(intense orchestral music) >> Hey, welcome back everybody, Jeff Frick here with theCUBE, we're at our Palo Alto studios for a CUBEConversation, and we're talking about startups today, which we don't often get to do but it's really one of the more exciting things that we get to do, because that's what really, what keeps Silicon Valley Silicon Valley; and this next new company is playing on a very hot space which is edge, you're all about cloud then the next big move is edge, especially with internet things and industrial internet things. So we're really happy to welcome Edgeworx here, fresh off the announcement of the new company and their funding. We got the, both Founders, we have Farah Papaioannou, and she is the President, and Kilton Hopkins, the CEO, both of Edgeworx, welcome. >> Thank you, >> Thanks. >> thanks for having us. >> So for those of us that aren't familiar, give us kind of the quick 101 on Edgeworx. >> So I've been looking at the space as a venture capitalist before I've joined up with Kilton, and I've been looking at edge computing for a long time because it just made intuitive sense to me. You're looking at all these devices that are now not just devices but they're compute platforms, or you know generating all this data; well how are we going to address all that data? If you think about sending all that back to the cloud, latency, bandwidth, and cost, you talk about breaking the internet, this is what's going to break the internet not Kim Kardashian's you know butt photo right? (guys laugh) So, how do you solve that problem? You know if you think about autonomous vehicles for example these are now computers on wheels, they're not just a transportation mechanism. If they're generating all this data, and they need to interact with each other, and make decisions in near realtime; how are they going to do that if they have to send all that data back to the cloud? >> Right, great. >> So that's where I came across Kilton's company, or actually the technology that he'd built, and we formed a company together. I looked at everything, and the technology that he'd developed, was far, leaps and bounds beyond anything anyone else had come to to date, so. >> So, Kilton, how did you start on that project? >> Yeah, so this actually goes way back, this goes way back to like about 2010. Back in Chicago I was looking at what architecture is going to allow us to do the types of processing that's really expensive, and do it close to where the data is? This architecture was in the back of my mind. When I came to the bay area, I jumped in with the city of San Francisco as an IOT Advisor; and everywhere I looked I saw the same problems. Nobody was doing secure processing at the edge in any kind of way that was manageable, so I started to solve it. Then, years later after doing, you know I did some deployments myself, and after seeing how was this stuff working, it finally arrived at an architecture that I thought: okay, this thing's passing all these trials, and now I think we've got this pretty well nailed, so. I basically got into it before the terms fog and edge computing were being thrown around, and just said this is what has to happen. And then of course, it turns out that the world catches up, and now of course there's terms for it, and everyone's talking about the edge. >> So it's an interesting problem, right, it's the same old problem we've been having forever, which is do you move the data to the compute or do you move the compute to the data? And then we've had these other things happening with suddenly this you know huge swell of data flow, and that's even before we start you know kind of the IOT connection on the data flow, luckily the networks are getting faster, 5G's around the corner, chips are getting faster and cheaper, memory's getting cheaper and faster. And then we had the development of the cloud and really the hyper growth of the public cloud. But that still doesn't help you with kind of these low latency applications that you have to execute on the edge. And obviously we've talked to GE a lot, and everyone wants to talk about turbines and you know harsh conditions and you know nasty weather, and it's not this pristine data center; how do you put compute, and how much compute do you put at the edge, and how do you manage kind of that data flow? What can you deal with there, what do you have to send up? And of course this pesky thing called physics and latency, which just prohibits, as you said, the ability to get stuff up to some compute and get it back in time necessarily to do something about it. So what is the approach that you guys are taking? What's a little bit different about what you've built with Edgeworx? >> Sure. >> So, in most cases, people think about the edge as like almost a lead into the cloud. They say: how can I pre-process the data, maybe curtail some of the bandwidth volume that I need in order to send data up to the cloud? But that doesn't actually solve the problem, you'll never get rid of cloud latency if you're sending just smaller packages. And in addition, you have done nothing to address the security issues of the edge, if you're just trying to package data, maybe reduce it a bit and send it to the cloud. So what's different about us is with us you can use the cloud, but you don't have to, we're completely at the edge. So you can run software with Edgeworx that stays within the four walls of a factory, if you so choose, and no data will ever leave the building; and that is a stark difference from the approaches that've been taken to date which've been tied to the cloud, but we do a little at the edge, it's like come on, this is real edge. >> Right, right. And so is it a software layer that sits on top of whatever kind of bios and firmware are on a lot of these dumb sensors, is that kind of the idea? >> Yeah, no actually it sits, exactly, it sits above the bios level, it sits above the firmware. It creates an application runtime, so it allows developers to write applications that are containerized, so we run containers at the edge, which allows our developers to run applications they've already developed for the cloud, to write new applications, but they don't have to learn an entirely new framework or an entirely new SDK, they can write using tools they already know: Java, C#, C++, Python, if you can write that language, we can run it, and at the edge. Which again allows people to use skillsets that they already know, they don't have to like learn specialized skillsets for the edge, why should they have to do that you know? >> I think, and you know good for you guys, to get Stacey Higginbotham to write a nice article about the company long before you launched, which is good. But I thought she had a really interesting breakdown on kind of edge computing, and she broke it down into four layers: the device, the sensors, as you said as dumb as it can be, right, you want a lot of these things. Then this gateway layer that collects the data. You know some level of compute close to the edge, not necessarily in the camera or in any of these sensors, but close, and then of course a connection back to the cloud. So you guys run in the sensor, or probably more likely in that gateway layer? Or do you see, in some of the early customers you're talkin' to, are they putting these like little micro data centers? I mean how are you actually seeing this stuff deployed in the field at scale? >> So we actually gave Stacey that four layer chart because were trying to explain people to the edge, to people who didn't understand what that was, and again, people refer to all these different layers at the edge. We actually think that the layer right above the sensors is actually the most difficult to solve for. And the reason we don't want to run on the sensor level is because sensors are becoming more and more commoditized, a customer would rather have a thousand dumb sensors where they could get more and more data, than have like 10 really smart sensors where they could run compute on them. So, unless there's special circumstances, like you know a case of a camera where we're actually working with a camera that has GPU capability, where they can actually run on the edge, we'd like to run at a level up there, and there's a couple of reasons for that. One is, if you run on the devices itself, you can't really aggregate each other's devices, you can't aggregate-- a temperature sensor cannot aggregate a pressure sensor's data, you need to set up a layer above. Also we're able to serve as a broker between low levels of you know Wi-Fi and Bluetooth, versus you know high levels of TCP/IP, right, which you also cannot do at the sensor level. If you were run at the sensor, you'd basically have to do what Amazon does, which is device-to-cloud; which doesn't really afford you the capability of running real software at the edge. >> Right. So, when you're out, let's just say the camera, we talked a little bit before we turned the cameras on about the surveillance and surveillance cameras, I mean where are those gateways, and where's the power and the connectivity to that gateway, what're you seeing in some of these early examples? >> So, you know, for cameras you've got basically two choices, either the camera is a dumb camera that puts a video feed to some kind of a compute box that's nearby, or is on a wired network, or wireless network that's private to it, so. In building cameras that are already in place, that are analog, you can put a box in the building that can take the feeds, but the better option than that even is to have smart cameras, so probably a new greenfield deploy would have smart cameras that have the ability to do the AI processing right there in the module. So the answer is: somewhere you have a feed of sensor data, whether it be video, audio, or just like a temperature, you know time series data, and then it hits a point of where you're still on the edge, but you can do compute. Sometimes they're in the same unit, sometimes they're a little spread out, sometimes they're over wireless; that first layer up is where we sit no matter how the compute is done. >> Okay. And I'm just curious on some of the early use cases. How do people see the opportunity now to have kind of a software-driven IOT device that's separate from the actual firmware that's in the in the sensor? What is that going to enable them to do that they're excited to do they couldn't do before? >> Yeah, so if you think about the older model, it's: how can I make this device, get it's sensor readings and somehow communicate that data, and I'm going to write low-level code, probably C code or whatever to operate that and it's how often do I pull the sensor? And you're really thinking about just jeez I need this data somewhere to make useable. And when you use us you think: okay, I have streams of data, what would I do if I wanted to run software right where the data is, I can increase my sampling frequency, I can undo everything we were going to do in the cloud, and do it right there for free once it's deployed there's no bandwidth cost. So it opens the world of, of thinking, we're now running software at the edge, instead of running firmware, so I can just move the data upstream. You stop moving the data, and you start moving the applications, and that's what's like the world changer for everybody. >> Right, right. >> Plus you can use the same skillsets you have for the cloud and up until now programming IOT devices has been a matter of saying oh, you know, if I know how to work the GPIO pins you know and you know I can write in C, maybe I can make it work. And now you say: I know Python, and I know how to do data analytics with Python, I can just move that into the sensor module, if it's smart enough, or the gateway right there, and I can pretty much push my code into the factory instead of waiting for the factory to wire the data to me. >> And we actually have a customer right now that's doing real-time surveillance at the edge, and they have smart city deployments and they're looking at an example of, border control for example. And what they want to be able to do is put these cameras out there and say: well, I've detected something on the maritime border here, is it a whale, is it debris, or is it a boat full of refugees, or is it a boat full of like pirates, or is it a boat full of migrants? Well before what they would have to do is okay well, as an edge device maybe I, at the basic level of processing I could run is to say let me compress that video data and send some of it back, right, and then do the analysis back there; well that's not really going to be that helpful, because if I have to send it back to some cloud and do some analysis, by the time I've recognized what's out there: too late. What we can do now with our software capability, because we have our platform running on these cameras is we can deploy software that says: okay well I can detect, right there, right at the edge, what we're seeing, and I can not just send back video data, which I don't really want to do, that's really you know heavy on bandwidth and latency, cost as well, is I can just send back text data and say: well, I've actually detected something, so let's take some sort of action on it, and say okay the next camera should be able to detect it or pick it up or send some notifications that we need to address it back here. If I'm sending textual data back, and say I've already done that processing right there and then, I can run thousands of cameras out there at the edge versus just 10 or you know, 10 or 12 because of the amount of cost and latency. And then the customer can decide, well you know what, I want to add another application that you know does target tracking of certain individual terrorists, right? Okay, well that's easy for me to deploy that software because our platform's already running. We can, you know, and just push it out there at the edge. Oh, you know what, I'm able to model train at the edge, and I can actually do better detection, I can go from 80% to 90%, well I can just push that data and do an upgrade right there at the edge as opposed to going out there and flashing that board, and you know upgrading that way, or sending out some sort of firmware upgrade; so it allows a lot of flexibility that we couldn't do before. >> Right. Well I just got to ask ya now, you got a pile of money, which is exciting, and congratulations. >> Thank you. >> I was going to say, kind of, where do you kind of focus on your go-to-market, you know within any particular vertical, or any specific horizontal application? But it sounds like, I think we've use cameras now three or four times (laughs) in the last three or four questions, so I'm guessin' that's a, that's a good-- >> That's been a strong one for us. >> You know kind of early early adopt to market for you guys. >> That one's been a strong one for us, yeah. We've had some real success with telco's, another use case that we've seen some real good traction is being able to detect quality-of-service issues on Wi-Fi routers, so, that's one that we're looking at as well that's had some adoption. Oil and gas has been pretty strong for us as well. So it seems to be kind of a horizontal play for us, and we're excited about the opportunity. >> Alright. Well thanks for comin' on and tellin' the story, and congratulations on your funding and launching the company, and, >> Thank you. >> And bringin' it to reality. >> Great, thanks. >> Alright, Kilton, Farah, I'm Jeff, you're watchin' theCUBE, thanks for watchin' we'll see ya next time. 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(light upbeat music) >> Hey, everyone. Welcome back to theCUBE's day long coverage of Women in Data Science 2023. Live from Stanford University, I'm Lisa Martin. We've had some amazing conversations today with my wonderful co-host, as you've seen. Tracy Zhang joins me next for a very interesting and inspiring conversation. I know we've been bringing them to you, we're bringing you another one here. Dr. Irene Dankwa-Mullan joins us, the Chief Medical Officer at Marti Health, and a speaker at WIDS. Welcome, Irene, it's great to have you. >> Thank you. I'm delighted to be here. Thank you so much for this opportunity. >> So you have an MD and a Master of Public Health. Covid must have been an interesting time for you, with an MPH? >> Very much so. >> Yeah, talk a little bit about you, your background, and Marti Health? This is interesting. This is a brand new startup. This is a digital health equity startup. >> Yes, yes. So, I'll start with my story a little bit about myself. So I was actually born in Ghana. I finished high school there and came here for college. What would I say? After I finished my undergraduate, I went to medical school at Dartmouth and I always knew I wanted to go into public health as well as medicine. So my medical education was actually five years. I did the MPH and my medical degree, at the same time, I got my MPH from Yale School of Public Health. And after I finished, I trained in internal medicine, Johns Hopkins, and after that I went into public health. I am currently living in Maryland, so I'm in Bethesda, Maryland, and that's where I've been. And really enjoyed public health, community health, combining that aspect of sort of prevention and wellness and also working in making sure that we have community health clinics and safety net clinics. So a great experience there. I also had the privilege, after eight years in public health, I went to the National Institute of Health. >> Oh, wow. >> Where I basically worked in clinical research, basically on minority health and health disparities. So, I was in various leadership roles and helped to advance the science of health equity, working in collaboration with a lot of scientists and researchers at the NIH, really to advance the science. >> Where did your interest in health equity come from? Was there a defining moment when you were younger and you thought "There's a lot of inequities here, we have to do something about this." Where did that interest start? >> That's a great question. I think this influence was basically maybe from my upbringing as well as my family and also what I saw around me in Ghana, a lot of preventable diseases. I always say that my grandfather on my father's side was a great influence, inspired me and influenced my career because he was the only sibling, really, that went to school. And as a result, he was able to earn enough money and built, you know, a hospital. >> Oh wow. >> In their hometown. >> Oh my gosh! >> It started as a 20 bed hospital and now it's a 350 bed hospital. >> Oh, wow, that's amazing! >> In our hometown. And he knew that education was important and vital as well for wellbeing. And so he really inspired, you know, his work inspired me. And I remember in residency I went with a group of residents to this hospital in Ghana just to help over a summer break. So during a summer where we went and helped take care of the sick patients and actually learned, right? What it is like to care for so many patients and- >> Yeah. >> It was really a humbling experience. But that really inspired me. I think also being in this country. And when I came to the U.S. and really saw firsthand how patients are treated differently, based on their background or socioeconomic status. I did see firsthand, you know, that kind of unconscious bias. And, you know, drew me to the field of health disparities research and wanted to learn more and do more and contribute. >> Yeah. >> Yeah. So, I was curious. Just when did the data science aspect tap in? Like when did you decide that, okay, data science is going to be a problem solving tool to like all the problems you just said? >> Yeah, that's a good question. So while I was at the NIH, I spent eight years there, and precision medicine was launched at that time and there was a lot of heightened interest in big data and how big data could help really revolutionize medicine and healthcare. And I got the opportunity to go, you know, there was an opportunity where they were looking for physicians or deputy chief health officer at IBM. And so I went to IBM, Watson Health was being formed as a new business unit, and I was one of the first deputy chief health officers really to lead the data and the science evidence. And that's where I realized, you know, we could really, you know, the technology in healthcare, there's been a lot of data that I think we are not really using or optimizing to make sure that we're taking care of our patients. >> Yeah. >> And so that's how I got into data science and making sure that we are building technologies using the right data to advance health equity. >> Right, so talk a little bit about health equity? We mentioned you're with Marti Health. You've been there for a short time, but Marti Health is also quite new, just a few months old. Digital health equity, talk about what Marti's vision is, what its mission is to really help start dialing down a lot of the disparities that you talked about that you see every day? >> Yeah, so, I've been so privileged. I recently joined Marti Health as their Chief Medical Officer, Chief Health Officer. It's a startup that is actually trying to promote a value-based care, also promote patient-centered care for patients that are experiencing a social disadvantage as a result of their race, ethnicity. And were starting to look at and focused on patients that have sickle cell disease. >> Okay. >> Because we realize that that's a population, you know, we know sickle cell disease is a genetic disorder. It impacts a lot of patients that are from areas that are endemic malaria. >> Yeah. >> Yeah. >> And most of our patients here are African American, and when, you know, they suffer so much stigma and discrimination in the healthcare system and complications from their sickle cell disease. And so what we want to do that we feel like sickle cell is a litmus test for disparities. And we want to make sure that they get in patient-centered care. We want to make sure that we are leveraging data and the research that we've done in sickle cell disease, especially on the continent of Africa. >> Okay. >> And provide, promote better quality care for the patients. >> That's so inspiring. You know, we've heard so many great stories today. Were you able to watch the keynote this morning? >> Yes. >> I loved how it always inspires me. This conference is always, we were talking about this all day, how you walk in the Arrillaga Alumni Center here where this event is held every year, the vibe is powerful, it's positive, it's encouraging. >> Inspiring, yeah. >> Absolutely. >> Inspiring. >> Yeah, yeah. >> It's a movement, WIDS is a movement. They've created this community where you feel, I don't know, kind of superhuman. "Why can't I do this? Why not me?" We heard some great stories this morning about data science in terms of applications. You have a great application in terms of health equity. We heard about it in police violence. >> Yes. >> Which is an epidemic in this country for sure, as we know. This happens too often. How can we use data and data science as a facilitator of learning more about that, so that that can stop? I think that's so important for more people to understand all of the broad applications of data science, whether it's police violence or climate change or drug discovery or health inequities. >> Irene: Yeah. >> The potential, I think we're scratching the surface. But the potential is massive. >> Tracy: It is. >> And this is an event that really helps women and underrepresented minorities think, "Why not me? Why can't I get involved in that?" >> Yeah, and I always say we use data to make an make a lot of decisions. And especially in healthcare, we want to be careful about how we are using data because this is impacting the health and outcomes of our patients. And so science evidence is really critical, you know? We want to make sure that data is inclusive and we have quality data. >> Yes. >> And it's transparent. Our clinical trials, I always say are not always diverse and inclusive. And if that's going to form the evidence base or data points then we're doing more harm than good for our patients. And so data science, it's huge. I mean, we need a robust, responsible, trustworthy data science agenda. >> "Trust" you just brought up "trust." >> Yeah. >> I did. >> When we talk about data, we can't not talk about security and privacy and ethics but trust is table stakes. We have to be able to evaluate the data and trust in it. >> Exactly. >> And what it says and the story that can be told from it. So that trust factor is, I think, foundational to data science. >> We all see what happened with Covid, right? I mean, when the pandemic came out- >> Absolutely. >> Everyone wanted information. We wanted data, we wanted data we could trust. There was a lot of hesitancy even with the vaccine. >> Yeah. >> Right? And so public health, I mean, like you said, we had to do a lot of work making sure that the right information from the right data was being translated or conveyed to the communities. And so you are totally right. I mean, data and good information, relevant data is always key. >> Well- >> Is there any- Oh, sorry. >> Go ahead. >> Is there anything Marti Health is doing in like ensuring that you guys get the right data that you can put trust in it? >> Yes, absolutely. And so this is where we are, you know, part of it would be getting data, real world evidence data for patients who are being seen in the healthcare system with sickle cell disease, so that we can personalize the data to those patients and provide them with the right treatment, the right intervention that they need. And so part of it would be doing predictive modeling on some of the data, risk, stratifying risk, who in the sickle cell patient population is at risk of progressing. Or getting, you know, they all often get crisis, vaso-occlusive crisis because the cells, you know, the blood cell sickles and you want to avoid those chest crisis. And so part of what we'll be doing is, you know, using predictive modeling to target those at risk of the disease progressing, so that we can put in preventive measures. It's all about prevention. It's all about making sure that they're not being, you know, going to the hospital or the emergency room where sometimes they end up, you know, in pain and wanting pain medicine. And so. >> Do you see AI as being a critical piece in the transformation of healthcare, especially where inequities are concerned? >> Absolutely, and and when you say AI, I think it's responsible AI. >> Yes. >> And making sure that it's- >> Tracy: That's such a good point. >> Yeah. >> Very. >> With the right data, with relevant data, it's definitely key. I think there is so much data points that healthcare has, you know, in the healthcare space there's fiscal data, biological data, there's environmental data and we are not using it to the full capacity and full potential. >> Tracy: Yeah. >> And I think AI can do that if we do it carefully, and like I said, responsibly. >> That's a key word. You talked about trust, responsibility. Where data science, AI is concerned- >> Yeah. >> It has to be not an afterthought, it has to be intentional. >> Tracy: Exactly. >> And there needs to be a lot of education around it. Most people think, "Oh, AI is just for the technology," you know? >> Yeah, right. >> Goop. >> Yes. >> But I think we're all part, I mean everyone needs to make sure that we are collecting the right amount of data. I mean, I think we all play a part, right? >> We do. >> We do. >> In making sure that we have responsible AI, we have, you know, good data, quality data. And the data sciences is a multi-disciplinary field, I think. >> It is, which is one of the things that's exciting about it is it is multi-disciplinary. >> Tracy: Exactly. >> And so many of the people that we've talked to in data science have these very non-linear paths to get there, and so I think they bring such diversity of thought and backgrounds and experiences and thoughts and voices. That helps train the AI models with data that's more inclusive. >> Irene: Yes. >> Dropping down the volume on the bias that we know is there. To be successful, it has to. >> Definitely, I totally agree. >> What are some of the things, as we wrap up here, that you're looking forward to accomplishing as part of Marti Health? Like, maybe what's on the roadmap that you can share with us for Marti as it approaches the the second half of its first year? >> Yes, it's all about promoting health equity. It's all about, I mean, there's so much, well, I would start with, you know, part of the healthcare transformation is making sure that we are promoting care that's based on value and not volume, care that's based on good health outcomes, quality health outcomes, and not just on, you know, the quantity. And so Marti Health is trying to promote that value-based care. We are envisioning a world in which everyone can live their full life potential. Have the best health outcomes, and provide that patient-centered precision care. >> And we all want that. We all want that. We expect that precision and that personalized experience in our consumer lives, why not in healthcare? Well, thank you, Irene, for joining us on the program today. >> Thank you. >> Talking about what you're doing to really help drive the volume up on health equity, and raise awareness for the fact that there's a lot of inequities in there we have to fix. We have a long way to go. >> We have, yes. >> Lisa: But people like you are making an impact and we appreciate you joining theCUBE today and sharing what you're doing, thank you. >> Thank you. >> Thank you- >> Thank you for having me here. >> Oh, our pleasure. For our guest and Tracy Zhang, this is Lisa Martin from WIDS 2023, the eighth Annual Women in Data Science Conference brought to you by theCUBE. Stick around, our show wrap will be in just a minute. Thanks for watching. (light upbeat music)

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> This past week we saw two of the Big 3 cloud providers present the latest update on their respective cloud visions, their business progress, their announcements and innovations. The content at these events had many overlapping themes, including modern cloud infrastructure at global scale, applying advanced machine intelligence, AKA AI, end-to-end data platforms, collaboration software. They talked a lot about the future of work automation. And they gave us a little taste, each company of the Metaverse Web 3.0 and much more. Despite these striking similarities, the differences between these two cloud platforms and that of AWS remains significant. With Microsoft leveraging its massive application software footprint to dominate virtually all markets and Google doing everything in its power to keep up with the frenetic pace of today's cloud innovation, which was set into motion a decade and a half ago by AWS. Hello and welcome to this week's Wikibon CUBE Insights, powered by ETR. In this Breaking Analysis, we unpack the immense amount of content presented by the CEOs of Microsoft and Google Cloud at Microsoft Ignite and Google Cloud Next. We'll also quantify with ETR survey data the relative position of these two cloud giants in four key sectors: cloud IaaS, BI analytics, data platforms and collaboration software. Now one thing was clear this past week, hybrid events are the thing. Google Cloud Next took place live over a 24-hour period in six cities around the world, with the main gathering in New York City. Microsoft Ignite, which normally is attended by 30,000 people, had a smaller event in Seattle, in person with a virtual audience around the world. AWS re:Invent, of course, is much different. Yes, there's a virtual component at re:Invent, but it's all about a big live audience gathering the week after Thanksgiving, in the first week of December in Las Vegas. Regardless, Satya Nadella keynote address was prerecorded. It was highly produced and substantive. It was visionary, energetic with a strong message that Azure was a platform to allow customers to build their digital businesses. Doing more with less, which was a key theme of his. Nadella covered a lot of ground, starting with infrastructure from the compute, highlighting a collaboration with Arm-based, Ampere processors. New block storage, 60 regions, 175,000 miles of fiber cables around the world. He presented a meaningful multi-cloud message with Azure Arc to support on-prem and edge workloads, as well as of course the public cloud. And talked about confidential computing at the infrastructure level, a theme we hear from all cloud vendors. He then went deeper into the end-to-end data platform that Microsoft is building from the core data stores to analytics, to governance and the myriad tooling Microsoft offers. AI was next with a big focus on automation, AI, training models. He showed demos of machines coding and fixing code and machines automatically creating designs for creative workers and how Power Automate, Microsoft's RPA tooling, would combine with Microsoft Syntex to understand documents and provide standard ways for organizations to communicate with those documents. There was of course a big focus on Azure as developer cloud platform with GitHub Copilot as a linchpin using AI to assist coders in low-code and no-code innovations that are coming down the pipe. And another giant theme was a workforce transformation and how Microsoft is using its heritage and collaboration and productivity software to move beyond what Nadella called productivity paranoia, i.e., are remote workers doing their jobs? In a world where collaboration is built into intelligent workflows, and he even showed a glimpse of the future with AI-powered avatars and partnerships with Meta and Cisco with Teams of all firms. And finally, security with a bevy of tools from identity, endpoint, governance, et cetera, stressing a suite of tools from a single provider, i.e., Microsoft. So a couple points here. One, Microsoft is following in the footsteps of AWS with silicon advancements and didn't really emphasize that trend much except for the Ampere announcement. But it's building out cloud infrastructure at a massive scale, there is no debate about that. Its plan on data is to try and provide a somewhat more abstracted and simplified solutions, which differs a little bit from AWS's approach of the right database tool, for example, for the right job. Microsoft's automation play appears to provide simple individual productivity tools, kind of a ground up approach and make it really easy for users to drive these bottoms up initiatives. We heard from UiPath that forward five last month, a little bit of a different approach of horizontal automation, end-to-end across platforms. So quite a different play there. Microsoft's angle on workforce transformation is visionary and will continue to solidify in our view its dominant position with Teams and Microsoft 365, and it will drive cloud infrastructure consumption by default. On security as well as a cloud player, it has to have world-class security, and Azure does. There's not a lot of debate about that, but the knock on Microsoft is Patch Tuesday becomes Hack Wednesday because Microsoft releases so many patches, it's got so much Swiss cheese in its legacy estate and patching frequently, it becomes a roadmap and a trigger for hackers. Hey, patch Tuesday, these are all the exploits that you can go after so you can act before the patches are implemented. And so it's really become a problem for users. As well Microsoft is competing with many of the best-of-breed platforms like CrowdStrike and Okta, which have market momentum and appear to be more attractive horizontal plays for customers outside of just the Microsoft cloud. But again, it's Microsoft. They make it easy and very inexpensive to adopt. Now, despite the outstanding presentation by Satya Nadella, there are a couple of statements that should raise eyebrows. Here are two of them. First, as he said, Azure is the only cloud that supports all organizations and all workloads from enterprises to startups, to highly regulated industries. I had a conversation with Sarbjeet Johal about this, to make sure I wasn't just missing something and we were both surprised, somewhat, by this claim. I mean most certainly AWS supports more certifications for example, and we would think it has a reasonable case to dispute that claim. And the other statement, Nadella made, Azure is the only cloud provider enabling highly regulated industries to bring their most sensitive applications to the cloud. Now, reasonable people can debate whether AWS is there yet, but very clearly Oracle and IBM would have something to say about that statement. Now maybe it's not just, would say, "Oh, they're not real clouds, you know, they're just going to hosting in the cloud if you will." But still, when it comes to mission-critical applications, you would think Oracle is really the the leader there. Oh, and Satya also mentioned the claim that the Edge browser, the Microsoft Edge browser, no questions asked, he said, is the best browser for business. And we could see some people having some questions about that. Like isn't Edge based on Chrome? Anyway, so we just had to question these statements and challenge Microsoft to defend them because to us it's a little bit of BS and makes one wonder what else in such as awesome keynote and it was awesome, it was hyperbole. Okay, moving on to Google Cloud Next. The keynote started with Sundar Pichai doing a virtual session, he was remote, stressing the importance of Google Cloud. He mentioned that Google Cloud from its Q2 earnings was on a $25-billion annual run rate. What he didn't mention is that it's also on a 3.6 billion annual operating loss run rate based on its first half performance. Just saying. And we'll dig into that issue a little bit more later in this episode. He also stressed that the investments that Google has made to support its core business and search, like its global network of 22 subsea cables to support things like, YouTube video, great performance obviously that we all rely on, those innovations there. Innovations in BigQuery to support its search business and its threat analysis that it's always had and its AI, it's always been an AI-first company, he's stressed, that they're all leveraged by the Google Cloud Platform, GCP. This is all true by the way. Google has absolutely awesome tech and the talk, as well as his talk, Pichai, but also Kurian's was forward thinking and laid out a vision of the future. But it didn't address in our view, and I talked to Sarbjeet Johal about this as well, today's challenges to the degree that Microsoft did and we expect AWS will at re:Invent this year, it was more out there, more forward thinking, what's possible in the future, somewhat less about today's problem, so I think it's resonates less with today's enterprise players. Thomas Kurian then took over from Sundar Pichai and did a really good job of highlighting customers, and I think he has to, right? He has to say, "Look, we are in this game. We have customers, 9 out of the top 10 media firms use Google Cloud. 8 out of the top 10 manufacturers. 9 out of the top 10 retailers. Same for telecom, same for healthcare. 8 out of the top 10 retail banks." He and Sundar specifically referenced a number of companies, customers, including Avery Dennison, Groupe Renault, H&M, John Hopkins, Prudential, Minna Bank out of Japan, ANZ bank and many, many others during the session. So you know, they had some proof points and you got to give 'em props for that. Now like Microsoft, Google talked about infrastructure, they referenced training processors and regions and compute optionality and storage and how new workloads were emerging, particularly data-driven workloads in AI that required new infrastructure. He explicitly highlighted partnerships within Nvidia and Intel. I didn't see anything on Arm, which somewhat surprised me 'cause I believe Google's working on that or at least has come following in AWS's suit if you will, but maybe that's why they're not mentioning it or maybe I got to do more research there, but let's park that for a minute. But again, as we've extensively discussed in Breaking Analysis in our view when it comes to compute, AWS via its Annapurna acquisition is well ahead of the pack in this area. Arm is making its way into the enterprise, but all three companies are heavily investing in infrastructure, which is great news for customers and the ecosystem. We'll come back to that. Data and AI go hand in hand, and there was no shortage of data talk. Google didn't mention Snowflake or Databricks specifically, but it did mention, by the way, it mentioned Mongo a couple of times, but it did mention Google's, quote, Open Data cloud. Now maybe Google has used that term before, but Snowflake has been marketing the data cloud concept for a couple of years now. So that struck as a shot across the bow to one of its partners and obviously competitor, Snowflake. At BigQuery is a main centerpiece of Google's data strategy. Kurian talked about how they can take any data from any source in any format from any cloud provider with BigQuery Omni and aggregate and understand it. And with the support of Apache Iceberg and Delta and Hudi coming in the future and its open Data Cloud Alliance, they talked a lot about that. So without specifically mentioning Snowflake or Databricks, Kurian co-opted a lot of messaging from these two players, such as life and tech. Kurian also talked about Google Workspace and how it's now at 8 million users up from 6 million just two years ago. There's a lot of discussion on developer optionality and several details on tools supported and the open mantra of Google. And finally on security, Google brought out Kevin Mandian, he's a CUBE alum, extremely impressive individual who's CEO of Mandiant, a leading security service provider and consultancy that Google recently acquired for around 5.3 billion. They talked about moving from a shared responsibility model to a shared fate model, which is again, it's kind of a shot across AWS's bow, kind of shared responsibility model. It's unclear that Google will pay the same penalty if a customer doesn't live up to its portion of the shared responsibility, but we can probably assume that the customer is still going to bear the brunt of the pain, nonetheless. Mandiant is really interesting because it's a services play and Google has stated that it is not a services company, it's going to give partners in the channel plenty of room to play. So we'll see what it does with Mandiant. But Mandiant is a very strong enterprise capability and in the single most important area security. So interesting acquisition by Google. Now as well, unlike Microsoft, Google is not competing with security leaders like Okta and CrowdStrike. Rather, it's partnering aggressively with those firms and prominently putting them forth. All right. Let's get into the ETR survey data and see how Microsoft and Google are positioned in four key markets that we've mentioned before, IaaS, BI analytics, database data platforms and collaboration software. First, let's look at the IaaS cloud. ETR is just about to release its October survey, so I cannot share the that data yet. I can only show July data, but we're going to give you some directional hints throughout this conversation. This chart shows net score or spending momentum on the vertical axis and overlap or presence in the data, i.e., how pervasive the platform is. That's on the horizontal axis. And we've inserted the Wikibon estimates of IaaS revenue for the companies, the Big 3. Actually the Big 4, we included Alibaba. So a couple of points in this somewhat busy data chart. First, Microsoft and AWS as always are dominant on both axes. The red dotted line there at 40% on the vertical axis. That represents a highly elevated spending velocity and all of the Big 3 are above the line. Now at the same time, GCP is well behind the two leaders on the horizontal axis and you can see that in the table insert as well in our revenue estimates. Now why is Azure bigger in the ETR survey when AWS is larger according to the Wikibon revenue estimates? And the answer is because Microsoft with products like 365 and Teams will often be considered by respondents in the survey as cloud by customers, so they fit into that ETR category. But in the insert data we're stripping out applications and SaaS from Microsoft and Google and we're only isolating on IaaS. The other point is when you take a look at the early October returns, you see downward pressure as signified by those dotted arrows on every name. The only exception was Dell, or Dell and IBM, which showing slightly improved momentum. So the survey data generally confirms what we know that AWS and Azure have a massive lead and strong momentum in the marketplace. But the real story is below the line. Unlike Google Cloud, which is on pace to lose well over 3 billion on an operating basis this year, AWS's operating profit is around $20 billion annually. Microsoft's Intelligent Cloud generated more than $30 billion in operating income last fiscal year. Let that sink in for a moment. Now again, that's not to say Google doesn't have traction, it does and Kurian gave some nice proof points and customer examples in his keynote presentation, but the data underscores the lead that Microsoft and AWS have on Google in cloud. And here's a breakdown of ETR's proprietary net score methodology, that vertical axis that we showed you in the previous chart. It asks customers, are you adopting the platform new? That's that lime green. Are you spending 6% or more? That's the forest green. Is you're spending flat? That's the gray. Is you're spending down 6% or worse? That's the pinkest color. Or are you replacing the platform, defecting? That's the bright red. You subtract the reds from the greens and you get a net score. Now one caveat here, which actually is really favorable from Microsoft, the Microsoft data that we're showing here is across the entire Microsoft portfolio. The other point is, this is July data, we'll have an update for you once ETR releases its October results. But we're talking about meaningful samples here, the ends. 620 for AWS over a thousand from Microsoft in more than 450 respondents in the survey for Google. So the real tell is replacements, that bright red. There is virtually no churn for AWS and Microsoft, but Google's churn is 5x, those two in the survey. Now 5% churn is not high, but you'd like to see three things for Google given it's smaller size. One is less churn, two is much, much higher adoption rates in the lime green. Three is a higher percentage of those spending more, the forest green. And four is a lower percentage of those spending less. And none of these conditions really applies here for Google. GCP is still not growing fast enough in our opinion, and doesn't have nearly the traction of the two leaders and that shows up in the survey data. All right, let's look at the next sector, BI analytics. Here we have that same XY dimension. Again, Microsoft dominating the picture. AWS very strong also in both axes. Tableau, very popular and respectable of course acquired by Salesforce on the vertical axis, still looking pretty good there. And again on the horizontal axis, big presence there for Tableau. And Google with Looker and its other platforms is also respectable, but it again, has some work to do. Now notice Streamlit, that's a recent Snowflake acquisition. It's strong in the vertical axis and because of Snowflake's go-to-market (indistinct), it's likely going to move to the right overtime. Grafana is also prominent in the Y axis, but a glimpse at the most recent survey data shows them slightly declining while Looker actually improves a bit. As does Cloudera, which we'll move up slightly. Again, Microsoft just blows you away, doesn't it? All right, now let's get into database and data platform. Same X Y dimensions, but now database and data warehouse. Snowflake as usual takes the top spot on the vertical axis and it is actually keeps moving to the right as well with again, Microsoft and AWS is dominant in the market, as is Oracle on the X axis, albeit it's got less spending velocity, but of course it's the database king. Google is well behind on the X axis but solidly above the 40% line on the vertical axis. Note that virtually all platforms will see pressure in the next survey due to the macro environment. Microsoft might even dip below the 40% line for the first time in a while. Lastly, let's look at the collaboration and productivity software market. This is such an important area for both Microsoft and Google. And just look at Microsoft with 365 and Teams up into the right. I mean just so impressive in ubiquitous. And we've highlighted Google. It's in the pack. It certainly is a nice base with 174 N, which I can tell you that N will rise in the next survey, which is an indication that more people are adopting. But given the investment and the tech behind it and all the AI and Google's resources, you'd really like to see Google in this space above the 40% line, given the importance of this market, of this collaboration area to Google's success and the degree to which they emphasize it in their pitch. And look, this brings up something that we've talked about before on Breaking Analysis. Google doesn't have a tech problem. This is a go-to-market and marketing challenge that Google faces and it's up against two go-to-market champs and Microsoft and AWS. And Google doesn't have the enterprise sales culture. It's trying, it's making progress, but it's like that racehorse that has all the potential in the world, but it's just missing some kind of key ingredient to put it over at the top. It's always coming in third, (chuckles) but we're watching and Google's obviously, making some investments as we shared with earlier. All right. Some final thoughts on what we learned this week and in this research: customers and partners should be thrilled that both Microsoft and Google along with AWS are spending so much money on innovation and building out global platforms. This is a gift to the industry and we should be thankful frankly because it's good for business, it's good for competitiveness and future innovation as a platform that can be built upon. Now we didn't talk much about multi-cloud, we haven't even mentioned supercloud, but both Microsoft and Google have a story that resonates with customers in cross cloud capabilities, unlike AWS at this time. But we never say never when it comes to AWS. They sometimes and oftentimes surprise you. One of the other things that Sarbjeet Johal and John Furrier and I have discussed is that each of the Big 3 is positioning to their respective strengths. AWS is the best IaaS. Microsoft is building out the kind of, quote, we-make-it-easy-for-you cloud, and Google is trying to be the open data cloud with its open-source chops and excellent tech. And that puts added pressure on Snowflake, doesn't it? You know, Thomas Kurian made some comments according to CRN, something to the effect that, we are the only company that can do the data cloud thing across clouds, which again, if I'm being honest is not really accurate. Now I haven't clarified these statements with Google and often things get misquoted, but there's little question that, as AWS has done in the past with Redshift, Google is taking a page out of Snowflake, Databricks as well. A big difference in the Big 3 is that AWS doesn't have this big emphasis on the up-the-stack collaboration software that both Microsoft and Google have, and that for Microsoft and Google will drive captive IaaS consumption. AWS obviously does some of that in database, a lot of that in database, but ISVs that compete with Microsoft and Google should have a greater affinity, one would think, to AWS for competitive reasons. and the same thing could be said in security, we would think because, as I mentioned before, Microsoft competes very directly with CrowdStrike and Okta and others. One of the big thing that Sarbjeet mentioned that I want to call out here, I'd love to have your opinion. AWS specifically, but also Microsoft with Azure have successfully created what Sarbjeet calls brand distance. AWS from the Amazon Retail, and even though AWS all the time talks about Amazon X and Amazon Y is in their product portfolio, but you don't really consider it part of the retail organization 'cause it's not. Azure, same thing, has created its own identity. And it seems that Google still struggles to do that. It's still very highly linked to the sort of core of Google. Now, maybe that's by design, but for enterprise customers, there's still some potential confusion with Google, what's its intentions? How long will they continue to lose money and invest? Are they going to pull the plug like they do on so many other tools? So you know, maybe some rethinking of the marketing there and the positioning. Now we didn't talk much about ecosystem, but it's vital for any cloud player, and Google again has some work to do relative to the leaders. Which brings us to supercloud. The ecosystem and end customers are now in a position this decade to digitally transform. And we're talking here about building out their own clouds, not by putting in and building data centers and installing racks of servers and storage devices, no. Rather to build value on top of the hyperscaler gift that has been presented. And that is a mega trend that we're watching closely in theCUBE community. While there's debate about the supercloud name and so forth, there little question in our minds that the next decade of cloud will not be like the last. All right, we're going to leave it there today. Many thanks to Sarbjeet Johal, and my business partner, John Furrier, for their input to today's episode. Thanks to Alex Myerson who's on production and manages the podcast and Ken Schiffman as well. Kristen Martin and Cheryl Knight helped get the word out on social media and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE, who does some wonderful editing. And check out SiliconANGLE, a lot of coverage on Google Cloud Next and Microsoft Ignite. Remember, all these episodes are available as podcast wherever you listen. Just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. And you can always get in touch with me via email, david.vellante@siliconangle.com or you can DM me at dvellante or comment on my LinkedIn posts. And please do check out etr.ai, the best survey data in the enterprise tech business. This is Dave Vellante for the CUBE Insights, powered by ETR. Thanks for watching and we'll see you next time on Breaking Analysis. (gentle music)

(upbeat music) >> Okay, we're back at AWS re:Invent 2021. You're watching theCUBE. My name is Dave Vellante, and we're here with Jay Theodore who's the CTO of Enterprise and AI at Esri and he's joined by Dave Cardella, who's the Principal Product Manager for Developer Technologies also at Esri. Guys, thanks for coming on. Welcome to theCUBE. >> Thanks, Dave. >> Thanks, Dave. >> Jay, maybe you could give us a little background on Esri. What do you guys do? What are you all about? >> Sure. Esri is an old timer, we are a 50-year old software company. We are the pioneers in GIS and the world leader in GIS - geographic information system. We build geospatial infrastructure that's built for the cloud, built for the edge, built for the field also, you can say. So, we do mapping and analytics. We help our customers solve very complex challenges by bringing location intelligence into the mix. Our customers sort of like run the world, transform the world and we sort of like empower them with the technology we have. So, that's what we do. >> The original edge, and now of course, AWS is coming to you. >> Yeah. (both interviewees chuckling) >> Who are your customers, your main customers? Maybe share that. >> Yeah. We've got over 350,000 customers in... (Dave Cardella chuckling) Yeah. We're all- >> Dave Vellante: Scale. >> Yeah. (Dave Vellante laughing) In the public sector, especially, commercial businesses, non-profit organizations, and that really represents tens of millions of users globally. >> So, let's talk a little bit more about how things are changing. As they say, the edge is coming to you. Maybe AI, you know, 50 years ago... Actually, 50 years ago is probably a lot of talk about AI. When I came into the business, you know, it was a lot of chatter about it. But now, it's real. All this data that we have and the compute power, the cost is coming down. So, AI is in your title? >> Jay: Yes. >> Tell us more about that. >> I think that AI's come to age. When I went to grad school, AI was still in theory because we didn't have the compute and of course we didn't have all the data that was collected, right? Now, there's a lot of observation data coming in through IOT and many senses and so on. So, what do you do with that? Like, human interpretation is pretty challenged, I would say. So, that's where AI comes in, to augment the intelligence that we have in terms of extracting information. So, geospatial AI, specifically which we focus on, is to try to take location that's embedded with this kind of information and sort of like extract knowledge and information out of them, right? Intelligence out of them. So, that's what we focus on: to compliment location intelligence with AI, which we call geospatial AI. >> So, you can observe how things are changing, maybe report on that and that's got to be a huge thing that we can talk about. So, maybe talk about some of the big trends that are driving your business. What are those? >> Yeah, that's a great question. So, I was listening to Sandy Carter's 'Keynote' yesterday and she really emphasized the importance of data. And, data is crucial to what we do as a technology company, and we curate data globally and we get our data from best of breed sources, and that includes commercial data providers, it includes natural mapping agencies, and also a community maps program where we get data from our customers, from our global network of distributors and partners, and we take that data, we curate it, we host it and we deliver it back. And so, just recently for example, we're really excited 'cause we released the 2020 Global Land Cover. And so, Esri is the first company to release this data at 10 meter resolution for the entire planet, and it's made up of well over 400,000 earth observations from various satellites. So, you know data is a... It's not only a nice-to-have anymore, it's actually a must-have. And so, so is location when we talk about data. They go hand in hand. >> 10 meters so I can look at the hole in the roof of my barn... >> Well... (Dave Cardella chuckling) >> Dave Vellante: Pretty much. >> It depends on what you're trying to do, right? So, I think you know, to talk about it, it's within context. GIS is all about context, right? It's bringing location into context in your decision-making process. It's sort of like the where along with the when, what, how and why. That's what GIS brings in. So, a lot of problems are challenging because we need to bring these things together. It's sort of like you're tearing various layers of data that you have and then bringing them within context. Very often, the context that human minds understand and reflected in the physical world is geographic location, right? So, that's what you bring in. And I would say that there's various kinds of data, also. Various types of data, formats of data: structured data, unstructured data, data captured from extraterrestrial, you know, like, you can say, satellite imagery from drones, from IOT. So, it's like on the ground, above the ground, under the ground. All these sensors are bringing in data, right? So, what GIS does is try of map that data to a place on the earth at very high precision, if you're looking at it locally, or at a certain position if it's regionally, trying to find patterns, trying to understand what's emerging, and then, as you take this and infuse geospatial layer into this, you can even predict what is going to happen based on the past. So, that's sort of like... You could say GIS being used for real world problems, like if you take some examples, COVID... The pandemic is one example. Being able to first discover where it happened, where it's spreading, you know, that's the tracking aspect and then how you respond to that and then how you recover, you know, recovering as humans, as businesses and so on. So, we have widespread use of that. The most popular would be the John Hopkins' Dashboard, >> Dave Vellante: Board, yeah. >> that everyone's seen. >> Vellante: We all use it... >> It's gotten trillions of hits and so on, right? That's one example. Another example is addressing racial equity by using location information. Similarly, social justice. Now, these are all problems that we face today, right? So, GIS is extensively used by our customers to solve such problems. And then of course, you have the climate change challenge itself, right? Where you're hovering all kinds of complex data that we can't comprehend because you have to go back decades and try to bring all that together to compute. So, all of this together comes in the form of a geospatial cloud that we have as an offering. >> So, okay. That's amazing. I mean, you're building a super cloud, we call it. You know, and... So, how do you deal with... How do you work with AWS? What's the relationship there? Where do developers fit in? Maybe you can talk about a little bit. >> Yeah. Yeah, that's a great question. So, we've got two main integration points with AWS. A lot of our location services that we expose data and capabilities through are built on AWS. So, we use storage, we use cloud caching and AWS's various data sets across the world quite heavily. So, that's one integration point. The other is a relatively new product that Amazon has released called Amazon Location Service. And so, what it does is it brings location and spatial intelligence directly into a developer's AWS dashboard. So, the experience that they're already used to, they now get the power of Esri services and location intelligence right at their fingertips. >> So, you're .. We started talking about the edge, your data architecture is very distributed, right? But, of course, you're bringing it back. So, how does that all work? You process it locally and then sending some data back? Are you sending all data back? What does that flow look like? >> I think the key thing is that our customers work with data of all kinds, all formats, all sizes and some are in real-time, some are big data and archive, right? So, most recently, just to illustrate that point, this year, we released RGS Enterprise on Kubernetes. It's the entire geospatial cloud made available for enterprise customers, and that's made available on AWS, on EKS. Now, when it's available on EKS, that means all these capabilities are microservices, so, they can be massively scaled. They're DevOps friendly and you've got the full mapping and analytics system that's made available for this. >> Dave Vellante: Oh. >> And we sort of like built it, you know, cloud native from the ground up and the more important thing that we have now is connectivity with Redshift. Why is that important? Because a lot of our customers have geospatial data in these cloud data warehouses. Redshift is very important for them. And so, you can connect to that, you can discover these massive petabytes of data sets and then you can set up what we call the query layer. It's basically pushing analytics into Redshift and being able to bring out that data for mapping, visualization, for AI workflows and so on. It's pretty amazing and it's pretty exciting at this time. >> And, I mean... So much data. And then... What, do you tear it down into glacier of just to save some cost, or is it going to all stay in S3 or is it... >> So, we already work with S3, we've worked with RDS, we support Amazon Aurora, our customers are very happy with that. So, Redshift is a new offering for us to connect to Redshift. >> Dave Vellante: AOK. >> So, the way the query layer works is all of your observation data is in Redshift, your other kinds of data... Your authoritative data sets could come from various other sources including in Amazon Aurora, for example, okay? And then, you overlay them and use them. Now, the data in Redshift is usually massive, so, when you run the analytic query, we let you cache that as a materialized view or as a snapshot that you can refresh and you can work against that. This is really good because it compliments our ability to actually take that data, to put it on a map image which we render service side, it's got very complex cartographic ready symbology and rendering and everything in there. And you get these beautiful rendering of maps that comes out of Redshift data. >> And you're pushing AI throughout your stack, is that, you know? >> Yeah. AI is just like infused, right? I mean, it's... I would say, human intelligence augmented for data scientists, for everyone, you know. Whether you're using it through notebooks or whether you're using it through applications that we have or the developer APIs themselves. >> So, what are some of the big initiatives you're working on near-term, mid-term? >> Yeah. So, you mentioned what's really driving innovation and it's related to the question that you just asked right now and I really believe developers drive innovation. They're force multipliers in the solutions that they build. And so, that's really the integration point that Esri has with AWS, it's developers. And earlier this year, we released the RGS platform which is our platform as a service offering that exposes these powerful location services that Jay just explained. There's a set of on-demand services that developers can bring in their applications as they want and they can bring in one, they can bring in two or three, whatever they need, but they're there when they need them. And also, developers have their client API of choice. So, we have our own client APIs that we offer but you're not pigeonholed into that when you're working with RGS platform. A developer can bring their own API. >> Okay, so he called the platform as a service. Are you making your data available as well? Your data, your tooling and then selling that as a service? >> Our data has always been available as a service, I would say. >> Okay, yeah. >> Everything that we do, our GIS tools, are accessible as a web service. >> Vellante: Is that new, or... that's always been the way? >> No, that's always been there. That's always been that way. The difference now is everything is built from the ground up to be cloud native. >> Dave Vellante: Okay. >> From the ground up to be connected to every data set that's available on AWS, every compute that can be exploited from small to massive in terms of compute, and also reaching out to bring all the apps and the developer experiences, pushing out to customers. >> So, 50 years ago, you weren't obviously using the cloud, but so, you were running everything on-prem now you're all in the cloud, or you're kind of got a mix? What is the clearer picture of that? >> So, we have two major offerings. There's RGS Online, where obviously it's offered as a service and it's GIS as a service provider for everyone. And that's available everywhere. The other offering we have is actually RGS Enterprise where some customers run them on premises, some run it in the cloud, especially AWS. Many run it on the edge, some in the field and there's connectivity between this. A lot of our customers are hybrid. So, they make the best of both. Depending on the kinds of data- >> Dave Vellante: You give them a choice. >> the kinds of workflows... Giving them the choice, exactly. And I would say, you know, taking Werner's 'Keynote' this morning, he talked about what's the next frontier, right? The next frontier could very well be when AWS gets to space and makes compute available there. It's sitting alongside the data that's captured and we've always, like I said, for 50 years, worked with satellite imagery, >> Dave Vellante: Yeah. >> or worked with IOT, or worked with drone data. It's just getting GIS closer to where the data is. >> So, the ultimate edge space. >> Yes. >> All right, I'll give you guys... Give us a quick wrap if you would. Final thoughts. >> I think its... Go ahead. >> Go, ahead Dave. >> Yeah. I really resonate with data and content. We're a technology company- there's no doubt about that- but without good data, not only supplied by ourselves, but our customers, Jay mentioned it earlier, our customers bring their own data to our platform and that's really what drives the analytics and the accuracy in the answers to the problems that people are trying to solve. >> Bring their first-party data with your data and then one plus one is... >> Yes. Yeah, and the key thing about that (Cardella chuckling) is not some of the data, it's all of the data that you have. You don't more need to be constrained. >> Yeah, you're not sampling. >> Yes, exactly. >> Yeah. >> All right, guys. Thanks so much. Really interesting story. Congratulations. >> Thank you, Dave. >> Dave, thank you. >> Nice meeting you. >> Thank you for watching. This is Dave Vellante for theCUBE, the leader in global tech coverage. We'll be right back. (upbeat music)

>>Mhm Hello and welcome to the cubes coverage of AWS amazon web services, Global public Sector partner awards program. I'm john for your host of the cube here we're gonna talk about the best covid solution to great guests. Benham or with healthcare and life sciences lead at palantir Ben welcome to the cube SAm Michaels, Director of automation and compound management and Cats. National Center for advancing translational sciences and Cats. Part of the NIH National sort of health Gentlemen, thank you for coming on and and congratulations on the best covid solution. >>Thank you so much john >>so I gotta, I gotta ask you the best solution is when can I get the vaccine? How fast how long it's gonna last but I really appreciate you guys coming on. I >>hope you're vaccinated. I would say john that's outside of our hands. I would say if you've not got vaccinated, go get vaccinated right now, have someone stab you in the arm, you know, do not wait and and go for it. That's not on us. But you got that >>opportunity that we have that done. I got to get on a plane and all kinds of hoops to jump through. We need a better solution anyway. You guys have a great technical so I wanna I wanna dig in all seriousness aside getting inside. Um you guys have put together a killer solution that really requires a lot of data can let's step back and and talk about first. What was the solution that won the award? You guys have a quick second set the table for what we're talking about. Then we'll start with you. >>So the national covered cohort collaborative is a secure data enclave putting together the HR records from more than 60 different academic medical centers across the country and they're making it available to researchers to, you know, ask many and varied questions to try and understand this disease better. >>See and take us through the challenges here. What was going on? What was the hard problem? I'll see everyone had a situation with Covid where people broke through and cloud as he drove it amazon is part of the awards, but you guys are solving something. What was the problem statement that you guys are going after? What happened? >>I I think the problem statement is essentially that, you know, the nation has the electronic health records, but it's very fragmented, right. You know, it's been is highlighted is there's there's multiple systems around the country, you know, thousands of folks that have E H. R. S. But there is no way from a research perspective to actually have access in any unified location. And so really what we were looking for is how can we essentially provide a centralized location to study electronic health records. But in a Federated sense because we recognize that the data exist in other locations and so we had to figure out for a vast quantity of data, how can we get data from those 60 sites, 60 plus that Ben is referencing from their respective locations and then into one central repository, but also in a common format. Because that's another huge aspect of the technical challenge was there's multiple formats for electronic health records, there's different standards, there's different versions. And how do you actually have all of this data harmonised into something which is usable again for research? >>Just so many things that are jumping in my head right now, I want to unpack one at the time Covid hit the scramble and the imperative for getting answers quickly was huge. So it's a data problem at a massive scale public health impact. Again, we were talking before we came on camera, public health records are dirty, they're not clean. A lot of things are weird. I mean, just just massive amount of weird problems. How did you guys pull together take me through how this gets done? What what happened? Take us through the the steps He just got together and said, let's do this. How does it all happen? >>Yeah, it's a great and so john, I would say so. Part of this started actually several years ago. I explain this when people talk about in three C is that and Cats has actually established what we like to call, We support a program which is called the Clinical translation Science Award program is the largest single grant program in all of NIH. And it constitutes the bulk of the Cats budget. So this is extra metal grants which goes all over the country. And we wanted this group to essentially have a common research environment. So we try to create what we call the secure scientific collaborative platforms. Another example of this is when we call the rare disease clinical research network, which again is a consortium of 20 different sites around the nation. And so really we started working this several years ago that if we want to Build an environment that's collaborative for researchers around the country around the world, the natural place to do that is really with a cloud first strategy and we recognize this as and cats were about 600 people now. But if you look at the size of our actual research community with our grantees were in the thousands. And so from the perspective that we took several years ago was we have to really take a step back. And if we want to have a comprehensive and cohesive package or solution to treat this is really a mid sized business, you know, and so that means we have to treat this as a cloud based enterprise. And so in cats several years ago had really gone on this strategy to bring in different commercial partners, of which one of them is Palin tear. It actually started with our intramural research program and obviously very heavy cloud use with AWS. We use your we use google workspace, essentially use different cloud tools to enable our collaborative researchers. The next step is we also had a project. If we want to have an environment, we have to have access. And this is something that we took early steps on years prior that there is no good building environment if people can't get in the front door. So we invested heavily and create an application which we call our Federated authentication system. We call it unified and cats off. So we call it, you know, for short and and this is the open source in house project that we built it and cats. And we wanted to actually use this for all sorts of implementation, acting as the front door to this collaborative environment being one of them. And then also by by really this this this interest in electronic health records that had existed prior to the Covid pandemic. And so we've done some prior work via mixture of internal investments in grants with collaborative partners to really look at what it would take to harmonize this data at scale. And so like you mentioned, Covid hit it. Hit really hard. Everyone was scrambling for answers. And I think we had a bit of these pieces um, in play. And then that's I think when we turned to ban and the team at volunteer and we said we have these components, we have these pieces what we really need. Something independent that we can stand up quickly to really address some of these problems. One of the biggest one being that data ingestion and the harmonization step. And so I can let Ben really speak to that one. >>Yeah. Ben Library because you're solving a lot of collaboration problems, not just the technical problem but ingestion and harmonization ingestion. Most people can understand is that the data warehousing or in the database know that what that means? Take us through harmonization because not to put a little bit of shade on this, but most people think about, you know, these kinds of research or non profits as a slow moving, you know, standing stuff up sandwich saying it takes time you break it down. By the time you you didn't think things are over. This was agile. So take us through what made it an agile because that's not normal. I mean that's not what you see normally. It's like, hey we'll see you next year. We stand that up. Yeah. At the data center. >>Yeah, I mean so as as Sam described this sort of the question of data on interoperability is a really essential problem for working with this kind of data. And I think, you know, we have data coming from more than 60 different sites and one of the reasons were able to move quickly was because rather than saying oh well you have to provide the data in a certain format, a certain standard. Um and three C. was able to say actually just give us the data how you have it in whatever format is easiest for you and we will take care of that process of actually transforming it into a single standard data model, converting all of the medical vocabularies, doing all of the data quality assessment that's needed to ensure that data is actually ready for research and that was very much a collaborative endeavor. It was run out of a team based at johns Hopkins University, but in collaboration with a broad range of researchers who are all adding their expertise and what we were able to do was to provide the sort of the technical infrastructure for taking the transformation pipelines that are being developed, that the actual logic and the code and developing these very robust kind of centralist templates for that. Um, that could be deployed just like software is deployed, have changed management, have upgrades and downgrades and version control and change logs so that we can roll that out across a large number of sites in a very robust way very quickly. So that's sort of that, that that's one aspect of it. And then there was a bunch of really interesting challenges along the way that again, a very broad collaborative team of researchers worked on and an example of that would be unit harmonization and inference. So really simple things like when a lab result arrives, we talked about data quality, um, you were expected to have a unit right? Like if you're reporting somebody's weight, you probably want to know if it's in kilograms or pounds, but we found that a very significant proportion of the time the unit was actually missing in the HR record. And so unless you can actually get that back, that becomes useless. And so an approach was developed because we had data across 60 or more different sites, you have a large number of lab tests that do have the correct units and you can look at the data distributions and decide how likely is it that this missing unit is actually kilograms or pounds and save a huge portion of these labs. So that's just an example of something that has enabled research to happen that would not otherwise have been able >>just not to dig in and rat hole on that one point. But what time saving do you think that saves? I mean, I can imagine it's on the data cleaning side. That's just a massive time savings just in for Okay. Based on the data sampling, this is kilograms or pounds. >>Exactly. So we're talking there's more than 3.5 billion lab records in this data base now. So if you were trying to do this manually, I mean, it would take, it would take to thousands of years, you know, it just wouldn't be a black, it would >>be a black hole in the dataset, essentially because there's no way it would get done. Ok. Ok. Sam take me through like from a research standpoint, this normalization, harmonization the process. What does that enable for the, for the research and who decides what's the standard format? So, because again, I'm just in my mind thinking how hard this is. And then what was the, what was decided? Was it just on the base records what standards were happening? What's the impact of researchers >>now? It's a great quite well, a couple things I'll say. And Ben has touched on this is the other real core piece of N three C is the community, right? You know, And so I think there's a couple of things you mentioned with this, johN is the way we execute this is, it was very nimble, it was very agile and there's something to be said on that piece from a procurement perspective, the government had many covid authorities that were granted to make very fast decisions to get things procured quickly. And we were able to turn this around with our acquisition shop, which we would otherwise, you know, be dead in the water like you said, wait a year ago through a normal acquisition process, which can take time, but that's only one half the other half. And really, you're touching on this and Ben is touching on this is when he mentions the research as we have this entire courts entire, you know, research community numbering in the thousands from a volunteer perspective. I think it's really fascinating. This is a really a great example to me of this public private partnership between the companies we use, but also the academic participants that are actually make up the community. Um again, who the amount of time they have dedicated on this is just incredible. So, so really, what's also been established with this is core governance. And so, you know, you think from assistance perspective is, you know, the Palin tear this environment, the N three C environment belongs to the government, but the N 33 the entire actually, you know, program, I would say, belongs to the community. We have co governance on this. So who decides really is just a mixture between the folks on End Cats, but not just end cast as folks at End Cats, folks that, you know, and I proper, but also folks and other government agencies, but also the, the academic communities and entire these mixed governance teams that actually set the stage for all of this. And again, you know, who's gonna decide the standard, We decide we're gonna do this in Oman 5.3 point one um is the standard we're going to utilize. And then once the data is there, this is what gets exciting is then they have the different domain teams where they can ask different research questions depending upon what has interest scientifically to them. Um and so really, you know, we viewed this from the government's perspective is how do we build again the secure platform where we can enable the research, but we don't really want to dictate the research. I mean, the one criteria we did put your research has to be covid focused because very clearly in response to covid, so you have to have a Covid focus and then we have data use agreements, data use request. You know, we have entire governance committees that decide is this research in scope, but we don't want to dictate the research types that the domain teams are bringing to the table. >>And I think the National Institutes of Health, you think about just that their mission is to serve the public health. And I think this is a great example of when you enable data to be surfaced and available that you can really allow people to be empowered and not to use the cliche citizen analysts. But in a way this is what the community is doing. You're doing research and allowing people from volunteers to academics to students to just be part of it. That is citizen analysis that you got citizen journalism. You've got citizen and uh, research, you've got a lot of democratization happening here. Is that part of it was a result of >>this? Uh, it's both. It's a great question. I think it's both. And it's it's really by design because again, we want to enable and there's a couple of things that I really, you know, we we clamor with at end cats. I think NIH is going with this direction to is we believe firmly in open science, we believe firmly in open standards and how we can actually enable these standards to promote this open science because it's actually nontrivial. We've had, you know, the citizen scientists actually on the tricky problem from a governance perspective or we have the case where we actually had to have students that wanted access to the environment. Well, we actually had to have someone because, you know, they have to have an institution that they come in with, but we've actually across some of those bridges to actually get students and researchers into this environment very much by design, but also the spirit which was held enabled by the community, which, again, so I think they go they go hand in hand. I planned for >>open science as a huge wave, I'm a big fan, I think that's got a lot of headroom because open source, what that's done to software, the software industry, it's amazing. And I think your Federated idea comes in here and Ben if you guys can just talk through the Federated, because I think that might enable and remove some of the structural blockers that might be out there in terms of, oh, you gotta be affiliate with this or that our friends got to invite you, but then you got privacy access and this Federated ID not an easy thing, it's easy to say. But how do you tie that together? Because you want to enable frictionless ability to come in and contribute same time you want to have some policies around who's in and who's not. >>Yes, totally, I mean so Sam sort of already described the the UNa system which is the authentication system that encounters has developed. And obviously you know from our perspective, you know we integrate with that is using all of the standard kind of authentication protocols and it's very easy to integrate that into the family platform um and make it so that we can authenticate people correctly. But then if you go beyond authentication you also then to actually you need to have the access controls in place to say yes I know who this person is, but now what should they actually be able to see? Um And I think one of the really great things in Free C has done is to be very rigorous about that. They have their governance rules that says you should be using the data for a certain purpose. You must go through a procedure so that the access committee approves that purpose. And then we need to make sure that you're actually doing the work that you said you were going to. And so before you can get your data back out of the system where your results out, you actually have to prove that those results are in line with the original stated purpose and the infrastructure around that and having the access controls and the governance processes, all working together in a seamless way so that it doesn't, as you say, increase the friction on the researcher and they can get access to the data for that appropriate purpose. That was a big component of what we've been building out with them three C. Absolutely. >>And really in line john with what NIH is doing with the research, all service, they call this raz. And I think things that we believe in their standards that were starting to follow and work with them closely. Multifactor authentication because of the point Ben is making and you raised as well, you know, one you need to authenticate, okay. This you are who you say you are. And and we're recognizing that and you're, you know, the author and peace within the authors. E what do you authorized to see? What do you have authorization to? And they go hand in hand and again, non trivial problems. And especially, you know, when we basis typically a lot of what we're using is is we'll do direct integrations with our package. We using commons for Federated access were also even using login dot gov. Um, you know, again because we need to make sure that people had a means, you know, and login dot gov is essentially a runoff right? If they don't have, you know an organization which we have in common or a Federated access to generate a login dot gov account but they still are whole, you know beholden to the multi factor authentication step and then they still have to get the same authorizations because we really do believe access to these environment seamlessly is absolutely critical, you know, who are users are but again not make it restrictive and not make it this this friction filled process. That's very that's very >>different. I mean you think about nontrivial, totally agree with you and if you think about like if you were in a classic enterprise, I thought about an I. T. Problem like bring your own device to work and that's basically what the whole world does these days. So like you're thinking about access, you don't know who's coming in, you don't know where they're coming in from, um when the churn is so high, you don't know, I mean all this is happening, right? So you have to be prepared two Provisions and provide resource to a very lightweight access edge. >>That's right. And that's why it gets back to what we mentioned is we were taking a step back and thinking about this problem, you know, an M three C became the use case was this is an enterprise I. T. Problem. Right. You know, we have users from around the world that want to access this environment and again we try to hit a really difficult mark, which is secure but collaborative, Right? That's that's not easy, you know? But but again, the only place this environment could take place isn't a cloud based environment, right? Let's be real. You know, 10 years ago. Forget it. You know, Again, maybe it would have been difficult, but now it's just incredible how much they advanced that these real virtual research organizations can start to exist and they become the real partnerships. >>Well, I want to Well, that's a great point. I want to highlight and call out because I've done a lot of these interviews with awards programs over the years and certainly in public sector and open source over many, many years. One of the things open source allows us the code re use and also when you start getting in these situations where, okay, you have a crisis covid other things happen, nonprofits go, that's the same thing. They, they lose their funding and all the code disappears. Saying with these covid when it becomes over, you don't want to lose the momentum. So this whole idea of re use this platform is aged deplatforming of and re factoring if you will, these are two concepts with a cloud enables SAM, I'd love to get your thoughts on this because it doesn't go away when Covid's >>over, research still >>continues. So this whole idea of re platform NG and then re factoring is very much a new concept versus the old days of okay, projects over, move on to the next one. >>No, you're absolutely right. And I think what first drove us is we're taking a step back and and cats, you know, how do we ensure that sustainability? Right, Because my background is actually engineering. So I think about, you know, you want to build things to last and what you just described, johN is that, you know, that, that funding, it peaks, it goes up and then it wanes away and it goes and what you're left with essentially is nothing, you know, it's okay you did this investment in a body of work and it goes away. And really, I think what we're really building are these sustainable platforms that we will actually grow and evolve based upon the research needs over time. And I think that was really a huge investment that both, you know, again and and Cats is made. But NIH is going in a very similar direction. There's a substantial investment, um, you know, made in these, these these these really impressive environments. How do we make sure the sustainable for the long term? You know, again, we just went through this with Covid, but what's gonna come next? You know, one of the research questions that we need to answer, but also open source is an incredibly important piece of this. I think Ben can speak this in a second, all the harmonization work, all that effort, you know, essentially this massive, complex GTL process Is in the N three Seagate hub. So we believe, you know, completely and the open source model a little bit of a flavor on it too though, because, you know, again, back to the sustainability, john, I believe, you know, there's a room for this, this marriage between commercial platforms and open source software and we need both. You know, as we're strong proponents of N cats are both, but especially with sustainability, especially I think Enterprise I. T. You know, you have to have professional grade products that was part of, I would say an experiment we ran out and cast our thought was we can fund academic groups and we can have them do open source projects and you'll get some decent results. But I think the nature of it and the nature of these environments become so complex. The experiment we're taking is we're going to provide commercial grade tools For the academic community and the researchers and let them use them and see how they can be enabled and actually focus on research questions. And I think, you know, N3C, which we've been very successful with that model while still really adhering to the open source spirit and >>principles as an amazing story, congratulated, you know what? That's so awesome because that's the future. And I think you're onto something huge. Great point, Ben, you want to chime in on this whole sustainability because the public private partnership idea is the now the new model innovation formula is about open and collaborative. What's your thoughts? >>Absolutely. And I mean, we uh, volunteer have been huge proponents of reproducibility and openness, um in analyses and in science. And so everything done within the family platform is done in open source languages like python and R. And sequel, um and is exposed via open A. P. I. S and through get repository. So that as SaM says, we've we've pushed all of that E. T. L. Code that was developed within the platform out to the cats get hub. Um and the analysis code itself being written in those various different languages can also sort of easily be pulled out um and made available for other researchers in the future. And I think what we've also seen is that within the data enclave there's been an enormous amount of re use across the different research projects. And so actually having that security in place and making it secure so that people can actually start to share with each other securely as well. And and and be very clear that although I'm sharing this, it's still within the range of the government's requirements has meant that the, the research has really been accelerated because people have been able to build and stand on the shoulders of what earlier projects have done. >>Okay. Ben. Great stuff. 1000 researchers. Open source code and get a job. Where do I sign up? I want to get involved. This is amazing. Like it sounds like a great party. >>We'll send you a link if you do a search on on N three C, you know, do do a search on that and you'll actually will come up with a website hosted by the academic side and I'll show you all the information of how you can actually connect and john you're welcome to come in. Billion by all means >>billions of rows of data being solved. Great tech he's working on again. This is a great example of large scale the modern era of solving problems is here. It's out in the open, Open Science. Sam. Congratulations on your great success. Ben Award winners. You guys doing a great job. Great story. Thanks for sharing here with us in the queue. Appreciate it. >>Thank you, john. >>Thanks for having us. >>Okay. It is. Global public sector partner rewards best Covid solution palantir and and cats. Great solution. Great story. I'm john Kerry with the cube. Thanks for watching. Mm mm. >>Mhm

>> Narrator: From theCUBE studios in Palo Alto in Boston connecting with our leaders all around the world. This is theCUBE conversation. >> Welcome to theCUBE and the special IBM Brocade panel. I'm Lisa Martin. And I'm having a great opportunity here to sit down for the next 20 minutes with three gentlemen please welcome Brian Sherman a distinguished engineer from IBM, Brian, great to have you joining us. >> Thanks for having me. >> And Matt key here. Flash systems SME from IBM, Matt, happy Friday. >> Happy Friday, Lisa. Thanks for having us. >> Our pleasure. And AIG Customer solution here from Brocade is here. AJ welcome. >> Thanks for having me along. >> AJ we're going to stick with you, IBM and Brocade have had a very long you said about 22 year strategic partnership. There's some new news. And in terms of the evolution of that talk to us about what's going on with with Brocade IBM and what is new in the storage industry? >> Yeah, so the the newest thing for us at the moment is that IBM just in mid-October launched our Gen seven platforms. So this is think about the stresses that are going on in the IT environments. This is our attempt to keep pace with with the performance levels that the IBM teams are now putting into their storage environments the All-Flash Data Centers and the new technologies around non-volatile memory express. So that's really, what's driving this along with the desire to say, "You know what people aren't allowed "to be in the data center." And so if they can't be in the data center then the fabrics actually have to be able to figure out what's going on and basically provide a lot of the automation pieces. So something we're referring to as the autonomous SAM. >> And we're going to dig into NBME of our fabrics in a second but I do want to AJ continue with you in terms of industries, financial services, healthcare airlines there's the biggest users, biggest need. >> Pretty much across the board. So if you look at the global 2000 as an example, something on the order of about 96, 97% of the global 2000 make use of fiber channel environments and in portions of their world generally tends to be a lot of the high end financial guys, a lot of the pharmaceutical guys, the automotive, the telcos, pretty much if the data matters, and it's something that's critical whether we talk about payment card information or healthcare environments, data that absolutely has to be retained, has to get there, has to perform then it's this combination that we're bringing together today around the new storage elements and the functionalities they have there. And then our ability in the fabric. So the concept of a 64 gig environment to help basically not be the bottleneck in the application demands, 'cause one thing I can promise you after 40 years in this industry is the software guys always figure out how to all the performance that the hardware guys put on the shelf, right? Every single time. >> Well there's gauntlet thrown down there. Matt, let's go to you. I want to get IBM's perspective on this. Again, as we said, a 22 year strategic partnership, as we look at things like not being able to get into the data center during these unprecedented times and also the need to be able to remove some of those bottlenecks how does IBM view this? >> Yeah, totally. It's certainly a case of raising the bar, right? So we have to as a vendor continue to evolve in terms of performance, in terms of capacity, cost density, escalating simplicity, because it's not just a case of not be able to touch the rates, but there's fewer people not being able to adjust the rates, right? It's a case where our operational density continues to have to evolve being able to raise the bar on the network and be able to still saturate those line rates and be able to provide that simply a cost efficiency that gets us to a utilization that raises the bar from our per capita ratio from not just talking about 200, 300 terabytes per admin but going beyond the petabyte scale per admin. And we can't do that unless people have access to the data. And we have to provide the resiliency. We have to provide the simplicity of presentation and automation from our side. And then this collaboration that we do with our network brother like Brocade here continued to stay out of the discussion when it comes to talking about networks and who threw the ball next. So we truly appreciate this Gen seven launch that they're doing we're happy to come in and fill that pipe on the flash side for them. >> Excellent and Brian as a distinguished engineer and let me get your perspectives on the evolution of the technology over this 22 year partnership. >> Thanks Lisa. It certainly has been a longstanding, a great relationship, great partnership all the way from inventing joint things, to developing, to testing and deploying to different technologies through the course of time. And it's been one of those that where we are today, like AJ had talked about being able to sustain what the applications require today in this always on time type of environment. And as Matt said, bringing together the density and operational simplicity to make that happen 'cause we have to make it easier from the storage side for operations to be able to manage this volume of data that we have coming out and our due diligence is to be able to serve the data up as fast as we can and as resilient as we can. >> And so sticking with you, Brian that simplicity is key because as we know as we get more and more advances in technology the IT environment is only becoming more complex. So really truly enabling organizations in any industry to simplify is absolute table stakes. >> Yeah, it definitely is. And that's core to what we're focused on and how do we make the storage environment simple. It's been one those through the years and historically, we've had entry-level us and the industry as a whole, is that an entry-level product mid range level products, high-end level products. And earlier this year, we said enough, enough of that it's one product portfolio. So it's the same software stack it's just, okay. Small, medium and large in terms of the appliances that get delivered. Again, building on what Matt said, from a density perspective where we can have a petabyte of uncompressed and data reduced storage in a two Enclosure. So it becomes from a overall administration perspective, again, one software stake, one automation stack, one way to do point in time copies, replication. So in focusing on how to make that as simple for the operations as we possibly can. >> I think we'd all take a little bit of that right now. Matt, let's go to you and then AJ view, let's talk a little bit more, dig into the IBM storage arrays. I mean, we're talking about advances in flash, we're talking about NBME as a forcing function for applications to change and evolve with the storage. Matt, give us your thoughts on that. >> We saw a monumental leap in where we take some simplicity pieces from how we deliver our arrays but also the technology within the arrays. About nine months ago, in February we launched into the latest generation of non technology and with that born the story of simplicity one of the pieces that we've been happily essentially negating of value prop is storage level tiering and be able to say, "Hey, well we still support the idea of going down "to near line SaaS and enterprise disc in different flavors "of solid state whether it's tier one short usage "the tier zero high performance, high usage, "all the way up to storage class memory." While we support those technologies and the automated tiering, this elegance of what we've done as latest generation technology that we launched nine months ago has been able to essentially homogenize the environments to we're able to deliver that petabyte per rack unit ratio that Brian was mentioning be able to deliver over into all tier zero solution that doesn't have to go through woes of software managed data reduction or any kind of software managed hearing just to be always fast, always essentially available from a 100% data availability guaranteed that we offer through a technology called hyper swap, but it's really kind of highlighting what we take in from that simplicity story, by going into that extra mile and meeting the market in technology refresh. I mean, if you say the words IBM over the Thanksgiving table, you're kind of thinking, how big blue, big mainframe, old iron stuff but it's very happy to say over in distributed systems that we are in fact leading this pack by multiple months not just the fact that, "Hey, we announced sooner." But actually coming to delivering on-prem the actual solution itself nine, 10 months prior to anybody else and when that gets us into new density flavors gets us into new efficiency offerings. Not just talk about, "Hey, I can do this petabyte scale "a couple of rack units but with the likes of Brocade." That actually equates to a terabyte per second and a floor tile, what's that do for your analytics story? And the fact that we're now leveraging NBME to undercut the value prop of spinning disc in your HBC analytics environments by five X, that's huge. So now let's take near line SaaS off the table for anything that's actually per data of an angle of value to us. So in simplicity elements, what we're doing now will be able to make our own flash we've been deriving from the tech memory systems acquisition eight years ago and then integrating that into some essentially industry proven software solutions that we do with the bird flies. That appliance form factor has been absolutely monumental for us in the distributed systems. >> And thanks for giving us a topic to discuss at our socially distant Thanksgiving table. We'll talk about IBM. I know now I have great, great conversation. AJ over to you lot of advances here also in such a dynamic times, I want to get Brocade's perspective on how you're taking advantage of these latest technologies with IBM and also from a customer's perspective, what are they feeling and really being able to embrace and utilize that simplicity that Matt talked about. >> So there's a couple of things that fall into that to be honest, one of which is that similar to what you heard Brian described across the IBM portfolio for storage in our SaaS infrastructure. It's a single operating system up and down the line. So from the most entry-level platform we have to the largest platform we have it's a single software up and down. It's a single management environment up and down and it's also intended to be extremely reliable and extremely performance because here's part of the challenge when Matt's talking about multiple petabytes in a two U rack height, but the conversation you want to flip on its head there a little bit is "Okay exactly how many virtual machines "and how many applications are you going to be driving "out of that?" Because it's going to be thousands like between six and 10,000 potentially out of that, right? So imagine then if you have some sort of little hiccup in the connectivity to the data store for 6,000 to 10,000 applications, that's not the kind of thing that people get forgiving about. When we're all home like this. When your healthcare, when your finance, when your entertainment, when everything is coming to you across the network and remotely in this version and it's all application driven, the one thing that you want to make sure of is that network doesn't hiccup because humans have a lot of really good characteristics. Patience would not be one of those. And so you want to make sure that everything is in fact in play and running. And that's as one of the things that we work very hard with our friends at IBM to make sure of is that the kinds of analytics that Matt was just describing are things that you can readily get done. Speed is the new currency of business is a phrase you hear from... A quote you hear from Marc Benioff at Salesforce, right. And he's right if you can get data out of intelligence out of the data you've been collecting, that's really cool. But one of the other sort of flip sides on the people not being able to be in the data center and then to Matt's point, not as many people around either is how are humans fast enough when you look... Honestly when you look at the performance of the platforms, these folks are putting up how is human response time going to be good enough? And we all sort of have this headset of a network operations center where you've got a couple dozen people in a half lit room staring at massive screens on the thing to pop. Okay, if the first time a red light pops the human begins the investigation at what point is that going to be good enough? And so our argument for the autonomy piece of of what we're doing in the fabrics is you can't wait on the humans. You need to augment it. I get that people still want to be in charge and that's good. Humans are still smarter than the Silicon. We're not as repeatable, but we're still so far smarter about it. And so we needed to be able to do that measurement. We need to be able to figure out what normal looks like. We need to be able to highlight to the storage platform and to the application admins, when things go sideways because the demand from the applications isn't going to slow down. The demands from your environment whether you want to think about take the next steps with not just your home entertainment home entertainment systems but learning augmented reality, right. Virtual reality environments for kids, right? How do you make them feel like they're part and parcel of the classroom, for as long as we have to continue living a modified world and perhaps past it, right? If you can take a grade school from your local area and give them a virtual walkthrough of the loop where everybody's got a perfect view and it all looks incredibly real to them those are cool things, right? Those are cool applications, right? If you can figure out a new vaccine faster, right. Not a bad thing, right. If we can model better, not a bad thing. So we need to enable those things we need to not be the bottleneck, which is you get Matt and Brian over an adult beverage at some point and ask them about the cycle time for the Silicon they're playing with. We've never had Moore's law applied to external storage before never in the history of external storage. Has that been true until now. And so their cycle times, Matt, right? >> Yeah you struck a nerve there AJ, cause it's pretty simple for us to follow the linear increase in capacity and computational horsepower, right. We just ride the X86 bandwagon, ride the Silicon bandwagon. But what we have to do in order to maintain But what we have to do in order to maintain the simplicity story is followed more important one is the resiliency factor, right? 'Cause as we increased the capacity as we increased the essentially the amount of data responsible for each admin we have to literally log rhythmically increase the resiliency of these boxes because we're going to talk about petabyte scale systems and hosting them really 10,000 virtual machines in the two U form factor. I need to be able to accommodate that to make sure things don't blip. I need resilient networks, right. Have redundancy and access. I need to have protection schemes at every single layer of the stack. And so we're quite happy to be able to provide that as we leapfrog the industry and go in literally situations that are three times the competitive density that we you see out there and other distributed systems that are still bound by the commercial offerings, then, hey we also have to own that risk from a vendor side we have to make these things is actually rate six protection scheme equivalent from a drive standpoint and act back from controllers everywhere. Be able to supply the performance and consistency of that service throughout even the bad situations. >> And to that point, one of the things that you talked about, that's interesting to me that I'd kind of like you to highlight is your recovery times, because bad things will happen. And so you guys do something very, very different about that. That's critical to a lot of my customers because they know that Murphy will show up one day. So, I mean 'cause it happens, so then what. >> Well, speaking of that, then what Brian I want to go over to you. You mentioned Matt mentioned resiliency. And if we think of the situation that we're in in 2020 many companies are used to DR and BC plans for natural disasters, pandemics. So as we look at the shift and then the the volume of ransomware, that's going up one ransomware attack every 11 seconds this year, right now. How Brian what's that change that businesses need to make from from cyber security to cyber resiliency? >> Yeah, it's a good point in, and I try to hammer that home with our clients that, you're used to having your business continuity disaster recovery this whole cyber resiliency thing is a completely separate practice that we have to set up and think about and go through the same thought process that you did for your DR What are you going to do? What are you going to pretest? How are you going to test it? How are you going to detect whether or not you've got ransomware? So I spent a lot of time with our clients on that theme of you have to think about and build your cyber resiliency plan 'cause it's going to happen. It's not like a DR plan where it's a pure insurance policy and went and like you said, every 11 seconds there's an event that takes place. It's going to be a win not then. Yeah and then we have to work with our customers to put in a place for cyber resiliency and then we spent a lot of discussion on, okay what does that mean for my critical applications, from a restore time of backup and mutability. What do we need for those types of services, right? In terms of quick restore, which are my tier zero applications that I need to get back as fast as possible, what other ones can I they'll stick out on tape or virtual tape in and do things like that. So again, there's a wide range of technology that we have available in the in the portfolio for helping our clients from cyber resiliency. And then we try to distinguish that cyber resiliency versus cyber security. So how do we help to keep every, everybody out from a cybersecurity view? And then what can we do from the cyber resiliency, from a storage perspective to help them once once it gets to us, that's a bad thing. So how can we help? How help our folks recover? Well, and that's the point that you're making Brian is that now it's not a matter of, could this happen to us? It's going to, how much can we tolerate? But ultimately we have to be able to recover. We can't restore that data and one of those things when you talk about ransomware and things, we go to that people as the weakest link insecurity AJ talked about that, there's the people. Yeah there's probably quite a bit of lack of patients going on right now. But as we look as I want to go back over to you to kind of look at, from a data center perspective and these storage solutions, being able to utilize things to help the people, AI and Machine Learning. You talked about AR VR. Talk to me a little bit more about that as you see, say in the next 12 months or so as moving forward, these trends these new solutions that are simplified. >> Yeah, so a couple of things around that one of which is iteration of technology the storage platforms the Silicon they're making use of Matt I think you told me 14 months is the roughly the Silicon cycle that you guys are seeing, right? So performance levels are going to continue to go up the speeds. The speeds are going to continue to go up. The scale is going to is going to continue to shift. And one of the things that does for a lot of the application owners is it lets them think broader. It lets them think bigger. And I wish I could tell you that I knew what the next big application was going to be but then we'd be having a conversation about which Island in the Pacific I was going to be retiring too. But they're going to come and they're going to consume this performance because if you look at the applications that you're dealing with in your everyday life, right. They continue to get broader. The scope of them continues to scale out, right. There's things that we do. I saw I think it was an MIT development recently where they're talking about being able to and they were originally doing it for Alzheimer's and dementia, but they're talking about being able to use the microphones in your smartphone to listen to the way you cough and use that as a predictor for people who have COVID that are not symptomatic yet. So asymptomatic COVID people, right? So when we start talking about where this, where this kind of technology can go and where it can lead us, right. There's sort of this unending possibility for it. But what that on, in part is that the infrastructure has to be extremely sound, right? The foundation has to be there. We have to have the resilience, the reliability and one of the points that Brian was just making is extremely key. We talk about disaster tolerance business continuous, so business continuance is how do you recover? Cyber resilience is the same conversation, right? So you have the protection side of it. Here's my defenses. Now what happens when they actually get in. And let's be honest, right? Humans are frequently that weak link, right. For a variety of behaviors that the humans that humans have. And so when that happens, where's the software in the storage that tells you, "Hey, wait there's an odd traffic behavior here "where data is being copied "at rates and to locations that that are not normal." And so that's part of when we talk about what we're doing in our side of the automation is how do you know what normal looks like? And once you know what normal looks like you can figure out where the outliers are. And that's one of the things that people use a lot for trying to determine whether or not ransomware is going on is, "Hey, this is a traffic pattern, that's new. "This is a traffic pattern. "That's different." Are they doing this because they're copying the dataset from here to here and encrypting it as they go, right? 'Cause that's one of the challenges you got to, you got to watch for. So I think you're going to see a lot of advancement in the application space. And not just the MIT stuff, which is great. The fact that people are actually able to or I may have misspoken, maybe Johns Hopkins. And I apologize to the Johns Hopkins folks that kind of scenario, right. There's no knowing what they can make use of here in terms of the data sets, right. Because we're gathering so much data, the internet of things is an overused phrase but the sheer volume of data that's being generated outside of the data center, but manipulated analyzed and stored internally. 'Cause you got to have it someplace secure. Right and that's one of the things that we look at from our side is we've got to be that as close to unbreakable as we can be. And then when things do break able to figure out exactly what happened as rapidly as possible and then the recovery cycle as well. >> Excellent and I want to finish with you. We just have a few seconds left, but as AJ was talking about this massive evolution and applications, for example when we talk about simplicity and we talk about resiliency and being able to recover when something happens, how did these new technologies that we've been unpacking today? How did these help the admin folks deal with all of the dynamics that are happening today? >> Yeah so I think the biggest the drop, the mic thing we can say right now is that we're delivering 100% tier zero in Vme without data reduction value props on top of it at a cost that undercuts off-prem S3 storage. So if you look at what you can do from an off-prem solution for air gap and from cyber resiliency you can put your data somewhere else. And it's going to take whatever long time to transfer that data back on prem, to read get back to your recover point. But when you work at economics that we're doing right now in the distributed systems, hey, you're DR side, your copies of data do not have to wait for that. Off-prem bandwidth to restore. You can actually literally restore it in place. And you couple that with all of the the technology on the software side that integrates with it I get incremental point in time. Recovery is either it's on the primary side of DRS side, wherever, but the fact that we get to approach this thing from a cost value then by all means I can naturally absorb a lot of the cyber resiliency value in that too. And because it's all getting all the same orchestrated capabilities, regardless of the big, small, medium, all that stuff, it's the same skillsets. And so I don't need to really learn new platforms or new solutions to providing cyber resiliency. It's just part of my day-to-day activity because fundamentally all of us have to wear that cyber resiliency hat. But as, as our job, as a vendor is to make that simple make it cost elegance, and be able to provide a essentially a homogenous solutions overall. So, hey, as your business grows, your risk gets averted on your recovery means also get the thwarted essentially by your incumbent solutions and architecture. So it's pretty cool stuff that we're doing, right. >> It is pretty cool. And I'd say a lot of folks would say, that's the Nirvana but I think the message that the three of you have given in the last 20 minutes or so is that IBM and Brocade together. This is a reality. You guys are a cornucopia of knowledge. Brian, Matt, AJ, thank you so much for joining me on this panel I really enjoyed our conversation. >> Thank you. >> Thank you again Lisa. >> My pleasure. From my guests I'm Lisa Martin. You've been watching this IBM Brocade panel on theCUBE.

>> Announcer: From around the globe, it's theCUBE, with digital coverage of AWS re:Invent 2020. Special coverage sponsored by AWS Worldwide Public Sector. >> Welcome to theCUBE Virtual and our coverage of AWS re:Invent 2020, the specialized programming for Worldwide Public Sector. I'm Lisa Martin. I'm joined by Nick Speece the chief federal technologist for Snowflake. Nick, welcome to theCUBE. >> Thank you, Lisa. It's great to be here. >> Likewise, chief federal technologist, that's the first time I've ever heard of that title. Tell me a little bit about that. >> It's probably the last time you'll hear it. So chief federal technologist is really somebody in the company who is focused on bringing the needs of the federal government back to our corporate headquarters, making sure that the product as it's developed and evolves has the federal requirements in mind. >> Excellent. So the last couple of months for Snowflake big, biggest software IPO and software history your market cap right now is at 66 billion 515 data workloads running on Snowflake's platform every day 250 petabytes of data under management, a lot is going on. Let's talk about Snowflake. You guys operate only in the cloud, why was that decision made and how does that impact businesses analysis of data? >> Yeah, so great question and the answer is actually in the opening that you gave for us and thank you for that reinforcement. Snowflake can't exist anywhere, but the cloud. Technology over the last five to 10 years has really seen a move from what the cloud originally was, which was I have a virtual machine in my data center, I'm going to run it on your stuff not mine, into more comprehensive service offerings like Snowflake. We can't reach the kind of scale that Snowflake operates at every day and that our customers demand without the technology of clouds like AWS. The technology has to be there, the underlying and underpinning architecture has to be there, otherwise our customers get left in the dark and we can't can't have that. >> And especially today as data volumes are massively increasing and we know that that's only going to go up. We know that IT is only going to be more complex but when we talk to businesses in any industry the value of the data is in the insights the ability to extract that data in real time glean insights from it so that businesses can make data-based decisions that pivot their business, especially critical during the year that we have now known as 2020. Talk to me a little bit about though digging into your marketing material, everyone, there's all these terms, right, that everyone uses and you guys use single source of truth. What does it actually mean for single source, for stuff like? >> Yeah, so we talk about cloud, we talk about single source of truth and when you're looking at data problems, the problem and the solution are the same thing. A massive amount of data is a raw resource, that's all it is. And trying to refine that raw resource into something that is insightful or something that is useful to a business process is a challenge that every customer in every market, in every region undergoes. And how you overcome that is critical. And one of the primary focuses of Snowflake is to evolve the data cloud. Snowflake platform is the underlying technology for the data cloud but the data cloud is where we're going. And what I mean by data cloud. If you have a data set, your internal data, that is your truth, but it might not be the truth. So in Snowflake we encourage our customers to collaborate on data sets. For example, if you want to know how many people are living in a certain borough in New York City you could go around with a clicker and count everyone, or you could just ask the Census Bureau. That's the nature of the data cloud and what we're talking about here. Going to the subject matter experts who have the data that you need, using our marketplace, using our private exchanges, using our data sharing to build your own data cloud and become part of the next gen architecture for data sharing and collaboration, to get to the source of the truth, to make better decisions, to gain better insights. It's great to combine your data with enrich data from other sources, especially when it comes to making federal decisions and governance decisions. >> Absolutely that's critical. That the biggest challenge customers have is being able to sort through all that and find. I like how you put this as their single source of truth. Can you give us some examples of some federal agencies maybe even just anonymously that are using the power of Snowflake to do just that? >> Absolutely. We've got customers in the healthcare space and in some of the law enforcement spaces and especially in public education that are trying to increase the awareness of the folks that are subscribing to their services, for example, folks that are looking for healthcare help. If you're filing claims for a certain healthcare providers or certain care facilities, we want to make sure that those claims that are forwarded to those entities are legitimate, first of all, for example, if you're filing a claim for knee surgery in Florida, you probably didn't have one in California, three hours later. So those kinds of enforcement activities, and not just trying to do audits but also to benefit everybody who's receiving care. There's a lot of push now about genetic sequencing, DNA and RNA vaccination is huge with COVID-19, getting access to massive amounts of data to do analysis against and figure out the best approach, that's critical for where we go in the next 10 to 15 years in healthcare. Snowflake is very, very honored and happy to be propelling that move in the healthcare space. >> It is that's going to be absolutely critical but we're also seeing it, you know, everywhere else, such as for universities and education, suddenly this need, the last few months for real-time learning. Talk to me about data analysis. Can Snowflake help companies, you talked about enriching data sets so not just companies sources of data but additional data sets that they can add in and evaluate and analyze to make great decisions, but from a historical real-time perspective, talk to me how Snowflake helps with that data analysis. >> Yeah, sure. Right. So Snowflake in and of itself can do some analysis work. We've got some great visualization tools in our new UI that was released recently on public preview. So there's some analysis tools built into Snowflake but really where the value comes from is in taking your tools that you already use today and connecting it to a data source or platform that can wrangle that data, that can move that data through automated pipelines to give you a model view of that data that's beneficial. For example, data scientists and data engineers spend 80% of their time, and I know a lot of statistics are made up on the spot, that was not a promise, but trying to move this data through and refine it and build features to get to the point where you can ask a question is 80% of these very valuable professionals time. Shortening those timelines is what Snowflake really aims to do in the analysis space. We're not trying to replace the analysis tools that you use today, we work fine with all of them. The big difference is presenting them with enough data volume to give you real insights and eliminate bias as much as we can in data sets. >> What are some of the things that differentiates Snowflake from data warehouses and other folks in the market? >> Yeah. Great question. The big difference is Snowflake was built natively for the cloud. We weren't adapted to the cloud, we didn't adopt the cloud at some point in the future, Snowflake was built from scratch to be in the cloud. And since this is the appropriate show to mention it the primary difference between us is we were built to use object storage foundationally underneath our technology. And I know that sounds really nerdy and it is, but it adds a tremendous amount of value. If you think about how we used to collaborate 10 years ago we'd have a spreadsheet that if I open that spreadsheet for my share drive and you tried to open it at the same time, you'd get locked out. You're told you couldn't have it. And if tradition stays true I would probably be on vacation for two weeks. Contrast that now with the massive Google Doc platform and Office 365, object storage has changed the way that we collaborate on the same kinds of documents. Multiple people interacting with one thing at one time without contention, that's the reason why Snowflake has to operate in the cloud. We bring that same paradigm, multiple actors on a single object and give you that source of truth the truth that you absolutely need to make decisions. >> And that's critical these days as we know. We're in living in uncertain times and one of the things I think we can expect is the uncertainty to continue, but also for many industries people to stay remote or some big percentage for quite a while. So the ability to have those collaboration tools and be able to collaborate in real time is table stakes for so many companies. But when we're talking about some of the things going on this year, security, we can't not talk about security. You know, all these folks from home accessing corporate networks, you know, maybe not through VPNs or behind firewalls, the cloud is paramount to that. How does Snowflake address the security issue? >> Absolutely. So I'll start by saying our security is inherited from the wonderful security platform that AWS has underneath it. So we inherit all the security around data storage the EC Compute, all of the different entities and end points that AWS already secures Snowflake takes the same precautions. More than that, we've also built and rolled this access control to ensure that people are getting access only to the data that they should be getting access to, we recently implemented data masking as well, so certain roles are not able to see unmasked data, but they can still do queries that use the underlying data to filter. So there's a lot of different capabilities built in, encryption at rest, encryption in flight, AES-256 encryption keys used in a hierarchial model. These are phenomenal security architectures that are paramount to the security of the folks that are using our platform. Because we know at the end of the day the first day we have a leak in Snowflake is probably our last day in business. We got to be good at that which is why it's our top priority. >> I didn't, to ever talk about security as an inherited, I must be a dominant trait if we're going to be talking about, you know, genetics and chromosomes and mRNA and things like that. So walk me through last question, a government organization, or say they're an AWS customer or they want to start using Snowflake, what's that process? How do they go about doing that to leverage those inherited security capabilities that you talked about? >> Well, thankfully AWS has helped us put a FedRAMP moderate certified Snowflake region together in AWS, East commercial, so we're very happy to have a FedRAMP moderate region. They can access Snowflake through the AWS Marketplace or from Snowflake.com, you can start a trial in just a couple of minutes. Our security is built into all of our regions although the FedRAMP regions are specialized in some of the encryption technology we use, but we always, always always protect our users' data, regardless of where it is. >> You make it sound easy, I got to say. (laughing) >> That's because it is. (laughing) Thank you cloud. >> That's good. And well, that's good and it should be, especially because there's so much complexity and uncertainty everywhere else in the world right now. Last question for you. As I mentioned in the beginning, the biggest IPO in software history, just a couple of months ago during probably one of the most strangest time of any of us have ever, and our relatives ever witnessed, what can we expect from Snowflake in 2021? Are you going to bring all the good vibes that we all need? (laughing) >> Well, good vibes is our business model. You know, Snowflake is a phenomenal platform. We've had a ton of success driven by the success of our cloud provider partners, driven by the success of our wonderful customers. We have over 4,000 people using Snowflake now to great effect. You can look for more features, you can look for more functions, but really the evolution of the data cloud, our big push is to help our customers get into the data cloud, get the truth out of their data and make better decisions every day. And you'll see more of that from us as time continues. >> One more question I wanted to sneak in, how did you work with those customers to evolve the data cloud? What's that feedback loop like? >> It's, a lot of it comes down to silos that the customers have built up over years and years and years of operation. That's the first step. In Snowflake there isn't such thing really as a data silo there's data put into Snowflake, everything is unified, you can do queries across databases, that's the first thing. The second thing is browsing our data marketplace. It's just like an App Store for your phone but instead it's data sets and the data sets are published by the experts who know that material better than anyone. I mentioned earlier bringing in everything from housing evaluation data to COVID-19 data from California and Boston, bringing World Health Organization data, John Hopkins University data, joining that with the data that you already use today along with weather and population counts, the main thing here, the strategy is almost endless. More and more data sets are being published over every day. We have over a hundred contributors in the marketplace now. >> That's exciting that we have the technology and the power like this to help the world re, you know, recover from such a crazy time. It's nice to know that, that there was the power of that behind that, and the smart folks like you chief federal technologists, helping to fine tune that and really ensure that organizations across the government can maximize the value of data and find their single source of truth. Nick, it's been a blast having you on theCUBE. Thank you for joining me. >> Thank you for having me. >> For next piece, I'm Lisa Martin. You're watching theCUBE Virtual. (upbeat music)

>>Hello, everyone. And welcome back to our wall to wall coverage of the data Cloud Summit. This is Dave a lot. And we're seeing the emergence of a next generation workload in the cloud were more facile access and governed. Sharing of data is accelerating. Time to insights and action. All right, allow me to introduce our next guest. Amy Irwin is here. She's the vice president of strategy for experience. And Matt Glickman is VP customer product strategy it snowflake with an emphasis on financial services. Folks, welcome to the Cube. Thanks so much for coming on. >>Thanks for >>having us >>nice to be here. Hey, >>So, Amy, I mean, obviously 2020 has been pretty unique and crazy and challenging time for a lot of people. I don't know why I've been checking my credit score a lot more for some reason. On the app I love the app I got hacked. I had a lock it the other day I locked my credit. Somebody tried to dio on and it worked. I was so happy. So thank you for that. But so we know experience, but there's a ton of data behind what you do. I wonder if you could share kind of where you sit in the data space and how you've seen organizations leverage data up to this point. And really, if you could address maybe some of the changes that you're seeing as a result of the pandemic, that would be great. >>Sure, sure. Well, Azaz, you mentioned experience Eyes best known as a credit bureau. Uh, I work in our marketing services business unit, and what we do is we really help brands leverage the power of data and technology to make the right marketing decisions and better understand and connect with consumers. Eso we offer markers products around data identity activation measurement. We have a consumer view data file that's based on off line P I and contains demographic interest, transaction data and other attributes on about 300 million people in the U. S. Uh, and on the identity side, we've always been known for our safe haven or privacy friendly matching that allows marketers to connect their first party data to experience or other third parties. Uh, but in today's world, with the growth and importance of digital advertising and consumer behavior shifting to digital, uh, experience also is working to connect that offline data to the digital world for a complete view of the customer you mentioned co vid, um, we actually we serve many different verticals. And what we're seeing from our clients during co vid is that there's a bearing impact of the pandemic. The common theme is that those that have successfully pivoted their businesses to digital are doing much better. Uh, as we all know, Kobe accelerated very strong trends to digital both in the commerce and immediate viewing habits. We work with a lot of retailers. Retail is a tale of two cities with big box and grocery growing and apparel retail really struggling. We've helped our clients leveraging our data to better understand the shifts in these consumer behaviors and better segment their customers during this really challenging time. Eso think about there's there's a group of customers that is still staying home that is sheltered in place. There's a group of customers starting that significantly varied their consumer behavior, but it's starting to venture out a little. And then there's a group of customers that's doing largely what they did before and a somewhat modified fashion. So we're helping our clients segment those customers into groups to try and understand the right messaging and right offers for each of those groups. And we're also helping them with at risk audiences. Eso That's more on the financial side. Which of your customers air really struggling? Do the endemic And how do you respond? >>It's awesome, thank you. You know, it's it's funny. I mean somebody I saw Twitter poll today asking if we measure our screen time and I said, Oh my no eso Matt, let me ask you. You spend a ton of time in financial services. You really kind of cut your teeth there, and it's always been very data oriented. You've seen a lot of changes tell us about how your customers are bringing together data, the skills that people obviously a big part of the equation and applications to really put data at the center of their universe. What's new and different that these companies were getting out of the investments in data and skills. >>That's a great question. Um, the acceleration that Amy mentioned Israel, Um, we're seeing it particularly this year, but I think even in the past few years, the reluctance of customers to embrace the cloud is behind us. And now there's this massive acceleration to be able to go faster on, and in some ways the new entrance into this category. Have an advantage versus, you know, the companies that have been in the space within its financial services or beyond. Um, and in a lot of ways they are are seeing the cloud and services like snowflake as a way toe not only catch up but leapfrog your competitors and really deliver a differentiated experience to your customers to your business, internally or externally. Um, and this past, you know, however long this crisis has been going on, has really only accelerated that, because now there's a new demand. Understand your customer better your your business better with with your traditional data sources and also new alternative data sources, Um, and also be able to take a pulse. One of things that we learned which was you know, I opening experience was as the crisis unfolded, one of our data partners decided to take the data sets about where the cases where were happening from the Johns Hopkins and World Health Organization and put that on our platform, and it became a runaway hit where now with thousands of our customers overnight, we're using this data to understand how their business was doing versus how the crisis was unfolding in real time. On this has been a game changer, and I think it's only it's only scratching the surface of what now the world will be able to do when data is really at their fingertips. You're not hindered by your legacy platforms. >>I wrote about that back in the early days of the pandemic when you guys did that and talked about some of the changes that you guys enabled and and, you know you're right about Cloud. I mean, financial services. Cloud used to be an evil word, and now it's almost become a mandate. Amy, I >>wonder if you >>could tell us a little bit more about what? What, you know your customers they're having to work through in order to achieve some of these outcomes. I mean, I'm interested in the starting point. I've been talking a lot and writing a lot on talking to practitioners about what I call the data lifecycle. Sometimes people call it the data pipeline. It za complicated matter, but those customers and companies that can put data at the center and really treat that pipeline is the heart of their organization, If you will, really succeeding. What are you seeing and what really is the starting point there? >>Yes, yes, that's a good question. And as you mentioned, first party, I mean, we start with first party data. Right? First party data is critical to understanding consumers on been in different verticals, different companies. Different brands have varying levels of first party data. So retailers gonna have a lot more first party data financial services company, then say an auto manufacturer. Uh, while many marketers have that first party data to really have a 3 60 view of the customer, they need third party data as well. And that's where experience comes in. We help brands connect those disparate data sets both 1st and 3rd party baked data to better understand consumers and create a single customer view, which has a number of applications. I think the last that I heard was that there's about eight devices on average per person. I always joke that we're gonna have these enormous. I mean, that that number is growing. We're gonna have these enormous charging stations in our house, and I think we're because all the different devices and way seamlessly move from device to device along our customer journey. And, um, if the brand doesn't understand who we are, it's much harder for the brand to connect with consumers and create a positive customer experience and way site that about 95% of companies are actually that they are looking to achieve that single customer view. They recognize, um, that they need that. And they've aligned various teams from e commerce to marketing to sales toe at a minimum in just their first party data and then connect that data to better understand, uh, consumers so consumers can interact with the brand through website and mobile app in store visits, um, by the phone, TV ads, etcetera. And a brand needs to use all of those touchpoints often collected by different parts of the organization and then adding that third party data to really understand the consumers in terms of specific use cases, Um, there's there's about three that come to mind, so there's first. There's relevant advertising and reaching the right customer. There's measurement s or being able to evaluate your advertising efforts. Uh, if you see an ad on the if I see it out of my mobile and then I by by visiting a desktop website understanding or get a direct mail piece, understanding that those connect those interactions are all connected to the same person is critical for measurement. And then there's, uh, there's personalization, um, which includes encourage customer experience amongst your own, um, touch points with that consumer personalized marketing communication and then, of course, um, analytics. So those are the use cases we're seeing? Great. >>Thank you, Amy. I'm out. You can't really talk about data without talking about, >>you know, >>governance and and and compliance. And I remember back in 2006, when the Federal Rules of Civil Procedure went in, it was easy. The lawyers just said, No, nobody can have access, but that's changed. One of things I like about what snowflakes doing with the data cloud is it's really about democratizing access, but doing so in a way that gives people confidence that they only have access to the right data. So maybe you could talk a little bit about how you're thinking about this topic, what you're doing to help customers navigate, which has traditionally been such a really challenging problem. >>No, it's another great question. Um, this is where I think the major disruption is happening. Um, and what Amy described being able to join together 1st and 3rd party data sets. Um, being able to do this was always a challenge because data had to be moved around, had a ship, my first party data to the other side. The third party data had to be shipped to me on being able to join those data sets together, um was problematic at best. And now, with the focus on privacy and protecting P, I, um, this is this is something that has to change. And the good news is with the data cloud data does not have to move. Data can stay where it belongs. Experiencing keep its data experience. Customers can hold on to their data. Yet the data can be joined together on this universal global platform that we call the data cloud. On top of that, and particularly with the regulations that are coming out that are gonna prevent data from being collected on either a mobile device or in wet warren as cookies and Web browsers, new approaches. And we're seeing this a lot in our space, both in financials and in media is to set up these data clean rooms where both sides can give access to one another, but not have to reveal any P i i to do that joint. Um, this is gonna be huge right now. You actually can protect your your customers, private your consumers, private identities, but still accomplish that. Join that Amy mentioned to be able to thio relate the cause and effect of these campaigns and really understand the signals. Um, that these data sets are trying to say about one another again without having to move data without having to reveal P. I We're seeing this happening now. This is this is the next big thing that we're gonna see explode over the next months and years to come. >>I totally agree. Massive changes coming in public policy in this area, and I wanted we only have a few minutes left. I wonder if for our audience members that you know, looking for some advice, what's the what's the one thing you'd recommend? They start doing differently or consider putting in place. That's going to set them up for success over the next decade. >>Yeah, that's a good question. Um, you know, I think e always say, you know, first harness all of your first party data across all touchpoints. Get that first party data in one place and working together Second back that data with trusted third parties and in mats, just in some ways to do that and then third, always with the customer first speak their language. Uh, where and when they want to be, uh, reached out thio on and use the information. You have to really create a better a better customer experience for your customers. >>Matt. What would you add to that? Bring us home if you would >>applications. Um, the idea that data can now be your data can now be pulled into your own business applications the same way that Netflix and Spotify are pulled into your consumer and lifestyle applications again without data moving these personalized applications experiences is what I encourage everyone to be thinking about from first principles. What would you do in your next app that you're gonna build? If you had all of your consumers, consumers had access to their data in the app and not having to think about things you know from scratch. Leverage the data cloud leverage these, you know, service providers like experience and build the applications of tomorrow. >>I'm super excited when I talked to practitioners like yourselves about the future of data Guys, Thanks so much for coming on. The Cube was really a pleasure having you and hope we can continue this conversation in the future. >>Thank you. >>All right. Thank you for watching. Keep it right there. We've got great content. Tons of content coming at the Snowflake Data Cloud Summit. This is Dave Volonte for the Cube. Keep it right there.

>>from around the globe. It's the Cube with coverage of Yukon and Cloud. Native Con North America 2020 Virtual brought to you by Red Hat The Cloud Native Computing Foundation and Ecosystem >>Partners Everyone welcome back to the cubes. Coverage of Coop con Cloud, native con North America 2020. Normally the Cuba's in person. But like the EU event, this is gonna be a remote virtual event. This is the Cube virtual. We are the Cube Virtual. This is a keynote and show review with our analysts and hosts Lisa Martin, GOP Scar and myself. Guys, welcome to the program. Lisa, Great to see you. You great to see you remotely. Thanks for coming on. >>Always great to be part of the Cuban acute virtual keeping us connected. >>So Coop Con Cloud Native cons November and I remember in 2016 the first Coop Con. That's when Hillary Clinton got defeated by Trump. And now this year the election's passed this time and, uh, Biden the winner. So, you know, election more good vibes this year in the community because everyone was kind of sad last time. So if you remember the first Cube con, it was in Seattle during that time, so that was important to kinda reminisce on. That other thing I want to bring up to you guys is the somber news of the passing of Dan Con who was the executive director of C N C F. He passed a few weeks ago on his home. It was illness and great legend. So we're gonna call that out, and there are thoughts and prayers. Go with the families. Condolences to his wife and kids. So what? I'm say, Dan. Godspeed. Funny dance story, Lisa. Yo, piece that I always always pronounce his name wrong on the queue was like, John, it's con, not Cohen. Okay. All right, Dan, Good to see you. Sorry, but a great guy friend to everyone And super great human being. So rest in peace. Okay. Que con, I >>think the big thing. >>This you wanna get your thoughts, you have to start with you, C and C F. What are they up to? Obviously remote. It's been a terrible year with the pandemic and all the disruptions on DCI change your thoughts on where they are now, this year. >>So you know, it's funny, even though it's remote. Even though reaching people, it's become harder. Uh, you know, we all have to deal with this from our you know, our living room, our office at home. But still, the C in C F is doing what it's been doing for a little while now. So instead of focusing on the technology part of RT world, there are focusing on you know, the community side of it. So they're fighting for inclusivity. They're fighting for diversity, for resilience in terms off their community. And they are really working on making the open source community more accessible, both for end user companies. A swell as offer developers thio enter the space, have their contribution and, you know, make sure that everyone can reap the full benefits off these open source products. >>You know, we talked to Priyanka Sharma and Stephen Augustus, and this was a big theme. There's there's been there's been a lot of engagement online, obviously, even though they have a remote platform, some people are thrilled with it. Some aren't. No one's ever happy these days. It's on the Web. It's always difficult, but the community been activated and a lot more diversity. I covered the big story around. You know, Master slave. The terminology now is gonna go main, you know, terminology and how that's gonna be safer. Also for diversity stem women in tech, This >>has been >>a big theme. I'd love to get your thoughts on that, because I think that's been a very positive thing. Uh, Lisa, you and I have been talking about this for years on the Cube around this diversity peace. What's your thoughts as well, like to get both your reactions on where this directions going. >>Yeah. You know, I think there's a number of things that have been catalyzed this year by the challenges that we've been through and the diversity pushed into the spotlight again. The spotlight is different, and it's really causing change for good. I think it's opening people's minds and perspective, as is, I think, this entire time, you know, it's for events like Yukon and all the other events that were normally getting a lot of airline miles for John and you were not getting. We're sitting at home with our in home studios, but at the same time, the engagement is increasing in every event, I imagine that the great Q. Khan and cognitive community that Dan Cohen has built is on Lee getting bigger and stronger, even though folks are physically separated. That's been just been my observation and something I felt from everything show I've covered every interview I've done that diversity is being raised now to a visibility level that we haven't seen in terms of a catalyzing action. >>You your reaction, Thio. >>No, I completely agree. And I want to add to that where you know, just like Lisa said. You know, we used to fly to these events. We were privileged and lucky to to be there to have the opportunity. But because everything is now digital and virtual, it opens the community up to so many other people who, for whatever reason, weren't able to join in person but are able to join virtually indigenously. So I think you know, even though there's a lot of downsides Thio to this pandemic, this is one of the, you know, the small nuggets of off seeing the sea NCF community opening up to a broader audience. >>Yeah, and that's a great point. You know, we aren't getting the airline miles we're getting Certainly the zoom and the cube mileage remote Lisa, because what's interesting you're saying is is that you know, we're getting more action with him coming in, doing some or hosting yourself, um, Eliana Gesu as well, Others. But we can get people more because remember, the people aren't we're not trying, but so aren't other people that were coming the big names, but also the fresh voices, the new names, names? We don't know yet. I think that's what we're seeing with the remote interviews is that it's one click away from being on the Cube now. So cute. Virtual is 24 73 65 we're gonna continue to do that. I think this is gonna change the makeup of the engagement in the conversation because you're gonna have mawr participation that's going to be highly accelerated. But also, these new voices are gonna bring a positive change. It might upset the hierarchy a little bit in the working groups at the top you, But you know they're open. I mean, I talked with Stephen Augustus. He's totally cool with this Chris, and I check is the same way he's like, Hey, bring on more people. This is the >>This is >>the vibe of the of the Lennox foundations always been. >>It's always been that way. And, you know, going back Teoh to the early open source events in Europe that I went to you. I started doing that as a teenager 15 years ago, and the vibe, you know, hasn't necessarily changed. The makeup of the audience certainly has changed right from it, being dominated by white males. It's totally opened up. And, you know, if we see that happening with the C N C F now as well, I think that's for you know, for the better. I think, um, our community, the i t community in the open source community need that resilience. Need all of those different perspectives from all of you know, different kinds of people from different walks of life with different histories. And I think that only makes the community stronger and more viable in the long run. I >>agree it's that >>open source needs. >>Sorry, it's not thought diversity that I think we're seeing even more now again. Just my perspective is just that the light that this challenging time is shining on, exposing things that are really opportunities and it's I think it's imperative to look at it in that way. But that thought diversity just opens up so many more opportunities that folks that are maybe a little bit more tunnel visioned aren't thinking of. But for businesses, thio and people Thio thrive and move forward and learn from this we need to be able Thio, take into consideration other concepts, other perspectives as we learn and grow. >>Yeah, that's a good point. You know, It was giving a a shout out to Dan Conn. And when I heard the news, I put a clip. One of my favorite clips over the interviews was really me kind of congratulating him on the success of C and C. I think it was, like two years ago or maybe last year. I forget, Um, but I >>was a >>critic of it ever initially, and I was publicly on the record on the Cube. Lisa, you remember, uh, with Stew, who's now having a great new career? Red hat Still and I were arguing, and I was saying, Stew, I think this is gonna fail, because if c. N. C F doesn't balance the end user peace with the logos that we're coming because remember, you about four years ago. It was like a NASCAR logo. Farmers like you know, it's like, you know, everything was like sponsored by Google this and then Amazon came in. You look at the sponsor list. It was like It's the who's who and cloud and now cloud native. It was the industry the entire industry was like, stacked up against reinvent. This is before Amazon made their move. I mean, uh, as your maid, they're moving for Google. Cloud kind of got their footing. So is essentially coop con against a W s. And I said, That's gonna fail, and I had to eat my words, and I did. It was rightfully so, But the balance, the balance between end user projects and vendor was very successful. And that's still plays out today. Lisa. This is important now because you said pandemic de ecosystem still needs to thrive, but there's no face to face anymore. >>What's the >>challenge? What's the opportunity there? I wanna put you on the spot. >>Sure. No, I think I think it's both challenging and opportunistic. I tend to look at it more from an opportunistic view. I think that it forced a lot of us, Even people like myself who worked from home a lot before, when I wasn't traveling for my marketing company or the Cube. You can really have very personal interactions. The people on Zoom and I found that it's connecting people in a deeper way than you even would get in the office. That's something that I actually really appreciate, how it has been an opportunity to really kind of expand relationships or toe open new doors that wouldn't be there if we were able to be studying together physically in person. And it's obviously changing. You know, all the vendors that we work with. It's very different to engage an audience when you are on Lee on camera, and it's something that, as we know, is we work with folks who haven't done it before. That's one of the things that I think a lot of the C suite I talked to Mrs is that opportunity Thio. You know, be on a stage and and be able to show your body language and your energy with your customers and your partners and your employees. But I actually do think that there is what we're doing through Zoom and and all these virtual platforms like the Cube virtual is well, we're opening up doors for a more intimate way that I think the conversations are more authentic. You know, people are have, like, three year old Discover occurs and they're running in the room when they're screaming behind that. That's how things are today. We're learning toe work with that, but we're also seeing people in a more human >>way. Containers Mitch, mainstream and shifting, left the role of security this year. What's your >>take? So I mean, if we're talking about security and nothing else, I think we're at a point where you know, the C N C. F has become mainstream. Its most popular products have become mainstream. Um, because if we're talking about security, there's, you know, not a lot left. And I say that with, you know, a little bit of sarcasm. I don't mean to offend anyone, but if I did, uh, I do apologize, but, you know, security. Even though it is super important again, it means that we have, you know, moved on from talking about kubernetes and and container Management, or we've moved on from storage. Um, it means that the technology part of the C N. C. F. Like the hard work has been done for 80%. We're now into the 20% where we're kind of, you know, dotting the I's and and making sure that we cover all of the bases. And so one of the news sandbox sandbox projects that has been accepted, I think, today even eyes certain Manager Thio to manage certificates Uh, you know, at scale, um, in an automated fashion. And I think that's, you know, 11 prime example of how security is becoming the theme and kind of the conversation at Yukon this year where, you know, we're again seeing that maturity come into play with even with sandbox projects now being able to help customers help end users with, you know, certificates which is, you know, in in the the macro picture a very specific, a very niche thing to be able to solve with open source software. But for every company, this is one of those vital, you know, kind of boilerplate security measures so that the, um the customer and all of their infrastructure remains safe. >>I think you what You're kind of really articulate, and there is the evolution of CNC off much to John Surprises. You said you thought in the very beginning that this wasn't gonna take off. It has. Clearly, Dan Cohen's left a great legacy there. But we're seeing the evolution of that. I do know John. Wanna ask because you did a lot of the interviews here. We've been talking for, what, nine months now on the Cube Virtual about the acceleration of transformation, of every business to go from that. Okay, how do we do this work in this in this weird environment? Keep the lights on. How do we actually be successful and actually become a thriving business? As things go forward, what are some of the things that you heard from the guests regarding? Kobe has an accelerator. >>Well, I think I think a couple of things. Good. Good question. I think it ranges. Right. So the new They had some news that they're trying to announce. Obviously, new survey certifications, K a security certification, new new tech radar support, diversity stats. You know, the normal stuff they do in the event, they gotta get the word out. So that was one thing I heard, but on the overall macro trend. You know, we saw the covert impact, and no >>one's >>afraid of it there. I mean, I think, you know, part of the legacy of these tech communities is they've been online. They're they're used to being online. So it's not a new thing. So I don't think that the work environment has been that much of a disruption to the people in the in the core community. Linux Foundation, for instance, had a great shot with Chris and a sticker on this. He's the CTO. He's been the CEO, brought a senior roles. Um, in fact, they're they're creating a template around C N C f. And then they're announced The Finn Finn Ops Foundation. Uh, Jr store meant, um, is an executive director. That's part of the new foundation. It's a practitioner community. So I think, um, teasing out the conversation is you're gonna see a template model of the C N. C f. Where you're going to see how groups work together. I think what cove? It has definitely shown in some of the things that you guys were saying around how people are gonna be more engaged, more diversity, more access. I >>think you're >>gonna start to see new social constructs emerge around distinct user groups. And I think this Finn ops Foundation is a tell sign around how groups of people going to start together, whether they're cube host coming together on Cube fans and cube alumni. I mean, let me think about the alumni that have been on the Cube. Lisa, you know Tim Hopkins, Sarah Novotny. Chelsie Hightower. Um, Dan Burns, Craig MK Lucky. I mean, we've had everybody on that's now Captain of the industry. So, um, way had capital one we've had, uh, you know, lift on. I mean, it's becoming a really tight knit. Everyone knows each other, and I think now they realize that they have a lot of, uh, power to infect change. And so when you're trying to affect change, um, that's a good thing, and people are pumped about. So I think the big focus was, um, CNC have a successful again. It's there's there's a somber note around Dan cons passing, but I think he had already moved on to a new position. So he was already passed the baton to management, But he did leave a mark, but I think there's Priyanka Sharma. She's doing a great job. People are upbeat and I think the theme is kubernetes. It happened. It's went next level, then it's going next level again and I think that's kind of what people really aren't saying is kind of the public secret, which is okay, this thing's going mainstream. Now you're gonna start to see it in, in, In commercial deployments. You're gonna start to see it scale into organizations. And that's not the cool kids or the Emerging Dev ops crowd. That's I t. So you know you know it's gonna happen is like, Hey, you know, I'm a nice guy, our developer. What is this? It has toe work. Well, that's the big I think I think people weren't talking about That's the most important story. >>I think another element to that John is the cultural shift. You know, we were talking when we talk about Dev ops who was think about speed and I talked to some folks who said, You know, it's it has to be the I T. Cultures on the business cultures coming together in a meaningful way to collaborate in a very new way. Thankfully, we have the technology to enable us to collaborate. But I think that's been another underlying thing that I've heard a lot through recent times. Is that that facilitator of of cultural change, which is always hard to dio? And there's a bit of a catalyst here for organizations to not just keep the lights on. But to be successful, going forward and and and find new ways of delighting their customers, >>we'll get the final word. I just want to say my big take away to the show is and we'll go down the line. I'll start Lisa in Europe, you could go is the usage of cloud and multi cloud is here. Everyone sees that. I think there's a financial aspect going on with security. You're gonna be tied in. I think you see new sets of services coming built on the foundation of the C N C F. But cloud and multi cloud is here. Multi cloud meeting edges. Well, that is definitely on everyone's radar. That was a big theme throughout the interview, so we'll see more of that. Lisa, your takeaways. >>Yeah, I would agree with that. And I think one of the biggest things that I hear consistently is the opportunities that have been uncovered, the the collaboration becoming tighter and folks having the opportunity to engage more with events like Coop Con and C and C F. Because of this virtual shift, I think there's only ah lot of positive things that we're going to stay to come. >>Yep. Yeah, my point of view is I mean, open source is validated completely right? It's a viable model to build around software. On the one hand, on the other hand, the C and C s role in making that open source community broadly accessible and inclusive is, I think, the biggest win Thio look back at at the last year. >>Well, I'm super excited for moving on to the next event. It's been great pleasure. Lisa. You you guys are great co host Virtual Cube. Thanks for participating. And we'll see you next time. Thank you. Okay, that's the cubes. Coverage of Coop con 2020 cloud Native con Virtual This the cube Virtual. We are the cube. Virtual. Thanks for watching

>> Hello everyone, and welcome back to our wall-to-wall coverage of the Datacloud summit, this is Dave Vellante, and we're seeing the emergence of a next generation workload in the cloud, more facile access, and governed sharing of data is accelerating time to insights and action. Alright, allow me to introduce our next guest. Aimee Irwin is here, she's the vice president of strategy for Experian, and Matt Glickman is VP of customer product strategy at Snowflake, with an emphasis on financial services, folks, welcome to theCUBE, thanks so much for coming on. >> Thanks Dave, nice to be here. >> Hey so Aimee, obviously 2020's been pretty unique and crazy and challenging time for a lot of people, I don't know why, I've been checking my credit score a lot more for some reason on the app, I love the app, I had to lock it the other day, I locked my credit, somebody tried to do, and it worked, I was so happy, so thank you for that. So, we know Experian, but there's a ton of data behind what you do, I wonder if you could share kind of where you sit in the data space, and how you've seen organizations leverage data up to this point, and really if you could address some of the changes you're seeing as a result of the pandemic, that would be great. >> Sure, sure. Well, as you mentioned, Experian is best known as a credit bureau. I work in our marketing services business unit, and what we do is we really help brands leverage the power of data and technology to make the right marketing decisions, and better understand and connect with consumers. So we offer marketers products around data, identity, activation, measurement, we have a consumer-view data file that's based on offline PII and contains demographic interest, transaction data, and other attributes on about 300 million people in the US. And on the identity side we've always been known for our safe haven, or privacy-friendly matching, that allows marketers to connect their first party data to Experian or other third parties, but in today's world, with the growth in importance of digital advertising, and consumer behavior shifting to digital, Experian also is working to connect that offline data to the digital world, for a complete view of the customer. You mentioned COVID, we actually, we serve many different verticals, and what we're seeing from our clients during COVID is that there's a varying impact of the pandemic. The common theme is that those who have successfully pivoted their businesses to digital are doing much better, as we all know, COVID accelerated very strong trends to digital, both in e-commerce and in media-viewing habits. We work with a lot of retailers, retail is a tale of two cities, with big box and grocery growing, and apparel retail really struggling. We've helped our clients, leveraging our data to better understand the shifts in these consumer behaviors, and better psych-map their customers during this really challenging time. So think about, there's a group of customers that is still staying home, that is sheltered in place, there's a group of customers starting to significantly vary their consumer behavior, but is starting to venture out a little, and then there's a group of customers that's doing largely what they did before, in a somewhat modified fashion, so we're helping our clients segment those customers into groups to try and understand the right messaging and right offers for each of those groups, and we're also helping them with at-risk audiences. So that's more on the financial side, which of your customers are really struggling due to the pandemic, and how do you respond. >> That's awesome, thank you. You know, it's funny, I saw a twitter poll today asking if we measure our screen time, and I said, "oh my, no." So, Matt, let me ask you, you spent a ton of time in financial services, you really kind of cut your teeth there, and it's always been very data-oriented, you're seeing a lot of changes, tell us about how your customers are bringing it together, data, the skills, the people, obviously a big part of the equation, and applications to really put data at the center of the universe, what's new and different that these companies are getting out of the investments in data and skills? >> That's a great question, the acceleration that Aimee mentioned is real. We're seeing, particularly this year, but I think even in the past few years, the reluctance of customers to embrace the cloud is behind us, and now there's this massive acceleration to be able to go faster, and in some ways, the new entrants into this category have an advantage versus the companies that have been in this space, whether it's financial services or beyond, and in a lot of ways, they all are seeing the cloud and services like Snowflake as a way to not only catch up, but leapfrog your competitors, and really deliver a differentiated experience to your customers, to your business, internally or externally. And this past, however long this crisis has been going on, has really only accelerated that, because now there's a new demand to understand your customer better, your business better, with your traditional data sources, and also new, alternative data sources, and also being able to take a pulse. One of the things that we learned, which was an eye-opening experience, was as the crisis unfolded, one of our data partners decided to take the datasets about where the cases were happening from the Johns Hopkins, and World Health Organization, and put that on our platform, and it became a runaway hit. Thousands of our customers overnight were using this data to understand how their business was doing, versus how the crisis was unfolding in real time. And this has been a game-changer, and it's only scratching the surface of what now the world will be able to do when data is really at their fingertips, and you're not hindered by your legacy platforms. >> I wrote about that back in the early days of the pandemic when you guys did that, and talked about some of the changes that you guys enabled, and you know, you're right about cloud, in financial services cloud used to be an evil word, and now it's almost, it's become a mandate. Aimee, I wonder if you could tell us a little bit more about what your customers are having to work through in order to achieve some of these outcomes. I mean, you know, I'm interested in the starting point, I've been talking a lot, and writing a lot, and talking to practitioners about what I call the data life cycle, sometimes people call it the data pipeline, it's a complicated matter, but those customers and companies that can put data at the center and really treat that pipeline as the heart of their organization, if you will, are really succeeding. What are you seeing, and what really is the starting point, there? >> Yes, yeah, that's a good question, and as you mentioned, first party, I mean we start with first party data, right? First party data is critical to understanding consumers. And different verticals, different companies, different brands have varying levels of first party data. So a retailers going to have a lot more first party data, a financial services company, than say, an auto manufacturer. And while many marketers have that first party data, to really have a 360 view of the customer, they need third party data as well, and that's where Experian comes in, we help brands connect those disparate datasets, both first and third party data to better understand consumers, and create a single customer view, which has a number of applications. I think the last stat I heard was that there's about eight devices, on average, per person. I always joke that we're going to have these enormous, and that number's growing, we're going to have these enormous charging stations in our house, and I think we already do, because of all the different devices. And we seamlessly move from device to device, along our customer journey, and, if the brand doesn't understand who we are, it's much harder for the brand to connect with consumers and create a positive customer experience. And we cite that about 95 percent of companies, they are looking to achieve that single customer view, they recognize that they need that, and they've aligned various teams from e-commerce, to marketing, to sales, to at a minimum adjust their first party data, and then connect that data to better understand consumers. So, consumers can interact with a brand through a website, a mobile app, in-store visits, you know, by the phone, TV ads, et cetera, and a brand needs to use all of those touchpoints, often collected by different parts of the organization, and then add in that third party data to really understand the consumers. In terms of specific use cases, there's about three that come to mind. So first there's relevant advertising, and reaching the right customer, there's measurement, so being able to evaluate your advertising efforts, if you see an ad on, if I see an ad on my mobile, and then I buy by visiting a desktop website, understanding, or I get a direct mail piece, understanding that those interactions are all connected to the same person is critical for measurement. And then there's personalization, which includes improved customer experience amongst your own touchpoints with that consumer, personalized marketing communication, and then of course analytics, so those are the use cases we're seeing. >> Great, thank you Aimee. Now Matt, you can't really talk about data without talking about governance and compliance, and I remember back in 2006, when the federal rules of civil procedure went in, it was easy, the lawyers just said, "no, nobody can have access," but that's changed, and one of the things I like about what Snowflake's doing with the data cloud is it's really about democratizing access, but doing so in a way that gives people confidence that they only have access to the right data. So maybe you could talk a little bit about how you're thinking about this topic, what you're doing to help customers navigate, which has traditionally been such a really challenging problem. >> Another great question, this is where I think the major disruption is happening. And what Aimee described, being able to join together first and third party datasets, being able to do this was always a challenge, because data had to be moved around, I had to ship my first party data to the other side, and the third party data had to be shipped to me, and being able to join those datasets together was problematic at best, and now with the focus on privacy and protecting PII, this is something that has to change, and the good news is, with the data cloud, data does not have to move. Data can stay where it belongs, Experian can keep its data, Experian's customers can hold onto their data, yet the data can be joined together on this universal, global platform that we call the data cloud. On top of that, and particularly with the regulations that are coming out that are going to prevent data from being collected on either a mobile device or as cookies on web browsers, new approaches, and we're seeing this a lot in our space, both in financials and media, is to set up these data clean rooms, where both sides can give access to one another, but not have to reveal any PII to do that join. This is going to be huge, now you actually can protect your customers' and your consumers' private identities, but still accomplish that join that Aimee mentioned, to be able to relate the cause and effect of these campaigns, and really understand the signals that these datasets are trying to say about one another, again without having to move data, without having to reveal PII, we're seeing this happening now, this is the next big thing, that we're going to see explode over the months and years to come. >> I totally agree, massive changes coming in public policy in this area, and we only have a few minutes left, and I wonder if for our audience members that are looking for some advice, what's the, Aimee, what's the one thing you'd recommend they start doing differently, or consider putting in place that's going to set them up for success over the next decade? >> Yeah, that's a good question. You know, I think, I always say, first, harness all of your first party data across all touchpoints, get that first party data in one place and working together, second, connect that data with trusted third parties, and Matt suggested some ways to do that, and then third, always put the customer first, speak their language, where and when they want to be reached out to, and use the information you have to really create a better customer experience for your customers. >> Matt, what would you add to that? Bring us home, if you would. >> Applications. The idea that data, your data can now be pulled into your own business applications the same way that Netflix and Spotify are pulled into your consumer and lifestyle applications, again, without data moving, these personalized application experiences is what I encourage everyone to be thinking about from first principles. What would you do in your next app that you're going to build, if you had all your consumers, if the consumers had access to their data in the app, and not having to think about things from scratch, leverage the data cloud, leverage these service providers like Experian, and build the applications of tomorrow. >> I'm super excited when I talk to practitioners like yourselves, about the future of data, guys, thanks so much for coming on theCUBE, it was a really a pleasure having you, and I hope we can continue this conversation in the future. >> Thank you. >> Thanks. >> Alright, thank you for watching, keep it right there, we got great content, and tons of content coming at the Snowflake data cloud summit, this is Dave Vellante for theCUBE, keep it right there.

>>Hello, everyone. And welcome back to our wall to wall coverage of the data Cloud Summit. This is Dave a lot. And we're seeing the emergence of a next generation workload in the cloud were more facile access and governed. Sharing of data is accelerating. Time to insights and action. All right, allow me to introduce our next guest. Amy Irwin is here. She's the vice president of strategy for experience. And Matt Glickman is VP customer product strategy it snowflake with an emphasis on financial services. Folks, welcome to the Cube. Thanks so much for coming on. >>Thanks for >>having us >>nice to be here. Hey, >>So, Amy, I mean, obviously 2020 has been pretty unique and crazy and challenging time for a lot of people. I don't know why I've been checking my credit score a lot more for some reason. On the app I love the app I got hacked. I had a lock it the other day I locked my credit. Somebody tried to dio on and it worked. I was so happy. So thank you for that. But so we know experience, but there's a ton of data behind what you do. I wonder if you could share kind of where you sit in the data space and how you've seen organizations leverage data up to this point. And really, if you could address maybe some of the changes that you're seeing as a result of the pandemic, that would be great. >>Sure, sure. Well, Azaz, you mentioned experience Eyes best known as a credit bureau. Uh, I work in our marketing services business unit, and what we do is we really help brands leverage the power of data and technology to make the right marketing decisions and better understand and connect with consumers. Eso We offer marketers products around data identity activation measurement. We have a consumer view data file that's based on offline P I and contains demographic interest, transaction data and other attributes on about 300 million people in the U. S. Uh, and on the identity side, we've always been known for our safe haven or privacy friendly matching that allows marketers to connect their first party data to experience or other third parties. Uh, but in today's world, with the growth and importance of digital advertising and consumer behavior shifting to digital, uh, experience also is working to connect that offline data to the digital world for a complete view of the customer you mentioned co vid, um, we actually, we start of many different verticals. And what we're seeing from our clients during co vid is that there's a bearing impact of the pandemic. The common theme is that those that have successfully pivoted their businesses to digital are doing much better. Uh, as we all know, Kobe accelerated very strong trends to digital both in the commerce and immediately eating habits. We work with a lot of retailers. Retail is a tale of two cities with big box and grocery growing and apparel retail really struggling. We've helped our clients leveraging our data to better understand the shifts in these consumer behaviors and better segment their customers during this really challenging time. Eso think about there's there's a group of customers that it's still staying home that is sheltered in place. There's a group of customers starting that significantly varied their consumer behavior, but it's starting to venture out a little. And then there's a group of customers that's doing largely what they did before in a somewhat modified fashion. So we're helping our clients segment those customers into groups to try and understand the right messaging and right offers for each of those groups. And we're also helping them with at risk. Audi's is S O. That's more on the financial side. Which of your customers are really struggling due to the pandemic. And how do you respond? >>So it's awesome. Thank you. You know it Zafon e I mean somebody. I saw Twitter poll today asking if we measure our screen time and I said, Oh my no eso Matt, let me ask you. You spend a ton of time and financial services. You really kind of cut your teeth there, and it's always been very data oriented. You've seen a lot of changes tell us about how your customers are bringing together data, the skills that people obviously a big part of the equation and applications to really put data at the center of their universe. What's new and different that these companies are getting out of the investments in data and skills. >>That's a great question. Um, the acceleration that Amy mentioned Israel, Um, we're seeing a particularly this year, but I think even in the past few years, the reluctance of customers to embrace. The cloud is behind us. And now there's this massive acceleration to be able to go faster on, and in some ways the new entrance into this category have an advantage versus, you know, the companies that have been in the space, whether it's financial services or beyond. Um, and in a lot of ways they are are seeing the cloud and services like snowflakes as a way toe not only catch up but leapfrog your competitors and really deliver a differentiated experience to your customers to your business, internally or externally. Um, and this past, you know, however long this crisis has been going on, has really only accelerated that, because now there's a new demand. Understand your customer better your your business better with with your traditional data sources and also new alternative data sources, Um, and also be able to take a pulse. One of things that we learned which was you know, I opening experience was as the crisis unfolded, one of our data partners decided to take the data sets about where the cases where were happening from the Johns Hopkins and World Health Organization and put that on our platform and it became a runaway hit. Where now, with thousands of our customers overnight, we're using this data to understand how their business was doing versus how the crisis was unfolding in real time. On this has been a game changer, and I think it's only it's only scratching the surface of what now the world will be able to do when data is really at their fingertips. You're not hindered by your legacy platforms. >>I wrote about that back in the early days of the pandemic when you guys did that and talked about some of the changes that you guys enabled. And you know you're right about Cloud. I mean, financial services. Cloud used to be an evil word, and now it's almost become a mandate. Amy, I >>wonder if you >>could tell us a little bit more about what? What you know your customers they're having to work through in order to achieve some of these outcomes. I mean, I'm interested in the starting point. I've been talking a lot and writing a lot on talking to practitioners about what I call the data lifecycle. Sometimes people call it the data pipeline. It's it's a complicated matter, but those customers and companies that can put data at the center and really treat that pipeline is, you know, the heart of their organization, if you will, Really succeeding. What are you seeing and what really is the starting point there? >>Yes, yes, that's a good question. And as you mentioned, first party, I mean, we start with first party data. Right? First party data is critical to understanding consumers on been in different verticals, different companies. Different brands have varying levels of first party data. So retailers gonna have a lot more first party data financial services company, then say an auto manufacturer. Uh, while many marketers have that first party data to really have a 3 60 view of the customer, they need third party data as well. And that's where experience comes in. We help brands connect those disparate data sets both 1st and 3rd party baked data to better understand consumers and create a single customer view, which has a number of applications. I think the last that I heard was that there's about eight devices on average per person. I always joke that we're gonna have these enormous. I mean, that that number is growing we're gonna have these enormous charging stations in our house, and I think we're because all the different devices and way seamlessly move from device to device along our customer journey. And, um, if the brand doesn't understand who we are, it's much harder for the brand to connect with consumers and create a positive customer experience and way site that about 95% of companies are actually that they are looking to achieve that single customer view. They recognize, um, that they need that. And they've aligned various teams from e commerce to marketing to sales so at a minimum in just their first party data, and then connect that data to better understand, uh, consumers. So consumers can interact with the brand through website and mobile app in store visits, um, by the phone TV ad, etcetera. And a brand needs to use all of those touchpoints often collected by different parts of the organization and then adding that third party data to really understand the consumers in terms of specific use cases, Um, there's there's about three that come to mind. So there's first. There's relevant advertising and reaching the right customer. There's measurement s or being able to evaluate your advertising efforts. Uh, if you see an ad on if I see it out of my mobile and then I by by visiting a desktop website, understanding or I get a direct mail piece understanding that those connect those interactions are all connected to the same person is critical for measurement. And then there's, uh, there's personalization, um, which includes improved customer experience amongst your own, um, touch points with that consumer Parsons marketing communication and then, of course, um, analytics. So those are the use cases we're seeing? Great. >>Thank you, Amy. I'm at you Can't really talk about data without talking about, >>you know, >>governance and and and compliance. And I remember back in 2006 when the Federal Rules of Civil Procedure went in, it was easy. The lawyers just said, No, nobody can have access, but that's changed. One of things I like about what snowflakes doing with the data cloud is it's really about democratizing access, but doing so in a way that gives people confidence that they only have access to the right data. So maybe you could talk a little bit about how you're thinking about this topic what you're doing to help customers navigate, which has traditionally been such a really challenging problem. >>No, it's another great question. Um, this is where I think the major disruption is happening. Um, and what Amy described being able to join together 1st and 3rd party data sets. Um, being able to do this was always a challenge because data had to be moved around, had to ship my first party data to the other side. The third party data had to be shipped to me. And being able to join those data sets together, um was problematic at best. And now, with the focus on privacy and protecting P, I, um, this is this is something that has to change. And the good news is with the data cloud data does not have to move. Data can stay where it belongs experience and keep its data experience. Customers can hold on to their data. Yet the data can be joined together on this universal global platform that we call the data cloud. On top of that, and particularly with the regulations that are coming out that are going to prevent data from being collected on either a mobile device or in wet warn as cookies and Web browsers. New approaches and we're seeing this a lot in our space, both in financials and in media is to set up these data clean rooms where both sides can give access to one another but not have to reveal any P i i to do that joint. Um, this is gonna be huge right now. You actually can protect your your customers, private your consumers, private identities, but still accomplish that. Join that Amy mentioned to be able to thio, relate the cause and effect of these campaigns and really understand the signals that these data sets are trying to say about one another again without having to move data without having to reveal P. I We're seeing this happening now. This is this is the next big thing that we're gonna see explode over the next months and years to come. >>I totally agree massive changes coming in public policy in this area, and I wanted we only have a few minutes left. I wonder if for our audience members that you know, looking for some advice, what's the what's the one thing you'd recommend? They start doing differently or consider putting in place That's going to set them up for success over the next decade. >>Yeah, that's a good question. Um, you know, I think e always say, you know, first harness all of your first party data across all touchpoints. Get that first party data in one place and working together psychic back that data with trusted third parties and mats, just in some ways to do that and then third, always with the customer first speak their language, uh, where and when they want to be, uh, reached out thio on and use the information. You have to really create a better a better customer experience for your customers. >>Matt. What would you add to that? Bring us home if you would >>applications. Um, the idea that data can now be your data can now be pulled into your own business applications the same way that Netflix and Spotify are pulled into your consumer and lifestyle applications again without data moving these personalized applications experiences is what I encourage everyone to be thinking about from first principles. What would you do in your next app that you're going to build? If you had all of your consumers. Consumers had access to their data in the APP and not having to think about things, you know, from scratch. Leverage the data cloud leverage these, you know, service providers like experience and build the applications of tomorrow. >>I'm super excited when I talked to practitioners like yourselves about the future of data Guys. Thanks so much for coming on. The Cube was really a pleasure having you and hope we can continue this conversation in the future. >>Thank you. >>Anything. >>All right. Thank you for watching. Keep it right there. We've got great content. Tons of content coming at the Snowflake Data Cloud Summit. This is Dave Volonte for the Cube. Keep it right there.

>> Hello, welcome to the final day of the NTT Research Summit Upgrade 2020. My name is Joe Alexander and I belong to the Medical and Health Informatics lab, so-called MEI lab, and I lead the development of the bio digital twin. I'd like to give you a high level overview of what we mean by bio digital twin, what some of our immediate research targets are, and a description of our overall approach. You will note that my title is not simply bio digital twin, but more specifically a cardiovascular bio digital twin and you'll soon understand why. What do we mean by digital twin? For our project, we're taking the definition on approach used in commercial aviation, mostly for predictive maintenance of jet engines. A digital twin is an up-to-date virtual representation, an electronic replica if you will. Now, if anything which gives you real-time insight into the status of the real-world asset to enable better management and to inform decision-making. It aims to merge the real and the virtual world. It enables one to design, simulate, and verify products digitally, including mechanics and multi-physics. It allows integration of complex systems. It allows for predictive maintenance through direct real-time monitoring of the health and structure of the plane parts, mitigating danger. It enables monitoring of all machines anywhere at all times. This allows feeding back insights to continuously optimize the digital twin of the product, which in turn leads to continuous improvement of the product in the real world. A robust platform is needed for digital twins to live, learn and run. Because we aim to apply these concepts to biological systems for predictive maintenance of health, we use the term bio digital twin. We're aiming for a precision medicine and predictive health maintenance. And while ultimately we intend to represent multiple organ systems and the diseases affecting them, we will start with the cardiovascular system. When we revisit concepts from the last slide, there's the one-to-one mapping as you can see on this slide. A cardiovascular bio digital twin is an up-to-date virtual representation as well, but of a cardiovascular system, which gives you real-time insight into the status of the cardiovascular system of a real world patient to enable better care management and to inform clinical decision-making. It does so by merging the real and virtual world. It enables one to design, simulate, and verify drug and device treatments digitally, including cardiovascular mechanics and multi-physics. It allows integration of complex organ systems. It allows for predictive maintenance of health care through direct real-time monitoring of the health and functional integration, or excuse me, functional integrity of body parts, mitigating danger. It enables monitoring of all patients anywhere at all times. This allows feedback to continuously optimize the digital twins of subjects, which in turn leads to continuous improvements to the health of subjects in the real world. Also a robust platform is needed for digital twins to live, learn, and run. One platform under evaluation for us is called embodied bio-sciences. And it is a cloud-based platform leveraging AWS distributed computing database and cuing solutions. There are many cardiovascular diseases that might be targeted by cardiovascular bio digital twin. We have chosen to focus on the two most common forms of heart failure, and those are ischemic heart failure and hypertensive heart failure. Ischemic heart failure is usually due to coronary artery disease and hypertensive heart failure usually is secondary to high blood pressure. By targeting heart failure, number one, it forces us to automatically incorporate biological mechanisms, common to many other cardiovascular diseases. And two, heart failure is an area of significant unmet medical need, especially given the world's aging population. The prevalence of heart failure is estimated to be one to one and a half. I'm sorry, one to 5% in the general population. Heart failure is a common cause of hospitalization. The risk of heart failure increases with age. About a third to a half of the total number of patients diagnosed with heart failure, have a normal ejection fraction. Ischemic heart failure occurs in the setting of an insult to the coronary arteries causing atherosclerosis. The key physiologic mechanisms of ischemic heart failure are increased myocardial oxygen demand in the face of a limited myocardial oxygen supply. And hypertensive heart failure is usually characterized by complex myocardial alterations resulting from the response to stress imposed by the left ventricle by a chronic increase in blood pressure. In order to achieve precision medicine or optimized and individualized therapies for heart failure, we will develop three computational platforms over a five-year period. A neuro-hormonal regulation platform, a mechanical adaptation platform and an energetics platform. The neuro-hormonal platform is critical for characterizing a fundamental feature of chronic heart failure, which is neuro-humoral activation and alterations in regulatory control by the autonomic nervous system. We will also develop a mechanical adaptation and remodeling platform. Progressive changes in the mechanical structure of the heart, such as thickening or thinning a bit muscular walls in response to changes in workloads are directly related to future deterioration in cardiac performance and heart failure. And we'll develop an energetics platform, which includes the model of the coronary circulation, that is the blood vessels that supply the heart organ itself. And will thus provide a mechanism for characterizing the imbalances between the oxygen and metabolic requirements of cardiac tissues and their lack of availability due to neuro-hormonal activation and heart failure progression. We consider it the landscape of other organizations pursuing innovative solutions that may be considered as cardiovascular bio digital twins, according to a similar definition or conceptualization as ours. Some are companies like the UT Heart, Siemens Healthineers, Computational Life. Some are academic institutions like the Johns Hopkins Institute for Computational Medicine, the Washington University Cardiac Bio Electricity and Arrhythmia Center. And then some are consortia such as echos, which stands for enhanced cardiac care through extensive sensing. And that's a consortium of academic and industrial partners. These other organizations have different aims of course, but most are focused on cardiac electrophysiology and disorders of cardiac rhythm. Most use both physiologically based and data driven methods, such as artificial intelligence and deep learning. Most are focused on the heart itself without robust representations of the vascular load, and none implement neuro hormonal regulation or mechanical adaptation and remodeling, nor aim for the ultimate realization of close loop therapeutics. By autonomous closed loop therapeutics, I mean, using the cardiovascular bio digital twin, not only to predict cardiovascular events and determine optimal therapeutic interventions for maintenance of health or for disease management, but also to actually deliver those therapeutic interventions. This means not only the need for smart sensors, but also for smart actuators, smart robotics, and various nanotechnology devices. Going back to my earlier comparisons to commercial aviation, autonomous closed loop therapeutics means not only maintenance of the plane and its parts, but also the actual flying of the plane in autopilot. In the beginning, we'll include the physician pilots in the loop, but the ultimate goal is an autonomous bio digital twin system for the cardiovascular system. The goal of realizing autonomous closed loop therapeutics in humans is obviously a more longterm goal. We're expecting to demonstrate that first in animal models. And our initial thinking was that this demonstration would be possible by the year 2030, that is 10 years. As of this month, we were planning ways of reaching this target even sooner. Finally, I would also like to add that by setting our aims at such a high ambition target, we drive the quality and accuracy of old milestones along the way. Thank you. This concludes my presentation. I appreciate your interest and attention. Please enjoy the remaining sessions, thank you.

>> Narrator: From around the globe, it's "theCUBE" with digital coverage of Smart Data Marketplaces brought to you by Io-Tahoe. >> We're back. We're talking about smart data and have been for several weeks now. Really it's all about injecting intelligence and automation into the data life cycle of the data pipeline. And today we're drilling into Smart Data Marketplaces, really trying to get to that self-serve, unified, trusted, secured, and compliant data models. And this is not trivial. And with me to talk about some of the nuances involved in actually getting there with folks that have experienced doing that. They'd send a series of digital evangelist with Tata Consultancy Services, TCS. And Ajay Vohora is back, he's the CEO of Io-Tahoe. Guys, great to see you, thanks so much for coming on. >> Good to see you, Dave. >> Hey Dave. >> Ajay, let's start with you. Let's set up the sort of smart data concept. What's that all about? What's your perspective? >> Yeah, so I mean, our way of thinking about this is you you've got data, it has latent value, and it's really about discovering what the properties of that data. Does it have value? Can you put that data to work? And the way we go about that with algorithms and machine learning, to generate signals in that data identified patterns, that means we can start to discover how can we apply that data to down stream? What value can we unlock for a customer and business? >> Well, so you've been on this, I mean, really like a laser, why? I mean, why this issue? Did you see a gap in the marketplace in terms of talking to customers and maybe you can help us understand the origin? >> Yeah, I think that the gap has always been there. They've been, it's become more apparent over recent times with big data. So the ability to manually work with volumes of data in petabytes is prohibitively complex and expensive. So you need the different routes, you need different set of tools and methods to do that. Metadata are data that you can understand about data. That's what we at Io-Tahoe focus on, discovering and generating that metadata. That ready, that analogy to automate those data ops processes. So the gap David, is being felt by a business owner prizes and all sectors, healthcare, telecoms, and putting that data to work. >> So Ved, Let's talk a little bit about your role. You work with a lot of customers. I see you as an individual in a company who's really trying to transform what is a very challenging industry. That's sort of ripe for transformation, but maybe you could give us your perspective on this, what kind of signals you're looking for from the data pipeline and we'll get into how you are helping transform healthcare? >> Thanks, David. You know I think this year has been one of those years where we've all realized about this idea of unknown unknowns, where something comes around the corner that you're completely not expecting. And that's really hard to plan for obviously. And I think what we need is the ability to find early signals and be able to act on things as soon as you can. Sometimes, and you know, the COVID-19 scenario of course, is hopefully once in a generation thing, but most businesses struggle with the idea that they may have the data there in their systems, but they still don't know which bit of that is really valuable and what are the signals they should be watching for. And I think the interesting thing here is the ability for us to extract from a massive data, the most critical and important signals. And I think that's where we want to focus on. >> And so, talk a little bit about healthcare in particular and sort of your role there, and maybe at a high level. How Tata and your eco-system are helping transform healthcare? >> So if you look at healthcare, you've got the bit where people need active intervention from a medical professional. And then you've got this larger body of people, typically elderly people who aren't unwell, but they have frailties. They have underlying conditions and they're very vulnerable, especially in the world that we're in now in the post-COVID-19 scenario. And what we were trying to look at is how do we keep people who are elderly, frail and vulnerable? How can we keep them safe in their own homes rather than moving to care homes, where there has been an incredibly high level of infection for things like COVID-19. So the world works better if you can keep people safe in their own homes, if you can see the slide we've got. We're also talking about a world where care is expensive. In most Western countries, especially in Western Europe, the number of elderly people is increasing as a percentage of the population, quite significantly, and resources just are not keeping up. We don't have enough people. We don't have enough funding to look after them effectively. And the care industry that used to do that job has been struggling of late. So it's kind of a perfect storm for the need for technology intervention there. And in that space, what we're saying is the data signal that we want to receive are exactly what as a relative, or a son or daughter you might want from a parent to say, "Everything's okay. "We know that today's been just like every other day "there are no anomalies in your daily living." If you could get the signals that might tell us that something's wrong, something not quite right. We don't need very complex diagnostics. We just need to know something's not quite right, that my dad hasn't woken up as has always at seven o'clock, but till nine o'clock there's no movement. Maybe he's a bit unwell. It's that kind of signal that if we can generate, can make a dramatic difference to how we can look out for these people, whether through professional carers or through family members. So what we're looking to do is to sensor-enable homes of vulnerable people so that those data signals can come through to us in a curated manner, in a way that protects privacy and security of the individual, but gives the right people, which is carers or chosen family members the access to the signals, which is alerts that might tell you there was too much movement at night, or the front door was been left open, things like that that would give you a reason to call him and check. Everybody has spoken to in this always has an example of an uncle or a relative or parent that they've looked after. And all they're looking for is a signal. Even stories like my father's neighbor calls me when he doesn't open his curtain by 11 o'clock, that actually, if you think about it is a data signal that something might be all right. And I think what we're trying to do with technology is create those kinds of data signals because ultimately, the healthcare system works much better if you can prevent rather than cure. So every dollar that you put into prevention saves maybe $3 to $5 downstream. The economic summit also are working our favor. >> And those signals give family members the confidence to act. Ajay, it is interesting to hear what Ved was talking about in terms of the unknowns, because when you think about the early days of the computer industry, there were a lot of knowns, the processes were known. It was like the technology was the big mystery. Now, I feel like it's flipped. We've certainly seen that with COVID. The technology is actually quite well understood and quite mature and reliable. One of the examples is automated data discovery, which is something that you guys have been been focused on at Io-Tahoe. Why is automated data discovery such an important component of a smart data life cycle? >> Yeah. I mean, if we look David at the schematic and this one moves from left to right where right at the outset with that latent data, the value is late because you don't know. Does it have? Can it be applied? Can that data be put to work or not? And the objective really is about driving some form of exchange or monetization of data. If you think about it in insurance or healthcare, you've got lots of different parties, providers, payers, patients, everybody's looking to make some kind of an exchange of information. The difficulty is in all of those organizations, that data sits within its own system. So data discovery, if we drill into the focus itself that, it's about understanding which data has value, classifying that data so that it can be applied and being able to tag it so that it can then be put to use it's the real enabler for DataOps. >> So maybe talk a little bit more about this. We're trying to get to self-service. It's something that we hear a lot about. You mentioned putting data to work. It seems to me that if the business can have access to that data and serve themselves, that's the way to put data to work. Do you have thoughts on that? >> Yeah, I mean, thinking back in terms of what IT and the IT function in a business could provide, there have been limitations around infrastructure, around scaling, around compute. Now that we're in an economy that is digital driven by API's your infrastructure, your data, your business rules, your intelligence, your models, all of those on the back of an API. So the options become limitless. How you can drive value and exchange that data. What that allows us to do is to be more creative, if we can understand what data has value for what use case. >> Ved, Let's talk a little bit about the US healthcare system. It's a good use case. I was recently at a chief data officer conference and listening to the CDO of Johns Hopkins, talk about the multiple different formats that they had to ingest to create that COVID map. They even had some PDFs, they had different definitions, and that's sort of underscored to me, the state of the US healthcare industry. I'm not as familiar with the UK and Europe generally, but I am familiar with the US healthcare system and the diversity that's there, the duplication of information and the like, maybe you could sort of summarize your perspectives and give us kind of the before and your vision of the after, if you will? >> The use of course, is particularly large and complex system. We all know that. We also know, I think there is some research that suggests that in the US the per-capita spend on healthcare is among the highest in the world. I think it's like 70%, and that compares to what just under 9%, which is going to be European, typical European figure. So it's almost double of that, but the outcomes are still vastly poor. When Ajay and I were talking earlier, I think we believe that there is a concept of a data friction. When you've got multiple players in an eco-system, trying to provide a single service as a patient, you're receiving a single health care service. There are probably a dozen up to 20 different organizations that have to collaborate to make sure you get that top of the line health care service. That kind of investment deserves. And what prevents it from happening very often is what we would call data friction, which is the ability to effectively share data. Something as simple as a healthcare record, which says, "This is Dave, this is Ved, this is Ajay." And when we go to hospital for anything, whatever happens, that healthcare record can capture all the information and tie to us as an individual. And if you go to a different hospital, then that record will follow you. This is how you would expect that to be implemented, but I think we're still on that journey. There are lots and lots of challenges. I've seen anecdotal data around people who suffered because they weren't carrying a card when they went into hospital, because that card has the critical elements of data, but in today's world, should you need to carry a piece of paper or can the entire thing be a digital data flow that can easily be, can certainly navigate through lack of paper and those kinds of things. So the vision that I think we need to be looking at is an effective data exchange or marketplace back with a kind of a backbone model where people agree and sign off a data standard, where each individual's data is always tied to the individual. So if you were to move States, if you would move providers, change insurance companies, none of that would impact your medical history, your data, and the ability to have the other care and medical professionals to access the data at the point of need and at the point of healthcare delivery. So I think that's the vision we're looking at, but as you rightly you said that there are enormous number of challenges, partly because of the history, of healthcare, I think it was technology enablement of healthcare started early. So there's a lot of legacy as well. So we shouldn't trivialize the challenges that the industry faces, but that I think is the way we want to go. >> Well, privacy is obviously a huge one, and a lot of the processes are built around non-digital processes and what you're describing as a flip for digital first. I mean, as a consumer, as a patient, I want an app for that. So I can see my own data. I can see price, price transparency, give access to people that I think need it. And that is a daunting task, isn't it? >> Absolutely. And I think the implicit idea and what you just said, which is very powerful is also on the app you want to control. >> Yes. >> And sometimes you want to be able to change access on data at that point. Right now, I'm at the hospital. I would like to access my data. And when I walk away or maybe three days later, I want to revoke that access. It's that level of control. And absolutely, it is by no means a trivial problem, but I think that's where you need the data automation tools. If you try to do any of this manually, we'd be here for another decade trying to solve this, but that's where tools like Io-Tahoe come in because to do this, a lot of the heavy lifting behind the scenes has to be automated. There has to be a machine churning that and presenting the simpler options. And I know you were talking about it just a little while ago Ajay. I was reminded of the example of a McDonald's or a Coke, because the sales store idea that you can go in and you can do your own ordering off a menu, or you can go in and select five different flavors from a Coke machine and choose your own particular blend of Coke. It's a very trivial example, but I think that's the word we want to get to with access of data as well. If it was that simple for consumers, for enterprise, business people, for doctors, then that's where we ultimately want to be able to arrive. But of course, to make something very simple for the end-user, somebody has to solve for complexity behind the scenes. >> So Ajay, it seems to me Ajay there're two major outcomes here. One is of course, the most important I guess, is patient outcomes, and the other is cost. I mean, they talked about the cost issues, we all, US especially understand the concerns about rising costs of healthcare. My question is this, how does a Smart Data Marketplace fit into achieving those two very important outcomes? >> When we think about how automation is enabling that, where we've got different data formats, the manual tasks are involved, duplication of information. The administrative overhead of that alone and the work, the rework, and the cycles of work that generates. That's really what we're trying to help with data is to eliminate that wasted effort. And with that wasted effort comes time and money to employ people to work through those siloed systems. So getting to the point where there is an exchange in a marketplace just as they would be for banking or insurance is really about automating the classification of data to make it available to a system that can pick it up through an API and to run a machine learning model and to manage a workflow, a process. >> Right, so you mentioned backing insurance, you're right. I mean, we've actually come a long way and just in terms of, know the customer and applying that to know the patient would be very powerful. I'm interested in what you guys are doing together, just in terms of your vision. Are you going to market together, kind of what you're seeing in terms of promoting or enabling this self-service, self-care. Maybe you could talk a little bit about Io-Tahoe and Tata, the intersection at the customer? >> Sure. I think we've been very impressed with the TCS vision of 4.0, how the re-imagining traditional industries, whether it's insurance, banking, healthcare, and bringing together automation, agile processes, robotics, AI, and once those enablers, technology may have brought together to re-imagine how those services can be delivered digitally. All of those are dependent on data. So we see that there's a really good fit here to enable understanding the legacy, the historic situation that has built up over time in an organization, a business and to help shine a light on what's meaningful in that to migrate to the cloud or to drive a digital twin, data science project. >> Ved, anything you can add to that? >> Sure. I mean, we do take the business 4.0 model quite seriously in terms of a lens with which you look at any industry, and what I talked about in healthcare was an example of that. And for us business 4.0, means a few very specific things. The technology that we use in today's verse should be agile, automated, intelligent, and cloud-based. These have become kind of hygiene factors now. On top of that, the businesses we build should be mass customized. They should be risk embracing. They should engage ecosystems, and they should strive for exponential value, not 10% growth year on year, but doubling, tripling every three, four years, because that's the competition that most businesses are facing today. And within that, the Tata group itself, is an extremely purpose-driven business. We really believe that we exist to serve communities, not just one specific set, i.e. shareholders, but the broader community in which we live and work. And I think this framework also allows us to apply that to things like healthcare, to education and to a whole vast range of areas where, everybody has a vision of using data science or doing really clever stuff at the gradients. But what becomes clear is, to do any of that, the first thing you need is a foundational piece. And as a foundation isn't right, then no matter how much you invest in the data science tools you won't get the answers you want. And the work we're doing with the Io-Tahoe really, for me, is particularly exciting because it sorts out that foundational piece. And at the end of it, to make all of this, again, I will repeat that, to make it simple and easy to use for the end user, whoever that is. And I realized that I'm probably the first person who's used fast food as a shining example for healthcare in this discussion, but you can make a lot of different examples. And today, if you press a button and start a car, that's simplicity, but someone has solved for that. And that's what we want to do with data as well. >> Yeah, that makes a lot of sense to me. We talk a lot about digital transformation and a digital business, and I would observe that a digital business puts data at the core. And you can certainly be the best example. There is, of course, Google is an all digital business, but take a company like Amazon, Who's got obviously a massive physical component to its business. Data is at the core. And that's exactly my takeaway from this discussion. Both of you are talking about putting data at the core, simplifying it, making sure that it's compliant, and healthcare it's taking longer, 'cause it's such a high risk industry, but it's clearly happening, COVID I guess, was an accelerant. Guys, Ajay, I'll start with you. Any final thoughts that you want to leave the audience with? _ Yeah, we're really pleased to be working with TCS. We've been able to explore how we're able to put dates to work in a range of different industries. Ved has mentioned healthcare, telecoms, banking and insurance are others. And the same impact they speak to whenever we see the exciting digital transformations that are being planned, being able to accelerate those, unlock the value from data is where we're having a purpose. And it's good that we can help patients in the healthcare sector, consumers in banking realize a better experience through having a more joined up marketplace with their data. >> Ved, you know what excites me about this conversation is that, as a patient or as a consumer, if I'm helping loved ones, I can go to the web and I can search, and I can find a myriad of possibilities. What you're envisioning here is really personalizing that with real time data. And that to me is a game changer. Your final thoughts? >> Thanks, David. I absolutely agree with you that the idea of data centricity and simplicity are absolutely forefront, but I think if we were to design an organization today, you might design it very differently to how most companies today are structured. And maybe Google and Amazon are probably better examples of that because you almost have to think of a business as having a data engine room at its core. A lot of businesses are trying to get to that stage, whereas what we call digital natives, are people who have started life with that premise. So I absolutely agree with you on that, but extending that a little bit. If you think of most industries as eco-systems that have to collaborate, then you've got multiple organizations who will also have to exchange data to achieve some shared outcomes. Whether you look at supply chains of automobile manufacturers or insurance companies or healthcares we've been talking about. So I think that's the next level of change we want to be able to make, which is to be able to do this at scale across organizations at industry level or in population scheme for healthcare. >> Yeah, Thank you for that. Go ahead Ajay. >> David that's where it comes back to again, the origination where we've come from in big data. The volume of data combined with the specificity of individualizing, personalizing a service around an individual amongst that massive data from different providers is where is exciting, that we're able to have an impact. >> Well, and you know Ajay, I'm glad you brought that up because in the early days of big data, there were only a handful of companies, the biggest financial institutions. Obviously, the internet giants who had all these engineers that were able to take advantage of it. But with companies like Io-Tahoe and others, and the investments that the industry has made in terms of providing the tools and simplifying that, especially with machine intelligence and AI and machine learning, these are becoming embedded into the tooling so that everybody can have access to them, small, medium, and large companies. That's really, to me, the exciting part of this new era that we're entering. >> Yeah, and we have placed those, take it down to the level of not-for-profits and smaller businesses that want to innovate and leapfrog into, to growing their digital delivery of their service. >> And I know a lot of time, but Ved, what you were saying about TCS's responsibility to society, I think is really, really important. Large companies like yours, I believe, and you clearly do as well, have a responsibility to society more than just a profit. And I think, Big Tech it's a better app in a lot of cases, but so thank you for that and thank you gentlemen for this great discussion. I really appreciate it. >> Thanks David. >> Thank you. >> All right, keep it right there. I'll be right back right after this short break. This is Dave Vellante for theCUBE. (calm music)

>> Narrator: From around the globe, it's "theCUBE" with digital coverage of Smart Data Marketplaces brought to you by Io-Tahoe. >> We're back. We're talking about smart data and have been for several weeks now. Really it's all about injecting intelligence and automation into the data life cycle of the data pipeline. And today we're drilling into Smart Data Marketplaces, really trying to get to that self-serve, unified, trusted, secured, and compliant data models. And this is not trivial. And with me to talk about some of the nuances involved in actually getting there with folks that have experienced doing that. They'd send a series of digital evangelist with Tata Consultancy Services, TCS. And Ajay Vohora is back, he's the CEO of Io-Tahoe. Guys, great to see you, thanks so much for coming on. >> Good to see you, Dave. >> Hey Dave. >> Ajay, let's start with you. Let's set up the sort of smart data concept. What's that all about? What's your perspective? >> Yeah, so I mean, our way of thinking about this is you you've got data, it has latent value, and it's really about discovering what the properties of that data. Does it have value? Can you put that data to work? And the way we go about that with algorithms and machine learning, to generate signals in that data identified patterns, that means we can start to discover how can we apply that data to down stream? What value can we unlock for a customer and business? >> Well, so you've been on this, I mean, really like a laser, why? I mean, why this issue? Did you see a gap in the marketplace in terms of talking to customers and maybe you can help us understand the origin? >> Yeah, I think that the gap has always been there. They've been, it's become more apparent over recent times with big data. So the ability to manually work with volumes of data in petabytes is prohibitively complex and expensive. So you need the different routes, you need different set of tools and methods to do that. Metadata are data that you can understand about data. That's what we at Io-Tahoe focus on, discovering and generating that metadata. That ready, that analogy to automate those data ops processes. So the gap David, is being felt by a business owner prizes and all sectors, healthcare, telecoms, and putting that data to work. >> So Ved, Let's talk a little bit about your role. You work with a lot of customers. I see you as an individual in a company who's really trying to transform what is a very challenging industry. That's sort of ripe for transformation, but maybe you could give us your perspective on this, what kind of signals you're looking for from the data pipeline and we'll get into how you are helping transform healthcare? >> Thanks, David. You know I think this year has been one of those years where we've all realized about this idea of unknown unknowns, where something comes around the corner that you're completely not expecting. And that's really hard to plan for obviously. And I think what we need is the ability to find early signals and be able to act on things as soon as you can. Sometimes, and you know, the COVID-19 scenario of course, is hopefully once in a generation thing, but most businesses struggle with the idea that they may have the data there in their systems, but they still don't know which bit of that is really valuable and what are the signals they should be watching for. And I think the interesting thing here is the ability for us to extract from a massive data, the most critical and important signals. And I think that's where we want to focus on. >> And so, talk a little bit about healthcare in particular and sort of your role there, and maybe at a high level. How Tata and your eco-system are helping transform healthcare? >> So if you look at healthcare, you've got the bit where people need active intervention from a medical professional. And then you've got this larger body of people, typically elderly people who aren't unwell, but they have frailties. They have underlying conditions and they're very vulnerable, especially in the world that we're in now in the post-COVID-19 scenario. And what we were trying to look at is how do we keep people who are elderly, frail and vulnerable? How can we keep them safe in their own homes rather than moving to care homes, where there has been an incredibly high level of infection for things like COVID-19. So the world works better if you can keep people safe in their own homes, if you can see the slide we've got. We're also talking about a world where care is expensive. In most Western countries, especially in Western Europe, the number of elderly people is increasing as a percentage of the population, quite significantly, and resources just are not keeping up. We don't have enough people. We don't have enough funding to look after them effectively. And the care industry that used to do that job has been struggling of late. So it's kind of a perfect storm for the need for technology intervention there. And in that space, what we're saying is the data signal that we want to receive are exactly what as a relative, or a son or daughter you might want from a parent to say, "Everything's okay. "We know that today's been just like every other day "there are no anomalies in your daily living." If you could get the signals that might tell us that something's wrong, something not quite right. We don't need very complex diagnostics. We just need to know something's not quite right, that my dad hasn't woken up as has always at seven o'clock, but till nine o'clock there's no movement. Maybe he's a bit unwell. It's that kind of signal that if we can generate, can make a dramatic difference to how we can look out for these people, whether through professional carers or through family members. So what we're looking to do is to sensor-enable homes of vulnerable people so that those data signals can come through to us in a curated manner, in a way that protects privacy and security of the individual, but gives the right people, which is carers or chosen family members the access to the signals, which is alerts that might tell you there was too much movement at night, or the front door was been left open, things like that that would give you a reason to call him and check. Everybody has spoken to in this always has an example of an uncle or a relative or parent that they've looked after. And all they're looking for is a signal. Even stories like my father's neighbor calls me when he doesn't open his curtain by 11 o'clock, that actually, if you think about it is a data signal that something might be all right. And I think what we're trying to do with technology is create those kinds of data signals because ultimately, the healthcare system works much better if you can prevent rather than cure. So every dollar that you put into prevention saves maybe $3 to $5 downstream. The economic summit also are working our favor. >> And those signals give family members the confidence to act. Ajay, it is interesting to hear what Ved was talking about in terms of the unknowns, because when you think about the early days of the computer industry, there were a lot of knowns, the processes were known. It was like the technology was the big mystery. Now, I feel like it's flipped. We've certainly seen that with COVID. The technology is actually quite well understood and quite mature and reliable. One of the examples is automated data discovery, which is something that you guys have been been focused on at Io-Tahoe. Why is automated data discovery such an important component of a smart data life cycle? >> Yeah. I mean, if we look David at the schematic and this one moves from left to right where right at the outset with that latent data, the value is late because you don't know. Does it have? Can it be applied? Can that data be put to work or not? And the objective really is about driving some form of exchange or monetization of data. If you think about it in insurance or healthcare, you've got lots of different parties, providers, payers, patients, everybody's looking to make some kind of an exchange of information. The difficulty is in all of those organizations, that data sits within its own system. So data discovery, if we drill into the focus itself that, it's about understanding which data has value, classifying that data so that it can be applied and being able to tag it so that it can then be put to use it's the real enabler for that per day drops. >> So maybe talk a little bit more about this. We're trying to get to self-service. It's something that we hear a lot about. You mentioned putting data to work. It seems to me that if the business can have access to that data and serve themselves, that's the way to put data to work. Do you have thoughts on that? >> Yeah, I mean, thinking back in terms of what IT and the IT function in a business could provide, there have been limitations around infrastructure, around scaling, around compute. Now that we're in an economy that is digital driven by API's your infrastructure, your data, your business rules, your intelligence, your models, all of those on the back of an API. So the options become limitless. How you can drive value and exchange that data. What that allows us to do is to be more creative, if we can understand what data has value for what use case. >> Ved, Let's talk a little bit about the US healthcare system. It's a good use case. I was recently at a chief data officer conference and listening to the CDO of Johns Hopkins, talk about the multiple different formats that they had to ingest to create that COVID map. They even had some PDFs, they had different definitions, and that's sort of underscored to me, the state of the US healthcare industry. I'm not as familiar with the UK and Europe generally, but I am familiar with the US healthcare system and the diversity that's there, the duplication of information and the like, maybe you could sort of summarize your perspectives and give us kind of the before and your vision of the after, if you will? >> The use of course, is particularly large and complex system. We all know that. We also know, I think there is some research that suggests that in the US the per-capita spend on healthcare is among the highest in the world. I think it's like 70%, and that compares to what just under 9%, which is going to be European, typical European figure. So it's almost double of that, but the outcomes are still vastly poor. When Ajay and I were talking earlier, I think we believe that there is a concept of a data friction. When you've got multiple players in an eco-system, trying to provide a single service as a patient, you're receiving a single health care service. There are probably a dozen up to 20 different organizations that have to collaborate to make sure you get that top of the line health care service. That kind of investment deserves. And what prevents it from happening very often is what we would call data friction, which is the ability to effectively share data. Something as simple as a healthcare record, which says, "This is Dave, this is Ved, this is Ajay." And when we go to hospital for anything, whatever happens, that healthcare record can capture all the information and tie to us as an individual. And if you go to a different hospital, then that record will follow you. This is how you would expect that to be implemented, but I think we're still on that journey. There are lots and lots of challenges. I've seen anecdotal data around people who suffered because they weren't carrying a card when they went into hospital, because that card has the critical elements of data, but in today's world, should you need to carry a piece of paper or can the entire thing be a digital data flow that can easily be, can certainly navigate through lack of paper and those kinds of things. So the vision that I think we need to be looking at is an effective data exchange or marketplace back with a kind of a backbone model where people agree and sign off a data standard, where each individual's data is always tied to the individual. So if you were to move States, if you would move providers, change insurance companies, none of that would impact your medical history, your data, and the ability to have the other care and medical professionals to access the data at the point of need and at the point of healthcare delivery. So I think that's the vision we're looking at, but as you rightly you said that there are enormous number of challenges, partly because of the history, of healthcare, I think it was technology enablement of healthcare started early. So there's a lot of legacy as well. So we shouldn't trivialize the challenges that the industry faces, but that I think is the way we want to go. >> Well, privacy is obviously a huge one, and a lot of the processes are built around non-digital processes and what you're describing as a flip for digital first. I mean, as a consumer, as a patient, I want an app for that. So I can see my own data. I can see price, price transparency, give access to people that I think need it. And that is a daunting task, isn't it? >> Absolutely. And I think the implicit idea and what you just said, which is very powerful is also on the app you want to control. >> Yes. >> And sometimes you want to be able to change access on data at that point. Right now, I'm at the hospital. I would like to access my data. And when I walk away or maybe three days later, I want to revoke that access. It's that level of control. And absolutely, it is by no means a trivial problem, but I think that's where you need the data automation tools. If you try to do any of this manually, we'd be here for another decade trying to solve this, but that's where tools like Io-Tahoe come in because to do this, a lot of the heavy lifting behind the scenes has to be automated. There has to be a machine churning that and presenting the simpler options. And I know you were talking about it just a little while ago Ajay. I was reminded of the example of a McDonald's or a Coke, because the sales store idea that you can go in and you can do your own ordering off a menu, or you can go in and select five different flavors from a Coke machine and choose your own particular blend of Coke. It's a very trivial example, but I think that's the word we want to get to with access of data as well. If it was that simple for consumers, for enterprise, business people, for doctors, then that's where we ultimately want to be able to arrive. But of course, to make something very simple for the end-user, somebody has to solve for complexity behind the scenes. >> So Ajay, it seems to me Ajay there're two major outcomes here. One is of course, the most important I guess, is patient outcomes, and the other is cost. I mean, they talked about the cost issues, we all, US especially understand the concerns about rising costs of healthcare. My question is this, how does a Smart Data Marketplace fit into achieving those two very important outcomes? >> When we think about how automation is enabling that, where we've got different data formats, the manual tasks are involved, duplication of information. The administrative overhead of that alone and the work, the rework, and the cycles of work that generates. That's really what we're trying to help with data is to eliminate that wasted effort. And with that wasted effort comes time and money to employ people to work through those siloed systems. So getting to the point where there is an exchange in a marketplace just as they would be for banking or insurance is really about automating the classification of data to make it available to a system that can pick it up through an API and to run a machine learning model and to manage a workflow, a process. >> Right, so you mentioned backing insurance, you're right. I mean, we've actually come a long way and just in terms of, know the customer and applying that to know the patient would be very powerful. I'm interested in what you guys are doing together, just in terms of your vision. Are you going to market together, kind of what you're seeing in terms of promoting or enabling this self-service, self-care. Maybe you could talk a little bit about Io-Tahoe and Tata, the intersection at the customer? >> Sure. I think we've been very impressed with the TCS vision of 4.0, how the re-imagining traditional industries, whether it's insurance, banking, healthcare, and bringing together automation, agile processes, robotics, AI, and once those enablers, technology may have brought together to re-imagine how those services can be delivered digitally. All of those are dependent on data. So we see that there's a really good fit here to enable understanding the legacy, the historic situation that has built up over time in an organization, a business and to help shine a light on what's meaningful in that to migrate to the cloud or to drive a digital twin, data science project. >> Ved, anything you can add to that? >> Sure. I mean, we do take the business 4.0 model quite seriously in terms of a lens with which you look at any industry, and what I talked about in healthcare was an example of that. And for us business 4.0, means a few very specific things. The technology that we use in today's verse should be agile, automated, intelligent, and cloud-based. These have become kind of hygiene factors now. On top of that, the businesses we build should be mass customized. They should be risk embracing. They should engage ecosystems, and they should strive for exponential value, not 10% growth year on year, but doubling, tripling every three, four years, because that's the competition that most businesses are facing today. And within that, the Tata group itself, is an extremely purpose-driven business. We really believe that we exist to serve communities, not just one specific set, i.e. shareholders, but the broader community in which we live and work. And I think this framework also allows us to apply that to things like healthcare, to education and to a whole vast range of areas where, everybody has a vision of using data science or doing really clever stuff at the gradients. But what becomes clear is, to do any of that, the first thing you need is a foundational piece. And as a foundation isn't right, then no matter how much you invest in the data science tools you won't get the answers you want. And the work we're doing with the Io-Tahoe really, for me, is particularly exciting because it sorts out that foundational piece. And at the end of it, to make all of this, again, I will repeat that, to make it simple and easy to use for the end user, whoever that is. And I realized that I'm probably the first person who's used fast food as a shining example for healthcare in this discussion, but you can make a lot of different examples. And today, if you press a button and start a car, that's simplicity, but someone has solved for that. And that's what we want to do with data as well. >> Yeah, that makes a lot of sense to me. We talk a lot about digital transformation and a digital business, and I would observe that a digital business puts data at the core. And you can certainly be the best example. There is, of course, Google is an all digital business, but take a company like Amazon, Who's got obviously a massive physical component to its business. Data is at the core. And that's exactly my takeaway from this discussion. Both of you are talking about putting data at the core, simplifying it, making sure that it's compliant, and healthcare it's taking longer, 'cause it's such a high risk industry, but it's clearly happening, COVID I guess, was an accelerant. Guys, Ajay, I'll start with you. Any final thoughts that you want to leave the audience with? _ Yeah, we're really pleased to be working with TCS. We've been able to explore how we're able to put dates to work in a range of different industries. Ved has mentioned healthcare, telecoms, banking and insurance are others. And the same impact they speak to whenever we see the exciting digital transformations that are being planned, being able to accelerate those, unlock the value from data is where we're having a purpose. And it's good that we can help patients in the healthcare sector, consumers in banking realize a better experience through having a more joined up marketplace with their data. >> Ved, you know what excites me about this conversation is that, as a patient or as a consumer, if I'm helping loved ones, I can go to the web and I can search, and I can find a myriad of possibilities. What you're envisioning here is really personalizing that with real time data. And that to me is a game changer. Your final thoughts? >> Thanks, David. I absolutely agree with you that the idea of data centricity and simplicity are absolutely forefront, but I think if we were to design an organization today, you might design it very differently to how most companies today are structured. And maybe Google and Amazon are probably better examples of that because you almost have to think of a business as having a data engine room at its core. A lot of businesses are trying to get to that stage, whereas what we call digital natives, are people who have started life with that premise. So I absolutely agree with you on that, but extending that a little bit. If you think of most industries as eco-systems that have to collaborate, then you've got multiple organizations who will also have to exchange data to achieve some shared outcomes. Whether you look at supply chains of automobile manufacturers or insurance companies or healthcares we've been talking about. So I think that's the next level of change we want to be able to make, which is to be able to do this at scale across organizations at industry level or in population scheme for healthcare. >> Yeah, Thank you for that. Go ahead Ajay. >> David that's where it comes back to again, the origination where we've come from in big data. The volume of data combined with the specificity of individualizing, personalizing a service around an individual amongst that massive data from different providers is where is exciting, that we're able to have an impact. >> Well, and you know Ajay, I'm glad you brought that up because in the early days of big data, there were only a handful of companies, the biggest financial institutions. Obviously, the internet giants who had all these engineers that were able to take advantage of it. But with companies like Io-Tahoe and others, and the investments that the industry has made in terms of providing the tools and simplifying that, especially with machine intelligence and AI and machine learning, these are becoming embedded into the tooling so that everybody can have access to them, small, medium, and large companies. That's really, to me, the exciting part of this new era that we're entering. >> Yeah, and we have placed those, take it down to the level of not-for-profits and smaller businesses that want to innovate and leapfrog into, to growing their digital delivery of their service. >> And I know a lot of time, but Ved, what you were saying about TCS's responsibility to society, I think is really, really important. Large companies like yours, I believe, and you clearly do as well, have a responsibility to society more than just a profit. And I think, Big Tech it's a better app in a lot of cases, but so thank you for that and thank you gentlemen for this great discussion. I really appreciate it. >> Thanks David. >> Thank you. >> All right, keep it right there. I'll be right back right after this short break. This is Dave Vellante for theCUBE. (calm music)

>> From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this a CUBE conversation. >> Hello, and welcome to this CUBE conversation, I'm John Furrier, host of theCUBE here, in our Palo Alto, California studios, here with our quarantine crew. We're getting all the remote interviews during this time of COVID-19. We've got two great remote guests here, Rachini Moosavi who's the Executive Director of Analytical Services and Data Governance at UNC Healthcare, and Sonya Jordan, Enterprise Analytics Manager of Data Governance at UNC Health. Welcome to theCUBE, thanks for coming on. >> Thank you. >> Thanks for having us. >> So, I'm super excited. University of North Carolina, my daughter will be a freshman this year, and she is coming, so hopefully she won't have to visit UNC Health, but looking forward to having more visits down there, it's a great place. So, thanks for coming on, really appreciate it. Okay, so the conversation today is going to be about how data and how analytics are helping solve problems, and ultimately, in your case, serve the community, and this is a super important conversation. So, before we get started, talk about UNC Health, what's going on there, how you guys organize, how big is it, what are some of the challenges that you have? >> SO UNC Health is comprised of about 12 different entities within our hospital system. We have physician groups as well as hospitals, and we serve, we're spread throughout all of North Carolina, and so we serve the patients of North Carolina, and that is our primary focus and responsibility for our mission. As part of the offices Sonya and I are in, we are in the Enterprise Analytics and Data Sciences Office that serves all of those entities and so we are centrally located in the triangle area of North Carolina, which is pretty central to the state, and we serve all of our entities equally from our Analytics and Data Governance needs. >> John: You guys got a different customer base, obviously you've got the clinical support, and you got the business applications, you got to be agile, that's what it's all about today, you don't need to rely on IT support. How do you guys do that? What's the framework? How do you guys tackle that problem of being agile, having the data be available, and you got two different customers, you got all the compliance issues with clinical, I can only imagine all the regulations involved, and you've got the business applications. How do you handle those? >> Yeah, so for us in the roles that we are in, we are fully responsible for more of the data and analytics needs of the organization, and so we provide services that truly are balanced across our clinician group, so we have physicians, and nurses, and all of the other ancillary clinical staff that we support, as well as the operational needs as well, so revenue cycle, finance, pharmacy, any of those groups that are required in order to run a healthcare system. So, we balance our time amongst all of those and for the work that we take on and how we continuously support them is really based on governance at the end of the day. How we make decisions around what the priorities are and what needs to happen next, and requires the best insights, is really how we focus on what work we do next. As for the applications that we build, in our office, we truly only build analytical applications or products like visualizations within Tableau as well as we support data governance platforms and services and so we provide some of the tools that enable our end users to be able to interact with the information that we're providing around analytics and insights, at the end of the day. >> Sonya, what's your job? Your title is Analytics Manager of Data Governance, obviously that sounds broad but governance is obviously required in all things. What is your job, what is your day-to-day roles like? What's your focus? >> Well, my day-to-day operations is first around building a data governance program. I try to work with identifying customers who we can start partnering with so that we can start getting documentation and utilizing a lot of the programs that we currently have, such as certification, so when we talk about initiatives, this is one of the initiatives that we use to partner with our stakeholders in order to start bringing visibilities to the various assets, such as metrics, or universes that we want to certify, or dashboards, algorithm, just various lists of different types of assets that we certify that we like to partner with the customers in order for them to start documenting within the tools, so that we can bring visibility to what's available, really focusing on data literacy, helping people to understand what assets are available, not only what assets are available, but who owns them, and who own the asset, and what can they do with it, making sure that we have great documentation in order to be able to leverage literacy as well. >> So, I can only imagine with how much volume you guys are dealing from a data standpoint, and the diversity, that the data warehouse must be massive, or it must be architected in a way that it can be agile because the needs, of the diverse needs. Can you guys share your thoughts on how you guys look on the data warehouse challenge and opportunity, and what you guys are currently doing? >> Well, so- >> Yeah you go ahead, Rachini. >> Go ahead, Sonya. >> Well, last year we implemented a tool, an enterprise warehouse, basically behind a tool that we implemented, and that was an opportunity for Data Governance to really lay some foundation and really bring visibility to the work that we could provide for the enterprise. We were able to embed into probably about six or seven of the 13 initiatives, I was actually within that project, and with that we were able to develop our stewardship committee, our data governance council, and because Rachini managed Data Solutions, our data solution manager was able to really help with the architect and integration of the tools. >> Rachini, your thoughts on running the data warehouse, because you've got to have flexibility for new types of data sources. How do you look at that? >> So, as Sonya just mentioned, we upgraded our data warehouse platform just recently because of these evolving needs, and like a lot of healthcare providers out there, a lot of them are either one or the other EMRs that are top in the market. With our EMR, they provide their own data warehouse, so you have to factor almost the impact of what they bring to the table in with an addition to all of those other sources of data that you're trying to co-mingle and bring together into the same data warehouse, and so for us, it was time for us to evolve our data warehouse. We ended up deciding on trying to create a virtual data warehouse, and in doing so, with virtualization, we had to upgrade our platform, which is what created that opportunity that Sonya was mentioning. And by moving to this new platform we are now able to bring all of that into one space and it's enabled us to think about how does the community of analysts interact with the data? How do we make that available to them in a secure way? In a way that they can take advantage of reusable master data files that could be our source of truth within our data warehouse, while also being able to have the flexibility to build what they need in their own functional spaces so that they can get the wealth of information that they need out of the same source and it's available to everyone. >> Okay, so I got to ask the question, and I was trying to get the good stuff out first, but let's get at the reality of COVID-19. You got pre-COVID-19 pandemic, we're kind of in the middle of it, and people are looking at strategies to come out of it, obviously the world will be changed, higher with a lot of virtualization, virtual meetings, and virtual workforce, but the data still needs to be, the business still needs to run, but data will be changing different sources, how are you guys responding to that crisis because you're going to be leaned on heavily for more and more support? >> Yeah it's been non-stop since March (laughs). So, I'm going to tell you about the reporting aspects of it, and then I'd love to turn it over to Sonya to tell you about some of the great things that we've actually been able to do to it and enhance our data governance program by not wasting this terrible event and this opportunity that's come up. So, with COVID, when it kicked off back in March, we actually formed a war room to address the needs around reporting analytics and just insights that our executives needed, and so in doing so, we created within the first week, our first weekend actually, our first dashboard, and within the next two weeks we had about eight or nine other dashboards that were available. And we continuously add to that. Information is so critical to our executives, to our clinicians, to be able to know how to address the evolving needs of COVID-19 and how we need to respond. We literally, and I'm not even exaggerating, at this very moment we have probably, let's see, I think it's seven different forecasts that we're trying to build all at the same time to try and help us prepare for this new recovery, this sort of ramp up efforts, so to your point, it started off as we're shutting down so that we can flatten the curve, but now as we try to also reopen at the same time while we're still meeting the needs of our COVID patients, there's this balancing act that we're trying to keep up with and so analytics is playing a critical factor in doing that. >> Sonya, your thoughts. First of all, congratulations, and action is what defines the players from the pretenders in my mind, you're seeing that play out, so congratulations for taking great action, I know you're working hard. Sonya, your thoughts, COVID, it's putting a lot of pressure? It highlights the weaknesses and strengths of what's kind of out there, what's your thoughts? >> Well, it just requires a great deal of collaboration and making sure that you're documenting metrics in a way where you're factoring true definition because at the end of the day, this information can go into a dashboard that's going to be visualized across the organization, I think what COVID has done was really enhanced the need and the understanding of why data governance is important and also it has allowed us to create a lot of standardization, where we we're standardizing a lot of processes that we currently had in correct place but just enhancing them. >> You know, not to go on a tangent, but I will, it's funny how the reality has kind of pulled back, exposed a lot of things, whether it's the remote work situation, people are VPNing, not under provision with the IT side. On the data side, everyone now understands the quality of the data. I mean, I got my kids talking progression analysis, "Oh, the curves are all wrong," I mean people are now seeing the science behind the data and they're looking at graphs all the time, you guys are in the visualization piece, this really highlights the need of data as a story, because there's an impact, and two, quality data. And if you don't have the data, the story isn't being told and then misinformation comes out of it, and this is actually playing out in real time, so it's not like it's just a use case for the most analytics but this again highlights the value of proposition of what you guys do. What's your personal thoughts on all this because this really is playing out globally. >> Yeah, it's been amazing how much information is out there. So, we have been extremely blessed at times but also burdened at times by that amount of information. So, there's the data that's going through our healthcare system that we're trying to manage and wrangle and do that data storytelling so that people can drive those insights to very effective decisions. But there's also all of this external data that we're trying to be able to leverage as well. And this is where the whole sharing of information can sometimes become really hard to try and get ahead of, we leverage the Johns Hopkins data for some time, but even that, too, can have some hiccups in terms of what's available. We try to use our State Department of Health and Human Services data and they just about updated their website and how information was being shared every other week and it was making it impossible for us to ingest that into our dashboards that we were providing, and so there's really great opportunities but also risks in some of the information that we're pulling. >> Sonya, what's your thoughts? I was just having a conversation this morning with the Chief of Analytics and Insight from NOA which is the National Oceanic Administration, about weather data and forecasting weather, and they've got this community model where they're trying to get the edges to kind of come in, this teases out a template. You guys have multiple locations. As you get more democratized in the connection points, whether it's third-party data, having a system managing that is hard, and again, this is a new trend that's emerging, this community connection points, where I think you guys might also might be a template, and your multiple locations, what's your general thoughts on that because the data's coming in, it's now connected in, whether it's first-party to the healthcare system or third-party. >> Yeah, well we have been leveraging our data governance tool to try to get that centralized location, making sure that we obtain the documentations. Due to COVID, everything is moving very fast, so it requires us to really sit down and capture the information and when you don't have enough resources in order to do that, it's easy to miss some very important information, so really trying to encourage people to understand the reason why we have data governance tools in order for them to leverage, in order to capture the documentation in a way that it can tell the story about the data, but most of all, to be able to capture it in a way so that if that person happened to leave the organization, we're not spending a lot of time trying to figure out how was this information created, how was this dashboard designed, where are the requirements, where are the specifications, where are the key elements, where does that information live, and making sure we capture that up front. >> So, guys, you guys are using Informatica, how are they helping you? Obviously, they have a system they're getting some great feedback on, how are you using Informatica, how is it going, and how has that enabled you guys to be successful? >> Yeah, so we decided on Informatica after doing a really thorough vetting of all of the other vendors in the industry that could provide us these services. We've really loved the capabilities that we've been able to provide to our customers at this point. It's evolving, I think, for us, the ability to partner with a group like Prominence, to be able to really leverage the capabilities of Informatica and then be really super, super hyper focused on providing data literacy back to our end users and making that the full intent of what we're doing within data governance has really enabled us to take the tools and make it something that's specific to UNC Health and the needs that our end users are verbalizing and provide that to them in a very positive way. >> Sonya, they talk about this master catalog, and I've talked to the CEO of Informatica and all their leaders, governance is a big part of it, and I've always said, I've always kind of had a hard time, I'm an entrepreneur, I like to innovate, move fast, break things, which is kind of not the way you work in the data world, you don't want to be breaking anything, so how do you balance governance and compliance with innovation? This has been a key topic and I know that you guys are using their enterprise data catolog. Is that helping? How does that fit in, is that part of it? >> Well, yeah, so during our COVID initiatives and building these telos dashboards, these visualizations and forecast models for executive leaders, we were able to document and EMPower you, which we rebranded Axon to EMPower, we were able to document a lot of our dashboards, which is a data set, and pretty much document attributes and show lineage from EMPower to EDC, so that users would know exactly when they start looking at the visualization not only what does this information mean, but they're also able to see what other sources that that information impacts as well as the data lineage, where did the information come from in EDC. >> So I got to ask the question to kind of wrap things up, has Informatica helped you guys out now that you're in this crisis? Obviously you've implemented before, now that you're in the middle of it, have you seen any things that jumped out at you that's been helpful, and are there areas that need to be worked on so that you guys continue to fight the good fight, come out of this thing stronger than before you came in? >> Yeah, there is a lot of new information, what we consider as "aha" moments that we've been learning about, and how EMPower, yes there's definitely a learning curve because we implemented EDC and EMPower last year doing our warehouse implementation, and so there's a lot of work that still needs to be done, but based on where we were the first of the year, I can say we have evolved tremendously due to a lot of the pandemic issues that arised, and we're looking to really evolve even greater, and pilot across the entire organization so that they can start leveraging these tools for their needs. >> Rachini you got any thoughts on your end on what's worked, what you see improvements coming, anything to share? >> Yeah, so we're excited about some of the new capabilities like the marketplace for example that's available in Axon, we're looking forward to being able to take advantage of some of these great new aspects of the tool so that we can really focus more on providing those insights back to our end users. I think for us, during COVID, it's really been about how do we take advantage of the immediate needs that are surfacing. How do we build all of these dashboards in record-breaking time but also make sure that folks understand exactly what's being represented within those dashboards, and so being able to provide that through our Informatica tools and service it back to our end users, almost in a seamless way like it's built into our dashboards, has been a really critical factor for us, and feeling like we can provide that level of transparency, and so I think that's where as we evolve that we would look for more opportunities, too. How do we make it simple for people to get that immediate answers to their questions, of what does the information need without it feeling like they're going elsewhere for the information. >> Rachini, thank you so much for your insight, Sonya as well, thanks for the insight, and stay safe. Sonya, behind you, I was pointing out, that's your artwork, you painted that picture. >> Yes. >> Looks beautiful. >> Yes, I did. >> You got two jobs, you're an artist, and you're doing data governance. >> Yes, I am, and I enjoy painting, that's how I relax (laughs). >> Looks great, get that on the market soon, get that on the marketplace, let's get that going. Appreciate the time, thank you so much for the insights, and stay safe and again, congratulations on the hard work you're doing, I know there's still a lot more to do, thanks for your time, appreciate it. >> Thank you. >> Thank you. >> It's theCUBE conversation, I'm John Furrier at the Palo Alto studios, for the remote interviews with Informatica, I'm John Furrier, thanks for watching. (upbeat music)

>>from around the globe. It's the Cube with digital coverage of CONMEBOL. Future ready 2020. Brought to you by combo. >>Hi and welcome back. I'm Stew Minuteman, and we're at the Cube's coverage of Con Volt Future Ready. You've got the power panel to really dig in on the product announcements that happened at the event today. Joining me? We have three guests. First of all, we have Brenda Rajagopalan. He's the vice president of products. Sitting next to him is Don Foster, vice president of Storage Solutions. And in the far piece of the panel Mersereau, vice president of Global Channels and Alliances. All three of them with Conn Volt. Gentlemen, thanks all three of you for joining us. Exactly. All right, so first of all, great job on the launch. You know, these days with a virtual event doing, you know, the announcements, the engagement with the press and analyst, you know, having demos, customer discussions. It's a challenge to put all those together. And it has been, you know, engaging in interesting watch today. So we're going to start with you. You've been quite busy today explaining all the pieces, so just at a very high level if you put this really looks like the culmination of the update with Conn Volt portfolio new team new products compared to kind of a year, year and 1/2 ago. So just if you could start us off with kind of the high points, >>thank you still, yeah, absolutely exciting day for us today. You did comrade multiple reasons for that excitement and go through that we announced an exciting new portfolio today knows to not the culmination. It's a continuation off our journey, a bunch of new products that we launched today Hyper scaler X as a new integrated data protection appliance. We've also announced new offerings in data protection, backup and recovery, disaster recovery and complete data protection and lots of exciting updates for Hedwig and a couple of weeks like we introduced updates for metallic. So, yes, it's been a really exciting pain. Also, today happens to be the data, and we got to know that we are the leader in Gartner Magic Quadrant for the ninth consecutive. I am so a lot of goodness today for us. >>Excellent. Lots of areas that we definitely want to dig deep in to the pieces done. You know, we just heard a little bit about Hedvig was an acquisition a year ago that everybody's kind of looking at and saying Okay, you know, will this make them compete against some of their traditional partners? How we get integrated in So, baby, just give us one level deeper on the Hedvig piece on what that means to the portfolio? Yeah, sure, So I >>guess I mean, one of the key things that the random mentioned was the fact that had hyper scale that's is built off the head Day files. So that's a huge milestone for us. As we teased out maybe 10 months ago. Remember, Tomball, Go on the Cube and talking about, you know, kind of what our vision and strategy was of unifying data and storage management. Those hyper hyper scale X applying is a definite milestone improving out that direction. But beyond just the hyper scale ECs, we've also been driving on some of the more primary or modern workloads such as containers and the really interesting stuff we've come out with your recently is the kubernetes native integration that ties in all of the advanced component of the head to distribute storage architecture on the platform itself across multi cloud and on premise environments, making it really easy and policy driven. Um, for Dev, ops users and infrastructure users, the tie ins applications from a group, Friction >>Great and Mercer. There's some updates to the partner program and help us understand how all of these product updates they're gonna affect the kind of the partnerships and alliances beasts that you want. >>Absolutely. So in the time since our last meeting that go in the fall, which is actually right after I had just doing combo, we spent a good portion of the following six months really talking with partners, understanding the understand the impact of the partner program that we introduced last summer, looking at the data and really looking at barriers to evolve the program, which fell around three difference specific. Once you bet one was simplicity of the simplicity of the program, simplicity of understanding, rewards, levers and so forth. The second was paying for value was really helping, helping our partners to be profitable around things like deal registration on other benefits and then third was around co investment. So making sure that we get the right members in place to support our partners and investing in practices. Another training, another enablement around combo and we launched in over these things last week is a part of an evolution of that program. Today is a great follow on because in addition to all of the program evolutions that we we launched last week now we have an opportunity with our partners to have many more opportunities or kind of a thin into the wedge to open up new discussions with our customers now around all of these different use cases and capabilities. So back to that simplification angle, really driving more and more opportunities for those partners toe specific conversations around use cases. >>Okay, for this next question, I think it makes sense for you to start. Maybe maybe Don, you can get some commentary in two. But when he's firstly the announcements, there are some new products in the piece that you discuss but trying to understand, you know, when you position it, you know, do you call the portfolio? Is it a platform? You know, if I'm an existing Conn Volt customer, you know, how do I approach this? If I use something like metallic, how does that interplay with some of the new pieces that were discussed today. >>Sure, I can take the business. I'm sure Don and mostly will have more data to it. The simplest way to think about it is as a port for you. But contrary to how you would think about portfolio as independent products, what we have is a set off data management services granular. We're very aligned to the use case, which can all inter operate with each other. So maybe launched backup and recovery and disaster recovery. These can be handled separately, purchased separately and deployed standalone or for customers who want a combination of those capabilities. We also have a complete data protection are fine storage optimization, data governance E discovery in complaints are data management services that build on top off any of these capabilities now a very differentiating factor in our platform owners. All the services that you're talking about are delivered off the same software to make it simpler to manage to the same year. So it's very easy to start with one service and then just turn on the license and go to other services so I can understand the confusion is coming from but it's all the same. The customer simplicity and flexibility in mind, and it's all delivered off the same platform. So it is a portfolio built on a single Don. Would you like to add more to it? >>Yeah, I think the interesting thing due to add on top of that is where we're going with Hedvig Infrastructure, the head of distributed storage platform, uh, to to run this point, how everything is integrated and feed and work off of one another. That's the same idea that we have. We talked about unifying data and storage manager. So the intricate storage architecture components the way data might be maneuvered, whether it's for kubernetes for virtual machines, database environments, secondary storage, you name it, um, we are. We're quickly working to continue driving that level of of unification and integration between the portfolio and heads storage, distribute storage platforms and also deliver. So what you're seeing today going back to, I think wrong his first point. It's definitely not the culmination. It's just another step in the direction as we continue to innovate and integrate this >>product, and I think for our partners what this really does, it allows them to sell around customer use cases because it'll ask now if I have a d. Our use case. I can go after just PR. If I have a backup use case, I can just go after backup, and I don't have to try to sell more than that. Could be on what the customer is looking for in parallel that we can steal these things in line with the customer use case. So the customer has a lot of remote offices. They want to scale Hedvig across those they want to use the art of the cloud. They can scale these things independently, and it really gives us a lot of optionality that we didn't have before when we had a few monolithic products. >>Excellent. Really reminds me more of how I look at products if I was gonna go buy it from some of the public cloud providers living in a hybrid cloud. World, of course, is what your customers are doing. Help us understand a little bit, you know, Mercer talked about metallic and the azure partnership, but for the rest of the products, the portfolio that we're talking about, you know, does this >>kind >>of work seamlessly across my own data center hosting providers Public Cloud, you know, how does this fit into the cloud environment for your customer? >>Yes, it does. And I can start with this one goes to, um it's our strategy is cloud first, right? And you see it in every aspect of our product portfolio. In fact, I don't know if you got to see a keynote today, but Ron from Johns Hopkins University was remarking that comment has the best cloud native architectures. And that's primarily because of the innovation that we drive into the multi cloud reality. We have very deep partnerships with pretty much all the cloud vendors, and we use that for delivering joint innovation, a few things that when you think of it from a hybrid customers perspective, the most important need for them is to continue working on pram while still leveraging the cloud. And we have a lot of optimization is built into that, and then the next step of the journey is of course, making sure that you can recover to the cloud would be it work load. Typically your data quality and there's a lot of automation that we provide to our solutions and finally, Of course, if you're already in the cloud, whether you're running a science parents or cloud native, our software protects across all those use cases, either true sass with metallic auto downloadable software, backup and recovery so we can cover the interest victims of actual presence. You. We do definitely help customers in every stage of their hybrid cloud acceleration journey. >>And if you take a look at the Hedvig protect if you take a look at the head back to, um, the ability to work in a cloud native fast, it is essentially a part of the DNA of that storage of the storage, right? So whether you're running on Prem, whether you're running it about adjacent, set up inside the cloud head, that can work with any compute environment and any storage environment that you went to essentially then feed, we build this distributed storage, and the reason that becomes important. It's pretty much highlighted with our announcement around the kubernetes and container support is that it makes it really easy to start maneuvering data from on Prem to the cloud, um, from cloud to cloud region to region, sort of that high availability that you know as customers make cloud first a reality and their organizations starts to become a critical requirement or ensuring the application of and some of the things that we've done now with kubernetes in making all of our integration for how we deliver storage for the kubernetes and container environments and being that they're completely kubernetes native and that they can support a Google in AWS and Azure. And of course, any on premises community set up just showcases the value that we can provide in giving them that level of data portability. And it basically provides a common foundation layer, or how any sort of the Dev ops teams will be operating in the way that those state full container state workloads. Donna Oh, sorry. Go >>ahead, mark area >>because you mentioned the metallic and azure partnership announcement and I just want to get on that. And one thing that run dimension, which is we are really excited about the announcement of partnership with Microsoft and all the different news cases that opens up that are SAS platform with Azure with office 3 65 and all of the great application stack it's on. If you're at the same time, to run this point. We are a multi cloud company. And whether that is other of the hyper scale clouds Mess GC, P. Ali at Oracle and IBM, etcetera, or Oliver, Great service writer burners. We continue to believe in customer choice, and we'll continue to drive unique event innovations across all of those platforms. >>All right, Don, I was wondering if we could just dig in a little bit more on some other kubernetes pieces you were talking about. Let me look at just the maturation of storage in general. You know, how do we had state back into containers in kubernetes environments? Help us see, You know what you're hearing from your customers. And you know how you how you're ready to meet their needs toe not only deliver storage, but as you say, Really? You know, full data protection in that environment? >>Certainly it So I mean, there's been a number of enhancements that happened in the kubernetes environment General over the last two years. One of the big ones was the creation of what the visit environment calls a persistent volume. And what that allows you to do is to really present storage to a a communities application. Do it be typically through what's called a CSR container storage interface that allows for state full data to be written, storage and be handled and reattached applications as you leverage them about that kubernetes. Um, as you can probably imagine that with the addition of the additional state full applications, some of the overall management now of stateless and state collapse become very talent. And that's primarily because many customers have been using some of the more traditional storage solutions to try to map that into these new state. Full scenario. And as you start to think about Dev ops organization, most Dev ops organizations want to work in the environment of their choice. Whether that's Google, whether that's AWS, Microsoft, uh, something that might be on Prem or a mix of different on Prem environments. What you typically find, at least in the kubernetes world, is there's seldom ever one single, very large kubernetes infrastructure cluster that's set to run, Dev asked. The way and production all at once. You usually have this spread out across a fairly global configuration, and so that's where some of these traditional mechanisms from traditional storage vendors really start to fall down because you can apply the same level of automation and controls in every single one of those environments. When you don't control the storage, let's say and that's really where interfacing Hedvig and allowing that sort of extension distribute storage platform brings about all of this automation policy control and really storage execution definition for the state. Full statehood workloads so that now managing the stateless and the state full becomes pretty easy and pretty easy to maintain when it comes to developing another Dev branch or simply trying to do disaster recovery or a J for production, >>any family actively do. That's a very interesting response, and the reality is customers are beginning to experiment with business. Very often they only have a virtual environment, and now they're also trying to expand into continuous. So Hedwig's ability to service primary storage for virtualization as well as containers actually gives their degree of flexibility and freedom for customers to try out containers and to start their contingent. Thank you familiar constructs. Everything is mellow where you just need to great with continuous >>Alright, bring a flexibility is something that I heard when you talk about the portfolio and the pricing as to how you put these pieces together. You actually talked about in the presentation this morning? Aggressive pricing. If you talk about, you know, kind of backup and recovery, help us understand, You know, convo 2020 how you're looking at your customers and you know how you put together your products, that to meet what they need at that. As you said, aggressive pricing? >>Absolutely. And you use this phrase a little bit earlier is to blow like flexibility. That's exactly what we're trying to get to the reason why we are reconstructing our portfolio so that we have these very granular use case aligned data management services to provide the cloud like flexibility. Customers don't have the same data management needs all the time. Great. So they can pick and choose the exact solution that need because there are delivered on the same platform that can enable out the solution investment, you know, And that's the reality. We know that many of our customers are going to start with one and keep adding more and more services, because that's what we see as ongoing conversations that gives us the ability to really praise the entry products very aggressively when compared to competition, especially when we go against single product windows. This uses a lot of slammed where we can start with a really aggressively priced product and enable more capabilities as we move forward to give you an idea, we launched disaster recovery today. I would say that compared to the so the established vendors India, we would probably come in at about 25 to 40% of the Priceline because it depends on the environment and what not. But you're going to see that that's the power of bringing to the table. You start small and then depending on what your needs are, you have the flexibility to run on either. More data management capabilities are more workloads, depending on what your needs will be. I think it's been a drag from a partner perspective, less with muscle. If you want a little bit more than that, >>yes, I mean, that goes back to the idea of being ableto simply scale across government use functionality. For example, things like the fact that our disaster recovery offering the Newman doesn't require backup really allows us to have those Taylor conversations around use cases, applications >>a >>zealous platforms. You think about one of the the big demands that we've had coming in from customers and partners, which is help me have a D R scenario or a VR set up in my environment that doesn't require people to go put their hands on boxes and cables, which was one of those things that a year ago we were having. This conversation would not necessarily have been as important as it is now, but that ability to target those specific, urgent use cases without having to go across on sort of sell things that aren't necessarily associated with the immediate pain points really makes those just makes us ineffective. Offer. >>Yeah, you bring up some changing priorities. I think almost everybody will agree that the number one priority we're hearing from customers is around security. So whether I'm adopting more cloud, I'm looking at different solutions out there. Security has to be front and center. Could we just kind of go down the line and give us the update as to how security fits and all the pieces we've been discussing? >>I guess I'm talking about change, right, so I'll start. The security for us is built into everything that we do the same view you're probably going to get from each of us because security is burden. It's not a board on, and you would see it across a lot of different images. If you take our backup and recovery and disaster recovery, for instance, a lot of ransomware protection capabilities built into the solution. For instance, we have anomaly detection that is built into the platform. If we see any kind of spurious activity happening all of a sudden, we know that that might be a potential and be reported so that the customer can take a quick look at air Gap isolation, encryption by default. So many features building. And when you come to disaster recovery, encryption on the wire, a lot of security aspects we've been to every part of the portfolio don't. >>Consequently, with Hedvig, it's probably no surprise that when that this platform was developed and as we've continued development, security has always been at the core of what we're doing is stored. So what? It's for something as simple as encryption on different volume, ensuring the communication between applications and the storage platform itself, and the way the distributors towards platform indicates those are all incredibly secured. Lock down almost such for our own our own protocols for ensuring that, um, you know, only we're able to talk within our own, our own system. Beyond that, though, I mean it comes down to ensure that data in rest data in transit. It's always it's always secure. It's also encrypted based upon the level of control that using any is there one. And then beyond just the fact of keeping the data secure. You have things like immutable snapshots. You have declared of data sovereignty to ensure that you can put essentially virtual fence barriers for where data can be transported in this highly distributed platform. Ah, and then, from a user perspective, there's always level security for providing all seeking roll on what groups organization and consume storage or leverage. Different resource is the storage platform and then, of course, from a service provider's perspective as well, providing that multi tenanted access s so that users can have access to what they want when they want it. It's all about self service, >>and the idea there is that obviously, we're all familiar with the reports of increased bad actors in the current environment to increased ransomware attacks and so forth. And be a part of that is addressed by what wrong and done said in terms of our core technology. Part of that also, though, is addressed by being able to work across platforms and environments because, you know, as we see the acceleration of state tier one applications or entire data center, evacuations into service provider or cloud environments has happened. You know, this could have taken 5 10 years in a in a normal cycle. But we've seen this happen overnight has cut this. Companies have needed to move those I T environments off science into managed environments and our ability to protect the applications, whether they're on premises, whether they're in the cloud or in the most difficult near where they live. In both cases, in both places at once, is something that it's really important to our customers to be able to ensure that in the end, security posture >>great Well, final thing I have for all three of you is you correctly noted that this is not the end, but along the journey that you're going along with your customers. So you know, with all three of you would like to get a little bit. Give us directionally. What should we be looking at? A convo. Take what was announced today and a little bit of look forward towards future. >>Directionally we should be looking at a place where we're delivering even greater simplicity to our customers. And that's gonna be achieved through multiple aspects. 1st 1 it's more technologies coming together. Integrating. We announced three important integration story. We announced the Microsoft partnership a couple of weeks back. You're gonna see us more longer direction. The second piece is technology innovation. We believe in it. That's what Differentiators has a very different company and we'll continue building it along the dimensions off data awareness, data, automation and agility. And the last one continued obsession with data. What more can we do with it? How can we drive more insights for our customers We're going to see is introducing more capabilities along those dimensions? No. >>And I think Rhonda tying directly into what you're highlighting there. I'm gonna go back to what we teased out 10 months ago at calm Bolt. Go there in Colorado in this very on this very program and talk about how, in the unification of ah ah, data and storage management, that vision, we're going to make more and more reality. I think the, uh, the announcements we've made here today let some of the things that we've done in between the lead up to this point is just proof of our execution. And ah, I can happily and excitedly tell you, we're just getting warmed up. It's going to be, ah, gonna be some fun future ahead. >>And I think studio in the running that out with the partner angle. Obviously, we're going to continue to produce great products and solutions that we're going to make our partners relevant. In those conversations with customers, I think we're also going to continue to invest in alternative business models, services, things like migration services, audit services, other things that build on top of this core technology to provide value for customers and additional opportunities for our partners >>to >>build out their their offerings around combo technologies. >>All right, well, thank you. All three of you for joining us. It was great to be able to dig in, understand those pieces. I know you've got lots of resources online for people to learn more. So thank you so much for joining us. Thank you too. Thank you. Alright, and stay with us. So we've got one more interview left for the Cube's coverage of con vault. Future Ready, students. Mannan. Thanks. As always for watching the Cube. Yeah, Yeah, yeah, yeah, yeah, yeah