(soft music) >> Hello, everyone. Welcome to theCUBE's presentation of the AWS Startup Showcase. AI and Machine Learning: Top Startups Building Foundational Model Infrastructure. This is season 3, episode 1 of the ongoing series covering the exciting stuff from the AWS ecosystem, talking about machine learning and AI. I'm your host, John Furrier and today we are excited to be joined by Luis Ceze who's the CEO of OctoML and Anna Connolly, VP of customer success and experience OctoML. Great to have you on again, Luis. Anna, thanks for coming on. Appreciate it. >> Thank you, John. It's great to be here. >> Thanks for having us. >> I love the company. We had a CUBE conversation about this. You guys are really addressing how to run foundational models faster for less. And this is like the key theme. But before we get into it, this is a hot trend, but let's explain what you guys do. Can you set the narrative of what the company's about, why it was founded, what's your North Star and your mission? >> Yeah, so John, our mission is to make AI sustainable and accessible for everyone. And what we offer customers is, you know, a way of taking their models into production in the most efficient way possible by automating the process of getting a model and optimizing it for a variety of hardware and making cost-effective. So better, faster, cheaper model deployment. >> You know, the big trend here is AI. Everyone's seeing the ChatGPT, kind of the shot heard around the world. The BingAI and this fiasco and the ongoing experimentation. People are into it, and I think the business impact is clear. I haven't seen this in all of my career in the technology industry of this kind of inflection point. And every senior leader I talk to is rethinking about how to rebuild their business with AI because now the large language models have come in, these foundational models are here, they can see value in their data. This is a 10 year journey in the big data world. Now it's impacting that, and everyone's rebuilding their company around this idea of being AI first 'cause they see ways to eliminate things and make things more efficient. And so now they telling 'em to go do it. And they're like, what do we do? So what do you guys think? Can you explain what is this wave of AI and why is it happening, why now, and what should people pay attention to? What does it mean to them? >> Yeah, I mean, it's pretty clear by now that AI can do amazing things that captures people's imaginations. And also now can show things that are really impactful in businesses, right? So what people have the opportunity to do today is to either train their own model that adds value to their business or find open models out there that can do very valuable things to them. So the next step really is how do you take that model and put it into production in a cost-effective way so that the business can actually get value out of it, right? >> Anna, what's your take? Because customers are there, you're there to make 'em successful, you got the new secret weapon for their business. >> Yeah, I think we just see a lot of companies struggle to get from a trained model into a model that is deployed in a cost-effective way that actually makes sense for the application they're building. I think that's a huge challenge we see today, kind of across the board across all of our customers. >> Well, I see this, everyone asking the same question. I have data, I want to get value out of it. I got to get these big models, I got to train it. What's it going to cost? So I think there's a reality of, okay, I got to do it. Then no one has any visibility on what it costs. When they get into it, this is going to break the bank. So I have to ask you guys, the cost of training these models is on everyone's mind. OctoML, your company's focus on the cost side of it as well as the efficiency side of running these models in production. Why are the production costs such a concern and where specifically are people looking at it and why did it get here? >> Yeah, so training costs get a lot of attention because normally a large number, but we shouldn't forget that it's a large, typically one time upfront cost that customers pay. But, you know, when the model is put into production, the cost grows directly with model usage and you actually want your model to be used because it's adding value, right? So, you know, the question that a customer faces is, you know, they have a model, they have a trained model and now what? So how much would it cost to run in production, right? And now without the big wave in generative AI, which rightfully is getting a lot of attention because of the amazing things that it can do. It's important for us to keep in mind that generative AI models like ChatGPT are huge, expensive energy hogs. They cost a lot to run, right? And given that model usage growth directly, model cost grows directly with usage, what you want to do is make sure that once you put a model into production, you have the best cost structure possible so that you're not surprised when it's gets popular, right? So let me give you an example. So if you have a model that costs, say 1 to $2 million to train, but then it costs about one to two cents per session to use it, right? So if you have a million active users, even if they use just once a day, it's 10 to $20,000 a day to operate that model in production. And that very, very quickly, you know, get beyond what you paid to train it. >> Anna, these aren't small numbers, and it's cost to train and cost to operate, it kind of reminds me of when the cloud came around and the data center versus cloud options. Like, wait a minute, one, it costs a ton of cash to deploy, and then running it. This is kind of a similar dynamic. What are you seeing? >> Yeah, absolutely. I think we are going to see increasingly the cost and production outpacing the costs and training by a lot. I mean, people talk about training costs now because that's what they're confronting now because people are so focused on getting models performant enough to even use in an application. And now that we have them and they're that capable, we're really going to start to see production costs go up a lot. >> Yeah, Luis, if you don't mind, I know this might be a little bit of a tangent, but, you know, training's super important. I get that. That's what people are doing now, but then there's the deployment side of production. Where do people get caught up and miss the boat or misconfigure? What's the gotcha? Where's the trip wire or so to speak? Where do people mess up on the cost side? What do they do? Is it they don't think about it, they tie it to proprietary hardware? What's the issue? >> Yeah, several things, right? So without getting really technical, which, you know, I might get into, you know, you have to understand relationship between performance, you know, both in terms of latency and throughput and cost, right? So reducing latency is important because you improve responsiveness of the model. But it's really important to keep in mind that it often leads diminishing returns. Below a certain latency, making it faster won't make a measurable difference in experience, but it's going to cost a lot more. So understanding that is important. Now, if you care more about throughputs, which is the time it takes for you to, you know, units per period of time, you care about time to solution, we should think about this throughput per dollar. And understand what you want is the highest throughput per dollar, which may come at the cost of higher latency, which you're not going to care about, right? So, and the reality here, John, is that, you know, humans and especially folks in this space want to have the latest and greatest hardware. And often they commit a lot of money to get access to them and have to commit upfront before they understand the needs that their models have, right? So common mistake here, one is not spending time to understand what you really need, and then two, over-committing and using more hardware than you actually need. And not giving yourself enough freedom to get your workload to move around to the more cost-effective choice, right? So this is just a metaphoric choice. And then another thing that's important here too is making a model run faster on the hardware directly translates to lower cost, right? So, but it takes a lot of engineers, you need to think of ways of producing very efficient versions of your model for the target hardware that you're going to use. >> Anna, what's the customer angle here? Because price performance has been around for a long time, people get that, but now latency and throughput, that's key because we're starting to see this in apps. I mean, there's an end user piece. I even seeing it on the infrastructure side where they're taking a heavy lifting away from operational costs. So you got, you know, application specific to the user and/or top of the stack, and then you got actually being used in operations where they want both. >> Yeah, absolutely. Maybe I can illustrate this with a quick story with the customer that we had recently been working with. So this customer is planning to run kind of a transformer based model for tech generation at super high scale on Nvidia T4 GPU, so kind of a commodity GPU. And the scale was so high that they would've been paying hundreds of thousands of dollars in cloud costs per year just to serve this model alone. You know, one of many models in their application stack. So we worked with this team to optimize our model and then benchmark across several possible targets. So that matching the hardware that Luis was just talking about, including the newer kind of Nvidia A10 GPUs. And what they found during this process was pretty interesting. First, the team was able to shave a quarter of their spend just by using better optimization techniques on the T4, the older hardware. But actually moving to a newer GPU would allow them to serve this model in a sub two milliseconds latency, so super fast, which was able to unlock an entirely new kind of user experience. So they were able to kind of change the value they're delivering in their application just because they were able to move to this new hardware easily. So they ultimately decided to plan their deployment on the more expensive A10 because of this, but because of the hardware specific optimizations that we helped them with, they managed to even, you know, bring costs down from what they had originally planned. And so if you extend this kind of example to everything that's happening with generative AI, I think the story we just talked about was super relevant, but the scale can be even higher, you know, it can be tenfold that. We were recently conducting kind of this internal study using GPT-J as a proxy to illustrate the experience of just a company trying to use one of these large language models with an example scenario of creating a chatbot to help job seekers prepare for interviews. So if you imagine kind of a conservative usage scenario where the model generates just 3000 words per user per day, which is, you know, pretty conservative for how people are interacting with these models. It costs 5 cents a session and if you're a company and your app goes viral, so from, you know, beginning of the year there's nobody, at the end of the year there's a million daily active active users in that year alone, going from zero to a million. You'll be spending about $6 million a year, which is pretty unmanageable. That's crazy, right? >> Yeah. >> For a company or a product that's just launching. So I think, you know, for us we see the real way to make these kind of advancements accessible and sustainable, as we said is to bring down cost to serve using these techniques. >> That's a great story and I think that illustrates this idea that deployment cost can vary from situation to situation, from model to model and that the efficiency is so strong with this new wave, it eliminates heavy lifting, creates more efficiency, automates intellect. I mean, this is the trend, this is radical, this is going to increase. So the cost could go from nominal to millions, literally, potentially. So, this is what customers are doing. Yeah, that's a great story. What makes sense on a financial, is there a cost of ownership? Is there a pattern for best practice for training? What do you guys advise cuz this is a lot of time and money involved in all potential, you know, good scenarios of upside. But you can get over your skis as they say, and be successful and be out of business if you don't manage it. I mean, that's what people are talking about, right? >> Yeah, absolutely. I think, you know, we see kind of three main vectors to reduce cost. I think one is make your deployment process easier overall, so that your engineering effort to even get your app running goes down. Two, would be get more from the compute you're already paying for, you're already paying, you know, for your instances in the cloud, but can you do more with that? And then three would be shop around for lower cost hardware to match your use case. So on the first one, I think making the deployment easier overall, there's a lot of manual work that goes into benchmarking, optimizing and packaging models for deployment. And because the performance of machine learning models can be really hardware dependent, you have to go through this process for each target you want to consider running your model on. And this is hard, you know, we see that every day. But for teams who want to incorporate some of these large language models into their applications, it might be desirable because licensing a model from a large vendor like OpenAI can leave you, you know, over provision, kind of paying for capabilities you don't need in your application or can lock you into them and you lose flexibility. So we have a customer whose team actually prepares models for deployment in a SaaS application that many of us use every day. And they told us recently that without kind of an automated benchmarking and experimentation platform, they were spending several days each to benchmark a single model on a single hardware type. So this is really, you know, manually intensive and then getting more from the compute you're already paying for. We do see customers who leave money on the table by running models that haven't been optimized specifically for the hardware target they're using, like Luis was mentioning. And for some teams they just don't have the time to go through an optimization process and for others they might lack kind of specialized expertise and this is something we can bring. And then on shopping around for different hardware types, we really see a huge variation in model performance across hardware, not just CPU vs. GPU, which is, you know, what people normally think of. But across CPU vendors themselves, high memory instances and across cloud providers even. So the best strategy here is for teams to really be able to, we say, look before you leap by running real world benchmarking and not just simulations or predictions to find the best software, hardware combination for their workload. >> Yeah. You guys sound like you have a very impressive customer base deploying large language models. Where would you categorize your current customer base? And as you look out, as you guys are growing, you have new customers coming in, take me through the progression. Take me through the profile of some of your customers you have now, size, are they hyperscalers, are they big app folks, are they kicking the tires? And then as people are out there scratching heads, I got to get in this game, what's their psychology like? Are they coming in with specific problems or do they have specific orientation point of view about what they want to do? Can you share some data around what you're seeing? >> Yeah, I think, you know, we have customers that kind of range across the spectrum of sophistication from teams that basically don't have MLOps expertise in their company at all. And so they're really looking for us to kind of give a full service, how should I do everything from, you know, optimization, find the hardware, prepare for deployment. And then we have teams that, you know, maybe already have their serving and hosting infrastructure up and ready and they already have models in production and they're really just looking to, you know, take the extra juice out of the hardware and just do really specific on that optimization piece. I think one place where we're doing a lot more work now is kind of in the developer tooling, you know, model selection space. And that's kind of an area that we're creating more tools for, particularly within the PyTorch ecosystem to bring kind of this power earlier in the development cycle so that as people are grabbing a model off the shelf, they can, you know, see how it might perform and use that to inform their development process. >> Luis, what's the big, I like this idea of picking the models because isn't that like going to the market and picking the best model for your data? It's like, you know, it's like, isn't there a certain approaches? What's your view on this? 'Cause this is where everyone, I think it's going to be a land rush for this and I want to get your thoughts. >> For sure, yeah. So, you know, I guess I'll start with saying the one main takeaway that we got from the GPT-J study is that, you know, having a different understanding of what your model's compute and memory requirements are, very quickly, early on helps with the much smarter AI model deployments, right? So, and in fact, you know, Anna just touched on this, but I want to, you know, make sure that it's clear that OctoML is putting that power into user's hands right now. So in partnership with AWS, we are launching this new PyTorch native profiler that allows you with a single, you know, one line, you know, code decorator allows you to see how your code runs on a variety of different hardware after accelerations. So it gives you very clear, you know, data on how you should think about your model deployments. And this ties back to choices of models. So like, if you have a set of choices that are equally good of models in terms of functionality and you want to understand after acceleration how are you going to deploy, how much they're going to cost or what are the options using a automated process of making a decision is really, really useful. And in fact, so I think these events can get early access to this by signing up for the Octopods, you know, this is exclusive group for insiders here, so you can go to OctoML.ai/pods to sign up. >> So that Octopod, is that a program? What is that, is that access to code? Is that a beta, what is that? Explain, take a minute and explain Octopod. >> I think the Octopod would be a group of people who is interested in experiencing this functionality. So it is the friends and users of OctoML that would be the Octopod. And then yes, after you sign up, we would provide you essentially the tool in code form for you to try out in your own. I mean, part of the benefit of this is that it happens in your own local environment and you're in control of everything kind of within the workflow that developers are already using to create and begin putting these models into their applications. So it would all be within your control. >> Got it. I think the big question I have for you is when do you, when does that one of your customers know they need to call you? What's their environment look like? What are they struggling with? What are the conversations they might be having on their side of the fence? If anyone's watching this, they're like, "Hey, you know what, I've got my team, we have a lot of data. Do we have our own language model or do I use someone else's?" There's a lot of this, I will say discovery going on around what to do, what path to take, what does that customer look like, if someone's listening, when do they know to call you guys, OctoML? >> Well, I mean the most obvious one is that you have a significant spend on AI/ML, come and talk to us, you know, putting AIML into production. So that's the clear one. In fact, just this morning I was talking to someone who is in life sciences space and is having, you know, 15 to $20 million a year cloud related to AI/ML deployment is a clear, it's a pretty clear match right there, right? So that's on the cost side. But I also want to emphasize something that Anna said earlier that, you know, the hardware and software complexity involved in putting model into production is really high. So we've been able to abstract that away, offering a clean automation flow enables one, to experiment early on, you know, how models would run and get them to production. And then two, once they are into production, gives you an automated flow to continuously updating your model and taking advantage of all this acceleration and ability to run the model on the right hardware. So anyways, let's say one then is cost, you know, you have significant cost and then two, you have an automation needs. And Anna please compliment that. >> Yeah, Anna you can please- >> Yeah, I think that's exactly right. Maybe the other time is when you are expecting a big scale up in serving your application, right? You're launching a new feature, you expect to get a lot of usage or, and you want to kind of anticipate maybe your CTO, your CIO, whoever pays your cloud bills is going to come after you, right? And so they want to know, you know, what's the return on putting this model essentially into my application stack? Am I going to, is the usage going to match what I'm paying for it? And then you can understand that. >> So you guys have a lot of the early adopters, they got big data teams, they're pushed in the production, they want to get a little QA, test the waters, understand, use your technology to figure it out. Is there any cases where people have gone into production, they have to pull it out? It's like the old lemon laws with your car, you buy a car and oh my god, it's not the way I wanted it. I mean, I can imagine the early people through the wall, so to speak, in the wave here are going to be bloody in the sense that they've gone in and tried stuff and get stuck with huge bills. Are you seeing that? Are people pulling stuff out of production and redeploying? Or I can imagine that if I had a bad deployment, I'd want to refactor that or actually replatform that. Do you see that too? >> Definitely after a sticker shock, yes, your customers will come and make sure that, you know, the sticker shock won't happen again. >> Yeah. >> But then there's another more thorough aspect here that I think we likely touched on, be worth elaborating a bit more is just how are you going to scale in a way that's feasible depending on the allocation that you get, right? So as we mentioned several times here, you know, model deployment is so hardware dependent and so complex that you tend to get a model for a hardware choice and then you want to scale that specific type of instance. But what if, when you want to scale because suddenly luckily got popular and, you know, you want to scale it up and then you don't have that instance anymore. So how do you live with whatever you have at that moment is something that we see customers needing as well. You know, so in fact, ideally what we want is customers to not think about what kind of specific instances they want. What they want is to know what their models need. Say, they know the SLA and then find a set of hybrid targets and instances that hit the SLA whenever they're also scaling, they're going to scale with more freedom, right? Instead of having to wait for AWS to give them more specific allocation for a specific instance. What if you could live with other types of hardware and scale up in a more free way, right? So that's another thing that we see customers, you know, like they need more freedom to be able to scale with whatever is available. >> Anna, you touched on this with the business model impact to that 6 million cost, if that goes out of control, there's a business model aspect and there's a technical operation aspect to the cost side too. You want to be mindful of riding the wave in a good way, but not getting over your skis. So that brings up the point around, you know, confidence, right? And teamwork. Because if you're in production, there's probably a team behind it. Talk about the team aspect of your customers. I mean, they're dedicated, they go put stuff into production, they're developers, there're data. What's in it for them? Are they getting better, are they in the beach, you know, reading the book. Are they, you know, are there easy street for them? What's the customer benefit to the teams? >> Yeah, absolutely. With just a few clicks of a button, you're in production, right? That's the dream. So yeah, I mean I think that, you know, we illustrated it before a little bit. I think the automated kind of benchmarking and optimization process, like when you think about the effort it takes to get that data by hand, which is what people are doing today, they just don't do it. So they're making decisions without the best information because it's, you know, there just isn't the bandwidth to get the information that they need to make the best decision and then know exactly how to deploy it. So I think it's actually bringing kind of a new insight and capability to these teams that they didn't have before. And then maybe another aspect on the team side is that it's making the hand-off of the models from the data science teams to the model deployment teams more seamless. So we have, you know, we have seen in the past that this kind of transition point is the place where there are a lot of hiccups, right? The data science team will give a model to the production team and it'll be too slow for the application or it'll be too expensive to run and it has to go back and be changed and kind of this loop. And so, you know, with the PyTorch profiler that Luis was talking about, and then also, you know, the other ways we do optimization that kind of prevents that hand-off problem from happening. >> Luis and Anna, you guys have a great company. Final couple minutes left. Talk about the company, the people there, what's the culture like, you know, if Intel has Moore's law, which is, you know, doubling the performance in few years, what's the culture like there? Is it, you know, more throughput, better pricing? Explain what's going on with the company and put a plug in. Luis, we'll start with you. >> Yeah, absolutely. I'm extremely proud of the team that we built here. You know, we have a people first culture, you know, very, very collaborative and folks, we all have a shared mission here of making AI more accessible and sustainable. We have a very diverse team in terms of backgrounds and life stories, you know, to do what we do here, we need a team that has expertise in software engineering, in machine learning, in computer architecture. Even though we don't build chips, we need to understand how they work, right? So, and then, you know, the fact that we have this, this very really, really varied set of backgrounds makes the environment, you know, it's say very exciting to learn more about, you know, assistance end-to-end. But also makes it for a very interesting, you know, work environment, right? So people have different backgrounds, different stories. Some of them went to grad school, others, you know, were in intelligence agencies and now are working here, you know. So we have a really interesting set of people and, you know, life is too short not to work with interesting humans. You know, that's something that I like to think about, you know. >> I'm sure your off-site meetings are a lot of fun, people talking about computer architectures, silicon advances, the next GPU, the big data models coming in. Anna, what's your take? What's the culture like? What's the company vibe and what are you guys looking to do? What's the customer success pattern? What's up? >> Yeah, absolutely. I mean, I, you know, second all of the great things that Luis just said about the team. I think one that I, an additional one that I'd really like to underscore is kind of this customer obsession, to use a term you all know well. And focus on the end users and really making the experiences that we're bringing to our user who are developers really, you know, useful and valuable for them. And so I think, you know, all of these tools that we're trying to put in the hands of users, the industry and the market is changing so rapidly that our products across the board, you know, all of the companies that, you know, are part of the showcase today, we're all evolving them so quickly and we can only do that kind of really hand in glove with our users. So that would be another thing I'd emphasize. >> I think the change dynamic, the power dynamics of this industry is just the beginning. I'm very bullish that this is going to be probably one of the biggest inflection points in history of the computer industry because of all the dynamics of the confluence of all the forces, which you mentioned some of them, I mean PC, you know, interoperability within internetworking and you got, you know, the web and then mobile. Now we have this, I mean, I wouldn't even put social media even in the close to this. Like, this is like, changes user experience, changes infrastructure. There's going to be massive accelerations in performance on the hardware side from AWS's of the world and cloud and you got the edge and more data. This is really what big data was going to look like. This is the beginning. Final question, what do you guys see going forward in the future? >> Well, it's undeniable that machine learning and AI models are becoming an integral part of an interesting application today, right? So, and the clear trends here are, you know, more and more competitional needs for these models because they're only getting more and more powerful. And then two, you know, seeing the complexity of the infrastructure where they run, you know, just considering the cloud, there's like a wide variety of choices there, right? So being able to live with that and making the most out of it in a way that does not require, you know, an impossible to find team is something that's pretty clear. So the need for automation, abstracting with the complexity is definitely here. And we are seeing this, you know, trends are that you also see models starting to move to the edge as well. So it's clear that we're seeing, we are going to live in a world where there's no large models living in the cloud. And then, you know, edge models that talk to these models in the cloud to form, you know, an end-to-end truly intelligent application. >> Anna? >> Yeah, I think, you know, our, Luis said it at the beginning. Our vision is to make AI sustainable and accessible. And I think as this technology just expands in every company and every team, that's going to happen kind of on its own. And we're here to help support that. And I think you can't do that without tools like those like OctoML. >> I think it's going to be an error of massive invention, creativity, a lot of the format heavy lifting is going to allow the talented people to automate their intellect. I mean, this is really kind of what we see going on. And Luis, thank you so much. Anna, thanks for coming on this segment. Thanks for coming on theCUBE and being part of the AWS Startup Showcase. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>> Narrator: theCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (upbeat music) >> Hey everyone and welcome back to Barcelona, Spain, it's theCUBE. We are live at MWC 23. This is day two of our coverage, we're giving you four days of coverage, but you already know that because you were here yesterday. Lisa Martin with Dave Nicholson. Dave this show is massive. I was walking in this morning and almost getting claustrophobic with the 80,000 people that are joining us. There is, seems to be at MWC 23 more interest in enterprise-class technology than we've ever seen before. What are some of the things that you've observed with that regard? >> Well I've observed a lot of people racing to the highest level messaging about how wonderful it is to have the kiss of a breeze on your cheek, and to feel the flowing wheat. (laughing) I want to hear about the actual things that make this stuff possible. >> Right. >> So I think we have a couple of guests here who can help us start to go down that path of actually understanding the real cool stuff that's behind the scenes. >> And absolutely we got some cool stuff. We've got two guests from Dell. Dave Lincoln is here, the VP of Networking and Emerging the Server Solutions, and Deania Davidson, Director Edge Server Product Planning and Management at Dell. So great to have you. >> Thank you. >> Two Daves, and a Davidson. >> (indistinct) >> Just me who stands alone here. (laughing) So guys talk about, Dave, we'll start with you the newest generation of PowerEdge servers. What's new? Why is it so exciting? What challenges for telecom operators is it solving? >> Yeah, well so this is actually Dell's largest server launch ever. It's the most expansive, which is notable because of, we have a pretty significant portfolio. We're very proud of our core mainstream portfolio. But really since the Supercompute in Dallas in November, that we started a rolling thunder of launches. MWC being part of that leading up to DTW here in May, where we're actually going to be announcing big investments in those parts of the market that are the growth segments of server. Specifically AIML, where we in, to address that. We're investing heavy in our XE series which we, as I said, we announced at Supercompute in November. And then we have to address the CSP segment, a big investment around the HS series which we just announced, and then lastly, the edge telecom segment which we're, we had the biggest investment, biggest announce in portfolio launch with XR series. >> Deania, lets dig into that. >> Yeah. >> Where we see the growth coming from you mentioned telecom CSPs with the edge. What are some of the growth opportunities there that organizations need Dell's help with to manage, so that they can deliver what they're demanding and user is wanting? >> The biggest areas being obviously, in addition the telecom has been the biggest one, but the other areas too we're seeing is in retail and manufacturing as well. And, so internally, I mean we're going to be focused on hardware, but we also have a solutions team who are working with us to build the solutions focused on retail, and edge and telecom as well on top of the servers that we'll talk about shortly. >> What are some of the biggest challenges that retailers and manufacturers are facing? And during the pandemic retailers, those that were successful pivoted very quickly to curbside delivery. >> Deania: Yeah. >> Those that didn't survive weren't able to do that digitally. >> Deania: Yeah. >> But we're seeing such demand. >> Yeah. >> At the retail edge. On the consumer side we want to get whatever we want right now. >> Yes. >> It has to be delivered, it has to be personalized. Talk a little bit more about some of the challenges there, within those two verticals and how Dell is helping to address those with the new server technologies. >> For retail, I think there's couple of things, the one is like in the fast food area. So obviously through COVID a lot of people got familiar and comfortable with driving through. >> Lisa: Yeah. >> And so there's probably a certain fast food restaurant everyone's pretty familiar with, they're pretty efficient in that, and so there are other customers who are trying to replicate that, and so how do we help them do that all, from a technology perspective. From a retail, it's one of the pickup and the online experience, but when you go into a store, I don't know about you but I go to Target, and I'm looking for something and I have kids who are kind of distracting you. Its like where is this one thing, and so I pull up the Target App for example, and it tells me where its at, right. And then obviously, stores want to make more money, so like hey, since you picked this thing, there are these things around you. So things like that is what we're having conversations with customers about. >> It's so interesting because the demand is there. >> Yeah, it is. >> And its not going to go anywhere. >> No. >> And it's certainly not going to be dialed down. We're not going to want less stuff, less often. >> Yeah (giggles) >> And as typical consumers, we don't necessarily make the association between what we're seeing in the palm of our hand on a mobile device. >> Deania: Right. >> And the infrastructure that's actually supporting all of it. >> Deania: Right. >> People hear the term Cloud and they think cloud-phone mystery. >> Yeah, magic just happens. >> Yeah. >> Yeah. >> But in fact, in order to support the things that we want to be able to do. >> Yeah. >> On the move, you have to optimize the server hardware. >> Deania: Yes. >> In certain ways. What does that mean exactly? When you say that its optimized, what are the sorts of decisions that you make when you're building? I think of this in the terms of Lego bricks. >> Yes, yeah >> Put together. What are some of the decisions that you make? >> So there were few key things that we really had to think about in terms of what was different from the Data center, which obviously supports the cloud environment, but it was all about how do we get closer to the customer right? How do we get things really fast and how do we compute that information really quickly. So for us, it's things like size. All right, so our server is going to weigh one of them is the size of a shoe box and (giggles), we have a picture with Dave. >> Dave: It's true. >> Took off his shoe. >> Its actually, its actually as big as a shoe. (crowd chuckles) >> It is. >> It is. >> To be fair, its a pretty big shoe. >> True, true. >> It is, but its small in relative to the old big servers that you see. >> I see what you're doing, you find a guy with a size 12, (crowd giggles) >> Yeah. >> Its the size of your shoe. >> Yeah. >> Okay. >> Its literally the size of a shoe, and that's our smallest server and its the smallest one in the portfolio, its the XR 4000, and so we've actually crammed a lot of technology in there going with the Intel ZRT processors for example to get into that compute power. The XR 8000 which you'll be hearing a lot more about shortly with our next guest is one I think from a telco perspective is our flagship product, and its size was a big thing there too. Ruggedization so its like (indistinct) certification, so it can actually operate continuously in negative 5 to 55 C, which for customers, or they need that range of temperature operation, flexibility was a big thing too. In meaning that, there are some customers who wanted to have one system in different areas of deployment. So can I take this one system and configure it one way, take that same system, configure another way and have it here. So flexibility was really key for us as well, and so we'll actually be seeing that in the next segment coming. >> I think one of, some of the common things you're hearing from this is our focus on innovation, purpose build servers, so yes our times, you know economic situation like in itself is tough yeah. But far from receding we've doubled down on investment and you've seen that with the products that we are launching here, and we will be launching in the years to come. >> I imagine there's a pretty sizeable day impact to the total adjustable market for PowerEdge based on the launch what you're doing, its going to be a tam, a good size tam expansion. >> Yeah, absolutely. Depending on how you look at it, its roughly we add about $30 Billion of adjustable tam between the three purposeful series that we've launched, XE, HS and XR. >> Can you comment on, I know Dell and customers are like this. Talk about, I'd love to get both of your perspective, I'm sure you have a favorite customer stories. But talk about the involvement of the customer in the generation, and the evolution of PowerEdge. Where are they in that process? What kind of feedback do they deliver? >> Well, I mean, just to start, one thing that is essential Cortana of Dell period, is it all is about the customer. All of it, everything that we do is about the customer, and so there is a big focus at our level, from on high to get out there and talk with customers, and actually we have a pretty good story around XR8000 which is call it our flagship of the XR line that we've just announced, and because of this deep customer intimacy, there was a last minute kind of architectural design change. >> Hm-mm. >> Which actually would have been, come to find out it would have been sort of a fatal flaw for deployment. So we corrected that because of this tight intimacy with our customers. This was in two Thanksgiving ago about and, so anyways it's super cool and the fact that we were able to make a change so late in development cycle, that's a testament to a lot of the speed and, speed of innovation that we're driving, so anyway that was that's one, just case of one example. >> Hm-mm. >> Let talk about AI, we can't go to any trade show without talking about AI, the big thing right now is ChatGPT. >> Yeah. >> I was using it the other day, it's so interesting. But, the growing demand for AI, talk about how its driving the evolution of the server so that more AI use cases can become more (indistinct). >> In the edge space primarily, we actually have another product, so I guess what you'll notice in the XR line itself because there are so many different use cases and technologies that support the different use cases. We actually have a range form factor, so we have really small, I guess I would say 350 ml the size of a shoe box, you know, Dave's shoe box. (crowd chuckles) And then we also have, at the other end a 472, so still small, but a little bit bigger, but we did recognize obviously AI was coming up, and so that is our XR 7620 platform and that does support 2 GPUs right, so, like for Edge infrencing, making sure that we have the capability to support customers in that too, but also in the small one, we do also have a GPU capability there, that also helps in those other use cases as well. So we've built the platforms even though they're small to be able to handle the GPU power for customers. >> So nice tight package, a lot of power there. >> Yes. >> Beside as we've all clearly demonstrated the size of Dave's shoe. (crowd chuckles) Dave, talk about Dell's long standing commitment to really helping to rapidly evolve the server market. >> Dave: Yeah. >> Its a pivotal payer there. >> Well, like I was saying, we see innovation, I mean, this is, to us its a race to the top. You talked about racing and messaging that sort of thing, when you opened up the show here, but we see this as a race to the top, having worked at other server companies where maybe its a little bit different, maybe more of a race to the bottom source of approach. That's what I love about being at Dell. This is very much, we understand that it's innovation is that is what's going to deliver the most value for our customers. So whether its some of the first to market, first of its kind sort of innovation that you find in the XR4000, or XR8000, or any of our XE line, we know that at the end of day, that is what going to propel Dell, do the best for our customers and thereby do the best for us. To be honest, its a little bit surprising walking by some of our competitors booths, there's been like a dearth of zero, like no, like it's almost like you wouldn't even know that there was a big launch here right? >> Yeah. >> Or is it just me? >> No. >> It was a while, we've been walking around and yet we've had, and its sort of maybe I should take this as a flattery, but a lot of our competitors have been coming by to our booth everyday actually. >> Deania: Yeah, everyday. >> They came by multiple times yesterday, they came by multiple times today, they're taking pictures of our stuff I kind of want to just send 'em a sample. >> Lisa: Or your shoe. >> Right? Or just maybe my shoe right? But anyway, so I suppose I should take it as an honor. >> Deania: Yeah. >> And conversely when we've walked over there we actually get in back (indistinct), maybe I need a high Dell (indistinct). (crowd chuckles) >> We just had that experience, yeah. >> Its kind of funny but. >> Its a good position to be in. >> Yeah. >> Yes. >> You talked about the involvement of the customers, talk a bit more about Dell's ecosystem is also massive, its part of what makes Dell, Dell. >> Wait did you say ego-system? (laughing) After David just. >> You caught that? Darn it! The talk about the influence or the part of the ecosystem and also some of the feedback from the partners as you've been rapidly evolving the server market and clearly your competitors are taking notice. >> Yeah, sorry. >> Deania: That's okay. >> Dave: you want to take that? >> I mean I would say generally, one of the things that Dell prides itself on is being able to deliver the worlds best innovation into the hands of our customers, faster and better that any other, the optimal solution. So whether its you know, working with our great partners like Intel, AMD Broadcom, these sorts of folks. That is, at the end of the day that is our core mantra, again its retractor on service, doing the best, you know, what's best for the customers. And we want to bring the world's best innovation from our technology partners, get it into the hands of our partners you know, faster and better than any other option out there. >> Its a satisfying business for all of us to be in, because to your point, I made a joke about the high level messaging. But really, that's what it comes down to. >> Lisa: Yeah. >> We do these things, we feel like sometimes we're toiling in obscurity, working with the hardware. But what it delivers. >> Deania: Hm-mm. >> The experiences. >> Dave: Absolutely. >> Deania: Yes. >> Are truly meaningful. So its a fun. >> Absolutely. >> Its a really fun thing to be a part of. >> It is. >> Absolutely. >> Yeah. Is there a favorite customer story that you have that really articulates the value of what Dell is doing, full PowerEdge, at the Edge? >> Its probably one I can't particularly name obviously but, it was, they have different environments, so, in one case there's like on flights or on sea vessels, and just being able to use the same box in those different environments is really cool. And they really appreciate having the small compact, where they can just take the server with them and go somewhere. That was really cool to me in terms of how they were using the products that we built for them. >> I have one that's kind of funny. It around XR8000. Again a customer I won't name but they're so proud of it, they almost kinds feel like they co defined it with us, they want to be on the patent with us so, anyways that's. >> Deania: (indistinct). >> That's what they went in for, yeah. >> So it shows the strength of the partnership that. >> Yeah, exactly. >> Of course, the ecosystem of partners, customers, CSVs, telecom Edge. Guys thank you so much for joining us today. >> Thank you. >> Thank you. >> Sharing what's new with the PowerEdge. We can't wait to, we're just, we're cracking open the box, we saw the shoe. (laughing) And we're going to be dealing a little bit more later. So thank you. >> We're going to be able to touch something soon? >> Yes, yes. >> Yeah. >> In couple of minutes? >> Next segment I think. >> All right! >> Thanks for setting the table for that guys. We really appreciate your time. >> Thank you for having us. >> Thank you. >> Alright, our pleasure. >> For our guests and for Dave Nicholson, I'm Lisa Martin . You're watching theCUBE. The leader in live tech coverage, LIVE in Barcelona, Spain, MWC 23. Don't go anywhere, we will be right back with our next guests. (gentle music)

(gentle instrumental music) >> Okay, we're back with Tom Christensen who's the global technology advisor and executive analyst at Hitachi Vantara. And we're exploring how Hitachi Vantara drives customer success, specifically with partners. You know Tom, it's funny, back in the early part of the last decade, there was this big push around, remember it was called green IT, and then the 07-08 financial crisis sort of put that on the back burner. But sustainability is back, and it seems to be emerging as a mega trend in IT. Are you seeing this? Is it same wine new label? How real is this trend and where's the pressure coming from? >> Well, we clearly see that sustainability is a mega trend in the IT sector. And when we talk to CIOs or senior IT leaders or simply just invite them in for a round table on this topic, they all tell us that they get the pressure from three different angles. The first one is really end consumers, and end consumers nowadays are beginning to ask questions about the green profile and what are the company doing for the environment. And this one here is both private and public companies as well. The second pressure that we see, is coming from the government. The government thinks that companies are not moving fast enough, so they want to put laws in that are forcing companies to move faster. And we see that in Germany as an example, where they are giving a law into enterprise companies to follow the human rights and sustainability, three levels back in the supply chain. But we also see that in EU they are talking about a new law that they want to put into action, and that one will replicate to 27 countries in Europe. But this one is not only Europe, it's the rest of the world where governments are talking about forcing companies to move faster than we have done in the past. So we see two types of pressure coming in, and at the same time, this one here starts off at the CEO at a company, because they want to have the competitive edge and be able to be relevant in the market. And for that reason they're beginning to put KPIs on themselves as the CEO, but they also are hiring sustainability officers with sustainability KPIs. And when that happens, it replicates down in the organization and we can now see that some CIOs, they have a KPI, others are indirectly measured. So we see direct and indirect. The same with CFOs and other C levels, they all get measured on it, and for that reason it replicates down to IT people. And that's what they tell us on these round tables. I get that pressure every day, every week, every quarter. But where is the pressure coming from? Well, the pressure is coming from end consumers and new laws that are put into action, that force companies to think differently and have focus on their green profile and doing something good for the environment. So those are the three pressures that we see. But when we talk to CFOs as an example, we are beginning to see that they have a new score system where they put out request for proposal, and this one is in about 58% of all request for proposal that we receive, that they are asking for our sustainability take, what are you doing as a vendor? And in their store system, cost has the highest priority and number two is sustainability. It weighs about 15, 20 to 25% when they look at your proposal that you submit to a CFO. But in some cases the CFO say, "I don't even know where the pressure is coming from. I'm asked to do it." But they're asked to do it because end consumers, laws, and so on, are forcing them to do it. But I would answer, yeah sustainability has become a maker trend this year and it's even growing faster and faster every month we move forward. >> Yeah, Tom, it feels like it's here to stay this time. And your point about public policy is right on, and we saw the EU leading with privacy and GDPR, and it looks like it's going to lead again here. Just shifting gears, I've been to a number of Hitachi facilities in my day. Odawara is my favorite, because on a clear day you can see Mount Fuji but other plants I've been to as well. What does Hitachi do in the production facility to reduce CO2 emissions? >> Yeah, I think you're hitting a good point here. So what we have, we have a facility in Japan and we have one in Europe and we have one in America as well, to keep our production close to our customers and reduce transportation for the factory out to our customers. But you know, in the EMEA region, back in 2013, we created a new factory. And when we did that, we were asked to do it in an energy neutral way, which means that we are moving from being powered by black energy to green energy in that factory. And we built a factory with concrete walls that were extremely thick to make it cold in the summertime and hot in the wintertime, with minimum energy consumption. But we also put 17,000 square meters of solar panels on the roof to power that factory. We were collecting rain water to flush it in the toilet. We were removing light bulbs with LED. And when we send out our equipment to our customers, we put it in a rack, instead of sending out 25 packages to a customer. We want to reduce the waste as much as possible. And you know, this one was pretty new back in 2013. It was actually the biggest project in EMEA at that time. I will say if you want to build a factory today that's the way you are going to do it. But it has a huge impact for us when electricity is going up in price and oil and gas prices are coming up. We are running with energy neutral in our facility, which is a big benefit for us going forward. But it is also a competitive advantage to be able to explain what we have been doing the last eight, nine years in that factory. We are actually walking the talk, and we make that decision, even though it was a really hard decision to do back in 2013. When you do decisions like this one here, the return of investment is not coming the first couple of years. It's something that comes far out in the future, but right now we are beginning to see the benefit of the decision we made back in 2013. >> I want to come back to the economics, but before I do, I want to pick up on something you just said, because you hear the slogan, "Sustainability by design." A lot of people might think, "Okay, that's just a marketing slogan to vector into this mega trend," but it sounds like it's something that you've been working on for quite some time based on your last comments. Can you add some color to that? >> Yeah, so, the factory is just one example of what you need to do to reduce the CO2 emission in that part of the life of a product. The other one is really innovating new technology to drive down the CO2 emission. And here we are laser focused on what we call decarbonization by design. And this one is something that we have done the last eight years, so this is far from new for us. So between each generation of products that we have put out over the last eight years, we've been able to reduce the CO2 emission by up to 30 to 60% between each generation of products that we have put into the market. So we are laser focused on driving that one down but we are far from done, we still got eight years before we hit our first target net zero in 2030. So we got a roadmap where we want to achieve even more with new technology. At its core it's a technology innovator and our answer is to reduce the CO2 emission, and the decarbonization of the data center is going to be through innovating new technology because it has the speed, the scale, and the impact to make it possible to reach your sustainability objectives going forward. >> How about recycling? Where does that fit? I mean, the other day it was... A lot of times at a hotel you used to get bottled water now you get plant-based waters in a box and so we are seeing it all around us. But for a manufacturer of your size, recycling and circular economy, how does that fit into your plans? >> Yeah, let me try to explain what we are doing here because one thing is how you produce it. Another thing is how you innovate all that new technology, but you also need to combine that with service and software, otherwise you won't get the full benefit. So what we are doing here when it comes to exploring circular economics, it's kind of where we have an eternity mindset. We want to see if it is possible to get nothing out to the landfill. This is the aim that we are looking at. So when you buy a product today you get an option to keep it in your data center for up to 10 years. But what we want to do when you keep it for 10 years, is to upgrade only parts of the system. So let's say that you need more CPU power, you just switch the controller to next generation controller and you get more CPU power in your storage system, to keep it those 10 years. But you can also expand with new disk media, flash media, even media that doesn't exist today will be supported over those 10 years. You can change your protocol in the front end of your system to have new protocols and connect to your server environment with the latest and greatest technology. See, the benefit here is that, you don't have to put your system into a truck and a recycle process after three years, four years, five years, you can actually postpone that one for 10 years. And this one is reducing the emission again. But once we take it back, you put it on the truck and we take it into our recycling facility. And here we take our own equipment, like computer network and switches, but we also take competitive equipment in and we recycle as much as we can. In many cases, it's only 1% that goes to the landfill or 2% that goes to the landfill. The remaining material will go into new products either in our cycle or in other parts of the electronic industry. So it will be reused for other products. So when we look at what we've been doing for many years that has been linear economics, where you buy material, you make your product, you put it into production, and it goes into the landfill afterwards. The recycling economics is really, you buy material, you make your product, you put it into production, and you recycle as much as possible. The remaining part will go into the landfill. But where we are right now is exploring circular economics, where you actually buy material, make it, put it into production, and you reuse as much as you can. And only 1-2% is going into the landfill right now. So we have come along, and we honestly believe that the circular economics is the new economics going forward for many industries in the world. >> Yeah, and that addresses some of the things that we were talking about earlier about sustainability by design. You have to design that so that you can take advantage of that circular economy. I do want to come back to the economics, because in the early days of so-called green IT, there was a lot of talk about, "Well I'll never be able to lower the power bill, and the facilities people don't talk to the IT people," and that's changed. So explain why sustainability is good business, not just an expense item, but can really drive bottom line profitability. I understand it's going to take some time, but help us understand your experience there Tom. >> Yeah, let me try to explain that one. You often get the question about sustainability. Isn't that a cost? I mean how much does it cost to get that green profile? But you know, in reality, when you do a deep dive into the data center, you realize that sustainability is a cost saving activity. And this one is quite interesting, and we have now done more than 1,200 data center assessment around the world, where we have looked at data centers. And let me give you just an average number from a global bank that we work with. And this one is not different from all the other cases that we are doing. So when we look at the storage area, what we can do on the electricity by moving an old legacy data center into a new modernized infrastructure, is to reduce the electricity by 96%. This is a very high number, and a lot of money that you save, but the CO2 emission is reduced by 96% as well. The floor space can go up to 35% reduction as well. When we move down to the compute part, we are talking about 61% reduction in electricity on the compute part, just by moving from legacy to new modern infrastructure, and 61% on the CO2 emission as well. And see this one here is quite interesting, because you save electricity and you do something really good for the environment at the same time. In this case I'm talking about here, the customer was paying 2.5 million U.S. dollar annually, and by just modernizing that infrastructure, we could bring it down to 1.1 million. This is 1.4 million savings straight into your pocket and you can start the next activity here, looking at moving from virtual machine to containers. Containers only use 10% of the CPU resources compared to a virtual machine. Move up to the application layer if you have that kind of capability in your organization. Modernizing your application with sustainability by design and you can reduce the CO2 emission by up to 50%. There's so much we can do in that data center, but we often start at the infrastructure first and then we move up in the chain and we give customers benefit in all these different layers. >> Yeah, a big theme of this program today is what you guys are doing with partners. Are partners aware of this in your view? Are they in tune with it? Are they demanding it? What message would you like to give the channel partners, resellers, and distributors who may be watching? >> So the way to look at it is that we offer a platform with product, service and software, and that platform can elevate the conversation much higher up in the organization, and partners get the opportunity here to go up and talk to sustainability officers about what we are doing. They can even take it up to the CEO, and talk about how can you reach your sustainability KPI in the data center. What we've see in this round table when we have sustainability officers in the room, is that they are very focused on the green profile, and what is going out of the company. They rarely have a deep understanding of what is going on in the data center. Why? Because it's really technical and they don't have that background. So just by elevating the conversation to these sustainability officers, you can tell them what they should measure and how they should measure that. And you can be sure that that will replicate down to the CIO and the CFO, and there will immediately be a request for proposal going forward. So this one here is really a golden opportunity to take that story, go out and talk to different people in the organization, to be relevant, and have an impact, and make it more easy for you to win that proposal when it gets out. >> Well, really solid story on a super important topic. Thanks Tom, really appreciate your time and taking us through your perspectives. >> Thank you Dave, for the invitation. >> Yeah, you bet. Okay, in a moment we'll be back to summarize our final thoughts, keep it right there. (gentle instrumental music)

>>Partnerships in the technology business, they take many forms. For example, technology engineering partnerships, they drive value in terms of things like integration and simplification for customers. There are product partnerships. They fill gaps to create more comprehensive portfolios and more fluid relationships. Partner ecosystems offer high touch services. They offer managed services, specialty services, and other types of value based off of strong customer knowledge and years of built up trust partner. Ecosystems have evolved quite dramatically over the last decade with the explosion of data and the popularity of cloud models. Public, private, hybrid cross clouds. You know, yes it's true. Partnerships are about selling solutions, but they're also about building long term sustainable trust, where a seller learns the ins and outs of a customer's organization and can anticipate needs that are gonna drive bottom line profits for both sides of the equation, the buyer and the seller. >>Hello and welcome to our program. My name is Dave Ante and along with Lisa Martin, we're going to explore how Hitachi Van Tara drives customer success with its partners. First up, Lisa speaks with Kim King. She's the senior vice president of Strategic Partners and Alliances at Hitachi Van. And they'll set the table for us with an overview of how Hitachi is working with partners and where their priorities are focused. Then Russell Kingsley, he's the CTO and global VP of Technical sales at Hitachi Van Tara. He joins Lisa for a discussion of the tech and they're gonna get into cloud generally and hybrid cloud specifically in the role that partners play in the growing as a service movement. Now, after that I'll talk with Tom Christensen, he's the global technology advisor and executive analyst at Hitachi Vitara. And we're gonna talk about a really important topic, sustainability. We're gonna discuss where it came from, why it matters, and how it can drive bottom line profitability for both customers and partners. Let's get right to it. >>Where for the data driven, for those who understand clarity is currency. Believe progress requires precision and no neutral is not an option. We're for the data driven. The ones who can't tolerate failure, who won't put up with downtime or allow access to just anyone. We're for the data driven who act on insight instead of instinct. Bank on privacy instead of probabilities and rely on resilience instead of reaction. We see ourselves in the obsessive, the incessant, progressive, and the meticulously engineered. We enable the incredible identify with the analytical and are synonymous with the mission critical. We know what it means to be data driven because data is in our dna. We were born industrial and and we breathe digital. We speak predictive analytics so you can keep supply chains moving. We bleed in store and online insights so you can accurately predict customer preferences. We sweat security and digital privacy so you can turn complex regulations into competitive advantage. We break down barriers and eliminate silos. So you can go from data rich to data driven because it's clear the future belongs to the data driven. >>Hey everyone, welcome to this conversation. Lisa Martin here with Kim King, the SVP of Strategic Partners and Alliances at Hitachi Ventera. Kim, it's great to have you on the program. Thank you so much for joining me today. >>Thanks Lisa. It's great to be here. >>Let's talk about, so as we know, we talk about cloud all the time, the landscape, the cloud infrastructure landscape increasingly getting more and more complex. What are some of the biggest challenges and pain points that you're hearing from customers today? >>Yeah, so lot. There are lots, but I would say the, the few that we hear consistently are cost the complexity, right? Really the complexity of where do they go, how do they do it, and then availability. They have a lot of available options, but again, going back to complexity and cost, where do they think that they should move and how, how do they make that a successful move to the cloud? >>So talk to me, Hitachi Ventura has a great partner ecosystem. Where do partners play a role in helping customers to address some of the challenges with respect to the cloud landscape? >>Yeah, so part, our partners are really leading the way in the area of cloud in terms of helping customers understand the complexities of the cloud. As we talked about, they're truly the trusted advisor. So when they look at a customer's complete infrastructure, what are the workloads, what are the CRI critical applications that they work with? What's the unique architecture that they have to drive with that customer for a successful outcome and help them architect that? And so partners are truly leading the way across the board, understanding the complexities of each individual customer and then helping them make the right decisions with and for them. And then bringing us along as part of that, >>Talk to me a little bit about the partner landscape, the partner ecosystem at Hitachi Ventura. How does this fit into the overall strategy for the company? >>So we really look at our ecosystem as an extension of our sales organization and and really extension across the board, I would say our goal is to marry the right customer with the right partner and help them achieve their goals, ensure that they keep costs in check, that they ensure they don't have any security concerns, and that they have availability for the solutions and applications that they're trying to move to the cloud, which is most important. So we really, we really look at our ecosystem as a specialty ecosystem that adds high value for the right customers. >>So Kim, talk to me about how partners fit into Hitachi van's overall strategy. >>So I think our biggest differentiators with partners is that they're not just another number. Our partner organization is that valued extension of our overall sales pre-sales services organization. And we treat them like an extension of our organization. It's funny because I was just on a call with an analyst earlier this week and they said that AWS has increased their number of partners to 150,000 partners from, it was just under a hundred thousand. And I'm really not sure how you provide quality engagement to partners, right? And is how is that really a sustainable strategy? So for us, we look at trusted engagement across the ecosystem as a def differentiation. Really our goal is to make their life simple and profitable and really become their primary trusted partner when we go to market with them. And we see that paying dividends with our partners as they engage with us and as they expand and grow across the segments and then grow globally with us as well. >>And that's key, right? That synergistic approach when you're in customer conversations, what do you articulate as the key competitive differentiators where it relates to your partners? >>So really the, that they're the trusted advisor for that partner, right? That they understand our solutions better than any solution out there. And because we're not trying to be all things to our customers and our partners that we being bring best breaths of breed, best of breed solutions to our customers through our partner community, they can truly provide that end user experience and the successful outcome that's needed without, you know, sort of all kinds of, you know, crazy cha challenges, right? When you look at it, they really wanna make sure that they're driving that co-developed solution and the successful outcome for that customer. >>So then how do you feel that Hitachi Ventura helps partners really to grow and expand their own business? >>Wow, so that's, there's tons of ways, but we've, we've created a very simplified, what we call digital selling platform. And in that digital selling platform, we have allowed our partners to choose their own price and pre-approve their pricing and their promotions. They've actually, we've expanded the way we go to market with our partners from a sort of a technical capabilities. We give them online what we call Hitachi online labs that allow them to really leverage all of the solutions and demo systems out there today. And they have complete access to any one of our resources, product management. And so we really have, like I said, we actually provide our partners with better tools and resources sometimes than we do our own sales and pre-sales organization. So we, we look at them as, because they have so many other solutions out there that we have to be one step ahead of everybody else to give them that solution capability and the expertise that they need for their customers. >>So if you dig in, where is it that Hiti is helping partners succeed with your portfolio? >>Wow. So I think just across the board, I think we're really driving that profitable, trusted, and simplified engagement with our partner community because it's a value base and ease of doing business. I say that we allow them to scale and drive that sort of double digit growth through all of the solutions and and offerings that we have today. And because we've taken the approach of a very complex technical sort of infrastructure from a high end perspective and scale it all the way through to our mid-size enterprise, that allows them to really enter any customer at any vertical and provide them a really quality solution with that 100% data availability guarantee that we provide all of our customers. >>So then if we look at the overall sales cycle and the engagement, where is it that you're helping cus your partners rather succeed with the portfolio? >>Say that again? Sorry, my brain broke. No, >>No worries. So if we look at the overall sales cycle, where is it specifically where you're helping customers to succeed with the portfolio? >>So from the sales cycle, I think because we have the, a solution that is simple, easy, and really scaled for the type of customer that we have out there, it allows them to basically right size their infrastructure based on the application, the workload, the quality or the need that application may have and ensure that we provide them with that best solution. >>So then from a partner's perspective, how is it that Hitachi van is helping them to actually close deals faster? >>Yeah, so lots of great ways I think between our pre-sales organization that's on call and available a hundred percent of the time, I think that we've seen, again, the trusted engagement with them from a pricing and packaging perspective. You know, we, you know, two years ago it would take them two to three weeks to get a pre-approved quote where today they preapproved their own quotes in less than an hour and can have that in the hands of a customer. So we've seen that the ability for our partners to create and close orders in very short periods of time and actually get to the customer's needs very quickly, >>So dramatically faster. Yes. Talk about overall, so the partner relationship's quite strong, very synergistic that, that Hitachi Ventura has with its customers. Let's kind of step back out and look at the cloud infrastructure. How do you see it evolving the market evolving overall in say the next six months, 12 months? >>Yeah, so we see it significantly, we've been doing a lot of studies around this specifically. So we have a couple of different teams. We have our sort of our standard partner team that's out there and now we have a specialty cloud service provider team that really focuses on partners that are building and their own infrastructure or leveraging the infrastructure of a large hyperscaler or another GSI and selling that out. And then what we found is when we dig down deeper into our standard sort of partner reseller or value added reseller market, what we're seeing is that they are want to have the capability to resell the solution, but they don't necessarily wanna have to own and manage the infrastructure themselves. So we're helping both of them through that transition. We see that it's gonna, so it's funny cuz you're seeing a combination of many customers move to really the hyperscale or public cloud and many of them want to repatriate their infrastructure back because they see costs and they see challenges around all of that. And so our partners are helping them understand, again, what is the best solution for them as opposed to let's just throw everything in the public cloud and hope that it works. We're we're really helping them make the right choices and decisions and we're putting the right partners together to make that happen. >>And how was that feedback, that data helping you to really grow and expand the partner program as a whole? >>Yeah, so it's been fantastic. We have a whole methodology that we, we created, which is called PDM plan, develop monetize with partners. And so we went specifically to market with cloud service providers that'll, and we really tested this out with them. We didn't just take a solution and say, here, go sell it, good luck and have, you know, have a nice day. Many vendors are doing that to their partners and the partners are struggling to monetize those solutions. So we spend a lot of time upfront planning with them what is not only the storage infrastructure but your potentially your data resiliency and, and everything else that you're looking at your security solutions. How do we package those all together? How do we help you monetize them? And then who do you target from a customer perspective so that they've built up a pipeline of opportunities that they can go and work with us on and we really sit side by side with them in a co-development environment. >>In terms of that side by side relationship, how does the partner ecosystem play a role in Hitachi Venturas as a service business? >>So our primary go to market with our, as a service business is with and through partners. So our goal is to drive all, almost all of of our as a service. Unless it's super highly complex and something that a partner cannot support, we will make sure that they really, we leverage that with them with all of our partners. >>So strong partner relationships, very strong partner ecosystem. What would you say, Kim, are the priorities for the partner ecosystem going forward? The next say year? >>Yeah, so we have tons of priorities, right? I think really it's double digit growth for them and for us and understanding how a simpler approach that's customized for the specific vertical or customer base or go to market that they have that helps them quickly navigate to be successful. Our goal is always to facilitate trusted engagements with our partners, right? And then really, as I said, directionally our goal is to be 95 to a hundred percent of all of our business through partners, which helps customers and then really use that trusted advisor status they have to provide that value base to the customer. And then going back on our core tenants, which are, you know, really a trusted, simplified, profitable engagement with our partner community that allows them to really drive successful outcomes and go to market with us. And the end users >>Trust is such an important word, we can't underutilize it in these conversations. Last question. Sure. From a channel business perspective, what are some of the priorities coming down the pi? >>Oh, again, my biggest priority right, is always to increase the number of partner success stories that we have and increase the value to our partners. So we really dig in, we, we right now sit about number one or number two in, in our space with our partners in ease of doing business and value to our channel community. We wanna be number one across the board, right? Our goal is to make sure that our partner community is successful and that they really have those profitable engagements and that we're globally working with them to drive that engagement and, and help them build more profitable businesses. And so we just take tons of feedback from our partners regularly to help them understand, but we, we act on it very quickly so that we can make sure we incorporate that into our new program and our go to markets as we roll out every year. >>It sounds like a great flywheel of communications from the partners. Kim, thank you so much for joining me today talking about what Hitachi Vanta is doing with its partner ecosystem, the value in IT for customers. We appreciate your insights. >>Thank you very much. >>Up next, Russell Kingsley joins me, TTO and global VP of technical sales at Hitachi van you watch in the cube, the leader in live tech coverage. Hey everyone, welcome back to our conversation with Hitachi van Tara, Lisa Martin here with Russell Skillings Lee, the CTO and global VP of technical sales at Hitachi Van Russell. Welcome to the program. >>Hi Lisa, nice to be here. >>Yeah, great to have you. So here we are, the end of calendar year 2022. What are some of the things that you're hearing out in the field in terms of customers priorities for 2023? >>Yeah, good one. Just to, to set the scene here, we tend to deal with enterprises that have mission critical IT environments and this has been been our heritage and continues to be our major strength. So just to set the scene here, that's the type of customers predominantly I'd be hearing from. And so that's what you're gonna hear about here. Now, in terms of 20 23, 1 of the, the macro concerns that's hitting almost all of our customers right now, as you can probably appreciate is power consumption. And closely related to that is the whole area of ESG and decarbonization and all of that sort of thing. And I'm not gonna spend a lot of time on that one because that would be a whole session in itself really, but sufficient to say it is a priority for us and we, we are very active in, in that area. >>So aside from from that one that that big one, there's also a couple that are pretty much in common for most of our customers and, and we're in areas that we can help. One of those is in an exponential growth of the amount of data. It's, it's predicted that the world's data is going to triple by 2025 as opposed to where it was in 2020. And I think everyone's contributing to that, including a lot of our customers. So just the, the act of managing that amount of data is, is a challenge in itself. And I think closely related to that, a desire to use that data better to be able to gain more business insights and potentially create new business outcomes and business ideas are, is another one of those big challenges in, in that sense, I think a lot of our customers are in what I would kind of call, I affectionately call the, the post Facebook awakening era. >>And that, and what I mean by that is our traditional businesses, you know, when Facebook came along, they kind of illustrated, hey, I can actually make some use out of what is seemingly an enormous amount of useless data, which is exactly what Facebook did. They took a whole lot of people's Yeah. The minutia of people's lives and turned it into, you know, advertising revenue by gaining insights from, from those, you know, sort of seemingly useless bits of data and, you know, right. And I think this actually gave rise to a lot of digital business at that time. You know, the, this whole idea of what all you really need to be successful and disrupt the business is, you know, a great idea, you know, an app and a whole bunch of data to, to power it. And I think that a lot of our traditional customers are looking at this and wondering how do they get into the act? Because they've been collecting data for decades, an enormous amount of data, right? >>Yes. I mean, every company these days has to be a data company, but to your point, they've gotta be able to extract those insights, monetize it, and create real value new opportunities for the business at record speed. >>Yes, that's exactly right. And so being able to, to wield that data somehow turn it, it kind of turns out our customer's attentions to the type of infrastructure they've got as well. I mean, if you think about those, those companies that have been really successful in leveraging that data, a lot of them have, especially in the early days, leverage the cloud to be able to build out their capabilities. And, and the reason why the cloud became such a pivotal part of that is because it offered self-service. IT and, you know, easy development platforms to those people that had these great ideas. All they needed was access to, to, you know, the provider's website and a credit card. And now all of a sudden they could start to build a business from that. And I think a lot of our traditional IT customers are looking at this and thinking, now how do I build a similar sort of infrastructure? How do I, how do I provide that kind of self-service capability to the owners of business inside my company rather than the IT company sort of being a gatekeeper to a selected set of software packages. How now do I provide this development platform for those internal users? And I think this, this is why really hybrid cloud has become the defacto IT sort of architectural standard, even even for quite traditional, you know, IT companies. >>So when it comes to hybrid cloud, what are some of the challenges the customers are facing? And then I know Hitachi has a great partner ecosystem. How are partners helping Hitachi Ventura and its customers to eliminate or solve some of those hybrid cloud challenges? >>Yeah, it's, it, it's a great question and you know, it's, it's not 1975 anymore. It's not, it's not like you're going to get all of your IT needs from, from one, from one vendor hybrid by sort of, it's, you know, by definition is going to involve multiple pieces. And so there basically is no hybrid at all without a partner ecosystem. You really can't get everything at, at a one stop shop like you used to. But even if you think about the biggest public cloud provider on the planet, aws even, it has a marketplace for partner solutions. So, so even they see, even for customers that might consider themselves to be all in on public cloud, they are still going to need other pieces, which is where their marketplace come comes in. Now for, for us, you know, we are, we're a company that, we've been in the IT business for over 60 years, one of one of the few that could claim that sort of heritage. >>And you know, we've seen a lot of this type of change ourselves, this change of attitude from being able to provide everything yourself to being someone who contributes to an overall ecosystem. So partners are absolutely essential. And so now we kind of have a, a partner first philosophy when it comes to our routes to market on, you know, not just our own products in terms of, you know, a resale channel or whatever, but also making sure that we are working with some of the biggest players in hybrid infrastructure and determining where we can add value to that in our, in our own solutions. And so, you know, when it comes to those, those partner ecosystems, we're always looking for the spaces where we can best add our own capability to those prevailing IT architectures that are successful in the marketplace. And, you know, I think that it's probably fair to say, you know, for us, first and foremost, we, we have a reputation for having the biggest, most reliable storage infrastructure available on the planet. >>And, and we make no apologies for the fact that we tout our speeds and feeds and uptime supremacy. You know, a lot of our, a lot of our competitors would suggest that, hey, speeds and feeds don't matter. But you know, that's kind of what you say when, when you're not the fastest or not the most reliable, you know, of course they matter. And for us, what we, the way that we look at this is we say, let's look at who's providing the best possible hybrid solutions and let's partner with them to make those solutions even better. That's the way we look at it. >>Can you peel the, the onion a little bit on the technology underpinning the solutions, give a glimpse into that and then maybe add some color in terms of how partners are enhancing that? >>Yeah, let me, let me do that with a few examples here, and maybe what I can do is I can sort of share some insight about the way we think with partnering with, with particular people and why it's a good blend or why we see that technologically it's a good blend. So for example, the work we do with VMware, which we consider to be one of our most important hybrid cloud partners and in, and in fact it's, it's my belief, they have one of the strongest hybrid cloud stories in the industry. It resonates really strongly with, with our customers as well. But you know, we think it's made so much better with the robust underpinnings that we provide. We're one of the, one of the few storage vendors that provides a 100% data availability guarantee. So we, we take that sort of level of reliability and we add other aspects like life cycle management of the underpinning infrastructure. >>We combine that with what VMware's doing, and then when you look at our converged or hyper-converged solutions with them, it's a better together story where you now have what is one of the best hybrid cloud stories in the industry with VMware. But now for the on premise part, especially, you've now added a hundred percent data, data availability guarantee, and you've made managing the underlying infrastructure so much easier through the tools that we provide that go down to that level A level underneath where VMware are. And so that's, that's VMware. I've got a couple, couple more examples just to sort of fill, fill that out a bit. Sure. Cisco is another part, very strong partner of ours, a key partner. And I mean, you look at Cisco, they're a 50 billion IT provider and they don't have a dedicated storage infrastructure of their own. So they're going to partner with someone. >>From our perspective, we look at Cisco's, Cisco's customers and we look at them and think they're very similar to our own in terms of they're known to appreciate performance and reliability and a bit of premium in quality, and we think we match them them quite well. They're already buying what we believe are the best converge platforms in the industry from Cisco. So it makes sense that those customers would want to compliment that investment with the best array, best storage array they can get. And so we think we are helping Cisco's customers make the most of their decision to be ucs customers. Final one for, for you, Lisa, by way of example, we have a relationship with, with Equinix and you know, Equinix is the world's sort of leading colo provider. And the way I think they like to think of themselves, and I too tend to agree with them, is their, they're one of the most compelling high-speed interconnect networks in the world. >>They're connected to all of the, the, the significant cloud providers in most of the locations around the world. We have a, a relationship with them where we find we have customers in common who really love the idea of compute from the cloud. Compute from the cloud is great because compute is something that you are doing for a set period of time and then it's over you. Like you have a task, you do some compute, it's done. Cloud is beautiful for that. Storage on the other hand is very long lived storage doesn't tend to operate in that same sort of way. It sort of just becomes a bigger and bigger blob over time. And so the cost model around public cloud and storage is not as compelling as it is for compute. And so our, with our relationship with Equinix, we help our customers to be able to create, let's call it a, a data anchor point where they put our arrays into, into an Equinix location, and then they utilize Equinix as high speeding interconnects to the, to the cloud providers, okay. To take the compute from them. So they take the compute from the cloud providers and they own their own storage, and in this way they feel like we've now got the best of all worlds. Right. What I hope that illustrates Lisa is with those three examples is we are always looking for ways to find our key advantages with any given, you know, alliance partners advantages, >>Right? What are, when you're in customer conversations, and our final few minutes here, I wanna get, what are some of the key differentiators that you talk about when you're in customer conversations, and then how does the partner ecosystem fit into Hitachi vans as a service business? We'll start with differentiators and then let's move into the as service business so we can round out with that. >>Okay. Let's start with the differentiators. Yeah. Firstly and I, and hopefully I've kind of, I've hit this point hard, hard enough. We do believe that we have the fastest and most reliable storage infrastructure on the planet. This is kind of what we are known for, and customers that are working with us already sort of have an appreciation for that. And so they're looking for, okay, you've got that now, how can you make my hybrid cloud aspirations better? So we do have that as a fundamental, right? So, but secondly I'd say, I think it's also because we go beyond just storage management and, and into the areas of data management. You know, we've got, we've got solutions that are not just about storing the bits. We do think that we do that very well, but we also have solutions that move into the areas of enrichment, of the data, cataloging of the data, classification of the data, and most importantly, analytics. >>So, you know, we, we think it's, some of our competitors just stop at storing stuff and some of our competitors are in the analytics space, but we feel that we can bridge that. And we think that that's a, that's a competitive advantage for us. One of the other areas that I think is key for us as well is, as I said, we're one of the few vendors who've been in the marketplace for 60 years and we think this, this, this gives us a more nuanced perspective about things. There are many things in the industry, trends that have happened over time where we feel we've seen this kind of thing before and I think we will see it again. But you only really get that perspective if you are, if you are long lived in the industry. And so we believe that our conversations with our customers bear a little bit more sophistication. It's not just, it's not just about what's the latest and greatest trends. >>Right. We've got about one minute left. Can you, can you round us out with how the partner ecosystem is playing a role in the as service business? >>They're absolutely pivotal in that, you know, we, we ourselves don't own data centers, right? So we don't provide our own cloud services out. So we are 100% partner focused when it comes to that aspect. Our formula is to help partners build their cloud services with our solutions and then onsell them to their customers as as as a service. You know, and by what quick way of example, VMware for example, they've got nearly 5,000 partners selling VMware cloud services. 5,000 blows me away. And many of them are our partners too. So we kind of see this as a virtuous cycle. We've got product, we've got an an alliance with VMware and we work together with partners in common for the delivery of an as a service business. >>Got it. So the, as you said, the partner ecosystem is absolutely pivotal. Russell, it's been a pleasure having you on the program talking about all things hybrid cloud challenges, how Hitachi van is working with its partner ecosystems to really help customers across industries solve those big problems. We really appreciate your insights and your time. >>Thank you very much, Lisa. It's been great. >>Yeah, yeah. For Russell Stingley, I'm Lisa Martin. In a moment we're gonna continue our conversation with Tom Christensen. Stay tuned. >>Sulfur Royal has always embraced digital technology. We were amongst the first hospitals in the UK to install a full electronic patient record system. Unfortunately, as a result of being a pioneer, we often find that there's gaps in the digital solutions. My involvement has been from the very start of this program, a group of us got together to discuss what the problems actually were in the hospital and how we could solve this. >>The digital control center is an innovation that's been designed in partnership between ourselves, anti touch, and it's designed to bring all of the information that is really critical for delivering effective and high quality patient care. Together the DCC is designed not only to improve the lives of patients, but also of our staff giving us information that our demand is going to increase in the number of patients needing support. The technology that we're building can be replicated across sulfur, the NCA, and the wider nhs, including social care and community services. Because it brings all of that information that is essential for delivering high quality efficient care. >>The DCC will save time for both staff and more importantly our patients. It will leave clinicians to care for patients rather than administrate systems and it will allow the system that I work with within the patient flow team to effectively and safely place patients in clinically appropriate environments. >>But we chose to partner with Hitachi to deliver the DCC here at Sulfur. They were willing to work with us to co-produce and design a product that really would work within the environment that we find ourselves in a hospital, in a community setting, in a social care setting. >>My hopes for the DCC is that ultimately we will provide more efficient and reliable care for our patients. >>I do believe the digital control center will improve the lives of staff and also the patients so that we can then start to deliver the real change that's needed for patient care. >>Okay, we're back with Tom Christensen, who's the global technology advisor and executive analyst at Hitachi Van Tara. And we're exploring how Hitachi Van Tower drives customer success specifically with partners. You know Tom, it's funny, back in the early part of the last decade, there was this big push around, remember it was called green it and then the oh 7 0 8 financial crisis sort of put that on the back burner. But sustainability is back and it seems to be emerging as a mega trend in in it is, are you seeing this, is it same wine new label? How real is this trend and where's the pressure coming from? >>Well, we clearly see that sustainability is a mega trend in the IT sector. And when we talk to CIOs or senior IT leaders or simply just invite them in for a round table on this topic, they all tell us that they get the pressure from three different angles. The first one is really end consumers and end consumers. Nowaday are beginning to ask questions about the green profile and what are the company doing for the environment. And this one here is both private and public companies as well. The second pressure that we see is coming from the government. The government thinks that companies are not moving fast enough so they want to put laws in that are forcing companies to move faster. And we see that in Germany as an example, where they are giving a law into enterprise companies to following human rights and sustainability tree levels back in the supply chain. >>But we also see that in EU they are talking about a new law that they want to put into action and that one will replicate to 27 countries in Europe. But this one is not only Europe, it's the rest of the world where governments are talking about forcing companies to move faster than we have done in the past. So we see two types of pressure coming in and at the same time, this one here starts off at the CEO at a company because they want to have the competitive edge and be able to be relevant in the market. And for that reason they're beginning to put KPIs on themself as the ceo, but they're also hiring sustainability officers with sustainability KPIs. And when that happens it replicates down in the organization and we can now see that some CIOs, they have a kpi, others are indirectly measured. >>So we see direct and indirect. The same with CFOs and other C levels. They all get measured on it. And for that reason it replicates down to IT people. And that's what they tell us on these round table. I get that pressure every day, every week, every quarter. But where is the pressure coming from? Well the pressure is coming from in consumers and new laws that are put into action that force companies to think differently and have focus on their green profile and doing something good for the environment. So those are the tree pressures that we see. But when we talk to CFOs as an example, we are beginning to see that they have a new store system where they put out request for proposal and this one is in about 58% of all request for proposal that we receive that they are asking for our sustainability take, what are you doing as a vendor? >>And in their score system cost has the highest priority and number two is sustainability. It waits about 15, 20 to 25% when they look at your proposal that you submit to a cfo. But in some cases the CFO say, I don't even know where the pressure is coming from. I'm asked to do it. Or they're asked to do it because end consumers laws and so on are forcing them to do it. But I would answer, yeah, sustainability has become a make trend this year and it's even growing faster and faster every month we move forward. >>Yeah, Tom, it feels like it's here to stay this time. And your point about public policy is right on, we saw the EU leading with privacy and GDPR and it looks like it's gonna lead again here. You know, just shifting gears, I've been to a number of Hitachi facilities in my day. OWA is my favorite because on a clear day you can see Mount Fuji, but other plants I've been to as well. What does Hitachi do in the production facility to reduce CO2 emissions? >>Yeah, I think you're hitting a good point here. So what we have, we have a, a facility in Japan and we have one in Europe and we have one in America as well to keep our production close to our customers and reduced transportation for the factory out to our customers. But you know, in the, in the, in the May region back in 2020 13, we created a new factory. And when we did that we were asked to do it in an energy, energy neutral way, which means that we are moving from being powered by black energy to green energy in that factory. And we build a factory with concrete walls that were extremely thick to make it cold in the summertime and hot in the winter time with minimum energy consumption. But we also put 17,000 square meters of solar panel on the roof to power that factory. >>We were collecting rain waters to flush it in the toilet. We were removing light bulbs with L E D and when we sent out our equipment to our customers, we put it in a, instead of sending out 25 packages to a customer, we want to reduce the waste as much as possible. And you know, this one was pretty new back in 2013. It was actually the biggest project in EA at that time. I will say if you want to build a factory today, that's the way you are going to do it. But it has a huge impact for us when electricity is going up and price and oil and gas prices are coming up. We are running with energy neutral in our facility, which is a big benefit for us going forward. But it is also a competitive advantage to be able to explain what we have been doing the last eight, nine years in that factory. We are actually walking to talk and we make that decision even though it was a really hard decision to do back in 2013, when you do decisions like this one here, the return of investment is not coming the first couple of years. It's something that comes far out in the future. But right now we are beginning to see the benefit of the decision we made back in 2013. >>I wanna come back to the economics, but before I do, I wanna pick up on something you just said because you know, you hear the slogan sustainability by design. A lot of people might think okay, that's just a marketing slogan, slogan to vector in into this mega trend, but it sounds like it's something that you've been working on for quite some time. Based on your last comments, can you add some color to that? >>Yeah, so you know, the factory is just one example of what you need to do to reduce the CO2 emission and that part of the life of a a product. The other one is really innovating new technology to drive down the CO2 emission. And here we are laser focused on what we call decarbonization by design. And this one is something that we have done the last eight years, so this is far from you for us. So between each generation of products that we have put out over the last eight years, we've been able to reduce the CO2 emission by up to 30 to 60% between each generation of products that we have put into the market. So we are laser focused on driving that one down, but we are far from done, we still got eight years before we hit our first target net zero in 2030. So we got a roadmap where we want to achieve even more with new technology. At its core, it is a technology innovator and our answers to reduce the CO2 emission and the decarbonization of a data center is going to be through innovating new technology because it has the speed, the scale, and the impact to make it possible to reach your sustainability objectives going forward. >>How about recycling? You know, where does that fit? I mean, the other day it was, you know, a lot of times at a hotel, you know, you used to get bottled water, now you get, you know, plant based, you know, waters in a box and, and so we are seeing it all around us. But for a manufacturer of your size, recycling and circular economy, how does that fit into your plans? >>Yeah, let me try to explain what we are doing here. Cause one thing is how you produce it. Another thing is how you innovate all that new technology, but you also need to combine that with service and software, otherwise you won't get the full benefit. So what we are doing here, when it comes to exploring circular economics, it's kind of where we have an eternity mindset. We want to see if it is possible to get nothing out to the landfill. This is the aim that we are looking at. So when you buy a product today, you get an option to keep it in your data center for up to 10 years. But what we wanna do when you keep it for 10 years is to upgrade only parts of the system. So let's say that you need more CBU power, use your switch the controller to next generation controller and you get more CPU power in your storage system to keep it those 10 years. >>But you can also expand with new this media flash media, even media that doesn't exist today will be supported over those 10 years. You can change your protocol in the, in the front end of your system to have new protocols and connect to your server environment with the latest and greatest technology. See, the benefit here is that you don't have to put your system into a truck and a recycle process after three years, four years, five years, you can actually postpone that one for 10 years. And this one is reducing the emission again. But once we take it back, you put it on the truck and we take it into our recycling facility. And here we take our own equipment like compute network and switches, but we also take competitor equipment in and we recycle as much as we can. In many cases, it's only 1% that goes to the landfill or 2% that goes to the landfill. >>The remaining material will go into new products either in our cycle or in other parts of the electronic industry. So it will be reused for other products. So when we look at what we've been doing for many years, that has been linear economics where you buy material, you make your product, you put it into production, and it goes into land feed afterwards. The recycling economics, it's really, you buy material, you make your product, you put it into production, and you recycle as much as possible. The remaining part will go into the landfill. But where we are right now is exploring circle economics where you actually buy material, make it, put it into production, and you reuse as much as you can. And only one 2% is going into the landfill right now. So we have come along and we honestly believe that the circular economics is the new economics going forward for many industries in the world. >>Yeah. And that addresses some of the things that we were talking about earlier about sustainability by design, you have to design that so that you can take advantage of that circular economy. I, I do wanna come back to the economics because, you know, in the early days of so-called green, it, there was a lot of talk about, well, I, I, I'll never be able to lower the power bill. And the facilities people don't talk to the IT people. And that's changed. So explain why sustainability is good business, not just an expense item, but can really drive bottom line profitability. I, I understand it's gonna take some time, but, but help us understand your experience there, Tom. >>Yeah, let me try to explain that one. You know, you often get the question about sustainability. Isn't that a cost? I mean, how much does it cost to get that green profile? But you know, in reality when you do a deep dive into the data center, you realize that sustainability is a cost saving activity. And this one is quite interesting. And we have now done more than 1,200 data center assessment around the world where we have looked at data centers. And let me give you just an average number from a global bank that we work with. And this one is, it is not different from all the other cases that we are doing. So when we look at the storage area, what we can do on the electricity by moving an old legacy data center into a new modernized infrastructure is to reduce the electricity by 96%. >>This is a very high number and a lot of money that you save, but the CO2 mission is reduced by 96% as well. The floor space can go up to 35% reduction as well. When we move down to the compute part, we are talking about 61% reduction in electricity on the compute part just by moving from legacy to new modern infrastructure and 61% on the CO2 emission as well. And see this one here is quite interesting because you save electricity and you and you do something really good for the environment. At the same time, in this case I'm talking about here, the customer was paying 2.5 million US dollar annually and by just modernizing that infrastructure, we could bring it down to 1.1 million. This is 1.4 million savings straight into your pocket and you can start the next activity here looking at moving from virtual machine to containers. Containers only use 10% of the CPU resources compared to a virtual machine. Move up to the application layer. If you have that kind of capability in your organization, modernizing your application with sustainability by design and you can reduce the C, the CO2 emission by up to 50%. There's so much we can do in that data center, but we often start at the infrastructure first and then we move up in the chain and we give customers benefit in all these different layers. >>Yeah, A big theme of this program today is what you guys are doing with partners do, are partners aware of this in your view? Are they in tune with it? Are they demanding it? What message would you like to give the channel partners, resellers and, and distributors who may be watching? >>So the way to look at it is that we offer a platform with product, service and software and that platform can elevate the conversation much higher up in the organization. And partners get the opportunity here to go up and talk to sustainability officers about what we are doing. They can even take it up to the CEO and talk about how can you reach your sustainability KPI in the data center. What we've seen this round table when we have sustainability officers in the room is that they're very focused on the green profile and what is going out of the company. They rarely have a deep understanding of what is going on at the data center. Why? Because it's really technical and they don't have that background. So just by elevating the conversation to these sustainability officers, you can tell them what they should measure and how they should measure that. And you can be sure that that will replicate down to the CIO and the CFO and that immediately your request for proposal going forward. So this one here is really a golden opportunity to take that story, go out and talk to different people in the organization to be relevant and have an impact and make it more easy for you to win that proposal when it gets out. >>Well really solid story on a super important topic. Thanks Tom. Really appreciate your time and taking us through your perspectives. >>Thank you Dave, for the invitation. >>Yeah, you bet. Okay, in a moment we'll be back. To summarize our final thoughts, keep it right there. >>Click by click. The world is changing. We make sense of our world by making sense of data. You can draw more meaning from more data than was ever possible before, so that every thought and every action can build your path to intelligent innovation to change the way the world works. Hitachi Van Tara. >>Okay, thanks for watching the program. We hope you gained a better understanding of how Hitachi Ventura drives customer success with its partners. If you wanna learn more about how you can partner for profit, check out the partner togetherPage@hitachiventera.com and there's a link on the webpage here that will take you right to that page. Okay, that's a wrap for Lisa Martin. This is Dave Valante with the Cube. You a leader in enterprise and emerging tech coverage.

(upbeat music) >> On June 21st in Cambridge mass at the Sinclair in Harvard Square, Tech Tackles Cancer is back after a COVID hiatus with live band karaoke and some local tech celebrities raising money for a great cause. The Cube is a media sponsor of the event and Josh Epstein, local marketing exec and one of the events organizers is here to tell us more. Josh, good to see you, welcome. >> Good to be here, Dave. >> So tell us about this event. What's going on? What are the logistics? How's that all work? >> Yeah, we're super excited. So as you said, June 21st at the Sinclair in Harvard Square, Sinclair, if you haven't been there is just the great old school rock club. So we'll be there from 6:00 to 10:00. We will have live band karaoke. So the main event and kind of the primary fundraising approach here is that we have some celebrity technology rock gods these featured performers like Chris Lynch who was the founder of Tech Tackles Cancer, who are are raising money from basically now, up until June 21st. Then at the event, their fundraising will culminate with them singing a live song backed by a live band. And the awards will be given out to the most money raised, the best performance and the best stage presence. So it will be a lot of fun. >> So the fundraising format is I sign up to sing do the karaoke with a live band which is a little bit different. And then I raise as much dough as possible. So obviously that's competitive. >> It's competitive, I think that we ask for a minimum of $10,000 targeted for each of the fundraisers but knowing these guys, knowing guys like Chris Lynch, they don't like to lose. So the bet here is that people are going to go out, they're going to hit their network and they are going to look to kind of raise the most money. So we anticipate this to be a great event with a lot of money raised and a lot of fun. >> So we have a graphic from Alex. If you could bring that up of the people who have signed up for this already. We got Steve Duplessie, founder of of ESG, senior analyst. They sold their company to Tech Target, which is awesome. Congratulations to those guys and thank you for stepping up. George Hope, who heads partner sales for HPE, Joe Lemay of Rocketbook Nathan Hall from Pure Storage, system engineering guy and of course, Steiny, Ken Steinhardt from Infinidat. He was at EMC, he's the field CTO now. He's going to be up there singing. So of course, Chris. >> Absolutely, these are just the early entrance here. So we just started really working our networks. And obviously, I'm a Boston tech guy kind of working the storage networks, the networking networks and kind of the other folks that are around. So as we come out of stealth here in April and start really recruiting, we anticipate having probably 10 to 15 of these featured performers, really fundraising performers that we'll sing. And then we're also obviously soliciting broader donations from anyone who wants to come to the event or just give to the cause and the corporate sponsorships as well. >> All right, so you got corporate sponsorships. You can sing, you can donate you can be there just to support it. That's fantastic and the awards, how's that work? >> Yeah, so we're excited. So first off, most money raised wins an award. So we'll have a leaderboard on the website, we'll be able to kind of track who's raised what, at the event, we're going to have some celebrity judges that will be actually voting for their favorites and then have a crowdsource component as well. So we'll introduce what that mechanism is. But as people, either at the events or a watching in streamed live on LinkedIn live, we'll actually vote for their favorite performance as well as their their pick for best stage presence which we know in rock and roll is half the battle. >> Now this cause has raised a bunch of, I think last time, you guys did this, it was probably a quarter million or close to it and you support multiple causes. What causes are you supporting? >> Sure, yeah, actually I think since they founded the event several years ago they raised over $2 million. This year for this format where we're looking, we can really up our game here but this year we're supporting two really great causes that are both focused on pediatric cancer. The first is St. Batrick's that is really committed to raising funds for research to really help stamp out pediatric cancer really. The approach to researching cures and treatments to pediatric cancer is very different from regular adult cancer. So St. Batrick's does a great job of picking those research projects that really target in on those pediatric cancer causes. And then the second is one mission. And one mission really outlooks to help make pediatric cancer patients that are spending time in the hospital, making their time less stressful, less painful, less sad, less boring. And so they do a lot of fundraising and contributions targeting children's hospitals, really around the country for those pediatric cancer floors. >> Josh, amazing cause. Thanks so much for coming onto the Cube and explaining all that. >> Great, thanks David. >> All right, June 21st, go to ttcfund.org, Tech Tackles Cancer fund, ttcffund.org for more information and you can donate. We'll see you there. (soft music)

(bright music) >> The challenges of legacy data warehouses and traditional business intelligence systems, they've been well-documented. They're built on rigid infrastructure, and they're managed by really specialized gatekeepers. Data warehouses of the past were, as one financial customer once said to me, like a snake swallowing a basketball, imagine that. The amount of data ingested into a data warehouse has just overwhelmed the system. Every time Intel came out with a new microprocessor, practitioners, they would chase the chip in an effort to try to compress the overly restrictive elapsed time to insights, and this cycle repeated itself for decades. Cloud data warehouses, generally, and Snowflake, specifically, changed all this. Not only were resources virtually infinite, but the ability to separate, compute from storage, it actually turned off the compute when you weren't using it, permanently altered the cost, the performance, the scale and the value equation. But as data makes its way into the cloud and is increasingly democratized as a shared resource across clouds and at the edge, practitioners have to bring Sec DevOps mindsets to securing their cloud data warehouses. Hello, and welcome to this week's Wikibon, "theCUBE Insights," powered by ETR. In this "Breaking Analysis," we take a closer look at the fundamentals of securing Snowflake and to do so, we welcome two guests into the program. Ben Herzberg is an experienced hacker and developer and an expert in several aspects of data security. He's currently working as the Chief Data Scientist at Satori, and he's joined by his colleague, Yoav Cohen, who is a technology visionary, and currently serving as CTO at Satori Cyber. Gentlemen, welcome to "theCUBE," great to see you. >> Great to be here. >> Thanks for having us, Dave. >> Now, these two individuals have co-authored a book on Snowflake Security. It's a comprehensive guide to what you need to know as a data practitioner using Snowflake. So guys, congratulations on the book. It's really detailed, packed with great information, best practices and practical advice and insights all in one place, so really good work. So, before we get into the discussion, I want to share some ETR survey data just to set the context. We're seeing cybersecurity and data, they're colliding in a really important way. And here's some data points that we've shared before from ETR's latest drill down survey. They asked more than 1200 respondents. We're talking CIOs, CSOs and IT professionals, "Which organizational priorities "will be most important in 2022?" And these were the top seven. There were a lot of others, but these were the most important. So, it's no surprise that security is number one, although, as we shared in our predictions post, the magnitude of its relative importance, it does vary by the degree of expertise within the organization. The Delta is maybe not as significant, for example, in large companies, and you can see where analytics and data fit. And we've tied these two domains together and picked up on a term that our two guests have used, in fact, you guys may have even coined it, called DataSecOps, which, to me, is the idea that you bring Agile DevOps practices to data operations and built-in security as part of the full cycle of managing, creating the data, using the data, accessing the data, not a bolt on, but it's fundamental, so guys, what do you make of this data, and what's your point of view on DataSecOps? >> So, definitely aligns with what we're seeing on the ground in the market. In between what you saw there, you had cybersecurity and data warehousing. In the middle you had cloud migration, and that's basically what's pushing companies to invest in both security and data and warehousing, because the cloud changed the game for cybersecurity. The tools that we use before are not the same tools that we need to use now. And also, it unlocks a lot of performance value and capabilities around data warehousing. So, all of that comes together to a big trend in the industry for investment, for replacement, and definitely we're seeing that on the Snowflake platform, which is doing really, really well recently. >> Yeah, well thank you, Yoav. And to that point, I want to share another data point and then dive in, maybe Ben, you can comment. And I want to address, why are we always talking about Snowflake? Of course, it's a hot company. Everybody knows that. You can see it in the company's financials, but the ETR survey data tells a really compelling story about the company. Here's a chart from the most recent ETR January survey. And so, you can see at the, at the top, that blue line, it represents net score or spending momentum, and the darker line at the bottom represents presence or pervasiveness in the survey sample. Just a background, there are 165 Snowflake customers that responded to this past survey. 10% of companies within the Fortune 500 were in the sample, and around 4% of Global 2000 companies participated. Just under 30% of the respondents were C-Suite executives, and about 20% were analysts or engineers or data specialist with around half were VP, director, manager roles that fat middle, with a very broad mix of industries, and there was a bias toward larger companies. Now, back to the chart, that net score for a moment, is that top line, is derived by asking customers, "Are you adopting Snowflake new in 2022?" That's the 27% lime green number. "Will you be spending 6% or more on Snowflake, "relative to 2021?" That's the 57% forest green. "Is your spending flat?" That's the gray. "Is it down by 6% or worse?" That's the other, the pink area. "Are you leaving the platform?" That's the bright red, and that's a zero defection, so there's none there. So you subtract the reds from the greens, and you get net score, which calculates out to 83% in his pet survey. But what's remarkable is that Snowflake has held this elevated score for more than 12 quarterly surveys. It's in the stratosphere among the many thousands and thousands of companies in the ETR survey. Remember, anything above that 40% line is elevated and Snowflake is like glued to the ceiling. So the bottom line shows that the company's market presence continues to grow, that darker line at the bottom, and that green shade shows us that the pace of last quarter is actually accelerating. Snowflake is becoming ubiquitous, and customers are becoming intimately familiar with its platform, and it's scaling like we've never seen before, and it's building a pretty hard to penetrate fortress, we think, and an ecosystem. Ben, I wonder, in your view, what accounts for Snowflake's performance? >> Okay, so I would say that we can spend a full session just about such thing, so I'll try to say what I think. I think, first of all, it does what it says on the box. You get from zero to being able to have a data warehouse easily, you have a very rich support of capability and features that you need for a cloud data warehouse. Your multi-cloud, you're not dependent on one of the big public clouds, and it's fast and scalable, and you don't need to worry yourself with the infrastructure behind. You don't need to, God-forbid, add any indexes or do things like that. You don't need to do that, at least not often, indexes never, but other maintenance. And the innovation rate, they innovate fast. They add a lot of new capabilities, like the move to unstructured data, like a lot of security and governance capabilities, high innovation rate as well. >> Okay, good, and we'll talk about that move. So let's get deeper into the topic now on securing Snowflake. My first question is look, Snowflake, when you talk to practitioners and customers, they get pretty high marks on security, largely because of the simplicity, so why did you feel the need to write a book on the subject? >> So, definitely Snowflake is investing a lot of effort and putting a lot of emphasis on security. However, it's connected to the cloud service, and like any other cloud service, there is a shared responsibility model between Snowflake and its customers when it comes to fully securing their data cloud. So Snowflake can build amazing features, but then customers have to really adopt them, implement them in the best way. One of the things that we've seen by working with Snowflake customers is that we typically interact with data engineers, but then they have to implement security features and security capability. We thought writing a book about the topic would help these customers to understand the features better, benefit from them better and really structure their implementation and decide what's most important to implement at every step of their journey. >> Yeah, and I think that when I was researching this topic, I could find a lot of good information on the web, but I kind of had to hunt and peck for it. It was really sort of dispersed, and you put the information all in one place. You have a nice table of contents, so I can just zip right to where I want to go, so that was quite useful, I thought. What are the very basic fundamentals of securing Snowflake? In other words, I'm interested in, you get this world of flexible, it's globally distributed. You get democratizing data. How do you really make sure that only those folks that should have access, do have access? I mean really, let's talk about that a little bit. >> Oh, I think that, of course there are a lot of different aspects, but I think that I would start with the big blocks. For example, when you get a Snowflake account out of the box, it's open to the world in terms of network. I would start by limiting that. That should be easy for an organization. It's a couple of commands, and you've lowered your risk significantly, both security and compliance. Then, one of the common things that you can get a good improvement in a decrease of your risk is around those indications. For example, do you have applications that are accessing Snowflake using user password? Okay, change that to using a key. Do you have users with username, password? Change that to Okta integration or your IDP integration. So I would start with the big blocks that can remove most of my risk, and then of course, there is a lot to do from getting to the data warehouse and to auditing and monitoring. >> Okay, thank you for that. But, Yoav, how are these fundamentals that we just heard from Ben, how are they different? Isn't this kind of common sense? What's unique about Snowflake? >> So, a couple things, first of all, security, we love to say that it's 80% good security hygiene. You have to make sure that your basics are locked and tightly configured and that brings a lot of value. But two points to consider, first of all, all of these types of controls are pretty static in the sense that once you get in, you get in, and then you have pretty broad access, and we'll talk about authorization concepts and everything, perhaps today, but these are really static gatekeepers around your data. Once you have access, then it's really free for all. When you compare it to other types of environments and what we're seeing in other domains, maybe a move to more dynamic type of controls, elevated access or elevated additional authentication steps before you get elevated access. And what we're thinking is that beyond those static controls, the market is going to move towards implementing more dynamic, more fine-grain control, especially because in Snowflake, but any other data warehouse or large-scale data store, which becomes an aggregation point of data in the company, and we work with really big companies, and they bring in data from multiple jurisdiction from across the world, so they can get an overview of the business and run the business in a much more efficient way, but that really creates a pressure point when it comes to securing that data. >> Okay, Ben, you touched on this a little bit. I want to kind of dig deeper. So, Snowflake takes a layered approach, of course, it's sensible, and the layers, network, which talked about identity, access and encryption. and so, with any cloud, as you guys mentioned, it's a shared responsibility model. So I want to break that down a bit, and let's start with the network. So my responsibility, as a customer, I'm going to be responsible to set up the DNS. How much public internet access am I going to have for other users and apps. So how should practitioners think about their end of the bargain on the network? What do they need to know? >> At the network level, as I mentioned before, a new account is open network-wise, it's open to the world. And one of the first thing I would do would be to set a network policy on the account to limit network access to that account. And of course, in many organizations, you would want to configure that with private link to your cloud environment, but that would be step two. (laughs) First step is simply set the network policy to make sure that it's not open to the public. >> Yeah, and that seems pretty straightforward, but let's talk about identity, 'cause it feels like that's where it starts to get tricky. You got to worry about setting up roles and managing users. You could even configure row and column base access, as I understand it, and I imagine access is where it really gets confusing for a lot of people, especially when you're crossing domain identities. Like for example, isn't a role-based security, let's land on that for a minute, I think you called it hierarchy hell in the book, so what should we think about in regards to identity? >> Well, first of all, it's hierarchy hell, in the book, it says that you can use hierarchy, but you should avoid getting to a hierarchy hell. Basically, we've seen that with several Snowflake customers where the ability to set roles in a hierarchy model, to set a role that inherits privileges from another role, that inherits privileges from other roles and maybe, of course, used in a good way, but it also in some of the cases, it leads to complexities and to access not being deterministic, at least not obvious to the person who gives access, who is usually the data engineer. So, whenever you start having a complex authorization model, whenever I want to give Yoav access to a certain data set, and because things are complex, I also, by mistake, give him access to the salary information of the company, that's when things become tricky. If your roles are messy and complex, then it may lead to data exposure within the organization or outside the organization. >> How do you find Snowflake's integrations? Like if I want to use Okta or I want to use a CyberArk, I mean, how would you grade them on their ability to integrate with popular third party platforms? >> So, I would say pretty high, actually. We haven't encountered many customers who haven't configured any of these... nowadays, really basic security integration, and it really, really helps, setting that good identity management foundation for the platform. So they're investing a lot in that area, and we've been following them for a couple of years now, and it's really, really coming along nicely. >> All right, let's talk about encryption. I mean, that seemed pretty straightforward. Correct me if I'm wrong. I think Snowflake auto rotates the keys every 30 days. It really seems like your responsibility there is monitoring, making sure you're in compliance. You got good log data or access to good log data. Is that right? >> So, this really depends. So, for the average company, I would say, yes. For some of the companies with higher security requirements or compliance requirements or both, sometimes there are issues like companies that do not want to have the data stored in clear text, in Snowflake, even encrypted as in the data warehouse encryption or the account encryption, even if someone accidentally gets access to the table, they want them not to be able to pull the data in clear text, and then it gets slightly more complicated. You have different ways of tackling this, but for the average company or companies who do not have such requirements, then everything in Snowflake is encrypted in transit and addressed, and of course, there are more advanced features for higher requirements. >> Okay, I'm interested in what you guys think of some of the more vulnerable aspects that Snowflake customers should really be aware of. Imagine I'm saying, "Guys, let's run a pen test. "Okay, make sure I have no open chest wounds, "but really try to fool me." What would you attack? Where should I be extra cautious? >> So, I would start with where data resides. And, if you look at the Snowflake architecture, there's a separation between storage and compute, but that also means storage is accessible without going through the compute. That can create opportunities for hackers to go and try and find access where access shouldn't be had. That's where I would focus on. >> I want to ask you about Virtual Private Snowflake. It seems to me, if I have sensitive data, if I don't use Virtual Private Snowflake, I feel like I'm increasing my risk that a security incident at the shared cloud services layer could impact multiple customers, and is this a valid concern? How should we think about reducing that risk, and when should I use that higher level of security? >> So, I think first of all, to the best of my knowledge, I'm not a Snowflake employee, but to the best of my knowledge, Virtual Private Snowflake is used by a minority of the customers, a small minority of the customers. There are other more popular ways within Snowflake, like private link, for example, I would say, to enhance your security and your account segregation. But I wouldn't say that simply because the platform is multi-tenant, it is vulnerable. Of course, in many cases, your security or compliance requirements requires you to eliminate even this risk, but I wouldn't say that there are a lot of other platforms in different areas that are multi-tenant and-- >> And probably better than your on-prem, your average on-prem installation. >> Probably, probably. >> Okay, so I buy that. >> I would say on that, that maybe a shared environment is a higher value target for hackers. So if you're on a shared environment with thousands of other customers, if I'm a hacker, I would go there, 'cause then I get data for thousands of customers instead of try to focus on just one target and getting data for just one company. I think that's the most significant advantage. And obviously, Snowflake are investing a lot in making all of their environments very, very secure, and from our interactions with large Snowflake customers, we know that Snowflake are going above and beyond in making sure these environments are secure. >> Yeah, that's good, that's good news, because if I don't have to spend up, I can put the budget elsewhere. How do you guys think Snowflake's recent moves... They're making a couple of big moves. They've recently added unstructured data. They used to have semi-structured data. They're going after the data science and data lake functionality. Do those kinds of moves, I guess they're two different things, but does that change the way that security pros should think about protecting their Snowflake environment? >> I would say that Snowflake is moving fast with adding new functionality, well fast, but not too fast. They're releasing it in a controlled way. I would say that for new capabilities, of course, in some cases there are new attack vectors or new risks and obviously, securing different types of data may bring new challenges, but the basics, I think, remains the same. The basics of the network, identity authentication, authorization and auditing monitoring. I would say they will be the same and perhaps new features or capability will need to be used. And the largest issue, as data democratization is growing within organizations, and more and more people are using your data cloud, that also needs to be addressed. >> All right, finally, I want to end, I want to talk a little bit about futures. Have you guys talked in your book about multi-cloud as a way to reduce your reliance on a single vendor? And of course, it happens through M and A, and that's cool. We've talked a lot about multi-cloud, and we've been using this term that we coined, called supercloud, and it references an abstraction layer that exists on top of, and floats across, if you will, multiple clouds, and it hides some of that underlying complexity, and we feel like Snowflake is a good example of a company that's moving in that direction, building value on top of all that hyperscale infrastructure. So I wonder how you see Snowflake's moves in that direction would impact the way you think about DataSecOps. >> So definitely, we also see the trend of companies adopting more and more types of cloud and cloud technologies. They're in one cloud today. They want to move to a second one, almost every company that I talk to have, nowadays, a multi-cloud strategy. With respect to Snowflake, they basically have it figured out, because they are an overlay, like a supercloud, super data cloud, that is spread across any cloud, and you can basically pick and choose where you want to put your data for what use cases, and that's really, really helpful, because then you don't have to manage the complexity of multiple solutions for multiple areas of the business. We see this also in other areas where companies are saying, "Hey, I prefer to not use a specific cloud technology "for that purpose, but use a vendor that can cover my needs "across the clouds," definitely on the security side, where they want one throat to choke, so to speak, but they want to control things on a central place. As Ben mentioned before, complexity is the enemy of security and having those multi-cloud operations, from a security perspective, definitely adds complexity, which adds risks, so simplifying that is really, really helpful. >> Hey, thank you for that, and thank you guys for coming on today. Why don't you give us a little bumper sticker on Satori. What do you guys do? Give us the quick commercial. >> So, we help companies secure access to their data on platforms like Snowflake and others. We build really innovative technology that decouples security controls from the actual data layer. So if you think about it, where you can put controls to govern how people access data. You can put it inside the database. You can put it somewhere on the client. We've actually invented a technology that can do that in the middle, so you don't have to coalesce and mix your security concerns with your data. You don't have to go to your clients' users' end-points, laptops and put technology there. We set technology that fits in the middle, that decouples that aspect of your DataSecOps operations, and really helps companies implement those security controls much faster, because it's detached from the rest of their operation. >> Nice thought, leaning into that simplicity trend that you talked about. Okay guys, that's all the time we have today. Really, I want to thank Ben and Yoav for coming on "theCUBE." It was really great to have you. I'd love to welcome you back at some point. >> Thank you, Dave. >> Thank you, it was a pleasure >> All right, remember these episodes, these episodes are all available as podcasts, wherever you listen. All you got to do is search breaking analysis podcasts. Check out ETR's website at ETI.ai. We also publish full report every week on Wikibon.com and SiliconAngle.com. You can get in touch with me. Email me, David.Vellante@SiliconANGLE.com @DVellante or comment on our LinkedIn posts. This is Dave Vellante for "theCUBE Insights," powered by ETR. Have a great week, stay safe, be well, and we'll see you next time. (bright music)

(upbeat music) >> Welcome to this Cube Conversation. I'm Lisa Martin. I'm talking next with Sanzio Bassini, the head of High Performance Computing at Cineca a Dell Technologies customer. Sanzio, welcome to theCUBE. >> Thank you, it's a pleasure. >> Lisa Martin: Likewise. Nice to see you. So tell us a little bit about Cineca, this is a large computing center, but a very large Italian non-profit consortium. Tell us about it. >> Yes, Cineca has been founded 50 years ago, from the university systems in Italy to support the scientific discovery and the industry innovations using the high-performance computing, and the correlated mythologies like intelligence together with the big data processing, and the simulations. We are a corsortium, which means that is a private not-for-profit organization. Currently our member of the consortium, almost all the universities in Italy and also all the national agencies. >> Lisa Martin: And I also read that you are the top 10 out of the top 500 of the world's fastest super computers. That's a pretty big accomplishment. >> Yes. That is a part of our statutory visions in the last 10 to 15 years , we have been to say, frequent buyers in the top 10. The idea is that we're enabling the scientific discovery by mean of the providing the most advanced systems, and the co-designing the the most advanced HPC systems to promote to support the accents in science. And being part of the European high-performance computing ecosystems. >> Now, talk to me about some of the challenges that Cineca is trying to solve in particular, the human brain project. Talk to us a little bit about that and how you're leveraging high-performance computing to accelerate scientific discovery. >> The human brain project is one of the flagship projects that has been co-funded by the European Commission and the participating member states that are two different, right now , flagships together with another that is just in progress, which is the the quantum of flagship we are participating indirectly together with the National Disaster Council. And we are core partners of the HPC constructors , that is the human brain project. One billion euro of investment, co-founded by the participating states and the European Commissions. it's a project that would combine both the technology issues and the designing of a high-performance computing systems that would meet the requirements of the community. And the big scientific challenges, correlated to the physiological functions of the human brains, including different related to the behavior of the, of the human brain, either from the pathological point of view either from the physiological point of view. In order to better understand the aging user, that it would impact the, the health the public health systems, some other that are correlating with what would be the support for the physiological knowledge of the human brains. And finally computational performance, the human brain is more than Exascale systems, but with a average consumption, which is very low. We are talking about some hundred of wards of energy would provide a, an extreme and computational performance. So if we put the organizing the technology high-performance computing in terms of interconnections now we're morphing the computing systems that would represent a tremendous step in order to facing the big challenges of our base and energies, personalized medicine, climate change, food for all those kinds of big social economic challenge that we are facing. >> Which reading them, besides the human brain project, there are other projects going on, such as that you mentioned. I'd like to understand how Cineca is working with Dell Technologies. You have to translate, as you mentioned a minute ago, the scientific requirements for discovery into high-performance computing requirements. Talk to me about how you've been doing that with partners like Dell Technologies. >> In our computing architectures. We had the need to address the capability to facing the big data processing involved with respect of the Human Brain Project and generally speaking that evolved with the respect of the science-driven that would provide cloud access to the systems by means of containers technologies. And the capability also to address what will be the creation of a Federation for high performance computing facility in Europe. So at the end we manage a competitive dialogue procurement the processor, that in a certain sense would share together with the different potential technology providers, what would be the visions and also the constraints with respect to the divisions including budget constraints and at the end Dell had shown the characteristics of the solution, that it will be more, let's say compliant. And at the same time, flexible with respect of the combinations of very different constraints and requirements. >> Dell Technologies has been sounds like a pretty flexible partner because you've got so many different needs and scientific needs to meet for different researchers. Talk to me about how you mentioned that this is a multi-national effort. How does Cineca serve and work with teams not only in Italy, but in other countries and from other institutes? >> The Italian commitment together with the European member states is that by mean of scientific methods and peer review process roughly speaking of the production capacity, would be shared at the European level, that it's a commitment that has been shared together with the France, Germany, Spain, and Switzerland. Where also of course, the Italian scientists, can apply and participate, but in a sort of emulations and the advanced competition for addressing what will be the excellence in science. The remaining 50% of our production capacity is for, for the national community and in somehow to support the Italian community to be competitive on the worldwide scenario that setting up would lead also to the agreement after the international level, with respect of some of the actions that are promoted in progress in the US and in Japan also that means the sharing options with the US researchers or Japanese researchers in an open space. >> It sounds like the human brain project, which the HPC is powering, which has been around since 2013 is really facilitating global collaboration. Talk to me about some of the results that the high-performance computing environment has helped the human brain project to achieve so far. >> The main outcomes that it will be consolidated in the next phase that will be lead by Euro SPC, which is called the phoenix that stands for Federation of a high-performance computing system in Europe. That provide open service based on two concepts One is the sharing of the ID at the European level. So it means that open the access to the Cineca system to the system in France , to UNIX system in Germany, to fifth system in Switzerland, and to the diocese the marine ocean system in Spain that is federated, ID management, others, et cetera, related to what will be the Federation of data access. The scientific community may share their data in a seamless mode, the actions is being supported by genetic, has to do with the two specific target. One is the elaborations of the data that are provided by the lens, laser, laboratory facility in Florence, that is one of the core parts of garnering the data that would come from the mouse brains, the time user for caviar. And the second part is for the meso scale studies of the cortex of the brain. In some situations they combinations of performance capability of the Federation systems for addressing what would be the simulations of the overall being of the human brain would take a lot of performance that are challenging simulation periodically that they would happen combining that they HPC facility as at European level. >> Right. So I was reading there's a case study by the way, on Cineca that Dell Technologies has published. And some of the results you talked about those at the HPC is facilitating research and results on epilepsy, spinal cord injury, brain prosthesis for the blind, as well as new insights into autism. So incredibly important work that you're doing here for the human brain project. One last question for you. What advice would you give to your peers who might be in similar situations that need to, to build and deploy and maintain high-performance computing environments? Where should they start? >> There is a continuous sharing, of knowledge, experience, best practices, where the situation is different in the sense that there are, what would we be the integration of the high-performance computing technology into their production workflow. That is the sharing of the experience in order to provide a spreads and amplifications of the opportunity for supporting the innovation. That is part of our social mission in Italy, but it's also the objective. that is supported by the European Commission. >> Excellent, that sharing and that knowledge transfer and collaboration. It seems to be absolutely fundamental and the environment that you've built, facilitates that. Sanzio, thank you so much for sharing with us, what Cineca is doing and the great research that's going on there. And across a lot of disciplines. We appreciate you joining the program today. Thank you. >> Thank you. Thank you very much. >> Likewise, for Sanzio Bassini. I'm Lisa Martin. You're watching this Cube Conversation. (upbeat music)

(upbeat music) >> Welcome to the CUBE Conversation. I'm Lisa Martin. I'm talking next with Sanzio Bassini, the Head of High Performance Computing at Cineca, at DELL technologies customer. Sanzio, welcome to the CUBE. >> Thank you, it's a pleasure, it's a pleasure. >> Likewise, nice to see you. So tell us a little bit about Cineca. This is a large computing center, but a very large Italian nonprofit consortium. Tell us about it. >> Yes, Cineca been founded 50 years ago, from the university systems in Italy. For a statutory mission, which is to support, the scientific discovery, and the industry innovations, using the High Performance Computing and the correlated methodologies like a, Artificial Intelligence, which is one of the, you see the more, in a, in a adopted in those days, but together with the big data processing and and simulation. Yes, we are a consortium, which means that this is a private not-for-profit organizations. Currently, our member of the consortium, almost all the universities in Italy and also all the national agencies for those selected structures. Uh. The main quarter of Cineca is in Bologna, which is in the heart Nation, with the bunch of presence in Milan, in Rome and in Naples, so we are a consultation organization. >> And I also read that you were, are the top 10 out of the top 500 of the world's fastest super computers. That's a pretty big accomplishment. >> Yes. That is a part of our institutional missions, the last 10 to 15 years we have been to say, frequent flyers in the top 10. There been at least two, three systems that have been ranked at the top 10. Apart, the.., whatever would be the meaning of such an advance market, there's a lot of its criticalities. We are well aware. The idea is that we're enabling the scientific discovery, by means of providing the most advanced systems and the co-designing, the most advanced HPC systems to promote and support the, what is the, excellence in science. And that being part of European high-performance computing IT system. That is the case. >> Excellent. Now, talk to me about some of the challenges that Cineca is trying to solve in particular, the Human Brain Project. Talk to us a little bit about that and how you're leveraging high-performance computing to accelerate scientific discovery. >> Um, The Human Brain Project is one of the flagship project that has been co-founded by the European commission and that the participating member states. Is not as another situations that are undertaking, it's definitely a joint collaboration between members states and the European commission. There are two different right now, flagships together with another, that is in progress, which is that the quantum of flagship, the first two flagship abroad that that has been lost. The commission for operation with the participating states has been one on the digraph vein on which also we are participating in directly together with the CNR, is the national business counselor. And the second for which we are core partners of the HPC that is, the Human Brain Project. That, that is a big flagship, one million offer, of newer investment, co-founded by the participating states and that the European commission. The project it's going to set up, in what to do be the, third strategic grant agreement that they will go over the next three years, the good, the complete that the, the whole process. Then we see what is going to happen at Africa. We thought that their would be some others progress offer these big projects. It's project that would combine both the technology issues, like the designing the off high-performance computing systems that meet the requirements of the community and the big challenge, scientific challenges correlated to the physiological functions of the human brain center, including the different farm show survey to do with the behavior of the human brain. A from the pathological point of view, from the physiological point of view, that better understand the could be the way for, for a facing that. Let's say the pathology, some of those are very much correlated with respect to aging, and that it would impact the, the health, the public health systems. Some other that are correlating with what would be the support for the physiological knowledge of the, of the human brains. And finally that they, let me say, technological transfer stuff that represented the knowing off at the physiological, behavior of the human brain. Just to use a sort of metaphor to have happen from the point of view of there computational performance, the human brain is a, a, a, more than Exoscale systems, but with a energy consumption, which is very low, we are talking about some hundreds of Watts. So some hundreds of watts of energy, would provide a an extreme and computational performance. So if would could organized the technology of the high-performance computing in terms of interconnections now we're morphing the computing systems and exploitations of that kind of technologies, in I build a system that it might provide the computational power that would represent a tremendous and tremendous step ahead, in order to facing the big challenges of our base, like energies, personalized medicine, try not to change food for all those kinds of big socioeconomic challenges that we are facing. >> Yes I was reading that besides, sorry Sanzio I was reading that besides the Human Brain Project, there are other projects going on, such as that you mentioned, I'd like to understand how Cineca is working with Dell technologies. You have to translate, as you've mentioned a minute ago, the scientific requirements for discovery into high-performance computing requirements. Talk to me about how you've been doing that with partners like Dell technologies. >> Yes, in particularly in our computing architectures, we had the need to address the capability to facing the data processing involved with backed off the Human Brain Project and general speaking that is backed off the science vendor, that would combine the capability also to provide the cloud access to the system. So by main soft containers technologies and the capability also, to address what would be the creation of a Federation. So Piper problems with people proceeded in a new world. So at the end that the requirements and the terms of reference of the would matter will decline and the terms of a system that would be capable to manage, let's say, in a holistic approach, the data processing, the cloud computing services and the opportunity before for being integrated that in a Federation of HSBC system in Europe's, and with this backed off, that kind of thing, we manage a competitive dialogue procurement processor, I think I the sentence would share together with the different potential technology providers, what would be the visuals and those are the constraints (inaudible) and those other kinds of constraints like, I don't want to say, I mean, environmental kind of constraints and uh, sharing with this back of the technology provider what would it be the vision for this solution, in a very, let's say hard, the competitive dialogue, and at the end, results in a sort of, I don't want to say Darwinian processes, okay. So I mean, the survivors in terms of the different technology providers being Dell that shown the characteristics of the solution that it will be more, let's say compliant. And at the same time are flexible with respect of the combinations of very different constraints and requirements that has been the, the process that has been the outcomes of such a process. >> I like that you mentioned that Darwinian survival of the fittest and that Dell technologies has been, it sounds like a pretty flexible partner because you've got so many different needs and scientific needs to meet for different researchers. Talk to me about how you mentioned that this is a multi-national effort. How does Cineca serve and work with teams not only in Italy, but in other countries and from other institutes? >> Definitely the volume commitment that together with the, European member states is that by means of scientific merits and the peer review process, roughly speaking the arc of the production capacity, would be shared at the European level. That it's a commitment that, that there's been, that there's been a shared of that together with France, Germany, Spain, and, and with the London. So, I mean, our, half of our production capacity, it's a share of that at the European level, where also of course the Italian scientist can apply in the participates, but in a sort of offer emulations and the advanced competition for addressing what it would be the excellence in science. The remaining 50% of our production capacity is for, for the national community and, somehow to prepare and support the Italian community to be competitive on the worldwide scenario on the European and international scenario, uh that setting up would lead also to the agreement at the international level, with respect of some of the options that, that are promoted the progress in a US and in Japan also. So from this point of view, that mean that in some cases also the, access that it would come from researchers the best collaborations and the sharing options with the US researchers their or Japanese researchers in an open space. >> Open space for, it sounds like the Human Brain Project, which the HPC is powering, which has been around since 2013 is really facilitating global collaboration. Talk to me about some of the results that the high-performance computing environment has helped the Human Brain Project to achieve so far. >> The main outcomes that it will be consolidated in the next phase that will be need the by rural SPC that is the Jared undertaking um entities, that has been created for consolidating and for progressing the high-performance computing ecosystem in Europe. It represented by the Federations of high-performance computing systems at European level, there is a, a, an option that, that has been encapsulated and the elaborated inside the human brain flagship project which is called the FEHIPCSE that stand for Federation of a High-Performance Computing System in Europe. That uh provide the open service based on the two concepts on one, one is the sharing of the Heidi at a European level, so it means that the, the high demand of the users or researchers more properly. It's unique and Universal at the European level. That didn't mean better the same, we see identity management, education management with the open, and the access to the Cineca system, to the SARS system in France, to the unique system in, uh Germany to the, Diocese system in a Switzerland, to the Morocco System in a Spain. That is the part related to what will be the federated, the ID management, the others, et cetera, related to what will be the Federation off the data access. So from the point of view, again, the scientific community, mostly the community of Human Brain Project, but that will be open at other domains and other community, make sure that data in a seamless mode after European language, from the technological point of view, or let's say from the infrastructure point of view, very strong up, from the scientific point of view, uh what they think they may not, will be the most of the options is being supported by Cineca has to do with the two specific target. One is the elaboration of the data that are provided by the lands. The laws are a laboratory facility in that Florence. That is one of the four parts, and from the bottom view of the provisions of the data that is for the scattering, the, the data that would come from the mouse brains, that are use for, for (inaudible) And then the second part is for the Mayor scale studies of the cortex of the of the human brain, and that got add-on by a couple of groups that are believing that action from a European level their group of the National Researcher Counsel the CNR, that are the two main outcome on which we are in some out reference high-performance computing facilities for supporting that kind of research. Then their is in some situations they combinations of the performance a, capability of the Federation systems for addressing what will be the simulations of the overall human brain would take a lot of performance challenge simulation with bacteria that they would happen combining that they SPC facility as at European level. >> Right! So I was reading there's a case study by the way, on Cynic that Dell technologies has published. And some of the results you talked about, those that the HPC is facilitating research and results on epilepsy, spinal cord injury, brain prostheses for the blind, as well as new insights into autism. So incredibly important work that you're doing here for the Human Brain Project. One last question Sanzio, for you, what advice would you give to your peers who might be in similar situations that need to, to build and deploy and maintain high-performance computing environments? Where should they start? >> (coughs laughs) I think that at, at a certain point, that specific know how would became sort of a know how that is been, I mean, accumulated and then by some facilities and institutions around the world. There are the, the federal labs in US, the main nation model centers in Europe, the big facilities in Japan. And of course the, the big university facilities in China that are becoming, how do you say, evident and our progressively occupied increasing the space, that to say that that is somehow it, that, that, that the, those institutions would continues collaborate and sharing that there are periods I would expect off what to do, be the top level systems. Then there is a continuous sharing of uh knowledge, the experience best practices with respect off, let's say the technologies transfers towards productions and services and boosterism. Where the situation is big parenta, in the sense that, their are focused what it would be, uh the integration of the high-performance computing technology into their production workflow. And from the point of view, there is the sharing of the experience in order to provide the, a sort of, let's say, spreads and amplifications of the opportunity for supporting innovation. That is part of are solution means, in a Italy but it also, eh, er sort of um, see objective, that is addressed by the European options er supported by the European commission. I think that that sort of (inaudible) supply that in US, the, that will be coming there, sort of you see the max practice for the technology transfer to support the innovation. >> Excellent, that sharing and that knowledge transfer and collaboration. It seems to be absolutely fundamental and the environment that you've built, facilitates that. Sanzio thank you so much for sharing with us, what Cineca is doing and the great research that's going on there, and across a lot of disciplines, we appreciate you joining the program today. Thank you. >> Thank you, it's been a pleasure, thank you very much for the opportunity. >> Likewise, for Sanzio Bassini. I'm Lisa Martin. You're watching this cube conversation. (calming music)

>>Welcome to this cube conversation. I'm Lisa Martin. Joining me next is bran Kirschner, the vice president of strategy at DataStax Brian. Welcome to the program. Thank you. Glad to be here. Excited to unpack this survey that DataStax recently did. This is with 500 or so it executives, technology practitioners talking about data strategy. Talk to me, first of all, about the state of the data, racist, the name of the survey. Why did data sect students? What was the impetus behind that? >>Yeah. Great question. Thank you. So, you know, um, we are in a race for our company. Every organization is in a race to find ways to use data in new ways to move the business forward, satisfy your customers and so on. Um, it's okay to have a strategy to be a leader. It's probably okay to have a strategy, to be a fast follower. It might even be okay to say we stayed in touch with best practices and once they're proven we adopt them, but what's not okay, is one to lose track of where you need to be relative to how the market's moving most important than your competitors. But in general, customer expectations, your employee partner expectations are going to be set by companies potentially in different industries. So you need to be at the right spot in your journey. So that's why we do a lot of benchmarking, but as important is as your particular company's context and history and situation and technical architecture, um, kind of comes in contact with a strategy that looks great on paper, you have to understand is something slowing us down that we didn't expect because of our culture or unspoken incentives or, you know, what is our next best step for us? >>So in this, in this dataset, we really look to identify the leaders who are having the most success and then work back from the patterns and practices we saw with them to how different, different types of companies at different stages of their journey can find their next best step to make the right progress. >>So the showed that a lot of companies have a data strategy. The execution piece is a, is a different story. Talk to me about how this survey defines a data leader. What is it, what are some of the key characteristics? Yes, >>There are quite a few. Um, in fact, what we've done over the last year that we fed into the survey was, you know, in the course of my work and my colleagues work, we talked with lots of CEOs, hands-on techno practitioners, CTOs, and so on. And we put all that conversation and qualitative insight together into, uh, about 70 measures. Um, and so that was all in the survey. And once we got the data back, uh, we did a cluster analysis, bringing some data science, the data strategy, if you will. Um, and that's surface these segments. Um, and for example, how much revenue you were generating from data was not part of the, so then we mapped these segments and these practices against that, and we say, oh, the leaders generating the most revenue from data. So that gave us some confidence in using these patterns and practices to bucketize folks. >>And you found that the data, those companies in the data leadership category were able to attribute more than 20% of their data of two gives me 20% of their revenue to data and analytics. Talk to me about that 20% benchmark is that considered where a lot of organizations need to aim to be because there's still a lot of money on the table. Yeah, >>That's right. That's right. So, you know, in common industry parlance as a standard, you know, materiality on the balance sheet is 10%. Um, and we've seen a pretty significant number of companies hit that mark. Um, what we saw, which is interesting in our data was, you know, that's kind of a comfortable benchmark to pick it's an industry standard data's material, Hey, congratulations. Uh, but you're actually drill down further and you look at that 20% mark and you say, well, 10% is probably not aiming nearly high enough because a significant proportion of these leaders have already gotten to that 20% mark. Um, and so it's in part, you know, again about that benchmarking where you are, where's your destination, your destination, probably isn't we're on the board, your destination probably isn't its material. Your destination is probably, you know, it's big and it keeps getting bigger. >>And where are these data leaders with respect to deploying a hybrid data strategy? What is it about how they're organized and structurally what they're doing that is positioning them to actually really drive incremental revenue from data? >>Yes. Yeah. What stands out about the leaders, um, is, and, you know, we see this in our data, you can see this in any number of analysts, firms and other data sources hybrid cloud strategy is, you know, the dominant strategy for large enterprises, right? It's about preserving your flexibility to operate in multiple clouds. And on-prem, so that's pretty well understood. What we saw in this data was overwhelmingly almost a hundred percent of the data leaders also say they're pursuing a hybrid data strategy. So they're already doing that kind of same level of thoughtfulness and planning about how can we get and deploy apps and compute everywhere to how can we store and deploy and redeploy data everywhere. And there's a real steep curve to the extent where the folks who are just starting out, who may have a strategy, but have taken very little action. None of them strongly agree that they have that type of hybrid data strategy. Um, and so the pattern qualitative pattern we see is companies go down this hybrid cloud compute strategy for good reasons, and it pays off, starts to pay off. And then they realize, oh, we should be doing the same thing for data. Um, and that's giving these leaders, you know, a lot of agility control, flexibility, um, and opportunities. >>One of the things I found interesting in the report from a statistics perspective is that those data leaders that you talked about that are able to, or able to attribute more than 20% of their revenue to data and analytics twice as many of those are two. And they're two X likely to be using a robust open source data stack talking about that as it plays into the computing strategy and the ability to convert data into revenue. >>That's right. So they're, they're, they're almost a hundred percent comparable to the hybrid data strategy. Almost a hundred percent are also increasing their use of open source software. And I kind of think about this from, from two dimensions, right? The, the hybrid cloud and hybrid data strategy gives you agility, optionality flexibility for your infrastructure, for your compute, for your storage and so on. Um, then it's about really making sure you're using the best of breed tools for the job of creating value with data. Um, and if you look backwards, um, you know, the track record of open source technologies, Apache Cassandra Kafka spark at some of just like, you know, the applications and experiences that are, you know, have, have, you know, validated the massive impact data can have on a business. Um, the track record of open source is strong and you look at the cycle of innovation and you see, you know, Kafka having emerged and now pulse are emerging as sort of a, a newer, more cloud friendly version of Kafka and flank kind of emerging as potentially a successor to spark that cycle of innovation, arguably is accelerating. >>Um, and so as you think about what's, you know, what's unique to us as a company, um, it's the data you have, right? No one has the customer interactions. You have, nobody has a business processes you have. So what you want to do is take those best of breed tools and have flexibility about the infrastructure services to support them and focus your people on doing great things with the data. So don't try to solve a problem that the open source ecosystem has already solved, right? If you're, if you're writing that code, instead of focusing on what differentiates your business, that's a miss. Um, so when you see the leaders leaning hard into, um, open source, you know, it's because they've got the clarity about, we differentiate by using these best of breed tools on our data, not reinventing the wheel, >>Are these companies, you mentioned culture a minute ago, and that's always something that I find intriguing because it's very hard to change. We've been in the last 16, 18 months in an, in a very fast pace of change, as we know, but are you seeing these data leaders that are companies that are reorienting towards a data culture where data is part of everyone's job? >>Yeah, absolutely. Absolutely. So they, so it's interesting. Um, a majority of all companies said that reacting to the COVID crisis did increase their pace of innovation, but again, it's almost universal among, among those leaders. Um, and one of the patterns that stands out is indeed, when you say making it everyone's job, I'll put finer point on it. It's saying accountability for creating value, generating revenue with data is the line of business is accountability. I'm in conversations. I've literally had CEOs say, it's not my problem anymore. It's my problem to help them execute on the ideas, right? And that can even raise the bar because now they're coming up with bolder, bigger ideas, but it's not about it being the custodians of the data, trying to go to the business and say, Hey, could you use some data it's business, general managers, VPs now accountable for how have you used data to drive revenue? >>How can it change the way you sell or the way you service customers? Um, and so on. And, and that, um, in part, what we heard from some folks was in organizations with progressive CEOs, chief data officers, they have been going to the side to the business side of things and saying, Hey, I think we've got ways to do business better, but there wasn't pressure on the business. They're like our business is going fine. Uh, but once COVID hit, it was okay. We need to take out costs. We need to find new ways to grow. Um, and there's sort of that that drove and organic embrace of, ah, I see, I want to pick up the reins and, you know, work with my technology partners to make it happen, but now I see we should be driving it on the business side. >>And have you seen in the COVID era data strategy become really a board level initiative and, and to your point, one of the things that you've found is, is it's not just the culture of data being core to everyone's job, it's the accountability level at the line of business level. But I imagine that that data strategy is indeed a board level initiative. >>That's right. That's the biggest, when you mentioned culture, the biggest of the segments is a group whose biggest challenge is cultural change about almost a third of, of all organizations. Um, and you see there, there's this big drop, you know, compared to the leaders of whether the data strategy is a board level discussion, right. And you see this big drop in other metrics where, you know, do you have a data strategy, mild agreement like, oh yeah, we talk about data of everybody talks about data. Um, but it's really about getting that top down. This is a true corporate priority, which kind of circles back to our initial conversation, you know, if the goal is 20% or more of your revenue from data, it better be a board level conversation. Right. And, and, you know, if you have an effective board, you want the board to be helping to drive toward that. Um, so it really closes the loop on, you know, again, calibrating, what's our aspiration, um, what's at stake. And if we believe in the data, you know, we shouldn't be hesitating to elevate this to the board level and get their attention on >>It. Right. Give me an example of a, of a customer that's doing that. That's a data leader that's doing this really well. And one that pivoted to be able to, to use data and extract value and revenue from it during the last year and a half, >>I would say it's a little bit less of a pivot and more of an amazing success story. Um, uh, because of you look backwards a few years ago, um, home Depot made a significant board level, you know, top-down, company-wide commitment to a very bold digital and data strategy. And so, you know, by 2019, um, for one example, you know, Forester ranked them as a top retail app, um, uh, for customers, um, and all that work, which is already paying off, right. They're making big investments, but they're getting big payoffs. Um, when COVID hits home, Depot is able to deploy curbside delivery as a service. They did not have a feature they did not have in weeks at scale, um, which drove even more outsized returns during COVID. Um, and so it's, it's a little, uh, you know, it's a less of a pivot, but more about the value of making that commitment. >>Um, because you know, they, weren't planning on deploying curbside delivery to the app in weeks, but when COVID hit, they were able to, because they already had the cultural change, the infrastructure, the metrics, the technologies in place. Um, and so, you know, it's really a message about don't wait, right? If you are going to fast follow, if you are going to be away for proven best practices, you don't want to start off the blocks at zero. When something disruptive happens, you want to have some success stories, some practice at it under your belt. So, you know, even if you're, if you're, if you're fortunate enough not to have been pushed into radical action because of COVID, don't, don't let that stop you from seizing the day and actually starting to move. >>I now I've, I think I'll never have the same opinion of, of home Depot. Again, I will always go on there looking for light bulbs and batteries and flashlights thinking of them as a data company, but as a company, that, to your point, committed to it and push that accountability out into those lines of business. How does, what did the survey show in terms of those data leaders embracing, uh, open source, embracing a hybrid data strategy? How does that facilitate that, driving that accountability into the lines of business so that that revenue that's sitting on the table from data can be unpacked. >>Yeah, it's, it's almost, I think, you know, if I look at it from the technology side, um, imagine, you know, in the past, you're the custodian of data, you know, as a CIO and your job is to kind of make, make, make, you know, data's not lost. We comply with regulations, you know, for the kind of way we run the business yesterday and today doesn't break tomorrow. And so if I think about the shifts to where the lines of business are now accountable for finding new ways to use data, what are the, to come up with? Like, you know, if you think about like, you know, innovating in business, um, taking data under the wing, right? Your job now, as a manager is innovate, innovate your business model, deliver something we never delivered before deliver something. No one in our industry delivers. So on the tech side, you know, it should be exciting, but it also means you may be on the hook for delivering some capability that your company had never thought about. >>Um, so that really gets back to this idea of like, do you have access to, you know, the best infrastructure services through hybrid cloud and data strategy? Are you set up to use best of breed tools, even if, you know, last year we didn't have a scenario that uses best of breed tools. Well, now that the businesses, I think it really hard on how we differentiate with data. They're probably going to come up with some big bold ideas, um, again, which should be exciting, but you gotta be ready to invest in change and something new as opposed to keeping the lights on. >>Right. I think that pace of innovation, I don't know, maybe it's permanently altered because of the scenario was one that nobody ever expected to be in. As we saw so much transformation in the last year and a half, and the pace of innovation change and, and the, you know, the places that are like the home Depot being able to radically change so quickly. And so we saw a lot of other businesses that could not do that. What are some of the market trends that you're seeing as we're now coming around the corner into the second half of 2021? >>I mean, the acceleration is a great point because when you're using data to deliver value to customers or create value for your business, things actually build on them on each other. Right. So, you know, data doesn't get used up until the, the amazing things about digital data. It can be used and reuse and recombined. So if you saw, for example, you know, leaders are well on the way before COVID, do you have real time inventory we'll share. Uh, but then once COVID hit, do you have real time inventory? And can you make a recommendation for somebody that's out of stock became like, wow, we should get that done ASAP. So then as you see folks do some necessary things, um, you start to see, well, if we've got real-time inventory and we can make recommendations, why are we getting a 360 degree view of the customer from that data plus marketing data, right? >>And now the value gets unlocked. Whereas if you said, you know, two years ago, how can we justify creating a 360 degree view of the customer, some organizations might've been like, well, we can, you know, it's hard to do. We can't see the value. Whereas once you're doing a couple of these use cases, it becomes obvious that they'd be better together. Right. And so, um, if you see, you know, the home Depot, I think you're going to see, um, you know, essentially every retailer that wants to stay competitive is going to follow in that path. >>Do you think that those companies that become data leaders or are on the path to become data leaders that have the hybrid data strategy that are embracing OpenStack? Is that mentality in your opinion, going to separate the winners and the losers going forward in the next year plus? >>Yeah, I mean, I think, I think in a sense it has to, uh, because again, as I think, you know, there was a trend already in place for all of us as consumers, right. We love, for example, delightful recommendations, you know, uh, companies and applications that know us and just make our lives better because they're smart, like Netflix and Spotify, right. The classic examples. Um, but now you think about for anything. So Cengage is an education platform company, and they talk about being the Netflix of education. Um, and you know, retailers like home Depot, like target have gotten super smart about things like recommendations. Um, and you know, in the case of home Depot, like connecting me with the data that explains how to do DIY projects and use the tools I'm trying to buy. So, you know, the bar just keeps getting raised to the point where, you know, you look at, you know, you look at a, the e-commerce site of the past, we just sort of a dumb e-commerce site where it's, I can pick things, put them in a cart and buy, you know, that's not acceptable by any stretch of the imagination today, right. >>Are there user reviews? Are there, you know, recommendations? We expect all of this. Um, and I think you'll see it, you know, obviously retail's heavily disrupted by COVID pointing into the sphere, so to speak, but I mean, telehealth is another example where, you know, I think the writing is on the wall. If you can't do telehealth as a health system or a hospital, you know, very soon you're going to have a big problem. >>Yeah. The consumer demand is incredible for, I want whatever it is, if it's I'm shopping on Amazon or if it's going to be, but I want them to know what to recommend to me next, based on what I just thought we have that expectation that the Netflix is and the Spotify is to your point have set. And we also have that expectation in our business life. So when folks are buying it, interacting with software, they want the same thing, right. It's not just limited to healthcare retailers. That's >>Right. And I that's that there's a virtuous cycle, right? If you think about companies, you know, making that cultural change, leaning into using data to make things better, it's not just for customers, it's for your employees, it's for your partners, it's for your business processes. Right. And how are you going to be able to hire people who are super excited about making things better for customers, if you're also not, you know, internally making things better for your employees, right. There's just a real disconnect in terms of, you know, culture and personnel. There. >>That's a great point. Those are in my opinion, inextricably linked, Brian, it's been great to have you on the program. Thank you for sharing with us. The state of the data raised very interesting sort of that you guys have done. Folks can get their hands on that lot of opportunity and a lot of money on the table for organizations in any industry. Thanks so much for joining me today, brand thank you for Brian Kirschner. I'm Lisa Martin. You're watching a cube conversation.

>>From around the globe. It's the queue with digital coverage of Dell technologies, world digital experience brought to you by Dell technologies. >>Hi, welcome to the cubes coverage of Dell technologies, world 2020, the virtual experience. I am Lisa Martin and I've got a couple of guests joining me. One of them is a longtime cube alumni. John Siegel is back the VP of product marketing for Dell technologies. John, it's great to see you. >>Great to be back. Thank you. >>And also joining us is Troy Massey the director of enterprise engagements from iron bow technologies. Troy, welcome to the cube. >>Hi, thank you. Grabbed him. >>So we're going to be talking about VxRail, how it's driving the future of HCI to the edge, but first let's get choice perspective. I would like the audience to understand who iron bow technologies is and what you do. And then we'll kind of look at it as what you're doing with the extra rail, as well as your channel partner business with Dell technologies. So Troy, take it away. >>Hi. Yeah. So, uh, Iron Bow is a global company. We're a value added reseller, uh, having partnered with Dell. Um, we have people physically living from Europe all the way through in Korea, um, from kind of based the globe, uh, primarily in wherever there's DOD or federal government agencies. >>And tell me about from a channel partner perspective, what you guys are doing together. >>Yeah, so we have a lot of efforts going on channel partner together, uh, specifically, uh, Iron target is, is a huge effort to where we're doing together. Uh, it's a on prem cloud, uh, that's uh, it's basis, VxRail VMware Cloud foundation on top, uh, with Intel all throughout. So there's an Intel Xeon processors and, uh, Optane drives. Uh, so just the perfect elegant OnPrem cloud, hybrid cloud solution that Dell and Iron Bow are driving together. >>So let's talk about the edge, cause a big focus of Dell technologies world this year is about the edge. How do you see Troy iron bow extending services to the edge and what do you anticipate from your customers in terms of what their needs are as they're changing? >>Great, great question. So, um, for one, I've gotta talk a little bit about what the edge and what the edges and the edges different things to different people. So I'm going to explain a little bit of the edge and what we're seeing and, and the federal government. So I'll give you one example and that's, um, uh, you know,  the air force reserves. So they have a, uh, an entire squadron that does all of the firefighting, uh, the large fires you see across California or whatever states engulfed in fires that year, um, where they take an entire squadron of airplanes out when they sort of water overall, the whole fire, uh, they don't just bring planes. They entire squatters military personnel to help communicate with the police and with the local fire and all of that takes information. So they need to bring information data with them. Is there a building over there? Do people live over there where we got to actually concentrate on site on fighting that fire priority-wise so it doesn't make a lot of sense to try to do that remotely over satellite it's large, large chunks of data that needs to be local to the customer. So, um, VxRail is, is the power beast of the HCI world VxRail at that edge provides them with the performance they need to get that job done. >>I think that's going to be a new new segment here in Silicon Valley. That thinking about all the fires we've had, and it's really VxRail at the edge, that's helping fight the fires. That's not something I knew. So thanks for sharing that. >>So there's all kinds of workings in that area, same deal. They need to know where to go rescue those people and it's all data. >>Exactly. And it's gotta be data that's that, as you said, it was not delayed sent over the wire, but obviously being able to be transmitted in real time so that actions can be taken, which is one of the things we talk about with data all the time. You have to be able to get the insight and act on it quickly. So, so John, the theme of this year's virtual Dell technologies world is the edge is a big part of the theme. So talk to us about driving the future of HCI at the edge with VxRail, how there's been a lot of growth, I think 9,600 plus customers so far. So talk to us about the future of HCI at the edge with VxRail as a driver. >>Absolutely. So first of all, I want to thank iron bow for being one of our nearly 10,000 customers for VxRail. Um, and you know, absolutely. So, you know, overall the edge is going to be a major theme for Dell tech world this week. Uh, and specifically for VxRail. Um, we of course continue to play with VxRail, a key role in modernizing data centers, uh, as well as hybrid cloud. And this week really wanted to highlight some of the recent innovations we have around extending the simplified operations of VxRail that many like, uh, iron bow and others are experiencing today in the core, uh, are in the cloud and extending those, that automation to the edge. Um, and you heard a lot about what the edge can do in the end and the implications and the value of the edge. Um, while we have lots of customers today, um, including IMO that are using VxRail at their edge locations, uh, we have others like large retail, uh, home improvement chains, financial institutions. Um, we expect the edge to soon explode. Um, we like to think that, uh, we are at the edge of the edge opportunity, um, in >>It in fact, IDC recently stated that by 2023, over 50% of new enterprise data that is generated is going to be generated outside the core data center and outside the cloud. That's up front 10% today. So this is, this is massive, um, edge locations. Um, of course come with their own challenges, whether it's sometimes less than ideal conditions around power and cooling, or they may not have typically, um, skilled it staff at the edge, right? So they, they need, they need new special configuration. They need operational efficiencies. And I think VxRail is uniquely positioned to help address that. >>Let's kind of dig into those operational challenges because in the last seven months, so much of what we all do has become remote and a good amount of that is going to be probably permanent. Right. So when you think about the volume of remote devices that VxRail could potentially manage, John, how, how do you see  VxRail being able to help in this sort of very distributed environment that might be very well much permanent? >>Yeah, I know. And like you said, it's going to just grow and grow the distributed environment and what that means for each company might be slightly different, but regardless what they do need to seamless operations across all of those different edge locations, um, and a, again, a big focus for us. So we're really doing three things to extend the, the automated operations of VxRail to the edge and doing so at scale. Uh, the first thing I want to say, talk about is that we did on avail just two VxRail platforms designed specifically for the edge, uh, the new VxRail E-Series, which is ideal for remote office locations, where space is limited. Um, the remote, uh, the VxRail D series, I think of D as in durable, uh, this is our ruggedized platform, uh, built from the ground up for harsh environments, you know, such as the DOD environments, like in the, um, in the desert. >>Um, and both of these VxRail platforms are fully automated. They automate everything from deployment to expansions to, to lifecycle management overall. Um, and now what we're doing now with extending that automation is the second thing we doing, uh, you know, to the edge from an operational perspective. And what we're doing first and foremost is we are introducing a new software as a service multi cluster management. Uh, this is part of the VxRail HCI system software that we deliver today as part of the VxRail. Uh, this not only provides a global view of the infrastructure performance, um, and capacity analysis across all the locations, but even more importantly, it actively ensures that all the clusters and the remote locate locations always stay in a continuously validated state. This means that it can automatically determine which software components need to be upgraded. Um, you know, and also automatically execute the full stack upgrades, right? >>Without any technical expertise at the site, it can be done centrally, further automating the lifecycle management process and process that we do, uh, at the core and the cloud, and now extending to the edge. So, yeah, imagine the operational efficiencies for customers with tens or hundreds or even thousands of edge sites. So this is we think truly a game changer from that perspective. And then in addition to that, we're also adding, uh, the support for BCS on VxRail. So, uh, just at VM world just a couple of weeks ago, uh, VMware announced, uh, remote edge cluster support for VCF. Uh, so those customers that run run BCF on VxRail now can get the, the, they can enjoy a consistent cloud operating model, um, you know, for those edge locations. So, you know, in summary, you're getting consistency, you're getting automation regardless of where your VxRail is located. >>And this is something that I saw in the notes. John is described as a curated experience. Can you describe what that is if I think of reference architectures and things of that, what is a curated experience and how is it different? >>Yeah,  a curated experience for VxRail...  really what it is it's about seamless. Uh, it it's about we, we have taken the burden if you will, of integrating infrastructure off of the customer's shoulders and onto ours, right? So what we do is we ensure VxRail is in fact, the only, um, jointly engineered HCI system in the market, that's doing the engineering with VMware, for VMware to enhance VMware environments. Uh, and so what we've done is we, uh, we have a pre-integrated, uh, full stack experience that we're providing the customer from deployment, uh, to, uh, again, to everyday operations, to making changes, et cetera. Uh, we've essentially what we've done here, um, is that we've, we've taken again, that, that burden off of customers, uh, and allowed them to spend more time innovating, uh, and less, you know, less time integrating >>That sounds good to everyone, right? Simplifying less time to troubleshoot more time to be able to be strategic and innovative, especially in such a rapidly changing world toy overview now, Oh, go ahead, John, >>To add to that, you know, we've seen a real acceleration this year to digital transformation, to your point earlier, just with remote everything. And I think a lot of the projects, and so including a shift that we've seen to consuming infrastructure overall, whether, you know, and that's, that's the, the onset of the cloud and wherever that cloud might be, right. It could be on prem, could it be on premises, could be off premises. Um, and so, you know, that focused on consuming infrastructure versus in that preference for consuming infrastructure versus building and maintaining it, that's something that we're going to continue to see accelerate over time. >>You're right. That digital transformation acceleration has been one of the biggest topics in the last seven months and looking at which businesses really are set up and have the foundation and the culture to be able to make those changes quickly, to not just survive in this environment, but win tomorrow. So talk over to you for a second, in terms of, of the edge. What are your thoughts on as a partner, with VxRail, you've got a solution built on it. What are your thoughts about what VxRail is going to be able to deliver, enable you to deliver at the edge? You know, you gave us that great example of the air force reserve, but what our iron bows thoughts there, what do you envision going forward? >>He talks about tens, hundreds, thousands of different sites that all need their data, they all need process and compute but those types of sites don't necessarily need to have and IT on staff at those sites, a great example is the army Corps of engineers. They have to have one or two people out at every dam to monitor the dam, but that mean it justifies an IT staffer out there with them. So the idea to remotely manage that VxRail, they're just industry leaders in the ability to deploy this  somewhere where there's not an it person and be able to manage it, but not just manage it, predictive analysis on when they're starting to run out of storage , give alerts so that we can start the upgrade. >>John talked to us about the engagement that you're expecting your customers to have with Dell technologies during this virtual event. >>Absolutely. I think so. First of all, um, yeah, virtual is different, but there's lot of advantages to that. Um, one of them is that we can have, um, an ongoing dialogue during, uh, a number of the sessions that we have, why some of the sessions might be prerecorded. There are alive chats all the way through, including a number of breakouts on VxRail, specifically, uh, as well as the edge, as well as a number of different, um, topics that you can imagine. Um, we've also just launched a new game, a fun game, uh, from mobile called data center sin, um, where, uh, customers can have some fun, uh, learning about VxRail, uh, the experience that takes and balancing the budget and staffing and capacity, uh, to address the needs of the business. So, uh, we're always looking for fun and engaging ways to experience, experience the real life benefits of our HCI platforms, such as VxRail. And, um, so customers can, uh, check that out as well, um, by searching their app store of choice for Dell technologies, data center, sin, uh, and have at it and have some fun. But again, whether it's playing the game online through it, I've met the reality experience or it's, um, you know, connecting directly with any of our subject matter experts. Um, there's going to be a lot of opportunity, uh, to learn more about how VxRail and ACI can help our customers thrive. >>Excellent. I like that game idea. Well, Troy, John, thank you for joining me today and letting me know what you guys are doing with the VxRail what's coming with the edge and the fact that they use cases are just going to proliferate. We appreciate your time. Thank you as well for my guests. I'm Lisa Martin. You're watching the cubes coverage of Dell technologies world 2020.

>> Announcer: From around the globe, it's theCUBE with digital coverage of AWS Public Sector Online, brought to you by Amazon Web Services. >> Everyone welcome back to theCUBE's coverage virtually of the AWS, Amazon Web Services, Public Sector Summit Online. Normally we're face to face in Bahrain or Asia Pacific, or even down in New Zealand and Australia, but we have to do it remotely. I'm John Furrier host of theCUBE, we've got a great segment here with a great guest, Ian McCrae, Founder and CEO of Orion Health, talking about the Global Healthcare Industry with Cloud Technology because now more than ever, we all know what it looks like, before COVID and after COVID, has upending the health care business, we're seeing it play out in real time, a lot of great benefits to technology. Ian, thank you for coming remotely from New Zealand and we're here in Palo Alto, California, thank you for joining me. >> Thank you for the invitation. >> You're the Founder and CEO of Orion Health global, award-winning provider of health information technology, supports the delivery of optimized healthcare throughout New Zealand, but now more than ever around the world, congratulations. But now COVID has hit, what is the impact of COVID because this is changing healthcare for the better and speed agility, is the services up to snuff, is it up to par? What is the situation of the post-COVID or the current COVID and then what we'll post-COVID look like for healthcare, what's your opinion? >> So, sir, I've never seen such a dramatic change in such a short time, as has happened over the last nine to 10 months. And you know what we're seeing is before COVID, a lot of focus on automating hospitals, probably primary care, et cetera, now all the focus is on putting medical records together, digital front doors giving patients access to their medical records, and much of the same way you have access to your bank records, when you travel you go into well, we don't travel now actually, but when you go into the lounges, the airline apps are very, very user friendly and the healthcare sector has been a laggard on this area, that's all about to change. And patients will be wanting, they don't want to go when they're feeling ill, they don't want to go down to their local physician practice because, well, there are other sick people there, they want to get the right care, at the right time, and the right place. And usually when they're not feeling well, they want to go online, probably symptom checking, if they need to have a consult they would like to do it there and then and not two or three days later, and they'd like to it virtually, and you know, there are definitely some things that can be done remotely and that's what people want. >> One of the things that comes up in all my interviews around innovation and certainly around AWS and cloud is the speed of innovation, and we were talking before we came on camera about I'm in Palo Alto, California, you're in Auckland, New Zealand, I don't have to fly there, although it's been quarantined for 14 days in New Zealand and summer is coming. but we can get remote services, we're talking and sharing knowledge right now. And when you were also talking before we went on about how healthcare is taking a trajectory similar to the financial industry, you saw our ATM machines, what an innovation, self service, then you got apps and then, you know, the rest is history just connect the dots. The same kind of thing is happening in healthcare, can you share your vision of how you see this playing out, why is it so successful, what are some of the things that need to be worked on and how does cloud bring it all together? >> Just on the banking front, I haven't been to the bank for many years because I understood all online, I had to go to the bank the other day, it was a novel experience. But you know I have a lot of, when I discussed with our developers and they say, well what are the requirements, I said, well, hold on, you're a patient you know what you want, you want your medical record pulled together, right, you want everything there, you can have easy access to it, perhaps you might like the computer to make some suggestions to you, it may want to give you warnings and alerts. And you know what we're also getting is a lot more data, and historically a medical record will be your lab, your radiology, your pharmacy, few procedures, maybe, but what we're getting now is genomic data getting added to its social determinants, where do you live, where do you work, behavioral and lots of other things are getting entered onto the medical record and it is going to get big. Oh, actually I forgot device data as well, all sorts of data. Now, within that vast amount of data, there will be signals that can be picked up, not by humans, but by machine learning and we need to pick the right suggestions that I give them back to the patients themselves, or the circle of care, be it their doctors, physicians, or maybe their family. So the picture I'm trying to paint here is health is going to, historically it's been all seated around physicians and hospitals, and it's all about to change. And it's going to happen quickly, you know normally health is very slow, it's a leg out it takes forever and forever to change, what we're seeing right across the world, I'm talking from Europe, Middle East, Asia, the North America, right across the world, the big health systems looking to provide firm or far richer services to their populations. >> Big joke in Silicon Valley used to be about a decade ago when big data was hitting the scene, we have the smartest data engineers, working on how to make an ad, be placed next to for you and on a page, which in concept is actually technically a challenge, you know, getting the right contextual, relevant piece of information in front of you, I guess it's smart. But if you take that construct to say medicine, you have precision needs, you also have contextual needs so if I need to get a physician, why not do virtually? If that gives me faster care, I got knowledge based system behind it, but if I want precision, I then can come in and it's much efficient, much more efficient. Can you share how the data, 'cause machine learning is a big part of it and machine learning is a consumer of data too, not just users, you're consuming data, but the results are still the same, how are you seeing that translate into value? >> I think the first thing is that if you can treat patients earlier more accurately, you can ultimately keep them healthier and using less health resources. And, you know, you notice around the world, different health systems take a different approach. The most interesting approach we see is when a payer also happens to own the hospitals, their approach changes dramatically and they start pouring a lot of money into primary care so they have to have less hospital beds, but, with data information, you can be more precise in the way you treat the patient. So I've had my genome done, probably quite a few times actually, I just one of the care pair, the different providers so I have avian called CYP2C19, I'm pretty sure I've got it right, and that means I hyper metabolize suite on drugs, so you give them to me they won't work. And so there's information in our medical records, with machine-learning, if you can keep a Tesla on the road, we must be able to use the same, in fact we're, we have a very big machine learning project here on this company, and to not only get the information out of the medical records but save it back up, this is the hard part, save it back up to the providers, and to the patients in a meaningful useful way, an actionable way, not too much, not too little and that's usually the challenge, actually. >> You're a customer in your business, and you guys are in New Zealand, but it's global, you've a global footprint, how are you leveraging cloud technology to address your customers? >> It's usually useful because we end up with one target platform so when we come to deploy in any part of the world, it's the same platform. And you know from a security point of view, if we're trying to secure all these on-prem installations, it's very, very hard so we have a lot of security features that are provided for us, there are lots of infrastructure tooling, deployment and monitoring all the stuff is just inherent within the cloud and I guess what's most important we have a standard platform that we can target right across the world. >> And you're using Amazon Web Services, I mean, I'd imagine that as you go outside and look at the edge, as you have to have these secure edge points where you're serving clients, that's important, how're you securing that edge? >> Well, fortunately for us as Amazon is increasingly getting right across the world so there are still some regions which, this tool are working on, but over time, we would be expecting officially every country in the world to have all sorts of services available. >> You see the future of health care going from your standpoint, I mean, if you had to throw a projectile in the future to say, you know, five years from now, where are we on the progress and innovation wave, how do you see that Ian, playing out? >> So, certainly last 30 years, we've had various ways of innovation on healthcare, I think this pandemic is going to transform healthcare in such a major way in such a short time, and we'll see it totally transform within two to four years. And the transformation will be just like your bank, your airline, or lots of other buying stuff actually via Amazon actually, we'll see that sort of transformation of healthcare. We have talked a lot about healthcare, historically being patient centric, it is really not true, our healthcare today and most parts of the world has been geared around the various healthcare facilities, so this change we're going to see now, it'll be geared around the patients themselves, which is really intriguing but exciting. >> Position, I want to get my genome done, you've reminded me, I got to get that done. >> Finding that out, you know, you know--- >> I want to know, (laughs) I want to kind of know in advance, so I can either go down the planes, have a good time or low the loam games. >> I find out I had the positivity gene, you know, I kind of knew that and you know, I'm the fairly positive individual, so (laughs). >> Yeah, well, so as you I'm going to get my, I've to go through that process. But you know, again, fundamentally, you know that I agree this industry is going to be right for change, I remember the old debates on HIPAA and having silos, and so the data protection was a big part of that business and privacy as a huge, but one area, I'll get to that in a second, but the one area I want to touch on first is that really an important one, for everyone around the world is how does technology help people, everywhere get access to healthcare? How do you see that unless there's one approach that the government do it all, some people like that, some people don't, but generally speaking technology should help you, what's your view on how technology helps us, get accessible healthcare? >> What it means no matter where you live or what you do, most people have access to the internet either via our phone or a computer. And so what you want to be able to do, what we need to do, as a society, is give everybody access, just like they have access to their banking records, have a similar access to their medical records. And again, you know, the standard features, you know, symptom checking for patients who have chronic conditions, advice, help, medication charts are really important, the ability to go online and do internet consult or the conditions that don't require a physical examination, be able to message your circle of care, it's basically the automation of healthcare, which, you know, sadly has legged other industries. >> It is a critical point, you mentioned that early, I want to get back on the date and we'll get to privacy right after. You mentioned AI and machine learning, obviously it's a huge part of it, having data models that are intelligent, I know I've covered Amazon SageMaker and a bunch of other stuff they're working on, so they're getting smarter and they're doing it by industry, which I think is smart. But I want to ask you about data, I was just having a conversation this morning with a colleague, and we hear about AI and AI and machine learning, they're consumers too, (chuckles) so if machines are going to automate humans, which they are, the machines are consuming data so the machine learning is now a consumer, not just a technology. So when you're consuming data, you got to have a good approach. You guys are doing a lot with data, how should people think about machine learning and data, because if you believe that machine learning will assist humans, then machines are going to talk to other machines and consume data, and create insights, et cetera, and spoil another systematic effects. How should people think about data who are in healthcare, what's your insight there? >> Well, the tricky thing with machine learning and healthcare is not so much the algorithms, the algorithms are readily available on Amazon and elsewhere, and the big problem that we have found, and we've been working on this for some time and have a lot of people working on it, the big problem we have is first of all marshaling, getting all the data together, wrangling the data, so and then there's a fun part where run the algorithms and then the next big problem is getting the results back into the clinical workflow. So we spent all our time upstream and downstream and a bit in the middle, which is the fun bit, takes a very small amount of time. And so it's probably the hardest part is getting it back into the clinical workflow, that's the hardest part, really, it's really difficult. >> You know, I really appreciate what you do, I think this is going to be the beginning of a big wave of innovation, I was talking with Max Peterson about some areas where they saw, you know, thousands and thousands of people being cared, that they never would have been cared for virtually with the systems and then cloud. Again, just the beginning, and I think this is a reconfiguration of the healthcare value chain and--- >> Configuration, I mean, at pre-COVID we as a company spend so much time on planes, traveling all over the world, I've hardly traveled this year and zoom and all the other technologies, I've quite enjoyed it to be fair. So, and I think that there's a reconfiguration of how business is done, it's started to happen in healthcare and--- >> If tell my wife, I'm coming to New Zealand, I get quarantined for 14 days. >> That's right. >> Yeah, I'm stuck down under summertime. >> You get one of those hotels with the view of the Harbor, very nice. >> And final question and just close it out here in the segments, I think this is super important, you mentioned at the top, COVID has upended the healthcare industry, remote health is what people want, whether it's for, you know, not to being around other sick people, or for convenience, or for just access. This is a game changer, you got iWatches now, I was just watching Apple discuss some of the new technologies and processes that they have in these things for heartbeat, so, you know how this signals. This is absolutely going to be a game changer, software needs to be written, it has to be so far defined, cloud is going to be at the center of it. What's your final assessment, share your partying thoughts? >> We are definitely, in a major reconfiguration of healthcare that's going to happen very quickly, I would've thought that 24 months, maybe no more than 36 and what we're going to end up with is a health system, just like your bank and the big challenge for our sector is first of all, the large amounts of data, how do you store it, where do you store, and the cloud is ideal place to do it, then how do you make sense of it, you know, how do you give just the right advice to an elderly patient versus a millennial who is very technology aware? So these, there's lots of innovation and problems to be solved and lots of opportunities I believe for startups and new innovative companies, and so it's interesting times. >> I think time's short, you know, it's just so much to do, great recruitment opportunity in Orion Health. Thank you for spending time, Ian McCrae, Founder and CEO of Orion health, an award winning provider of health information global based out of New Zealand, thank you for taking the time to come on, appreciate it. >> Thank you. >> Okay, I'm John Furrier with theCUBE coverage of AWS Public Sector Summit Online. We're not face to face, normally we'd be in person, but we're doing it remotely due to the pandemic, thank you for watching theCUBE. (soft upbeat music)

>> From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world. This is theCUBE conversation. >> Hello everyone, David Vellante here and welcome to the special CUBE conversation with a colleague and friend of mine, Randy Seidl is a accomplished CEO, he's an executive, sales pro, and he's a founder of the Sales Community, this newly formed social network, Randy, good to see you again, welcome. >> Hey, great to see you, it's been a lot of great years, great relationship with you and congratulations with all your success with SiliconANGLE and theCUBE. I was remembering back, I think it's been probably since 1985, so 35 years ago when we were both Cub Scouts, I was at EMC, and you were at IDC. >> Yeah, I mean, first of all, I love where you are, your man-cave there, we heard you held a great little networking event that you do periodically with some of our joint colleagues. And yeah, wow, we were both in our twenties, I was a young pop and Dicky Eagan, and Jack and Mike, and they would have me talk to you guys, you know, sort of brief you on the market, what little I knew now looking back. But wow, Randy, I mean. >> We knew! >> Right, I mean, and then just the whole thing just took off, but we had a good instinct, that storage was going to matter, everything back then was mainframe and IBM was the king of the world, and then you guys just crushed it. Wow, what a run, amazing. >> Yeah, absolutely. >> So tell me about Sales Community. What are you trying to accomplish with this new social network? >> Well, it was kind of really my COVID moment. I was talking to Peter Bell I know, you know well as well, and it was right in the beginning of COVID we were kind of comparing notes and long story short, he said, hey Randy, you do all this work with these technology companies, and channel partners, and use your customers, CIO, CTO, CSOs, but you're really not doing much for those that you know the best, which are really technology sales professionals, CROs, STRs kind of up and down the food chain. And that really got me thinking, then he introduced me to one of his companies that sells to CROs and I was going through with them and they were kind of calling me on the carpet saying, okay, do I really know these people? I'm like, oh my gosh! They basically just said, I'm a dope, I haven't really done anything here. So, one thing led to another and ended up developing a Sales Community, a big thing and big help for me was talking to probably 150 or so during the course of the summer, CROs, VPs of sales, Reps STRs to really kind of help get some feedback from them in terms of I caught now they call product-market fit, but kind of what they think it's missing, what's needed, what are their teams need, what do they want? So, it's kind of all a perfect storm, which to be honest without COVID probably wouldn't have created Sales Community. >> Well, I joined and it was a great onboarding experience and love participating with colleagues. I mean, sales is hard, I mean, you've got your ups and your downs and you just got to keep pressing on, but who's participating in Sales Community. >> We're targeting STRs on up to CROs and the kind of the tagline is learn more so you can sell more. We have a lot of great different kind of content areas and we're going to kind of bob and weave based on the feedback that we get, but we've got some great virtual events and interviews. We have an executive coach, Tony Jerry, who's doing nine sessions on designing your life. We did a recording, a live session last week on personal goal setting. We did one yesterday, it was a live session that'll be posted shortly on strategic health. Next one's on branding, so that's not necessarily specific to tech sales, but kind of adding value. We also have Dave Knorr, another executive coach doing a weekly interview series that we're calling tech sales insights with some of the leading CROs, CEOs, Jim Sullivan, who I know you know well, he's going to be the first one, it's going to be next Wednesday, he runs a NWN and he's done a lot of great things and a lot of other great leaders from there. Also still on the interview virtual events side, Michael Cotoia from Tech Target he's going to do a CMO insights series. His Tech Target International editors are also going to do regional ones. So CIO interviews from AMEA, Asia Pac, Latin America, Australia, also on the CSO side, we have somebody focused on doing a CSO interviews, Paul Salamanca of channel interviews, I think this channel, by and large gets missed a lot. CEO's and then Steve Duplessie, I know you know well as well is going to do and focus on CIO, sub-CIO insights, but basically creating virtual events and interview series that are really targeted at people that we sell to. So that covers the kind of virtual event and interview side. And I maybe more quickly go through some of the other key segments. So another one is a content library. There's the guy who's a STR at ServiceNow went through, send me note the other day that said, hey, I found out you have some great feedback on prospecting cold calling, I shared it with my team helped me a lot. So a lot of good things in terms of content library, also opportunity to network. So you could be say selling to Fidelity, you could send a note to the community and members and say anybody else trying to sell the Fidelity, let's network, let's compare notes, also great opportunities for channel partners. So channel partner could raise their hand and say, hey, I know Fidelity, let me help with you. A lot of sharing of best practices. And also just in terms of communication, slack channels, and then opportunities to create round tables. So you might have CROs from startups that want to have maybe six to 10 of them get together. So they can kind of commiserate, ask questions, you could have CROs, companies that are maybe transforming going from on-prem to kind of SAS model. So a lot of different great things, ultimately really to serve the folks in the tech Sales Community. >> Yeah, it sounds like, I mean, first of all tons of content, the other thing I like about it is we all read books on sales, some of them are so like gimmicky, some of them are inspirational. Some of them have really great suggestions. Some of them can be life changing, but what's always been missing in my opinion, is this notion of a network, a social network, if you will, where people can help each other, you just gave a ton of good examples. So you're really trying to differentiate from a lot of the things that have worked over the years, but have really sort of one way communication, some sales guru either training or you're reading his or her book. >> Yes, and we're also fortunate on the content side, we have some of the best kind of consulting sales methodology companies that love what we're doing. So they're likewise providing a lot of content and as you said, it's crazy. You think of any other industry, restaurant, hotel, lawyers, landscape, they have these big, kind of user groups, even technology companies user groups within the larger field of technology sales enterprise B2B sales, there's really nothing that looks like this that exists. So far the feedback's been great. >> Well, so just to what you're describing, I mean, I've known you for a long, long time, and one of the principles of great salespeople is, you help others, right? You make as many friends as you can, and you're the master of that. But essentially you're bringing a lot of the things that have worked, a lot of the principles that have worked in your career to this community. Maybe talk about that a little bit. >> Yeah, I mean, especially I think some of the younger sales folks, it's not kind of off the cuff as we know, but it's really kind of training, being disciplined, being prepared, what are you going to do, how are you going to do it in this COVID moment? You know, I'm seeing lots of friends where the companies that have great relationships, they can do really well and kind of lean in a lot. If you're kind of cold calling and this environment, and it's tough, so kind of, how can you be best prepared, how can you do the best homework? How can you have the kind of right agenda, when you're going to do the sales calls? And then it's not really as much follow up, but really follow through in terms of what you do afterwards. So kind of what is the training? What can you do, how can you do it? And, you know, it's crazy, a lot of companies spend lots of money on training, but if you think about it they're really tied in specifically to tech sales, hopefully this will be great. Plus being able to just kind of throw out questions here and there works out well as well. >> Well that's what I'm looking forward to, say, hey, I got some challenges, how do others deal with this? You know, one of the things that is, I think, paramount to being a great salesperson is the attitude you hear it all the time. How do you stay pumped up? (laughing) Like I said before, we've all been through ups and downs, and what do you tell people there? >> In terms of staying pumped up, interestingly enough, the session we did yesterday on strategic health, probably plays a key role. So yeah, there's the work aspects and how are you going to focus and wake up and get fired up. But ultimately, I think you really got to take several steps back and saying are you taking care of yourself? Are you sleeping, are you eating and drinking correctly? Are you drinking enough water, are you exercising? So, in this moment, I think that's probably something that gets missed a lot in terms of getting fired up. And then ultimately just being excited about kind of what you're doing, how are you doing it, taking care of the customers and serving those around you. And you had mentioned in terms of giving it back, but a lot of us that have been around, love the idea of kind of paying it forward, helping out others and seeing a lot of the great younger folks really rise up and become stars. >> I think that's one of the most exciting things is somebody has been around for awhile. Like (laughing) we all get cold calls and say, hey, how you doing today? You know, (laughing) you really had that dead air, and you actually want to reach out and help these individuals. A lot of times they'll call you, they have no idea what you do, well I've read your website, and I think we'd be a great fit for, you know, something that would not be a great fit. So, there's a level of preparation we always talk about in sales, you got to be prepared, but there's also sometimes... I was talking to a sales pro the other day, you know, sometimes you can over prepare he said, I've been on sales calls, I prepare for hours and hours and hours, and then they get there, and it was just a lot of wasted hours. I probably could have done it in 15 minutes. I mean, so there's a really a balance there. And it comes with experience, I guess. >> Yeah, I mean, I don't know how anybody could prepare hours and hours, so that's a whole different subject to think. >> Well, he said, my technique now is just 15 minutes before the call I'll jump on and just, you know, cram as much as I can. And it actually, it worked for him. So, different approaches, right? >> Yeah, absolutely. The other thing I'd like to mention is the advisory board I'm fortunate to have a work with, and be friends with several of the best in industry like you. So if anybody goes to the website, you can click on an advisory board and there's a 200 plus and haven't count them exactly. But you know, some of the best in technology, we've got them sorted on the sales side and the channel side, the consulting side, the coaching side, analyst side, but, really just such a tremendous each head of talent that can really help us continue to go and grow and pivot and you're making sure that we are serving our Sales Community and making sure everybody's learning more so they can sell more. And then I guess I should add onto that also, earning more and making more money. >> So I got to ask you where you land on this. I mean, you're a sports fan, I am too and for a while there once the "Moneyball" came out, you saw Billy Bean and it was this sort of formulaic approach. The guy, you know, we would joke the team with the best nerds would win. But it seems like there's an equilibrium. It used to be all gut feel and experience, and then it became the data nerds. And it seems like in our industry, it's following a similar pattern, the marketing ops, Martech, becoming very, very data driven. But it feels to me, Randy, especially in these COVID times that there really is this equilibrium, this balance between experience, and tribal knowledge, gut feel, network, which is something you're building and the data. How do you see that role, that CRO role, that sales role evolving, especially in the context of what I just talked about with the data nerds? (laughing) >> Yeah, absolutely, I think I heard two points there since you brought up Billy Bean, I forgot the guy's name, but in the movie is kind of nerd. I've got Jesse and Tucker who have been tremendously helpful for us putting together a Sales Community. But to answer the question on the CMOs side, the CMOs out there frankly not going to like this answer, but I think more and more, you see CMOs and CROs kind of separated and it's kind of different agendas, my belief is that eventually the CMO function or marketing is really going to come under sales and sales are really going to take a much more active role in driving and leveraging that marketing function in terms of what's the best bang for the buck, what are they doing, how are they doing it? And I've got a lot of friends, I won't name names, but they're not on the sales side and they're doing what they can, but they just see what I'd call it kind of wasted money or inefficiencies on the marketing side. So, if I maybe I spin that a different way, I think given kind of analytics and those companies that do have best practices, and I write things on the marketing side, you know, they're going to continue to go and grow, you know, on cert with the right sales team. So I think that you bring up a great point and that area is going to continue to evolve a lot. >> Does that principle apply to product marketing? In other words do you feel like product marketing should be more aligned with engineering or sales and maybe sales and finance, where do you land on that? >> Yeah, I mean, I'm kind of old school, so I go back to Dick and Jack and Roger and Mike Rutgers, and you all in terms of, hey, you have those silos, but you get everybody at the table, kind of what we're working well together. It is interesting though in today's world, the PLG, Product-Led Growth models, where a lot of companies now are trying to get in maybe almost like a VMware, maybe BMC did in the early days where you're kind of getting into the low level developers and then kind of things bubble up so that you think Product-Led Growth model, having a lower cost insight sales model, works when I'll say the kind of the product sells itself. But I would argue, that I think some of those PLG led companies really miss out on leveraging the high end enterprise relationships, to kind of turbocharge and supersize and expedite larger sales deals, larger (indistinct). >> Well, and you mentioned earlier a channel you said a lot of times that's overlooked and I couldn't agree more, channel increasingly important. That's where a lot of the relationships live, it gives you scale, it just gives you a lot of leverage, maybe you talk about the importance of channel and how it relates to Sales Community. >> Yeah, I mean, it's interesting they're really unto themselves, there's some things that are channel channel, but if you think about, you know, go to market tech sales, pick the company on average is probably half of the business goes through the channel. And it used to be way back when just kind of fulfillment, but now the best companies really are those that have the right relationships, that are adding value, that can help on the pre sales, that can help on the post sales, that can help kind of cross sale. You know, if I'm a customer, I don't want to deal with whatever five or 10 different vendors if I can have a one stop shop with one bar solution provider, partner, SI, or whatever you want to call them, you know, that certainly makes life a lot easier. And I think a lot of companies almost been kind of a second class citizen, but I think those companies that really bring them into the fold as really partners at the table, whether it be an account planning sessions, whether you're doing sales calls, but kind of leveraging that I call it a variable cost kind of off balance sheet, sales force really is where the future is going to continue to go. >> So you've been a successful individual sales contributor. You've been a CEO, you've run large sales organizations. I mean, you basically ran sales at HP for Donna Telly, and so you've seen it all, and you've been helping startups. When you look at hiring sales people, what are the attributes that you look for? Is it intelligence, is it hard work, is it coach ability? What are some of the things that are most important to you, and do you apply different attributes in different situations? What are your thoughts on that? >> Great question in a little plug, maybe for a recruiting business, top talent recruiting, (laughing) but one of the key things that we do, which I think is different from others in the recruiting side is the relationships. So a lot of people don't dig in, when we're talking to candidates, they say, well, nobody really asked me this before. And I would argue a key differentiator, and this is way before COVID, but especially now with COVID is okay, who do you have relationships with? So I could be talking to a candidate that maybe somebody is hiring, wants to cover financial services in New York. And then I'll say, okay, well, who do you know what City JPB Bay and I'll know more people than they know. And I'll probably say, just so you know, that's weird me up in Boston. I know more than the council you probably know the best. So really trying to unearth, really kind of who has the right relationships and then separate from that in terms of a reference check, being able to reference checks sooner in the process with somebody that know well firsthand, as opposed to second hand. And a lot of times I've seen even some of the larger, more expensive recruiting firms, you're kind of wait until somebody is the final say, when do an offer, then they do a reference check and they do the reference check with somebody that they don't know. And to me, I mean, that's totally useless which quite with LinkedIn today, I could be say if we're looking at you for candidate, maybe a bad example, but I don't know, we probably have a 1000 in common, and from those, we probably have 200 that we both know, well, that I could check. And when you do reference checking, it's not a maybe it's either, hey, the person is a yes, or the person's a no. So trying to do that early in the process, I think is a big differentiator. And then last and probably third piece I'd highlight is, if it's a startup company, you can't get somebody that's just from a big company. If it's a big company role, you can't get somebody that just from a small company, you got to really make sure you kind of peel back the onions and see where they're from. And you could have somebody from a big company, but they were kind of wearing a smaller division. So again, you have to kind of, you can't judge a book by the cover. You got to kind of peel back the onion. >> So Randy, how do people learn more about Sales Community? Where do they go to engage, sign up, et cetera? >> Absolutely, it's salescommunity.com. So it should be pretty straight forward. A lot of great information there. You can go subscribe, and if you like it spread the word and a lot of great content and you can ping me there. And if not I'm randy@salescommunity.com. So love to get any feedback, help out in any way we can. >> Well, I think it's critical that you're putting this network together and you are probably the best networker that I know I've seen you in action at gatherings and you really have been a great inspiration and a friend. So, Randy, thanks so much for doing the Sales Community and coming on theCUBE and sharing your experience with us. >> Great, thanks Dave, appreciate it. >> All right you're very welcome and thank you for watching everybody. This is Dave Vellante for theCUBE, and we'll see you next time. (upbeat music)

>> Hello everyone. On today's panel, the theme is Drug Discovery and how Artificial Intelligence can make a difference. On the panel today, we are honored to have Dr. Ryan Yates, principal scientist at The National Center for Natural Products Research, with a focus on botanicals specifically the pharmacokinetics, which is essentially how the drug changes over time in our body and pharmacodynamics which is essentially how drugs affects our body. And of particular interest to him is the use of AI in preclinical screening models to identify chemical combinations that can target chronic inflammatory processes such as fatty liver disease, cognitive impairment and aging. Welcome, Ryan. Thank you for coming. >> Good morning. Thank you for having me. >> The other distinguished panelist is Dr. Rangan Sukumar, our very own, is a distinguished technologist at the CTO office for High Performance Computing and Artificial Intelligence with a PHD in AI and 70 publications that can be applied in drug discovery, autonomous vehicles and social network analysis. Hey Rangan, welcome. Thank you for coming, by sparing the time. We have also our distinguished Chris Davidson. He is leader of our HPC and AI Application and Performance Engineering team. His job is to tune and benchmark applications, particularly in the applications of weather, energy, financial services and life sciences. Yes so particular interest is life sciences he spent 10 years in biotech and medical diagnostics. Hi Chris, welcome. Thank you for coming. >> Nice to see you. >> Well let's start with your Chris, yes, you're regularly interfaced with pharmaceutical companies and worked also on the COVID-19 White House Consortium. You know tell us, let's kick this off and tell us a little bit about your engagement in the drug discovery process. >> Right and that's a good question I think really setting the framework for what we're talking about here is to understand what is the drug discovery process. And that can be kind of broken down into I would say four different areas, there's the research and development space, the preclinical studies space, clinical trial and regulatory review. And if you're lucky, hopefully approval. Traditionally this is a slow arduous process it costs a lot of money and there's a high amount of error. Right, however this process by its very nature is highly iterate and has just huge amounts of data, right it's very data intensive, right and it's these characteristics that make this process a great target for kind of new approaches in different ways of doing things. Right, so for the sake of discussion, right, go ahead. >> Oh yes, so you mentioned data intensive brings to mind Artificial Intelligence, you know, so Artificial Intelligence making the difference here in this process, is that so? >> Right, and some of those novel approaches are actually based on Artificial Intelligence whether it's deep learning and machine learning, et cetera, you know, prime example would say, let's just say for the sake of discussion, let's say there's a brand new virus, causes flu-like symptoms, shall not be named if we focus kind of on the R and D phase, right our goal is really to identify target for the treatment and then screen compounds against it see which, you know, which ones we take forward right to this end, technologies like cryo-electron, cryogenic electron microscopy, just a form of microscopy can provide us a near atomic biomolecular map of the samples that we're studying, right whether that's a virus, a microbe, the cell that it's attaching to and so on, right AI, for instance, has been used in the particle picking aspect of this process. When you take all these images, you know, there are only certain particles that we want to take and study, right whether they have good resolution or not whether it's in the field of the frame and image recognition is a huge part of this, it's massive amounts of data in AI can be very easily, you know, used to approach that. Right, so with docking, you can take the biomolecular maps that you achieved from cryo-electron microscopy and you can take those and input that into the docking application and then run multiple iterations to figure out which will give you the best fit. AI again, right, this is iterative process it's extremely data intensive, it's an easy way to just apply AI and get that best fit doing something in a very, you know, analog manner that would just take humans very long time to do or traditional computing a very long time to do. >> Oh, Ryan, Ryan, you work at the NCNPR, you know, very exciting, you know after all, you know, at some point in history just about all drugs were from natural products yeah, so it's great to have you here today. Please tell us a little bit about your work with the pharmaceutical companies, especially when it is often that drug cocktails or what they call Polypharmacology, is the answer to complete drug therapy. Please tell us a bit more with your work there. >> Yeah thank you again for having me here this morning Dr. Goh, it's a pleasure to be here and as you said, I'm from the National Center for Natural Products Research you'll hear me refer to it as the NCNPR here in Oxford, Mississippi on the Ole Miss Campus, beautiful setting here in the South and so, what, as you said historically, what the drug discovery process has been, and it's really not a drug discovery process is really a therapy process, traditional medicine is we've looked at natural products from medicinal plants okay, in these extracts and so where I'd like to begin is really sort of talking about the assets that we have here at the NCNPR one of those prime assets, unique assets is our medicinal plant repository which comprises approximately 15,000 different medicinal plants. And what that allows us to do, right is to screen mine, that repository for activities so whether you have a disease of interest or whether you have a target of interest then you can use this medicinal plant repository to look for actives, in this case active plants. It's really important in today's environment of drug discovery to really understand what are the actives in these different medicinal plants which leads me to the second unique asset here at the NCNPR and that is our what I'll call a plant deconstruction laboratory so without going into great detail, but what that allows us to do is through a how to put workstation, right, is to facilitate rapid isolation and identification of phytochemicals in these different medicinal plants right, and so things that have historically taken us weeks and sometimes months, think acetylsalicylic acid from salicylic acid as a pain reliever in the willow bark or Taxol, right as an anti-cancer drug, right now we can do that with this system on the matter of days or weeks so now we're talking about activity from a plant and extract down to phytochemical characterization on a timescale, which starts to make sense in modern drug discovery, alright and so now if you look at these phytochemicals, right, and you ask yourself, well sort of who is interested in that and why, right what are traditional pharmaceutical companies, right which I've been working with for 20, over 25 years now, right, typically uses these natural products where historically has used these natural products as starting points for new drugs. Right, so in other words, take this phytochemical and make chemicals synthetic modifications in order to achieve a potential drug. But in the context of natural products, unlike the pharmaceutical realm, there is often times a big knowledge gap between a disease and a plant in other words I have a plant that has activity, but how to connect those dots has been really laborious time consuming so it took us probably 50 years to go from salicylic acid and willow bark to synthesize acetylsalicylic acid or aspirin it just doesn't work in today's environment. So casting about trying to figure out how we expedite that process that's when about four years ago, I read a really fascinating article in the Los Angeles Times about my colleague and business partner, Dr. Rangan Sukumar, describing all the interesting things that he was doing in the area of Artificial Intelligence. And one of my favorite parts of this story is basically, unannounced, I arrived at his doorstep in Oak Ridge, he was working Oak Ridge National Labs at the time, and I introduced myself to him didn't know what was coming, didn't know who I was, right and I said, hey, you don't know me you don't know why I'm here, I said, but let me tell you what I want to do with your system, right and so that kicked off a very fruitful collaboration and friendship over the last four years using Artificial Intelligence and it's culminated most recently in our COVID-19 project collaborative research between the NCNPR and HP in this case. >> From what I can understand also as Chris has mentioned highly iterative, especially with these combination mixture of chemicals right, in plants that could affect a disease. We need to put in effort to figure out what are the active components in that, that affects it yeah, the combination and given the layman's way of understanding it you know and therefore iterative and highly data intensive. And I can see why Rangan can play a huge significant role here, Rangan, thank you for joining us So it's just a nice segue to bring you in here, you know, given your work with Ryan over so many years now, tell I think I'm also quite interested in knowing a little about how it developed the first time you met and the process and the things you all work together on that culminated into the progress at the advanced level today. Please tell us a little bit about that history and also the current work. Rangan. >> So, Ryan, like he mentioned, walked into my office about four years ago and he was like hey, I'm working on this Omega-3 fatty acid, what can your system tell me about this Omega-3 fatty acid and I didn't even know how to spell Omega-3 fatty acids that's the disconnect between the technologist and the pharmacologist, they have terms of their own right since then we've come a long way I think I understand his terminologies now and he understands that I throw words like knowledge graphs and page rank and then all kinds of weird stuff that he's probably never heard in his life before right, so it's been on my mind off to different domains and terminologies in trying to accept each other's expertise in trying to work together on a collaborative project. I think the core of what Ryan's work and collaboration has led me to understanding is what happens with the drug discovery process, right so when we think about the discovery itself, we're looking at companies that are trying to accelerate the process to market, right an average drug is taking 12 years to get to market the process that Chris just mentioned, Right and so companies are trying to adopt what's called the in silico simulation techniques and in silico modeling techniques into what was predominantly an in vitro, in silico, in vivo environment, right. And so the in silico techniques could include things like molecular docking, could include Artificial Intelligence, could include other data-driven discovery methods and so forth, and the essential component of all the things that you know the discovery workflows have is the ability to augment human experts to do the best by assisting them with what computers do really really well. So, in terms of what we've done as examples is Ryan walks in and he's asking me a bunch of questions and few that come to mind immediately, the first few are, hey, you are an Artificial Intelligence expert can you sift through a database of molecules the 15,000 compounds that he described to prioritize a few for next lab experiments? So that's question number one. And he's come back into my office and asked me about hey, there's 30 million publications in PubMag and I don't have the time to read everything can you create an Artificial Intelligence system that once I've picked these few molecules will tell me everything about the molecule or everything about the virus, the unknown virus that shows up, right. Just trying to understand what are some ways in which he can augment his expertise, right. And then the third question, I think he described better than I'm going to was how can technology connect these dots. And typically it's not that the answer to a drug discovery problem sits in one database, right he probably has to think about uniproduct protein he has to think about phytochemical, chemical or informatics properties, data and so forth. Then he talked about the phytochemical interaction that's probably in another database. So when he is trying to answer other question and specifically in the context of an unknown virus that showed up in late last year, the question was, hey, do we know what happened in this particular virus compared to all the previous viruses? Do we know of any substructure that was studied or a different disease that's part of this unknown virus and can I use that information to go mine these databases to find out if these interactions can actually be used as a repurpose saying, hook, say this drug does not interact with this subsequence of a known virus that also seems to be part of this new virus, right? So to be able to connect that dot I think the abstraction that we are learning from working with pharma companies is that this drug discovery process is complex, it's iterative, and it's a sequence of needle in the haystack search problems, right and so one day, Ryan would be like, hey, I need to match genome, I need to match protein sequences between two different viruses. Another day it would be like, you know, I need to sift through a database of potential compounds, identified side effects and whatnot other day it could be, hey, I need to design a new molecule that never existed in the world before I'll figure out how to synthesize it later on, but I need a completely new molecule because of patentability reasons, right so it goes through the entire spectrum. And I think where HP has differentiated multiple times even the recent weeks is that the technology infusion into drug discovery, leads to several aha! Moments. And, aha moments typically happened in the other few seconds, and not the hours, days, months that Ryan has to laboriously work through. And what we've learned is pharma researchers love their aha moments and it leads to a sound valid, well founded hypothesis. Isn't that true Ryan? >> Absolutely. Absolutely. >> Yeah, at some point I would like to have a look at your, peak the list of your aha moments, yeah perhaps there's something quite interesting in there for other industries too, but we'll do it at another time. Chris, you know, with your regular work with pharmaceutical companies especially the big pharmas, right, do you see botanicals, coming, being talked about more and more there? >> Yeah, we do, right. Looking at kind of biosimilars and drugs that are already really in existence is kind of an important point and Dr. Yates and Rangan, with your work with databases this is something important to bring up and much of the drug discovery in today's world, isn't from going out and finding a brand new molecule per se. It's really looking at all the different databases, right all the different compounds that already exist and sifting through those, right of course data is mind, and it is gold essentially, right so a lot of companies don't want to share their data. A lot of those botanicals data sets are actually open to the public to use in many cases and people are wanting to have more collaborative efforts around those databases so that's really interesting to kind of see that being picked up more and more. >> Mm, well and Ryan that's where NCNPR hosts much of those datasets, yeah right and it's interesting to me, right you know, you were describing the traditional way of drug discovery where you have a target and a compound, right that can affect that target, very very specific. But from a botanical point of view, you really say for example, I have an extract from a plant that has combination of chemicals and somehow you know, it affects this disease but then you have to reverse engineer what those chemicals are and what the active ones are. Is that very much the issue, the work that has to be put in for botanicals in this area? >> Yes Doctor Goh, you hit it exactly. >> Now I can understand why a highly iterative intensive and data intensive, and perhaps that's why Rangan, you're highly valuable here, right. So tell us about the challenge, right the many to many intersection to try and find what the targets are, right given these botanicals that seem to affect the disease here what methods do you use, right in AI, to help with this? >> Fantastic question, I'm going to go a little bit deeper and speak like Ryan in terminology, but here we go. So with going back to about starting of our conversation right, so let's say we have a database of molecules on one side, and then we've got the database of potential targets in a particular, could be a virus, could be bacteria, could be whatever, a disease target that you've identified, right >> Oh this process so, for example, on a virus, you can have a number of targets on the virus itself some have the spike protein, some have the other proteins on the surface so there are about three different targets and others on a virus itself, yeah so a lot of people focus on the spike protein, right but there are other targets too on that virus, correct? >> That is exactly right. So for example, so the work that we did with Ryan we realized that, you know, COVID-19 protein sequence has an overlap, a significant overlap with previous SARS-CoV-1 virus, not only that, but it overlap with MERS, that's overlapped with some bad coronavirus that was studied before and so forth, right so knowing that and it's actually broken down into multiple and Ryan I'm going to steal your words, non-structural proteins, envelope proteins, S proteins, there's a whole substructure that you can associate an amino acid sequence with, right so on the one hand, you have different targets and again, since we did the work it's 160 different targets even on the COVID-19 mark, right and so you find a match, that we say around 36, 37 million molecules that are potentially synthesizable and try to figure it out which one of those or which few of those is actually going to be mapping to which one of these targets and actually have a mechanism of action that Ryan's looking for, that'll inhibit the symptoms on a human body, right so that's the challenge there. And so I think the techniques that we can unrule go back to how much do we know about the target and how much do we know about the molecule, alright. And if you start off a problem with I don't know anything about the molecule and I don't know anything about the target, you go with the traditional approaches of docking and molecular dynamics simulations and whatnot, right. But then, you've done so much docking before on the same database for different targets, you'll learn some new things about the ligands, the molecules that Ryan's talking about that can predict potential targets. So can you use that information of previous protein interactions or previous binding to known existing targets with some of the structures and so forth to build a model that will capture that essence of what we have learnt from the docking before? And so that's the second level of how do we infuse Artificial Intelligence. The third level, is to say okay, I can do this for a database of molecules, but then what if the protein-protein interactions are all over the literature study for millions of other viruses? How do I connect the dots across different mechanisms of actions too? Right and so this is where the knowledge graph component that Ryan was talking about comes in. So we've put together a database of about 150 billion medical facts from literature that Ryan is able to connect the dots and say okay, I'm starting with this molecule, what interactions do I know about the molecule? Is there a pretty intruding interaction that affects the mechanism of pathway for the symptoms that a disease is causing? And then he can go and figure out which protein and protein in the virus could potentially be working with this drug so that inhibiting certain activities would stop that progression of the disease from happening, right so like I said, your method of options, the options you've got is going to be, how much do you know about the target? How much do you know the drug database that you have and how much information can you leverage from previous research as you go down this pipeline, right so in that sense, I think we mix and match different methods and we've actually found that, you know mixing and matching different methods produces better synergies for people like Ryan. So. >> Well, the synergies I think is really important concept, Rangan, in additivities, synergistic, however you want to catch that. Right. But it goes back to your initial question Dr. Goh, which is this idea of polypharmacology and historically what we've done with traditional medicines there's more than one active, more than one network that's impacted, okay. You remember how I sort of put you on both ends of the spectrum which is the traditional sort of approach where we really don't know much about target ligand interaction to the completely interpretal side of it, right where now we are all, we're focused on is, in a single molecule interacting with a target. And so where I'm going with this is interesting enough, pharma has sort of migrate, started to migrate back toward the middle and what I mean by that, right, is we had these in a concept of polypharmacology, we had this idea, a regulatory pathway of so-called, fixed drug combinations. Okay, so now you start to see over the last 20 years pharmaceutical companies taking known, approved drugs and putting them in different combinations to impact different diseases. Okay. And so I think there's a really unique opportunity here for Artificial Intelligence or as Rangan has taught me, Augmented Intelligence, right to give you insight into how to combine those approved drugs to come up with unique indications. So is that patentability right, getting back to right how is it that it becomes commercially viable for entities like pharmaceutical companies but I think at the end of the day what's most interesting to me is sort of that, almost movement back toward that complex mixture of fixed drug combination as opposed to single drug entity, single target approach. I think that opens up some really neat avenues for us. As far as the expansion, the applicability of Artificial Intelligence is I'd like to talk to, briefly about one other aspect, right so what Rang and I have talked about is how do we take this concept of an active phytochemical and work backwards. In other words, let's say you identify a phytochemical from an in silico screening process, right, which was done for COVID-19 one of the first publications out of a group, Dr. Jeremy Smith's group at Oak Ridge National Lab, right, identified a natural product as one of the interesting actives, right and so it raises the question to our botanical guy, says, okay, where in nature do we find that phytochemical? What plants do I go after to try and source botanical drugs to achieve that particular end point right? And so, what Rangan's system allows us to do is to say, okay, let's take this phytochemical in this case, a phytochemical flavanone called eriodictyol and say, where else in nature is this found, right that's a trivial question for an Artificial Intelligence system. But for a guy like me left to my own devices without AI, I spend weeks combing the literature. >> Wow. So, this is brilliant I've learned something here today, right, If you find a chemical that actually, you know, affects and addresses a disease, right you can actually try and go the reverse way to figure out what botanicals can give you those chemicals as opposed to trying to synthesize them. >> Well, there's that and there's the other, I'm going to steal Rangan's thunder here, right he always teach me, Ryan, don't forget everything we talk about has properties, plants have properties, chemicals have properties, et cetera it's really understanding those properties and using those properties to make those connections, those edges, those sort of interfaces, right. And so, yes, we can take something like an eriodictyol right, that example I gave before and say, okay, now, based upon the properties of eriodictyol, tell me other phytochemicals, other flavonoid in this case, such as that phytochemical class of eriodictyols part right, now tell me how, what other phytochemicals match that profile, have the same properties. It might be more economically viable, right in other words, this particular phytochemical is found in a unique Himalayan plant that I've never been able to source, but can we find something similar or same thing growing in, you know a bush found all throughout the Southeast for example, like. >> Wow. So, Chris, on the pharmaceutical companies, right are they looking at this approach of getting, building drugs yeah, developing drugs? >> Yeah, absolutely Dr. Goh, really what Dr. Yates is talking about, right it doesn't help us if we find a plant and that plant lives on one mountain only on the North side in the Himalayas, we're never going to be able to create enough of a drug to manufacture and to provide to the masses, right assuming that the disease is widespread or affects a large enough portion of the population, right so understanding, you know, not only where is that botanical or that compound but understanding the chemical nature of the chemical interaction and the physics of it as well where which aspect affects the binding site, which aspect of the compound actually does the work, if you will and then being able to make that at scale, right. If you go to these pharmaceutical companies today, many of them look like breweries to be honest with you, it's large scale, it's large back everybody's clean room and it's, they're making the microbes do the work for them or they have these, you know, unique processes, right. So. >> So they're not brewing beer okay, but drugs instead. (Christopher laughs) >> Not quite, although there are pharmaceutical companies out there that have had a foray into the brewery business and vice versa, so. >> We should, we should visit one of those, yeah (chuckles) Right, so what's next, right? So you've described to us the process and how you develop your relationship with Dr. Yates Ryan over the years right, five years, was it? And culminating in today's, the many to many fast screening methods, yeah what would you think would be the next exciting things you would do other than letting me peek at your aha moments, right what would you say are the next exciting steps you're hoping to take? >> Thinking long term, again this is where Ryan and I are working on this long-term project about, we don't know enough about botanicals as much as we know about the synthetic molecules, right and so this is a story that's inspired from Simon Sinek's "Infinite Game" book, trying to figure it out if human population has to survive for a long time which we've done so far with natural products we are going to need natural products, right. So what can we do to help organizations like NCNPR to stage genomes of natural products to stage and understand the evolution as we go to understand the evolution to map the drugs and so forth. So the vision is huge, right so it's not something that we want to do on a one off project and go away but in the process, just like you are learning today, Dr. Goh I'm going to be learning quite a bit, having fun with life. So, Ryan what do you think? >> Ryan, we're learning from you. >> So my paternal grandfather lived to be 104 years of age. I've got a few years to get there, but back to "The Infinite Game" concept that Rang had mentioned he and I discussed that quite frequently, I'd like to throw out a vision for you that's well beyond that sort of time horizon that we have as humans, right and that's this right, is our current strategy and it's understandable is really treatment centric. In other words, we have a disease we develop a treatment for that disease. But we all recognize, whether you're a healthcare practitioner, whether you're a scientist, whether you're a business person, right or whatever occupation you realize that prevention, right the old ounce, prevention worth a pound of cure, right is how can we use something like Artificial Intelligence to develop preventive sorts of strategies that we are able to predict with time, right that's why we don't have preventive treatment approach right, we can't do a traditional clinical trial and say, did we prevent type two diabetes in an 18 year old? Well, we can't do that on a timescale that is reasonable, okay. And then the other part of that is why focus on botanicals? Is because, for the most part and there are exceptions I want to be very clear, I don't want to paint the picture that botanicals are all safe, you should just take botanicals dietary supplements and you'll be safe, right there are exceptions, but for the most part botanicals, natural products are in fact safe and have undergone testing, human testing for thousands of years, right. So how do we connect those dots? A preventive strategy with existing extent botanicals to really develop a healthcare system that becomes preventive centric as opposed to treatment centric. If I could wave a magic wand, that's the vision that I would figure out how we could achieve, right and I do think with guys like Rangan and Chris and folks like yourself, Eng Lim, that that's possible. Maybe it's in my lifetime I got 50 years to go to get to my grandfather's age, but you never know, right? >> You bring really, up two really good points there Ryan, it's really a systems approach, right understanding that things aren't just linear, right? And as you go through it, there's no impact to anything else, right taking that systems approach to understand every aspect of how things are being impacted. And then number two was really kind of the downstream, really we've been discussing the drug discovery process a lot and kind of the kind of preclinical in vitro studies and in vivo models, but once you get to the clinical trial there are many drugs that just fail, just fail miserably and the botanicals, right known to be safe, right, in many instances you can have a much higher success rate and that would be really interesting to see, you know, more of at least growing in the market. >> Well, these are very visionary statements from each of you, especially Dr. Yates, right, prevention better than cure, right, being proactive better than being reactive. Reactive is important, but we also need to focus on being proactive. Yes. Well, thank you very much, right this has been a brilliant panel with brilliant panelists, Dr. Ryan Yates, Dr. Rangan Sukumar and Chris Davidson. Thank you very much for joining us on this panel and highly illuminating conversation. Yeah. All for the future of drug discovery, that includes botanicals. Thank you very much. >> Thank you. >> Thank you.

>> Narrator: From theCUBE's studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hello everyone, this is Dave Vellante. Welcome to this CUBE conversation. We got a really cool company that we're going to introduce you to, and Anthony Brooks Williams is here. He's the CEO of that company, HVR. Anthony, good to see you. Thanks for coming on. >> Hey Dave, good to see you again, appreciate it. >> Yeah cheers, so tell us a little bit about HVR. Give us the background of the company, we'll get into a little bit of the history. >> Yeah sure, so at HVR we are changing the way companies routes and access their data. And as we know, data really is the lifeblood of organizations today, and if that stops moving, or stop circulating, well, there's a problem. And people want to make decisions on the freshest data. And so what we do is we move critical business data around these organizations, the most predominant place today is to the cloud, into platforms such as Snowflake, where we've seen massive traction. >> Yeah boy, have we ever. I mean, of course, last week, we saw the Snowflake IPO. The industry is abuzz with that, but so tell us a little bit more about the history of the company. What's the background of you guys? Where did you all come from? >> Sure, the company originated out of the Netherlands, at Amsterdam, founded in 2012, helping solve the issue that customer's was having moving data efficiently at scale across all across a wide area network. And obviously, the cloud is one of those endpoint. And therefore a company, such as the Dutch Postal Service personnel, where today we now move the data to Azure and AWS. But it was really around how you can efficiently move data at scale across these networks. And I have a bit of a background in this, dating back from early 2000s, when I founded a company that did auditing recovery, or SQL Server databases. And we did that through reading the logs. And so then sold that company to Golden Gate, and had that sort of foundation there, in those early days. So, I mean again, Azure haven't been moving data efficiently as we can across these organizations with it, with the key aim of allowing customers to make decisions on the freshest data. Which today's really, table stakes. >> Yeah, so, okay, so we should think about you, as I want to invoke Einstein here, move as much data as you need to, but no more, right? 'Cause it's hard to move data. So your high speeds kind of data mover, efficiency at scale. Is that how we should think about you? >> Absolutely, I mean, at our core, we are CDC trades that capture moving incremental workloads of data, moving the updates across the network, you mean, combined with the distributed architecture that's highly flexible and extensible. And these days, just that one point, customers want to make decisions on us as much as they can get. We have companies that we're doing this for, a large apparel company that's taking some of their not only their core sales data, but some of that IoT data that they get, and sort of blending that together. And given the ability to have a full view of the organization, so they can make better decisions. So it's moving as much data as they can, but also, you need to do that in a very efficient way. >> Yeah, I mean, you mentioned Snowflake, so what I'd like to do is take my old data warehouse, and whatever, let it do what it does, reporting and compliance, stuff like that, but then bring as much data as I need into my Snowflake, or whatever modern cloud database I'm using, and then apply whatever machine intelligence, and really analyze it. So really that is kind of the problem that you're solving, is getting all that data to a place where it actually can be acted on, and turned into insights, is that right? >> Absolutely, I mean, part of what we need to do is there's a whole story around multi-cloud, and that's obviously where Snowflake fit in as well. But from our point of views of supporting over 30 different platforms. I mean data is generated, data is created in a number of different source systems. And so our ability to support each of those in this very efficient way, using these techniques such as CDCs, is going to capture the data at source, and then weaving it together into some consolidated platform where they can do the type of analysis they need to do on that. And obviously, the cloud is the predominant target system of choice with something like a Snowflake there in either these clouds. I mean, we support a number of different technologies in there. But yeah, it's about getting all that data together so they can make decisions on all areas of the business. So I'd love to get into the secret sauce a little bit. I mean we've heard luminaries like Andy Jassie stand up at last year at Reinvent, he talked about Nitro, and the big pipes, and how hard it is to move data at scale. So what's the secret sauce that you guys have that allow you to be so effective at this? >> Absolutely, I mean, it starts with  how you going to acquire data? And you want to do that in the least obtrusive way to the database. So we'll actually go in, and we read the transaction logs of each of these databases. They all generate logs. And we go read the logs systems, all these different source systems, and then put it through our webs and secret sauce, and how we how we move the data, and how we compress that data as well. So, I mean, if you want to move data across a wide area network, I mean, the technique that a few companies use, such as ourselves, is change data capture. And you're moving incremental updates, incremental workloads, the change data across a network. But then combine that with the ability that we have around some of the compression techniques that we use, and, and then just into very distributed architecture, that was one of the things that made me join HVR after my previous experiences, and seeing that how that really fits in today's world of real time and cloud. I mean, those are table stakes things. >> Okay, so it's that change data capture? >> Yeah. >> Now, of course, you've got to initially seed the target. And so you do that, if I understand you use data reduction techniques, so that you're minimizing the amount of data. And then what? Do you use asynchronous methodologies, dial it down, dial it up, off hours, how does that work? >> Absolutely, exactly what you've said they mean. So we're going to we're, initially, there's an initial association, or an initial concept, where you take a copy of all of that data that sits in that source system, and replicating that over to the target system, you turn on that CDC mechanism, which is then weaving that change data. At the same time, you're compressing it, you're encrypting it, you're making sure it's highly secure, and loading that in the most efficient way into their target systems. And so we either do a lot of that, or we also work with, if there's a ETL vendor involved, that's doing some level of transformations, and they take over the transformation capabilities, or loading. We obviously do a fair amount of that ourselves as well. But it depends on what is the architecture that's in there for the customer as well. The key thing is that what we also have is, we have this compare and repair ability that's built into the product. So we will move data across, and we make sure that data that gets moved from A to B is absolutely accurate. I mean people want to know that their data can move faster, they want it to be efficient, but they also want it to be secure. They want to know that they have a peace of mind to make decisions on accurate data. And that's some stuff that we have built into the products as well, supported across all the different platforms as well. So something else that just sets us apart in that as well. >> So I want to understand the business case, if you will. I mean, is it as simple as, "Hey, we can move way more data faster. "We can do it at a lower cost." What's the business case for you guys, and the business impact? >> Absolutely, so I mean, the key thing is the business case is moving that data as efficiently as we can across this, so they can make these decisions. So our biggest online retailer in the US uses us, on the biggest busiest system. They have some standard vendors in there, but they use us, because of the scalability that we can achieve there, of making decisions on their financial data, and all the transactions that happen between the main E-commerce site, and all the third party vendors. That's us moving that data across there as efficiently as they can. And first we look at it as pretty much it's subscription based, and it's all connection based type pricing as well. >> Okay, I want to ask you about pricing. >> Yeah. >> Pricing transparency is a big topic in the industry today, but how do you how do you price? Let's start there. >> Yeah, we charge a simple per connection price. So what are the number of source systems, a connection is a source system or a target system. And we try to very simply, we try and keep it as simple as possible, and charge them on the connections. So they will buy a packet of five connections, they have source systems, two target systems. And it's pretty much as simple as that. >> You mentioned security before. So you're encrypting the data. So your data in motion's encrypted. What else do we need to know about security? >> Yeah, you mean, that we have this concept and how we handle, and we have this wallet concept, and how we integrate with the standard security systems that those customers have already, in the in this architecture. So it's something that we're constantly doing. I mean, there's there's a data encryption at rest. And initially, the whole aim is to make sure that the customer feels safe, that the data that is moving is highly secure. >> Let's talk a little bit about cloud, and maybe the architecture. Are you running in the cloud, are you running on prem, both, across clouds. How does that work? >> Yeah, all of the above. So I mean, what we see today is majority of the data is still generated on prem. And then the majority of the talks we see are in the cloud, and this is not a one time thing, this is continuous. I mean, they've moved their analytical workload into the cloud. You mean they have these large events a few times a year, and they want the ability to scale up and scale down. So we typically see you mean, right now, you need analytics, data warehouses, that type of workload is sitting in the cloud, because of the elasticity, and the scalability, and the reasons the cloud was brought on. So absolutely, we can support the cloud to cloud, we can support on prem to cloud, I think you mean, a lot of companies adopting this hybrid strategy that we've seen certainly for the foreseeable next five years. But yeah, absolutely. The source of target systems considered on prem or in the cloud. >> And where's the point of control? Is it wherever I want it to be? >> Absolutely. >> Is it in one of the clouds on prem? >> Yeah absolutely, you can put that point of control where you want it to be. We have a concept of agents, these agents search on the source and target systems. And then we have the, it's at the edge of your brain, the hub that is controlling what is happening. This data movement that can be sitting with a source system, separately, or on target system. So it's highly extensible and flexible architecture there as well. >> So if something goes wrong, it's the HVR brain that helps me recover, right? And make sure that I don't have all kinds of data corruption. Maybe you could explain that a little bit, what happens when something goes wrong? >> Yeah absolutely, I mean, we have things that are built into the product that help us highlight what has gone wrong, and how we can correct those. And then there's alerts that get sent back to us to the to the end customer. And there's been a whole bunch of training, and stuff that's taken place for then what actions they can take, but there's a lot of it is controlled through HVR core system that handles that. So we are working next step. So as we move as a service into more of an autonomous data integration model ourselves, whichever, a bunch of exciting things coming up, that just takes that off to the next levels. >> Right, well Golden Gate Heritage just sold that to Oracle, they're pretty hardcore about things like recovery. Anthony, how do you think about the market? The total available market? Can you take us through your opportunity broadly? >> Yeah absolutely, you mean, there's the core opportunity in the space that we play, as where customers want to move data, they don't want to do data integration, they want to move data from A to B. There's those that are then branching out more to moving a lot of their business workloads to the cloud on a continuous basis. And then where we're seeing a lot of traction around this particular data that resides in these critical business systems such as SAP, that is something you're asking earlier about, what are some core things on our product. We have the ability to unpack, to unlock that data that sits in some of these SAP environments. So we can go, and then decode this data that sits between these cluster pool tables, combine that with our CDC techniques, and move their data across a network. And so particularly, sort of bringing it back a little bit, what we're seeing today, people are adopting the cloud, the massive adoption of Snowflake. I mean, as we see their growth, a lot of that is driven through consumption, why? It's these big, large enterprises that are now ready to consume more. We've seen that tail wind from our perspective, as well as taking these workloads such as SAP, and moving that into something like these cloud platforms, such as a Snowflake. And so that's where we see the immediate opportunity for us. And then and then branching out from there further, but I mean, that is the core immediate area of focus right now. >> Okay, so we've talked about Snowflake a couple of times, and other platforms, they're not the only one, but they're the hot one right now. When you think about what organizations are doing, they're trying to really streamline their data pipeline to get to turn raw data into insights. So you're seeing that emerging organizations, that data pipeline, we've been talking about it for quite some time. I mean, Snowflake, obviously, is one piece of that. Where's your value in that pipeline? Is it all about getting the data into that stream? >> Yeah, you just mentioned something there that we have an issue internally that's called raw data to ready data. And that's about capturing this data, moving that across. And that's where we building value on that data as well, particularly around some of our SAP type initiatives, and solutions related to that, that we're bringing out as well. So one it's absolutely going in acquiring that data. It's then moving it as efficiently as we can at scale, which a lot of people talk about, we truly operate at scale, the biggest companies in the world use us to do that, across there and giving them that ability to make decisions on the freshest data. Therein lies the value of them being able to make decisions on data that is a few seconds, few minutes old, versus some other technology they may be using that takes hours days. You mean that is it, keeping large companies that we work with today. I mean keeping toner paper on shelves, I mean one thing that happened after COVID. I mean one of our big customers was making them out their former process, and making the shelves are full. Another healthcare provider being able to do analysis on what was happening on supplies from the hospital, and the other providers during this COVID crisis. So that's where it's a lot of that value, helping them reinvent their businesses, drive down that digital transformation strategy, is the key areas there. No data, they can't make those type of decisions. >> Yeah, so I mean, your vision really, I mean, you're betting on data. I always say don't bet against the data. But really, that's kind of the premise here. Is the data is going to continue to grow. And data, I often say data is plentiful insights aren't. And we use the Broma you said before. So really, maybe, good to summarize the vision for us, where you want to take this thing? Yeah, absolutely so we're going to continue building on what we have, making it easier to use. Certainly, as we move, as more customers move into the cloud. And then from there, I mean, we have some strategic initiatives of looking at some acquisitions as well, just to build on around offering, and some of the other core areas. But ultimately, it's getting closer to the business user. In today's world, there is many IT tech-savvy people sitting in the business side of organization, as they are in IT, if not more. And so as we go down that flow with our product, it's getting closer to those end users, because they're at the forefront of wanting this data. As we said that the data is the lifeblood of an organization. And so given an ability to drive the actual power that they need to run the data, is a core part of that vision. So we have some some strategic initiatives around some acquisitions, as well, but also continue to build on the product. I mean, there's, as I say, I mean sources and targets come and go, there's new ones that are created each week, and new adoptions, and so we've got to support those. That's our table stakes, and then continue to make it easier to use, scale even quicker, more autonomous, those type of things. >> And you're working with a lot of big companies, the company's well funded if Crunchbase is up to date, over $50 million in funding. Give us up right there. >> Yeah absolutely, I mean a company is well funded, we're on a good footing. Obviously, it's a very hot space to be in. With COVID this year, like everybody, we sat down and looked in sort of everyone said, "Okay well, let's have a look how "this whole thing's going to shake out, "and get good plan A, B and C in action." And we've sort of ended up with Plan A plus, we've done an annual budget for the year. We had our best quarter ever, and Q2, 193% year over year growth. And it's just, the momentum is just there, I think at large. I mean obviously, it sounds cliche, a lot of people say it around digital transformation and COVID. Absolutely, we've been building this engine for a few years now. And it's really clicked into gear. And I think projects due to COVID and things that would have taken nine, 12 months to happen, they're sort of taking a month or two now. It's been getting driven down from the top. So all of that's come together for us very fortunately, the timing has been ideal. And then tie in something like a Snowflake traction, as you said, we support many other platforms. But all of that together, it just set up really nicely for us, fortunately. >> That's amazing, I mean, with all the turmoil that's going on in the world right now. And all the pain in many businesses. I tell you, I interview people all day every day, and the technology business is really humming. So that's awesome to hear that you guys. I mean, especially if you're in the right place, and data is the place to be. Anthony, thanks so much for coming on theCUBE and summarizing your thoughts, and give us the update on HVR, really interesting. >> Absolutely, I appreciate the time and opportunity. >> Alright, and thank you for watching everybody. This is Dave Vellante for theCUBE, and we'll see you next time. (upbeat music)

>>from the Cube Studios in Palo Alto in Boston, connecting with thought leaders all around the world. This is a cube conversation, >>Everyone. Welcome to this cube conversation. I'm John for host of the Cube here in the Cubes Palo Alto studios during the co vid crisis. Square Quarantine with our crew, but we got the remote interviews. Got great to get great guests here from 44 to guard Fortinet, 40 Guard Labs, Derek Manky chief Security Insights and Global Threat alliances. At 14 it's 40 guard labs and, um, are Lakhani. Who's the lead researcher for the Guard Labs. Guys, great to see you. Derek. Good to see you again. Um, are you meet you? >>Hey, it's it's it's been a while and that it happened so fast, >>it just seems, are say it was just the other day. Derek, we've done a couple interviews in between. A lot of flow coming out of Florida net for the guards. A lot of action, certainly with co vid everyone's pulled back home. The bad actors taking advantage of the situation. The surface areas increased really is the perfect storm for security. Uh, in terms of action, bad actors are at all time high new threats here is going on. Take us through what you guys were doing. What's your team makeup look like? What are some of the roles and you guys were seeing on your team? And how's that transcend to the market? >>Yeah, sure, Absolutely. So you're right. I mean, like, you know, like I was saying earlier this this is all this always happens fast and furious. We couldn't do this without, you know, a world class team at 40 guard labs eso we've grown our team now to over 235 globally. There's different rules within the team. You know, if we look 20 years ago, the rules used to be just very pigeonholed into, say, anti virus analysis. Right now we have Thio account for when we're looking at threats. We have to look at that growing attack surface. We have to look at where these threats coming from. How frequently are they hitting? What verticals are they hitting? You know what regions? What are the particular techniques? Tactics, procedures, You know, we have threat. This is the world of threat Intelligence, Of course. Contextualizing that information and it takes different skill sets on the back end, and a lot of people don't really realize the behind the scenes. You know what's happening on bears. A lot of magic happen not only from what we talked about before in our last conversation from artificial intelligence and machine learning, that we do a 40 yard labs and automation, but the people. And so today we want to focus on the people on and talk about you know how on the back ends, we approach a particular threat. We're going to talk to the world, a ransom and ransomware. Look at how we dissect threats. How correlate that how we use tools in terms of threat hunting as an example, And then how we actually take that to that last mile and and make it actionable so that, you know, customers are protected. How we share that information with Keith, right until sharing partners. But again it comes down to the people. We never have enough people in the industry. There's a big shortages, we know, but it it's a really key critical element, and we've been building these training programs for over a decade within 40 guard lab. So you know, you know, John, this this to me is why, exactly why, I always say, and I'm sure Americans share this to that. There's never a dull day in the office. I know we hear that all the time, but I think today you know, all the viewers really get a new idea of why that is, because this is very dynamic. And on the back end, there's a lot of things that doing together our hands dirty with this, >>you know, the old expression started playing Silicon Valley is if you're in the arena, that's where the action and it's different than sitting in the stands watching the game. You guys are certainly in that arena. And, you know, we've talked and we cover your your threat report that comes out, Um, frequently. But for the folks that aren't in the weeds on all the nuances of security, can you kind of give the 101 ransomware. What's going on? What's the state of the ransomware situation? Um, set the stage because that's still continues to be a threat. I don't go a week, but I don't read a story about another ransomware and then it leaks out. Yeah, they paid 10 million in Bitcoin or something like I mean, this Israel. That's a real ongoing threat. What is it, >>quite a bit? Yeah, eso I'll give sort of the one on one and then maybe capacity toe mark, who's on the front lines dealing with this every day. You know, if we look at the world of I mean, first of all, the concept to ransom, obviously you have people that that has gone extended way, way before, you know, cybersecurity. Right? Um, in the world of physical crime s Oh, of course. You know the world's first ransom, where viruses actually called PC cyborg. This is in 1989. The ransom payment was demanded to appeal box from leave. It was Panama City at the time not to effective on floppy disk. Very small audience. Not a big attack surface. I didn't hear much about it for years. Um, you know, in really it was around 2000 and 10. We started to see ransomware becoming prolific, and what they did was somewhat cybercriminals. Did was shift on success from ah, fake antivirus software model, which was, you know, popping up a whole bunch of, you know said your computer is infected with 50 or 60 viruses. Chaos will give you an anti virus solution, Which was, of course, fake. You know, people started catching on. You know, the giggles up people caught onto that. So they weren't making a lot of money selling this project software. Uh, enter Ransomware. And this is where ransomware really started to take hold because it wasn't optional to pay for the software. It was mandatory almost for a lot of people because they were losing their data. They couldn't reverse engineer the current. Uh, the encryption kind of decrypt it with any universal tool. Ransomware today is very rigid. We just released our threat report for the first half of 2020. And we saw we've seen things like master boot record nbr around somewhere. This is persistent. It sits before your operating system when you boot up your computer. So it's hard to get rid of, um, very strong. Um, you know, public by the key cryptography that's being so each victim is infected with the different key is an example. The list goes on, and you know I'll save that for for the demo today. But that's basically it's It's very it's prolific and we're seeing shit. Not only just ransomware attacks for data, we're now starting to see ransom for extortion, for targeted ransom cases that we're going after, you know, critical business. Essentially, it's like a D O s holding revenue streams around too. So the ransom demands were getting higher because of this is Well, it's complicated. >>Yeah, I was mentioning, Omar, I want you to weigh in. I mean, 10 million is a lot we reported earlier this month. Garment was the company that was act I t guy completely locked down. They pay 10 million. Um, garment makes all those devices and a Z. We know this is impacting That's real numbers. So I mean, it's another little ones, but for the most part, it's new. It's, you know, pain in the butt Thio full on business disruption and extortion. Can you explain how it all works before I got it? Before we go to the demo, >>you know, you're you're absolutely right. It is a big number, and a lot of organizations are willing to pay that number to get their data back. Essentially their organization and their business is at a complete standstill. When they don't pay, all their files are inaccessible to them. Ransomware in general, what does end up from a very basic or review is it basically makes your files not available to you. They're encrypted. They have a essentially a pass code on them that you have to have the correct pass code to decode them. Ah, lot of times that's in the form of a program or actually a physical password you have type in. But you don't get that access to get your files back unless you pay the ransom. Ah, lot of corporations these days, they are not only paying the ransom, they're actually negotiating with the criminals as well. They're trying to say, Oh, you want 10 million? How about four million? Sometimes that it goes on as well, but it's Ah, it's something that organizations know that if they don't have the proper backups and the Attackers are getting smart, they're trying to go after the backups as well. They're trying to go after your duplicate files, so sometimes you don't have a choice, and organizations will will pay the ransom >>and it's you know they're smart. There's a business they know the probability of buy versus build or pay versus rebuild, so they kind of know where to attack. They know the tactics. The name is vulnerable. It's not like just some kitty script thing going on. This is riel system fistic ated stuff. It's and it's and this highly targeted. Can you talk about some use cases there and what's goes on with that kind of attack? >>Absolutely. The cybercriminals are doing reconnaissance. They're trying to find out as much as they can about their victims. And what happens is they're trying to make sure that they can motivate their victims in the fastest way possible to pay the ransom as well. Eh? So there's a lot of attacks going on. We usually we're finding now is ransomware is sometimes the last stage of an attack, so an attacker may go into on organization. They may already be taking data out of that organization. They may be stealing customer data P I, which is personal, identifiable information such as Social Security numbers or or driver's licenses or credit card information. Once they've done their entire attack, once they've gone, everything they can Ah, lot of times their end stage. There last attack is ransomware, and they encrypt all the files on the system and try and try and motivate the victim to pay as fast as possible and as much as possible as well. >>You know, it's interesting. I thought of my buddy today. It's like casing the joint. They check it out. They do their re kon reconnaissance. They go in, identify what's the move that's move to make. How to extract the most out of the victim in this case, Target. Um, and it really I mean, it's just go on a tangent, you know? Why don't we have the right to bear our own arms? Why can't we fight back? I mean, the end of the day, Derek, this is like, Who's protecting me? I mean, >>e do >>what? To protect my own, build my own army, or does the government help us? I mean, that's at some point, I got a right to bear my own arms here, right? I mean, this is the whole security paradigm. >>Yeah, so I mean, there's a couple of things, right? So first of all, this is exactly why we do a lot of that. I was mentioning the skills shortage and cyber cyber security professionals. Example. This is why we do a lot of the heavy lifting on the back end. Obviously, from a defensive standpoint, you obviously have the red team blue team aspect. How do you first, Um, no. There is what is to fight back by being defensive as well, too, and also by, you know, in the world that threat intelligence. One of the ways that we're fighting back is not necessarily by going and hacking the bad guys, because that's illegal in jurisdictions, right? But how we can actually find out who these people are, hit them where it hurts. Freeze assets go after money laundering that works. You follow the cash transactions where it's happening. This is where we actually work with key law enforcement partners such as Inter Pool is an example. This is the world, the threat intelligence. That's why we're doing a lot of that intelligence work on the back end. So there's other ways toe actually go on the offense without necessarily weaponizing it per se right like he's using, you know, bearing your own arms, Aziz said. There's different forms that people may not be aware of with that and that actually gets into the world of, you know, if you see attacks happening on your system, how you how you can use security tools and collaborate with threat intelligence? >>Yeah, I think that I think that's the key. I think the key is these new sharing technologies around collective intelligence is gonna be, ah, great way to kind of have more of an offensive collective strike. But I think fortifying the defense is critical. I mean, that's there's no other way to do that. >>Absolutely. I mean the you know, we say that's almost every week, but it's in simplicity. Our goal is always to make it more expensive for the cyber criminal to operate. And there's many ways to do that right you could be could be a pain to them by by having a very rigid, hard and defense. That means that if if it's too much effort on their end, I mean, they have roos and their in their sense, right, too much effort on there, and they're gonna go knocking somewhere else. Um, there's also, you know, a zay said things like disruption, so ripping infrastructure offline that cripples them. Yeah, it's wack a mole they're going to set up somewhere else. But then also going after people themselves, Um, again, the cash networks, these sorts of things. So it's sort of a holistic approach between anything. >>Hey, it's an arms race. Better ai better cloud scale always helps. You know, it's a ratchet game. Okay, tomorrow I want to get into this video. It's of ransomware four minute video. I'd like you to take us through you to lead you to read. Researcher, >>take us >>through this video and, uh, explain what we're looking at. Let's roll the video. >>All right? Sure s. So what we have here is we have the victims. That's top over here. We have a couple of things on this. Victims that stop. We have ah, batch file, which is essentially going to run the ransom where we have the payload, which is the code behind the ransomware. And then we have files in this folder, and this is where you typically find user files and, ah, really world case. This would be like Microsoft Microsoft Word documents or your Power point presentations. Over here, we just have a couple of text files that we've set up we're going to go ahead and run the ransomware and sometimes Attackers. What they do is they disguise this like they make it look like a like, important word document. They make it look like something else. But once you run, the ransomware usually get a ransom message. And in this case, the ransom message says your files are encrypted. Uh, please pay this money to this Bitcoin address. That obviously is not a real Bitcoin address that usually they look a little more complicated. But this is our fake Bitcoin address, but you'll see that the files now are encrypted. You cannot access them. They've been changed. And unless you pay the ransom, you don't get the files. Now, as the researchers, we see files like this all the time. We see ransomware all the all the time. So we use a variety of tools, internal tools, custom tools as well as open source tools. And what you're seeing here is open source tool is called the cuckoo sandbox, and it shows us the behavior of the ransomware. What exactly is a ransom we're doing in this case? You can see just clicking on that file launched a couple of different things that launched basically a command execute herbal, a power shell. It launched our windows shell and then it did things on the file. It basically had registry keys. It had network connections. It changed the disk. So this kind of gives us behind the scenes. Look at all the processes that's happening on the ransomware and just that one file itself. Like I said, there's multiple different things now what we want to do As researchers, we want to categorize this ransomware into families. We wanna try and determine the actors behind that. So we dump everything we know in the ransomware in the central databases. And then we mind these databases. What we're doing here is we're actually using another tool called malt ego and, uh, use custom tools as well as commercial and open source tools. But but this is a open source and commercial tool. But what we're doing is we're basically taking the ransomware and we're asking malty, go to look through our database and say, like, do you see any like files? Or do you see any types of incidences that have similar characteristics? Because what we want to do is we want to see the relationship between this one ransomware and anything else we may have in our system because that helps us identify maybe where the ransom that's connecting to where it's going thio other processes that may be doing. In this case, we can see multiple I P addresses that are connected to it so we can possibly see multiple infections weaken block different external websites. If we can identify a command and control system, we can categorize this to a family. And sometimes we can even categorize this to a threat actor that has claimed responsibility for it. Eso It's essentially visualizing all the connections and the relationship between one file and everything else we have in our database in this example. Off course, we put this in multiple ways. We can save these as reports as pdf type reports or, you know, usually HTML or other searchable data that we have back in our systems. And then the cool thing about this is this is available to all our products, all our researchers, all our specialty teams. So when we're researching botnets when we're researching file based attacks when we're researching, um, you know, I P reputation We have a lot of different IOC's or indicators of compromise that we can correlate where attacks goes through and maybe even detective new types of attacks as well. >>So the bottom line is you got the tools using combination of open source and commercial products. Toe look at the patterns of all ransomware across your observation space. Is that right? >>Exactly. I should you like a very simple demo. It's not only open source and commercial, but a lot of it is our own custom developed products as well. And when we find something that works, that logic that that technique, we make sure it's built into our own products as well. So our own customers have the ability to detect the same type of threats that we're detecting as well. At four of our labs intelligence that we acquire that product, that product of intelligence, it's consumed directly by our projects. >>Also take me through what, what's actually going on? What it means for the customers. So border guard labs. You're looking at all the ransom where you see in the patterns Are you guys proactively looking? Is is that you guys were researching you Look at something pops on the radar. I mean, take us through What is what What goes on? And then how does that translate into a customer notification or impact? >>So So, yeah, if you look at a typical life cycle of these attacks, there's always proactive and reactive. That's just the way it is in the industry, right? So of course we try to be a wear Some of the solutions we talked about before. And if you look at an incoming threat, first of all, you need visibility. You can't protect or analyze anything that you can't see. So you got to get your hands on visibility. We call these I, O. C s indicators a compromise. So this is usually something like, um, actual execute herbal file, like the virus from the malware itself. It could be other things that are related to it, like websites that could be hosting the malware as an example. So once we have that seed, we call it a seed. We could do threat hunting from there, so we can analyze that right? If it's ah piece of malware or a botnet weaken do analysis on that and discover more malicious things that this is doing. Then we go investigate those malicious things and we really you know, it's similar to the world of C. S. I write have these different gods that they're connecting. We're doing that at hyper scale on DWI. Use that through these tools that Omar was talking. So it's really a life cycle of getting, you know, the malware incoming seeing it first, um, analyzing it on, then doing action on that. Right? So it's sort of a three step process, and the action comes down to what tomorrow is saying water following that to our customers so that they're protected. But then in tandem with that, we're also going further. And I'm sharing it, if if applicable to, say, law enforcement partners, other threat Intel sharing partners to And, um, there's not just humans doing that, right? So the proactive peace again, This is where it comes to artificial intelligence machine learning. Um, there's a lot of cases where we're automatically doing that analysis without humans. So we have a I systems that are analyzing and actually creating protection on its own. Two. So it Zack white interest technology. >>A decision. At the end of the day, you want to protect your customers. And so this renders out if I'm afford a net customer across the portfolio. The goal here is to protect them from ransomware. Right? That's the end of game. >>Yeah, And that's a very important thing when you start talking these big dollar amounts that were talking earlier comes Thio the damages that air down from estimates. >>E not only is a good insurance, it's just good to have that fortification. Alright, So dark. I gotta ask you about the term the last mile because, you know, we were before we came on camera. You know, I'm band with junkie, always want more bandwidth. So the last mile used to be a term for last mile to the home where there was telephone lines. Now it's fiber and by five. But what does that mean to you guys and security is that Does that mean something specific? >>Yeah, Yeah, absolutely. The easiest way to describe that is actionable, right? So one of the challenges in the industry is we live in a very noisy industry when it comes thio cybersecurity. What I mean by that is because of that growing attacks for fists on do you know, you have these different attack vectors. You have attacks not only coming in from email, but websites from, you know, DDOS attacks. There's there's a lot of volume that's just going to continue to grow is the world of I G N O T. S O. What ends up happening is when you look at a lot of security operation centers for customers as an example, um, there are it's very noisy. It's, um you can guarantee that every day you're going to see some sort of probe, some sort of attack activity that's happening. And so what that means is you get a lot of protection events, a lot of logs, and when you have this worldwide shortage of security professionals, you don't have enough people to process those logs and actually started to say, Hey, this looks like an attack. I'm gonna go investigate it and block it. So this is where the last mile comes in because ah, lot of the times that you know these logs, they light up like Christmas. And I mean, there's a lot of events that are happening. How do you prioritize that? How do you automatically add action? Because The reality is, if it's just humans, doing it on that last mile is often going back to your bandwidth terms. There's too much too much lately. See right, So how do you reduce that late and see? That's where the automation the AI machine learning comes in. Thio solve that last mile problem toe automatically either protection. Especially important because you have to be quicker than the attacker. It's an arms race like E. >>I think what you guys do with four to Guard Labs is super important. Not like the industry, but for society at large, as you have kind of all this, you know, shadow, cloak and dagger kind of attacks systems, whether it's National Security international or just for, you know, mafias and racketeering and the bad guys. Can you guys take a minute and explain the role of 40 guards specifically and and why you guys exist? I mean, obviously there's a commercial reason you both on the four net that you know trickles down into the products. That's all good for the customers. I get that, but there's more to the fore to guard than just that. You guys talk about this trend and security business because it is very clear that there's a you know, uh, collective sharing culture developing rapidly for societal benefit. Can you take them into something that, >>Yeah, sure, I'll get my thoughts. Are you gonna that? So I'm going to that Teoh from my point of view, I mean, there's various functions, So we've just talked about that last mile problem. That's the commercial aspect we create through 40 yard labs, 40 yards, services that are dynamic and updated to security products because you need intelligence products to be ableto protect against intelligence attacks. That's just the defense again, going back to How can we take that further? I mean, we're not law enforcement ourselves. We know a lot about the bad guys and the actors because of the intelligence work that you do. But we can't go in and prosecute. We can share knowledge and we can train prosecutors, right? This is a big challenge in the industry. A lot of prosecutors don't know how to take cybersecurity courses to court, and because of that, a lot of these cybercriminals rain free. That's been a big challenge in the industry. So, you know, this has been close to my heart over 10 years, I've been building a lot of these key relationships between private public sector as an example, but also private sector things like Cyber Threat Alliance, where a founding member of the Cyber Threat Alliance, if over 28 members and that alliance. And it's about sharing intelligence to level that playing field because Attackers room freely. What I mean by that is there's no jurisdictions for them. Cybercrime has no borders. Um, they could do a million things, uh, wrong and they don't care. We do a million things right. One thing wrong, and it's a challenge. So there's this big collaboration that's a big part of 40 guard. Why exists to is to make the industry better. Thio, you know, work on protocols and automation and and really fight fight this together. Well, remaining competitors. I mean, we have competitors out there, of course, on DSO it comes down to that last mile problem. John is like we can share intelligence within the industry, but it's on Lee. Intelligence is just intelligence. How do you make it useful and actionable? That's where it comes down to technology integration. And, >>um, are what's your take on this, uh, societal benefit because, you know, I've been saying since the Sony hack years ago that, you know, when you have nation states that if they put troops on our soil, the government would respond. Um, but yet virtually they're here, and the private sector's defend for themselves. No support. So I think this private public partnership thing is very relevant. I think is ground zero of the future build out of policy because, you know, we pay for freedom. Why don't we have cyber freedom is if we're gonna run a business. Where's our help from the government? Pay taxes. So again, if a military showed up, you're not gonna see, you know, cos fighting the foreign enemy, right? So, again, this is a whole new change over it >>really is. You have to remember that cyberattacks puts everyone on even playing field, right? I mean, you know, now don't have to have a country that has invested a lot in weapons development or nuclear weapons or anything like that, right? Anyone can basically come up to speed on cyber weapons as long as they have an Internet connection. So it evens the playing field, which makes it dangerous, I guess, for our enemies, you know, But absolutely that I think a lot of us, You know, from a personal standpoint, a lot of us have seen researchers have seen organizations fail through cyber attacks. We've seen the frustration we've seen. Like, you know, besides organization, we've seen people like, just like grandma's loser pictures of their, you know, other loved ones because they can being attacked by ransom, where I think we take it very personally when people like innocent people get attacked and we make it our mission to make sure we can do everything we can to protect them. But But I will add that the least here in the U. S. The federal government actually has a lot of partnerships and ah, lot of programs to help organizations with cyber attacks. Three us cert is always continuously updating, you know, organizations about the latest attacks. Infra Guard is another organization run by the FBI, and a lot of companies like Fortinet and even a lot of other security companies participate in these organizations so everyone can come up to speed and everyone share information. So we all have a fighting chance. >>It's a whole new wave paradigm. You guys on the cutting edge, Derek? Always great to see a mark. Great to meet you remotely looking forward to meeting in person when the world comes back to normal as usual. Thanks for the great insights. Appreciate it. >>All right. Thank God. Pleasure is always >>okay. Q conversation here. I'm John for a host of the Cube. Great insightful conversation around security Ransomware with a great demo. Check it out from Derek and, um, are from 14 guard labs. I'm John Ferrier. Thanks for watching.