zencoder, that we're doing again here at MUX, was building four developers first. So our number one persona is a software developer. Not necessarily a video expert, just we think any developer should be able to build with video. It shouldn't be like, yeah, got to go be a specialist to use this technology, because it should become just of the internet. Video should just be something that any developer can work with. So yeah, so we build for developers first, which means we spend a lot of time thinking about API design, we spend a lot of time thinking about documentation, transparent pricing, the right features, great support and all those kind of things that tend to be characteristics of good developer companies. >> Tell me about the pipe lining of the products. I'm a developer, I work for a company, my boss is putting pressure on me. We need video, we have all this library, it's all stacking up. We hired some people, they left. Where's the video, we've stored it somewhere. I mean, it's a nightmare, right? So I'm like, okay, I'm cloud native, I got an API. I need to get my product to market fast, 'cause that is what Agile developers want. So how do you describe that acceleration for time to market? You mentioned you guys are API first, video first. How do these customers get their product into the market as fast as possible? >> Yeah, well, I mean the first thing we do is we put what we think is probably on average, three to four months of hard engineering work behind a single API call. So if you want to build a video platform, we tell our customers like, "Hey, you can do that." You probably need a team, you probably need video experts on your team so hire them or train them. And then it takes several months just to kind of to get video flowing. One API call at MUX gives you on-demand video or live video that works at scale, works around the world with good performance, good reliability, a rich feature set. So maybe just a couple specific examples, we worked with Robin Hood a few years ago to bring video into their newsfeed, which was hugely successful for them. And they went from talking to us for the first time to a big launch in, I think it was three months, but the actual code time there was like really short. I want to say they had like a proof of concept up and running in a couple days, and then the full launch in three months. Another customer of ours, Bandcamp, I think switched from a legacy provider to MUX in two weeks in band. So one of the big advantages of going a little bit higher in the abstraction layer than just building it yourself is that time to market. >> Talk about this notion of video pipeline 'cause I know I've heard people I talk about, "Hey, I just want to get my product out there. I don't want to get stuck in the weeds on video pipeline." What does that mean for folks that aren't understanding the nuances of video? >> Yeah, I mean, it's all the steps that it takes to publish video. So from ingesting the video, if it's live video from making sure that you have secure, reliable ingest of that live feed potentially around the world to the transcoding, which is we talked a little bit about, but it is a, you know, on its own is a massively complicated problem. And doing that, well, doing that well is hard. Part of the reason it's hard is you really have to know where you're publishing too. And you might want to transcode video differently for different devices, for different types of content. You know, the pipeline typically would also include all of the workflow items you want to do with the video. You want to thumbnail a video, you want clip, create clips of the video, maybe you want to restream the video to Facebook or Twitter or a social platform. You want to archive the video, you want it to be available for downloads after an event. If it's just a, if it's a VOD upload, if it's not live in the first place. You have all those things and you might want to do simulated live with the video. You might want to actually record something and then play it back as a live stream. So, the pipeline Ty typically refers to everything from the ingest of the video to the time that the bits are delivered to a device. >> You know, I hear a lot of people talking about video these days, whether it's events, training, just want peer to peer experience, video is powerful, but customers want to own their own platform, right? They want to have the infrastructure as a service. They kind of want platform as a service, this is cloud talk now, but they want to have their own capability to build it out. This allows them to get what they want. And so you see this, like, is it SaaS? Is it platform? People want customization? So kind of the general purpose video solution does it really exist or doesn't? I mean, 'cause this is the question. Can I just buy software and work or is it going to be customized always? How do you see that? Because this becomes a huge discussion point. Is it a SaaS product or someone's going to make a SaaS product? >> Yeah, so I think one of the most important elements of designing any software, but especially when you get into infrastructure is choosing an abstraction level. So if you think of computing, you can go all the way down to building a data center, you can go all the way down to getting a colo and racking a server like maybe some of us used to do, who are older than others. And that's one way to run a server. On the other extreme, you have just think of the early days of cloud competing, you had app engine, which was a really fantastic, really incredible product. It was one push deploy of, I think Python code, if I remember correctly, and everything just worked. But right in the middle of those, you had EC2, which was, EC2 is basically an API to a server. And it turns out that that abstraction level, not Colo, not the full app engine kind of platform, but the API to virtual server was the right abstraction level for maybe the last 15 years. Maybe now some of the higher level application platforms are doing really well, maybe the needs will shift. But I think that's a little bit of how we think about video. What developers want is an API to video. They don't want an API to the building blocks of video, an API to transcoding, to video storage, to edge caching. They want an API to video. On the other extreme, they don't want a big application that's a drop in white label video in a box like a Shopify kind of thing. Shopify is great, but developers don't want to build on top of Shopify. In the payments world developers want Stripe. And that abstraction level of the API to the actual thing you're getting tends to be the abstraction level that developers want to build on. And the reason for that is, it's the most productive layer to build on. You get maximum flexibility and also maximum velocity when you have that API directly to a function like video. So, we like to tell our customers like you, you own your video when you build on top of MUX, you have full control over everything, how it's stored, when it's stored, where it goes, how it's published, we handle all of the hard technology and we give our customers all of the flexibility in terms of designing their products. >> I want to get back some use case, but you brought that up I might as well just jump to my next point. I'd like you to come back and circle back on some references 'cause I know you have some. You said building on infrastructure that you own, this is a fundamental cloud concept. You mentioned API to a server for the nerds out there that know that that's cool, but the people who aren't super nerdy, that means you're basically got an interface into a server behind the scenes. You're doing the same for video. So, that is a big thing around building services. So what wide range of services can we expect beyond MUX? If I'm going to have an API to video, what could I do possibly? >> What sort of experience could you build? >> Yes, I got a team of developers saying I'm all in API to video, I don't want to do all that transit got straight there, I want to build experiences, video experiences on my app. >> Yeah, I mean, I think, one way to think about it is that, what's the range of key use cases that people do with video? We tend to think about six at MUX, one is kind of the places where the content is, the prop. So one of the things that use video is you can create great video. Think of online courses or fitness or entertainment or news or things like that. That's kind of the first thing everyone thinks of, when you think video, you think Netflix, and that's great. But we see a lot of really interesting uses of video in the world of social media. So customers of ours like Visco, which is an incredible photo sharing application, really for photographers who really care about the craft. And they were able to bring video in and bring that same kind of Visco experience to video using MUX. We think about B2B tools, videos. When you think about it, all video is, is a high bandwidth way of communicating. And so customers are as like HubSpot use video for the marketing platform, for business collaboration, you'll see a lot of growth of video in terms of helping businesses engage their customers or engage with their employees. We see live events obviously have been a massive category over the last few years. You know, we were all forced into a world where we had to do live events two years ago, but I think now we're reemerging into a world where the online part of a conference will be just as important as the in-person component of a conference. So that's another big use case we see. >> Well, full disclosure, if you're watching this live right now, it's being powered by MUX. So shout out, we use MUX on theCUBE platform that you're experiencing in this. Actually in real time, 'cause this is one application, there's many more. So video as code, is data as code is the theme, that's going to bring up the data ops. Video also is code because (laughs) it's just like you said, it's just communicating, but it gets converted to data. So data ops, video ops could be its own new category. What's your reaction to that? >> Yeah, I mean, I think, I have a couple thoughts on that. The first thought is, video is a way that, because the way that companies interact with customers or users, it's really important to have good monitoring and analytics of your video. And so the first product we ever built was actually a product called MUX video, sorry, MUX data, which is the best way to monitor a video platform at scale. So we work with a lot of the big broadcasters, we work with like CBS and Fox Sports and Discovery. We work with big tech companies like Reddit and Vimeo to help them monitor their video. And you just get a huge amount of insight when you look at robust analytics about video delivery that you can use to optimize performance, to make sure that streaming works well globally, especially in hard to reach places or on every device. That's we actually build a MUX data platform first because when we started MUX, we spent time with some of our friends at companies like YouTube and Netflix, and got to know how they use data to power their video platforms. And they do really sophisticated things with data to ensure that their streams well, and we wanted to build the product that would help everyone else do that. So, that's one use. I think the other obvious use is just really understanding what people are doing with their video, who's watching what, what's engaging, those kind of things. >> Yeah, data is definitely there. You guys mentioned some great brands that are working with you guys, and they're doing it because of the developer experience. And I'd like you to explain, if you don't mind, in your words, why is the MUX developer experience so good? What are some of the results you're seeing from your customers? What are they saying to you? Obviously when you win, you get good feedback. What are some of the things that they're saying and what specific develop experiences do they like the best? >> Yeah, I mean, I think that the most gratifying thing about being a startup founder is when your customers like what you're doing. And so we get a lot of this, but it's always, we always pay attention to what customers say. But yeah, people, the number one thing developers say when they think about MUX is that the developer experience is great. I think when they say that, what they mean is two things, first is it's easy to work with, which helps them move faster, software velocity is so important. Every company in the world is investing and wants to move quickly and to build quickly. And so if you can help a team speed up, that's massively valuable. The second thing I think when people like our developer experience is, you know, in a lot of ways that think that we get out of the way and we let them do what they want to do. So well, designed APIs are a key part of that, coming back to abstraction, making sure that you're not forcing customers into decisions that they actually want to make themselves. Like, if our video player only had one design, that that would not be, that would not work for most developers, 'cause developers want to bring their own design and style and workflow and feel to their video. And so, yeah, so I think the way we do that is just think comprehensively about how APIs are designed, think about the workflows that users are trying to accomplish with video, and make sure that we have the right APIs, make sure they're the right information, we have the right webhooks, we have the right SDKs, all of those things in place so that they can build what they want. >> We were just having a conversation on theCUBE, Dave Vellante and I, and our team, and I'd love to get you a reaction to this. And it's more and more, a riff real quick. We're seeing a trend where video as code, data as code, media stack, where you're starting to see the emergence of the media developer, where the application of media looks a lot like kind of software developer, where the app, media as an app. It could be a chat, it could be a peer to peer video, it could be part of an event platform, but with all the recent advances, in UX designers, coders, the front end looks like an emergence of these creators that are essentially media developers for all intent and purpose, they're coding media. What's your reaction to that? How do you see that evolving? >> I think the. >> Or do you agree with it? >> It's okay. >> Yeah, yeah. >> Well, I think a couple things. I think one thing, I think this goes along through saying, but maybe it's disagreement, is that we don't think you should have to be an expert at video or at media to create and produce or create and publish good video, good audio, good images, those kind of things. And so, you know, I think if you look at software overall, I think of 10 years ago, the kind of DevOps movement, where there was kind of a movement away from specialization in software where the same software developer could build and deploy the same software developer maybe could do front end and back end. And we want to bring that to video as well. So you don't have to be a specialist to do it. On the other hand, I do think that investments and tooling, all the way from video creation, which is not our world, but there's a lot of amazing companies out there that are making it easier to produce video, to shoot video, to edit, a lot of interesting innovations there all the way to what we do, which is helping people stream and publish video and video experiences. You know, I think another way about it is, that tool set and companies doing that let anyone be a media developer, which I think is important. >> It's like DevOps turning into low-code, no-code, eventually it's just composability almost like just, you know, "Hey Siri, give me some video." That kind of thing. Final question for you why I got you here, at the end of the day, the decision between a lot of people's build versus buy, "I got to get a developer. Why not just roll my own?" You mentioned data center, "I want to build a data center." So why MUX versus do it yourself? >> Yeah, I mean, part of the reason we started this company is we have a pretty, pretty strong opinion on this. When you think about it, when we started MUX five years ago, six years ago, if you were a developer and you wanted to accept credit cards, if you wanted to bring payment processing into your application, you didn't go build a payment gateway. You just probably used Stripe. And if you wanted to send text messages, you didn't build your own SMS gateway, you probably used Twilio. But if you were a developer and you wanted to stream video, you built your own video gateway, you built your own video application, which was really complex. Like we talked about, you know, probably three, four months of work to get something basic up and running, probably not live video that's probably only on demand video at that point. And you get no benefit by doing it yourself. You're no better than anyone else because you rolled your own video stack. What you get is risk that you might not do a good job, maybe you do worse than your competitors, and you also get distraction where you've just taken, you take 10 engineers and 10 sprints and you apply it to a problem that doesn't actually really give you differentiated value to your users. So we started MUX so that people would not have to do that. It's fine if you want to build your own video platform, once you get to a certain scale, if you can afford a dozen engineers for a VOD platform and you have some really massively differentiated use case, you know, maybe, live is, I don't know, I don't have the rule of thumb, live videos maybe five times harder than on demand video to work with. But you know, in general, like there's such a shortage of software engineers today and software engineers have, frankly, are in such high demand. Like you see what happens in the marketplace and the hiring markets, how competitive it is. You need to use your software team where they're maximally effective, and where they're maximally effective is building differentiation into your products for your customers. And video is just not that, like very few companies actually differentiate on their video technology. So we want to be that team for everyone else. We're 200 people building the absolute best video infrastructure as APIs for developers and making that available to everyone else. >> John, great to have you on with the showcase, love the company, love what you guys do. Video as code, data as code, great stuff. Final plug for the company, for the developers out there and prospects watching for MUX, why should they go to MUX? What are you guys up to? What's the big benefit? >> I mean, first, just check us out. Try try our APIs, read our docs, talk to our support team. We put a lot of work into making our platform the best, you know, as you dig deeper, I think you'd be looking at the performance around, the global performance of what we do, looking at our analytics stack and the insight you get into video streaming. We have an emerging open source video player that's really exciting, and I think is going to be the direction that open source players go for the next decade. And then, you know, we're a quickly growing team. We're 60 people at the beginning of last year. You know, we're one 50 at the beginning of this year, and we're going to a add, we're going to grow really quickly again this year. And this whole team is dedicated to building the best video structure for developers. >> Great job, Jon. Thank you so much for spending the time sharing the story of MUX here on the show, Amazon Startup Showcase season two, episode two, thanks so much. >> Thank you, John. >> Okay, I'm John Furrier, your host of theCUBE. This is season two, episode two, the ongoing series cover the most exciting startups from the AWS Cloud Ecosystem. Talking data analytics here, video cloud, video as a service, video infrastructure, video APIs, hottest thing going on right now, and you're watching it live here on theCUBE. Thanks for watching. (upbeat music)

>>from around the globe. It's the >>cube >>With digital coverage of IBM think 2021 brought to you by IBM. Welcome back everyone to the cube coverage of IBM Think 2021. I'm john for a host of the cube got a great guest here scott heaven or vice president of marketing at IBM for data and AI cube. Alumni has been around the wave around data, had many conversations over the years scott. Welcome back to the Cuban, I wish we were in person but we're remote for the virtual conference for think 2021. Thanks for coming on >>john great to be here. And yeah, I guess we have adapted to the world of being on the screen. >>Well, great, great to have you in. One of the things about virtualization of media is that we get more content this year. There's so many more signature stories around um, IBM think and one of the things that's really fun for us is the data conversations in a I as as the transformation and innovation equations are coming together at scale. You're seeing an accelerated piece here. My first question for you is this digital shift that's going on? The preferences are shifting to virtual now digital in the wake of Covid, what do companies need to adapt from your perspective as you see this playing out? What's your perspective? >>It's interesting to use that term. So we've been calling it the great digital shift. And uh yeah, there's an there was an interesting survey, a pretty big survey of global C suite that Mackenzie did. And they pointed out that 79% of those leaders felt that Covid highlighted the immaturity of their digital capability. And while they thought they were on the right path and they were building strong digital capabilities, the whole world of the pandemic remote work, how you engage with customers call centers going, you know, off the hooks in terms of people calling, it just goes on and on and on. And And they also pointed out that 90, I think it was 96 of them are going to speed their digital reinvention. And you mentioned data, if you think about it, it's data that a few fuels digital capabilities. Right? What good is digital if it's not data? Right? It's all data. So it's the fuel that makes it all work. And when you think about the ability to leverage all your dad, you got to democratize it, it's siloed all over the place, it's growing at six times rate over the next three years. It's really all over the place, every touch point across the digital ecosystem. Um and the only way to deal with the data in to unlock its value, particularly in predictive ways is to Ai Right? And so what we're seeing is a huge amount of investment in multi cloud, really bringing together this notion of hybrid and then applying AI as the intelligence to create a more predictable and resilient business right through a digital model, right? Yeah, it's really the investment is really going through the roof. You >>know, I think AI has been, it's been demystified over the years, been a lot of people saw the machine learning and now you got NLP and data control planes that are making it more addressable. But the real thing that comes up here, I think this year is this role between business and consumer and AI has that kind of dynamic. And I want to ask you because I was just having a conversation with one of your partner, IBM partner Samsung, KC Joy runs E V P E V P for the B to B B to G Group at Samsung. It's a huge I. O. T. Thing. And AI is a big part of that consumer and we talked about the consumer electronics business issues, how is A I different for business versus the consumer is obviously an industrial iot edge and you've got automation piece. What's the difference? I mean, someone asked you that between business and consumer AI. >>Yeah, actually, I think that's one of the areas that we really differentiate ourselves and we're putting the bulk of investments, this notion of AI for business, Right? And you know, a lot of people think of A I sometimes they think of Siri and Alexa and things that go on in your car and all that. Obviously that's a big part of applying machine learning and all that, but when we talk about AI for business, we're thinking about four core attributes. Uh One is that it needs to understand the unique language of your business and industry, right? And that's not just natural language but it's the ability to debate, it's the ability to read documents, interpret documents. Um It's the ability to really understand the context because you and I can ask the same question five or six different ways and it needs to understand the business to be able to interpret that and help answer the question unlike like Siri or Alexa where you really got to have the right semantics and you know, it won't understand the nuances as well, so understand the language of businesses. 12 is that we believe ai is the engine for automation. Um So Ai is really about automating workflows and experiences because anything that you want to automate and make more productive you have to have some predictive capabilities to it to understand what to do and you have to learn about you know, what's trying to be accomplished which is always unique and personalized. So that's the second one is about automation. The third is it is about driving trust and outcomes right in the business outcomes, which means, you know, if you were to, if some a model say scott go jump off a bridge, you know, I probably wouldn't want to do that unless it really explained to me, prove instantly that I should do that and they will but explain ability and trust is such a critical part of aI for business and then finally it needs to run everywhere. It has to integrate everything. And we believe unlike a lot of the competitors where you have to bring the data to a I we're saying leave the data where it lives and bring ai to the data so it runs anywhere from the data center to the edge. The same model, the same capabilities in a distributed environment. Um So those four kind of attributes come together to what we call A I for business. Um And that's what's gonna allow call centers and supply chains and business planning and risk and regulatory, you know, mitigation. I mean those kind of things to really come to life in a predictive way without those attributes, it's much harder to do a lot more coding and you're not gonna as much accuracy. >>Yeah, I mean what you're just walking through there is interesting and if you think about consumer, okay yeah, Alexa, go get me, you know, what's the weather like in Palo alto or whatever, you know, those kinds of all back in pretty complicated but it's not as complicated as moving data to the edge and moving computer around. And the complexity of dealing with data has always been an open discussion but now with ai such at the center point of the value pressure and becoming table stakes. I mean we're hearing companies say if you don't have an Ai innovation strategy you're going to be you know irrelevant or even delisted from the stock market. That's some radical views. But um talk about this complexity and how it's being tamed for customers because if you don't have the data exposed, you're only as good as the data that you have. And this has been a conversation we've had on the cube many times before with you and some of your peers here at IBM you can't get the data. What good is it? The insights are only as good as what you can program. So this means that date is gonna be accessible and it's also complexity to move it around. So can you unpack that equation? >>Yeah, it's the whole notion of garbage in garbage out and ai you know ai its lifeblood is data and we have equipped that we always say that there's no Ai without an I. A. An information architecture And we are well over 30,000 engagements um among our clients around A I you know we have the AI ladder which is a prescriptive approach. We've learned a ton over the years and and we said before, you know the great digital shift, well the great inhibitor is the complexity of all this data and the average large enterprise has over 1000 repositories and sources of data as things go out into the edge that's just multiply. Um there's more and more movement to put applications, you know software as a service applications on the cloud and most businesses have multiple clouds so you're further fragmenting all the data and if you look at what the gardener has said and many others, these big data projects in the past are very slow and costly and they've had limited impact. This idea of moving data replicating data. It's just not going to work as the explosion of data increases in terms of touch points in terms of types and in terms of pure velocity and also at the same time the value of data, it's lifespan is rapidly decreasing. A customer record that was created yesterday may not be as valuable a year from now or even in three months from now because things change so much. Right. >>Alright. Alright. So I gotta ask you the question then because this is kind of from a customer. What's in it for me? At the end of the day I got data problem. You take it you got my attention. Um I gotta move date. I got to edge Hybrid cloud has been defined as a bona fide. A done deal is hybrid multi clouds around the corner. But that's just a subsystem of the operating system that's business now. So Hybrid cloud is the operating model data. Supercritical. What does IBM offer? What can you offer me as a customer and why is it good you guys got some announcements with cloud pack for data specifically here? Think what's the solution? How do I solve this? What's IBM offering? >>Yeah. So I think it starts with the fact that we have a fully unified data and AI platform meaning that they're not separate thoughts. They're all unified together as one on life cycle. And it runs anywhere on any cloud data center. To the answer starts with that notion and it helps you collect, organize and analyze data and infuse ai um throughout the business. Now, when it comes to the data complexity three core principles that were put into the next version of call Pat for data, one is automation is inevitable. It's the only way to deal with all this complexity. Uh leave the data where it is, where it lives, where it thrives and bring ai to the data. And so what we are putting into the next generation of compact for data is an intelligent data fabric, right? That is fueled by A. I. And that is going to abstract a lot of the complexity out of all this. Let you keep the data where it's at and be able to discover that data intelligently, be able to catalogue it, be able to understand it right? And more importantly, to do unified queries and updates across all these distributed sources of data and bring the records together without having to take weeks and months to build new data pipelines and across that entire ecosystem, be able to enforce universal privacy and usage policies which is absolutely critical. Forrester estimates that 50 of data is not used because they're afraid that it's gonna break policy. Oh >>yeah, I mean that's a huge trust issue. I mean I I was talking to a practitioner and he's like you know, we don't even want to do some of these transactions that are interesting experiments and and cloud opportunities because of the compliance risk, they're afraid to get sued. Yeah, >>that's right. And each one of those data stores just think about the ecosystem we're talking about here of sources and consumers, data consumers, ai consumers and of course all the sources that are silent all over the place. A lot of these repositories and a lot of these different cloud violence have different policies in terms of usage and in privacy. Right? So how do you bring all that together? What we're delivering the next version of compact? Her dad is a universal privacy plane if you will, which called auto privacy and it will basically abstract all the complexity of the different policies allow you to create them and enforce it universally. And you couldn't imagine the productivity of being to deliver that versus having a hand deal with this in a manual way. Yeah, that's an example with the data fabric. You know, what's interesting >>is you're getting at these. I mean I'm hearing the conversation about the solution, it's okay. I'm not in mind going okay, what's the benefits? I hear, I hear uh speed, um I hear, you know, ease of use, compliance trust, but what you're really getting at is agility and there's a, there's a upside for agility that's moving fast and getting taking advantage of new opportunities or automating something away. But you mentioned trust peace because you know, that's where I see people afraid like, okay, if I move too fast, will I trip on over or some governance issue? Like that's a huge thing. This is a big problem. >>It's a massive problem. I mean, I think there's four, Four areas from a business perspective, right? One is think about digital experiences and we know that six and 10 customers that defect from a brand because of some bad experience usually don't return. And it's estimated that is costing the industry, you know, close to $500 billion responsive experiences, which is You have to bring the data together to be able to do that, right? The second is the regulatory and reputational risk. Um that's another 180 billion or so. Which in many cases eight of revenue just to mitigate all that risk of using data. Not only regulatory but reputational. This thing about lost productivity, how many, how many hours every week is a worker doing mundane tasks, low value work because it's not automated. Um That's like another 100 or so billion dollars of costs for enterprises um can go on with interact with planning and forecasting. Um Supply chains being inefficient. All this is being fueled by the data, right? So the more you can bring all this data together, unify it, create new views that are aggregate and nature and uncover hidden insights that you couldn't do before. Um That's the magic sauce here. Right. >>Well my last question for you on the on this product before we wrap up is there's a huge trend towards ecosystem network effect integration. Right there more more integration. People are partnering. I mean you have solutions where that rely on different people in the supply chain or value chain of a of a solution whether you're a concession at a ballpark or an enterprise you're connecting with other a piece. This is cloud, right? How does your cloud pack for data handle that integration and that trust? Because this is really the deployment scenario. Your thoughts? >>Yeah. I mean I think the core of top after data is it's going to greatly enhance productivity. It's going to lower costs of these, you know, complex data states. It's going to lower risk of all this and it's going to help you uncover hidden insights that you couldn't see before. Not only because of A I, but because when you unify the data to get more out of it, we then go on to really point out that it's a truly open platform with an open ecosystem. So we are partnering with all the cloud partners. Right. We have a vast network of software providers that can extend and intimacy customized the platform. We have Integrator partners and it's all based on open source communities. So it is fully extensible and customizable to unique needs of every customer on any cloud yuan or across the city college. All >>right, scott. That's great stuff. Thanks for coming on the cube. Great to see you scott, Wapner. Vice President Marketing at IBM for data. And they are the hottest area. Great. Great cube alumni. Great insight. Thanks scott for coming on. Thank you. Okay, I'm jennifer with the cube You're watching ibn think 2021 coverage. Thanks for watching. Yeah. >>Mm

>> Narrator: From around the globe, it's theCUBE with coverage of KubeCon and CloudNativeCon Europe 2021 virtual. Brought to you by Red Hat, The Cloud Native Computing Foundation, and ecosystem partners. >> Hey, welcome back to theCUBE's coverage of KubeCon 2021 CloudNativeCon Europe. Part of the CNCF and ongoing, could be in there from the beginning, love this community, theCUBE's proud to support and continue to cover it. We're virtual this year again because of the pandemic but it looks like we'll be right around the corner for a physical event, hopefully for the next one, fingers crossed. Got a great guest here from Red Hat. Siamak Sadeghianfar, a Senior Principal Product Manager. Welcome to theCUBE. Thanks for coming in. >> Thank you for having me. >> So, this topic's about GitOps, Pipelines, code. Obviously Infrastructure as Code has been the ethos since I can remember going back to 2008 and the original cloutaroti vision. And we were always talking about that. Now it's mainstream. Now it's DevSecOps. So, it's now, day two operations, shifting left with security. OpenShift is continuing to get, take ground. Congratulations on that. So my first question is you guys announced the general availability of OpenShift Pipelines and GitOps at KubeCon. What are, what's this about? And what's the benefits for the customer. Let's get into the news >> Thanks for, to begin with for the Congress and this, this is definitely a hot topic around the DevSecOps. And the different variations of that year about some versions that during in, in FinTech and other verticals as well. The idea is here really is that CI/CD has been around for a long time, continuous integration and continuous delivery, as one of the core practices of the DevOps movement. DevOps movement is quite widespread, now. You, you see reports of above 90% of organizations are in the process of adoption in their journey. And this is one of the main practices but something that has become quite apparent is that many of these organizations that are investing more and more in Cloud Native apps and adopting Cloud Native ways of building applications the tooling and technology that they use for CI/CD since CI/CD is nothing new is from 10 years old, five years old pre Kubernetes era which is not quite Cloud Native. So there is always a clash of how do I build Cloud Natives application using these technologies that are not really built for Cloud Native space and an OpenShift Pipelines OpenShift GitOps is really an opening in this direction and bring more Cloud Native ways of continuous integration and continuous delivery to customers on OpenShift. >> Got it, so I got to ask you, so a couple of questions on this topic, I really want to dig into. Can you describe the Cloud Native CI/CD process versus traditional CI/CD? >> Sure, so traditional when we think about CI/CD there is usually this monolithic solutions that are running on a virtual machine on a type of infrastructure that they use to deploy applications as well. 'Cause you, you need reliability and you have to be making an assumption about an infrastructure that you're running on. And when you come to Cloud Native infrastructure you have a much more dynamic infrastructure. We have a lot less assumptions. You might be running on a public cloud or on premise infrastructure or different types of public cloud. So these environments are often also containerized. So there are, there's a high chance you're running on a container platform, regardless if it's a public or on premises. And with the whole containers, you, you have different types of disciplines and principals to think in, about your infrastructure. So in the Cloud Native ways of CI/CD, you're running most likely in a container platform. You don't have dedicated infrastructure. You are running mostly on demand. You scale when there is a demand for running CI/CD, for example, rather than dedicated infrastructure to it. And also from the mode of operation from organization perspective, they are more adapted to this decentralized ways of ownership. As a part of the DevOps culture, this comes really with that movement, that more and more development teams are getting ownership of some portion of the delivery of their applications. And it's cognitive CS/CD solutions, they focus on supporting these models that you go away from that central model of control to decentralize and have more ownership, more capabilities within the development teams for delivering application. >> Okay, so I then have to ask you the next question. It's like you, like a resource, you'd say: Hey Siri, what is, what is GitOps? What is GitOps? 'Cause that's the topic that's been getting a lot of traction, everyone's talking about it. I mean we know DevOps. So what is the GitOps model? Can you define that? And is that what a, it that what comes after DevOps? Is it DevOps 2.0, what is the GitOps model? >> That's a very good question. GitOps is nothing really new. It's rather a more descriptive way of DevOps principles. DevOps talks about the cultural changes and mindset and ways of working. And when it comes to the, to the concrete work flow it is quite open for interpretation. So GitOps is one, a specific interpretation of how you, you do continuous integration and continuous delivery, how we implement DevOps. And the concept have been around for a couple of years. But just recently, it's got a lot of traction within the Cloud Native space. >> So how does GitOps fit into Kubernetes then? 'Cause that's going to be the next dot that we want to connect. What is that, what is, how, how. How does GitOps fit into Kubernetes? >> So GitOps is really the, the core principle of GitOps is that you, you, you think about everything in your infrastructure and application in a declarative manner. So everything needs to be declared in, in, in a number of gate repositories and you drive your operations through Git Workflows. Which if you think about it is quite similar to how Kubernetes operates. The, the reason Kubernetes became so popular is because of this declarative way of thinking about your infrastructure. You declare what you expect and Kubernetes actualizes that on, on some sort of infrastructure. So GitOps is, is, is exact same concept, but the, but applied not to the infrastructure itself, but to the operations of that infrastructure, operations of those applications. It becomes a really nice fit together. It's the same mindset really applied in different place. >> It's like Kubernetes is like the linchpin or the enabler for GitOps. Just a whole nother level of, I mean, I think GitOps essentially DevOps 2.0 in my opinion because it takes this whole nother level above that for the developer modern developer because it allows them to do more. So it's been around for a while. We've been talking about this, it's got a new name but GitOps is kind of concept has been around. Why is the increase adoption happening now in your opinion or do you have any data on or any facts or opinion on why it's such an increase in, in conversation and adoption? >> You had the, you had like very accurate point there that Kubernetes has been a great enabler for, for DevOps and later the same applies to GitOps as well because of that, that great fit. It has been, GitOps the concept has been there but implementation of that has been quite difficult before Kubernetes and also for non-containerized environments. Kubernetes is, is a very potent platform for this kind of operation because the the mindset and the ways of working is really native to how Kubernetes thinks. But there is also another driver that has been influential in, in the rise of GitOps in the last year or two. And this is an observation we see at a lot of our customers, that the number of clusters that organizations are deploying, Kubernetes clusters increasing. As their maturity increases they get more comfortable with Cloud Native way of working and transfer the workflows to become Cloud Native, they are, they are having, they move more and more of their infrastructure to Kubernetes clusters. So a new challenge rises with this. And now that I have a larger number of clusters how do I ensure consistency across all these, all these clusters? So before I had to deploy an application to production environment, perhaps, which meant two clusters across two geographical zones. Now I have to deploy to 20 clusters. And these 20 clusters also change over time. So this week is a different 20 clusters then three weeks from now. So this, this dynamic ways of working and the customers maturing in, in dealing with Kubernetes operating communities has increased really the pace of adoption of GitOps because it addresses a lot of those challenges that customers are dealing with in this space. >> Yeah, you bring up a really good challenge there. And I think that's worth calling out, this idea of expansion. And I won't say sprawl because it's not a sprawl of cluster. It's more a state provisioning and standing up clusters. And you said they they're changing because the environment has needs and the workloads might have requirements. This makes total sense in a DevOps kind of GitOps way. So I get that and I see that definitely happening. So this brings up the question, if I'm a customer, what I'm worried about is I don't want to have that Hadoop factor where I build a cluster and it takes too long to manage it, or I can't measure it, or understand the data, or have any observability. So I want to have an ease of provisioning and standing up and I want to have consistency that my apps who are using it, don't have to be, you know mangled with or coded with. So, you know, this combination of ease of deploying, ease of integrating, ease of consuming the clusters becomes a service model. Can you share your thoughts on how that gets solved? >> Yeah, absolutely. So that, that's a great point because as, as this is happening, there is also heterogenesis in this, this type of Kubernetes infrastructure window. Like, they're all Kubernetes but this problem also has multiple facets as customers running on multiple public clouds and, and combination of that with their on-premise Kubernetes clusters. And that is, they may as well be OpenShift across all this, all this infrastructure. But the, the problem that GitOps helps its customers advise that they can have the exact same operational model across all these apps and infrastructure, regardless of what kind of application it is. And regardless of where OpenShift is installed or if you're using that combined with a public cloud managed a Kubernetes stats, is the exact same process because you're relying on, on the Gits Workflows, right? And even beyond that, this standard workflow has the benefit of something that many organizations are already familiar with. So if you think about what GitOps operations mean it is essentially what developers have been always using for developing applications. So this standardizes the operations of both application and infrastructure as solvers. >> Listen to me, I got to ask you as the product manager on the whole pipelining in Kubernetes deployments. In your opinion, share your perspective on, real quick, on Kubernetes, where we're at? Because just the accelerated adoption has been phenomenal. We've seen it mature this year at KubeCon. And certainly when KubeCon North America happens, you're going to see more and more end user participation. You're going to see much more end-user use cases. You mentioned clusters are growing. What's the state of Kubernetes from your perspective, from a developer mindset? >> So Kubernetes, I think it has moved from a place that it was seen as only a, a type of infrastructure for Cloud Native applications because of the capability that it provides to a type of infrastructure for any type of application, any type of workload. I think what we have seen over the last two years is, is a shift to expansion of the use cases. And if, if you are, you talked about head open if you are a data scientist, or if you are an AIML type of developer or any type of workload really, see use cases that are coming to the Kubernetes platform as the targets type of infrastructure. So that's really where we see Kubernetes at right now is the really, the preferred infrastructure for any type of workload. And I believe this trend going to to keep continuing to address any of the challenge that exists that prevents maybe part of the, a particular type of workload to address that within the platform and opens that to add to, to developers. Which means for the developers now, once you learn the platform you are really proficient in a, you have this skills for any type of application or any type of infrastructure because they're all standardized, regardless of what type of application or workloads or technology you're specialized in. They're all going to the exact same platform. So it's very standardized type of skills across organizations, different type of teams that they have. >> Awesome, great, thanks for sharing that insight and definition. You're like a walking dictionary today for our CUBE audience. Thank you for all this good stuff. Appreciate it. Final question for you is, what does it mean for developers that are using Jenkins or other cloud-based CI solutions like GitHub Actions? What, what's the impact to them with all this from a working standpoint? 'Cause obviously you've got to make it workable. >> Right, so it's CI/CD also like it's, it's it's great to see like with DevOps adoption, there are many organizations that already have processes in place. They have, they're already using a CI tool or a CD tool. They might be using Jenkins. A lot of organizations really use, use Jenkins even though it comes with challenges and you might be using public cloud services or cloud-based CI tools, like you have Actions, you have pipelines and so on. So we are very well aware of the existing investment that many organizational teams have made. And we make sure that OpenShift as a platform works really well alongside all these different types of CI and CD technology that exists. We want to make sure that for developers starting on OpenShift, they, they have a really solid Cloud Native foundation for CI/CD. They have of strategies included but replaceable type of strategies. So they, they have a supportive platform that is Cloud Native, that gives them capability that matches the type of Cloud Native workloads that they have on the platform but also integrate well with existing tooling that exists around CI/CD. So that they can match and choose if they want to replace a piece of that with an existing investment that they have done, integrated with the rest of the platform. >> Awesome, well, great to have you on. Having the principal product manager is awesome, to talk about the two new announcements here. OpenShift pipe, Pipelines, and OpenShift GitOps. Final, final question, bumper sticker this for the audience. What's the bottom line with OpenShift Pipelines and GitOps? What's the, what's the bottom line benefit for customers? >> It's a, so OpenShift Pipeline and OpenShift GitOps makes it really simple for customers to create Cloud Native Pipelines and GitOps model for delivering application. And also making cluster changes across a large range of clusters that they have, make it really simple to grow from that point to many, many clusters and still manage the complexity of this complex infrastructure that it will be growing into. >> All right, Siamak Sadeghianfar, Senior Principal Product Manager at Red Hat. Here for the KubeCon + CloudNativeCon, Europe. CUBE conversation, thanks for coming on, appreciate it. >> Thanks John, thanks for having me. Okay, CUBE coverage continues. I'm John Farrow with theCUBE. Thanks for watching. (upbeat music)

>>from around the >>globe. It's >>the cube >>with digital >>coverage of IBM >>Think 2021 >>brought to you by IBM. Welcome back everyone to the cube coverage of IBM Think 2021. I'm john for a host of the cube. Got a great guest here scott, senior Vice president of marketing at IBM for data and ai cube alumni has been around the wave around data, had many conversations over the years. Scott welcome back to the Cuban, I wish we were in person but we're remote for the virtual conference for think 2021. Thanks for coming on >>john great to be here. And yeah, I guess we have adapted to the world of being on the screen. >>Well, great, great to have you in. One of the things about virtualization of media is that we get more content this year. There's so many more signature stories around um, IBM think and one of the things that's really fun for us is the data conversations in A I as as the transformation and innovation equations are coming together at scale, you're seeing an accelerated piece here. My first question for you is, you know, this digital shift that's going on, the preferences are shifting to virtual now digital in the wake of Covid, what do companies need to adapt from your perspective as you see this playing out? What's your perspective? >>It's interesting to use that term. So we've been calling it the great digital shift. And uh yeah, there's an there was an interesting survey, a pretty big survey of global C suite that Mackenzie did. And they pointed out that 79% of those leaders felt that Covid highlighted the immaturity of their digital capability. And while they thought they were on the right path and they were building strong digital capabilities, the whole world of the pandemic remote work, how you engage with customers call centers going off the hooks in terms of people calling, it just goes on and on and on. And And they also pointed out that 90, I think it was, 96 of them are going to speed their digital reinvention. And you mentioned data, if you think about it, it's data that a few fuels digital capabilities. Right? What good is digital if it's not data, right, It's all data. So it's the fuel that makes it all work. And when you think about the ability to leverage all your dad, you got to democratize it, It's siloed all over the place, it's growing at six times rate over the next three years. It's really all over the place, every touch point across the digital ecosystem. Um, and the only way to deal with the data in to unlock its value, particularly in predictive ways is to AI. Right. And so what we're seeing is a huge amount of investment in multi cloud, really bringing together this notion of hybrid and then applying AI as the intelligence to create a more predictable and resilient business right through a digital model, right? Yeah, it's really the investment is really going through the roof. >>You know, I think AI has been, it's been demystified over the years, been a lot of people saw the machine learning and now you got NLP and data control planes that are making it more addressable. But the real thing that comes up here, I think this year is this role between business and consumer and AI has that kind of dynamic. And I want to ask you because I was just having a conversation with one of your partner, IBM partner Samsung, KC Joy runs E V P E V P for the B to B B to G Group at Samsung. It's a huge I. O. T. Thing. And AI is a big part of that consumer and we talked about the consumer electronics business issues, how is A I different for business versus the consumer is obviously got industrial iot edge and you got automation piece, what's the difference? And someone asked you that between business and consumer Ai. >>Yeah, actually I think that's one of the areas that we really differentiate ourselves and we're putting the bulk of investments this notion of AI for business, right? And you know, a lot of people think of A I sometimes they think of Siri and Alexa and things that go on in your car and all that. Obviously that's a big part of applying machine learning and all that, but when we talk about AI for business, we're thinking about four core attributes. Uh one is that it needs to understand the unique language of your business and industry, right? And that's not just natural language but it's the ability to debate, it's the ability to read documents, interpret documents. Um It's the ability to really understand the context because you and I can ask the same question in five or six different ways and it needs to understand the business to be able to interpret that and help answer the question unlike like Siri or Alexa, where you really got to have the right semantics and you know, it won't understand the nuances as well, so understand the language of businesses. 12 is that we believe ai is the engine for automation. Um So Ai is really about automating workflows and experiences because anything that you want to automate and make more productive, you have to have some predictive capabilities to it to understand what to do and you have to learn about you know, what's trying to be accomplished which is always unique and personalized. So that's the second one is about automation. The third is it is about driving trust and outcomes right? In the business outcomes, which means, you know, if you were to if some a model say scott go jump off a bridge, you know I probably wouldn't want to do that unless it really explained to me convincingly that I should do that well but explain ability and trust is such a critical part of aI for business and then finally it needs to run everywhere. It has to integrate everything. And we believe unlike a lot of the competitors where you have to bring the data to a I we're saying leave the data where it lives and bring ai to the data. So it runs anywhere from the data center to the edge, The same model, the same capabilities in a distributed environment. Um So those four kind of attributes come together to what we call a I for business. Um And that's what's gonna allow call centers and supply chains and business planning and risk and regulatory, you know, mitigation, I mean those kind of things to really come to life in a predictive way without those attributes, it's much harder to do a lot more coding and you're not gonna as much accuracy. >>Yeah, I mean what you're just walking through there is interesting and if you think about consumer, okay, yeah, Alexa, go get me, you know, what's the weather like in Palo alto or whatever, you know, those kinds of all back in pretty complicated but it's not as complicated as moving data to the edge and moving compute around. And the complexity of dealing with data has always been an open discussion. But now with ai such at the center point of the value pressure and becoming table stakes. I mean we're hearing companies say if you don't have an Ai innovation strategy, you're going to be you know, irrelevant or even delisted from the stock market. That's some radical views. But um talk about this complexity and how it's being tamed for customers because if you don't have the data exposed, you're only as good as the data that you have and this has been a conversation we've had on the cube many times before with you and some of your other peers here at IBM you can't get the data. What good is it? The insights are only as good as what you can program. So this means that date is gonna be accessible and it's also complexity to move it around. So can you unpack that equation? >>Yeah, it's the whole notion of garbage in garbage out and ai you know ai its lifeblood is data and we have equipped that we always say that there's no Ai without an I. A. An information architecture And we are well over 30,000 engagements um among our clients around A I you know we have the AI ladder which is a prescriptive approach. We've learned a ton over the years and and we said before, you know the great digital shift, well the great inhibitor is the complexity of all this data and the average large enterprise has over 1000 repositories and sources of data as things go out into the edge that's just multiply. Um there's more and more movement to put applications, you know software as a service applications on the cloud and most businesses have multiple clouds so you're further fragmenting all the data and if you look at what the gardener has said and many others, these big data projects in the past are very slow, costly and they've had limited impact. This idea of moving data replicating data. It's just not going to work as the explosion of data increases in terms of touch points in terms of types and in terms of pure velocity and also at the same time the value of data, it's lifespan is rapidly decreasing. A customer record that was created yesterday may not be as valuable a year from now or even in three months from now because things change so much. Right. >>Alright. So I gotta ask you the question then because this is kind of from a customer. What's in it for me? At the end of the day I got data problem. You take it you got my attention. Um I gotta move date. I got the edge. Hybrid cloud has been defined as a bona fide is done deals Hybrid multi clouds around the corner. But that's just a subsystem of the operating system that's business now. So Hybrid cloud is the operating model. Data. Supercritical. What does IBM offer? What can you offer me as a customer and why is it good? You guys got some announcements with cloud pack for data specifically here? Think what's the solution? How do I solve this? What's IBM offering? >>Yeah. So I think it starts with the fact that we have a fully unified data and AI platform meaning that they're not separate thoughts. They're all unified together as one on life cycle. And it runs anywhere on any cloud data center. To the answer starts with that notion. It helps you collect, organize and analyze data and infuse ai um throughout the business. Now, when it comes to the data complexity three core principles that were put into the next version of call Pat for data, one is automation is inevitable. It's the only way to deal with all this complexity. Uh leave the data where it is, where it lives, where it thrives and bring ai to the data. And so what we are putting into the next generation of compact for data is an intelligent data fabric, right? That is fueled by A. I. And that is going to abstract a lot of the complexity out of all this. Let me keep the data where it's at and be able to discover that data intelligently be able to catalogue it, be able to understand it right? And more importantly, to do unified queries and updates across all these distributed sources of data and bring the records together without having to take weeks and months to build new data pipelines and across that entire ecosystem, be able to enforce universal privacy and usage policies which is absolutely critical. Forrester estimates that 50 of data is not used because they're afraid that it's gonna break policy. Oh >>yeah. I mean that's a huge trust issue. I mean I I was talking to a practitioner and he's like, you know, we don't even want to do some of these transactions that are interesting experiments and and cloud opportunities because of the compliance risk, they're afraid to get sued. Yeah, >>that's right. And each one of those data stores, so if you think about the ecosystem we're talking about here of sources and consumers, data consumers, ai consumers and of course all the sources that are silent all over the place. A lot of these repositories and a lot of these different cloud violence have different policies in terms of usage and pump in privacy. Right? So how do you bring all that together? What we're delivering? The next version of compact for dad is a universal privacy plane if you will, which called auto privacy and it will basically abstract all the complexity of the different policies allow you to create them and enforce it universally. And you couldn't imagine the productivity of being to deliver that versus having a hand deal with this in a manual way. That's an example of what the data fabric, >>you know, what's interesting is you're getting at this? I'm hearing the conversation about the solution. It's okay. I'm not a mind going okay, what's the benefits? I hear I hear uh speed, um, I hear, you know, ease of use, compliance trust. But what you're really getting at is agility and there's a, there's a upside for agility that's moving fast and getting taking advantage of new opportunities or automating something away. But you mentioned the trust piece because that's where I see people afraid like, okay, if I move too fast, will I trip on over or some governance issue? Like that's a huge thing. This is a big problem. >>It's a massive problem. I mean, I mean, I think there's four, Four areas from a business perspective, right? One is think about digital experiences and we know that six and 10 customers that defect from a brand because of some bad experience usually don't return. And it's estimated that is costing the industry, you know close to $500 billion responsive experiences, which is, You have to bring the data together to do that, right? The second is the regulatory and reputational risk. Um that's another 180 billion or so. Which in many cases eight of revenue just to mitigate all that risk of using data. Not only regulatory reputational. This thing about lost productivity, how many, how many hours every week is a worker doing mundane tasks, low value work because it's not automated. Um that's like another 100 or so billion dollars of costs for enterprises. Um go on with interact with planning and forecasting. Um supply chains being inefficient. All this is being fueled by the data, right? So the more you can bring all this data together, unify it create new views that are aggregate and nature and uncover hidden insights that you couldn't do before. Um That's the magic sauce here. Right. >>Well, my last question for you on the on this product before we wrap up is there's a huge trend towards ecosystem network effect integration right there more more integration and people are partnering. I mean you have solutions where that rely on different people in the supply chain or value chain of a of a solution whether you're a concession at a ballpark or an enterprise you're connecting with other a piece, this is cloud, right? How does your cloud pack for data handle that integration and that trust? Because this is really the deployment scenario. Your thoughts? >>Yeah. I mean I think the core of top after data is it's going to greatly enhance productivity. It's going to lower costs of these, you know, complex data states. It's going to lower risk of all this and it's going to help you uncover hidden insights that you couldn't see before. Not only because of A I but because when you unify the data to get more out of it, we then go on to really point out that it's a truly open platform with an open ecosystem. So we are partnering with all the cloud partners. Right? We have a vast network of software providers that can extend and intimacy customized the platform. We have integrator partners and it's all based on open source communities. So it is fully extensible and customizable to the unique needs of every customer on any Juwan or across the city college. All >>right scott. That's great stuff. Thanks for coming on the cube. Great to see you scott, Wapner. Vice president Marketing at IBM for data and they are the hottest area. Great. Great cube alumni. Great insight. Thanks scott for coming on. Thank you. Okay, I'm jennifer with the cube You're watching ibn think 2021 coverage. Thanks for watching. Mhm >>mm. >>Yeah.

>>from around the globe. It's the Cube presenting enterprise. Digital resilience on hybrid and multi cloud Brought to You by Iota Ho. Hello, everyone, and welcome to our continuing Siri's covering data automation brought to you by Io Tahoe. Today we're gonna look at how to ensure enterprise resilience for hybrid and multi cloud. Let's welcome in age. Eva Hora, who is the CEO of Iota A J. Always good to see you again. Thanks for coming on. >>Great to be back. David Pleasure. >>And he's joined by Fozzy Coons, who is a global principal architect for financial services. The vertical of financial services. That red hat. He's got deep experiences in that sector. Welcome, Fozzie. Good to see you. >>Thank you very much. Happy to be here. >>Fancy. Let's start with you. Look, there are a lot of views on cloud and what it is. I wonder if you could explain to us how you think about what is a hybrid cloud and and how it works. >>Sure, yes. So the hybrid cloud is a 90 architecture that incorporates some degree off workload, possibility, orchestration and management across multiple clouds. Those clouds could be private cloud or public cloud or even your own data centers. And how does it all work? It's all about secure interconnectivity and on demand. Allocation of resources across clouds and separate clouds can become hydrate when they're similarly >>interconnected. And >>it is that interconnectivity that allows the workloads workers to be moved and how management can be unified in off the street. You can work and how well you have. These interconnections has a direct impact on how well your hybrid cloud will work. >>Okay, so we'll fancy staying with you for a minute. So in the early days of Cloud that turned private Cloud was thrown a lot around a lot, but often just meant virtualization of an on PREM system and a network connection to the public cloud. Let's bring it forward. What, in your view, does a modern hybrid cloud architecture look like? >>Sure. So for modern public clouds, we see that, um, teams organizations need to focus on the portability off applications across clouds. That's very important, right? And when organizations build applications, they need to build and deploy these applications as small collections off independently, loosely coupled services, and then have those things run on the same operating system which means, in other words, running it on Lenox everywhere and building cloud native applications and being able to manage and orchestrate thes applications with platforms like KUBERNETES or read it open shit, for example. >>Okay, so that Z, that's definitely different from building a monolithic application that's fossilized and and doesn't move. So what are the challenges for customers, you know, to get to that modern cloud? Aziz, you've just described it. Is it skill sets? Is that the ability to leverage things like containers? What's your view there? >>So, I mean, from what we've seen around around the industry, especially around financial services, where I spent most of my time, we see that the first thing that we see is management right now because you have all these clouds and all these applications, you have a massive array off connections off interconnections. You also have massive array off integrations, possibility and resource allocations as well, and then orchestrating all those different moving pieces. Things like storage networks and things like those are really difficult to manage, right? That's one. What s O Management is the first challenge. The second one is workload, placement, placement. Where do you place this? How do you place this cloud? Native applications. Do you or do you keep on site on Prem? And what do you put in the cloud? That is the the the other challenge. The major one. The third one is security. Security now becomes the key challenge and concern for most customers. And we could talk about how hundreds? Yeah, >>we're definitely gonna dig into that. Let's bring a J into the conversation. A J. You know, you and I have talked about this in the past. One of the big problems that virtually every companies face is data fragmentation. Um, talk a little bit about how I owe Tahoe unifies data across both traditional systems legacy systems. And it connects to these modern I t environments. >>Yeah, sure, Dave. I mean, fancy just nailed it. There used to be about data of the volume of data on the different types of data. But as applications become or connected and interconnected at the location of that data really matters how we serve that data up to those those app. So working with red hat in our partnership with Red Hat being able Thio, inject our data Discovery machine learning into these multiple different locations. Would it be in AWS on IBM Cloud or A D. C p R. On Prem being able thio Automate that discovery? I'm pulling that. That single view of where is all my data then allows the CEO to manage cast that can do things like one. I keep the data where it is on premise or in my Oracle Cloud or in my IBM cloud on Connect. The application that needs to feed off that data on the way in which you do that is machine learning. That learns over time is it recognizes different types of data, applies policies to declassify that data. Andi and brings it all together with automation. >>Right? And that's one of the big themes and we've talked about this on earlier episodes. Is really simplification really abstracting a lot of that heavy lifting away so we can focus on things A. J A. Z. You just mentioned e nifaz e. One of the big challenges that, of course, we all talk about his governance across thes disparity data sets. I'm curious as your thoughts. How does Red Hat really think about helping customers adhere to corporate edicts and compliance regulations, which, of course, are are particularly acute within financial services. >>Oh, yeah, Yes. So for banks and the payment providers, like you've just mentioned their insurers and many other financial services firms, Um, you know, they have to adhere Thio standards such as a PC. I. D. S s in Europe. You've got the G g d p g d p r, which requires strange and tracking, reporting documentation. And you know, for them to to remain in compliance and the way we recommend our customers to address these challenges is by having an automation strategy. Right. And that type of strategy can help you to improve the security on compliance off the organization and reduce the risk after the business. Right. And we help organizations build security and compliance from the start without consulting services residencies. We also offer courses that help customers to understand how to address some of these challenges. And that's also we help organizations build security into their applications without open sources. Mueller, where, um, middle offerings and even using a platform like open shift because it allows you to run legacy applications and also continue rights applications in a unified platform right And also that provides you with, you know, with the automation and the truly that you need to continuously monitor, manage and automate the systems for security and compliance >>purposes. Hey, >>Jay, anything. Any color you could add to this conversation? >>Yeah, I'm pleased. Badly brought up Open shift. I mean, we're using open shift to be able. Thio, take that security application of controls to to the data level. It's all about context. So, understanding what data is there being able to assess it to say who should have access to it. Which application permission should be applied to it. Um, that za great combination of Red Hat tonight. Tahoe. >>But what about multi Cloud? Doesn't that complicate the situation even even further? Maybe you could talk about some of the best practices to apply automation across not only hybrid cloud, but multi >>cloud a swell. Yeah, sure. >>Yeah. So the right automation solution, you know, can be the difference between, you know, cultivating an automated enterprise or automation caress. And some of the recommendations we give our clients is to look for an automation platform that can offer the first thing is complete support. So that means have an automation solution that provides that provides, um, you know, promotes I t availability and reliability with your platform so that you can provide, you know, enterprise great support, including security and testing, integration and clear roadmaps. The second thing is vendor interoperability interoperability in that you are going to be integrating multiple clouds. So you're going to need a solution that can connect to multiple clouds. Simples lee, right? And with that comes the challenge off maintain ability. So you you you're going to need to look into a automation Ah, solution that that is easy to learn or has an easy learning curve. And then the fourth idea that we tell our customers is scalability in the in the hybrid cloud space scale is >>is >>a big, big deal here, and you need a to deploy an automation solution that can span across the whole enterprise in a constituent, consistent manner, right? And then also, that allows you finally to, uh, integrate the multiple data centers that you have, >>So A J I mean, this is a complicated situation, for if a customer has toe, make sure things work on AWS or azure or Google. Uh, they're gonna spend all their time doing that, huh? What can you add really? To simplify that that multi cloud and hybrid cloud equation? >>Yeah. I could give a few customer examples here Warming a manufacturer that we've worked with to drive that simplification Onda riel bonuses for them is has been a reduction cost. We worked with them late last year to bring the cost bend down by $10 million in 2021 so they could hit that reduced budget. Andre, What we brought to that was the ability thio deploy using open shift templates into their different environments. Where there is on premise on bond or in as you mentioned, a W s. They had G cps well, for their marketing team on a cross, those different platforms being out Thio use a template, use pre built scripts to get up and running in catalog and discover that data within minutes. It takes away the legacy of having teams of people having Thio to jump on workshop cause and I know we're all on a lot of teens. The zoom cause, um, in these current times, they just sent me is in in of hours in the day Thio manually perform all of this. So yeah, working with red hat applying machine learning into those templates those little recipes that we can put that automation toe work, regardless of which location the data is in allows us thio pull that unified view together. Right? >>Thank you, Fozzie. I wanna come back to you. So the early days of cloud, you're in the big apple, you know, financial services. Really well. Cloud was like an evil word within financial services, and obviously that's changed. It's evolved. We talked about the pandemic, has even accelerated that, Um And when you really, you know, dug into it when you talk to customers about their experiences with security in the cloud it was it was not that it wasn't good. It was great, whatever. But it was different. And there's always this issue of skill, lack of skills and multiple tools suck up teams, they're really overburdened. But in the cloud requires new thinking. You've got the shared responsibility model you've got obviously have specific corporate requirements and compliance. So this is even more complicated when you introduce multiple clouds. So what are the differences that you can share from your experience is running on a sort of either on Prem or on a mono cloud, um, or, you know, and versus across clouds. What? What? What do you suggest there? >>Yeah, you know, because of these complexities that you have explained here, Miss Configurations and the inadequate change control the top security threats. So human error is what we want to avoid because is, you know, as your clouds grow with complexity and you put humans in the mix, then the rate off eras is going to increase, and that is going to exposure to security threat. So this is where automation comes in because automation will streamline and increase the consistency off your infrastructure management. Also application development and even security operations to improve in your protection, compliance and change control. So you want to consistently configure resources according to a pre approved um, you know, pre approved policies and you want to proactively maintain a to them in a repeatable fashion over the whole life cycle. And then you also want to rapid the identified system that require patches and and reconfiguration and automate that process off patching and reconfiguring so that you don't have humans doing this type of thing, right? And you want to be able to easily apply patches and change assistant settings. According Thio, Pre defined, based on like explained before, you know, with the pre approved policies and also you want is off auditing and troubleshooting, right? And from a rate of perspective, we provide tools that enable you to do this. We have, for example, a tool called danceable that enables you to automate data center operations and security and also deployment of applications and also obvious shit yourself, you know, automates most of these things and obstruct the human beings from putting their fingers on, causing, uh, potentially introducing errors right now in looking into the new world off multiple clouds and so forth. The difference is that we're seeing here between running a single cloud or on prem is three main areas which is control security and compliance. Right control here it means if your on premise or you have one cloud, um, you know, in most cases you have control over your data and your applications, especially if you're on Prem. However, if you're in the public cloud, there is a difference there. The ownership, it is still yours. But your resources are running on somebody else's or the public clouds. You know, e w s and so forth infrastructure. So people that are going to do this need to really especially banks and governments need to be aware off the regulatory constraints off running, uh, those applications in the public cloud. And we also help customers regionalize some of these choices and also on security. You will see that if you're running on premises or in a single cloud, you have more control, especially if you're on Prem. You can control this sensitive information that you have, however, in the cloud. That's a different situation, especially from personal information of employees and things like that. You need to be really careful off that. And also again, we help you rationalize some of those choices. And then the last one is compliant. Aziz. Well, you see that if you're running on Prem or a single cloud, um, regulations come into play again, right? And if you're running a problem, you have control over that. You can document everything you have access to everything that you need. But if you're gonna go to the public cloud again, you need to think about that. We have automation, and we have standards that can help you, uh, you know, address some of these challenges for security and compliance. >>So that's really strong insights, Potsie. I mean, first of all, answerable has a lot of market momentum. Red hats in a really good job with that acquisition, your point about repeatability is critical because you can't scale otherwise. And then that idea you're you're putting forth about control, security compliance It's so true is I called it the shared responsibility model. And there was a lot of misunderstanding in the early days of cloud. I mean, yeah, maybe a W s is gonna physically secure the, you know, s three, but in the bucket. But we saw so many Miss configurations early on. And so it's key to have partners that really understand this stuff and can share the experiences of other clients. So this all sounds great. A j. You're sharp, you know, financial background. What about the economics? >>You >>know, our survey data shows that security it's at the top of the spending priority list, but budgets are stretched thin. E especially when you think about the work from home pivot and and all the areas that they had toe the holes that they had to fill their, whether it was laptops, you know, new security models, etcetera. So how do organizations pay for this? What's the business case look like in terms of maybe reducing infrastructure costs so I could, you know, pay it forward or there's a There's a risk reduction angle. What can you share >>their? Yeah. I mean, the perspective I'd like to give here is, um, not being multi cloud is multi copies of an application or data. When I think about 20 years, a lot of the work in financial services I was looking at with managing copies of data that we're feeding different pipelines, different applications. Now what we're saying I talk a lot of the work that we're doing is reducing the number of copies of that data so that if I've got a product lifecycle management set of data, if I'm a manufacturer, I'm just gonna keep that in one location. But across my different clouds, I'm gonna have best of breed applications developed in house third parties in collaboration with my supply chain connecting securely to that. That single version of the truth. What I'm not going to do is to copy that data. So ah, lot of what we're seeing now is that interconnectivity using applications built on kubernetes. Um, that decoupled from the data source that allows us to reduce those copies of data within that you're gaining from the security capability and resilience because you're not leaving yourself open to those multiple copies of data on with that. Couldn't come. Cost, cost of storage on duh cost of compute. So what we're seeing is using multi cloud to leverage the best of what each cloud platform has to offer That goes all the way to Snowflake and Hiroko on Cloud manage databases, too. >>Well, and the people cost to a swell when you think about yes, the copy creep. But then you know when something goes wrong, a human has to come in and figured out um, you brought up snowflake, get this vision of the data cloud, which is, you know, data data. I think this we're gonna be rethinking a j, uh, data architectures in the coming decade where data stays where it belongs. It's distributed, and you're providing access. Like you said, you're separating the data from the applications applications as we talked about with Fozzie. Much more portable. So it Z really the last 10 years will be different than the next 10 years. A. >>J Definitely. I think the people cast election is used. Gone are the days where you needed thio have a dozen people governing managing black policies to data. Ah, lot of that repetitive work. Those tests can be in power automated. We've seen examples in insurance were reduced teams of 15 people working in the the back office China apply security controls compliance down to just a couple of people who are looking at the exceptions that don't fit. And that's really important because maybe two years ago the emphasis was on regulatory compliance of data with policies such as GDP are in CCP a last year, very much the economic effect of reduce headcounts on on enterprises of running lean looking to reduce that cost. This year, we can see that already some of the more proactive cos they're looking at initiatives such as net zero emissions how they use data toe under understand how cape how they can become more have a better social impact. Um, and using data to drive that, and that's across all of their operations and supply chain. So those regulatory compliance issues that may have been external we see similar patterns emerging for internal initiatives that benefiting the environment, social impact and and, of course, course, >>great perspectives. Yeah, Jeff Hammer, Bucker once famously said, The best minds of my generation are trying to get people to click on ads and a J. Those examples that you just gave of, you know, social good and moving. Uh, things forward are really critical. And I think that's where Data is gonna have the biggest societal impact. Okay, guys, great conversation. Thanks so much for coming on the program. Really appreciate your time. Keep it right there from, or insight and conversation around, creating a resilient digital business model. You're watching the >>Cube digital resilience, automated compliance, privacy and security for your multi cloud. Congratulations. You're on the journey. You have successfully transformed your organization by moving to a cloud based platform to ensure business continuity in these challenging times. But as you scale your digital activities, there is an inevitable influx of users that outpaces traditional methods of cybersecurity, exposing your data toe underlying threats on making your company susceptible toe ever greater risk to become digitally resilient. Have you applied controls your data continuously throughout the data Lifecycle? What are you doing to keep your customer on supply data private and secure? I owe Tahoe's automated, sensitive data. Discovery is pre programmed with over 300 existing policies that meet government mandated risk and compliance standards. Thes automate the process of applying policies and controls to your data. Our algorithm driven recommendation engine alerts you to risk exposure at the data level and suggests the appropriate next steps to remain compliant on ensure sensitive data is secure. Unsure about where your organization stands In terms of digital resilience, Sign up for a minimal cost commitment. Free data Health check. Let us run our sensitive data discovery on key unmapped data silos and sources to give you a clear understanding of what's in your environment. Book time within Iot. Tahoe Engineer Now >>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 Iot, Tahoe 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. >>Trust 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 Mind is a 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 41 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 said you had up data services for Iot Tahoe. We've talked in the past. Of course you're out of London. What >>do you what? 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 Hyo Tahoe on happiest minds as a great mutual fit. A Suresh has said, uh, 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. Uh, we're using machine learning algorithms to make data discovery data cataloging, understanding, data done. See, 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 that 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 on. Then finally, I think they're both Challenger brands on happiest minds have a really positive, fresh ethical approach to people and customers that really resonates with us at Ideo Tahoe to >>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 say 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 organisation's trying to adopt onto the digital technologies. Right there has bean lot off data which has been to manage 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, right where we bring in the data. Complaints as a service were ableto manage the resilience much ahead off other competitors in the market. We were ableto bring in our business continuity processes from day one, where we were ableto deliver our services without any interruption to the services. What we were delivered to our customers So that is where the digital resilience with business community process enabled was very helpful for us. Toe enable our customers continue their business without any interruptions during pandemics. >>So I mean, some of the challenges that customers tell me they obviously they had to figure out how to get laptops to remote workers and that that whole remote 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 that, for the first step is to identify the critical data. Right. So we this is a six step 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 assert 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 digital ourselves and then as 1/5 step, we work as a back up plan in terms of bringing in a very comprehensive and a 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 their 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, you're, 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 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 soldiers. It could be on data basis, or it could be even on the data legs. Or it could be a 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. On finally, we also bringing the automated data governance where we can manage the sensory data policies on their later relationships in terms off mapping on manage their business roots 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. I'm gonna be great if you had an example is well, but maybe you could pick it up from there, >>John. I mean, at a high level, assertions are clearly articulated. Really? Um, Hyoty, who delivers business agility. So that's by, um accelerating the time to operationalize data, automating, putting in place controls and actually 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 often met manually, making multiple copies of the same data. So you have a d b A. They would copy the data to various different places, and then 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 on. So you don't have the same need thio 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 attack 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, discovering what's dubica? What's redundant? Uh, 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 our tire, you could do it really very quickly on you can have tangible results within weeks and months on Ben, 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 toward the business outcomes. Uh, 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, it's 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. Um, this is a is a 2 to 3 week process, uh, to really quickly start to understand on 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 they discovery those catalog e on 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 the 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. Um, Brasiliense 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 and the partnership Be well, >>Thank you, David Suresh. Thank you. Thank >>you for watching everybody, This is Dave Volonte for the Cuban are ongoing Siris on data automation without >>Tahoe, digital resilience, automated compliance, privacy and security for your multi cloud. Congratulations. You're on the journey. You have successfully transformed your organization by moving to a cloud based platform to ensure business continuity in these challenging times. But as you scale your digital activities, there is an inevitable influx of users that outpaces traditional methods of cybersecurity, exposing your data toe underlying threats on making your company susceptible toe ever greater risk to become digitally resilient. Have you applied controls your data continuously throughout the data lifecycle? What are you doing to keep your customer on supply data private and secure? I owe Tahoe's automated sensitive data. Discovery is pre programmed with over 300 existing policies that meet government mandated risk and compliance standards. Thes automate the process of applying policies and controls to your data. Our algorithm driven recommendation engine alerts you to risk exposure at the data level and suggests the appropriate next steps to remain compliant on ensure sensitive data is secure. Unsure about where your organization stands in terms of digital resilience. Sign up for our minimal cost commitment. Free data health check. Let us run our sensitive data discovery on key unmapped data silos and sources to give you a clear understanding of what's in your environment. Book time within Iot. Tahoe Engineer. Now. >>Okay, now we're >>gonna go into the demo. We want to get a better understanding of how you can leverage open shift. And I owe Tahoe to facilitate faster application deployment. Let me pass the mic to Sabetta. Take it away. >>Uh, thanks, Dave. Happy to be here again, Guys, uh, they've mentioned names to be the Davis. I'm the enterprise account executive here. Toyota ho eso Today we just wanted to give you guys a general overview of how we're using open shift. Yeah. Hey, I'm Noah Iota host data operations engineer, working with open ship. And I've been learning the Internets of open shift for, like, the past few months, and I'm here to share. What a plan. Okay, so So before we begin, I'm sure everybody wants to know. Noel, what are the benefits of using open shift. Well, there's five that I can think of a faster time, the operation simplicity, automation control and digital resilience. Okay, so that that's really interesting, because there's an exact same benefits that we had a Tahoe delivered to our customers. But let's start with faster time the operation by running iota. Who on open shift? Is it faster than, let's say, using kubernetes and other platforms >>are >>objective iota. Who is to be accessible across multiple cloud platforms, right? And so by hosting our application and containers were able to achieve this. So to answer your question, it's faster to create and use your application images using container tools like kubernetes with open shift as compared to, like kubernetes with docker cry over container D. Okay, so we got a bit technical there. Can you explain that in a bit more detail? Yeah, there's a bit of vocabulary involved, uh, so basically, containers are used in developing things like databases, Web servers or applications such as I have top. What's great about containers is that they split the workload so developers can select the libraries without breaking anything. And since Hammond's can update the host without interrupting the programmers. Uh, now, open shift works hand in hand with kubernetes to provide a way to build those containers for applications. Okay, got It s basically containers make life easier for developers and system happens. How does open shift differ from other platforms? Well, this kind of leads into the second benefit I want to talk about, which is simplicity. Basically, there's a lot of steps involved with when using kubernetes with docker. But open shift simplifies this with their source to image process that takes the source code and turns it into a container image. But that's not all. Open shift has a lot of automation and features that simplify working with containers, an important one being its Web console. Here. I've set up a light version of open ship called Code Ready Containers, and I was able to set up her application right from the Web console. And I was able to set up this entire thing in Windows, Mac and Lennox. So its environment agnostic in that sense. Okay, so I think I've seen the top left that this is a developers view. What would a systems admin view look like? It's a good question. So here's the administrator view and this kind of ties into the benefit of control. Um, this view gives insights into each one of the applications and containers that are running, and you could make changes without affecting deployment. Andi can also, within this view, set up each layer of security, and there's multiple that you can prop up. But I haven't fully messed around with it because with my luck, I'd probably locked myself out. So that seems pretty secure. Is there a single point security such as you use a log in? Or are there multiple layers of security? Yeah, there are multiple layers of security. There's your user login security groups and general role based access controls. Um, but there's also a ton of layers of security surrounding like the containers themselves. But for the sake of time, I won't get too far into it. Okay, eso you mentioned simplicity In time. The operation is being two of the benefits. You also briefly mention automation. And as you know, automation is the backbone of our platform here, Toyota Ho. So that's certainly grabbed my attention. Can you go a bit more in depth in terms of automation? Open shift provides extensive automation that speeds up that time the operation. Right. So the latest versions of open should come with a built in cryo container engine, which basically means that you get to skip that container engine insulation step and you don't have to, like, log into each individual container host and configure networking, configure registry servers, storage, etcetera. So I'd say, uh, it automates the more boring kind of tedious process is Okay, so I see the iota ho template there. What does it allow me to do? Um, in terms of automation in application development. So we've created an open shift template which contains our application. This allows developers thio instantly, like set up our product within that template. So, Noah Last question. Speaking of vocabulary, you mentioned earlier digital resilience of the term we're hearing, especially in the banking and finance world. Um, it seems from what you described, industries like banking and finance would be more resilient using open shift, Correct. Yeah, In terms of digital resilience, open shift will give you better control over the consumption of resource is each container is using. In addition, the benefit of containers is that, like I mentioned earlier since Hammond's can troubleshoot servers about bringing down the application and if the application does go down is easy to bring it back up using templates and, like the other automation features that open ship provides. Okay, so thanks so much. Know us? So any final thoughts you want to share? Yeah. I just want to give a quick recap with, like, the five benefits that you gained by using open shift. Uh, the five are timeto operation automation, control, security and simplicity. You could deploy applications faster. You could simplify the workload you could automate. A lot of the otherwise tedious processes can maintain full control over your workflow. And you could assert digital resilience within your environment. Guys, >>Thanks for that. Appreciate the demo. Um, I wonder you guys have been talking about the combination of a Iot Tahoe and red hat. Can you tie that in subito Digital resilience >>Specifically? Yeah, sure, Dave eso when we speak to the benefits of security controls in terms of digital resilience at Io Tahoe, we automated detection and apply controls at the data level, so this would provide for more enhanced security. >>Okay, But so if you were trying to do all these things manually. I mean, what what does that do? How much time can I compress? What's the time to value? >>So with our latest versions, Biota we're taking advantage of faster deployment time associated with container ization and kubernetes. So this kind of speeds up the time it takes for customers. Start using our software as they be ableto quickly spin up io towel on their own on premise environment are otherwise in their own cloud environment, like including aws. Assure or call GP on IBM Cloud a quick start templates allow flexibility deploy into multi cloud environments all just using, like, a few clicks. Okay, so so now just quickly add So what we've done iota, Who here is We've really moved our customers away from the whole idea of needing a team of engineers to apply controls to data as compared to other manually driven work flows. Eso with templates, automation, previous policies and data controls. One person can be fully operational within a few hours and achieve results straight out of the box on any cloud. >>Yeah, we've been talking about this theme of abstracting the complexity. That's really what we're seeing is a major trend in in this coming decade. Okay, great. Thanks, Sabina. Noah, How could people get more information or if they have any follow up questions? Where should they go? >>Yeah, sure. They've. I mean, if you guys are interested in learning more, you know, reach out to us at info at iata ho dot com to speak with one of our sales engineers. I mean, we love to hear from you, so book a meeting as soon as you can. All >>right. Thanks, guys. Keep it right there from or cube content with.

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle, everybody to Cuban cloud. My name is Dave Volonte, and I'll be here throughout the day with my co host, John Ferrier, who was quarantined in an undisclosed location in California. He's all good. Don't worry. Just precautionary. John, how are you doing? >>Hey, great to see you. John. Quarantine. My youngest daughter had covitz, so contact tracing. I was negative in quarantine at a friend's location. All good. >>Well, we wish you the best. Yeah, well, right. I mean, you know what's it like, John? I mean, you're away from your family. Your basically shut in, right? I mean, you go out for a walk, but you're really not in any contact with anybody. >>Correct? Yeah. I mean, basically just isolation, Um, pretty much what everyone's been kind of living on, kind of suffering through, but hopefully the vaccines are being distributed. You know, one of the things we talked about it reinvent the Amazon's cloud conference. Was the vaccine on, but just the whole workflow around that it's gonna get better. It's kind of really sucky. Here in the California area, they haven't done a good job, a lot of criticism around, how that's rolling out. And, you know, Amazon is now offering to help now that there's a new regime in the U. S. Government S o. You know, something to talk about, But certainly this has been a terrible time for Cove it and everyone in the deaths involved. But it's it's essentially pulled back the covers, if you will, on technology and you're seeing everything. Society. In fact, um, well, that's big tech MIT disinformation campaigns. All these vulnerabilities and cyber, um, accelerated digital transformation. We'll talk about a lot today, but yeah, it's totally changed the world. And I think we're in a new generation. I think this is a real inflection point, Dave. You know, modern society and the geo political impact of this is significant. You know, one of the benefits of being quarantined you'd be hanging out on these clubhouse APS, uh, late at night, listening to experts talk about what's going on, and it's interesting what's happening with with things like water and, you know, the island of Taiwan and China and U. S. Sovereignty, data, sovereignty, misinformation. So much going on to talk about. And, uh, meanwhile, companies like Mark injuries in BC firm starting a media company. What's going on? Hell freezing over. So >>we're gonna be talking about a lot of that stuff today. I mean, Cuba on cloud. It's our very first virtual editorial event we're trying to do is bring together our community. It's a it's an open forum and we're we're running the day on our 3 65 software platform. So we got a great lineup. We got CEO Seo's data Practitioners. We got a hard core technologies coming in, cloud experts, investors. We got some analysts coming in and we're creating this day long Siri's. And we've got a number of sessions that we've developed and we're gonna unpack. The future of Cloud computing in the coming decade is, John said, we're gonna talk about some of the public policy new administration. What does that mean for tech and for big tech in General? John, what can you add to that? >>Well, I think one of the things that we talked about Cove in this personal impact to me but other people as well. One of the things that people are craving right now is information factual information, truth texture that we call it. But hear this event for us, Davis, our first inaugural editorial event. Robbo, Kristen, Nicole, the entire Cube team Silicon angle, really trying to put together Morva cadence we're gonna doom or of these events where we can put out feature the best people in our community that have great fresh voices. You know, we do interview the big names Andy Jassy, Michael Dell, the billionaires with people making things happen. But it's often the people under there that are the rial newsmakers amid savory, for instance, that Google one of the most impressive technical people, he's gotta talk. He's gonna present democratization of software development in many Mawr riel people making things happen. And I think there's a communal element. We're going to do more of these. Obviously, we have, uh, no events to go to with the Cube. So we have the cube virtual software that we have been building and over years and now perfecting and we're gonna introduce that we're gonna put it to work, their dog footing it. We're gonna put that software toe work. We're gonna do a lot mawr virtual events like this Cuban cloud Cuban startup Cuban raising money. Cuban healthcare, Cuban venture capital. Always think we could do anything. Question is, what's the right story? What's the most important stories? Who's telling it and increase the aperture of the lens of the industry that we have and and expose that and fastest possible. That's what this software, you'll see more of it. So it's super exciting. We're gonna add new features like pulling people up on stage, Um, kind of bring on the clubhouse vibe and more of a community interaction with people to meet each other, and we'll roll those out. But the goal here is to just showcase it's cloud story in a way from people that are living it and providing value. So enjoy the day is gonna be chock full of presentations. We're gonna have moderated chat in these sessions, so it's an all day event so people can come in, drop out, and also that's everything's on demand immediately after the time slot. But you >>want to >>participate, come into the time slot into the cube room or breakout session. Whatever you wanna call it, it's a cube room, and the people in there chatting and having a watch party. So >>when you're in that home page when you're watching, there's a hero video there. Beneath that, there's a calendar, and you'll see that red line is that red horizontal line of vertical line is rather, it's a linear clock that will show you where we are in the day. If you click on any one of those sessions that will take you into the chat, we'll take you through those in a moment and share with you some of the guests that we have upcoming and and take you through the day what I wanted to do. John is trying to set the stage for the conversations that folks are gonna here today. And to do that, I wanna ask the guys to bring up a graphic. And I want to talk to you, John, about the progression of cloud over time and maybe go back to the beginning and review the evolution of cloud and then really talk a little bit about where we think it Z headed. So, guys, if you bring up that graphic when a W S announced s three, it was March of 2000 and six. And as you recall, John you know, nobody really. In the vendor and user community. They didn't really pay too much attention to that. And then later that year, in August, it announced E C two people really started. They started to think about a new model of computing, but they were largely, you know, chicken tires. And it was kind of bleeding edge developers that really leaned in. Um what? What were you thinking at the time? When when you saw, uh, s three e c to this retail company coming into the tech world? >>I mean, I thought it was totally crap. I'm like, this is terrible. But then at that time, I was thinking working on I was in between kind of start ups and I didn't have a lot of seed funding. And then I realized the C two was freaking awesome. But I'm like, Holy shit, this is really great because I don't need to pay a lot of cash, the Provisional Data center, or get a server. Or, you know, at that time, state of the art startup move was to buy a super micro box or some sort of power server. Um, it was well past the whole proprietary thing. But you have to assemble probably anyone with 5 to 8 grand box and go in, and we'll put a couple ghetto rack, which is basically, uh, you know, you put it into some coasting location. It's like with everybody else in the tech ghetto of hosting, still paying monthly fees and then maintaining it and provisioning that's just to get started. And then Amazon was just really easy. And then from there you just It was just awesome. I just knew Amazon would be great. They had a lot of things that they had to fix. You know, custom domains and user interface Council got better and better, but it was awesome. >>Well, what we really saw the cloud take hold from my perspective anyway, was the financial crisis in, you know, 709 It put cloud on the radar of a number of CFOs and, of course, shadow I T departments. They wanted to get stuff done and and take I t in in in, ah, pecs, bite sized chunks. So it really was. There's cloud awakening and we came out of that financial crisis, and this we're now in this 10 year plus boom um, you know, notwithstanding obviously the economic crisis with cove it. But much of it was powered by the cloud in the decade. I would say it was really about I t transformation. And it kind of ironic, if you will, because the pandemic it hits at the beginning of this decade, >>and it >>creates this mandate to go digital. So you've you've said a lot. John has pulled forward. It's accelerated this industry transformation. Everybody talks about that, but and we've highlighted it here in this graphic. It probably would have taken several more years to mature. But overnight you had this forced march to digital. And if you weren't a digital business, you were kind of out of business. And and so it's sort of here to stay. How do you see >>You >>know what this evolution and what we can expect in the coming decades? E think it's safe to say the last 10 years defined by you know, I t transformation. That's not gonna be the same in the coming years. How do you see it? >>It's interesting. I think the big tech companies are on, but I think this past election, the United States shows um, the power that technology has. And if you look at some of the main trends in the enterprise specifically around what clouds accelerating, I call the second wave of innovations coming where, um, it's different. It's not what people expect. Its edge edge computing, for instance, has talked about a lot. But industrial i o t. Is really where we've had a lot of problems lately in terms of hacks and malware and just just overall vulnerabilities, whether it's supply chain vulnerabilities, toe actual disinformation, you know, you know, vulnerabilities inside these networks s I think this network effects, it's gonna be a huge thing. I think the impact that tech will have on society and global society geopolitical things gonna be also another one. Um, I think the modern application development of how applications were written with data, you know, we always been saying this day from the beginning of the Cube data is his integral part of the development process. And I think more than ever, when you think about cloud and edge and this distributed computing paradigm, that cloud is now going next level with is the software and how it's written will be different. You gotta handle things like, where's the compute component? Is it gonna be at the edge with all the server chips, innovations that Amazon apple intel of doing, you're gonna have compute right at the edge, industrial and kind of human edge. How does that work? What's Leighton see to that? It's it really is an edge game. So to me, software has to be written holistically in a system's impact on the way. Now that's not necessarily nude in the computer science and in the tech field, it's just gonna be deployed differently. So that's a complete rewrite, in my opinion of the software applications. Which is why you're seeing Amazon Google VM Ware really pushing Cooper Netease and these service messes in the micro Services because super critical of this technology become smarter, automated, autonomous. And that's completely different paradigm in the old full stack developer, you know, kind of model. You know, the full stack developer, his ancient. There's no such thing as a full stack developer anymore, in my opinion, because it's a half a stack because the cloud takes up the other half. But no one wants to be called the half stack developer because it doesn't sound as good as Full Stack, but really Cloud has eliminated the technology complexity of what a full stack developer used to dio. Now you can manage it and do things with it, so you know, there's some work to done, but the heavy lifting but taking care of it's the top of the stack that I think is gonna be a really critical component. >>Yeah, and that that sort of automation and machine intelligence layer is really at the top of the stack. This this thing becomes ubiquitous, and we now start to build businesses and new processes on top of it. I wanna I wanna take a look at the Big Three and guys, Can we bring up the other The next graphic, which is an estimate of what the revenue looks like for the for the Big three. And John, this is I asked and past spend for the Big Three Cloud players. And it's It's an estimate that we're gonna update after earning seasons, and I wanna point a couple things out here. First is if you look at the combined revenue production of the Big Three last year, it's almost 80 billion in infrastructure spend. I mean, think about that. That Z was that incremental spend? No. It really has caused a lot of consolidation in the on Prem data center business for guys like Dell. And, you know, um, see, now, part of the LHP split up IBM Oracle. I mean, it's etcetera. They've all felt this sea change, and they had to respond to it. I think the second thing is you can see on this data. Um, it's true that azure and G C P they seem to be growing faster than a W s. We don't know the exact numbers >>because >>A W S is the only company that really provides a clean view of i s and pass. Whereas Microsoft and Google, they kind of hide the ball in their numbers. I mean, I don't blame them because they're behind, but they do leave breadcrumbs and clues about growth rates and so forth. And so we have other means of estimating, but it's it's undeniable that azure is catching up. I mean, it's still quite distance the third thing, and before I want to get your input here, John is this is nuanced. But despite the fact that Azure and Google the growing faster than a W s. You can see those growth rates. A W s I'll call this out is the only company by our estimates that grew its business sequentially last quarter. Now, in and of itself, that's not significant. But what is significant is because AWS is so large there $45 billion last year, even if the slower growth rates it's able to grow mawr and absolute terms than its competitors, who are basically flat to down sequentially by our estimates. Eso So that's something that I think is important to point out. Everybody focuses on the growth rates, but it's you gotta look at also the absolute dollars and, well, nonetheless, Microsoft in particular, they're they're closing the gap steadily, and and we should talk more about the competitive dynamics. But I'd love to get your take on on all this, John. >>Well, I mean, the clouds are gonna win right now. Big time with the one the political climate is gonna be favoring Big check. But more importantly, with just talking about covert impact and celebrating the digital transformation is gonna create a massive rising tide. It's already happening. It's happening it's happening. And again, this shift in programming, uh, models are gonna really kinda accelerating, create new great growth. So there's no doubt in my mind of all three you're gonna win big, uh, in the future, they're just different, You know, the way they're going to market position themselves, they have to be. Google has to be a little bit different than Amazon because they're smaller and they also have different capabilities, then trying to catch up. So if you're Google or Microsoft, you have to have a competitive strategy to decide. How do I wanna ride the tide If you will put the rising tide? Well, if I'm Amazon, I mean, if I'm Microsoft and Google, I'm not going to try to go frontal and try to copy Amazon because Amazon is just pounding lead of features and scale and they're different. They were, I would say, take advantage of the first mover of pure public cloud. They really awesome. It passed and I, as they've integrated in Gardner, now reports and integrated I as and passed components. So Gardner finally got their act together and said, Hey, this is really one thing. SAS is completely different animal now Microsoft Super Smart because they I think they played the right card. They have a huge installed base converted to keep office 3 65 and move sequel server and all their core jewels into the cloud as fast as possible, clarified while filling in the gaps on the product side to be cloud. So you know, as you're doing trends job, they're just it's just pedal as fast as you can. But Microsoft is really in. The strategy is just go faster trying. Keep pedaling fast, get the features, feature velocity and try to make it high quality. Google is a little bit different. They have a little power base in terms of their network of strong, and they have a lot of other big data capabilities, so they have to use those to their advantage. So there is. There is there is competitive strategy game application happening with these companies. It's not like apples, the apples, In my opinion, it never has been, and I think that's funny that people talk about it that way. >>Well, you're bringing up some great points. I want guys bring up the next graphic because a lot of things that John just said are really relevant here. And what we're showing is that's a survey. Data from E. T. R R Data partners, like 1400 plus CEOs and I T buyers and on the vertical axis is this thing called Net score, which is a measure of spending momentum. And the horizontal axis is is what's called market share. It's a measure of the pervasiveness or, you know, number of mentions in the data set. There's a couple of key points I wanna I wanna pick up on relative to what John just said. So you see A W S and Microsoft? They stand alone. I mean, they're the hyper scale er's. They're far ahead of the pack and frankly, they have fall down, toe, lose their lead. They spend a lot on Capex. They got the flywheel effects going. They got both spending velocity and large market shares, and so, but they're taking a different approach. John, you're right there living off of their SAS, the state, their software state, Andi, they're they're building that in to their cloud. So they got their sort of a captive base of Microsoft customers. So they've got that advantage. They also as we'll hear from from Microsoft today. They they're building mawr abstraction layers. Andy Jassy has said We don't wanna be in that abstraction layer business. We wanna have access to those, you know, fine grain primitives and eso at an AP level. So so we can move fast with the market. But but But so those air sort of different philosophies, John? >>Yeah. I mean, you know, people who know me know that I love Amazon. I think their product is superior at many levels on in its way that that has advantages again. They have a great sass and ecosystem. They don't really have their own SAS play, although they're trying to add some stuff on. I've been kind of critical of Microsoft in the past, but one thing I'm not critical of Microsoft, and people can get this wrong in the marketplace. Actually, in the journalism world and also in just some other analysts, Microsoft has always had large scale eso to say that Microsoft never had scale on that Amazon owned the monopoly on our franchise on scales wrong. Microsoft had scale from day one. Their business was always large scale global. They've always had infrastructure with MSN and their search and the distributive how they distribute browsers and multiple countries. Remember they had the lock on the operating system and the browser for until the government stepped in in 1997. And since 1997 Microsoft never ever not invested in infrastructure and scale. So that whole premise that they don't compete well there is wrong. And I think that chart demonstrates that there, in there in the hyper scale leadership category, hands down the question that I have. Is that there not as good and making that scale integrate in because they have that legacy cards. This is the classic innovator's dilemma. Clay Christensen, right? So I think they're doing a good job. I think their strategy sound. They're moving as fast as they can. But then you know they're not gonna come out and say We don't have the best cloud. Um, that's not a marketing strategy. Have to kind of hide in this and get better and then double down on where they're winning, which is. Clients are converting from their legacy at the speed of Microsoft, and they have a huge client base, So that's why they're stopping so high That's why they're so good. >>Well, I'm gonna I'm gonna give you a little preview. I talked to gear up your f Who's gonna come on today and you'll see I I asked him because the criticism of Microsoft is they're, you know, they're just good enough. And so I asked him, Are you better than good enough? You know, those are fighting words if you're inside of Microsoft, but so you'll you'll have to wait to see his answer. Now, if you guys, if you could bring that that graphic back up I wanted to get into the hybrid zone. You know where the field is. Always got >>some questions coming in on chat, Dave. So we'll get to those >>great Awesome. So just just real quick Here you see this hybrid zone, this the field is bunched up, and the other companies who have a large on Prem presence and have been forced to initiate some kind of coherent cloud strategy included. There is Michael Michael, multi Cloud, and Google's there, too, because they're far behind and they got to take a different approach than a W s. But as you can see, so there's some real progress here. VM ware cloud on AWS stands out, as does red hat open shift. You got VM Ware Cloud, which is a VCF Cloud Foundation, even Dell's cloud. And you'd expect HP with Green Lake to be picking up momentum in the future quarters. And you've got IBM and Oracle, which there you go with the innovator's dilemma. But there, at least in the cloud game, and we can talk about that. But so, John, you know, to your point, you've gotta have different strategies. You're you're not going to take out the big too. So you gotta play, connect your print your on Prem to your cloud, your hybrid multi cloud and try to create new opportunities and new value there. >>Yeah, I mean, I think we'll get to the question, but just that point. I think this Zeri Chen's come on the Cube many times. We're trying to get him to come on lunch today with Features startup, but he's always said on the Q B is a V C at Greylock great firm. Jerry's Cloud genius. He's been there, but he made a point many, many years ago. It's not a winner. Take all the winner. Take most, and the Big Three maybe put four or five in there. We'll take most of the markets here. But I think one of the things that people are missing and aren't talking about Dave is that there's going to be a second tier cloud, large scale model. I don't want to say tear to cloud. It's coming to sound like a sub sub cloud, but a new category of cloud on cloud, right? So meaning if you get a snowflake, did I think this is a tale? Sign to what's coming. VM Ware Cloud is a native has had huge success, mainly because Amazon is essentially enabling them to be successful. So I think is going to be a wave of a more of a channel model of indirect cloud build out where companies like the Cube, potentially for media or others, will build clouds on top of the cloud. So if Google, Microsoft and Amazon, whoever is the first one to really enable that okay, we'll do extremely well because that means you can compete with their scale and create differentiation on top. So what snowflake did is all on Amazon now. They kind of should go to azure because it's, you know, politically correct that have multiple clouds and distribution and business model shifts. But to get that kind of performance they just wrote on Amazon. So there's nothing wrong with that. Because you're getting paid is variable. It's cap ex op X nice categorization. So I think that's the way that we're watching. I think it's super valuable, I think will create some surprises in terms of who might come out of the woodwork on be a leader in a category. Well, >>your timing is perfect, John and we do have some questions in the chat. But before we get to that, I want to bring in Sargi Joe Hall, who's a contributor to to our community. Sargi. Can you hear us? All right, so we got, uh, while >>bringing in Sarpy. Let's go down from the questions. So the first question, Um, we'll still we'll get the student second. The first question. But Ronald ask, Can a vendor in 2021 exist without a hybrid cloud story? Well, story and capabilities. Yes, they could live with. They have to have a story. >>Well, And if they don't own a public cloud? No. No, they absolutely cannot. Uh hey, Sergey. How you doing, man? Good to see you. So, folks, let me let me bring in Sergeant Kohala. He's a He's a cloud architect. He's a practitioner, He's worked in as a technologist. And there's a frequent guest on on the Cube. Good to see you, my friend. Thanks for taking the time with us. >>And good to see you guys to >>us. So we were kind of riffing on the competitive landscape we got. We got so much to talk about this, like, it's a number of questions coming in. Um, but Sargi we wanna talk about you know, what's happening here in Cloud Land? Let's get right into it. I mean, what do you guys see? I mean, we got yesterday. New regime, new inaug inauguration. Do you do you expect public policy? You'll start with you Sargi to have What kind of effect do you think public policy will have on, you know, cloud generally specifically, the big tech companies, the tech lash. Is it gonna be more of the same? Or do you see a big difference coming? >>I think that there will be some changing narrative. I believe on that. is mainly, um, from the regulators side. A lot has happened in one month, right? So people, I think are losing faith in high tech in a certain way. I mean, it doesn't, uh, e think it matters with camp. You belong to left or right kind of thing. Right? But parlor getting booted out from Italy s. I think that was huge. Um, like, how do you know that if a cloud provider will not boot you out? Um, like, what is that line where you draw the line? What are the rules? I think that discussion has to take place. Another thing which has happened in the last 23 months is is the solar winds hack, right? So not us not sort acknowledging that I was Russia and then wish you watching it now, new administration might have a different sort of Boston on that. I think that's huge. I think public public private partnership in security arena will emerge this year. We have to address that. Yeah, I think it's not changing. Uh, >>economics economy >>will change gradually. You know, we're coming out off pandemic. The money is still cheap on debt will not be cheap. for long. I think m and a activity really will pick up. So those are my sort of high level, Uh, >>thank you. I wanna come back to them. And because there's a question that chat about him in a But, John, how do you see it? Do you think Amazon and Google on a slippery slope booting parlor off? I mean, how do they adjudicate between? Well, what's happening in parlor? Uh, anything could happen on clubhouse. Who knows? I mean, can you use a I to find that stuff? >>Well, that's I mean, the Amazons, right? Hiding right there bunkered in right now from that bad, bad situation. Because again, like people we said Amazon, these all three cloud players win in the current environment. Okay, Who wins with the U. S. With the way we are China, Russia, cloud players. Okay, let's face it, that's the reality. So if I wanted to reset the world stage, you know what better way than the, you know, change over the United States economy, put people out of work, make people scared, and then reset the entire global landscape and control all with cash? That's, you know, conspiracy theory. >>So you see the riches, you see the riches, get the rich, get richer. >>Yeah, well, that's well, that's that. That's kind of what's happening, right? So if you start getting into this idea that you can't actually have an app on site because the reason now I'm not gonna I don't know the particular parlor, but apparently there was a reason. But this is dangerous, right? So what? What that's gonna do is and whether it's right or wrong or not, whether political opinion is it means that they were essentially taken offline by people that weren't voted for that. Weren't that when people didn't vote for So that's not a democracy, right? So that's that's a different kind of regime. What it's also going to do is you also have this groundswell of decentralized thinking, right. So you have a whole wave of crypto and decentralized, um, cyber punks out there who want to decentralize it. So all of this stuff in January has created a huge counterculture, and I had predicted this so many times in the Cube. David counterculture is coming and and you already have this kind of counterculture between centralized and decentralized thinking and so I think the Amazon's move is dangerous at a fundamental level. Because if you can't get it, if you can't get buy domain names and you're completely blackballed by by organized players, that's a Mafia, in my opinion. So, uh, and that and it's also fuels the decentralized move because people say, Hey, if that could be done to them, it could be done to me. Just the fact that it could be done will promote a swing in the other direction. I >>mean, independent of of, you know, again, somebody said your political views. I mean Parlor would say, Hey, we're trying to clean this stuff up now. Maybe they didn't do it fast enough, but you think about how new parlor is. You think about the early days of Twitter and Facebook, so they were sort of at a disadvantage. Trying to >>have it was it was partly was what it was. It was a right wing stand up job of standing up something quick. Their security was terrible. If you look at me and Cory Quinn on be great to have him, and he did a great analysis on this, because if you look the lawsuit was just terrible. Security was just a half, asshole. >>Well, and the experience was horrible. I mean, it's not It was not a great app, but But, like you said, it was a quick stew. Hand up, you know, for an agenda. But nonetheless, you know, to start, get to your point earlier. It's like, you know, Are they gonna, you know, shut me down? If I say something that's, you know, out of line, or how do I control that? >>Yeah, I remember, like, 2019, we involved closing sort of remarks. I was there. I was saying that these companies are gonna be too big to fail. And also, they're too big for other nations to do business with. In a way, I think MNCs are running the show worldwide. They're running the government's. They are way. Have seen the proof of that in us this year. Late last year and this year, um, Twitter last night blocked Chinese Ambassador E in us. Um, from there, you know, platform last night and I was like, What? What's going on? So, like, we used to we used to say, like the Chinese company, tech companies are in bed with the Chinese government. Right. Remember that? And now and now, Actually, I think Chinese people can say the same thing about us companies. Uh, it's not a good thing. >>Well, let's >>get some question. >>Let's get some questions from the chat. Yeah. Thank you. One is on M and a subject you mentioned them in a Who do you see is possible emanate targets. I mean, I could throw a couple out there. Um, you know, some of the cdn players, maybe aka my You know, I like I like Hashi Corp. I think they're doing some really interesting things. What do you see? >>Nothing. Hashi Corp. And anybody who's doing things in the periphery is a candidate for many by the big guys, you know, by the hyper scholars and number two tier two or five hyper scholars. Right. Uh, that's why sales forces of the world and stuff like that. Um, some some companies, which I thought there will be a target, Sort of. I mean, they target they're getting too big, because off their evaluations, I think how she Corpuz one, um, >>and >>their bunch in the networking space. Uh, well, Tara, if I say the right that was acquired by at five this week, this week or last week, Actually, last week for $500 million. Um, I know they're founder. So, like I found that, Yeah, there's a lot going on on the on the network side on the anything to do with data. Uh, that those air too hard areas in the cloud arena >>data, data protection, John, any any anything you could adhere. >>And I think I mean, I think ej ej is gonna be where the gaps are. And I think m and a activity is gonna be where again, the bigger too big to fail would agree with you on that one. But we're gonna look at white Spaces and say a white space for Amazon is like a monster space for a start up. Right? So you're gonna have these huge white spaces opportunities, and I think it's gonna be an M and a opportunity big time start ups to get bought in. Given the speed on, I think you're gonna see it around databases and around some of these new service meshes and micro services. I mean, >>they there's a There's a question here, somebody's that dons asking why is Google who has the most pervasive tech infrastructure on the planet. Not at the same level of other to hyper scale is I'll give you my two cents is because it took him a long time to get their heads out of their ads. I wrote a piece of around that a while ago on they just they figured out how to learn the enterprise. I mean, John, you've made this point a number of times, but they just and I got a late start. >>Yeah, they're adding a lot of people. If you look at their who their hiring on the Google Cloud, they're adding a lot of enterprise chops in there. They realized this years ago, and we've talked to many of the top leaders, although Curry and hasn't yet sit down with us. Um, don't know what he's hiding or waiting for, but they're clearly not geared up to chicken Pete. You can see it with some some of the things that they're doing, but I mean competed the level of Amazon, but they have strength and they're playing their strength, but they definitely recognize that they didn't have the enterprise motions and people in the DNA and that David takes time people in the enterprise. It's not for the faint of heart. It's unique details that are different. You can't just, you know, swing the Google playbook and saying We're gonna home The enterprises are text grade. They knew that years ago. So I think you're going to see a good year for Google. I think you'll see a lot of change. Um, they got great people in there. On the product marketing side is Dev Solution Architects, and then the SRE model that they have perfected has been strong. And I think security is an area that they could really had a lot of value it. So, um always been a big fan of their huge network and all the intelligence they have that they could bring to bear on security. >>Yeah, I think Google's problem main problem that to actually there many, but one is that they don't They don't have the boots on the ground as compared to um, Microsoft, especially an Amazon actually had a similar problem, but they had a wide breath off their product portfolio. I always talk about feature proximity in cloud context, like if you're doing one thing. You wanna do another thing? And how do you go get that feature? Do you go to another cloud writer or it's right there where you are. So I think Amazon has the feature proximity and they also have, uh, aske Compared to Google, there's skills gravity. Larger people are trained on AWS. I think Google is trying there. So second problem Google is having is that that they're they're more focused on, I believe, um, on the data science part on their sort of skipping the cool components sort of off the cloud, if you will. The where the workloads needs, you know, basic stuff, right? That's like your compute storage and network. And that has to be well, talk through e think e think they will do good. >>Well, so later today, Paul Dillon sits down with Mids Avery of Google used to be in Oracle. He's with Google now, and he's gonna push him on on the numbers. You know, you're a distant third. Does that matter? And of course, you know, you're just a preview of it's gonna say, Well, no, we don't really pay attention to that stuff. But, John, you said something earlier that. I think Jerry Chen made this comment that, you know, Is it a winner? Take all? No, but it's a winner. Take a lot. You know the number two is going to get a big chunk of the pie. It appears that the markets big enough for three. But do you? Does Google have to really dramatically close the gap on be a much, much closer, you know, to the to the leaders in orderto to compete in this race? Or can they just kind of continue to bump along, siphon off the ad revenue? Put it out there? I mean, I >>definitely can compete. I think that's like Google's in it. Then it they're not. They're not caving, right? >>So But But I wrote I wrote recently that I thought they should even even put mawr oven emphasis on the cloud. I mean, maybe maybe they're already, you know, doubling down triple down. I just I think that is a multi trillion dollar, you know, future for the industry. And, you know, I think Google, believe it or not, could even do more. Now. Maybe there's just so much you could dio. >>There's a lot of challenges with these company, especially Google. They're in Silicon Valley. We have a big Social Justice warrior mentality. Um, there's a big debate going on the in the back channels of the tech scene here, and that is that if you want to be successful in cloud, you have to have a good edge strategy, and that involves surveillance, use of data and pushing the privacy limits. Right? So you know, Google has people within the country that will protest contract because AI is being used for war. Yet we have the most unstable geopolitical seen that I've ever witnessed in my lifetime going on right now. So, um, don't >>you think that's what happened with parlor? I mean, Rob Hope said, Hey, bar is pretty high to kick somebody off your platform. The parlor went over the line, but I would also think that a lot of the employees, whether it's Google AWS as well, said, Hey, why are we supporting you know this and so to your point about social justice, I mean, that's not something. That >>parlor was not just social justice. They were trying to throw the government. That's Rob e. I think they were in there to get selfies and being protesters. But apparently there was evidence from what I heard in some of these clubhouse, uh, private chats. Waas. There was overwhelming evidence on parlor. >>Yeah, but my point is that the employee backlash was also a factor. That's that's all I'm saying. >>Well, we have Google is your Google and you have employees to say we will boycott and walk out if you bid on that jet I contract for instance, right, But Microsoft one from maybe >>so. I mean, that's well, >>I think I think Tom Poole's making a really good point here, which is a Google is an alternative. Thio aws. The last Google cloud next that we were asked at they had is all virtual issue. But I saw a lot of I T practitioners in the audience looking around for an alternative to a W s just seeing, though, we could talk about Mano Cloud or Multi Cloud, and Andy Jassy has his his narrative around, and he's true when somebody goes multiple clouds, they put you know most of their eggs in one basket. Nonetheless, I think you know, Google's got a lot of people interested in, particularly in the analytic side, um, in in an alternative, hedging their bets eso and particularly use cases, so they should be able to do so. I guess my the bottom line here is the markets big enough to have Really? You don't have to be the Jack Welch. I gotta be number one and number two in the market. Is that the conclusion here? >>I think so. But the data gravity and the skills gravity are playing against them. Another problem, which I didn't want a couple of earlier was Google Eyes is that they have to boot out AWS wherever they go. Right? That is a huge challenge. Um, most off the most off the Fortune 2000 companies are already using AWS in one way or another. Right? So they are the multi cloud kind of player. Another one, you know, and just pure purely somebody going 200% Google Cloud. Uh, those cases are kind of pure, if you will. >>I think it's gonna be absolutely multi cloud. I think it's gonna be a time where you looked at the marketplace and you're gonna think in terms of disaster recovery, model of cloud or just fault tolerant capabilities or, you know, look at the parlor, the next parlor. Or what if Amazon wakes up one day and said, Hey, I don't like the cubes commentary on their virtual events, so shut them down. We should have a fail over to Google Cloud should Microsoft and Option. And one of people in Microsoft ecosystem wants to buy services from us. We have toe kind of co locate there. So these are all open questions that are gonna be the that will become certain pretty quickly, which is, you know, can a company diversify their computing An i t. In a way that works. And I think the momentum around Cooper Netease you're seeing as a great connective tissue between, you know, having applications work between clouds. Right? Well, directionally correct, in my opinion, because if I'm a company, why wouldn't I wanna have choice? So >>let's talk about this. The data is mixed on that. I'll share some data, meaty our data with you. About half the companies will say Yeah, we're spreading the wealth around to multiple clouds. Okay, That's one thing will come back to that. About the other half were saying, Yeah, we're predominantly mono cloud we didn't have. The resource is. But what I think going forward is that that what multi cloud really becomes. And I think John, you mentioned Snowflake before. I think that's an indicator of what what true multi cloud is going to look like. And what Snowflake is doing is they're building abstraction, layer across clouds. Ed Walsh would say, I'm standing on the shoulders of Giants, so they're basically following points of presence around the globe and building their own cloud. They call it a data cloud with a global mesh. We'll hear more about that later today, but you sign on to that cloud. So they're saying, Hey, we're gonna build value because so many of Amazon's not gonna build that abstraction layer across multi clouds, at least not in the near term. So that's a really opportunity for >>people. I mean, I don't want to sound like I'm dating myself, but you know the date ourselves, David. I remember back in the eighties, when you had open systems movement, right? The part of the whole Revolution OS I open systems interconnect model. At that time, the networking stacks for S N A. For IBM, decadent for deck we all know that was a proprietary stack and then incomes TCP I p Now os I never really happened on all seven layers, but the bottom layers standardized. Okay, that was huge. So I think if you look at a W s or some of the comments in the chat AWS is could be the s n a. Depends how you're looking at it, right? And you could say they're open. But in a way, they want more Amazon. So Amazon's not out there saying we love multi cloud. Why would they promote multi cloud? They are a one of the clouds they want. >>That's interesting, John. And then subject is a cloud architect. I mean, it's it is not trivial to make You're a data cloud. If you're snowflake, work on AWS work on Google. Work on Azure. Be seamless. I mean, certainly the marketing says that, but technically, that's not trivial. You know, there are latent see issues. Uh, you know, So that's gonna take a while to develop. What? Do your thoughts there? >>I think that multi cloud for for same workload and multi cloud for different workloads are two different things. Like we usually put multiple er in one bucket, right? So I think you're right. If you're trying to do multi cloud for the same workload, that's it. That's Ah, complex, uh, problem to solve architecturally, right. You have to have a common ap ice and common, you know, control playing, if you will. And we don't have that yet, and then we will not have that for a for at least one other couple of years. So, uh, if you if you want to do that, then you have to go to the lower, lowest common denominator in technical sort of stock, if you will. And then you're not leveraging the best of the breed technology off their from different vendors, right? I believe that's a hard problem to solve. And in another thing, is that that that I always say this? I'm always on the death side, you know, developer side, I think, uh, two deaths. Public cloud is a proxy for innovative culture. Right. So there's a catch phrase I have come up with today during shower eso. I think that is true. And then people who are companies who use the best of the breed technologies, they can attract the these developers and developers are the Mazen's off This digital sort of empires, amazingly, is happening there. Right there they are the Mazen's right. They head on the bricks. I think if you don't appeal to developers, if you don't but extensive for, like, force behind educating the market, you can't you can't >>put off. It's the same game Stepping story was seeing some check comments. Uh, guard. She's, uh, linked in friend of mine. She said, Microsoft, If you go back and look at the Microsoft early days to the developer Point they were, they made their phones with developers. They were a software company s Oh, hey, >>forget developers, developers, developers. >>You were if you were in the developer ecosystem, you were treated his gold. You were part of the family. If you were outside that world, you were competitors, and that was ruthless times back then. But they again they had. That was where it was today. Look at where the software defined businesses and starve it, saying it's all about being developer lead in this new way to program, right? So the cloud next Gen Cloud is going to look a lot like next Gen Developer and all the different tools and techniques they're gonna change. So I think, yes, this kind of developer ecosystem will be harnessed, and that's the power source. It's just gonna look different. So, >>Justin, Justin in the chat has a comment. I just want to answer the question about elastic thoughts on elastic. Um, I tell you, elastic has momentum uh, doing doing very well in the market place. Thea Elk Stack is a great alternative that people are looking thio relative to Splunk. Who people complain about the pricing. Of course it's plunks got the easy button, but it is getting increasingly expensive. The problem with elk stack is you know, it's open source. It gets complicated. You got a shard, the databases you gotta manage. It s Oh, that's what Ed Walsh's company chaos searches is all about. But elastic has some riel mo mentum in the marketplace right now. >>Yeah, you know, other things that coming on the chat understands what I was saying about the open systems is kubernetes. I always felt was that is a bad metaphor. But they're with me. That was the TCP I peep In this modern era, C t c p I p created that that the disruptor to the S N A s and the network protocols that were proprietary. So what KUBERNETES is doing is creating a connective tissue between clouds and letting the open source community fill in the gaps in the middle, where kind of way kind of probably a bad analogy. But that's where the disruption is. And if you look at what's happened since Kubernetes was put out there, what it's become kind of de facto and standard in the sense that everyone's rallying around it. Same exact thing happened with TCP was people were trashing it. It is terrible, you know it's not. Of course they were trashed because it was open. So I find that to be very interesting. >>Yeah, that's a good >>analogy. E. Thinks the R C a cable. I used the R C. A cable analogy like the VCRs. When they started, they, every VC had had their own cable, and they will work on Lee with that sort of plan of TV and the R C. A cable came and then now you can put any TV with any VCR, and the VCR industry took off. There's so many examples out there around, uh, standards And how standards can, you know, flair that fire, if you will, on dio for an industry to go sort of wild. And another trend guys I'm seeing is that from the consumer side. And let's talk a little bit on the consuming side. Um, is that the The difference wouldn't be to B and B to C is blood blurred because even the physical products are connected to the end user Like my door lock, the August door lock I didn't just put got get the door lock and forget about that. Like I I value the expedience it gives me or problems that gives me on daily basis. So I'm close to that vendor, right? So So the middle men, uh, middle people are getting removed from from the producer off the technology or the product to the consumer. Even even the sort of big grocery players they have their APs now, uh, how do you buy stuff and how it's delivered and all that stuff that experience matters in that context, I think, um, having, uh, to be able to sell to thes enterprises from the Cloud writer Breuder's. They have to have these case studies or all these sample sort off reference architectures and stuff like that. I think whoever has that mawr pushed that way, they are doing better like that. Amazon is Amazon. Because of that reason, I think they have lot off sort off use cases about on top of them. And they themselves do retail like crazy. Right? So and other things at all s. So I think that's a big trend. >>Great. Great points are being one of things. There's a question in there about from, uh, Yaden. Who says, uh, I like the developer Lead cloud movement, But what is the criticality of the executive audience when educating the marketplace? Um, this comes up a lot in some of my conversations around automation. So automation has been a big wave to automate this automate everything. And then everything is a service has become kind of kind of the the executive suite. Kind of like conversation we need to make everything is a service in our business. You seeing people move to that cloud model. Okay, so the executives think everything is a services business strategy, which it is on some level, but then, when they say Take that hill, do it. Developers. It's not that easy. And this is where a lot of our cube conversations over the past few months have been, especially during the cova with cute virtual. This has come up a lot, Dave this idea, and start being around. It's easy to say everything is a service but will implement it. It's really hard, and I think that's where the developer lead Connection is where the executive have to understand that in order to just say it and do it are two different things. That digital transformation. That's a big part of it. So I think that you're gonna see a lot of education this year around what it means to actually do that and how to implement it. >>I'd like to comment on the as a service and subject. Get your take on it. I mean, I think you're seeing, for instance, with HP Green Lake, Dell's come out with Apex. You know IBM as its utility model. These companies were basically taking a page out of what I what I would call a flawed SAS model. If you look at the SAS players, whether it's salesforce or workday, service now s a P oracle. These models are They're really They're not cloud pricing models. They're they're basically you got to commit to a term one year, two year, three year. We'll give you a discount if you commit to the longer term. But you're locked in on you. You probably pay upfront. Or maybe you pay quarterly. That's not a cloud pricing model. And that's why I mean, they're flawed. You're seeing companies like Data Dog, for example. Snowflake is another one, and they're beginning to price on a consumption basis. And that is, I think, one of the big changes that we're going to see this decade is that true cloud? You know, pay by the drink pricing model and to your point, john toe, actually implement. That is, you're gonna need a whole new layer across your company on it is quite complicated it not even to mention how you compensate salespeople, etcetera. The a p. I s of your product. I mean, it is that, but that is a big sea change that I see coming. Subject your >>thoughts. Yeah, I think like you couldn't see it. And like some things for this big tech exacts are hidden in the plain >>sight, right? >>They don't see it. They they have blind spots, like Look at that. Look at Amazon. They went from Melissa and 200 millisecond building on several s, Right, Right. And then here you are, like you're saying, pay us for the whole year. If you don't use the cloud, you lose it or will pay by month. Poor user and all that stuff like that that those a role models, I think these players will be forced to use that term pricing like poor minute or for a second, poor user. That way, I think the Salesforce moral is hybrid. They're struggling in a way. I think they're trying to bring the platform by doing, you know, acquisition after acquisition to be a platform for other people to build on top off. But they're having a little trouble there because because off there, such pricing and little closeness, if you will. And, uh, again, I'm coming, going, going back to developers like, if you are not appealing to developers who are writing the latest and greatest code and it is open enough, by the way open and open source are two different things that we all know that. So if your platform is not open enough, you will have you know, some problems in closing the deals. >>E. I want to just bring up a question on chat around from Justin didn't fitness. Who says can you touch on the vertical clouds? Has your offering this and great question Great CP announcing Retail cloud inventions IBM Athena Okay, I'm a huge on this point because I think this I'm not saying this for years. Cloud computing is about horizontal scalability and vertical specialization, and that's absolutely clear, and you see all the clouds doing it. The vertical rollouts is where the high fidelity data is, and with machine learning and AI efforts coming out, that's accelerated benefits. There you have tow, have the vertical focus. I think it's super smart that clouds will have some sort of vertical engine, if you will in the clouds and build on top of a control playing. Whether that's data or whatever, this is clearly the winning formula. If you look at all the successful kind of ai implementations, the ones that have access to the most data will get the most value. So, um if you're gonna have a data driven cloud you have tow, have this vertical feeling, Um, in terms of verticals, the data on DSO I think that's super important again, just generally is a strategy. I think Google doing a retail about a super smart because their whole pitches were not Amazon on. Some people say we're not Google, depending on where you look at. So every of these big players, they have dominance in the areas, and that's scarce. Companies and some companies will never go to Amazon for that reason. Or some people never go to Google for other reasons. I know people who are in the ad tech. This is a black and we're not. We're not going to Google. So again, it is what it is. But this idea of vertical specialization relevant in super >>forts, I want to bring to point out to sessions that are going on today on great points. I'm glad you asked that question. One is Alan. As he kicks off at 1 p.m. Eastern time in the transformation track, he's gonna talk a lot about the coming power of ecosystems and and we've talked about this a lot. That that that to compete with Amazon, Google Azure, you've gotta have some kind of specialization and vertical specialization is a good one. But of course, you see in the big Big three also get into that. But so he's talking at one o'clock and then it at 3 36 PM You know this times are strange, but e can explain that later Hillary Hunter is talking about she's the CTO IBM I B M's ah Financial Cloud, which is another really good example of specifying vertical requirements and serving. You know, an audience subject. I think you have some thoughts on this. >>Actually, I lost my thought. E >>think the other piece of that is data. I mean, to the extent that you could build an ecosystem coming back to Alan Nancy's premise around data that >>billions of dollars in >>their day there's billions of dollars and that's the title of the session. But we did the trillion dollar baby post with Jazzy and said Cloud is gonna be a trillion dollars right? >>And and the point of Alan Answer session is he's thinking from an individual firm. Forget the millions that you're gonna save shifting to the cloud on cost. There's billions in ecosystems and operating models. That's >>absolutely the business value. Now going back to my half stack full stack developer, is the business value. I've been talking about this on the clubhouses a lot this past month is for the entrepreneurs out there the the activity in the business value. That's the new the new intellectual property is the business logic, right? So if you could see innovations in how work streams and workflow is gonna be a configured differently, you have now large scale cloud specialization with data, you can move quickly and take territory. That's much different scenario than a decade ago, >>at the point I was trying to make earlier was which I know I remember, is that that having the horizontal sort of features is very important, as compared to having vertical focus. You know, you're you're more healthcare focused like you. You have that sort of needs, if you will, and you and our auto or financials and stuff like that. What Google is trying to do, I think that's it. That's a good thing. Do cook up the reference architectures, but it's a bad thing in a way that you drive drive away some developers who are most of the developers at 80 plus percent, developers are horizontal like you. Look at the look into the psyche of a developer like you move from company to company. And only few developers will say I will stay only in health care, right? So I will only stay in order or something of that, right? So they you have to have these horizontal capabilities which can be applied anywhere on then. On top >>of that, I think that's true. Sorry, but I'll take a little bit different. Take on that. I would say yes, that's true. But remember, remember the old school application developer Someone was just called in Application developer. All they did was develop applications, right? They pick the framework, they did it right? So I think we're going to see more of that is just now mawr of Under the Covers developers. You've got mawr suffer defined networking and software, defined storage servers and cloud kubernetes. And it's kind of like under the hood. But you got your, you know, classic application developer. I think you're gonna see him. A lot of that come back in a way that's like I don't care about anything else. And that's the promise of cloud infrastructure is code. So I think this both. >>Hey, I worked. >>I worked at people solved and and I still today I say into into this context, I say E r P s are the ultimate low code. No code sort of thing is right. And what the problem is, they couldn't evolve. They couldn't make it. Lightweight, right? Eso um I used to write applications with drag and drop, you know, stuff. Right? But But I was miserable as a developer. I didn't Didn't want to be in the applications division off PeopleSoft. I wanted to be on the tools division. There were two divisions in most of these big companies ASAP. Oracle. Uh, like companies that divisions right? One is the cooking up the tools. One is cooking up the applications. The basketball was always gonna go to the tooling. Hey, >>guys, I'm sorry. We're almost out of time. I always wanted to t some of the sections of the day. First of all, we got Holder Mueller coming on at lunch for a power half hour. Um, you'll you'll notice when you go back to the home page. You'll notice that calendar, that linear clock that we talked about that start times are kind of weird like, for instance, an appendix coming on at 1 24. And that's because these air prerecorded assets and rather than having a bunch of dead air, we're just streaming one to the other. So so she's gonna talk about people, process and technology. We got Kathy Southwick, whose uh, Silicon Valley CEO Dan Sheehan was the CEO of Dunkin Brands and and he was actually the c 00 So it's C A CEO connecting the dots to the business. Daniel Dienes is the CEO of you I path. He's coming on a 2:47 p.m. East Coast time one of the hottest companies, probably the fastest growing software company in history. We got a guy from Bain coming on Dave Humphrey, who invested $750 million in Nutanix. He'll explain why and then, ironically, Dheeraj Pandey stew, Minuteman. Our friend interviewed him. That's 3 35. 1 of the sessions are most excited about today is John McD agony at 403 p. M. East Coast time, she's gonna talk about how to fix broken data architectures, really forward thinking stuff. And then that's the So that's the transformation track on the future of cloud track. We start off with the Big Three Milan Thompson Bukovec. At one oclock, she runs a W s storage business. Then I mentioned gig therapy wrath at 1. 30. He runs Azure is analytics. Business is awesome. Paul Dillon then talks about, um, IDs Avery at 1 59. And then our friends to, um, talks about interview Simon Crosby. I think I think that's it. I think we're going on to our next session. All right, so keep it right there. Thanks for watching the Cuban cloud. Uh huh.

>>from around the globe. It's the Cube presenting innovation for good, brought to you by on shape. >>Hello, everyone, and welcome to Innovation for Good Program, hosted by the Cuban. Brought to You by on Shape, which is a PTC company. My name is Dave Valentin. I'm coming to you from our studios outside of Boston. I'll be directing the conversations today. It's a very exciting, all live program. We're gonna look at how product innovation has evolved, where it's going and how engineers, entrepreneurs and educators are applying cutting edge, cutting edge product development techniques and technology to change our world. You know, the pandemic is, of course, profoundly impacted society and altered how individuals and organizations they're gonna be thinking about and approaching the coming decade. Leading technologists, engineers, product developers and educators have responded to the new challenges that we're facing from creating lifesaving products to helping students learn from home toe how to apply the latest product development techniques and solve the world's hardest problems. And in this program, you'll hear from some of the world's leading experts and practitioners on how product development and continuous innovation has evolved, how it's being applied toe positive, positively affect society and importantly where it's going in the coming decades. So let's get started with our first session fueling Tech for good. And with me is John Herstek, who is the president of the Suffers, a service division of PTC, which required on shape just over a year ago, where John was the CEO and co founder. And Dana Grayson is here. She is the co founder and general partner at Construct Capital, a new venture capital firm. Folks, welcome to the program. Thanks so much for coming on. Great >>to be here, Dave. >>All right, John. You're very welcome. Dana. Look, John, let's get into it for first. A belated congratulations on the acquisition of Von Shape. That was an awesome seven year journey for your company. Tell our audience a little bit about the story of on shape, but take us back to Day zero. Why did you and your co founders start on shape? Well, >>actually, start before on shaping the You know, David, I've been in this business for almost 40 years. The business of building software tools for product developers and I had been part of some previous products in the industry and companies that had been in their era. Big changes in this market and about, you know, a little Before founding on shape, we started to see the problems product development teams were having with the traditional tools of that era years ago, and we saw the opportunity presented by Cloud Web and Mobile Technology. And we said, Hey, we could use Cloud Web and Mobile to solve the problems of product developers make their Their business is run better. But we have to build an entirely new system, an entirely new company, to do it. And that's what on shapes about. >>Well, so notwithstanding the challenges of co vid and difficulties this year, how is the first year been as, Ah, division of PTC for you guys? How's business? Anything you can share with us? >>Yeah, our first year of PTC has been awesome. It's been, you know, when you get acquired, Dave, you never You know, you have great optimism, but you never know what life will really be like. It's sort of like getting married or something, you know, until you're really doing it, you don't know. And so I'm happy to say that one year into our acquisition, a TPI TC on shape is thriving. It's worked out better than I could have imagined a year ago. Along always, I mean sales are up. In Q four, our new sales rate grew 80% vs Excuse me, our fiscal Q four Q three. In the calendar year, it grew 80% compared to the year before. Our educational uses skyrocketing with around 400% growth, most recently year to year of students and teachers and co vid. And we've launched a major cloud platform using the core of on shape technology called Atlas. So, um, just tons of exciting things going on a TTC. >>That's awesome. But thank you for sharing some of those metrics. And of course, you're very humble individual. You know, people should know a little bit more about you mentioned, you know, we founded solid works, go founded solid, where I actually found it solid works. You had a great exit in the late nineties. But what I really appreciate is, you know, you're an entrepreneur. You've got a passion for the babies that you helped birth. You stayed with the salt systems for a number of years. The company that quiet, solid works well over a decade. And and, of course, you and I have talked about how you participated in the M I t blackjack team. You know, back in the day a Z I say you're very understated, for somebody was so accomplished. So well, >>that's kind of you. But I tend to I tend Thio always keep my eye more on what's ahead. You know what's next then? And you know, I look back Sure to enjoy it and learn from it about what I can put toe work, making new memories, making new successes. >>I love it. Okay, let's bring Dana into the conversation. Hello, Dana. And you Look, you were fairly early investor in on shape when you were with any A. And I think it was like it was a Siri's B. But it was very right close after the A raise. And and you were and still are a big believer in industrial transformation. So take us back. What did you see about on shape back then? That that excited you? >>Thanks. Thanks for that. Yeah. I was lucky to be a early investment in shape. You know, the things that actually attracted me. Don shape were largely around John and, uh, the team. They're really setting out to do something, as John says humbly, something totally new, but really building off of their background was a large part of it. Um, but, you know, I was really intrigued by the design collaboration side of the product. Um, I would say that's frankly what originally attracted me to it. What kept me in the room, you know, in terms of the industrial world was seeing just if you start with collaboration around design what that does to the overall industrial product lifecycle accelerating manufacturing just, you know, modernizing, manufacturing, just starting with design. So I'm really thankful to the on shape guys, because it was one of the first investments I've made that turned me on to the whole sector. And, wow, just such a great pleasure to work with with John and the whole team there. Now see what they're doing inside PTC, >>and you just launched construct capital this year, right in the middle of a pandemic and which is awesome. I love it. And you're focused on early stage investing. Maybe tell us a little bit about construct capital. What? Your investment thesis is and you know, one of the big waves that you're hoping to ride. >>Sure, it construct it is literally lifting out of any what I was doing there, um uh, you're on shape. I went on to invest in companies such as desktop metal and Tulip, to name a couple of them form labs, another one in and around the manufacturing space. But our thesis it construct is broader than just, you know, manufacturing and industrial. It really incorporates all of what we'd call foundational industries that have let yet to be fully tech enabled or digitized. Manufacturing is a big piece of it. Supply chain, logistics, transportation and mobility or not, or other big pieces of it. And together they really drive, you know, half of the GDP in the US and have been very under invested. And frankly, they haven't attracted really great founders like Iran in droves. And I think that's going to change. We're seeing, um, entrepreneurs coming out of the tech world or staggolee into these industries and then bringing them back into the tech world, which is which is something that needs to happen. So John and team were certainly early pioneers and I think, you know, frankly, obviously, that voting with my feet that the next set, a really strong companies are going to come out of this space over the next decade. >>I think there's a huge opportunity to digitize the sort of traditionally non digital organizations. But Dana, you focused. I think it's it's accurate to say you're focused on even Mawr early stage investing now. And I want to understand why you feel it's important to be early. I mean, it's obviously riskier and reward e er, but what do you look for in companies and and founders like John >>Mhm, Um, you know, I think they're different styles of investing all the way up to public market investing. I've always been early stage investors, so I like to work with founders and teams when they're, you know, just starting out. Um, I happened to also think that we were just really early in the whole digital transformation of this world. You know, John and team have been, you know, back from solid works, etcetera around the space for a long time. But again, the downstream impact of what they're doing really changes the whole industry and and so we're pretty early and in digitally transforming that market. Um, so that's another reason why I wanna invest early now, because I do really firmly believe that the next set of strong companies and strong returns for my own investors will be in the spaces. Um, you know, what I look for in Founders are people that really see the world in a different way. And, you know, sometimes some people think of founders or entrepreneurs is being very risk seeking. You know, if you asked John probably and another successful entrepreneurs, they would call themselves sort of risk averse, because by the time they start the company, they really have isolated all the risk out of it and think that they have given their expertise or what they're seeing their just so compelled to go change something, eh? So I look for that type of attitude experience a Z. You can also tell from John. He's fairly humble. So humility and just focus is also really important. Um, that there's a that's a lot of it. Frankly, >>excellent. Thank you. And John, you got such a rich history in the space in one of you could sort of connect the dots over time. I mean, when you look back, what were the major forces that you saw in the market in in the early days? Uh, particularly days of on shape on how is that evolved? And what are you seeing today? Well, I >>think I touched on it earlier. Actually, could I just reflect on what Dana said about risk taking for just a quick one and say, throughout my life, from blackjack to starting solid works on shape, it's about taking calculated risks. Yes, you try to eliminate the risk sa's much as you can, but I always say, I don't mind taking a risk that I'm aware of, and I've calculated through as best I can. I don't like taking risks that I don't know I'm taking. >>That's right. You like to bet on >>sure things as much sure things, or at least where you feel you. You've done the research and you see them and you know they're there and you know, you, you you keep that in mind in the room, and I think that's great. And Dana did so much for us. Dana, I want to thank you again for all that you did it every step of the way from where we started. Thio, Thio You know your journey with us ended formally but continues informally. Now back to you. Um, Dave, I think question about the opportunity and how it's shaped up. Well, I think I touched on it earlier when I said It's about helping product developers. You know, our customers of the people build the future of manufactured goods. Anything you think of that would be manufacturing factory. You know, the chair you're sitting in machine that made your coffee. You know, the computer you're using that trucks that drive by on the street, all the covert product research, the equipment being used to make vaccines. All that stuff is designed by someone, and our job is given the tools to do it better. And I could see the problems that those product developers had that we're slowing them down with using the computing systems of the time. When we built solid works, that was almost 30 years ago. People don't realize that it was in the early >>nineties, and, you know, we did the >>best we could for the early nineties, but what we did, we didn't anticipate the world of today. And so people were having problems with just installing the systems. Dave, you wouldn't believe how hard it is to install these systems. You need a spec up a special windows computer, you know, and make sure you've got all the memory and graphics you need and getting to get that set up. You need to make sure the device drivers air, right, install a big piece of software. Ah, license key. I'm not making this up. They're still around. You may not even know what those are. You know, Dennis laughing because, you know, zero cool people do things like this anymore on but only runs some windows. You want a second user to use it? They need a copy. They need a code. Are they on the same version? It's a nightmare. The teams change. You know? You just say, Well, get everyone on the software. Well, who's everyone? You know? You got a new vendor today? A new customer tomorrow, a new employee. People come on and off the team. The other problem is the data stored in files, thousands of files. This isn't like a spreadsheet or word processor where there's one file to pass around these air thousands of files to make one, even a simple product. People were tearing their hair out. John, what do we do? I've got copies everywhere. I don't know where the latest version is. We tried like, you know, locking people out so that only one person can change it at the time that works against speed. It works against innovation. We saw what was happening with Cloud Web and mobile. So what's happened in the years since is every one of the forces that product developers experience the need for speed, the need for innovation, the need to be more efficient with their people in their capital. Resource is every one of those trends have been amplified since we started on shape by a lot of forces in the world. And covert is amplified all those the need for agility and remote work cove it is amplified all that the same time, The acceptance of cloud. You know, a few years ago, people were like cloud, you know, how is that gonna work now They're saying to me, you know, increasingly, how would you ever even have done this without the cloud? How do you make solid works Work without the cloud? How would that even happen? You know, And once people understand what on shapes about >>and we're the >>Onley full SAS solution software as a service, full SAS solution in our industry. So what's happened in those years? Same problems we saw earlier, but turn up the gain, their bigger problems. And with cloud, we've seen skepticism of years ago turn into acceptance. And now even embracement in the cova driven new normal. >>Yeah. So a lot of friction in the previous environments cloud obviously a huge factor on, I guess. I guess Dana John could see it coming, you know, in the early days of solid works with Salesforce, which is kind of the first major independent SAS player. Well, I guess that was late nineties. So it was post solid works, but pre in shape and their work day was, you know, pre on shape in the mid two thousands. And and but But, you know, the bet was on the SAS model was right for Crick had and and product development, you know, which Maybe the time wasn't a no brainer. Or maybe it was I don't know, but Dana is there. Is there anything that you would invest in today that's not Cloud based? >>Um, that's a great question. I mean, I think we still see things all the time in the manufacturing world that are not cloud based. I think you know, the closer you get to the shop floor in the production environment. Um, e think John and the PTC folks would agree with this, too, but that it's, you know, there's reliability requirements. There's performance requirements. There's still this attitude of, you know, don't touch the printing press. So the cloud is still a little bit scary sometimes. And I think hybrid cloud is a real thing for those or on premise. Solutions, in some cases is still a real thing. What, what were more focused on. And, um, despite whether it's on premise or hybrid or or SAS and Cloud is a frictionless go to market model, um, in the companies we invest in so sass and cloud, or really make that easy to adopt for new users, you know, you sign up, start using a product, um, but whether it's hosted in the cloud, whether it's as you can still distribute buying power. And, um, I would I'm just encouraging customers in the customer world and the more industrial environment to entrust some of their lower level engineers with more budget discretionary spending so they can try more products and unlock innovation. >>Right? The unit economics are so compelling. So let's bring it, you know, toe today's you know, situation. John, you decided to exit about a year ago. You know? What did you see in PTC? Other than the obvious money? What was the strategic fit? >>Yeah, Well, David, I wanna be clear. I didn't exit anything. Really? You >>know, I love you and I don't like that term exit. I >>mean, Dana had exit is a shareholder on and so it's not It's not exit for me. It's just a step in the journey. Um, what we saw in PTC was a partner. First of all, that shared our vision from the top down at PTC. Jim Hempleman, the CEO. He had a great vision for for the impact that SAS can make based on cloud technology. And really is Dana of highlighted so much. It's not just the technology is how you go to market and the whole business being run and how you support and make the customers successful. So Jim shared a vision for the potential. And really, really, um said Hey, come join us and we can do this bigger, Better, faster. We expanded the vision really to include this Atlas platform for hosting other SAS applications. That P D. C. I mean, David Day arrived at PTC. I met the head of the academic program. He came over to me and I said, You know, and and how many people on your team? I thought he'd say 5 40 people on the PTC academic team. It was amazing to me because, you know, we were we were just near about 100 people were required are total company. We didn't even have a dedicated academic team and we had ah, lot of students signing up, you know, thousands and thousands. Well, now we have hundreds of thousands of students were approaching a million users, and that shows you the power of this team that PTC had combined with our product and technology whom you get a big success for us and for the teachers and students to the world. We're giving them great tools. So so many good things were also putting some PTC technology from other parts of PTC back into on shape. One area, a little spoiler, little sneak peek. Working on taking generative design. Dana knows all about generative design. We couldn't acquire that technology were start up, you know, just to too much to do. But PTC owns one of the best in the business. This frustrated technology we're working on putting that into on shaping our customers. Um, will be happy to see it, hopefully in the coming year sometime. >>It's great to see that two way exchange. Now, you both know very well when you start a company, of course, a very exciting time. You know, a lot of baggage, you know, our customers pulling you in a lot of different directions and asking you for specials. You have this kind of clean slate, so to speak in it. I would think in many ways, John, despite you know, your install base, you have a bit of that dynamic occurring today especially, you know, driven by the forced march to digital transformation that cove it caused. So when you sit down with the team PTC and talk strategy, you now have more global resource is you got cohorts selling opportunities. What's the conversation like in terms of where you want to take the division? >>Well, Dave, you actually you sounds like we should have you coming in and talking about strategy because you've got the strategy down. I mean, we're doing everything said global expansion were able to reach across selling. We've got some excellent PTC customers that we can reach reach now and they're finding uses for on shape. I think the plan is to, you know, just go, go, go and grow, grow, grow where we're looking for this year, priorities are expand the product. I mentioned the breath of the product with new things PTC did recently. Another technology that they acquired for on shape. We did an acquisition. It was it was small, wasn't widely announced. It, um, in an area related to interfacing with electrical cad systems. So? So we're doing We're expanding the breath of on shape. We're going Maura. Depth in the areas were already in. We have enormous opportunity. Add more features and functions that's in the product. Go to market. You mentioned it global global presence. That's something we were a little light on a year ago. Now we have a team. Dana may not even know what we have a non shape, dedicated team in Barcelona, based in Barcelona but throughout Europe were doing multiple languages. Um, the academic program just introduced a new product into that space. That's that's even fueling more success and growth there, Um, and of course, continuing to to invest in customer success. And this Atlas platform story I keep mentioning, we're going to soon have We're gonna soon have four other major PTC brands shipping products on our Atlas Saas platform. And so we're really excited about that. That's good for the other PTC products. It's also good for on shape because now there's there's. There's other interesting products that are on shape customers can use take advantage of very easily using, say, a common log in conventions about user experience there used to invest of all their SAS based, so they that makes it easier to begin with. So that's some of the exciting things going on. I think you'll see P. D. C. Um expanding our lead in saas based applications for this sector for our target sectors, not just in in cat and data management. But another area, PTC's Big and his augmented reality with of euphoria, product line leader and industrial uses of a R. That's a whole other story we should do. A whole nother show augmented reality. But these products are amazing. You can You can help factory workers people on, uh, people who are left out of the digital transformation. Sometimes we're standing from machine >>all day. >>They can't be sitting like we are doing Zoom. They could wear a R headset in our tools. Let them create great content. This is an area Dana is invested in in other companies, but what I wanted to note is the new releases of our authoring software. For this, our content getting released this month, used through the Atlas platform, the SAS components of on shape for things like revision management and collaboration on duh workflow activity. All that those are tools that we're able to share leverage. We get a lot of synergy. It's just really good. It's really fun to We'll have a good time, >>that's awesome. And then we're gonna be talking to John MacLean later about Atlas and do a little deeper dive on that. And, Dana, what is your involvement today with with on shape? But you're looking for you know, which of their customers air actually adopting, and they're gonna disrupt their industries. You get good pipeline from that. How do you collaborate today? >>That sounds like a great idea. Um, a Z John will tell you I'm constantly just ask him for advice and impressions of other entrepreneurs and picking his brain on ideas. No formal relationship clearly, but continue to count John and and John and other people in on shaping in the circle of experts that I rely on for their opinions. >>All right, so we have some questions from the crowd here. Uh, one of the questions is for the dream team. You know, John and Dana. What's your next next collective venture? I don't think we're there yet, are we? No. I >>just say, as Dana said, we love talking to her about. You know, Dana, you just returned the compliment. We would try and give you advice and the deals you're looking at, and I'm sort of casually mentoring at least one of your portfolio entrepreneurs, and that's been a lot of fun for May on hopefully a value to them. But also Dana, We uran important pipeline to us in the world of some new things that are happening that we wouldn't see if you know you've shown us some things that you've said. What do you think of this business? And for us, it's like, Wow, it's cool to see that's going on And that's what's supposed to work in an ecosystem like this. So we we deeply value the ongoing relationship. And no, we're not starting something new. I got a lot of work left to do with what I'm doing and really happy. But we can We can collaborate in this way on other ventures. >>I like this question to somebody asking with the cloud options like on shape, Wilmore students have stem opportunities s Oh, that's a great question. Are you because of sass and cloud? Are you able to reach? You know, more students? Much more cost effectively. >>Yeah, Dave, I'm so glad that that that I was asked about this because Yes, and it's extremely gratifying us. Yes, we are because of cloud, because on shape is the only full cloud full SAS system. Our industry were able to reach stem education brings able to be part of bringing step education to students who couldn't get it otherwise. And one of most gratifying gratifying things to me is the emails were getting from teachers, um, that that really, um, on the phone calls that were they really pour their heart out and say We're able to get to students in areas that have very limited compute resource is that don't have an I T staff where they don't know what computer that the students can have at home, and they probably don't even have a computer. We're talking about being able to teach them on a phone to have an android phone a low end android phone. You could do three D modeling on there with on shape. Now you can't do it any other system, but with on shape, you could do it. And so the teacher can say to the students, They have to have Internet access, and I know there's a huge community that doesn't even have Internet access, and we're not able, unfortunately to help that. But if you have Internet and you have even an android phone, we can enable the educator to teach them. And so we have case after case of saving a stem program or expanding it into the students that need it most is the ones we're helping here. So really excited about that. And we're also able to let in addition to the run on run on whatever computing devices they have, we also offer them the tools they need for remote teaching with a much richer experience. You know, could you teach solid works remotely? Well, maybe if the student ran it had a windows workstation, you know, big, big, high and workstation. Maybe it could, but it would be like the difference between collaborating with on shape and collaborate with solid works. Like the difference between a zoom video call and talking on the landline phone. You know, it's a much richer experience, and that's what you need in stem teaching. Stem is hard. So, yeah, we're super super excited about bringing stem to more students because of clouds. >>Yeah, we're talking about innovation for good, and then the discussion, John, you just had it. Really? There could be a whole another vector here. We could discuss on diversity, and I wanna end with just pointing out So, Dana, your new firm. It's a woman led firm, too. Two women leaders, you know, going forward. So that's awesome to see, so really? Yeah, thumbs up on that. Congratulations on getting that off the ground. Yeah. Thank you. Okay. So thank you guys. Really appreciate It was a great discussion. I learned a lot, and I'm sure the audience did a swell in a moment. We're gonna talk with on shape customers to see how they're applying tech for good and some of the products that they're building. So keep it right there. I'm Dave Volonte. You're watching innovation for good on the Cube, the global leader in digital tech event coverage. Stay right there. Yeah.

>>Yeah, yeah. >>Welcome back, everyone. And let's get ready for session number two, which is all around picking the right use cases. We're going to take a look at how to make the most of your data driven journey through the lens of some instructive customer examples. So today we're joined by thought squads David Copay, who is a director of business value consulting like Daniel, who's a customer success manager and then engagement manager. Andrea Frisk, who not so long ago was actually a product manager. Canadian Tire, who are one of our customers. And she was responsible for the thoughts. What implementation? So we figured Who better to get involved? But yeah, let's Let's take it away, David. >>Thanks, Gina. Welcome, everybody. And Andrea Blake looking forward to this session with you. A zoo. We all know preparation early is key to success on Duin. Any project having the right team on sponsorship Thio, build and deploy. Ah, use case is critical being focused on three outcome that you have in mind both the business deliverables and then also the success criteria of how you're going to manage, uh, manage and define success. When you get there, Eyes really critical to to set you up in the right direction initially. So, Andrea, as as we mentioned, uh, you came from an organization that quite several use cases on thoughts about. So maybe you can talk us through some of those preparation steps that, yeah, that you went through and and share some insights on how folks can come prepare appropriately. >>Eso having the right team members makes such a difference. Executive support really helped the Canadian tire adoption spread. It gave the project presence and clout in leadership meetings and helped to drive change from the top down. We had clear goals and success criteria from our executive that we used to shape the go forward plan with training and frame the initial use case roadmap. One of the other key benefits over executive sponsor was that the reporting team for our initial use case rolled up by underhand. So there was a very clear directive for a rapid phase out of the old tools once thought Spot supported the same data story. And this is key because as you start to roll through use cases, you wanna realize the value. And if you're still executing the old the same time as the new. That's not gonna happen. As we expanded into areas where we were unfamiliar with the data in business utilization, we relied on the data experts and and users to inform what success would look like in the new use cases. We learned early on that those who got volunteer old and helping didn't always become the champions. That would help you drive value from the use case. Using the thoughts about it meant tables. We started to seek out users who are consistently logging in after an initial training, indicating their curiosity and appetite to learn more. We also looked for activities outside of just pin board views toe identify users that had the potential to build and guide new users as subject matter experts, not just in a data but in thought spot. This helps us find the right people to cultivate who were already excited about the potential of thought spot and could help us champion a use case. >>That's really helpful, great, great insight for someone who's been there and done that. Blake is as a customer success manager. Obviously, you approach many of the same situations, anything you'd like to add that >>I still along with the right team. My first question with any use cases. Why Why are we doing this? You've gathered all this data and now we want to use it. But But what for? When you get that initial response on Why this use case? Don't stop there. Keep asking Why keep digging? Keep digging. Keep digging. So what you're essentially trying to get at is what does the decision is that we will be made or potentially be made because of this use case. For example, let's say that we're looking at an expenses use case. What will be done with the insides gathered with this use case? Are those insights going? Thio change the expense approval process Now, Once you have that, why defined now it becomes a lot easier to define the success criteria. Success criteria they use. Face can sometimes be difficult to truly defined. But when you understand why it becomes much easier, so now you can document that success criteria. And the hard part at that point is to actually track that success over time, track the success of the use case, which is something that is easily miss but It's something that is incredibly useful to the overall initiative. >>Right measure. Measure the outcomes. You can't manage what you what? You can't what you don't measure right? As the old adage goes, and you know it's part of the business consulting team. That's really where we come in. Is helping customers really fundamentally define? How are we going to measure a success? Aziz. We move forward. Andi, I think you know, I think we've alluded to this a little bit in terms of that sort of ongoing nature of This is, you know, after the title of the session, eyes choosing the right news cases in the plural right? So it's very important to remember that this is not a single point in time event that happens once. This is a constant framework or process, because most organizations will find that there's many use cases, potentially dozens of use cases that thoughts what could be used for, and clearly you can't move forward with all of them. At the same time, eso. Another thing that our team helps customers walk through is what's the impact, the potential value, other particular use case. You know, you, Blake, you mentioned some of those outcomes, is it? Changing the expense processes it around? Reducing customer churn is an increasing speed toe insight and speak the market on defining those measurable outcomes that define the vertical axis here. The strategic importance off that use case. Um, but that's not the only dimension that you're gonna look at the East to deploy factors into that you could have the most valuable use case ever. But if it's going to take you to three years to get it implemented for various reasons, you're not really gonna start with that one, right? So the combination of east to deploy, aligned with the strategic importance or business value really gives you that road map of where to focus to prioritize on use cases. Eso again, Andrea, you've been through this, um, in your prior time at Canadian time. Maybe you can share some thoughts on how you approach that. >>Yeah. So our initial use case was a great launching platform because the merchandizing team had a huge amount across full engagement. So once we had the merchants on board, we started to plan or use case roadmap looking for other areas, and departments were thought spot had already started to spread by word of mouth and we where we felt there was a high strategic importance. As we started to scope these areas, the ease of deployment started to get more complicated. We struggled to get the right people engaged and didn't always have the top down support for resources in the new use case area. We wanted to maintain momentum with the adoption, but it was starting to feel like we were stalling out on the freeway. Then the strategic marketing team reached out and was really excited about getting into thought spot. This was an underserved team where when it came to data, they always had someone else running it for them, and they'd have to request reports and get the information in. Um, and our initial roadmap focused on the biggest impact areas where we could get the most users, and this team was not on the radar. But when we started to engage with them, we realized that this was gonna be an easy deployment. We already had the data and thought spot to support their needs, and it turned into such a great win because as a marketing team, they were so thrilled to have thought spot and to get the data when they needed it and wanted it. They continued to spread the word and let everyone know. But it also gave the project team a quick win to put some gas in the tank and keep us moving. So you want to plan your use case trajectory, but you also need to be willing to adapt to keep the momentum going. >>Yeah, no, that's a That's a really great point. So So Blake is a customer success manager. I'm sure you lived through some integration of this all the time. So any anything you wanted to add that >>Yes. So to Andrew's point, continuous delivery is key for technical folks out there were talking and agile methodology mindset versus a waterfall. So to show value, there's many different factors that air at play. You need to look at the overall business initiatives. We need to look at financial considerations. We need to look at different career objectives and also resource limitations. So when you start thinking about all those different factors, this becomes a mixture of art and science. So, for example, at the beginning of a project when thought spot is has just been purchased or whatever tool has just been purchased. You want to show immediate value to justify that purchase. So in order to show immediate value, you might want to look at a project or a use case that is tightly aligned to a business objective. Therefore, it shows value, and it has data that is ready to go without many different transformations. But as you move forward, you have to come up with a plan that is going to mix together these difficult use cases with the easier use cases and high business values cases versus the lower. So in order to do that, my most successful customers are evaluating those different business factors and putting those into place with an overall use case development plan. >>Really good feedback. That's great. Thank you. Thanks, Blake. Um, I think s a little bit of a reality check here. Right. So I think we all recognize that any technology implementation, um, is gonna have her bumps in the road. It's not gonna be smooth sailing all along the way. You know, we talk about people, process and technology. The technology wrote wrote roadblocks can be infrastructure related there could be some of the data quality issues that you're alluding to there. Like Onda, people in process fall into the sort of the cultural, uh, cultural cultural side of it. Blake, maybe you can spend a couple minutes going through. What? What if some of those bigger roadblocks that people may face on that, um, technical side on how they could both prepare for them and then address them as they come along? >>Yeah. So the most intimidating part of any business intelligence or analytics initiative is that it's going to put the data directly into the hands of the business users. And this is especially true with ocelot. So why this is intimidating is because it's going toe, lay bare and expose any data issues that exist. So this is going to lead to the most common objective that I hear to starting. Any new use case or any FBI initiative overall, which is our data isn't ready. And essentially that is fear of failure. So when data isn't ready and companies aren't ready to start these projects, what happens is to get around those data issues. There's a lot of patchwork that's happening, you know, this patchwork is necessary just to keep the wheels in motion just to keep things going. So what I mean by the patchwork is extracting the data from a source doing some manual manipulation, doing some manipulation directly within the within the database in order to satisfy those business users request. So this keeps things going, but it's not addressing the key issues that are in place now. While it's intimidating to start these initiatives, the beauty of starting these B I initiatives is it's going to force your company to address and fix these issues. And this, to me, is somewhere where thoughts what is a gigantic benefit? It's not something that we talk about necessarily or market, but thought Spot is really good at helping fix these data issues. And I say this for two reasons. One his data quality. So, with thoughts about you can run, searches directly against your most granular level data and find where those data issues exist, and now, especially with embrace, you're running it directly against the source. So thats what is going to really help you figure out those data quality issues. So as you develop a use case, we can uncover those data quality issues and address them accordingly. And second is data governance. So especially again with embrace and our cloud, our cloud structure is you are going to be bringing Companies are going to be bringing data sources from all over the place all into one source and into one logical view. And so traditionally, the problem with that is that your data and source a might be the theoretically the same data and source B. But the numbers are different. And so you have different versions of the truth. So what thoughts about helps you do is when you bring those sources together. Now you're gonna identify those issues, and now you're gonna be forced to address them. You're gonna be forced to address naming convention issues, business logic issues, which business logic translates to the technical logic toe transform that data and then also security and access. Who was actually able to see this data across these different data sources. So overall, the biggest objective eye here is our data isn't ready. But I challenge that. And I say that by taking on this initiative with thought spot, you were going to be directly addressing that issue and thoughts. What's going to help you fix it? >>Yeah, that's Ah, I'd love that observation that, you know, data quality issues. They're not gonna go away by themselves. And if thoughts, thoughts what could be part of the solution, then even better. So that's a That's a really great observation. Eso Andrea, looking at the sort of the cultural side of things the people in process, Um, what are some of the challenges that you've seen there that folks in the audience could that could learn from? >>Yeah. So think about the last time you learned a new system or tool. How long did it take you to get adjusted and get the performance you wanted from it? Maybe you hit the ground running, but maybe you still feel like you're not quite getting the most out of it. Everyone deals with change differently, and sometimes we get stuck in the change curve and never fully adapt. Companies air no different. Ah, lot of the roadblocks you may face are not only from individual struggling to get on board, but can be the result of an organizational culture that may not be used to change or managing it. Their external impacts on how we accept change such as Was there a clear message about the upcoming changes and impacts? Was there a communication channel for questions and concerns? Did individuals feel like their input was sought after and valued? Where there are multiple mediums, toe learn from was their time to learn? Organizational change is hard. And if there isn't a culture that allocates time and resources to training, then realizing success is gonna be an uphill battle. It will be harder to move people forward if they don't have the time to get comfortable and feel acclimated to the new way of doing things. Without the training and change support from the organization, you'll end up running the old and the new simultaneously, which we talked about not in our live supporting users, in both eyes going to negate that value. There were times at Canadian Tire where we really struggled to get key stakeholders engaged or to get leadership by it on the time of the resources that we're gonna be needed and committed Thio to make a use case successful. So gauging where people and the organization are in the change curve is the first step in moving them along the path towards acceptance and integration. So you'll wanna have an action plan to address the concerns and resistance and a way to solicit and channel feedback. >>Yeah, that's Zo great feedback. And I particularly like what you talked about sort of the old and the new because, you know, we've talked about success and measurement on value quite a bit in this session, and ultimately that's that's the goal, right? Is to live a Value s o. This is a framework that we found really helpful visit. Value Team is defining those success criteria really actually falls into two categories on the right hand side. Better decisions. Um, that's ultimately what you're looking to drive with thoughts about right. You're looking to get newer inside faster to be able to drive action and outcomes based on decisions that do. Maybe we're using your gut for previously on the words under that heading. They're going to change by organizations. So you know, those don't get too caught up on those, but it's really around defining, you know, one. Are those better decisions that you're looking to drive, Who what's the persona is gonna be making them one of their actually looking to accomplish when inside. So they're looking to get one of what are the actions they're going to take on those insights? And then how do we measure Thean pact of those actions that then provides us with the the foundation of a business case in our I, um, in parallel to that, it's important to remember that this use case is not just operating in a vacuum, right? Every organization has a Siri's off strategic transformational initiatives move to the cloud democratized data, etcetera. And to the extent that you can tie particular use cases into those key strategic initiatives, really elevates the importance off that use case outside of its own unique business case. In our calculation on Bazzaz several purposes, right, it raises the visibility project. It raises the visibility of the person championing project on. Do you know reality here is that every idea organization has tons of projects have taken invest in, but the ones they're gonna be more likely to invest in other ones that are tied to those strategic initiatives. So it increases the likelihood of getting the support and funding that you need to drive this forward um, that's really around defining the success success criteria upfront. Um, and >>what >>we find is a lot of organizations do that pretty well, and they've got a solid, really solid business case to move forward. But then over time, they kind of forget about that on. Do you know, a year down the line two years down the line, Maybe even, you know, three months, six months down the line. Maybe people have rotated through the business. People have come and gone, and you almost forget the benefit that you're driving, right? And so it's really important to not do that and keep an eye on and track Onda, look back and analyze and realize the value that use cases have driven on. Obviously, the structure of that and what you measure is gonna very significantly by escape. But it's really important there Thio to make sure that you're counting your success and measuring your success. Um, Andrea, I don't any any thoughts on that from from your past experience. >>Yeah, um, success will be different For each use case, 1 may be focused on reducing the time to insights in a fast competitive market, while another may be driven by a need to increase data fluency to reduce risk. The weighting of each of these criterias will shift and and the value perception should as well. Um, but one thing that we don't want to forget is to share your personal successes. So be proud of the work that you've done in the value it's created. Um, if you're a user who has taken advantage of thought spot and managed to grab a competitive edge by having faster in depth access to data, share that in your business reviews. If you're managing the adoption at your company, share your use case winds and user adoption stories. Your customer success team is here to help you articulate the value and leverage the great work being done in and because of thought spot. >>Yeah, long story short here. This benefits everybody. This is something that's easily overlooked and something that it ZZ not to do this to track adoption to define the r o I, but it benefits those benefits. Start spot benefits of customers. Everybody wins. When we do this, >>that's Ah, that's a great point. So, um, so if we talk about you know, as we wrap the session up. You know what can what can folks in the audience dio right now to start making some of this stuff happened? You know, you're Blake again, coming back to you in customer success. How have you and your role help customers take that next step and start executing on some of the things that we've talked about? >>Yeah. So to start off with, I would just say for each use case as much as possible, define the why and to find the success criteria. Just start off with those two, those two elements and over time that that process we'll get more and more refined and our goal within the CSCE or within within thoughts. But overall, not just the C s order is to enable all of our all of our customers to be able to do all these things on their own. And to be a successful, it's possible to be able to pick the right use cases to be able to execute those right use cases as effectively as possible. So we are here to help with that. CS is here to help with that. Your account executives here to help with that, we have use case workshops. We have our professional services team that can get in and help develop use cases. So lots of options available in goal. We all mutually benefit when we try to track towards thes best possible use cases. >>All right, that we're here to help. That's Ah, that's a great way. Thio, wrap up the session there. Thanks, Blake. For all of your thoughts and Andrea to hope everyone in the audience got some valuable insights here on how to choose the right news case and be successful with thoughts about, um, with that being, I'll hand it back over to you. >>Amazing. That was an awesome session. Thank you so much, guys. So our third session is up next, and we're going to be going Global s. Oh, hang on tight as we explore best practices from the extended ecosystem of cloud based analytics. >>Yeah,

>>Yeah, yeah. >>Welcome back to session, too. Thoughts about everywhere. Unlock new revenue streams with embedded search and I Today we're joined by our senior director of Global Oh am Rick Dimel, along with speakers from our thoughts about customer Hayes to discuss how thought spot is open for everyone by unlocking unprecedented value through data search in A I, you'll see how thoughts about compound analytics in your applications and hear how industry leaders are creating new revenue streams with embedded search and a I. You'll also learn how to increase app stickiness on how to create an autonomous this experience for your end users. I'm delighted to introduce our senior director of Global OPM from Phillips Spot, Rick DeMARE on then British Ramesh, chief technology officer, and Leon Roof, director of product management, both from Hayes over to you. Rick, >>Thank you so much. I appreciate it. Hi, everybody. We're here to talk to you about Fox Spot everywhere are branded version of our embedded analytics application. It really our analytics application is all about user experience. And in today's world, user experience could mean a lot of things in ux design methodologies. We want to talk about the things that make our product different from an embedded perspective. If you take a look at what product managers and product design people and engineers are doing in this space, they're looking at a couple of key themes when they design applications for us to consume. One of the key things in the marketplace today is about product led growth, where the product is actually the best marketing tool for the business, not even the sales portion or the marketing department. The product, by the word of mouth, is expanding and getting more people onto the system. Why is that important? It's important because within the first few days of any application, regardless of what it is being used binding users, 70% of those users will lose. Interest will stop coming back. Why do they stop coming back? Because there's no ah ha moment through them. To get engaged within the technology, today's technologies need to create a direct relationship with the user. There can't be a gatekeeper between the user and the products, such as marketing or sales or information. In our case. Week to to make this work, we have toe leverage learning models in leverage learning as it's called Thio. Get the user is engaged, and what that means is we have to give them capabilities they already know how to use and understand. There are too many applications on the marketplace today for for users to figure out. So if we can leverage the best of what other APS have, we can increase the usage of our systems. Because in today's world, what we don't want to do from a product perspective is lead the user to a dead end or from a product methodology. Our perspective. It's called an empty state, and in our world we do that all the time. In the embedded market place. If you look at at the embedded marketplace, it's all visualizations and dashboards, or what I call check engine lights in your application's Well, guess what happens when you hit a check engine life. You've got to call the dealer to get more information about what just took place. The same thing happens in the analytic space where we provide visualizations to users. They get an indicator, but they have to go through your gatekeepers to get access to the real value of that data. What am I looking at? Why is it important the best user experiences out on the marketplace today? They are autonomous. If we wanna leverage the true value of digital transformation, we have to allow our developers to develop, not have them, the gatekeepers to the rial, content to users want. And in today's world, with data growing at much larger and faster levels than we've ever seen. And with that shelf life or value of that data being much shorter and that data itself being much more fragmented, there's no developer or analysts that can create enough visualizations or dashboards in the world to keep the consumption or desire for these users to get access to information up to speed. Clients today require the ability to sift through this information on their own to customize their own content. And if we don't support this methodology, our users are gonna end up feeling powerless and frustrated and coming back to us. The gatekeepers of that information for more information. Loyalty, conversely, can be created when we give the users the ability toe access this information on their own. That is what product like growth is all about in thought spot, as you know we're all about search. It's simple. It's guided as we type. It gives a super fast responses, but it's also smart on the back end handling complexities, and it's really safe from a governance and as well as who gets access to what perspective it's unknown learned environment. Equally important in that learned environment is this expectation that it's not just search on music. It's actually gonna recommend content to me on the fly instantly as I try content I might not even thought of before. Just the way Spotify recommends music to us or Netflix recommends a movie. This is a expected learned behavior, and we don't want to support that so that they can get benefit and get to the ah ha moments much quicker. In the end, which consumption layer do you want to use, the one that leads you to the Dead End Street or the one that gets you to the ah ha moment quickly and easily and does it in an autonomous fashion. Needless to say, the benefits of autonomous user access are well documented today. Natural language search is the wave of the future. It is today. By 2004 75% of organizations are going to be using it. The dashboard is dead. It's no longer going to be utilized through search today, I if we can improve customer satisfaction and customer productivity, we're going to increase pretensions of our retention of our applications. And if we do that just a little bit, it's gonna have a tremendous impact to our bottom line. The way we deploy hotspots. As you know, from today's conversations in the cloud, it could be a manage class, not offering or could be software that runs in your own VPC. We've talked about that at length at this conference. We've also talked about the transformation of application delivery from a Cloud Analytics perspective at length here it beyond. But we apply those same principles to your product development. The benefits are astronomical because not only do you get architectural flexibility to scale up and scale down and right size, but your engineers will increase their productivity because their offerings, because their time and effort is not going to be spent on delivering analytics but delivering their offerings. The speed of innovation isn't gonna be released twice a year or four times a year. It's gonna It can happen on a weekly basis, so your time to market in your margins should increase significantly. At this point, I want a hand. The microphone over to Revert. Tesche was going to tell you a little bit about what they're doing. It hes for cash. >>Thanks, Rick. I just want to introduce myself to the audience. My name is Rotational. Mention the CTO Europe ace. I'm joined my today by my colleague Gillian Ruffles or doctor of product management will be demoing what we have built with thoughts about, >>um but >>just to my introduction, I'm going to talk about five key things. Talk about what we do. What hes, uh we have Really, um what we went through the select that spot with other competitors What we have built with that spot very quickly and last but not least, some lessons learned during the implementation. So just to start with what we do, uh, we're age. We are health care compliance and revenue integrity platform were a saas platform voter on AWS were very short of l A. That's it. Use it on these around 1 50 customers across the U. S. On these include large academic Medical Insight on. We have been in the compliant space for the last 30 plus years, and we were traditionally consulting company. But very recently we have people did more towards software platform model, uh, in terms off why we chose that spot. There were three business problems that I faced when I took this job last year. At age number one is, uh, should be really rapidly deliver new functionality, nor platform, and he agile because some of our product development cycles are in weeks and not months. Hey had a lot of data, which we collected traditionally from the SAS platform, and all should be really create inside stretch experience for our customers. And then the third Big one is what we saw Waas large for customers but really demanding self service capabilities. But they were really not going for the static dash boats and and curated content, but instead they wanted to really use the cell service capabilities. Thio mind the data and get some interesting answers during their questions. So they elevated around three products around these problems statements, and there were 14 reasons why we just start spot number one wars off course. The performance and speed to insights. Uh, we had around 800 to a billion robot of data and we wanted to really kind of mind the data and set up the data in seconds on not minutes and hours. We had a lot of out of the box capabilities with that spot, be it natural language search, predictive algorithms. And also the interactive visualization, which, which was which, Which gave us the agility Thio deliver these products very quickly. And then, uh, the end user experience. We just wanted to make sure that I would users can use this interface s so that they can very quickly, um, do some discovery of data and get some insights very quickly. On last but not least, talksport add a lot of robust AP ice around the platform which helped us embed tot spot into are offering. But those are the four key reasons which we went for thoughts part which we thought was, uh, missing in in the other products we evaluated performance and search, uh, the interactive visualization, the end user experience, and last but not least flexible AP ice, which we could customize into our platform in terms of what we built. We were trying to solve to $50 billion problem in health care, which is around denials. Um so every year, around 2, 50 to $300 billion are denied by players thes air claims which are submitted by providers. And we built offering, which we called it US revenue optimizer. But in plain English, what revenue optimizer does is it gives the capability tow our customers to mind that denials data s so that they can really understand why the claims were being denied. And under what category? Recent reasons. We're all the providers and quarters who are responsible for these claims, Um, that were dryland denials, how they could really do some, uh, prediction off. It is trending based on their historical denial reasons. And then last but not least, we also build some functionality in the platform where we could close the loop between insights, action and outcome that Leon will be showing where we could detect some compliance and revenue risks in the platform. On more importantly, we could, uh, take those risks, put it in a I would say, shopping card and and push it to the stakeholders to take corrective action so the revenue optimizer is something which we built in three months from concept to lunch and and that that pretty much prove the value proposition of thoughts. But while we could kind of take it the market within a short period of time Next leopard >>in terms >>off lessons learned during the implementation thes air, some of the things that came to my mind asses, we're going through this journey. The first one is, uh, focus on the use case formulation, outcomes and wishful story boarding. And that is something that hot spot that's really balance. Now you can you can focus on your business problem formulation and not really focus on your custom dash boarding and technology track, etcetera. So I think it really helped our team to focus on the versus problem, to focus on the outcomes from the problem and more importantly, really spend some time on visualizing What story are we say? Are we trying to say to our customers through revenue optimizer The second lesson learned first When we started this implementation, we did not dualistic data volume and capacity planning exercise and we learned it our way. When we are we loaded a lot of our data sets into that spot. And then Aziz were doing performance optimization. XYZ. We figured out that we had to go back and shot the infrastructure because the data volumes are growing exponentially and we did not account for it. So the biggest lesson learned This is part of your architectural er planning, exercise, always future proof your infrastructure and make sure that you work very closely with the transport engineering team. Um, to make sure that the platform can scale. Uh, the last two points are passport as a robust set of AP Ice and we were able to plug into those AP ice to seamlessly ended the top spot software into a platform. And last but not least, one thing I would like to closest as we start these projects, it's very common that the solution design we run into a lot of surprises. The one thing I should say is, along those 12 weeks, we very closely work with the thoughts, part architecture and accounting, and they were a great partner to work with us to really understand our business problem, and they were along the way to kind of government suggested, recommends and workarounds and more importantly, also, helpers put some other features and functionality which you requested in their engineering roadmap. So it's been a very successful partnership. Um, So I think the biggest take of it is please make sure that you set up your project and operating model value ember thoughts what resources and your team to make sure that they can help you as you. It's some obstacles in the projects so that you can meet your time ones. Uh, those are the key lessons learned from the implementation. And with that, I would pass this to my colleague Leon Rough was going to show you a demo off what we go. >>Thanks for Tesh. So when we were looking Thio provide this to our customer base, we knew that not everyone needed do you access or have available to them the same types of information or at the same particular level of information. And we do have different roles within RMD auto Enterprise platform. So we did, uh, minimize some roles to certain information. We drew upon a persona centric approach because we knew that those different personas had different goals and different reasons for wanting to drive into these insights, and those different personas were on three different levels. So we're looking at the executive level, which is more on the C suite. Chief Compliance Officer. We have a denial trending analyses pin board, which is more for the upper, uh, managers and also exact relatives if they're interested. And then really, um, the targeted denial analysis is more for the day to day analysts, um, the usage so that they could go in and they can really see where the trends are going and how they need to take action and launch into the auditing workflow so within the executive or review, Um, and not to mention that we were integrating and implementing this when everyone was we were focused on co vid. So as you can imagine, just without covert in the picture, our customers are concentrated on denials, and that's why they utilize our platform so they could minimize those risks and then throw in the covert factor. Um, you know, those denial dollars increase substantially over the course of spring and the summer, and we wanted to be able to give them ah, good view of the denials in aggregate as well as's we focus some curated pin boards specific to those areas that were accounting for those high developed denials. So on the Executive Overview Board, we created some banner tiles. The banner tiles are pretty much a blast of information for executives thes air, particular areas where there concentrating and their look looking at those numbers consistently so it provides them away to take a good look at that and have that quick snapshot. Um, more importantly, we did offer as I mentioned some curated pin boards so that it would give customers this turnkey access. They wouldn't necessarily have to wonder, You know, what should I be doing now on Day one, but the day one that we're providing to them these curated insights leads the curiosity and increases that curiosity so that they can go in and start creating their own. But the base curated set is a good overview of their denial dollars and those risks, and we used, um, a subject matter expert within our organization who worked in the field. So it's important to know you know what you're targeting and why you're targeting it and what's important to these personas. Um, not everyone is necessarily interests in all the same information, and you want to really hit on those critical key point to draw them and, um, and allowed them that quick access and answer those questions they may have. So in this particular example, the curated insight that we created was a monthly denial amount by functional area. And as I was mentioning being uber focused on co vid, you know, a lot of scrutiny goes back to those organizations, especially those coding and H i M departments, um, to ensure that their coding correctly, making sure that players aren't sitting on, um, those payments or denying those payments. So if I were in executive and I came in here and this was interesting to me and I want to drill down a little bit, I might say, You know, let me focus more on the functional area than I know probably is our main concern. And that's coating and h i M. And because of it hit in about the early winter. I know that those claims came in and they weren't getting paid until springtime. So that's where I start to see a spike. And what's nice is that the executive can drill down, they may have a hunch, or they can utilize any of the data attributes we made available to them from the Remittance file. So all of these data, um, attributes are related to what's being sent on the 8 35 fear familiar with the anti 8 35 file. So in particular, if I was curious and had a suspicion that these were co vid related or just want to concentrate in that area, um, we have particular flag set up. So the confirmed and suspected cases are pulling in certain diagnosis and procedure codes. And I might say 1.27 million is pretty high. Um, toe look at for that particular month, and then they have the ability to drill down even further. Maybe they want to look at a facility level or where that where that's coming from. Furthermore, on the executive level, we did take advantage of Let me stop here where, um also provided some lagged a so leg. This is important to organizations in this area because they wanna know how long does it take before they re submit a claim that was originally denied before they get paid industry benchmark is about 10 days of 10 days is a fairly good, good, um, basis to look at. And then, obviously anything over that they're going to take a little bit more scrutiny on and want to drill in and understand why that is. And again, they have that capabilities in order to drill down and really get it. Those answers that they're looking for, we also for this particular pin board. And these users thought it would be helpful to utilize the time Siri's forecasting that's made available. So again, thes executives need thio need to keep track and forecast where they're trends were going or what those numbers may look like in the future. And we thought by providing the prediction pins and we have a few prediction pins, um would give them that capability to take a look at that and be able to drill down and use that within, um, certain reporting and such for their organization. Another person, a level that I will go to is, um, Mawr on the analyst side, where those folks are utilizing, um, are auditing workflow and being in our platform, creating audits, completing audits, we have it segregated by two different areas. And this is by claim types so professional or institutional, I'm going to jump in here. And then I am going to go to present mode. So in this particular, um, in this particular view or insight, we're providing that analysts view with something that's really key and critical in their organization is denials related Thio HCC s andi. That's a condition category that kind of forecast, the risk of treatment. And, you know, if that particular patient is probably going to be seen again and have more conditions and higher costs, higher health care spending. So in this example, we're looking at the top 15 attending providers that had those HCC denials. And this is, um, critical because at this point, it really peaks in analyst curiosity. Especially, You know, they'll see providers here and then see the top 15 on the top is generating Ah, hide denial rate. Hi, denial. The dollars for those HCC's and that's a that's a real risk to the organization, because if that behavior continues, um, then those those dollars won't go down. That number won't go down so that analysts then can go in and they can drill down um, I'm going to drill down on diagnosis and then look at the diagnosis name because I have a suspicion, but I'm not exactly sure. And what's great is that they can easily do this. Change the view. Um, you know, it's showing a lot of diagnoses, but what's important is the first one is sepsis and substance is a big one. Substances something that those organizations see a lot of. And if they hover, they can see that 49.57 million, um, is attributed to that. So they may want to look further into that. They'd probably be interested in closing that loop and creating an audit. And so what allowed us to be able to do that for them is we're launching directly into our auditing workflow. So they noticed something in the carried insight. It sparked some investigation, and then they don't have to leave that insight to be able to jump into the auditing workflow and complete that. Answer that question. Okay, so now they're at the point where we've pulled back all the cases that attributed to that dollar amount that we saw on the Insight and the users launching into their auditing workflow. They have the ability Thio select be selective about what cases they wanna pull into the audit or if they were looking, um, as we saw with sepsis, they could pull in their 1600 rose, but they could take a sampling size, which is primarily what they would do. They went audit all 1600 cases, and then from this point in they're into, they're auditing workflow and they'd continue down the path. Looking at those cases they just pulled in and being able Thio finalized the audit and determine, you know, if further, um, education with that provider is needed. So that concludes the demo of how we integrated thought spot into our platform. >>Thank you, LeAnn. And thank you. Re test for taking the time to walk us through. Not only your company, but how Thought spot is helping you Power analytics for your clients. At this point, we want to open this up for a little Q and A, but we want to leave you with the fact that thought spot everywhere. Specifically, it cannot only do this for Hayes, but could do it for any company anywhere they need. Analytical applications providing these applications for their customers, their partners, providers or anybody within their network for more about this, you can see that the website attached below >>Thanks, Rick and thanks for tests and Leon that I find it just fascinating hearing what our customers are doing with our technology. And I certainly have learned 100% more about sepsis than I ever knew before this session. So thank you so much for sharing that it's really is great to see how you're taking our software and putting it into your application. So that's it for this session. But do stay tuned for the next session, which is all about getting the most out of your data and amplifying your insights. With the help of A, I will be joined by two thought spot leaders who will share their first hand experiences. So take a quick breather and come right back