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Irene Dankwa-Mullan, Marti Health | WiDS 2023


 

(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)

Published Date : Mar 9 2023

SUMMARY :

we're bringing you another one here. Thank you so much for this opportunity. So you have an MD and This is a brand new startup. I did the MPH and my medical and researchers at the NIH, and you thought "There's and built, you know, a hospital. and now it's a 350 bed hospital. And so he really inspired, you I did see firsthand, you know, to like all the problems you just said? And I got the opportunity to go, you know, that we are building that you see every day? It's a startup that is that that's a population, you know, and when, you know, they care for the patients. the keynote this morning? how you walk in the community where you feel, all of the broad But the potential is massive. Yeah, and I always say we use data And if that's going to form the We have to be able to evaluate and the story that can be told from it. We wanted data, we wanted And so you are totally right. Is there any- And so this is where we are, you know, Absolutely, and and when you say AI, that healthcare has, you know, And I think AI can do That's a key word. It has to be And there needs to be a I mean, I think we all play a part, right? we have, you know, good the things that's exciting And so many of the that we know is there. and not just on, you know, the quantity. and that personalized experience and raise awareness for the fact and we appreciate you brought to you by theCUBE.

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Adam Wenchel, Arthur.ai | CUBE Conversation


 

(bright upbeat music) >> Hello and welcome to this Cube Conversation. I'm John Furrier, host of theCUBE. We've got a great conversation featuring Arthur AI. I'm your host. I'm excited to have Adam Wenchel who's the Co-Founder and CEO. Thanks for joining us today, appreciate it. >> Yeah, thanks for having me on, John, looking forward to the conversation. >> I got to say, it's been an exciting world in AI or artificial intelligence. Just an explosion of interest kind of in the mainstream with the language models, which people don't really get, but they're seeing the benefits of some of the hype around OpenAI. Which kind of wakes everyone up to, "Oh, I get it now." And then of course the pessimism comes in, all the skeptics are out there. But this breakthrough in generative AI field is just awesome, it's really a shift, it's a wave. We've been calling it probably the biggest inflection point, then the others combined of what this can do from a surge standpoint, applications. I mean, all aspects of what we used to know is the computing industry, software industry, hardware, is completely going to get turbo. So we're totally obviously bullish on this thing. So, this is really interesting. So my first question is, I got to ask you, what's you guys taking? 'Cause you've been doing this, you're in it, and now all of a sudden you're at the beach where the big waves are. What's the explosion of interest is there? What are you seeing right now? >> Yeah, I mean, it's amazing, so for starters, I've been in AI for over 20 years and just seeing this amount of excitement and the growth, and like you said, the inflection point we've hit in the last six months has just been amazing. And, you know, what we're seeing is like people are getting applications into production using LLMs. I mean, really all this excitement just started a few months ago, with ChatGPT and other breakthroughs and the amount of activity and the amount of new systems that we're seeing hitting production already so soon after that is just unlike anything we've ever seen. So it's pretty awesome. And, you know, these language models are just, they could be applied in so many different business contexts and that it's just the amount of value that's being created is again, like unprecedented compared to anything. >> Adam, you know, you've been in this for a while, so it's an interesting point you're bringing up, and this is a good point. I was talking with my friend John Markoff, former New York Times journalist and he was talking about, there's been a lot of work been done on ethics. So there's been, it's not like it's new. It's like been, there's a lot of stuff that's been baking over many, many years and, you know, decades. So now everyone wakes up in the season, so I think that is a key point I want to get into some of your observations. But before we get into it, I want you to explain for the folks watching, just so we can kind of get a definition on the record. What's an LLM, what's a foundational model and what's generative ai? Can you just quickly explain the three things there? >> Yeah, absolutely. So an LLM or a large language model, it's just a large, they would imply a large language model that's been trained on a huge amount of data typically pulled from the internet. And it's a general purpose language model that can be built on top for all sorts of different things, that includes traditional NLP tasks like document classification and sentiment understanding. But the thing that's gotten people really excited is it's used for generative tasks. So, you know, asking it to summarize documents or asking it to answer questions. And these aren't new techniques, they've been around for a while, but what's changed is just this new class of models that's based on new architectures. They're just so much more capable that they've gone from sort of science projects to something that's actually incredibly useful in the real world. And there's a number of companies that are making them accessible to everyone so that you can build on top of them. So that's the other big thing is, this kind of access to these models that can power generative tasks has been democratized in the last few months and it's just opening up all these new possibilities. And then the third one you mentioned foundation models is sort of a broader term for the category that includes LLMs, but it's not just language models that are included. So we've actually seen this for a while in the computer vision world. So people have been building on top of computer vision models, pre-trained computer vision models for a while for image classification, object detection, that's something we've had customers doing for three or four years already. And so, you know, like you said, there are antecedents to like, everything that's happened, it's not entirely new, but it does feel like a step change. >> Yeah, I did ask ChatGPT to give me a riveting introduction to you and it gave me an interesting read. If we have time, I'll read it. It's kind of, it's fun, you get a kick out of it. "Ladies and gentlemen, today we're a privileged "to have Adam Wenchel, Founder of Arthur who's going to talk "about the exciting world of artificial intelligence." And then it goes on with some really riveting sentences. So if we have time, I'll share that, it's kind of funny. It was good. >> Okay. >> So anyway, this is what people see and this is why I think it's exciting 'cause I think people are going to start refactoring what they do. And I've been saying this on theCUBE now for about a couple months is that, you know, there's a scene in "Moneyball" where Billy Beane sits down with the Red Sox owner and the Red Sox owner says, "If people aren't rebuilding their teams on your model, "they're going to be dinosaurs." And it reminds me of what's happening right now. And I think everyone that I talk to in the business sphere is looking at this and they're connecting the dots and just saying, if we don't rebuild our business with this new wave, they're going to be out of business because there's so much efficiency, there's so much automation, not like DevOps automation, but like the generative tasks that will free up the intellect of people. Like just the simple things like do an intro or do this for me, write some code, write a countermeasure to a hack. I mean, this is kind of what people are doing. And you mentioned computer vision, again, another huge field where 5G things are coming on, it's going to accelerate. What do you say to people when they kind of are leaning towards that, I need to rethink my business? >> Yeah, it's 100% accurate and what's been amazing to watch the last few months is the speed at which, and the urgency that companies like Microsoft and Google or others are actually racing to, to do that rethinking of their business. And you know, those teams, those companies which are large and haven't always been the fastest moving companies are working around the clock. And the pace at which they're rolling out LLMs across their suite of products is just phenomenal to watch. And it's not just the big, the large tech companies as well, I mean, we're seeing the number of startups, like we get, every week a couple of new startups get in touch with us for help with their LLMs and you know, there's just a huge amount of venture capital flowing into it right now because everyone realizes the opportunities for transforming like legal and healthcare and content creation in all these different areas is just wide open. And so there's a massive gold rush going on right now, which is amazing. >> And the cloud scale, obviously horizontal scalability of the cloud brings us to another level. We've been seeing data infrastructure since the Hadoop days where big data was coined. Now you're seeing this kind of take fruit, now you have vertical specialization where data shines, large language models all of a set up perfectly for kind of this piece. And you know, as you mentioned, you've been doing it for a long time. Let's take a step back and I want to get into how you started the company, what drove you to start it? Because you know, as an entrepreneur you're probably saw this opportunity before other people like, "Hey, this is finally it, it's here." Can you share the origination story of what you guys came up with, how you started it, what was the motivation and take us through that origination story. >> Yeah, absolutely. So as I mentioned, I've been doing AI for many years. I started my career at DARPA, but it wasn't really until 2015, 2016, my previous company was acquired by Capital One. Then I started working there and shortly after I joined, I was asked to start their AI team and scale it up. And for the first time I was actually doing it, had production models that we were working with, that was at scale, right? And so there was hundreds of millions of dollars of business revenue and certainly a big group of customers who were impacted by the way these models acted. And so it got me hyper-aware of these issues of when you get models into production, it, you know. So I think people who are earlier in the AI maturity look at that as a finish line, but it's really just the beginning and there's this constant drive to make them better, make sure they're not degrading, make sure you can explain what they're doing, if they're impacting people, making sure they're not biased. And so at that time, there really weren't any tools to exist to do this, there wasn't open source, there wasn't anything. And so after a few years there, I really started talking to other people in the industry and there was a really clear theme that this needed to be addressed. And so, I joined with my Co-Founder John Dickerson, who was on the faculty in University of Maryland and he'd been doing a lot of research in these areas. And so we ended up joining up together and starting Arthur. >> Awesome. Well, let's get into what you guys do. Can you explain the value proposition? What are people using you for now? Where's the action? What's the customers look like? What do prospects look like? Obviously you mentioned production, this has been the theme. It's not like people woke up one day and said, "Hey, I'm going to put stuff into production." This has kind of been happening. There's been companies that have been doing this at scale and then yet there's a whole follower model coming on mainstream enterprise and businesses. So there's kind of the early adopters are there now in production. What do you guys do? I mean, 'cause I think about just driving the car off the lot is not, you got to manage operations. I mean, that's a big thing. So what do you guys do? Talk about the value proposition and how you guys make money? >> Yeah, so what we do is, listen, when you go to validate ahead of deploying these models in production, starts at that point, right? So you want to make sure that if you're going to be upgrading a model, if you're going to replacing one that's currently in production, that you've proven that it's going to perform well, that it's going to be perform ethically and that you can explain what it's doing. And then when you launch it into production, traditionally data scientists would spend 25, 30% of their time just manually checking in on their model day-to-day babysitting as we call it, just to make sure that the data hasn't drifted, the model performance hasn't degraded, that a programmer did make a change in an upstream data system. You know, there's all sorts of reasons why the world changes and that can have a real adverse effect on these models. And so what we do is bring the same kind of automation that you have for other kinds of, let's say infrastructure monitoring, application monitoring, we bring that to your AI systems. And that way if there ever is an issue, it's not like weeks or months till you find it and you find it before it has an effect on your P&L and your balance sheet, which is too often before they had tools like Arthur, that was the way they were detected. >> You know, I was talking to Swami at Amazon who I've known for a long time for 13 years and been on theCUBE multiple times and you know, I watched Amazon try to pick up that sting with stage maker about six years ago and so much has happened since then. And he and I were talking about this wave, and I kind of brought up this analogy to how when cloud started, it was, Hey, I don't need a data center. 'Cause when I did my startup that time when Amazon, one of my startups at that time, my choice was put a box in the colo, get all the configuration before I could write over the line of code. So the cloud became the benefit for that and you can stand up stuff quickly and then it grew from there. Here it's kind of the same dynamic, you don't want to have to provision a large language model or do all this heavy lifting. So that seeing companies coming out there saying, you can get started faster, there's like a new way to get it going. So it's kind of like the same vibe of limiting that heavy lifting. >> Absolutely. >> How do you look at that because this seems to be a wave that's going to be coming in and how do you guys help companies who are going to move quickly and start developing? >> Yeah, so I think in the race to this kind of gold rush mentality, race to get these models into production, there's starting to see more sort of examples and evidence that there are a lot of risks that go along with it. Either your model says things, your system says things that are just wrong, you know, whether it's hallucination or just making things up, there's lots of examples. If you go on Twitter and the news, you can read about those, as well as sort of times when there could be toxic content coming out of things like that. And so there's a lot of risks there that you need to think about and be thoughtful about when you're deploying these systems. But you know, you need to balance that with the business imperative of getting these things into production and really transforming your business. And so that's where we help people, we say go ahead, put them in production, but just make sure you have the right guardrails in place so that you can do it in a smart way that's going to reflect well on you and your company. >> Let's frame the challenge for the companies now that you have, obviously there's the people who doing large scale production and then you have companies maybe like as small as us who have large linguistic databases or transcripts for example, right? So what are customers doing and why are they deploying AI right now? And is it a speed game, is it a cost game? Why have some companies been able to deploy AI at such faster rates than others? And what's a best practice to onboard new customers? >> Yeah, absolutely. So I mean, we're seeing across a bunch of different verticals, there are leaders who have really kind of started to solve this puzzle about getting AI models into production quickly and being able to iterate on them quickly. And I think those are the ones that realize that imperative that you mentioned earlier about how transformational this technology is. And you know, a lot of times, even like the CEOs or the boards are very personally kind of driving this sense of urgency around it. And so, you know, that creates a lot of movement, right? And so those companies have put in place really smart infrastructure and rails so that people can, data scientists aren't encumbered by having to like hunt down data, get access to it. They're not encumbered by having to stand up new platforms every time they want to deploy an AI system, but that stuff is already in place. There's a really nice ecosystem of products out there, including Arthur, that you can tap into. Compared to five or six years ago when I was building at a top 10 US bank, at that point you really had to build almost everything yourself and that's not the case now. And so it's really nice to have things like, you know, you mentioned AWS SageMaker and a whole host of other tools that can really accelerate things. >> What's your profile customer? Is it someone who already has a team or can people who are learning just dial into the service? What's the persona? What's the pitch, if you will, how do you align with that customer value proposition? Do people have to be built out with a team and in play or is it pre-production or can you start with people who are just getting going? >> Yeah, people do start using it pre-production for validation, but I think a lot of our customers do have a team going and they're starting to put, either close to putting something into production or about to, it's everything from large enterprises that have really sort of complicated, they have dozens of models running all over doing all sorts of use cases to tech startups that are very focused on a single problem, but that's like the lifeblood of the company and so they need to guarantee that it works well. And you know, we make it really easy to get started, especially if you're using one of the common model development platforms, you can just kind of turn key, get going and make sure that you have a nice feedback loop. So then when your models are out there, it's pointing out, areas where it's performing well, areas where it's performing less well, giving you that feedback so that you can make improvements, whether it's in training data or futurization work or algorithm selection. There's a number of, you know, depending on the symptoms, there's a number of things you can do to increase performance over time and we help guide people on that journey. >> So Adam, I have to ask, since you have such a great customer base and they're smart and they got teams and you're on the front end, I mean, early adopters is kind of an overused word, but they're killing it. They're putting stuff in the production's, not like it's a test, it's not like it's early. So as the next wave comes of fast followers, how do you see that coming online? What's your vision for that? How do you see companies that are like just waking up out of the frozen, you know, freeze of like old IT to like, okay, they got cloud, but they're not yet there. What do you see in the market? I see you're in the front end now with the top people really nailing AI and working hard. What's the- >> Yeah, I think a lot of these tools are becoming, or every year they get easier, more accessible, easier to use. And so, you know, even for that kind of like, as the market broadens, it takes less and less of a lift to put these systems in place. And the thing is, every business is unique, they have their own kind of data and so you can use these foundation models which have just been trained on generic data. They're a great starting point, a great accelerant, but then, in most cases you're either going to want to create a model or fine tune a model using data that's really kind of comes from your particular customers, the people you serve and so that it really reflects that and takes that into account. And so I do think that these, like the size of that market is expanding and its broadening as these tools just become easier to use and also the knowledge about how to build these systems becomes more widespread. >> Talk about your customer base you have now, what's the makeup, what size are they? Give a taste a little bit of a customer base you got there, what's they look like? I'll say Capital One, we know very well while you were at there, they were large scale, lot of data from fraud detection to all kinds of cool stuff. What do your customers now look like? >> Yeah, so we have a variety, but I would say one area we're really strong, we have several of the top 10 US banks, that's not surprising, that's a strength for us, but we also have Fortune 100 customers in healthcare, in manufacturing, in retail, in semiconductor and electronics. So what we find is like in any sort of these major verticals, there's typically, you know, one, two, three kind of companies that are really leading the charge and are the ones that, you know, in our opinion, those are the ones that for the next multiple decades are going to be the leaders, the ones that really kind of lead the charge on this AI transformation. And so we're very fortunate to be working with some of those. And then we have a number of startups as well who we love working with just because they're really pushing the boundaries technologically and so they provide great feedback and make sure that we're continuing to innovate and staying abreast of everything that's going on. >> You know, these early markups, even when the hyperscalers were coming online, they had to build everything themselves. That's the new, they're like the alphas out there building it. This is going to be a big wave again as that fast follower comes in. And so when you look at the scale, what advice would you give folks out there right now who want to tee it up and what's your secret sauce that will help them get there? >> Yeah, I think that the secret to teeing it up is just dive in and start like the, I think these are, there's not really a secret. I think it's amazing how accessible these are. I mean, there's all sorts of ways to access LLMs either via either API access or downloadable in some cases. And so, you know, go ahead and get started. And then our secret sauce really is the way that we provide that performance analysis of what's going on, right? So we can tell you in a very actionable way, like, hey, here's where your model is doing good things, here's where it's doing bad things. Here's something you want to take a look at, here's some potential remedies for it. We can help guide you through that. And that way when you're putting it out there, A, you're avoiding a lot of the common pitfalls that people see and B, you're able to really kind of make it better in a much faster way with that tight feedback loop. >> It's interesting, we've been kind of riffing on this supercloud idea because it was just different name than multicloud and you see apps like Snowflake built on top of AWS without even spending any CapEx, you just ride that cloud wave. This next AI, super AI wave is coming. I don't want to call AIOps because I think there's a different distinction. If you, MLOps and AIOps seem a little bit old, almost a few years back, how do you view that because everyone's is like, "Is this AIOps?" And like, "No, not kind of, but not really." How would you, you know, when someone says, just shoots off the hip, "Hey Adam, aren't you doing AIOps?" Do you say, yes we are, do you say, yes, but we do differently because it's doesn't seem like it's the same old AIOps. What's your- >> Yeah, it's a good question. AIOps has been a term that was co-opted for other things and MLOps also has people have used it for different meanings. So I like the term just AI infrastructure, I think it kind of like describes it really well and succinctly. >> But you guys are doing the ops. I mean that's the kind of ironic thing, it's like the next level, it's like NextGen ops, but it's not, you don't want to be put in that bucket. >> Yeah, no, it's very operationally focused platform that we have, I mean, it fires alerts, people can action off them. If you're familiar with like the way people run security operations centers or network operations centers, we do that for data science, right? So think of it as a DSOC, a Data Science Operations Center where all your models, you might have hundreds of models running across your organization, you may have five, but as problems are detected, alerts can be fired and you can actually work the case, make sure they're resolved, escalate them as necessary. And so there is a very strong operational aspect to it, you're right. >> You know, one of the things I think is interesting is, is that, if you don't mind commenting on it, is that the aspect of scale is huge and it feels like that was made up and now you have scale and production. What's your reaction to that when people say, how does scale impact this? >> Yeah, scale is huge for some of, you know, I think, I think look, the highest leverage business areas to apply these to, are generally going to be the ones at the biggest scale, right? And I think that's one of the advantages we have. Several of us come from enterprise backgrounds and we're used to doing things enterprise grade at scale and so, you know, we're seeing more and more companies, I think they started out deploying AI and sort of, you know, important but not necessarily like the crown jewel area of their business, but now they're deploying AI right in the heart of things and yeah, the scale that some of our companies are operating at is pretty impressive. >> John: Well, super exciting, great to have you on and congratulations. I got a final question for you, just random. What are you most excited about right now? Because I mean, you got to be pretty pumped right now with the way the world is going and again, I think this is just the beginning. What's your personal view? How do you feel right now? >> Yeah, the thing I'm really excited about for the next couple years now, you touched on it a little bit earlier, but is a sort of convergence of AI and AI systems with sort of turning into AI native businesses. And so, as you sort of do more, get good further along this transformation curve with AI, it turns out that like the better the performance of your AI systems, the better the performance of your business. Because these models are really starting to underpin all these key areas that cumulatively drive your P&L. And so one of the things that we work a lot with our customers is to do is just understand, you know, take these really esoteric data science notions and performance and tie them to all their business KPIs so that way you really are, it's kind of like the operating system for running your AI native business. And we're starting to see more and more companies get farther along that maturity curve and starting to think that way, which is really exciting. >> I love the AI native. I haven't heard any startup yet say AI first, although we kind of use the term, but I guarantee that's going to come in all the pitch decks, we're an AI first company, it's going to be great run. Adam, congratulations on your success to you and the team. Hey, if we do a few more interviews, we'll get the linguistics down. We can have bots just interact with you directly and ask you, have an interview directly. >> That sounds good, I'm going to go hang out on the beach, right? So, sounds good. >> Thanks for coming on, really appreciate the conversation. Super exciting, really important area and you guys doing great work. Thanks for coming on. >> Adam: Yeah, thanks John. >> Again, this is Cube Conversation. I'm John Furrier here in Palo Alto, AI going next gen. This is legit, this is going to a whole nother level that's going to open up huge opportunities for startups, that's going to use opportunities for investors and the value to the users and the experience will come in, in ways I think no one will ever see. So keep an eye out for more coverage on siliconangle.com and theCUBE.net, thanks for watching. (bright upbeat music)

Published Date : Mar 3 2023

SUMMARY :

I'm excited to have Adam Wenchel looking forward to the conversation. kind of in the mainstream and that it's just the amount Adam, you know, you've so that you can build on top of them. to give me a riveting introduction to you And you mentioned computer vision, again, And you know, those teams, And you know, as you mentioned, of when you get models into off the lot is not, you and that you can explain what it's doing. So it's kind of like the same vibe so that you can do it in a smart way And so, you know, that creates and make sure that you out of the frozen, you know, and so you can use these foundation models a customer base you got there, that are really leading the And so when you look at the scale, And so, you know, go how do you view that So I like the term just AI infrastructure, I mean that's the kind of ironic thing, and you can actually work the case, is that the aspect of and so, you know, we're seeing exciting, great to have you on so that way you really are, success to you and the team. out on the beach, right? and you guys doing great work. and the value to the users and

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Prem Balasubramanian and Suresh Mothikuru | Hitachi Vantara: Build Your Cloud Center of Excellence


 

(soothing music) >> Hey everyone, welcome to this event, "Build Your Cloud Center of Excellence." I'm your host, Lisa Martin. In the next 15 minutes or so my guest and I are going to be talking about redefining cloud operations, an application modernization for customers, and specifically how partners are helping to speed up that process. As you saw on our first two segments, we talked about problems enterprises are facing with cloud operations. We talked about redefining cloud operations as well to solve these problems. This segment is going to be focusing on how Hitachi Vantara's partners are really helping to speed up that process. We've got Johnson Controls here to talk about their partnership with Hitachi Vantara. Please welcome both of my guests, Prem Balasubramanian is with us, SVP and CTO Digital Solutions at Hitachi Vantara. And Suresh Mothikuru, SVP Customer Success Platform Engineering and Reliability Engineering from Johnson Controls. Gentlemen, welcome to the program, great to have you. >> Thank. >> Thank you, Lisa. >> First question is to both of you and Suresh, we'll start with you. We want to understand, you know, the cloud operations landscape is increasingly complex. We've talked a lot about that in this program. Talk to us, Suresh, about some of the biggest challenges and pin points that you faced with respect to that. >> Thank you. I think it's a great question. I mean, cloud has evolved a lot in the last 10 years. You know, when we were talking about a single cloud whether it's Azure or AWS and GCP, and that was complex enough. Now we are talking about multi-cloud and hybrid and you look at Johnson Controls, we have Azure we have AWS, we have GCP, we have Alibaba and we also support on-prem. So the architecture has become very, very complex and the complexity has grown so much that we are now thinking about whether we should be cloud native or cloud agnostic. So I think, I mean, sometimes it's hard to even explain the complexity because people think, oh, "When you go to cloud, everything is simplified." Cloud does give you a lot of simplicity, but it also really brings a lot more complexity along with it. So, and then next one is pretty important is, you know, generally when you look at cloud services, you have plenty of services that are offered within a cloud, 100, 150 services, 200 services. Even within those companies, you take AWS they might not know, an individual resource might not know about all the services we see. That's a big challenge for us as a customer to really understand each of the service that is provided in these, you know, clouds, well, doesn't matter which one that is. And the third one is pretty big, at least at the CTO the CIO, and the senior leadership level, is cost. Cost is a major factor because cloud, you know, will eat you up if you cannot manage it. If you don't have a good cloud governance process it because every minute you are in it, it's burning cash. So I think if you ask me, these are the three major things that I am facing day to day and that's where I use my partners, which I'll touch base down the line. >> Perfect, we'll talk about that. So Prem, I imagine that these problems are not unique to Johnson Controls or JCI, as you may hear us refer to it. Talk to me Prem about some of the other challenges that you're seeing within the customer landscape. >> So, yeah, I agree, Lisa, these are not very specific to JCI, but there are specific issues in JCI, right? So the way we think about these are, there is a common issue when people go to the cloud and there are very specific and unique issues for businesses, right? So JCI, and we will talk about this in the episode as we move forward. I think Suresh and his team have done some phenomenal step around how to manage this complexity. But there are customers who have a lesser complex cloud which is, they don't go to Alibaba, they don't have footprint in all three clouds. So their multi-cloud footprint could be a bit more manageable, but still struggle with a lot of the same problems around cost, around security, around talent. Talent is a big thing, right? And in Suresh's case I think it's slightly more exasperated because every cloud provider Be it AWS, JCP, or Azure brings in hundreds of services and there is nobody, including many of us, right? We learn every day, nowadays, right? It's not that there is one service integrator who knows all, while technically people can claim as a part of sales. But in reality all of us are continuing to learn in this landscape. And if you put all of this equation together with multiple clouds the complexity just starts to exponentially grow. And that's exactly what I think JCI is experiencing and Suresh's team has been experiencing, and we've been working together. But the common problems are around security talent and cost management of this, right? Those are my three things. And one last thing that I would love to say before we move away from this question is, if you think about cloud operations as a concept that's evolving over the last few years, and I have touched upon this in the previous episode as well, Lisa, right? If you take architectures, we've gone into microservices, we've gone into all these server-less architectures all the fancy things that we want. That helps us go to market faster, be more competent to as a business. But that's not simplified stuff, right? That's complicated stuff. It's a lot more distributed. Second, again, we've advanced and created more modern infrastructure because all of what we are talking is platform as a service, services on the cloud that we are consuming, right? In the same case with development we've moved into a DevOps model. We kind of click a button put some code in a repository, the code starts to run in production within a minute, everything else is automated. But then when we get to operations we are still stuck in a very old way of looking at cloud as an infrastructure, right? So you've got an infra team, you've got an app team, you've got an incident management team, you've got a soft knock, everything. But again, so Suresh can talk about this more because they are making significant strides in thinking about this as a single workload, and how do I apply engineering to go manage this? Because a lot of it is codified, right? So automation. Anyway, so that's kind of where the complexity is and how we are thinking, including JCI as a partner thinking about taming that complexity as we move forward. >> Suresh, let's talk about that taming the complexity. You guys have both done a great job of articulating the ostensible challenges that are there with cloud, especially multi-cloud environments that you're living in. But Suresh, talk about the partnership with Hitachi Vantara. How is it helping to dial down some of those inherent complexities? >> I mean, I always, you know, I think I've said this to Prem multiple times. I treat my partners as my internal, you know, employees. I look at Prem as my coworker or my peers. So the reason for that is I want Prem to have the same vested interest as a partner in my success or JCI success and vice versa, isn't it? I think that's how we operate and that's how we have been operating. And I think I would like to thank Prem and Hitachi Vantara for that really been an amazing partnership. And as he was saying, we have taken a completely holistic approach to how we want to really be in the market and play in the market to our customers. So if you look at my jacket it talks about OpenBlue platform. This is what JCI is building, that we are building this OpenBlue digital platform. And within that, my team, along with Prem's or Hitachi's, we have built what we call as Polaris. It's a technical platform where our apps can run. And this platform is automated end-to-end from a platform engineering standpoint. We stood up a platform engineering organization, a reliability engineering organization, as well as a support organization where Hitachi played a role. As I said previously, you know, for me to scale I'm not going to really have the talent and the knowledge of every function that I'm looking at. And Hitachi, not only they brought the talent but they also brought what he was talking about, Harc. You know, they have set up a lot and now we can leverage it. And they also came up with some really interesting concepts. I went and met them in India. They came up with this concept called IPL. Okay, what is that? They really challenged all their employees that's working for GCI to come up with innovative ideas to solve problems proactively, which is self-healing. You know, how you do that? So I think partners, you know, if they become really vested in your interests, they can do wonders for you. And I think in this case Hitachi is really working very well for us and in many aspects. And I'm leveraging them... You started with support, now I'm leveraging them in the automation, the platform engineering, as well as in the reliability engineering and then in even in the engineering spaces. And that like, they are my end-to-end partner right now? >> So you're really taking that holistic approach that you talked about and it sounds like it's a very collaborative two-way street partnership. Prem, I want to go back to, Suresh mentioned Harc. Talk a little bit about what Harc is and then how partners fit into Hitachi's Harc strategy. >> Great, so let me spend like a few seconds on what Harc is. Lisa, again, I know we've been using the term. Harc stands for Hitachi application reliability sectors. Now the reason we thought about Harc was, like I said in the beginning of this segment, there is an illusion from an architecture standpoint to be more modern, microservices, server-less, reactive architecture, so on and so forth. There is an illusion in your development methodology from Waterfall to agile, to DevOps to lean, agile to path program, whatever, right? Extreme program, so on and so forth. There is an evolution in the space of infrastructure from a point where you were buying these huge humongous servers and putting it in your data center to a point where people don't even see servers anymore, right? You buy it, by a click of a button you don't know the size of it. All you know is a, it's (indistinct) whatever that name means. Let's go provision it on the fly, get go, get your work done, right? When all of this is advanced when you think about operations people have been solving the problem the way they've been solving it 20 years back, right? That's the issue. And Harc was conceived exactly to fix that particular problem, to think about a modern way of operating a modern workload, right? That's exactly what Harc. So it brings together finest engineering talent. So the teams are trained in specific ways of working. We've invested and implemented some of the IP, we work with the best of the breed partner ecosystem, and I'll talk about that in a minute. And we've got these facilities in Dallas and I am talking from my office in Dallas, which is a Harc facility in the US from where we deliver for our customers. And then back in Hyderabad, we've got one more that we opened and these are facilities from where we deliver Harc services for our customers as well, right? And then we are expanding it in Japan and Portugal as we move into 23. That's kind of the plan that we are thinking through. However, that's what Harc is, Lisa, right? That's our solution to this cloud complexity problem. Right? >> Got it, and it sounds like it's going quite global, which is fantastic. So Suresh, I want to have you expand a bit on the partnership, the partner ecosystem and the role that it plays. You talked about it a little bit but what role does the partner ecosystem play in really helping JCI to dial down some of those challenges and the inherent complexities that we talked about? >> Yeah, sure. I think partners play a major role and JCI is very, very good at it. I mean, I've joined JCI 18 months ago, JCI leverages partners pretty extensively. As I said, I leverage Hitachi for my, you know, A group and the (indistinct) space and the cloud operations space, and they're my primary partner. But at the same time, we leverage many other partners. Well, you know, Accenture, SCL, and even on the tooling side we use Datadog and (indistinct). All these guys are major partners of our because the way we like to pick partners is based on our vision and where we want to go. And pick the right partner who's going to really, you know make you successful by investing their resources in you. And what I mean by that is when you have a partner, partner knows exactly what kind of skillset is needed for this customer, for them to really be successful. As I said earlier, we cannot really get all the skillset that we need, we rely on the partners and partners bring the the right skillset, they can scale. I can tell Prem tomorrow, "Hey, I need two parts by next week", and I guarantee it he's going to bring two parts to me. So they let you scale, they let you move fast. And I'm a big believer, in today's day and age, to get things done fast and be more agile. I'm not worried about failure, but for me moving fast is very, very important. And partners really do a very good job bringing that. But I think then they also really make you think, isn't it? Because one thing I like about partners they make you innovate whether they know it or not but they do because, you know, they will come and ask you questions about, "Hey, tell me why you are doing this. Can I review your architecture?" You know, and then they will try to really say I don't think this is going to work. Because they work with so many different clients, not JCI, they bring all that expertise and that's what I look from them, you know, just not, you know, do a T&M job for me. I ask you to do this go... They just bring more than that. That's how I pick my partners. And that's how, you know, Hitachi's Vantara is definitely one of a good partner from that sense because they bring a lot more innovation to the table and I appreciate about that. >> It sounds like, it sounds like a flywheel of innovation. >> Yeah. >> I love that. Last question for both of you, which we're almost out of time here, Prem, I want to go back to you. So I'm a partner, I'm planning on redefining CloudOps at my company. What are the two things you want me to remember from Hitachi Vantara's perspective? >> So before I get to that question, Lisa, the partners that we work with are slightly different from from the partners that, again, there are some similar partners. There are some different partners, right? For example, we pick and choose especially in the Harc space, we pick and choose partners that are more future focused, right? We don't care if they are huge companies or small companies. We go after companies that are future focused that are really, really nimble and can change for our customers need because it's not our need, right? When I pick partners for Harc my ultimate endeavor is to ensure, in this case because we've got (indistinct) GCI on, we are able to operate (indistinct) with the level of satisfaction above and beyond that they're expecting from us. And whatever I don't have I need to get from my partners so that I bring this solution to Suresh. As opposed to bringing a whole lot of people and making them stand in front of Suresh. So that's how I think about partners. What do I want them to do from, and we've always done this so we do workshops with our partners. We just don't go by tools. When we say we are partnering with X, Y, Z, we do workshops with them and we say, this is how we are thinking. Either you build it in your roadmap that helps us leverage you, continue to leverage you. And we do have minimal investments where we fix gaps. We're building some utilities for us to deliver the best service to our customers. And our intention is not to build a product to compete with our partner. Our intention is to just fill the wide space until they go build it into their product suite that we can then leverage it for our customers. So always think about end customers and how can we make it easy for them? Because for all the tool vendors out there seeing this and wanting to partner with Hitachi the biggest thing is tools sprawl, especially on the cloud is very real. For every problem on the cloud. I have a billion tools that are being thrown at me as Suresh if I'm putting my installation and it's not easy at all. It's so confusing. >> Yeah. >> So that's what we want. We want people to simplify that landscape for our end customers, and we are looking at partners that are thinking through the simplification not just making money. >> That makes perfect sense. There really is a very strong symbiosis it sounds like, in the partner ecosystem. And there's a lot of enablement that goes on back and forth it sounds like as well, which is really, to your point it's all about the end customers and what they're expecting. Suresh, last question for you is which is the same one, if I'm a partner what are the things that you want me to consider as I'm planning to redefine CloudOps at my company? >> I'll keep it simple. In my view, I mean, we've touched upon it in multiple facets in this interview about that, the three things. First and foremost, reliability. You know, in today's day and age my products has to be reliable, available and, you know, make sure that the customer's happy with what they're really dealing with, number one. Number two, my product has to be secure. Security is super, super important, okay? And number three, I need to really make sure my customers are getting the value so I keep my cost low. So these three is what I would focus and what I expect from my partners. >> Great advice, guys. Thank you so much for talking through this with me and really showing the audience how strong the partnership is between Hitachi Vantara and JCI. What you're doing together, we'll have to talk to you again to see where things go but we really appreciate your insights and your perspectives. Thank you. >> Thank you, Lisa. >> Thanks Lisa, thanks for having us. >> My pleasure. For my guests, I'm Lisa Martin. Thank you so much for watching. (soothing music)

Published Date : Mar 2 2023

SUMMARY :

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Prem Balasubramanian and Suresh Mothikuru | Hitachi Vantara: Build Your Cloud Center of Excellence


 

(soothing music) >> Hey everyone, welcome to this event, "Build Your Cloud Center of Excellence." I'm your host, Lisa Martin. In the next 15 minutes or so my guest and I are going to be talking about redefining cloud operations, an application modernization for customers, and specifically how partners are helping to speed up that process. As you saw on our first two segments, we talked about problems enterprises are facing with cloud operations. We talked about redefining cloud operations as well to solve these problems. This segment is going to be focusing on how Hitachi Vantara's partners are really helping to speed up that process. We've got Johnson Controls here to talk about their partnership with Hitachi Vantara. Please welcome both of my guests, Prem Balasubramanian is with us, SVP and CTO Digital Solutions at Hitachi Vantara. And Suresh Mothikuru, SVP Customer Success Platform Engineering and Reliability Engineering from Johnson Controls. Gentlemen, welcome to the program, great to have you. >> Thank. >> Thank you, Lisa. >> First question is to both of you and Suresh, we'll start with you. We want to understand, you know, the cloud operations landscape is increasingly complex. We've talked a lot about that in this program. Talk to us, Suresh, about some of the biggest challenges and pin points that you faced with respect to that. >> Thank you. I think it's a great question. I mean, cloud has evolved a lot in the last 10 years. You know, when we were talking about a single cloud whether it's Azure or AWS and GCP, and that was complex enough. Now we are talking about multi-cloud and hybrid and you look at Johnson Controls, we have Azure we have AWS, we have GCP, we have Alibaba and we also support on-prem. So the architecture has become very, very complex and the complexity has grown so much that we are now thinking about whether we should be cloud native or cloud agnostic. So I think, I mean, sometimes it's hard to even explain the complexity because people think, oh, "When you go to cloud, everything is simplified." Cloud does give you a lot of simplicity, but it also really brings a lot more complexity along with it. So, and then next one is pretty important is, you know, generally when you look at cloud services, you have plenty of services that are offered within a cloud, 100, 150 services, 200 services. Even within those companies, you take AWS they might not know, an individual resource might not know about all the services we see. That's a big challenge for us as a customer to really understand each of the service that is provided in these, you know, clouds, well, doesn't matter which one that is. And the third one is pretty big, at least at the CTO the CIO, and the senior leadership level, is cost. Cost is a major factor because cloud, you know, will eat you up if you cannot manage it. If you don't have a good cloud governance process it because every minute you are in it, it's burning cash. So I think if you ask me, these are the three major things that I am facing day to day and that's where I use my partners, which I'll touch base down the line. >> Perfect, we'll talk about that. So Prem, I imagine that these problems are not unique to Johnson Controls or JCI, as you may hear us refer to it. Talk to me Prem about some of the other challenges that you're seeing within the customer landscape. >> So, yeah, I agree, Lisa, these are not very specific to JCI, but there are specific issues in JCI, right? So the way we think about these are, there is a common issue when people go to the cloud and there are very specific and unique issues for businesses, right? So JCI, and we will talk about this in the episode as we move forward. I think Suresh and his team have done some phenomenal step around how to manage this complexity. But there are customers who have a lesser complex cloud which is, they don't go to Alibaba, they don't have footprint in all three clouds. So their multi-cloud footprint could be a bit more manageable, but still struggle with a lot of the same problems around cost, around security, around talent. Talent is a big thing, right? And in Suresh's case I think it's slightly more exasperated because every cloud provider Be it AWS, JCP, or Azure brings in hundreds of services and there is nobody, including many of us, right? We learn every day, nowadays, right? It's not that there is one service integrator who knows all, while technically people can claim as a part of sales. But in reality all of us are continuing to learn in this landscape. And if you put all of this equation together with multiple clouds the complexity just starts to exponentially grow. And that's exactly what I think JCI is experiencing and Suresh's team has been experiencing, and we've been working together. But the common problems are around security talent and cost management of this, right? Those are my three things. And one last thing that I would love to say before we move away from this question is, if you think about cloud operations as a concept that's evolving over the last few years, and I have touched upon this in the previous episode as well, Lisa, right? If you take architectures, we've gone into microservices, we've gone into all these server-less architectures all the fancy things that we want. That helps us go to market faster, be more competent to as a business. But that's not simplified stuff, right? That's complicated stuff. It's a lot more distributed. Second, again, we've advanced and created more modern infrastructure because all of what we are talking is platform as a service, services on the cloud that we are consuming, right? In the same case with development we've moved into a DevOps model. We kind of click a button put some code in a repository, the code starts to run in production within a minute, everything else is automated. But then when we get to operations we are still stuck in a very old way of looking at cloud as an infrastructure, right? So you've got an infra team, you've got an app team, you've got an incident management team, you've got a soft knock, everything. But again, so Suresh can talk about this more because they are making significant strides in thinking about this as a single workload, and how do I apply engineering to go manage this? Because a lot of it is codified, right? So automation. Anyway, so that's kind of where the complexity is and how we are thinking, including JCI as a partner thinking about taming that complexity as we move forward. >> Suresh, let's talk about that taming the complexity. You guys have both done a great job of articulating the ostensible challenges that are there with cloud, especially multi-cloud environments that you're living in. But Suresh, talk about the partnership with Hitachi Vantara. How is it helping to dial down some of those inherent complexities? >> I mean, I always, you know, I think I've said this to Prem multiple times. I treat my partners as my internal, you know, employees. I look at Prem as my coworker or my peers. So the reason for that is I want Prem to have the same vested interest as a partner in my success or JCI success and vice versa, isn't it? I think that's how we operate and that's how we have been operating. And I think I would like to thank Prem and Hitachi Vantara for that really been an amazing partnership. And as he was saying, we have taken a completely holistic approach to how we want to really be in the market and play in the market to our customers. So if you look at my jacket it talks about OpenBlue platform. This is what JCI is building, that we are building this OpenBlue digital platform. And within that, my team, along with Prem's or Hitachi's, we have built what we call as Polaris. It's a technical platform where our apps can run. And this platform is automated end-to-end from a platform engineering standpoint. We stood up a platform engineering organization, a reliability engineering organization, as well as a support organization where Hitachi played a role. As I said previously, you know, for me to scale I'm not going to really have the talent and the knowledge of every function that I'm looking at. And Hitachi, not only they brought the talent but they also brought what he was talking about, Harc. You know, they have set up a lot and now we can leverage it. And they also came up with some really interesting concepts. I went and met them in India. They came up with this concept called IPL. Okay, what is that? They really challenged all their employees that's working for GCI to come up with innovative ideas to solve problems proactively, which is self-healing. You know, how you do that? So I think partners, you know, if they become really vested in your interests, they can do wonders for you. And I think in this case Hitachi is really working very well for us and in many aspects. And I'm leveraging them... You started with support, now I'm leveraging them in the automation, the platform engineering, as well as in the reliability engineering and then in even in the engineering spaces. And that like, they are my end-to-end partner right now? >> So you're really taking that holistic approach that you talked about and it sounds like it's a very collaborative two-way street partnership. Prem, I want to go back to, Suresh mentioned Harc. Talk a little bit about what Harc is and then how partners fit into Hitachi's Harc strategy. >> Great, so let me spend like a few seconds on what Harc is. Lisa, again, I know we've been using the term. Harc stands for Hitachi application reliability sectors. Now the reason we thought about Harc was, like I said in the beginning of this segment, there is an illusion from an architecture standpoint to be more modern, microservices, server-less, reactive architecture, so on and so forth. There is an illusion in your development methodology from Waterfall to agile, to DevOps to lean, agile to path program, whatever, right? Extreme program, so on and so forth. There is an evolution in the space of infrastructure from a point where you were buying these huge humongous servers and putting it in your data center to a point where people don't even see servers anymore, right? You buy it, by a click of a button you don't know the size of it. All you know is a, it's (indistinct) whatever that name means. Let's go provision it on the fly, get go, get your work done, right? When all of this is advanced when you think about operations people have been solving the problem the way they've been solving it 20 years back, right? That's the issue. And Harc was conceived exactly to fix that particular problem, to think about a modern way of operating a modern workload, right? That's exactly what Harc. So it brings together finest engineering talent. So the teams are trained in specific ways of working. We've invested and implemented some of the IP, we work with the best of the breed partner ecosystem, and I'll talk about that in a minute. And we've got these facilities in Dallas and I am talking from my office in Dallas, which is a Harc facility in the US from where we deliver for our customers. And then back in Hyderabad, we've got one more that we opened and these are facilities from where we deliver Harc services for our customers as well, right? And then we are expanding it in Japan and Portugal as we move into 23. That's kind of the plan that we are thinking through. However, that's what Harc is, Lisa, right? That's our solution to this cloud complexity problem. Right? >> Got it, and it sounds like it's going quite global, which is fantastic. So Suresh, I want to have you expand a bit on the partnership, the partner ecosystem and the role that it plays. You talked about it a little bit but what role does the partner ecosystem play in really helping JCI to dial down some of those challenges and the inherent complexities that we talked about? >> Yeah, sure. I think partners play a major role and JCI is very, very good at it. I mean, I've joined JCI 18 months ago, JCI leverages partners pretty extensively. As I said, I leverage Hitachi for my, you know, A group and the (indistinct) space and the cloud operations space, and they're my primary partner. But at the same time, we leverage many other partners. Well, you know, Accenture, SCL, and even on the tooling side we use Datadog and (indistinct). All these guys are major partners of our because the way we like to pick partners is based on our vision and where we want to go. And pick the right partner who's going to really, you know make you successful by investing their resources in you. And what I mean by that is when you have a partner, partner knows exactly what kind of skillset is needed for this customer, for them to really be successful. As I said earlier, we cannot really get all the skillset that we need, we rely on the partners and partners bring the the right skillset, they can scale. I can tell Prem tomorrow, "Hey, I need two parts by next week", and I guarantee it he's going to bring two parts to me. So they let you scale, they let you move fast. And I'm a big believer, in today's day and age, to get things done fast and be more agile. I'm not worried about failure, but for me moving fast is very, very important. And partners really do a very good job bringing that. But I think then they also really make you think, isn't it? Because one thing I like about partners they make you innovate whether they know it or not but they do because, you know, they will come and ask you questions about, "Hey, tell me why you are doing this. Can I review your architecture?" You know, and then they will try to really say I don't think this is going to work. Because they work with so many different clients, not JCI, they bring all that expertise and that's what I look from them, you know, just not, you know, do a T&M job for me. I ask you to do this go... They just bring more than that. That's how I pick my partners. And that's how, you know, Hitachi's Vantara is definitely one of a good partner from that sense because they bring a lot more innovation to the table and I appreciate about that. >> It sounds like, it sounds like a flywheel of innovation. >> Yeah. >> I love that. Last question for both of you, which we're almost out of time here, Prem, I want to go back to you. So I'm a partner, I'm planning on redefining CloudOps at my company. What are the two things you want me to remember from Hitachi Vantara's perspective? >> So before I get to that question, Lisa, the partners that we work with are slightly different from from the partners that, again, there are some similar partners. There are some different partners, right? For example, we pick and choose especially in the Harc space, we pick and choose partners that are more future focused, right? We don't care if they are huge companies or small companies. We go after companies that are future focused that are really, really nimble and can change for our customers need because it's not our need, right? When I pick partners for Harc my ultimate endeavor is to ensure, in this case because we've got (indistinct) GCI on, we are able to operate (indistinct) with the level of satisfaction above and beyond that they're expecting from us. And whatever I don't have I need to get from my partners so that I bring this solution to Suresh. As opposed to bringing a whole lot of people and making them stand in front of Suresh. So that's how I think about partners. What do I want them to do from, and we've always done this so we do workshops with our partners. We just don't go by tools. When we say we are partnering with X, Y, Z, we do workshops with them and we say, this is how we are thinking. Either you build it in your roadmap that helps us leverage you, continue to leverage you. And we do have minimal investments where we fix gaps. We're building some utilities for us to deliver the best service to our customers. And our intention is not to build a product to compete with our partner. Our intention is to just fill the wide space until they go build it into their product suite that we can then leverage it for our customers. So always think about end customers and how can we make it easy for them? Because for all the tool vendors out there seeing this and wanting to partner with Hitachi the biggest thing is tools sprawl, especially on the cloud is very real. For every problem on the cloud. I have a billion tools that are being thrown at me as Suresh if I'm putting my installation and it's not easy at all. It's so confusing. >> Yeah. >> So that's what we want. We want people to simplify that landscape for our end customers, and we are looking at partners that are thinking through the simplification not just making money. >> That makes perfect sense. There really is a very strong symbiosis it sounds like, in the partner ecosystem. And there's a lot of enablement that goes on back and forth it sounds like as well, which is really, to your point it's all about the end customers and what they're expecting. Suresh, last question for you is which is the same one, if I'm a partner what are the things that you want me to consider as I'm planning to redefine CloudOps at my company? >> I'll keep it simple. In my view, I mean, we've touched upon it in multiple facets in this interview about that, the three things. First and foremost, reliability. You know, in today's day and age my products has to be reliable, available and, you know, make sure that the customer's happy with what they're really dealing with, number one. Number two, my product has to be secure. Security is super, super important, okay? And number three, I need to really make sure my customers are getting the value so I keep my cost low. So these three is what I would focus and what I expect from my partners. >> Great advice, guys. Thank you so much for talking through this with me and really showing the audience how strong the partnership is between Hitachi Vantara and JCI. What you're doing together, we'll have to talk to you again to see where things go but we really appreciate your insights and your perspectives. Thank you. >> Thank you, Lisa. >> Thanks Lisa, thanks for having us. >> My pleasure. For my guests, I'm Lisa Martin. Thank you so much for watching. (soothing music)

Published Date : Feb 27 2023

SUMMARY :

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Prem Balasubramanian & Suresh Mothikuru


 

(soothing music) >> Hey everyone, welcome to this event, "Build Your Cloud Center of Excellence." I'm your host, Lisa Martin. In the next 15 minutes or so my guest and I are going to be talking about redefining cloud operations, an application modernization for customers, and specifically how partners are helping to speed up that process. As you saw on our first two segments, we talked about problems enterprises are facing with cloud operations. We talked about redefining cloud operations as well to solve these problems. This segment is going to be focusing on how Hitachi Vantara's partners are really helping to speed up that process. We've got Johnson Controls here to talk about their partnership with Hitachi Vantara. Please welcome both of my guests, Prem Balasubramanian is with us, SVP and CTO Digital Solutions at Hitachi Vantara. And Suresh Mothikuru, SVP Customer Success Platform Engineering and Reliability Engineering from Johnson Controls. Gentlemen, welcome to the program, great to have you. >> Thank. >> Thank you, Lisa. >> First question is to both of you and Suresh, we'll start with you. We want to understand, you know, the cloud operations landscape is increasingly complex. We've talked a lot about that in this program. Talk to us, Suresh, about some of the biggest challenges and pin points that you faced with respect to that. >> Thank you. I think it's a great question. I mean, cloud has evolved a lot in the last 10 years. You know, when we were talking about a single cloud whether it's Azure or AWS and GCP, and that was complex enough. Now we are talking about multi-cloud and hybrid and you look at Johnson Controls, we have Azure we have AWS, we have GCP, we have Alibaba and we also support on-prem. So the architecture has become very, very complex and the complexity has grown so much that we are now thinking about whether we should be cloud native or cloud agnostic. So I think, I mean, sometimes it's hard to even explain the complexity because people think, oh, "When you go to cloud, everything is simplified." Cloud does give you a lot of simplicity, but it also really brings a lot more complexity along with it. So, and then next one is pretty important is, you know, generally when you look at cloud services, you have plenty of services that are offered within a cloud, 100, 150 services, 200 services. Even within those companies, you take AWS they might not know, an individual resource might not know about all the services we see. That's a big challenge for us as a customer to really understand each of the service that is provided in these, you know, clouds, well, doesn't matter which one that is. And the third one is pretty big, at least at the CTO the CIO, and the senior leadership level, is cost. Cost is a major factor because cloud, you know, will eat you up if you cannot manage it. If you don't have a good cloud governance process it because every minute you are in it, it's burning cash. So I think if you ask me, these are the three major things that I am facing day to day and that's where I use my partners, which I'll touch base down the line. >> Perfect, we'll talk about that. So Prem, I imagine that these problems are not unique to Johnson Controls or JCI, as you may hear us refer to it. Talk to me Prem about some of the other challenges that you're seeing within the customer landscape. >> So, yeah, I agree, Lisa, these are not very specific to JCI, but there are specific issues in JCI, right? So the way we think about these are, there is a common issue when people go to the cloud and there are very specific and unique issues for businesses, right? So JCI, and we will talk about this in the episode as we move forward. I think Suresh and his team have done some phenomenal step around how to manage this complexity. But there are customers who have a lesser complex cloud which is, they don't go to Alibaba, they don't have footprint in all three clouds. So their multi-cloud footprint could be a bit more manageable, but still struggle with a lot of the same problems around cost, around security, around talent. Talent is a big thing, right? And in Suresh's case I think it's slightly more exasperated because every cloud provider Be it AWS, JCP, or Azure brings in hundreds of services and there is nobody, including many of us, right? We learn every day, nowadays, right? It's not that there is one service integrator who knows all, while technically people can claim as a part of sales. But in reality all of us are continuing to learn in this landscape. And if you put all of this equation together with multiple clouds the complexity just starts to exponentially grow. And that's exactly what I think JCI is experiencing and Suresh's team has been experiencing, and we've been working together. But the common problems are around security talent and cost management of this, right? Those are my three things. And one last thing that I would love to say before we move away from this question is, if you think about cloud operations as a concept that's evolving over the last few years, and I have touched upon this in the previous episode as well, Lisa, right? If you take architectures, we've gone into microservices, we've gone into all these server-less architectures all the fancy things that we want. That helps us go to market faster, be more competent to as a business. But that's not simplified stuff, right? That's complicated stuff. It's a lot more distributed. Second, again, we've advanced and created more modern infrastructure because all of what we are talking is platform as a service, services on the cloud that we are consuming, right? In the same case with development we've moved into a DevOps model. We kind of click a button put some code in a repository, the code starts to run in production within a minute, everything else is automated. But then when we get to operations we are still stuck in a very old way of looking at cloud as an infrastructure, right? So you've got an infra team, you've got an app team, you've got an incident management team, you've got a soft knock, everything. But again, so Suresh can talk about this more because they are making significant strides in thinking about this as a single workload, and how do I apply engineering to go manage this? Because a lot of it is codified, right? So automation. Anyway, so that's kind of where the complexity is and how we are thinking, including JCI as a partner thinking about taming that complexity as we move forward. >> Suresh, let's talk about that taming the complexity. You guys have both done a great job of articulating the ostensible challenges that are there with cloud, especially multi-cloud environments that you're living in. But Suresh, talk about the partnership with Hitachi Vantara. How is it helping to dial down some of those inherent complexities? >> I mean, I always, you know, I think I've said this to Prem multiple times. I treat my partners as my internal, you know, employees. I look at Prem as my coworker or my peers. So the reason for that is I want Prem to have the same vested interest as a partner in my success or JCI success and vice versa, isn't it? I think that's how we operate and that's how we have been operating. And I think I would like to thank Prem and Hitachi Vantara for that really been an amazing partnership. And as he was saying, we have taken a completely holistic approach to how we want to really be in the market and play in the market to our customers. So if you look at my jacket it talks about OpenBlue platform. This is what JCI is building, that we are building this OpenBlue digital platform. And within that, my team, along with Prem's or Hitachi's, we have built what we call as Polaris. It's a technical platform where our apps can run. And this platform is automated end-to-end from a platform engineering standpoint. We stood up a platform engineering organization, a reliability engineering organization, as well as a support organization where Hitachi played a role. As I said previously, you know, for me to scale I'm not going to really have the talent and the knowledge of every function that I'm looking at. And Hitachi, not only they brought the talent but they also brought what he was talking about, Harc. You know, they have set up a lot and now we can leverage it. And they also came up with some really interesting concepts. I went and met them in India. They came up with this concept called IPL. Okay, what is that? They really challenged all their employees that's working for GCI to come up with innovative ideas to solve problems proactively, which is self-healing. You know, how you do that? So I think partners, you know, if they become really vested in your interests, they can do wonders for you. And I think in this case Hitachi is really working very well for us and in many aspects. And I'm leveraging them... You started with support, now I'm leveraging them in the automation, the platform engineering, as well as in the reliability engineering and then in even in the engineering spaces. And that like, they are my end-to-end partner right now? >> So you're really taking that holistic approach that you talked about and it sounds like it's a very collaborative two-way street partnership. Prem, I want to go back to, Suresh mentioned Harc. Talk a little bit about what Harc is and then how partners fit into Hitachi's Harc strategy. >> Great, so let me spend like a few seconds on what Harc is. Lisa, again, I know we've been using the term. Harc stands for Hitachi application reliability sectors. Now the reason we thought about Harc was, like I said in the beginning of this segment, there is an illusion from an architecture standpoint to be more modern, microservices, server-less, reactive architecture, so on and so forth. There is an illusion in your development methodology from Waterfall to agile, to DevOps to lean, agile to path program, whatever, right? Extreme program, so on and so forth. There is an evolution in the space of infrastructure from a point where you were buying these huge humongous servers and putting it in your data center to a point where people don't even see servers anymore, right? You buy it, by a click of a button you don't know the size of it. All you know is a, it's (indistinct) whatever that name means. Let's go provision it on the fly, get go, get your work done, right? When all of this is advanced when you think about operations people have been solving the problem the way they've been solving it 20 years back, right? That's the issue. And Harc was conceived exactly to fix that particular problem, to think about a modern way of operating a modern workload, right? That's exactly what Harc. So it brings together finest engineering talent. So the teams are trained in specific ways of working. We've invested and implemented some of the IP, we work with the best of the breed partner ecosystem, and I'll talk about that in a minute. And we've got these facilities in Dallas and I am talking from my office in Dallas, which is a Harc facility in the US from where we deliver for our customers. And then back in Hyderabad, we've got one more that we opened and these are facilities from where we deliver Harc services for our customers as well, right? And then we are expanding it in Japan and Portugal as we move into 23. That's kind of the plan that we are thinking through. However, that's what Harc is, Lisa, right? That's our solution to this cloud complexity problem. Right? >> Got it, and it sounds like it's going quite global, which is fantastic. So Suresh, I want to have you expand a bit on the partnership, the partner ecosystem and the role that it plays. You talked about it a little bit but what role does the partner ecosystem play in really helping JCI to dial down some of those challenges and the inherent complexities that we talked about? >> Yeah, sure. I think partners play a major role and JCI is very, very good at it. I mean, I've joined JCI 18 months ago, JCI leverages partners pretty extensively. As I said, I leverage Hitachi for my, you know, A group and the (indistinct) space and the cloud operations space, and they're my primary partner. But at the same time, we leverage many other partners. Well, you know, Accenture, SCL, and even on the tooling side we use Datadog and (indistinct). All these guys are major partners of our because the way we like to pick partners is based on our vision and where we want to go. And pick the right partner who's going to really, you know make you successful by investing their resources in you. And what I mean by that is when you have a partner, partner knows exactly what kind of skillset is needed for this customer, for them to really be successful. As I said earlier, we cannot really get all the skillset that we need, we rely on the partners and partners bring the the right skillset, they can scale. I can tell Prem tomorrow, "Hey, I need two parts by next week", and I guarantee it he's going to bring two parts to me. So they let you scale, they let you move fast. And I'm a big believer, in today's day and age, to get things done fast and be more agile. I'm not worried about failure, but for me moving fast is very, very important. And partners really do a very good job bringing that. But I think then they also really make you think, isn't it? Because one thing I like about partners they make you innovate whether they know it or not but they do because, you know, they will come and ask you questions about, "Hey, tell me why you are doing this. Can I review your architecture?" You know, and then they will try to really say I don't think this is going to work. Because they work with so many different clients, not JCI, they bring all that expertise and that's what I look from them, you know, just not, you know, do a T&M job for me. I ask you to do this go... They just bring more than that. That's how I pick my partners. And that's how, you know, Hitachi's Vantara is definitely one of a good partner from that sense because they bring a lot more innovation to the table and I appreciate about that. >> It sounds like, it sounds like a flywheel of innovation. >> Yeah. >> I love that. Last question for both of you, which we're almost out of time here, Prem, I want to go back to you. So I'm a partner, I'm planning on redefining CloudOps at my company. What are the two things you want me to remember from Hitachi Vantara's perspective? >> So before I get to that question, Lisa, the partners that we work with are slightly different from from the partners that, again, there are some similar partners. There are some different partners, right? For example, we pick and choose especially in the Harc space, we pick and choose partners that are more future focused, right? We don't care if they are huge companies or small companies. We go after companies that are future focused that are really, really nimble and can change for our customers need because it's not our need, right? When I pick partners for Harc my ultimate endeavor is to ensure, in this case because we've got (indistinct) GCI on, we are able to operate (indistinct) with the level of satisfaction above and beyond that they're expecting from us. And whatever I don't have I need to get from my partners so that I bring this solution to Suresh. As opposed to bringing a whole lot of people and making them stand in front of Suresh. So that's how I think about partners. What do I want them to do from, and we've always done this so we do workshops with our partners. We just don't go by tools. When we say we are partnering with X, Y, Z, we do workshops with them and we say, this is how we are thinking. Either you build it in your roadmap that helps us leverage you, continue to leverage you. And we do have minimal investments where we fix gaps. We're building some utilities for us to deliver the best service to our customers. And our intention is not to build a product to compete with our partner. Our intention is to just fill the wide space until they go build it into their product suite that we can then leverage it for our customers. So always think about end customers and how can we make it easy for them? Because for all the tool vendors out there seeing this and wanting to partner with Hitachi the biggest thing is tools sprawl, especially on the cloud is very real. For every problem on the cloud. I have a billion tools that are being thrown at me as Suresh if I'm putting my installation and it's not easy at all. It's so confusing. >> Yeah. >> So that's what we want. We want people to simplify that landscape for our end customers, and we are looking at partners that are thinking through the simplification not just making money. >> That makes perfect sense. There really is a very strong symbiosis it sounds like, in the partner ecosystem. And there's a lot of enablement that goes on back and forth it sounds like as well, which is really, to your point it's all about the end customers and what they're expecting. Suresh, last question for you is which is the same one, if I'm a partner what are the things that you want me to consider as I'm planning to redefine CloudOps at my company? >> I'll keep it simple. In my view, I mean, we've touched upon it in multiple facets in this interview about that, the three things. First and foremost, reliability. You know, in today's day and age my products has to be reliable, available and, you know, make sure that the customer's happy with what they're really dealing with, number one. Number two, my product has to be secure. Security is super, super important, okay? And number three, I need to really make sure my customers are getting the value so I keep my cost low. So these three is what I would focus and what I expect from my partners. >> Great advice, guys. Thank you so much for talking through this with me and really showing the audience how strong the partnership is between Hitachi Vantara and JCI. What you're doing together, we'll have to talk to you again to see where things go but we really appreciate your insights and your perspectives. Thank you. >> Thank you, Lisa. >> Thanks Lisa, thanks for having us. >> My pleasure. For my guests, I'm Lisa Martin. Thank you so much for watching. (soothing music)

Published Date : Feb 24 2023

SUMMARY :

In the next 15 minutes or so and pin points that you all the services we see. Talk to me Prem about some of the other in the episode as we move forward. that taming the complexity. and play in the market to our customers. that you talked about and it sounds Now the reason we thought about Harc was, and the inherent complexities But at the same time, we like a flywheel of innovation. What are the two things you want me especially in the Harc space, we pick for our end customers, and we are looking it sounds like, in the partner ecosystem. make sure that the customer's happy showing the audience how Thank you so much for watching.

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Show Wrap | CloudNativeSecurityCon 23


 

>> Hey everyone. Welcome back to theCUBE's coverage day two of CloudNative Security CON 23. Lisa Martin here in studio in Palo Alto with John Furrier. John, we've had some great conversations. I've had a global event. This was a global event. We had Germany on yesterday. We had the Boston Studio. We had folks on the ground in Seattle. Lot of great conversations, a lot of great momentum at this event. What is your number one takeaway with this inaugural event? >> Well, first of all, our coverage with our CUBE alumni experts coming in remotely this remote event for us, I think this event as an inaugural event stood out because one, it was done very carefully and methodically from the CNCF. I think they didn't want to overplay their hand relative to breaking out from CUBE CON So Kubernetes success and CloudNative development has been such a success and that event and ecosystem is booming, right? So that's the big story is they have the breakout event and the question was, was it a good call? Was it successful? Was it going to, would the dog hunt as they say, in this case, I think the big takeaway is that it was successful by all measures. One, people enthusiastic and confident that this has the ability to stand on its own and still contribute without taking away from the benefits and growth of Kubernetes CUBE CON and CloudNative console. So that was the key. Hallway conversations, the sessions all curated and developed properly to be different and focused for that reason. So I think the big takeaway is that the CNCF did a good job on how they rolled this out. Again, it was very intimate event small reminds me of first CUBE CON in Seattle, kind of let's test it out. Let's see how it goes. Again, clearly it was people successful and they understood why they're doing it. And as we commented out in our earlier segments this is not something new. Amazon Web Services has re:Invent and re:Inforce So a lot of parallels there. I see there. So I think good call. CNCF did the right thing. I think this has legs. And then as Dave pointed out, Dave Vellante, on our last keynote analysis was the business model of the hackers is better than the business model of the industry. They're making more money, it costs less so, you know, they're playing offense and the industry playing defense. That has to change. And as Dave pointed out we have to make the cost of hacking and breaches and cybersecurity higher so that the business model crashes. And I think that's the strategic imperative. So I think the combination of the realities of the market globally and open source has to go faster. It's good to kind of decouple and be highly cohesive in the focus. So to me that's the big takeaway. And then the other one is, is that there's a lot more security problems still unresolved. The emphasis on developers productivity is at risk here, if not solved. You saw supply chain software, again, front and center and then down in the weeds outside of Kubernetes, things like BIND and DNS were brought up. You're seeing the Linux kernel. Really important things got to be paid attention to. So I think very good call, very good focus. >> I would love if for us to be able to, as the months go on talk to some of the practitioners that actually got to attend. There were 72 sessions, that's a lot of content for a small event. Obviously to your point, very well curated. We did hear from some folks yesterday who were just excited to get the community back together in person. To your point, having this dedicated focus on CloudNativesecurity is incredibly important. You talked about, you know, the offense defense, the fact that right now the industry needs to be able to pivot from being on defense to being on offense. This is a challenging thing because it is so lucrative for hackers. But this seems to be from what we've heard in the last couple days, the right community with the right focus to be able to make that pivot. >> Yeah, and I think if you look at the success of Kubernetes, 'cause again we were there at theCUBE first one CUBE CON, the end user stories really drove end user participation. Drove the birth of Kubernetes. Left some of these CloudNative early adopters early pioneers that were using cloud hyperscale really set the table for CloudNative CON. I think you're seeing that here with this CloudNative SecurityCON where I think we're see a lot more end user stories because of the security, the hairs on fire as we heard from Madrona Ventures, you know, as they as an investor you have a lot of use cases out there where customers are leaning in with getting the rolling up their sleeves, working with open source. This has to be the driver. So I'm expecting to see the next level of SecurityCON to be end user focused. Much more than vendor focused. Where CUBECON was very end user focused and then attracted all the vendors in that grew the industry. I expect the similar pattern here where end user action will be very high at the beginning and that will essentially be the rising tide for the vendors to be then participating. So I expect almost a similar trajectory to CUBECON. >> That's a good path that it needs to all be about all the end users. One of the things I'm curious if what you heard was what are some of the key factors that are going to move CloudNative Security forward? What did you hear the last two days? >> I heard that there's a lot of security problems and no one wants to kind of brag about this but there's a lot of under the hood stuff that needs to get taken care of. So if automation scales, and we heard that from one of the startups we've just interviewed. If automation and scale continues to happen and with the business model of the hackers still booming, security has to be refactored quickly and there's going to be an opportunity structurally to use the cloud for that. So I think it's a good opportunity now to get dedicated focus on fixing things like the DNS stuff old school under the hood, plumbing, networking protocols. You're going to start to see this super cloud-like environment emerge where data's involved, everything's happening and so security has to be re imagined. And I think there's a do over opportunity for the security industry with CloudNative driving that. And I think this is the big thing that I see as an opportunity to, from a story standpoint from a coverage standpoint is that it's a do-over for security. >> One of the things that we heard yesterday is that there's a lot of it, it's a pretty high percentage of organizations that either don't have a SOCK or have a very primitive SOCK. Which kind of surprised me that at this day and age the risks are there. We talked about that today's focus and the keynote was a lot about the software supply chain and what's going on there. What did you hear in terms of the appetite for organizations through the voice of the practitioner to say, you know what guys, we got to get going because there's going to be the hackers are they're here. >> I didn't hear much about that in the coverage 'cause we weren't in the hallways. But from reading the tea leaves and talking to the folks on the ground, I think there's an implied like there's an unlimited money from customers. So it's a very robust from the data infrastructure stack building we cover with the angel investor Kane you're seeing data infrastructure's going to be part of the solution here 'cause data and security go hand in hand. So everyone's got basically checkbook wide open everyone wants to have the answer. And we commented that the co-founder of Palo Alto you had on our coverage yesterday was saying that you know, there's no real platform, there's a lot of tools out there. People will buy anything. So there's still a huge appetite and spend in security but the answer's not going to more tool sprawling. It's going to more platform auto, something that enables automation, fix some of the underlying mechanisms involved and fix it fast. So to me I think it's going to be a robust monetary opportunity because of the demand on the business side. So I don't see that changing at all and I think it's going to accelerate. >> It's a great point in terms of the demand for the business side because as we know as we said yesterday, the next Log4j is out there. It's not a matter of if this happens again it's when, it's the extent, it's how frequent we know that. So organizations all the way up to the board have to be concerned about brand reputation. Nobody wants to be the next big headline in terms of breaches and customer data being given to hackers and hackers making all this money on that. That has to go all the way up to the board and there needs to be alignment between the board and the executives at the organization in terms of how they're going to deal with security, and now. This is not a conversation that can wait. Yeah, I mean I think the five C's we talked about yesterday the culture of companies, the cloud is an enabler, you've got clusters of servers and capabilities, Kubernetes clusters, you've got code and you've got all kinds of, you know, things going on there. Each one has elements that are at risk for hacking, right? So that to me is something that's super important. I think that's why the focus on security's different and important, but it's not going to fork the main event. So that's why I think the spin out was, spinout, or the new event is a good call by the CNCF. >> One of the things today that struck me they're talking a lot about software supply chain and that's been in the headlines for quite a while now. And a stat that was shared this morning during the keynote just blew my brains that there was a 742% increase in the software supply chain attacks occurring over the last three years. It's during Covid times, that is a massive increase. The threat landscape is just growing so amorphously but organizations need to help dial that down because their success and the health of the individuals and the end users is at risk. Well, Covid is an environment where everyone's kind of working at home. So there was some disruption to infrastructure. Also, when you have change like that, there's opportunities for hackers, they'll arbitrage that big time. But I think general the landscape is changing. There's no perimeter anymore. It's CloudNative, this is where it is and people who are moving from old IT to CloudNative, they're at risk. That's why there's tons of ransomware. That's why there's tons of risk. There's just hygiene, from hygiene to architecture and like Nick said from Palo Alto, the co-founder, there's not a lot of architecture in security. So yeah, people have bulked up their security teams but you're going to start to see much more holistic thinking around redoing security. I think that's the opportunity to propel CloudNative, and I think you'll see a lot more coming out of this. >> Did you hear any specific information on some of the CloudNative projects going on that really excite you in terms of these are the right people going after the right challenges to solve in the right direction? >> Well I saw the sessions and what jumped out to me at the sessions was it's a lot of extensions of what we heard at CUBECON and I think what they want to do is take out the big items and break 'em out in security. Kubescape was one we just covered. They want to get more sandbox type stuff into the security side that's very security focused but also plays well with CUBECON. So we'll hear more about how this plays out when we're in Amsterdam coming up in April for CUBECON to hear how that ecosystem, because I think it'll be kind of a relief to kind of decouple security 'cause that gives more focus to the stakeholders in CUBECON. There's a lot of issues going on there and you know service meshes and whatnot. So it's a lot of good stuff happening. >> A lot of good stuff happening. One of the things that'll be great about CUBECON is that we always get the voice of the customer. We get vendors coming on with the voice of the customer talking about and you know in that case how they're using Kubernetes to drive the business forward. But it'll be great to be able to pull in some of the security conversations that spin out of CloudNative Security CON to understand how those end users are embracing the technology. You brought up I think Nir Zuk from Palo Alto Networks, one of the themes there when Dave and I did their Ignite event in December was, of 22, was really consolidation. There are so many tools out there that organizations have to wrap their heads around and they need to be able to have the right enablement content which this event probably delivered to figure out how do we consolidate security tools effectively, efficiently in a way that helps dial down our risk profile because the risks just seem to keep growing. >> Yeah, and I love the technical nature of all that and I think this is going to be the continued focus. Chris Aniszczyk who's the CTO listed like E and BPF we covered with Liz Rice is one of the most three important points of the conference and it's just, it's very nerdy and that's what's needed. I mean it's technical. And again, there's no real standards bodies anymore. The old days developers I think are super important to be the arbiters here. And again, what I love about the CNCF is that they're developer focused and we heard developer first even in security. So you know, this is a sea change and I think, you know, developers' choice will be the standards bodies. >> Lisa: Yeah, yeah. >> They decide the future. >> Yeah. >> And I think having the sandboxing and bringing this out will hopefully accelerate more developer choice and self-service. >> You've been talking about kind of putting the developers in the driver's seat as really being the key decision makers for a while. Did you hear information over the last couple of days that validates that? >> Yeah, absolutely. It's clearly the fact that they did this was one. The other one is, is that engineering teams and dev teams and script teams, they're blending together. It's not just separate silos and the ones that are changing their team dynamics, again, back to the culture are winning. And I think this has to happen. Security has to be embedded everywhere in making it frictionless and to provide kind of the guardrail so developers don't slow down. And I think where security has become a drag or an anchor or a blocker has been just configuration of how the organization's handling it. So I think when people recognize that the developers are in charge and they're should be driving the application development you got to make sure that's secure. And so that's always going to be friction and I think whoever does it, whoever unlocks that for the developer to go faster will win. >> Right. Oh, that's what I'm sure magic to a developer's ear is the ability to go faster and be able to focus on co-development in a secure fashion. What are some of the things that you're excited about for CUBECON. Here we are in February, 2023 and CUBECON is just around the corner in April. What are some of the things that you're excited about based on the groundswell momentum that this first inaugural CloudNative Security CON is generating from a community, a culture perspective? >> I think this year's going to be very interesting 'cause we have an economic challenge globally. There's all kinds of geopolitical things happening. I think there's going to be very entrepreneurial activity this year more than ever. I think you're going to see a lot more innovative projects ideas hitting the table. I think it's going to be a lot more entrepreneurial just because the cycle we're in. And also I think the acceleration of mainstream deployments of out of the CNCF's main event CUBECON will happen. You'll see a lot more successes, scale, more clarity on where the security holes are or aren't. Where the benefits are. I think containers and microservices are continuing to surge. I think the Cloud scale hyperscale as Amazon, Azure, Google will be more aggressive. I think AI will be a big theme this year. I think you can see how data is going to infect some of the innovation thinking. I'm really excited about the data infrastructure because it powers a lot of things in the Cloud. So I think the Amazon Web Services, Azure next level gen clouds will impact what happens in the CloudNative foundation. >> Did you have any conversations yesterday or today with respect to AI and security? Was that a focus of anybody's? Talk to me about that. >> Well, I didn't hear any sessions on AI but we saw some demos on stage. But they're teasing out that this is an augmentation to their mission, right? So I think a lot of people are looking at AI as, again, like I always said there's the naysayers who think it's kind of a gimmick or nothing to see here, and then some are just going to blown away. I think the people who are alpha geeks and the industry connect the dots and understand that AI is going to be an accelerant to a lot of heavy lifting that was either manual, you know, hard to do things that was boring or muck as they say. I think that's going to be where you'll see the AI stories where it's going to accelerate either ways to make security better or make developers more confident and productive. >> Or both. >> Yeah. So definitely AI will be part of it. Yeah, definitely. One of the things too that I'm wondering if, you know, we talk about CloudNative and the goal of it, the importance of it. Do you think that this event, in terms of what we were able to see, obviously being remote the event going on in Seattle, us being here in Palo Alto and Boston and guests on from Seattle and Germany and all over, did you hear the really the validation for why CloudNative Security why CloudNative is important for organizations whether it's a bank or a hospital or a retailer? Is that validation clear and present? >> Yeah, absolutely. I think it was implied. I don't think there was like anyone's trying to debate that. I think this conference was more of it's assumed and they were really trying to push the ability to make security less defensive, more offensive and more accelerated into the solving the problems with the businesses that are out there. So clearly the CloudNative community understands where the security challenges are and where they're emerging. So having a dedicated event will help address that. And they've got great co-chairs too that put it together. So I think that's very positive. >> Yeah. Do you think, is it possible, I mean, like you said several times today so eloquently the industry's on the defense when it comes to security and the hackers are on the offense. Is it really possible to make that switch or obviously get some balances. As technology advances and industry gets to take advantage of that, so do the hackers, is that balance achievable? >> Absolutely. I mean, I think totally achievable. The question's going to be what's the environment going to be like? And I remember as context to understanding whether it's viable or not, is to look at, just go back 13 years ago, I remember in 2010 Amazon was viewed as an unsecure environment. Everyone's saying, "Oh, the cloud is not secure." And I remember interviewing Steve Schmidt at AWS and we discussed specifically how Amazon Cloud was being leveraged by hackers. They made it more complex for the hackers. And he said, "This is just the beginning." It's kind of like barbed wire on a fence. It's yeah, you're not going to climb it so people can get over it. And so since then what's happened is the Cloud has become more secure than on premises for a lot of either you know, personnel reasons, culture reasons, not updating, you know, from patches to just being insecure to be more insecure. So that to me means that the flip the script can be flipped. >> Yeah. And I think with CloudNative they can build in automation and code to solve some of these problems and make it more complex for the hacker. >> Lisa: Yes. >> And increase the cost. >> Yeah, exactly. Make it more complex. Increase the cost. That'll be in interesting journey to follow. So John, here we are early February, 2023 theCUBE starting out strong as always. What year are we in, 12? Year 12? >> 13th year >> 13! What's next for theCUBE? What's coming up that excites you? >> Well, we're going to do a lot more events. We got the theCUBE in studio that I call theCUBE Center as kind of internal code word, but like, this is more about getting the word out that we can cover events remotely as events are starting to change with hybrid, digital is going to be a big part of that. So I think you're going to see a lot more CUBE on location. We're going to do, still do theCUBE and have theCUBE cover events from the studio to get deeper perspective because we can then bring people in remote through our our studio team. We can bring our CUBE alumni in. We have a corpus of content and experts to bring to table. So I think the coverage will be increased. The expertise and data will be flowing through theCUBE and so Cube Center, CUBE CUBE Studio. >> Lisa: Love it. >> Will be a integral part of our coverage. >> I love that. And we have such great conversations with guests in person, but also virtually, digitally as well. We still get the voices of the practitioners and the customers and the vendors and the partner ecosystem really kind of lauded loud and clear through theCUBE megaphone as I would say. >> And of course getting the clips out there, getting the highlights. >> Yeah. >> Getting more stories. No stories too small for theCUBE. We can make it easy to get the best content. >> The best content. John, it's been fun covering CloudNative security CON with you with you. And Dave and our guests, thank you so much for the opportunity and looking forward to the next event. >> John: All right. We'll see you at Amsterdam. >> Yeah, I'll be there. We want to thank you so much for watching TheCUBES's two day coverage of CloudNative Security CON 23. We're live in Palo Alto. You are live wherever you are and we appreciate your time and your view of this event. For John Furrier, Dave Vellante, I'm Lisa Martin. Thanks for watching guys. We'll see you at the next show.

Published Date : Feb 3 2023

SUMMARY :

We had folks on the ground in Seattle. and be highly cohesive in the focus. that right now the because of the security, the hairs on fire One of the things I'm and there's going to be an One of the things that and I think it's going to accelerate. and the executives at One of the things today that struck me at the sessions was One of the things that'll be great Yeah, and I love the And I think having the kind of putting the developers for the developer to go faster will win. the ability to go faster I think it's going to be Talk to me about that. I think that's going to be One of the things too that So clearly the CloudNative and the hackers are on the offense. So that to me means that the and make it more complex for the hacker. Increase the cost. and experts to bring to table. Will be a integral and the customers and the getting the highlights. get the best content. for the opportunity and looking We'll see you at Amsterdam. and we appreciate your time

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Ben Hirschberg, Armo Ltd | CloudNativeSecurityCon 23


 

(upbeat music) >> Hello everyone, welcome back to theCUBE's coverage of Cloud Native SecurityCon North America 2023. Obviously, CUBE's coverage with our CUBE Center Report. We're not there on the ground, but we have folks and our CUBE Alumni there. We have entrepreneurs there. Of course, we want to be there in person, but we're remote. We've got Ben Hirschberg, CTO and Co-Founder of Armo, a cloud native security startup, well positioned in this industry. He's there in Seattle. Ben, thank you for coming on and sharing what's going on with theCUBE. >> Yeah, it's great to be here, John. >> So we had written on you guys up on SiliconANGLE. Congratulations on your momentum and traction. But let's first get into what's going on there on the ground? What are some of the key trends? What's the most important story being told there? What is the vibe? What's the most important story right now? >> So I think, I would like to start here with the I think the most important thing was that I think the event is very successful. Usually, the Cloud Native Security Day usually was part of KubeCon in the previous years and now it became its own conference of its own and really kudos to all the organizers who brought this up in, actually in a short time. And it wasn't really clear how many people will turn up, but at the end, we see a really nice turn up and really great talks and keynotes around here. I think that one of the biggest trends, which haven't started like in this conference, but already we're talking for a while is supply chain. Supply chain is security. I think it's, right now, the biggest trend in the talks, in the keynotes. And I think that we start to see companies, big companies, who are adopting themselves into this direction. There is a clear industry need. There is a clear problem and I think that the cloud native security teams are coming up with tooling around it. I think for right now we see more tools than adoption, but the adoption is always following the tooling. And I think it already proves itself. So we have just a very interesting talk this morning about the OpenSSL vulnerability, which was I think around Halloween, which came out and everyone thought that it's going to be a critical issue for the whole cloud native and internet infrastructure and at the end it turned out to be a lesser problem, but the reason why I think it was understood that to be a lesser problem real soon was that because people started to use (indistinct) store software composition information in the environment so security teams could look into, look up in their systems okay, what, where they're using OpenSSL, which version they are using. It became really soon real clear that this version is not adopted by a wide array of software out there so the tech surface is relatively small and I think it already proved itself that the direction if everyone is talking about. >> Yeah, we agree, we're very bullish on this move from the Cloud Native Foundation CNCF that do the security conference. Amazon Web Services has re:Invent. That's their big show, but they also have re:Inforce, the security show, so clearly they work together. I like the decoupling, very cohesive. But you guys have Kubescape of Kubernetes security. Talk about the conversations that are there and that you're hearing around why there's different event what's different around KubeCon and CloudNativeCon than this Cloud Native SecurityCon. It's not called KubeSucSecCon, it's called Cloud Native SecurityCon. What's the difference? Are people confused? Is it clear? What's the difference between the two shows? What are you hearing? >> So I think that, you know, there is a good question. Okay, where is Cloud Native Computing Foundation came from? Obviously everyone knows that it was somewhat coupled with the adoption of Kubernetes. It was a clear understanding in the industry that there are different efforts where the industry needs to come together without looking be very vendor-specific and try to sort out a lot of issues in order to enable adoption and bring great value and I think that the main difference here between KubeCon and the Cloud Native Security Conference is really the focus, and not just on Kubernetes, but the whole ecosystem behind that. The way we are delivering software, the way we are monitoring software, and all where Kubernetes is only just, you know, maybe the biggest clog in the system, but, you know, just one of the others and it gives great overview of what you have in the whole ecosystem. >> Yeah, I think it's a good call. I would add that what I'm hearing too is that security is so critical to the business model of every company. It's so mainstream. The hackers have a great business model. They make money, their costs are lower than the revenue. So the business of hacking in breaches, ransomware all over the place is so successful that they're playing offense, everyone's playing defense, so it's about time we can get focus to really be faster and more nimble and agile on solving some of these security challenges in open source. So I think that to me is a great focus and so I give total props to the CNC. I call it the event operating system. You got the security group over here decoupled from the main kernel, but they work together. Good call and so this brings back up to some of the things that are going on so I have to ask you, as your startup as a CTO, you guys have the Kubescape platform, how do you guys fit into the landscape and what's different from your tools for Kubernetes environments versus what's out there? >> So I think that our journey is really interesting in the solution space because I think that our mode really tries to understand where security can meet the actual adoption because as you just said, somehow we have to sort out together how security is going to be automated and integrated in its best way. So Kubescape project started as a Kubernetes security posture tool. Just, you know, when people are really early in their adoption of Kubernetes systems, they want to understand whether the installation is is secure, whether the basic configurations are look okay, and giving them instant feedback on that, both in live systems and in the CICD, this is where Kubescape came from. We started as an open source project because we are big believers of open source, of the power of open source security, and I can, you know I think maybe this is my first interview when I can say that Kubescape was accepted to be a CNCF Sandbox project so Armo was actually donating the project to the CNCF, I think, which is a huge milestone and a great way to further the adoption of Kubernetes security and from now on we want to see where the users in Armo and Kubescape project want to see where the users are going, their Kubernetes security journey and help them to automatize, help them to to implement security more fast in the way the developers are using it working. >> Okay, if you don't mind, I want to just get clarification. What's the difference between the Armo platform and Kubescape because you have Kubescape Sandbox project and Armo platform. Could you talk about the differences and interaction? >> Sure, Kubescape is an open source project and Armo platform is actually a managed platform which runs Kubescape in the cloud for you because Kubescape is part, it has several parts. One part is, which is running inside the Kubernetes cluster in the CICD processes of the user, and there is another part which we call the backend where the results are stored and can be analyzed further. So Armo platform gives you managed way to run the backend, but I can tell you that backend is also, will be available within a month or two also for everyone to install on their premises as well, because again, we are an open source company and we are, we want to enable users, so the difference is that Armo platform is a managed platform behind Kubescape. >> How does Kubescape differ from closed proprietary sourced solutions? >> So I can tell you that there are closed proprietary solutions which are very good security solutions, but I think that the main difference, if I had to pick beyond the very specific technicalities is the worldview. The way we see that our user is not the CISO. Our user is not necessarily the security team. From our perspective, the user is the DevOps and the developers who are working on the Kubernetes cluster day to day and we want to enable them to improve their security. So actually our approach is more developer-friendly, if I would need to define it very shortly. >> What does this risk calculation score you guys have in Kubscape? That's come up and we cover that in our story. Can you explain to the folks how that fits in? Is it Kubescape is the platform and what's the benefit, what's the purpose? >> So the risk calculation is actually a score we are giving to clusters in order for the users to understand where they are standing in the general population, how they are faring against a perfect hardened cluster. It is based on the number of different tests we are making. And I don't want to go into, you know, the very specifics of the mathematical functions, but in general it takes into account how many functions are failing, security tests are failing inside your cluster. How many nodes you are having, how many workloads are having, and creating this number which enables you to understand where you are standing in the global, in the world. >> What's the customer value that you guys pitching? What's the pitch for the Armo platform? When you go and talk to a customer, are they like, "We need you." Do they come to you? Is it word of mouth? You guys have a strategy? What's the pitch? What's so appealing to the customers? Why are they enthusiastic about you guys? >> So John, I can tell you, maybe it's not so easy to to say the words, but I nearly 20 years in the industry and though I've been always around cyber and the defense industry and I can tell you that I never had this journey where before where I could say that the the customers are coming to us and not we are pitching to customers. Simply because people want to, this is very easy tool, very very easy to use, very understandable and it very helps the engineers to improve security posture. And they're coming to us and they're saying, "Well, awesome, okay, how we can like use it. Do you have a graphical interface?" And we are pointing them to the Armor platform and they are falling in love and coming to us even more and we can tell you that we have a big number of active users behind the platform itself. >> You know, one of the things that comes up every time at KubeCon and Cloud NativeCon when we're there, and we'll be in Amsterdam, so folks watching, you know, we'll see onsite, developer productivity is like the number one thing everyone talks about and security is so important. It's become by default a blocker or anchor or a drag on productivity. This is big, the things that you're mentioning, easy to use, engineering supporting it, developer adoption, you know we've always said on theCUBE, developers will be the de facto standards bodies by their choices 'cause developers make all the decisions. So if I can go faster and I can have security kind of programmed in, I'm not shifting left, it's just I'm just having security kind of in there. That's the dream state. Is that what you guys are trying to do here? Because that's the nirvana, everyone wants to do that. >> Yeah, I think your definition is like perfect because really we had like this, for a very long time we had this world where we decoupled security teams from developers and even for sometimes from engineering at all and I think for multiple reasons, we are more seeing a big convergence. Security teams are becoming part of the engineering and the engineering becoming part of the security and as you're saying, okay, the day-to-day world of developers are becoming very tangled up in the good way with security, so the think about it that today, one of my developers at Armo is creating a pull request. He's already, code is already scanned by security scanners for to test for different security problems. It's already, you know, before he already gets feedback on his first time where he's sharing his code and if there is an issue, he already can solve it and this is just solving issues much faster, much cheaper, and also you asked me about, you know, the wipe in the conference and we know no one can deny the current economic wipe we have and this also relates to security teams and security teams has to be much more efficient. And one of the things that everyone is talking, okay, we need more automation, we need more, better tooling and I think we are really fitting into this. >> Yeah, and I talked to venture capitalists yesterday and today, an angel investor. Best time for startup is right now and again, open source is driving a lot of value. Ben, it's been great to have you on and sharing with us what's going on on the ground there as well as talking about some of the traction you have. Just final question, how old's the company? How much funding do you have? Where you guys located? Put a plug in for the company. You guys looking to hire? Tell us about the company. Were you guys located? How much capital do you have? >> So, okay, the company's here for three years. We've passed a round last March with Tiger and Hyperwise capitals. We are located, most of the company's located today in Israel in Tel Aviv, but we have like great team also in Ukraine and also great guys are in Europe and right now also Craig Box joined us as an open source VP and he's like right now located in New Zealand, so we are a really global team, which I think it's really helps us to strengthen ourselves. >> Yeah, and I think this is the entrepreneurial equation for the future. It's really great to see that global. We heard that in Priyanka Sharma's keynote. It's a global culture, global community. >> Right. >> And so really, really props you guys. Congratulations on Armo and thanks for coming on theCUBE and sharing insights and expertise and also what's happening on the ground. Appreciate it, Ben, thanks for coming on. >> Thank you, John. >> Okay, cheers. Okay, this is CUB coverage here of the Cloud Native SecurityCon in North America 2023. I'm John Furrier for Lisa Martin, Dave Vellante. We're back with more of wrap up of the event after this short break. (gentle upbeat music)

Published Date : Feb 3 2023

SUMMARY :

and sharing what's going on with theCUBE. What is the vibe? and at the end it turned that do the security conference. the way we are monitoring software, I call it the event operating system. the project to the CNCF, What's the difference between in the CICD processes of the user, is the worldview. Is it Kubescape is the platform It is based on the number of What's the pitch for the Armo platform? and the defense industry This is big, the things and the engineering becoming the traction you have. So, okay, the company's Yeah, and I think this is and also what's happening on the ground. of the Cloud Native SecurityCon

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Opher Kahane, Sonoma Ventures | CloudNativeSecurityCon 23


 

(uplifting music) >> Hello, welcome back to theCUBE's coverage of CloudNativeSecurityCon, the inaugural event, in Seattle. I'm John Furrier, host of theCUBE, here in the Palo Alto Studios. We're calling it theCUBE Center. It's kind of like our Sports Center for tech. It's kind of remote coverage. We've been doing this now for a few years. We're going to amp it up this year as more events are remote, and happening all around the world. So, we're going to continue the coverage with this segment focusing on the data stack, entrepreneurial opportunities around all things security, and as, obviously, data's involved. And our next guest is a friend of theCUBE, and CUBE alumni from 2013, entrepreneur himself, turned, now, venture capitalist angel investor, with his own firm, Opher Kahane, Managing Director, Sonoma Ventures. Formerly the founder of Origami, sold to Intuit a few years back. Focusing now on having a lot of fun, angel investing on boards, focusing on data-driven applications, and stacks around that, and all the stuff going on in, really, in the wheelhouse for what's going on around security data. Opher, great to see you. Thanks for coming on. >> My pleasure. Great to be back. It's been a while. >> So you're kind of on Easy Street now. You did the entrepreneurial venture, you've worked hard. We were on together in 2013 when theCUBE just started. XCEL Partners had an event in Stanford, XCEL, and they had all the features there. We interviewed Satya Nadella, who was just a manager at Microsoft at that time, he was there. He's now the CEO of Microsoft. >> Yeah, he was. >> A lot's changed in nine years. But congratulations on your venture you sold, and you got an exit there, and now you're doing a lot of investments. I'd love to get your take, because this is really the biggest change I've seen in the past 12 years, around an inflection point around a lot of converging forces. Data, which, big data, 10 years ago, was a big part of your career, but now it's accelerated, with cloud scale. You're seeing people building scale on top of other clouds, and becoming their own cloud. You're seeing data being a big part of it. Cybersecurity kind of has not really changed much, but it's the most important thing everyone's talking about. So, developers are involved, data's involved, a lot of entrepreneurial opportunities. So I'd love to get your take on how you see the current situation, as it relates to what's gone on in the past five years or so. What's the big story? >> So, a lot of big stories, but I think a lot of it has to do with a promise of making value from data, whether it's for cybersecurity, for Fintech, for DevOps, for RevTech startups and companies. There's a lot of challenges in actually driving and monetizing the value from data with velocity. Historically, the challenge has been more around, "How do I store data at massive scale?" And then you had the big data infrastructure company, like Cloudera, and MapR, and others, deal with it from a scale perspective, from a storage perspective. Then you had a whole layer of companies that evolved to deal with, "How do I index massive scales of data, for quick querying, and federated access, et cetera?" But now that a lot of those underlying problems, if you will, have been solved, to a certain extent, although they're always being stretched, given the scale of data, and its utility is becoming more and more massive, in particular with AI use cases being very prominent right now, the next level is how to actually make value from the data. How do I manage the full lifecycle of data in complex environments, with complex organizations, complex use cases? And having seen this from the inside, with Origami Logic, as we dealt with a lot of large corporations, and post-acquisition by Intuit, and a lot of the startups I'm involved with, it's clear that we're now onto that next step. And you have fundamental new paradigms, such as data mesh, that attempt to address that complexity, and responsibly scaling access, and democratizing access in the value monetization from data, across large organizations. You have a slew of startups that are evolving to help the entire lifecycle of data, from the data engineering side of it, to the data analytics side of it, to the AI use cases side of it. And it feels like the early days, to a certain extent, of the revolution that we've seen in transition from traditional databases, to data warehouses, to cloud-based data processing, and big data. It feels like we're at the genesis of that next wave. And it's super, super exciting, for me at least, as someone who's sitting more in the coach seat, rather than being on the pitch, and building startups, helping folks as they go through those motions. >> So that's awesome. I want to get into some of these data infrastructure dynamics you mentioned, but before that, talk to the audience around what you're working on now. You've been a successful entrepreneur, you're focused on angel investing, so, super-early seed stage. What kind of deals are you looking at? What's interesting to you? What is Sonoma Ventures looking for, and what are some of the entrepreneurial dynamics that you're seeing right now, from a startup standpoint? >> Cool, so, at a macro level, this is a little bit of background of my history, because it shapes very heavily what it is that I'm looking at. So, I've been very fortunate with entrepreneurial career. I founded three startups. All three of them are successful. Final two were sold, the first one merged and went public. And my third career has been about data, moving data, passing data, processing data, generating insights from it. And, at this phase, I wanted to really evolve from just going and building startup number four, from going through the same motions again. A 10 year adventure, I'm a little bit too old for that, I guess. But the next best thing is to sit from a point whereby I can be more elevated in where I'm dealing with, and broaden the variety of startups I'm focused on, rather than just do your own thing, and just go very, very deep into it. Now, what specifically am I focused on at Sonoma Ventures? So, basically, looking at what I refer to as a data-driven application stack. Anything from the low-level data infrastructure and cloud infrastructure, that helps any persona in the data universe maximize value for data, from their particular point of view, for their particular role, whether it's data analysts, data scientists, data engineers, cloud engineers, DevOps folks, et cetera. All the way up to the application layer, in applications that are very data-heavy. And what are very typical data-heavy applications? FinTech, cyber, Web3, revenue technologies, and product and DevOps. So these are the areas we're focused on. I have almost 23 or 24 startups in the portfolio that span all these different areas. And this is in terms of the aperture. Now, typically, focus on pre-seed, seed. Sometimes a little bit later stage, but this is the primary focus. And it's really about partnering with entrepreneurs, and helping them make, if you will, original mistakes, avoid the mistakes I made. >> Yeah. >> And take it to the next level, whatever the milestone they're driving with. So I'm very, very hands-on with many of those startups. Now, what is it that's happening right now, initially, and why is it so exciting? So, on one hand, you have this scaling of data and its complexity, yet lagging value creation from it, across those different personas we've touched on. So that's one fundamental opportunity which is secular. The other one, which is more a cyclic situation, is the fact that we're going through a down cycle in tech, as is very evident in the public markets, and everything we're hearing about funding going slower and lower, terms shifting more into the hands of typical VCs versus entrepreneur-friendly market, and so on and so forth. And a very significant amount of layoffs. Now, when you combine these two trends together, you're observing a very interesting thing, that a lot of folks, really bright folks, who have sold a startup to a company, or have been in the guts of the large startup, or a large corporation, have, hands-on, experienced all those challenges we've spoken about earlier, in turf, maximizing value from data, irrespective of their role, in a specific angle, or vantage point they have on those challenges. So, for many of them, it's an opportunity to, "Now, let me now start a startup. I've been laid off, maybe, or my company's stock isn't doing as well as it used to, as a large corporation. Now I have an opportunity to actually go and take my entrepreneurial passion, and apply it to a product and experience as part of this larger company." >> Yeah. >> And you see a slew of folks who are emerging with these great ideas. So it's a very, very exciting period of time to innovate. >> It's interesting, a lot of people look at, I mean, I look at Snowflake as an example of a company that refactored data warehouses. They just basically took data warehouse, and put it on the cloud, and called it a data cloud. That, to me, was compelling. They didn't pay any CapEx. They rode Amazon's wave there. So, a similar thing going on with data. You mentioned this, and I see it as an enabling opportunity. So whether it's cybersecurity, FinTech, whatever vertical, you have an enablement. Now, you mentioned data infrastructure. It's a super exciting area, as there's so many stacks emerging. We got an analytics stack, there's real-time stacks, there's data lakes, AI stack, foundational models. So, you're seeing an explosion of stacks, different tools probably will emerge. So, how do you look at that, as a seasoned entrepreneur, now investor? Is that a good thing? Is that just more of the market? 'Cause it just seems like more and more kind of decomposed stacks targeted at use cases seems to be a trend. >> Yeah. >> And how do you vet that, is it? >> So it's a great observation, and if you take a step back and look at the evolution of technology over the last 30 years, maybe longer, you always see these cycles of expansion, fragmentation, contraction, expansion, contraction. Go decentralize, go centralize, go decentralize, go centralize, as manifested in different types of technology paradigms. From client server, to storage, to microservices, to et cetera, et cetera. So I think we're going through another big bang, to a certain extent, whereby end up with more specialized data stacks for specific use cases, as you need performance, the data models, the tooling to best adapt to the particular task at hand, and the particular personas at hand. As the needs of the data analysts are quite different from the needs of an NL engineer, it's quite different from the needs of the data engineer. And what happens is, when you end up with these siloed stacks, you end up with new fragmentation, and new gaps that need to be filled with a new layer of innovation. And I suspect that, in part, that's what we're seeing right now, in terms of the next wave of data innovation. Whether it's in a service of FinTech use cases, or cyber use cases, or other, is a set of tools that end up having to try and stitch together those elements and bridge between them. So I see that as a fantastic gap to innovate around. I see, also, a fundamental need in creating a common data language, and common data management processes and governance across those different personas, because ultimately, the same underlying data these folks need, albeit in different mediums, different access models, different velocities, et cetera, the subject matter, if you will, the underlying raw data, and some of the taxonomies right on top of it, do need to be consistent. So, once again, a great opportunity to innovate, whether it's about semantic layers, whether it's about data mesh, whether it's about CICD tools for data engineers, and so on and so forth. >> I got to ask you, first of all, I see you have a friend you brought into the interview. You have a dog in the background who made a little cameo appearance. And that's awesome. Sitting right next to you, making sure everything's going well. On the AI thing, 'cause I think that's the hot trend here. >> Yeah. >> You're starting to see, that ChatGPT's got everyone excited, because it's kind of that first time you see kind of next-gen functionality, large-language models, where you can bring data in, and it integrates well. So, to me, I think, connecting the dots, this kind of speaks to the beginning of what will be a trend of really blending of data stacks together, or blending of models. And so, as more data modeling emerges, you start to have this AI stack kind of situation, where you have things out there that you can compose. It's almost very developer-friendly, conceptually. This is kind of new, but kind of the same concept's been working on with Google and others. How do you see this emerging, as an investor? What are some of the things that you're excited about, around the ChatGPT kind of things that's happening? 'Cause it brings it mainstream. Again, a million downloads, fastest applications get a million downloads, even among all the successes. So it's obviously hit a nerve. People are talking about it. What's your take on that? >> Yeah, so, I think that's a great point, and clearly, it feels like an iPhone moment, right, to the industry, in this case, AI, and lots of applications. And I think there's, at a high level, probably three different layers of innovation. One is on top of those platforms. What use cases can one bring to the table that would drive on top of a ChatGPT-like service? Whereby, the startup, the company, can bring some unique datasets to infuse and add value on top of it, by custom-focusing it and purpose-building it for a particular use case or particular vertical. Whether it's applying it to customer service, in a particular vertical, applying it to, I don't know, marketing content creation, and so on and so forth. That's one category. And I do know that, as one of my startups is in Y Combinator, this season, winter '23, they're saying that a very large chunk of the YC companies in this cycle are about GPT use cases. So we'll see a flurry of that. The next layer, the one below that, is those who actually provide those platforms, whether it's ChatGPT, whatever will emerge from the partnership with Microsoft, and any competitive players that emerge from other startups, or from the big cloud providers, whether it's Facebook, if they ever get into this, and Google, which clearly will, as they need to, to survive around search. The third layer is the enabling layer. As you're going to have more and more of those different large-language models and use case running on top of it, the underlying layers, all the way down to cloud infrastructure, the data infrastructure, and the entire set of tools and systems, that take raw data, and massage it into useful, labeled, contextualized features and data to feed the models, the AI models, whether it's during training, or during inference stages, in production. Personally, my focus is more on the infrastructure than on the application use cases. And I believe that there's going to be a massive amount of innovation opportunity around that, to reach cost-effective, quality, fair models that are deployed easily and maintained easily, or at least with as little pain as possible, at scale. So there are startups that are dealing with it, in various areas. Some are about focusing on labeling automation, some about fairness, about, speaking about cyber, protecting models from threats through data and other issues with it, and so on and so forth. And I believe that this will be, too, a big driver for massive innovation, the infrastructure layer. >> Awesome, and I love how you mentioned the iPhone moment. I call it the browser moment, 'cause it felt that way for me, personally. >> Yep. >> But I think, from a business model standpoint, there is that iPhone shift. It's not the BlackBerry. It's a whole 'nother thing. And I like that. But I do have to ask you, because this is interesting. You mentioned iPhone. iPhone's mostly proprietary. So, in these machine learning foundational models, >> Yeah. >> you're starting to see proprietary hardware, bolt-on, acceleration, bundled together, for faster uptake. And now you got open source emerging, as two things. It's almost iPhone-Android situation happening. >> Yeah. >> So what's your view on that? Because there's pros and cons for either one. You're seeing a lot of these machine learning laws are very proprietary, but they work, and do you care, right? >> Yeah. >> And then you got open source, which is like, "Okay, let's get some upsource code, and let people verify it, and then build with that." Is it a balance? >> Yes, I think- >> Is it mutually exclusive? What's your view? >> I think it's going to be, markets will drive the proportion of both, and I think, for a certain use case, you'll end up with more proprietary offerings. With certain use cases, I guess the fundamental infrastructure for ChatGPT-like, let's say, large-language models and all the use cases running on top of it, that's likely going to be more platform-oriented and open source, and will allow innovation. Think of it as the equivalent of iPhone apps or Android apps running on top of those platforms, as in AI apps. So we'll have a lot of that. Now, when you start going a little bit more into the guts, the lower layers, then it's clear that, for performance reasons, in particular, for certain use cases, we'll end up with more proprietary offerings, whether it's advanced silicon, such as some of the silicon that emerged from entrepreneurs who have left Google, around TensorFlow, and all the silicon that powers that. You'll see a lot of innovation in that area as well. It hopefully intends to improve the cost efficiency of running large AI-oriented workloads, both in inference and in learning stages. >> I got to ask you, because this has come up a lot around Azure and Microsoft. Microsoft, pretty good move getting into the ChatGPT >> Yep. >> and the open AI, because I was talking to someone who's a hardcore Amazon developer, and they said, they swore they would never use Azure, right? One of those types. And they're spinning up Azure servers to get access to the API. So, the developers are flocking, as you mentioned. The YC class is all doing large data things, because you can now program with data, which is amazing, which is amazing. So, what's your take on, I know you got to be kind of neutral 'cause you're an investor, but you got, Amazon has to respond, Google, essentially, did all the work, so they have to have a solution. So, I'm expecting Google to have something very compelling, but Microsoft, right now, is going to just, might run the table on developers, this new wave of data developers. What's your take on the cloud responses to this? What's Amazon, what do you think AWS is going to do? What should Google be doing? What's your take? >> So, each of them is coming from a slightly different angle, of course. I'll say, Google, I think, has massive assets in the AI space, and their underlying cloud platform, I think, has been designed to support such complicated workloads, but they have yet to go as far as opening it up the same way ChatGPT is now in that Microsoft partnership, and Azure. Good question regarding Amazon. AWS has had a significant investment in AI-related infrastructure. Seeing it through my startups, through other lens as well. How will they respond to that higher layer, above and beyond the low level, if you will, AI-enabling apparatuses? How do they elevate to at least one or two layers above, and get to the same ChatGPT layer, good question. Is there an acquisition that will make sense for them to accelerate it, maybe. Is there an in-house development that they can reapply from a different domain towards that, possibly. But I do suspect we'll end up with acquisitions as the arms race around the next level of cloud wars emerges, and it's going to be no longer just about the basic tooling for basic cloud-based applications, and the infrastructure, and the cost management, but rather, faster time to deliver AI in data-heavy applications. Once again, each one of those cloud suppliers, their vendor is coming with different assets, and different pros and cons. All of them will need to just elevate the level of the fight, if you will, in this case, to the AI layer. >> It's going to be very interesting, the different stacks on the data infrastructure, like I mentioned, analytics, data lake, AI, all happening. It's going to be interesting to see how this turns into this AI cloud, like data clouds, data operating systems. So, super fascinating area. Opher, thank you for coming on and sharing your expertise with us. Great to see you, and congratulations on the work. I'll give you the final word here. Give a plugin for what you're looking for for startup seats, pre-seeds. What's the kind of profile that gets your attention, from a seed, pre-seed candidate or entrepreneur? >> Cool, first of all, it's my pleasure. Enjoy our chats, as always. Hopefully the next one's not going to be in nine years. As to what I'm looking for, ideally, smart data entrepreneurs, who have come from a particular domain problem, or problem domain, that they understand, they felt it in their own 10 fingers, or millions of neurons in their brains, and they figured out a way to solve it. Whether it's a data infrastructure play, a cloud infrastructure play, or a very, very smart application that takes advantage of data at scale. These are the things I'm looking for. >> One final, final question I have to ask you, because you're a seasoned entrepreneur, and now coach. What's different about the current entrepreneurial environment right now, vis-a-vis, the past decade? What's new? Is it different, highly accelerated? What advice do you give entrepreneurs out there who are putting together their plan? Obviously, a global resource pool now of engineering. It might not be yesterday's formula for success to putting a venture together to get to that product-market fit. What's new and different, and what's your advice to the folks out there about what's different about the current environment for being an entrepreneur? >> Fantastic, so I think it's a great question. So I think there's a few axes of difference, compared to, let's say, five years ago, 10 years ago, 15 years ago. First and foremost, given the amount of infrastructure out there, the amount of open-source technologies, amount of developer toolkits and frameworks, trying to develop an application, at least at the application layer, is much faster than ever. So, it's faster and cheaper, to the most part, unless you're building very fundamental, core, deep tech, where you still have a big technology challenge to deal with. And absent that, the challenge shifts more to how do you manage my resources, to product-market fit, how are you integrating the GTM lens, the go-to-market lens, as early as possible in the product-market fit cycle, such that you reach from pre-seed to seed, from seed to A, from A to B, with an optimal amount of velocity, and a minimal amount of resources. One big difference, specifically as of, let's say, beginning of this year, late last year, is that money is no longer free for entrepreneurs, which means that you need to operate and build startup in an environment with a lot more constraints. And in my mind, some of the best startups that have ever been built, and some of the big market-changing, generational-changing, if you will, technology startups, in their respective industry verticals, have actually emerged from these times. And these tend to be the smartest, best startups that emerge because they operate with a lot less money. Money is not as available for them, which means that they need to make tough decisions, and make verticals every day. What you don't need to do, you can kick the cow down the road. When you have plenty of money, and it cushions for a lot of mistakes, you don't have that cushion. And hopefully we'll end up with companies with a more agile, more, if you will, resilience, and better cultures in making those tough decisions that startups need to make every day. Which is why I'm super, super excited to see the next batch of amazing unicorns, true unicorns, not just valuation, market rising with the water type unicorns that emerged from this particular era, which we're in the beginning of. And very much enjoy working with entrepreneurs during this difficult time, the times we're in. >> The next 24 months will be the next wave, like you said, best time to do a company. Remember, Airbnb's pitch was, "We'll rent cots in apartments, and sell cereal." Boy, a lot of people passed on that deal, in that last down market, that turned out to be a game-changer. So the crazy ideas might not be that bad. So it's all about the entrepreneurs, and >> 100%. >> this is a big wave, and it's certainly happening. Opher, thank you for sharing. Obviously, data is going to change all the markets. Refactoring, security, FinTech, user experience, applications are going to be changed by data, data operating system. Thanks for coming on, and thanks for sharing. Appreciate it. >> My pleasure. Have a good one. >> Okay, more coverage for the CloudNativeSecurityCon inaugural event. Data will be the key for cybersecurity. theCUBE's coverage continues after this break. (uplifting music)

Published Date : Feb 2 2023

SUMMARY :

and happening all around the world. Great to be back. He's now the CEO in the past five years or so. and a lot of the startups What kind of deals are you looking at? and broaden the variety of and apply it to a product and experience And you see a slew of folks and put it on the cloud, and new gaps that need to be filled You have a dog in the background but kind of the same and the entire set of tools and systems, I call it the browser moment, But I do have to ask you, And now you got open source and do you care, right? and then build with that." and all the use cases I got to ask you, because and the open AI, and it's going to be no longer What's the kind of profile These are the things I'm looking for. about the current environment and some of the big market-changing, So it's all about the entrepreneurs, and to change all the markets. Have a good one. for the CloudNativeSecurityCon

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HPE Compute Engineered for your Hybrid World - Accelerate VDI at the Edge


 

>> Hello everyone. Welcome to theCUBEs coverage of Compute Engineered for your Hybrid World sponsored by HPE and Intel. Today we're going to dive into advanced performance of VDI with the fourth gen Intel Zion scalable processors. Hello I'm John Furrier, the host of theCUBE. My guests today are Alan Chu, Director of Data Center Performance and Competition for Intel as well as Denis Kondakov who's the VDI product manager at HPE, and also joining us is Cynthia Sustiva, CAD/CAM product manager at HPE. Thanks for coming on, really appreciate you guys taking the time. >> Thank you. >> So accelerating VDI to the Edge. That's the topic of this topic here today. Let's get into it, Dennis, tell us about the new HPE ProLiant DL321 Gen 11 server. >> Okay, absolutely. Hello everybody. So HP ProLiant DL320 Gen 11 server is the new age center CCO and density optimized compact server, compact form factor server. It enables to modernize and power at the next generation of workloads in the diverse rec environment at the Edge in an industry standard designed with flexible scale for advanced graphics and compute. So it is one unit, one processor rec optimized server that can be deployed in the enterprise data center as well as at the remote office at end age. >> Cynthia HPE has announced another server, the ProLiant ML350. What can you tell us about that? >> Yeah, so the HPE ProLiant ML350 Gen 11 server is a powerful tower solution for a wide range of workloads. It is ideal for remote office compute with NextGen performance and expandability with two processors in tower form factor. This enables the server to be used not only in the data center environment, but also in the open office space as a powerful workstation use case. >> Dennis mentioned both servers are empowered by the fourth gen Intel Zion scale of process. Can you talk about the relationship between Intel HPE to get this done? How do you guys come together, what's behind the scenes? Share as much as you can. >> Yeah, thanks a lot John. So without a doubt it takes a lot to put all this together and I think the partnership that HPE and Intel bring together is a little bit of a critical point for us to be able to deliver to our customers. And I'm really thrilled to say that these leading Edge solutions that Dennis and Cynthia just talked about, they're built on the foundation of our fourth Gen Z on scalable platform that's trying to meet a wide variety of deployments for today and into the future. So I think the key point of it is we're together trying to drive leading performance with built-in acceleration and in order to deliver a lot of the business values to our customers, both HP and Intels, look to scale, drive down costs and deliver new services. >> You got the fourth Gen Z on, you got the Gen 11 and multiple ProLiants, a lot of action going on. Again, I love when these next gens come out. Can each of you guys comment and share what are the use cases for each of the systems? Because I think what we're looking at here is the next level innovation. What are some of the use cases on the systems? >> Yeah, so for the ML350, in the modern world where more and more data are generated at the Edge, we need to deploy computer infrastructure where the data is generated. So smaller form factor service will satisfy the requirements of S&B customers or remote and branch offices to deliver required performance redundancy where we're needed. This type of locations can be lacking dedicated facilities with strict humidity, temperature and noise isolation control. The server, the ML350 Gen 11 can be used as a powerful workstation sitting under a desk in the office or open space as well as the server for visualized workloads. It is a productivity workhorse with the ability to scale and adapt to any environment. One of the use cases can be for hosting digital workplace for manufacturing CAD/CAM engineering or oil and gas customers industry. So this server can be used as a high end bare metal workstation for local end users or it can be virtualized desktop solution environments for local and remote users. And talk about the DL320 Gen 11, I will pass it on to Dennis. >> Okay. >> Sure. So when we are talking about age of location we are talking about very specific requirements. So we need to provide solution building blocks that will empower and performance efficient, secure available for scaling up and down in a smaller increments than compared to the enterprise data center and of course redundant. So DL 320 Gen 11 server is the perfect server to satisfy all of those requirements. So for example, S&B customers can build a video solution, for example starting with just two HP ProLiant TL320 Gen 11 servers that will provide sufficient performance for high density video solution and at the same time be redundant and enable it for scaling up as required. So for VGI use cases it can be used for high density general VDI without GP acceleration or for a high performance VDI with virtual VGPU. So thanks to the modern modular architecture that is used on the server, it can be tailored for GPU or high density storage deployment with software defined compute and storage environment and to provide greater details on your Intel view I'm going to pass to Alan. >> Thanks a lot Dennis and I loved how you're both seeing the importance of how we scale and the applicability of the use cases of both the ML350 and DL320 solutions. So scalability is certainly a key tenant towards how we're delivering Intel's Zion scalable platform. It is called Zion scalable after all. And we know that deployments are happening in all different sorts of environments. And I think Cynthia you talked a little bit about kind of a environmental factors that go into how we're designing and I think a lot of people think of a traditional data center with all the bells and whistles and cooling technology where it sometimes might just be a dusty closet in the Edge. So we're defining fortunes you see on scalable to kind of tackle all those different environments and keep that in mind. Our SKUs range from low to high power, general purpose to segment optimize. We're supporting long life use cases so that all goes into account in delivering value to our customers. A lot of the latency sensitive nature of these Edge deployments also benefit greatly from monolithic architectures. And with our latest CPUs we do maintain quite a bit of that with many of our SKUs and delivering higher frequencies along with those SKUs optimized for those specific workloads in networking. So in the end we're looking to drive scalability. We're looking to drive value in a lot of our end users most important KPIs, whether it's latency throughput or efficiency and 4th Gen Z on scalable is looking to deliver that with 60 cores up to 60 cores, the most builtin accelerators of any CPUs in the market. And really the true technology transitions of the platform with DDR5, PCIE, Gen five and CXL. >> Love the scalability story, love the performance. We're going to take a break. Thanks Cynthia, Dennis. Now we're going to come back on our next segment after a quick break to discuss the performance and the benefits of the fourth Gen Intel Zion Scalable. You're watching theCUBE, the leader in high tech coverage, be right back. Welcome back around. We're continuing theCUBE's coverage of compute engineer for your hybrid world. I'm John Furrier, I'm joined by Alan Chu from Intel and Denis Konikoff and Cynthia Sistia from HPE. Welcome back. Cynthia, let's start with you. Can you tell us the benefits of the fourth Gen Intel Zion scale process for the HP Gen 11 server? >> Yeah, so HP ProLiant Gen 11 servers support DDR five memory which delivers increased bandwidth and lower power consumption. There are 32 DDR five dim slots with up to eight terabyte total on ML350 and 16 DDR five dim slots with up to two terabytes total on DL320. So we deliver more memory at a greater bandwidth. Also PCIE 5.0 delivers an increased bandwidth and greater number of lanes. So when we say increased number of lanes we need to remember that each lane delivers more bandwidth than lanes of the previous generation plus. Also a flexible storage configuration on HPDO 320 Gen 11 makes it an ideal server for establishing software defined compute and storage solution at the Edge. When we consider a server for VDI workloads, we need to keep the right balance between the number of cords and CPU frequency in order to deliver the desire environment density and noncompromised user experience. So the new server generation supports a greater number of single wide and global wide GPU use to deliver more graphic accelerated virtual desktops per server unit than ever before. HPE ProLiant ML 350 Gen 11 server supports up to four double wide GPUs or up to eight single wide GPUs. When the signing GPU accelerated solutions the number of GPUs available in the system and consistently the number of BGPUs that can be provisioned for VMs in the binding factor rather than CPU course or memory. So HPE ProLiant Gen 11 servers with Intel fourth generation science scalable processors enable us to deliver more virtual desktops per server than ever before. And with that I will pass it on to Alan to provide more details on the new Gen CPU performance. >> Thanks Cynthia. So you brought up I think a really great point earlier about the importance of achieving the right balance. So between the both of us, Intel and HPE, I'm sure we've heard countless feedback about how we should be optimizing efficiency for our customers and with four Gen Z and scalable in HP ProLiant Gen 11 servers I think we achieved just that with our built-in accelerator. So built-in acceleration delivers not only the revolutionary performance, but enables significant offload from valuable core execution. That offload unlocks a lot of previously unrealized execution efficiency. So for example, with quick assist technology built in, running engine X, TLS encryption to drive 65,000 connections per second we can offload up to 47% of the course that do other work. Accelerating AI inferences with AMX, that's 10X higher performance and we're now unlocking realtime inferencing. It's becoming an element in every workload from the data center to the Edge. And lastly, so with faster and more efficient database performance with RocksDB, we're executing with Intel in-memory analytics accelerator we're able to deliver 2X the performance per watt than prior gen. So I'll say it's that kind of offload that is really going to enable more and more virtualized desktops or users for any given deployment. >> Thanks everyone. We still got a lot more to discuss with Cynthia, Dennis and Allen, but we're going to take a break. Quick break before wrapping things up. You're watching theCUBE, the leader in tech coverage. We'll be right back. Okay, welcome back everyone to theCUBEs coverage of Compute Engineered for your Hybrid World. I'm John Furrier. We'll be wrapping up our discussion on advanced performance of VDI with the fourth gen Intel Zion scalable processers. Welcome back everyone. Dennis, we'll start with you. Let's continue our conversation and turn our attention to security. Obviously security is baked in from day zero as they say. What are some of the new security features or the key security features for the HP ProLiant Gen 11 server? >> Sure, I would like to start with the balance, right? We were talking about performance, we were talking about density, but Alan mentioned about the balance. So what about the security? The security is really important aspect especially if we're talking about solutions deployed at the H. When the security is not active but other aspects of the environment become non-important. And HP is uniquely positioned to deliver the best in class security solution on the market starting with the trusted supply chain and factories and silicon route of trust implemented from the factory. So the new ISO6 supports added protection leveraging SPDM for component authorization and not only enabled for the embedded server management, but also it is integrated with HP GreenLake compute ops manager that enables environment for secure and optimized configuration deployment and even lifecycle management starting from the single server deployed on the Edge and all the way up to the full scale distributed data center. So it brings uncompromised and trusted solution to customers fully protected at all tiers, hardware, firmware, hypervisor, operational system application and data. And the new intel CPUs play an important role in the securing of the platform. So Alan- >> Yeah, thanks. So Intel, I think our zero trust strategy toward security is a really great and a really strong parallel to all the focus that HPE is also bringing to that segment and market. We have even invested in a lot of hardware enabled security technologies like SGX designed to enhance data protection at rest in motion and in use. SGX'S application isolation is the most deployed, researched and battle tested confidential computing technology for the data center market and with the smallest trust boundary of any solution in market. So as we've talked about a little bit about virtualized use cases a lot of virtualized applications rely also on encryption whether bulk or specific ciphers. And this is again an area where we've seen the opportunity for offload to Intel's quick assist technology to encrypt within a single data flow. I think Intel and HP together, we are really providing security at all facets of execution today. >> I love that Software Guard Extension, SGX, also silicon root of trust. We've heard a lot about great stuff. Congratulations, security's very critical as we see more and more. Got to be embedded, got to be completely zero trust. Final question for you guys. Can you share any messages you'd like to share with the audience each of you, what should they walk away from this? What's in it for them? What does all this mean? >> Yeah, so I'll start. Yes, so to wrap it up, HPR Proliant Gen 11 servers are built on four generation science scalable processors to enable high density and extreme performance with high performance CDR five memory and PCI 5.0 plus HP engine engineered and validated workload solutions provide better ROI in any consumption model and prefer by a customer from Edge to Cloud. >> Dennis? >> And yeah, so you are talking about all of the great features that the new generation servers are bringing to our customers, but at the same time, customer IT organization should be ready to enable, configure, support, and fine tune all of these great features for the new server generation. And this is not an obvious task. It requires investments, skills, knowledge and experience. And HP is ready to step up and help customers at any desired skill with the HP Greenlake H2 cloud platform that enables customers for cloud like experience and convenience and the flexibility with the security of the infrastructure deployed in the private data center or in the Edge. So while consuming all of the HP solutions, customer have flexibility to choose the right level of the service delivered from HP GreenLake, starting from hardwares as a service and scale up or down is required to consume the full stack of the hardwares and software as a service with an option to paper use. >> Awesome. Alan, final word. >> Yeah. What should we walk away with? >> Yeah, thanks. So I'd say that we've talked a lot about the systems here in question with HP ProLiant Gen 11 and they're delivering on a lot of the business outcomes that our customers require in order to optimize for operational efficiency or to optimize for just to, well maybe just to enable what they want to do in, with their customers enabling new features, enabling new capabilities. Underpinning all of that is our fourth Gen Zion scalable platform. Whether it's the technology transitions that we're driving with DDR5 PCIA Gen 5 or the raw performance efficiency and scalability of the platform in CPU, I think we're here for our customers in delivering to it. >> That's great stuff. Alan, Dennis, Cynthia, thank you so much for taking the time to do a deep dive in the advanced performance of VDI with the fourth Gen Intel Zion scalable process. And congratulations on Gen 11 ProLiant. You get some great servers there and again next Gen's here. Thanks for taking the time. >> Thank you so much for having us here. >> Okay, this is theCUBEs keeps coverage of Compute Engineered for your Hybrid World sponsored by HP and Intel. I'm John Furrier for theCUBE. Accelerate VDI at the Edge. Thanks for watching.

Published Date : Dec 27 2022

SUMMARY :

the host of theCUBE. That's the topic of this topic here today. in the enterprise data center the ProLiant ML350. but also in the open office space by the fourth gen Intel deliver a lot of the business for each of the systems? One of the use cases can be and at the same time be redundant So in the end we're looking and the benefits of the fourth for VMs in the binding factor rather than from the data center to the Edge. for the HP ProLiant Gen 11 server? and not only enabled for the is the most deployed, got to be completely zero trust. by a customer from Edge to Cloud. of the HP solutions, Alan, final word. What should we walk away with? lot of the business outcomes the time to do a deep dive Accelerate VDI at the Edge.

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Kevin Miller and Ed Walsh | AWS re:Invent 2022 - Global Startup Program


 

hi everybody welcome back to re invent 2022. this is thecube's exclusive coverage we're here at the satellite set it's up on the fifth floor of the Venetian Conference Center and this is part of the global startup program the AWS startup showcase series that we've been running all through last year and and into this year with AWS and featuring some of its its Global Partners Ed wallson series the CEO of chaos search many times Cube Alum and Kevin Miller there's also a cube Alum vice president GM of S3 at AWS guys good to see you again yeah great to see you Dave hi Kevin this is we call this our Super Bowl so this must be like your I don't know uh World Cup it's a pretty big event yeah it's the World Cup for sure yeah so a lot of S3 talk you know I mean that's what got us all started in 2006 so absolutely what's new in S3 yeah it's been a great show we've had a number of really interesting launches over the last few weeks and a few at the show as well so you know we've been really focused on helping customers that are running Mass scale data Lakes including you know whether it's structured or unstructured data we actually announced just a few just an hour ago I think it was a new capability to give customers cross-account access points for sharing data securely with other parts of the organization and that's something that we'd heard from customers is as they are growing and have more data sets and they're looking to to get more out of their data they are increasingly looking to enable multiple teams across their businesses to access those data sets securely and that's what we provide with cross-count access points we also launched yesterday our multi-region access point failover capabilities and so again this is where customers have data sets and they're using multiple regions for certain critical workloads they're now able to to use that to fail to control the failover between different regions in AWS and then one other launch I would just highlight is some improvements we made to storage lens which is our really a very novel and you need capability to help customers really understand what storage they have where who's accessing it when it's being accessed and we added a bunch of new metrics storage lens has been pretty exciting for a lot of customers in fact we looked at the data and saw that customers who have adopted storage lens typically within six months they saved more than six times what they had invested in turning storage lens on and certainly in this environment right now we have a lot of customers who are it's pretty top of mind they're looking for ways to optimize their their costs in the cloud and take some of those savings and be able to reinvest them in new innovation so pretty exciting with the storage lens launch I think what's interesting about S3 is that you know pre-cloud Object Store was this kind of a niche right and then of course you guys announced you know S3 in 2006 as I said and okay great you know cheap and deep storage simple get put now the conversations about how to enable value from from data absolutely analytics and it's just a whole new world and Ed you've talked many times I love the term yeah we built chaos search on the on the shoulders of giants right and so the under underlying that is S3 but the value that you can build on top of that has been key and I don't think we've talked about his shoulders and Giants but we've talked about how we literally you know we have a big Vision right so hard to kind of solve the challenge to analytics at scale we really focus on the you know the you know Big Data coming environment get analytics so we talk about the on the shoulders Giants obviously Isaac Newton's you know metaphor of I learned from everything before and we layer on top so really when you talk about all the things come from S3 like I just smile because like we picked it up naturally we went all in an S3 and this is where I think you're going Dave but everyone is so let's just cut the chase like so any of the data platforms you're using S3 is what you're building but we did it a little bit differently so at first people using a cold storage like you said and then they ETL it up into a different platforms for analytics of different sorts now people are using it closer they're doing caching layers and cashing out and they're that's where but that's where the attributes of a scale or reliability are what we did is we actually make S3 a database so literally we have no persistence outside that three and that kind of comes in so it's working really well with clients because most of the thing is we pick up all these attributes of scale reliability and it shows up in the clients environments and so when you launch all these new scalable things we just see it like our clients constantly comment like one of our biggest customers fintech in uh Europe they go to Black Friday again black Friday's not one days and they lose scale from what is it 58 terabytes a day and they're going up to 187 terabytes a day and we don't Flinch they say how do you do that well we built our platform on S3 as long as you can stream it to S3 so they're saying I can't overrun S3 and it's a natural play so it's it's really nice that but we take out those attributes but same thing that's why we're able to you know help clients get you know really you know Equifax is a good example maybe they're able to consolidate 12 their divisions on one platform we couldn't have done that without the scale and the performance of what you can get S3 but also they saved 90 I'm able to do that but that's really because the only persistence is S3 and what you guys are delivering but and then we really for focus on shoulders Giants we're doing on top of that innovating on top of your platforms and bringing that out so things like you know we have a unique data representation that makes it easy to ingest this data because it's kind of coming at you four v's of big data we allow you to do that make it performant on s3h so now you're doing hot analytics on S3 as if it's just a native database in memory but there's no memory SSC caching and then multi-model once you get it there don't move it leverage it in place so you know elasticsearch access you know Cabana grafana access or SQL access with your tools so we're seeing that constantly but we always talk about on the shoulders of giants but even this week I get comments from our customers like how did you do that and most of it is because we built on top of what you guys provided so it's really working out pretty well and you know we talk a lot about digital transformation of course we had the pleasure sitting down with Adam solipski prior John Furrier flew to Seattle sits down his annual one-on-one with the AWS CEO which is kind of cool yeah it was it's good it's like study for the test you know and uh and so but but one of the interesting things he said was you know we're one of our challenges going forward is is how do we go Beyond digital transformation into business transformation like okay well that's that's interesting I was talking to a customer today AWS customer and obviously others because they're 100 year old company and they're basically their business was they call them like the Uber for for servicing appliances when your Appliance breaks you got to get a person to serve it a service if it's out of warranty you know these guys do that so they got to basically have a you know a network of technicians yeah and they gotta deal with the customers no phone right so they had a completely you know that was a business transformation right they're becoming you know everybody says they're coming a software company but they're building it of course yeah right on the cloud so wonder if you guys could each talk about what's what you're seeing in terms of changing not only in the sort of I.T and the digital transformation but also the business transformation yeah I know I I 100 agree that I think business transformation is probably that one of the top themes I'm hearing from customers of all sizes right now even in this environment I think customers are looking for what can I do to drive top line or you know improve bottom line or just improve my customer experience and really you know sort of have that effect where I'm helping customers get more done and you know it is it is very tricky because to do that successfully the customers that are doing that successfully I think are really getting into the lines of businesses and figuring out you know it's probably a different skill set possibly a different culture different norms and practices and process and so it's it's a lot more than just a like you said a lot more than just the technology involved but when it you know we sort of liquidate it down into the data that's where absolutely we see that as a critical function for lines of businesses to become more comfortable first off knowing what data sets they have what data they they could access but possibly aren't today and then starting to tap into those data sources and then as as that progresses figuring out how to share and collaborate with data sets across a company to you know to correlate across those data sets and and drive more insights and then as all that's being done of course it's important to measure the results and be able to really see is this what what effect is this having and proving that effect and certainly I've seen plenty of customers be able to show you know this is a percentage increase in top or bottom line and uh so that pattern is playing out a lot and actually a lot of how we think about where we're going with S3 is related to how do we make it easier for customers to to do everything that I just described to have to understand what data they have to make it accessible and you know it's great to have such a great ecosystem of partners that are then building on top of that and innovating to help customers connect really directly with the businesses that they're running and driving those insights well and customers are hours today one of the things I loved that Adam said he said where Amazon is strategically very very patient but tactically we're really impatient and the customers out there like how are you going to help me increase Revenue how are you going to help me cut costs you know we were talking about how off off camera how you know software can actually help do that yeah it's deflationary I love the quote right so software's deflationary as costs come up how do you go drive it also free up the team and you nail it it's like okay everyone wants to save money but they're not putting off these projects in fact the digital transformation or the business it's actually moving forward but they're getting a little bit bigger but everyone's looking for creative ways to look at their architecture and it becomes larger larger we talked about a couple of those examples but like even like uh things like observability they want to give this tool set this data to all the developers all their sres same data to all the security team and then to do that they need to find a way an architect should do that scale and save money simultaneously so we see constantly people who are pairing us up with some of these larger firms like uh or like keep your data dog keep your Splunk use us to reduce the cost that one and one is actually cheaper than what you have but then they use it either to save money we're saving 50 to 80 hard dollars but more importantly to free up your team from the toil and then they they turn around and make that budget neutral and then allowed to get the same tools to more people across the org because they're sometimes constrained of getting the access to everyone explain that a little bit more let's say I got a Splunk or data dog I'm sifting through you know logs how exactly do you help so it's pretty simple I'll use dad dog example so let's say using data dog preservability so it's just your developers your sres managing environments all these platforms are really good at being a monitoring alerting type of tool what they're not necessarily great at is keeping the data for longer periods like the log data the bigger data that's where we're strong what you see is like a data dog let's say you're using it for a minister for to keep 30 days of logs which is not enough like let's say you're running environment you're finding that performance issue you kind of want to look to last quarter in last month in or maybe last Black Friday so 30 days is not enough but will charge you two eighty two dollars and eighty cents a gigabyte don't focus on just 280 and then if you just turn the knob and keep seven days but keep two years of data on us which is on S3 it goes down to 22 cents plus our list price of 80 cents goes to a dollar two compared to 280. so here's the thing what they're able to do is just turn a knob get more data we do an integration so you can go right from data dog or grafana directly into our platform so the user doesn't see it but they save money A lot of times they don't just save the money now they use that to go fund and get data dog to a lot more people make sense so it's a creativity they're looking at it and they're looking at tools we see the same thing with a grafana if you look at the whole grafana play which is hey you can't put it in one place but put Prometheus for metrics or traces we fit well with logs but they're using that to bring down their costs because a lot of this data just really bogs down these applications the alerting monitoring are good at small data they're not good at the big data which is what we're really good at and then the one and one is actually less than you paid for the one so it and it works pretty well so things are really unpredictable right now in the economy you know during the pandemic we've sort of lockdown and then the stock market went crazy we're like okay it's going to end it's going to end and then it looked like it was going to end and then it you know but last year it reinvented just just in that sweet spot before Omicron so we we tucked it in which which was awesome right it was a great great event we really really missed one physical reinvent you know which was very rare so that's cool but I've called it the slingshot economy it feels like you know you're driving down the highway and you got to hit the brakes and then all of a sudden you're going okay we're through it Oh no you're gonna hit the brakes again yeah so it's very very hard to predict and I was listening to jassy this morning he was talking about yeah consumers they're still spending but what they're doing is they're they're shopping for more features they might be you know buying a TV that's less expensive you know more value for the money so okay so hopefully the consumer spending will get us out of this but you don't really know you know and I don't yeah you know we don't seem to have the algorithms we've never been through something like this before so what are you guys seeing in terms of customer Behavior given that uncertainty well one thing I would highlight that I think particularly going back to what we were just talking about as far as business and digital transformation I think some customers are still appreciating the fact that where you know yesterday you may have had to to buy some Capital put out some capital and commit to something for a large upfront expenditure is that you know today the value of being able to experiment and scale up and then most importantly scale down and dynamically based on is the experiment working out am I seeing real value from it and doing that on a time scale of a day or a week or a few months that is so important right now because again it gets to I am looking for a ways to innovate and to drive Top Line growth but I I can't commit to a multi-year sort of uh set of costs to to do that so and I think plenty of customers are finding that even a few months of experimentation gives them some really valuable insight as far as is this going to be successful or not and so I think that again just of course with S3 and storage from day one we've been elastic pay for what you use if you're not using the storage you don't get charged for it and I think that particularly right now having the applications and the rest of the ecosystem around the storage and the data be able to scale up and scale down is is just ever more important and when people see that like typically they're looking to do more with it so if they find you usually find these little Department projects but they see a way to actually move faster and save money I think it is a mix of those two they're looking to expand it which can be a nightmare for sales Cycles because they take longer but people are looking well why don't you leverage this and go across division so we do see people trying to leverage it because they're still I don't think digital transformation is slowing down but a lot more to be honest a lot more approvals at this point for everything it is you know Adam and another great quote in his in his keynote he said if you want to save money the Cloud's a place to do it absolutely and I read an article recently and I was looking through and I said this is the first time you know AWS has ever seen a downturn because the cloud was too early back then I'm like you weren't paying attention in 2008 because that was the first major inflection point for cloud adoption where CFO said okay stop the capex we're going to Opex and you saw the cloud take off and then 2010 started this you know amazing cycle that we really haven't seen anything like it where they were doubling down in Investments and they were real hardcore investment it wasn't like 1998 99 was all just going out the door for no clear reason yeah so that Foundation is now in place and I think it makes a lot of sense and it could be here for for a while where people are saying Hey I want to optimize and I'm going to do that on the cloud yeah no I mean I've obviously I certainly agree with Adam's quote I think really that's been in aws's DNA from from day one right is that ability to scale costs with with the actual consumption and paying for what you use and I think that you know certainly moments like now are ones that can really motivate change in an organization in a way that might not have been as palatable when it just it didn't feel like it was as necessary yeah all right we got to go give you a last word uh I think it's been a great event I love all your announcements I think this is wonderful uh it's been a great show I love uh in fact how many people are here at reinvent north of 50 000. yeah I mean I feel like it was it's as big if not bigger than 2019. people have said ah 2019 was a record when you count out all the professors I don't know it feels it feels as big if not bigger so there's great energy yeah it's quite amazing and uh and we're thrilled to be part of it guys thanks for coming on thecube again really appreciate it face to face all right thank you for watching this is Dave vellante for the cube your leader in Enterprise and emerging Tech coverage we'll be right back foreign

Published Date : Dec 7 2022

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Hitachi Vantara | Tom Christensen


 

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

Published Date : Dec 6 2022

SUMMARY :

and it seems to be emerging and be able to be relevant in the market. and we saw the EU leading and hot in the wintertime, with because you hear the slogan, and the impact to make it possible and so we are seeing it all around us. This is the aim that we are looking at. and the facilities people and a lot of money that you save, is what you guys are doing with partners. in the organization, to be and taking us through your perspectives. Yeah, you bet.

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Hitachi Vantara Drives Customer Success with Partners


 

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

Published Date : Dec 5 2022

SUMMARY :

Ecosystems have evolved quite dramatically over the last decade with the explosion of data and the popularity And they'll set the table for us with an overview of how Hitachi is working the incredible identify with the analytical and are synonymous with Kim, it's great to have you on the program. What are some of the biggest challenges and pain points that you're hearing from Really the complexity of where do they go, a role in helping customers to address some of the challenges with respect to the the right decisions with and for them. Talk to me a little bit about the partner landscape, the partner ecosystem at Hitachi Ventura. and really extension across the board, I would say our goal is to marry the right customer with So Kim, talk to me about how partners fit into Hitachi van's overall And we see that paying dividends with our partners as they engage with us and the successful outcome that's needed without, you know, sort of all kinds of, And so we really have, like I said, we actually provide our partners with better I say that we allow them to scale and drive Say that again? So if we look at the overall sales cycle, where is it specifically where So from the sales cycle, I think because we have the, a solution that the trusted engagement with them from a pricing and packaging perspective. Let's kind of step back out and look at the cloud infrastructure. So we have a couple of different teams. So we spend a lot of time upfront planning with them what is not only So our primary go to market with our, as a service business is with and through partners. Kim, are the priorities for the partner ecosystem going forward? And then going back on our core tenants, which are, you know, really a trusted, From a channel business perspective, what are some of the priorities coming down the pi? into our new program and our go to markets as we roll out every year. for joining me today talking about what Hitachi Vanta is doing with its partner ecosystem, Russell Skillings Lee, the CTO and global VP of technical sales at Hitachi Van So here we are, the end of calendar year 2022. And closely related to that is the whole area of ESG and decarbonization And I think everyone's contributing to that, And that, and what I mean by that is our traditional businesses, you know, monetize it, and create real value new opportunities for the business at record speed. especially in the early days, leverage the cloud to be able to build out their capabilities. How are partners helping Hitachi Ventura and its customers to even for customers that might consider themselves to be all in on public cloud, And you know, we've seen a lot of this type of change ourselves, this change of attitude not the most reliable, you know, of course they matter. So for example, the work we do with VMware, which we consider to be one We combine that with what VMware's doing, and then when you look at our converged And the way I think they like to think of themselves, and I too tend to agree with them, And so the cost I wanna get, what are some of the key differentiators that you talk about when you're in customer conversations, We do believe that we have the fastest and most reliable storage And so we believe that our conversations with our customers bear a little bit more sophistication. is playing a role in the as service business? So we are 100% partner focused when it comes to that aspect. So the, as you said, the partner ecosystem is absolutely pivotal. conversation with Tom Christensen. in the UK to install a full electronic patient record system. DCC is designed not only to improve the lives of patients, but also of our staff and it will allow the system that I work with within the patient flow team to effectively But we chose to partner with Hitachi to deliver the DCC here at Sulfur. My hopes for the DCC is that ultimately we will provide more efficient and so that we can then start to deliver the real change that's needed for oh 7 0 8 financial crisis sort of put that on the back burner. The second pressure that we see is coming from the government. replicates down in the organization and we can now see that some CIOs, And for that reason it replicates down to IT people. But in some cases the CFO say, I don't even know where the pressure is coming from. we saw the EU leading with privacy and GDPR and it looks like it's gonna lead again And we build a factory with concrete that's the way you are going to do it. I wanna come back to the economics, but before I do, I wanna pick up on something you just said because you know, And this one is something that we have done the last eight years, so this is far from you for I mean, the other day it was, you know, the controller to next generation controller and you get more CPU power in the landfill or 2% that goes to the landfill. And only one 2% is going into the landfill right now. And the facilities people don't talk to the IT people. And we have now done more than 1,200 data center assessment around the in electricity on the compute part just by moving from legacy to new modern infrastructure So the way to look at it is that we offer a platform with product, Really appreciate your time and taking us through your perspectives. Yeah, you bet. so that every thought and every action can build your path and there's a link on the webpage here that will take you right to that page.

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Holger Mueller, Constellation Research | AWS re:Invent 2022


 

(upbeat music) >> Hey, everyone, welcome back to Las Vegas, "theCube" is on our fourth day of covering AWS re:Invent, live from the Venetian Expo Center. This week has been amazing. We've created a ton of content, as you know, 'cause you've been watching. But, there's been north of 55,000 people here, hundreds of thousands online. We've had amazing conversations across the AWS ecosystem. Lisa Martin, Paul Gillan. Paul, what's your, kind of, take on day four of the conference? It's still highly packed. >> Oh, there's lots of people here. (laughs) >> Yep. Unusual for the final day of a conference. I think Werner Vogels, if I'm pronouncing it right kicked things off today when he talked about asymmetry and how the world is, you know, asymmetric. We build symmetric software, because it's convenient to do so, but asymmetric software actually scales and evolves much better. And I think that that was a conversation starter for a lot of what people are talking about here today, which is how the cloud changes the way we think about building software. >> Absolutely does. >> Our next guest, Holger Mueller, that's one of his key areas of focus. And Holger, welcome, thanks for joining us on the "theCube". >> Thanks for having me. >> What did you take away from the keynote this morning? >> Well, how do you feel on the final day of the marathon, right? We're like 23, 24 miles. Hit the ball yesterday, right? >> We are going strong Holger. And, of course, >> Yeah. >> you guys, we can either talk about business transformation with cloud or the World Cup. >> Or we can do both. >> The World Cup, hands down. World Cup. (Lisa laughs) Germany's out, I'm unbiased now. They just got eliminated. >> Spain is out now. >> What will the U.S. do against Netherlands tomorrow? >> They're going to win. What's your forecast? U.S. will win? >> They're going to win 2 to 1. >> What do you say, 2:1? >> I'm optimistic, but realistic. >> 3? >> I think Netherlands. >> Netherlands will win? >> 2 to nothing. >> Okay, I'll vote for the U.S.. >> Okay, okay >> 3:1 for the U.S.. >> Be optimistic. >> Root for the U.S.. >> Okay, I like that. >> Hope for the best wherever you work. >> Tomorrow you'll see how much soccer experts we are. >> If your prediction was right. (laughs) >> (laughs) Ja, ja. Or yours was right, right, so. Cool, no, but the event, I think the event is great to have 50,000 people. Biggest event of the year again, right? Not yet the 70,000 we had in 2019. But it's great to have the energy. I've never seen the show floor going all the way down like this, right? >> I haven't either. >> I've never seen that. I think it's a record. Often vendors get the space here and they have the keynote area, and the entertainment area, >> Yeah. >> and the food area, and then there's an exposition, right? This is packed. >> It's packed. >> Maybe it'll pay off. >> You don't see the big empty booths that you often see. >> Oh no. >> Exactly, exactly. You know, the white spaces and so on. >> No. >> Right. >> Which is a good thing. >> There's lots of energy, which is great. And today's, of course, the developer day, like you said before, right now Vogels' a rockstar in the developer community, right. Revered visionary on what has been built, right? And he's becoming a little professorial is my feeling, right. He had these moments before too, when it was justifying how AWS moved off the Oracle database about the importance of data warehouses and structures and why DynamoDB is better and so on. But, he had a large part of this too, and this coming right across the keynotes, right? Adam Selipsky talking about Antarctica, right? Scott against almonds and what went wrong. He didn't tell us, by the way, which often the tech winners forget. Scott banked on technology. He had motorized sleds, which failed after three miles. So, that's not the story to tell the technology. Let everything down. Everybody went back to ponies and horses and dogs. >> Maybe goes back to these asynchronous behavior. >> Yeah. >> The way of nature. >> And, yesterday, Swami talking about the bridges, right? The root bridges, right? >> Right. >> So, how could Werner pick up with his video at the beginning. >> Yeah. >> And then talk about space and other things? So I think it's important to educate about event-based architecture, right? And we see this massive transformation. Modern software has to be event based, right? Because, that's how things work and we didn't think like this before. I see this massive transformation in my other research area in other platforms about the HR space, where payrolls are being rebuilt completely. And payroll used to be one of the three peaks of ERP, right? You would size your ERP machine before the cloud to financial close, to run the payroll, and to do an MRP manufacturing run if you're manufacturing. God forbid you run those three at the same time. Your machine wouldn't be able to do that, right? So it was like start the engine, start the boosters, we are running payroll. And now the modern payroll designs like you see from ADP or from Ceridian, they're taking every payroll relevant event. You check in time wise, right? You go overtime, you take a day of vacation and right away they trigger and run the payroll, so it's up to date for you, up to date for you, which, in this economy, is super important, because we have more gig workers, we have more contractors, we have employees who are leaving suddenly, right? The great resignation, which is happening. So, from that perspective, it's the modern way of building software. So it's great to see Werner showing that. The dirty little secrets though is that is more efficient software for the cloud platform vendor too. Takes less resources, gets less committed things, so it's a much more scalable architecture. You can move the events, you can work asynchronously much better. And the biggest showcase, right? What's the biggest transactional showcase for an eventually consistent asynchronous transactional application? I know it's a mouthful, but we at Amazon, AWS, Amazon, right? You buy something on Amazon they tell you it's going to come tomorrow. >> Yep. >> They don't know it's going to come tomorrow by that time, because it's not transactionally consistent, right? We're just making every ERP vendor, who lives in transactional work, having nightmares of course, (Lisa laughs) but for them it's like, yes we have the delivery to promise, a promise to do that, right? But they come back to you and say, "Sorry, we couldn't make it, delivery didn't work and so on. It's going to be a new date. We are out of the product.", right? So these kind of event base asynchronous things are more and more what's going to scale around the world. It's going to be efficient for everybody, it's going to be better customer experience, better employee experience, ultimately better user experience, it's going to be better for the enterprise to build, but we have to learn to build it. So big announcement was to build our environment to build better eventful applications from today. >> Talk about... This is the first re:Invent... Well, actually, I'm sorry, it's the second re:Invent under Adam Selipsky. >> Right. Adam Selipsky, yep. >> But his first year. >> Right >> We're hearing a lot of momentum. What's your takeaway with what he delivered with the direction Amazon is going, their vision? >> Ja, I think compared to the Jassy times, right, we didn't see the hockey stick slide, right? With a number of innovations and releases. That was done in 2019 too, right? So I think it's a more pedestrian pace, which, ultimately, is good for everybody, because it means that when software vendors go slower, they do less width, but more depth. >> Yeah. >> And depth is what customers need. So Amazon's building more on the depth side, which is good news. I also think, and that's not official, right, but Adam Selipsky came from Tableau, right? >> Yeah. So he is a BI analytics guy. So it's no surprise we have three data lake offerings, right? Security data lake, we have a healthcare data lake and we have a supply chain data lake, right? Where all, again, the epigonos mentioned them I was like, "Oh, my god, Amazon's coming to supply chain.", but it's actually data lakes, which is an interesting part. But, I think it's not a surprise that someone who comes heavily out of the analytics BI world, it's off ringside, if I was pitching internally to him maybe I'd do something which he's is familiar with and I think that's what we see in the major announcement of his keynote on Tuesday. >> I mean, speaking of analytics, one of the big announcements early on was Amazon is trying to bridge the gap between Aurora. >> Yep. >> And Redshift. >> Right. >> And setting up for continuous pipelines, continuous integration. >> Right. >> Seems to be a trend that is common to all database players. I mean, Oracle is doing the same thing. SAP is doing the same thing. MariaDB. Do you see the distinction between transactional and analytical databases going away? >> It's coming together, right? Certainly coming together, from that perspective, but there's a fundamental different starting point, right? And with the big idea part, right? The universal database, which does everything for you in one system, whereas the suite of specialized databases, right? Oracle is in the classic Oracle database in the universal database camp. On the other side you have Amazon, which built a database. This is one of the first few Amazon re:Invents. It's my 10th where there was no new database announced. Right? >> No. >> So it was always add another one specially- >> I think they have enough. >> It's a great approach. They have enough, right? So it's a great approach to build something quick, which Amazon is all about. It's not so great when customers want to leverage things. And, ultimately, which I think with Selipsky, AWS is waking up to the enterprise saying, "I have all this different database and what is in them matters to me." >> Yeah. >> "So how can I get this better?" So no surprise between the two most popular database, Aurora and RDS. They're bring together the data with some out of the box parts. I think it's kind of, like, silly when Swami's saying, "Hey, no ETL.". (chuckles) Right? >> Yeah. >> There shouldn't be an ETL from the same vendor, right? There should be data pipes from that perspective anyway. So it looks like, on the overall value proposition database side, AWS is moving closer to the universal database on the Oracle side, right? Because, if you lift, of course, the universal database, under the hood, you see, well, there's different database there, different part there, you do something there, you have to configure stuff, which is also the case but it's one part of it, right, so. >> With that shift, talk about the value that's going to be in it for customers regardless of industry. >> Well, the value for customers is great, because when software vendors, or platform vendors, go in depth, you get more functionality, you get more maturity you get easier ways of setting up the whole things. You get ways of maintaining things. And you, ultimately, get lower TCO to build them, which is super important for enterprise. Because, here, this is the developer cloud, right? Developers love AWS. Developers are scarce, expensive. Might not be want to work for you, right? So developer velocity getting more done with same amount of developers, getting less done, less developers getting more done, is super crucial, super important. So this is all good news for enterprise banking on AWS and then providing them more efficiency, more automation, out of the box. >> Some of your customer conversations this week, talk to us about some of the feedback. What's the common denominator amongst customers right now? >> Customers are excited. First of all, like, first event, again in person, large, right? >> Yeah. >> People can travel, people meet each other, meet in person. They have a good handle around the complexity, which used to be a huge challenge in the past, because people say, "Do I do this?" I know so many CXOs saying, "Yeah, I want to build, say, something in IoT with AWS. The first reference built it like this, the next reference built it completely different. The third one built it completely different again. So now I'm doubting if my team has the skills to build things successfully, because will they be smart enough, like your teams, because there's no repetitiveness and that repetitiveness is going to be very important for AWS to come up with some higher packaging and version numbers.", right? But customers like that message. They like that things are working better together. They're not missing the big announcement, right? One of the traditional things of AWS would be, and they made it even proud, as a system, Jassy was saying, "If we look at the IT spend and we see something which is, like, high margin for us and not served well and we announced something there, right?" So Quick Start, Workspaces, where all liaisons where AWS went after traditional IT spend and had an offering. We haven't had this in 2019, we don't have them in 2020. Last year and didn't have it now. So something is changing on the AWS side. It's a little bit too early to figure out what, but they're not chewing off as many big things as they used in the past. >> Right. >> Yep. >> Did you get the sense that... Keith Townsend, from "The CTO Advisor", was on earlier. >> Yep. >> And he said he's been to many re:Invents, as you have, and he said that he got the sense that this is Amazon's chance to do a victory lap, as he called it. That this is a way for Amazon to reinforce the leadership cloud. >> Ja. >> And really, kind of, establish that nobody can come close to them, nobody can compete with them. >> You don't think that- >> I don't think that's at all... I mean, love Keith, he's a great guy, but I don't think that's the mindset at all, right? So, I mean, Jassy was always saying, "It's still the morning of the day in the cloud.", right? They're far away from being done. They're obsessed over being right. They do more work with the analysts. We think we got something right. And I like the passion, from that perspective. So I think Amazon's far from being complacent and the area, which is the biggest bit, right, the biggest. The only thing where Amazon truly has floundered, always floundered, is the AI space, right? So, 2018, Werner Vogels was doing more technical stuff that "Oh, this is all about linear regression.", right? And Amazon didn't start to put algorithms on silicon, right? And they have a three four trail and they didn't announce anything new here, behind Google who's been doing this for much, much longer than TPU platform, so. >> But they have now. >> They're keen aware. >> Yep. >> They now have three, or they own two of their own hardware platforms for AI. >> Right. >> They support the Intel platform. They seem to be catching up in that area. >> It's very hard to catch up on hardware, right? Because, there's release cycles, right? And just the volume that, just talking about the largest models that we have right now, to do with the language models, and Google is just doing a side note of saying, "Oh, we supported 50 less or 30 less, not little spoken languages, which I've never even heard of, because they're under banked and under supported and here's the language model, right? And I think it's all about little bit the organizational DNA of a company. I'm a strong believer in that. And, you have to remember AWS comes from the retail side, right? >> Yeah. >> Their roll out of data centers follows their retail strategy. Open secret, right? But, the same thing as the scale of the AI is very very different than if you take a look over at Google where it makes sense of the internet, right? The scale right away >> Right. >> is a solution, which is a good solution for some of the DNA of AWS. Also, Microsoft Azure is good. There has no chance to even get off the ship of that at Google, right? And these leaders with Google and it's not getting smaller, right? We didn't hear anything. I mean so much focused on data. Why do they focus so much on data? Because, data is the first step for AI. If AWS was doing a victory lap, data would've been done. They would own data, right? They would have a competitor to BigQuery Omni from the Google side to get data from the different clouds. There's crickets on that topic, right? So I think they know that they're catching up on the AI side, but it's really, really hard. It's not like in software where you can't acquire someone they could acquire in video. >> Not at Core Donovan. >> Might play a game, but that's not a good idea, right? So you can't, there's no shortcuts on the hardware side. As much as I'm a software guy and love software and don't like hardware, it's always a pain, right? There's no shortcuts there and there's nothing, which I think, has a new Artanium instance, of course, certainly, but they're not catching up. The distance is the same, yep. >> One of the things is funny, one of our guests, I think it was Tuesday, it was, it was right after Adam's keynote. >> Sure. >> Said that Adam Selipsky stood up on stage and talked about data for 52 minutes. >> Yeah. Right. >> It was timed, 52 minutes. >> Right. >> Huge emphasis on that. One of the things that Adam said to John Furrier when they were able to sit down >> Yeah >> a week or so ago at an event preview, was that CIOs and CEOs are not coming to Adam to talk about technology. They want to talk about transformation. They want to talk about business transformation. >> Sure, yes, yes. >> Talk to me in our last couple of minutes about what CEOs and CIOs are coming to you saying, "Holger, help us figure this out. We have to transform the business." >> Right. So we advise, I'm going quote our friends at Gartner, once the type A company. So we'll use technology aggressively, right? So take everything in the audience with a grain of salt, followers are the laggards, and so on. So for them, it's really the cusp of doing AI, right? Getting that data together. It has to be in the cloud. We live in the air of infinite computing. The cloud makes computing infinite, both from a storage, from a compute perspective, from an AI perspective, and then define new business models and create new best practices on top of that. Because, in the past, everything was fine out on premise, right? We talked about the (indistinct) size. Now in the cloud, it's just the business model to say, "Do I want to have a little more AI? Do I want a to run a little more? Will it give me the insight in the business?". So, that's the transformation that is happening, really. So, bringing your data together, this live conversation data, but not for bringing the data together. There's often the big win for the business for the first time to see the data. AWS is banking on that. The supply chain product, as an example. So many disparate systems, bring them them together. Big win for the business. But, the win for the business, ultimately, is when you change the paradigm from the user showing up to do something, to software doing stuff for us, right? >> Right. >> We have too much in this operator paradigm. If the user doesn't show up, doesn't find the click, doesn't find where to go, nothing happens. It can't be done in the 21st century, right? Software has to look over your shoulder. >> Good point. >> Understand one for you, autonomous self-driving systems. That's what CXOs, who're future looking, will be talked to come to AWS and all the other cloud vendors. >> Got it, last question for you. We're making a sizzle reel on Instagram. >> Yeah. >> If you had, like, a phrase, like, or a 30 second pitch that would describe re:Invent 2022 in the direction the company's going. What would that elevator pitch say? >> 30 second pitch? >> Yeah. >> All right, just timing. AWS is doing well. It's providing more depth, less breadth. Making things work together. It's catching up in some areas, has some interesting offerings, like the healthcare offering, the security data lake offering, which might change some things in the industry. It's staying the course and it's going strong. >> Ah, beautifully said, Holger. Thank you so much for joining Paul and me. >> Might have been too short. I don't know. (laughs) >> About 10 seconds left over. >> It was perfect, absolutely perfect. >> Thanks for having me. >> Perfect sizzle reel. >> Appreciate it. >> We appreciate your insights, what you're seeing this week, and the direction the company is going. We can't wait to see what happens in the next year. And, yeah. >> Thanks for having me. >> And of course, we've been on so many times. We know we're going to have you back. (laughs) >> Looking forward to it, thank you. >> All right, for Holger Mueller and Paul Gillan, I'm Lisa Martin. You're watching "theCube", the leader in live enterprise and emerging tech coverage. (upbeat music)

Published Date : Dec 1 2022

SUMMARY :

across the AWS ecosystem. of people here. and how the world is, And Holger, welcome, on the final day of the marathon, right? And, of course, or the World Cup. They just got eliminated. What will the U.S. do They're going to win. Hope for the best experts we are. was right. Biggest event of the year again, right? and the entertainment area, and the food area, the big empty booths You know, the white spaces in the developer community, right. Maybe goes back to So, how could Werner pick up and run the payroll, the enterprise to build, This is the first re:Invent... Right. a lot of momentum. compared to the Jassy times, right, more on the depth side, in the major announcement one of the big announcements early on And setting up for I mean, Oracle is doing the same thing. This is one of the first to build something quick, So no surprise between the So it looks like, on the overall talk about the value Well, the value for customers is great, What's the common denominator First of all, like, So something is changing on the AWS side. Did you get the sense that... and he said that he got the sense that can come close to them, And I like the passion, or they own two of their own the Intel platform. and here's the language model, right? But, the same thing as the scale of the AI from the Google side to get The distance is the same, yep. One of the things is funny, Said that Adam Selipsky Yeah. One of the things that are not coming to Adam coming to you saying, for the first time to see the data. It can't be done in the come to AWS and all the We're making a sizzle reel on Instagram. 2022 in the direction It's staying the course Paul and me. I don't know. It was perfect, and the direction the company is going. And of course, we've the leader in live enterprise

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Krishnaprasath Hari & Sid Sharma, Hitachi Vantara | AWS re:Invent 2022


 

(upbeat music) >> Hello, brilliant cloud community, and welcome back to AWS re:Invent. We are here in Las Vegas, Nevada. I'm Savannah Peterson, joined by my co-host Dave Vellante. Dave, how you doing? >> I'm doing well, thanks, yeah. >> Yeah, I feel like... >> I'm hanging in there. >> you've got a lot of pep in your step today for the fourth day. >> I think my voice is coming back, actually. >> (laughs) Look at you, resilient. >> I was almost lost yesterday, yeah. >> Yeah. (laughs) >> So, I actually, at a Hitachi event one time almost completely lost my voice. The production guys pulled me off. They said, "You're done." (Savannah laughing) They gave me the hook. >> You got booted? >> Dave: Yeah, yeah. >> Yeah, yeah, you actually (laughs) got the hook, wow. >> So, I have good memories of Hitachi. >> I was going to say (Dave laughing) interesting that you mentioned Hitachi. Our two guests this morning are from Hitachi. Sid and KP, welcome to the show. >> Thank you. >> Savannah: How you guys doing? Looking great for day four. >> Great. Thank you. >> Great. >> Hanging in there. >> Thank you, Dave and Savannah. (Savannah laughing) >> Dave: Yeah, cool. >> Savannah: Yeah. (laughs) >> Yeah, it was actually a Pentaho thing, right? >> Oh, Pentaho? Yeah. >> Which kind of you guys into that software edge. It was right when you announced the name change to Hitachi Vantara, which is very cool. I had Brian Householder on. You remember Brian? >> Yeah, I know. >> He was explaining the vision, and yeah (indistinct). >> Yeah. Well, look at you a little Hitachi (indistinct). >> Yeah, I've been around a long time, yeah. >> Yeah, all right. (Dave laughing) >> Just a casual flex to start us off there, Dave. I love it. I love it. Sid, we've talked a lot on the show about delivering outcomes. It's a hot theme. Everyone wants to actually have tangible business outcomes from all of this. How are customers realizing value from the cloud? What does that mean? >> See, still 2007, 2008, it was either/or kind of architecture. Either I'm going to execute my use cases on cloud or I'm going to keep my use cases and outcomes through edge. But in the last four or five years and specifically we are in re:Invent, I would talk about AWS. Lot of the power of hyperscalers has been brought to edge. If you talk about the snowball family of AWS, if you talk about monitor on edge devices, if you talk about the entire server list being brought into Lambda coupled inside snowball, now the architecture premise, if I talk about logical shift is end. Now the customers are talking about executing the use cases between edge and cloud. So, there is a continuum rather than a binary bullion decision. So, if you are talking about optimizing a factory, earlier I'll do the analytics at cloud, and I'll do machine on edge. Now it is optimization of a factory outcome at scale across my entire manufacturing where edge, private cloud, AWS, hyperscalers, everything is a continuum. And the customer is not worried about where, which part of my data ops, network ops, server ops storage ops is being executed. >> Savannah: It's like (indistinct). >> The customer is enjoying the use cases. And the orchestration is abstracted through an industrial player like Hitachi working very collaboratively with AWS. So, that is how we are working on industrial use cases right now. >> You brought up manufacturing. I don't think there's been a hotter conversation around supply chain and manufacturing than there has been the last few years. I can imagine taking that guessing game out for customers is a huge deal for you guys. >> Big because if you look at the world today, right from a safety pin, to a cell phone jacket, to a cell phone, the entire supply chain is throttled. The supply chain is throttled because there are various choke points. >> Savannah: Yeah. >> And each choke points is surrounded by different kind of supply and geopolitical issues. >> Savannah: 100%. >> Now, if we talk about the wheat crisis happening because of the Ukraine-Russia war, but the wheat crisis actually creates a multiple string of impacts which impact everything. Silicon, now we talk about silicon, but we then forget about nickel. Nickel is also controlled in one part of that geopolitical conflict. So, everything is getting conflagrated into a very big supply issue. So, if your factories are not performing beyond optimum, if they are not performing at real, I'm, we are talking about factory, hyperscale of the factory. The factory needs to perform at hyperscale to provide what the world needs today. So, we are in a very different kind of a scenario. Some of the economists call it earlier the recession was because of a demand constraint. The demand used to go down. Today's recession is because the supply is going down. The demand is there, but the supply is going down. And there is a different kind of recession in the world. The supply is what is getting throttled. >> And the demand is somewhat unpredictable too. People, you know, retailers, they've... >> Especially right now. >> kind of messed up their inventory. And so, the data is still siloed. And that's where, you know, you get to, okay, can I have the same experience across clouds, on-prem, out to the edge? Kind of bust those silos. >> Yep. >> You know, I dunno if it's, it's certainly not entirely a data problem. There's (laughs), like you say, geopolitical and social issues. >> Savannah: There's so much complexity. >> But there's a data problem too. >> Yes. >> Big. >> So, I wonder if you could talk about your sort of view of, point of view on that cross-cloud, hybrid, out to the edge, what I call super cloud? >> Absolutely. So, today, if you look at how enterprises are adopting cloud or how they're leveraging cloud, it's not just a hosting platform, right? It is the platform from where they can draw business capabilities. You heard in the re:Invent that Amazon is coming up with a supply chain service out of the box in the cloud. That's the kind of capabilities that business wants to draw from cloud today. So, the kind of multicloud or like hybrid cloud, public cloud, private cloud, those are the things which are kind of going to be behind the scenes. At the end of the day, the cloud needs to be able to support businesses by providing their services closer to their consumers. So, the challenges are going to be there in terms of like reliability, resilience, cost, security. Those are the ones that, you know, many of the enterprises are grappling with in terms of the challenges. And the way to solve that, the way how we approach our customers and work with them is to be able to bring resilience into the cloud, into the services which are running in cloud, and by driving automation, making autonomous in everything that you do, how you are monitoring your services, how we are making it available, how we are securing it, how we are making it very cost-effective as well. It cannot be manually executed; it has to be automated. So, automation is the key in terms of making the services leveraged from all of this cloud. >> That's your value add. >> Absolutely. >> And how do I consume that value add? Is it sort of embedded into infrastructure? Is it a service layer on top? >> Yeah, so everything that we do today in terms of like how these services have to be provided, how the services have to be consumed, there has to be a modern operating model, right? I think this is where Hitachi has come up with what we are calling as Hitachi Application Reliability Center and Services. That is focusing on modern operating, modern ways of like, you know, how you support these cloud workloads and driving this automation. So, whether we provide a hyper-converged infrastructure that is going to be at the edge location, or we are going to be able to take a customer through the journey of modernization or migrating onto cloud, the operating model that is going to be able to establish the foundation on cloud and then to be able to operate with the right levels of reliability, security, cost is the key. And that's the value added service that we provide. And then the way we do that is essentially by looking at three principles: one, to look at the service in totality. Gone are the days you look at infrastructure separately, applications separately, data and security separately, right? >> Savannah: No more silos. >> No more silos. You look at it as a workload, and you look at it as a service. And number two is to make sure that the DevOps that you bring and what you do at the table is totally integrated and it's end to end. It's not a product team developing a feature and then ops team trying to keep the lights on. It has to be a common backlog with the error budget that looks at you know, product releases, product functionalities, and even what ops needs to do to evolve the product as well. And then the third is to make sure that reliability and resiliency is inbuilt. Cloud offers native durability, native availability. But if your service doesn't take advantage of that, it's kind of going to still be not available. So, how do you kind of ingrain and embed all of these things as a value add that we provide? >> There's a lot of noise. We've got hybrid cloud. We've got multicloud. We've got a lot going on. It adds to the complexity. How do you help customers solve that complexity as they begin their transformation journey? I mean, I'm sure you're working with the biggest companies, making really massive change. How do you guide them through that process? >> So, it is to look at the outcome working backwards, like what AWS does, right? Like, you know, how do you look at the business outcome? What is the value that you're looking to drive? Again, it's not to be pinned through one particular cloud. I know there is lot of technology choices that you can make and lot of deployment models that you can choose from. But at the end of the day, having a common operating model which is kind of like modern, agile, and it is kind of like keeping the outcomes in the mind, that is what we do with our customers to be able to create that operating model, which completes the transformation, by the way. And cloud is just one part of the LEGO blocks which provides that overall scheme and then the view for driving that overall transformation. >> So, let's paint a picture. Let's say you've got this resilient foundation; you've kind of helped the customers build that out. How do they turn that into value for their customers? Do you have any examples that you can share? That'd be great. >> Yeah, I can start with what we're doing for one of the, you know, world's largest facility, infrastructure, power, cooling, security, monitoring company that has their products deployed in 2,000 locations across the globe. For them, and always on business means you are monitoring the temperature. You are monitoring the safety of people who are within the facility, right? A temperature shift of one to two degree can affect even the sustainability goals of NARC, our customer, but also their end consumers. So, how do you monitor these kind of like critical parameters? How do you have a platform? >> Savannah: Great example, yeah. >> How you have cloud resources that are going to be always on, that are going to be reliable, that are going to be cost-effective as well is what we are doing for one of our customers. Sid can talk about another example as well. >> Great. >> Yeah, go for it, Sid. >> So, there are examples: rail. We are working with a group in England; it's called West Coast Partnership. And they had a edge device which was increasing in size. Now, this edge device was becoming big because the parameters which go into the edge device were increasing because of regulation and because the rail is part of national security infrastructure. We have worked with West Coast Partnership and Hitachi Rail, which is a group company, to create a miniaturization of this edge device, because if the size of the edge device is increasing on the train, then the weight of the train increases, and the speed profile, velocity profile, everything goes down. So, we have miniaturized the edge device. Secondly, all the data profiles, signal control, traction control, traction motors, direction control, timetable compliance, everything has been kept uniform. And we have done analytics on cloud. So, what is the behavior of the driver? What is a big breaking parameter of the driver? If the timetable has being missed, is there an erratic behavior being demonstrated by the driver to just meet the timetable? And the timetable is a pretty important criteria in rail because if you miss one, then... So, what we have done is we have created an edge-to-cloud environment where the entire rail analytics is happening. Similarly, in another group company, Hitachi Energy, they had a problem that arguably one of the largest transformer manufacturer in the world. The transformer is a pretty common name now because you're seeing what is happening in Ukraine. Russia went after the transformers and substations before the start of the winter so that their district heating can be meddled with. Now, the transformer, it had a lead time of 17 weeks before COVID. So, if you put me an order of a three-phase transformer, I can deliver it to you in 17 weeks. After and during COVID, the entire lead time increased to 57 to 58 weeks. In cases of a complex transformer, it even went up to something like two years. >> Savannah: Ooh! >> Now, they wanted to increase the productivity of their existing plant because there is only that much sheet metal, that much copper for solenoid, that much microprocessor and silicon. So, they wanted to increase the output of their factory from 95 to 105, 10 more transformers every day, which is 500 and, which is 3,650 every- >> Savannah: Year. >> Year. Now, to do that, we went to a very complex machine; it's called a guard machine. And we increased the productivity of the guard machine by just analyzing all the throttles and all the wastages which are happening there. There are multiple case studies because, see, Hitachi is an industrial giant with 105 years of body of work. KP and I just represent the tip of the digital tip of the arrow. But what we are trying to do through HARC, through industry cloud, through partnership with AWS is basically containerizing and miniaturizing our entire body of work into a democratized environment, an industrial app store, if I may say, where people can come and take their industrial outcomes at ease without worrying about their computational and network orchestration between edge and cloud. That's what we are trying to do. >> I love that analogy of an industrial app cloud. Makes it feel easier in decreasing the complexity of all the different things that everyone's factoring into making their products, whatever they're making. So, we have a new challenge here on theCUBE at AWS re:Invent, where we are looking for your 30-second hot take, your Instagram reel, sound bite. What's the most important story or theme either for you as a team or coming out of the show? You can ponder it for a second. >> It might be different. See, for me, it is industrial security. Industrial OT security should be the theme of the Western world. Western world is on the crosshairs of multiple bad actors. And the industrial security is in the chemical plants, is in the industrial plants, is in the power grids, is in our postal networks and our rail networks. They need to be secured; otherwise, we are geopolitically very weak. Gone are the days when anyone is going to pick up a battle with America or Western world on a field. The battle is going to be pretty clandestine on an cyber world. And that is why industrial security is very important. >> Critical infrastructure and protecting it. >> Absolutely. >> Well said, Sid. KP, what's your hot take? >> My take is going to be a modern operating model, which is going to complete the transformation and to be able to tap into business services from cloud. So, a modern operating model through HARC, that is going to be my take. >> Fantastic. Well, can't wait to see what comes out of Hitachi next. Sid, KP... >> KP: Thank you. >> thank you so much for being here. >> Sid: Thank you. >> Absolutely. >> Dave: Thanks, guys. >> Savannah: This is I could talk to you all about supply chain all day long. And thank all of you for tuning in to our continuous live coverage here from AWS re:Invent in fantastic Sin City. I'm Savannah. Oh, excuse me. With Dave Vellante, I'm Savannah Peterson. You're watching theCUBE, the leader in high tech coverage. (digital xylophone music)

Published Date : Dec 1 2022

SUMMARY :

Dave, how you doing? for the fourth day. I think my voice is They gave me the hook. (laughs) got the hook, wow. interesting that you mentioned Hitachi. Savannah: How you guys doing? Thank you. Thank you, Dave and Savannah. Yeah. announced the name change He was explaining the Well, look at you a little Yeah, I've been Yeah, all right. to start us off there, Dave. Lot of the power of hyperscalers The customer is enjoying the use cases. for customers is a huge deal for you guys. look at the world today, by different kind of supply of recession in the world. And the demand is And so, the data is still siloed. There's (laughs), like you say, So, the challenges are going to be there how the services have to be consumed, that the DevOps that you the biggest companies, What is the value that that you can share? You are monitoring the safety that are going to be always on, by the driver to just meet the timetable? the output of their factory of the guard machine by just of all the different things of the Western world. and protecting it. KP, what's your hot take? that is going to be my take. Well, can't wait to see what could talk to you all

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Brad Peterson, NASDAQ & Scott Mullins, AWS | AWS re:Invent 2022


 

(soft music) >> Welcome back to Sin City, guys and girls we're glad you're with us. You've been watching theCUBE all week, we know that. This is theCUBE's live coverage of AWS re:Invent 22, from the Venetian Expo Center where there are tens of thousands of people, and this event if you know it, covers the entire strip. There are over 55,000 people here, hundreds of thousands online. Dave, this has been a fantastic show. It is clear everyone's back. We're hearing phenomenal stories from AWS and it's ecosystem. We got a great customer story coming up next, featured on the main stage. >> Yeah, I mean, you know, post pandemic, you start to think about, okay, how are things changing? And one of the things that we heard from Adam Selipsky, was, we're going beyond digital transformation into business transformation. Okay. That can mean a lot of things to a lot of people. I have a sense of what it means. And I think this next interview really talks to business transformation beyond digital transformation, beyond the IT. >> Excellent. We've got two guests. One of them is an alumni, Scott Mullins joins us, GM, AWS Worldwide Financial Services, and Brad Peterson is here, the EVP, CIO and CTO of NASDAQ. Welcome guys. Great to have you. >> Hey guys. >> Hey guys. Thanks for having us. >> Yeah >> Brad, talk a little bit, there was an announcement with NASDAQ and AWS last year, a year ago, about how they're partnering to transform capital markets. It was a highlight of last year. Remind us what you talked about and what's gone on since then. >> Yeah, so, we are very excited. I work with Adena Friedman, she's my boss, CEO of NASDAQ, and she was on stage with Adam for his first Keynote as CEO of AWS. And we made the commitment that we were going to move our markets to the Cloud. And we've been a long time customer of AWS and everyone said, you know the last piece, the last frontier to be moved was the actual matching where all the messages, the quotes get matched together to become confirmed orders. So that was what we committed to less than a year ago. And we said we were going to move one of our options markets. In the US, we have six of them. And options markets are the most challenging, they're the most high volume and high performance. So we said, let's start with something really challenging and prove we can do it together with AWS. So we committed to that. >> And? Results so far? >> So, I can sit here and say that November 7th so we are live, we're in production and the MRX Exchange is called Mercury, so we shorten it for MRX, we like acronyms in technology. And so, we started with a phased launch of symbols, so you kind of allow yourself to make sure you have all the functionality working then you add some volume on it, and we are going to complete the conversion on Monday. So we are all good so far. And I have some results I can share, but maybe Scott, if you want to talk about why we did that together. >> Yeah. >> And what we've done together over many years. >> Right. You know, Brian, I think it's a natural extension of our relationship, right? You know, you look at the 12 year relationship that AWS and NASDAQ have had together, it's just the next step, in the way that we're going to help the industry transform itself. And so not just NASDAQ's business transformation for itself, but really a blueprint and a template for the entire capital markets industry. And so many times people will ask me, who's using Cloud well? Who's doing well in the Cloud? And NASDAQ is an easy example to point to, of somebody who's truly taking advantage of these capabilities because the Cloud isn't a place, it's a set of capabilities. And so, this is a shining example of how to use these capabilities to actually deliver real business benefit, not just to to your organization, but I think the really exciting part is the market technology piece of how you're serving other exchanges. >> So last year before re:Invent, we said, and it's obvious within the tech ecosystem, that technology companies are building on top of the Cloud. We said, the big trend that we see in the 2020s is that, you know, consumers of IT, historically, your customers are going to start taking their stacks, their software, their data, their services and sassifying, putting it on the Cloud and delivering new services to customers. So when we saw Adena on stage last year, we called it by the way, we called it Super Cloud. >> Yeah. >> Okay. Some people liked the term but I love it. And so yeah, Super Cloud. So when we saw Adena on stage, we said that's a great example. We've seen Capital One doing some similar things, we've had some conversations with US West, it's happening, right? So talk about how you actually do that. I mean, because you've got a lot, you've got a big on-premises stay, are you connecting to that? Is it all in the Cloud? Paint a picture of what the architecture looks like? >> Yeah. And there's, so you started with the business transformation, so I like that. >> Yeah. >> And the Super Cloud designation, what we are is, we own and operate exchanges in the United States and in Europe and in Canada. So we have our own markets that we're looking at modernizing. So we look at this, as a modernization of the capital market infrastructure, but we happen to be the leading technology provider for other markets around the world. So you either build your own or you source from us. And we're by far the leading provider. So a lot of our customers said, how about if you go first? It's kind of like Mikey, you know, give it to Mikey, let him try it. >> See if Mikey likes it. >> Yeah. >> Penguin off the iceberg thing. >> Yeah. And so what we did is we said, to make this easy for our customers, so you want to ask your customers, you want to figure out how you can do it so that you don't disrupt their business. So we took the Edge Compute that was announced a few years ago, Amazon Outposts, and we were one of their early customers. So we started immediately to innovate with, jointly innovate with Amazon. And we said, this looks interesting for us. So we extended the region into our Carteret data center in Northern New Jersey, which gave us all the services that we know and love from Amazon. So our technical operations team has the same tools and services but then, we're able to connect because in the markets what we're doing is we need to connect fairly. So we need to ensure that you still have that fairness element. So by bringing it into our building and extending the Edge Compute platform, the AWS Outpost into Carteret, that allowed us to also talk very succinctly with our regulators. It's a familiar territory, it's all buttoned up. And that simplified the conversion conversation with the regulators. It simplified it with our customers. And then it was up to us to then deliver time and performance >> Because you had alternatives. You could have taken a more mature kind of on-prem legacy stack, figured out how to bolt that in, you know, less cloudy. So why did you choose Outposts? I am curious. >> Well, Outposts looked like when it was announced, that it was really about extending territory, so we had our customers in mind, our global customers, and they don't always have an AWS region in country. So a lot of you think about a regulator, they're going to say, well where is this region located? So finally we saw this ability to grow the Cloud geographically. And of course we're in Sweden, so we we work with the AWS region in Stockholm, but not every country has a region yet. >> And we're working as fast as we can. - Yes, you are. >> Building in every single location around the planet. >> You're doing a good job. >> So, we saw it as an investment that Amazon had to grow the geographic footprint and we have customers in many smaller countries that don't have a region today. So maybe talk a little bit about what you guys had in mind and it's a multi-industry trend that the Edge Compute has four or five industries that you can say, this really makes a lot of sense to extend the Cloud. >> And David, you said it earlier, there's a trend of ecosystems that are coming onto the Cloud. This is our opportunity to bring the Cloud to an ecosystem, to an existing ecosystem. And if you think about NASDAQ's data center in Carteret, there's an ecosystem of NASDAQ's clients there that are there to be with NASDAQ. And so, it was actually much easier for us as we worked together over a really a four year period, thinking about this and how to make this technological transition, to actually bring the capabilities to that ecosystem, rather than trying to bring the ecosystem to AWS in one of our public regions. And so, that's been our philosophy with Outpost all along. It's actually extending our capabilities that our customers know and love into any environment that they need to be able to use that in. And so to Brad's point about servicing other markets in different countries around the world, it actually gives us that ability to do that very quickly, very nimbly and very succinctly and successfully. >> Did you guys write a working backwards document for this initiative? >> We did. >> Yeah, we actually did. So to be, this is one of the fully exercised. We have a couple of... So by the way, Scott used to work at NASDAQ and we have a number of people who have gone from NASDAQ data to AWS, and from AWS to NASDAQ. So we have adopted, that's one of the things that we think is an effective way to really clarify what you're trying to accomplish with a project. So I know you're a little bit kidding on that, but we did. >> No, I was close. Because I want to go to the like, where are we in the milestone? And take us through kind of what we can expect going forward now that we've worked backwards. >> Yep, we did. >> We did. And look, I think from a milestone perspective, as you heard Brad say, we're very excited that we've stood up MRX in production. Having worked at NASDAQ myself, when you make a change and when you stand up a market that's always a moment where you're working with your community, with your clients and you've got a market-wide call that you're working and you're wanting to make sure that everything goes smoothly. And so, when that call went smoothly and that transition went smoothly I know you were very happy, and in AWS, we were also very happy as well that we hit that milestone within the timeframe that Adena set. And that was very important I know to you. >> Yeah. >> And for us as well. >> Yeah. And our commitment, so the time base of this one was by the end of 2022. So November 7th, checked. We got that one done. >> That's awesome. >> The other one is we said, we wanted the performance to be as good or better than our current platform that we have. And we were putting a new version of our derivative or options software onto this platform. We had confidence because we already rolled it to one market in the US then we rolled it earlier this year and that was last year. And we rolled it to our nordic derivatives market. And we saw really good customer feedback. So we had confidence in our software was going to run. Now we had to marry that up with the Outpost platform and we said we really want to achieve as good or better performance and we achieved better performance, so that's noticeable by our customers. And that one was the biggest question. I think our customers understand when we set a date, we test them with them. We have our national test facility that they can test in. But really the big question was how is it going to perform? And that was, I think one of the biggest proof points that we're really proud about, jointly together. And it took both, it took both of us to really innovate and get the platform right, and we did a number of iterations. We're never done. >> Right. >> But we have a final result that says it is better. >> Well, congratulations. - Thank you. >> It sounds like you guys have done a tremendous job. What can we expect in 2023? From NASDAQ and AWS? Any little nuggets you can share? >> Well, we just came from the partner, the partner Keynote with Adam and Ruba and we had another colleague on stage, so Nick Ciubotariu, so he is actually someone who brought digital assets and cryptocurrencies onto the Venmo, PayPal platform. He joined NASDAQ about a year ago and we announced that in our marketplace, the Amazon marketplace, we are going to offer digital custody, digital assets custody solution. So that is certainly going to be something we're excited about in 2023. >> I know we got to go, but I love this story because it fits so great at the Super cloud but we've learned so much from Amazon over the years. Two pieces of teams, we talked about working backwards, customer obsession, but this is a story of NASDAQ pointing its internal capabilities externally. We're already on that journey and then, bringing that to the Cloud. Very powerful story. I wonder what's next in this, because we learn a lot and we, it's like the NFL, we copy it. I think about product market fit. You think about scientific, you know, go to market and seeing that applied to the financial services industry and obviously other industries, it's really exciting to see. So congratulations. >> No, thank you. And look, I think it's an example of Invent and Simplify, that's another Amazon principle. And this is, I think a great example of inventing on behalf of an industry and then continually working to simplify the way that the industry works with all of us. >> Last question and we've got only 30 seconds left. Brad, I'm going to direct it to you. If you had the opportunity to take over the NASDAQ sign in Times Square and say a phrase that summarizes what NASDAQ and AWS are doing together, what would it say? >> Oh, and I think I'm going to put that up on Monday. So we're going to close the market together and it's going to say, "Modernizing the capital market's infrastructure together." >> Very cool. >> Excellent. Drop the mic. Guys, this was fantastic. Thank you so much for joining us. We appreciate you joining us on the show, sharing your insights and what NASDAQ and AWS are doing. We're going to have to keep watching this. You're going to have to come back next year. >> All right. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage. (soft music)

Published Date : Dec 1 2022

SUMMARY :

and this event if you know it, And one of the things that we heard and Brad Peterson is here, the Thanks for having us. Remind us what you talked about In the US, we have six of them. And so, we started with a And what we've done And NASDAQ is an easy example to point to, that we see in the 2020s So talk about how you actually do that. so you started with the So we have our own markets And that simplified the So why did you choose So a lot of you think about a regulator, as we can. location around the planet. and we have customers in that are there to be with NASDAQ. and we have a number of people now that we've worked backwards. and in AWS, we were so the time base of this one And we rolled it to our But we have a final result - Thank you. What can we expect in So that is certainly going to be something and seeing that applied to the that the industry works with all of us. and say a phrase that summarizes and it's going to say, We're going to have to keep watching this. the leader in live enterprise

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Steve Mullaney, CEO, Aviatrix | AWS re:Invent 2022


 

(upbeat music) >> You got it, it's theCUBE. We are in Vegas. This is the Cube's live coverage day one of the full event coverage of AWS reInvent '22 from the Venetian Expo Center. Lisa Martin here with Dave Vellante. We love being in Vegas, Dave. >> Well, you know, this is where Super Cloud sort of was born. >> It is. >> Last year, just about a year ago. Steve Mullaney, CEO of of Aviatrix, you know, kind of helped us think it through. And we got some fun stories around. It's happening, but... >> It is happening. We're going to be talking about Super Cloud guys. >> I guess I just did the intro, Steve Mullaney >> You did my intro, don't do it again. >> Sorry I stole that from you, yeah. >> Steve Mullaney, joined just once again, one of our alumni. Steve, great to have you back on the program. >> Thanks for having me back. >> Dave: It's happening. >> It is happening. >> Dave: We talked about a year ago. Net Studio was right there. >> That was two years. Was that year ago, that was a year ago. >> Dave: It was last year. >> Yeah, I leaned over >> What's happening? >> so it's happening. It's happening. You know what, the thing I noticed what's happening now is the maturity of the cloud, right? So, if you think about this whole journey to cloud that has been, what, AWS 12 years. But really over the last few years is when enterprises have really kind of joined that journey. And three or four years ago, and this is why I came out of retirement and went to Aviatrix, was they all said, okay, now we're going to do cloud. You fast forward now three, four years from now, all of a sudden those five-year plans of evacuating the data center, they got one year left, two year left, and they're going, oh crap, we don't have five years anymore. We're, now the maturity's starting to say, we're starting to put more apps into the cloud. We're starting to put business critical apps like SAP into the cloud. This is not just like the low-hanging fruit anymore. So what's happening now is the business criticality, the scale, the maturity. And they're all now starting to hit a lot of limits that have been put into the CSPs that you never used to hit when you didn't have business critical and you didn't have that scale. They were always there. The rocks were always there. Just it was, you never hit 'em. People are starting to hit 'em now. So what's happening now is people are realizing, and I'm going to jump the gun, you asked me for my bumper sticker. The bumper sticker for Aviatrix is, "Good enough is no longer good enough." Now it's funny, it came in a keynote today, but what we see from our customers is it's time to upgrade the native constructs of networking and network security to be enterprise-grade now. It's no longer good enough to just use the native constructs because of a lack of visibility, the lack of controls, the lack of troubleshooting capabilities, all these things. "I now need enterprise grade networking." >> Let me ask you a question 'cause you got a good historical perspective on the industry. When you think about when Maritz was running VMWare. He was like any app, he said basically we're building a software mainframe. And they kind of did that, right? But then they, you know, hit the issue with scale, right? And they can't replicate the cloud. Are there things that we can draw from that experience and apply that to the cloud? What's the same, what's different? >> Oh yeah. So, 1992, do you remember what happened in 1992? I do this, weird German software company called SAP >> Yeah, R3. announced a release as R/3. Which was their first three-tier client-server application of SAP. Before that it ran on mainframes, TCP/IP. Remember that Protocol War? Guess what happened post-1992, everybody goes up like this. Infrastructure completely changes. Cisco, EMC, you name it, builds out these PCE client-server architectures. The WAN changes, MPLS, the campus, everything's home running back to that data center running SAP. That was the last 30 years ago. Great transformation of SAP. They've did it again. It's called S/4Hana. And now it's running and people are switching to S/4Hana and they're moving to the cloud. It's just starting. And that is going to alter how you build infrastructure. And so when you have that, being able to troubleshoot in hours versus minutes is a big deal. This is business critical, millions of dollars. This is not fun and games. So again, back to my, what was good enough for the last three or four years for enterprises no longer good enough, now I'm running business critical apps like SAP, and it's going to completely change infrastructure. That's happening in the cloud right now. And that's obviously a significant seismic shift, but what are some of the barriers that customers have been able to eliminate in order to get there? Or is it just good enough isn't good enough anymore? >> Barriers in terms of, well, I mean >> Lisa: The adoption. Yeah well, I mean, I think it's all the things that they go to cloud is, you know, the complexity, really, it's the agility, right? So the barrier that they have to get over is how do I keep the developer happy because the developer went to the cloud in the first place, why? Swipe the credit card because IT wasn't doing their job, 'cause every time I asked them for something, they said no. So I went around 'em. We need that. That's what they have to overcome in the move to the cloud. That is the obstacle is how do I deliver that visibility, that control, the enterprise, great functionality, but yet give the developer what they want. Because the minute I stop giving them that swipe the card operational model, what do you think they're going to do? They're going to go around me again and I can't, and the enterprise can't have that. >> That's a cultural shift. >> That's the main barrier they've got to overcome. >> Let me ask you another question. Is what we think of as mission critical, the definition changing? I mean, you mentioned SAP, obviously that's mission critical for operations, but you're also seeing new applications being developed in the cloud. >> I would say anything that's, I call business critical, same thing, but it's, business critical is internal to me, like SAP, but also anything customer-facing. That's business critical to me. If that app goes down or it has a problem, I'm not collecting revenue. So, you know, back 30 years ago, we didn't have a lot of customer-facing apps, right? It really was just SAP. I mean there wasn't a heck of a lot of cust- There were customer-facing things. But you didn't have all the digitalization that we have now, like the digital economy, where that's where the real explosion has come, is you think about all the customer-facing applications. And now every enterprise is what? A technology, digital company with a customer-facing and you're trying to get closer and closer to who? The consumer. >> Yeah, self-service. >> Self-service, B2C, everybody wants to do that. Get out of the middle man. And those are business critical applications for people. >> So what's needed under the covers to make all this happen? Give us a little double click on where you guys fit. >> You need consistent architecture. Obviously not just for one cloud, but for any cloud. But even within one cloud, forget multicloud, it gets worst with multicloud. You need a consistent architecture, right? That is automated, that is as code. I can't have the human involved. These are all, this is the API generation, you've got to be able to use automation, Terraform. And all the way from the application development platform you know, through Jenkins and all other software, through CICD pipeline and Terraform, when you, when that developer says, I want infrastructure, it has to go build that infrastructure in real time. And then when it says, I don't need it anymore it's got to take it away. And you cannot have a human involved in that process. That's what's completely changed. And that's what's giving the agility. And that's kind of a cloud model, right? Use software. >> Well, okay, so isn't that what serverless does, right? >> That's part of it. Absolutely. >> But I might still want control sometimes over the runtime if I'm running those mission critical applications. Everything in enterprise is a heterogeneous thing. It's like people, people say, well there's going to, the people going to repatriate back to on-prem, they are not repatriating back to on-prem. >> We were just talking about that, I'm like- >> Steve: It's not going to happen, right? >> It's a myth, it's a myth. >> And there's things that maybe shouldn't have ever gone into the cloud, I get that. Look, do people still have mainframes? Of course. There's certain things that you just, doesn't make sense to move to the new generation. There were things, certain applications that are very static, they weren't dynamic. You know what, keeping it on-prem it's, probably makes sense. So some of those things maybe will go back, but they never should have gone. But we are not repatriating ever, you know, that's not going to happen. >> No I agree. I mean, you know, there was an interesting paper by Andreessen, >> Yeah. >> But, I mean- >> Steve: Yeah it was a little self-serving for some company that need more funding, yeah. You look at the numbers. >> Steve: Yeah. >> It tells the story. It's just not happening. >> No. And the reason is, it's that agility, right? And so that's what people, I would say that what you need to do is, and in order to get that agility, you have to have that consistency. You have to have automation, you have to get these people out of the way. You have to use software, right? So it's that you have that swipe the card operational model for the developers. They don't want to hear the word no. >> Lisa: Right. >> What do you think is going to happen with AWS? Because we heard, I don't know if you heard Selipsky's keynote this morning, but you've probably heard the hallway talk. >> Steve: I did, yeah. >> Okay. You did. So, you know, connecting the dots, you know doubling down on all the primitives, that we expected. We kind of expected more of the higher level stuff, which really didn't see much of that, a little bit. >> Steve: Yeah. So, you know, there's a whole thing about, okay, does the cloud get commoditized? Does it not? I think the secret weapon's the ecosystem, right? Because they're able to sell through with guys like you. Make great margins on that. >> Steve: Yeah, well, yeah. >> What are your thoughts though on the future of AWS? >> IAS is going to get commoditized. So this is the fallacy that a lot of the CSPs have, is they thought that they were going to commoditize enterprise. It never happens that way. What's going to happen is infrastructure as a service, the lower level, which is why you see all the CSPs talking about what? Oracle Cloud, industry cloud. >> Well, sure, absolutely, yeah. >> We got to get to the apps, we got to get to SAP, we got to get to all that, because that's not going to get commoditized, right. But all the infrastructural service where AWS is king that is going to get commoditized, absolutely. >> Okay, so, but historically, you know Cisco's still got 60% plus gross margins. EMC always had good margin. How pure is the lone survivor in Flash? They got 70% gross margins. So infrastructure actually has always been a pretty good business. >> Yeah that's true. But it's a hell of a lot easier, particularly with people like Aviatrix and others that are building these common architectural things that create simplicity and abstract the way the complexities of underneath such that we allow your network to run an AWS, Azure, Google, Oracle, whatever, exactly the same. So it makes it a hell of a lot easier >> Dave: Super cloud. >> to go move. >> But I want to tap your brain because you have a good perspective of this because servers used to be a great margin business too on-prem and now it's not. It's a low margin business 'cause all the margin went to Intel. >> Yeah. But the cloud guys, you know, AWS in particular, makes a ton of dough on servers, so, or compute. So it's going to be interesting to see over time if that gets com- that's why they're going so hard after silicon. >> I think if they can, I think if you can capture the workload. So AWS and everyone else, as another example, this SAP, they call that a gravity workload. You know what gravity workload is? It's a black hole. It drags everything else with it. If you get SAP or Oracle or a mainframe app, it ain't going anywhere. And then what's going to happen is all your other apps are going to follow it. So that's what they're all going to fight for, is type of app. >> You said something earlier about, forget multicloud, for a moment, but, that idea of the super cloud, this abstraction layer, I mean, is that a real business value for customers other than, oh I got all these clouds, I need 'em to work together. You know, from your perspective from Aviatrix perspective, is it an opportunity for you to build on top of that? Or are you just looking at, look, I'm going to do really good work in AWS, in Azure? Now we're making the same experience. >> I hear this every single day from our customers is they look and they say, good enough isn't good enough. I've now hit the point, I'm hitting route limitations. I'm hitting, I'm doing things manually, and that's fine when I don't have that many applications or I don't have mission critical. The dogs are eating the dog food, we're going into the cloud and they're looking and then saying this is not an operational model for me. I've hit the point where I can't keep doing this, I can't throw bodies at this, I need software. And that's the opportunity for us, is they look and they say, I'm doing it in one cloud, but, and there's zero chance I'm going to be able to figure that out in the two or three other clouds. Every enterprise I talk to says multicloud is inevitable. Whether they're in it now, they all know they're going to go, because it's the business units that demand it. It's not the IT teams that demand it, it's the line of business that says, I like GCP for this reason. >> The driver's functionality that they're getting. >> It's the app teams that say, I have this service and GCP's better at it than AWS. >> Yeah, so it's not so much a cost game or the end all coffee mug, right? >> No, no. >> Google does this better than Microsoft, or better than- >> If you asked an IT person, they would rather not have multicloud. They actually tried to fight it. No, why would you want to support four clouds when you could support one right? That's insane. >> Dave and Lisa: Right. If they didn't have a choice and, and so it, the decision was made without them, and actually they weren't even notified until day before. They said, oh, good news, we're going to GCP tomorrow. Well, why wasn't I notified? Well, we're notifying you now. >> Yeah, you would've said, no. >> Steve: This is cloud bottle, let's go. >> Super cloud again. Did you see the Berkeley paper, sky computing I think they call it? Down at Berkeley, yep Dave Linthicum from Deloitte. He's talking about, I think he calls it meta cloud. It's happening. >> Yeah, yeah, yeah. >> It's happening. >> No, and because customers, customers want that. They... >> And talk about some customer example or two that you think really articulates the value of why it's happening and the outcomes that it's generating. >> I mean, I was just talking to Lamb Weston last night. So we had a reception, Lamb Weston, huge, frozen potatoes. They serve like, I dunno, some ungodly percentage of all the french fries to all the fast food. It's unbelievable what they do. Do you know, they have special chemicals they put on the french fries. So when you get your DoorDash, they stay crispy longer. They've invented that patented it. But anyway, it's all these businesses you've never heard of and they do all the, and again, they're moving to SAP or they're actually SAP in the cloud, they're one of the first ones. They did it through Accenture. They're pulling it back off from Accenture. They're not happy with the service they're getting. They're going to use us for their networking and network security because they're going to get that visibility and control back. And they're going to repatriate it back from a managed service and bring it back and run it in-house. And the SAP basis engineers want it to happen because they see the visibility and control that the infrastructure guy's going to get because of us, which leads to, all they care about is uptime and performance. That's it. And they're going to say the infrastructure team's going to lead to better uptime and better performance if it's running on Aviatrix. >> And business performance and uptime, business critical >> That is the business. That is the business. >> It is. So what are some of the things next coming down the pike from Aviatrix? Any secret sauce you can share? >> Lot of secrets. So, two secrets. One, the next thing people really want to do, embedded network security into the network. We've kind of talked about this. You're going to be seeing some things from us. Where does network security belong? In the network. Embedded in the fabric of the network, not as this dumb device called the next-gen firewall that you steer traffic to. It has to be into the fabric of what we do, what we call airspace. You're going to see us talk about that. And then the next thing, back to the maturity of the cloud, as they build out the core, guess what they're doing? It's this thing called edge, Dave, right? And guess what they're going to do? It's not about connecting the cloud to the edge to the cloud with dumb things like SD-WAN, right? Or SaaS. It's actually the other way around. Go into the cloud, turn around, look out at the edge and say, how do I extend the cloud out to the edge, and make it look like a VPC. That's what people are doing. Why, 'cause I want the operational model. I want all the things that I can do in the cloud out at the edge. And everyone knows it's been in networking. I've been in networking for 37 years. He who wins the core does what? Wins the edge, 'cause that's what happens. You do it first in the core and then you want one architecture, one common architecture, one consistent way of doing everything. And that's going to go out to the edge and it's going to look like a VPC from an operational model. >> And Amazon's going to support that, no doubt. >> Yeah, I mean every, you know, every, and then it's just how do you want to go do that? And us as the networking and network security provider, we're getting dragged to the edge by our customer. Because you're my networking provider. And that means, end to end. And they're trying to drag us into on-prem too, yeah. >> Lot's going on, you're going to have to come back- >> Because they want one networking vendor. >> But wait, and you say what? >> We will never do like switches and any of the keep Arista, the Cisco, and all that kind of stuff. But we will start sucking in net flow. We will start doing, from an operational perspective, we will integrate a lot of the things that are happening in on-prem into our- >> No halfway house. >> Copilot. >> No halfway house, no two architectures. But you'll take the data in. >> You want one architecture. >> Yeah. >> Yeah, totally. >> Right play. >> Amazing stuff. >> And he who wins the core, guess what's more strategic to them? What's more strategic on-prem or cloud? Cloud. >> It flipped three years ago. >> Dave: Yeah. >> So he who wins in the clouds going to win everywhere. >> Got it, We'll keep our eyes on that. >> Steve: Cause and effect. >> Thank you so much for joining us. We've got your bumper sticker already. It's been a great pleasure having you on the program. You got to come back, there's so, we've- >> You posting the bumper sticker somewhere? >> Lisa: It's going to be our Instagram. >> Oh really, okay. >> And an Instagram sto- This is new for you guys. Always coming up with new ideas. >> Raising the bar. >> It is, it is. >> Me advance, I mean, come on. >> I love it. >> All right, for our guest Steve Mullaney and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage.

Published Date : Nov 29 2022

SUMMARY :

This is the Cube's live coverage day one Well, you know, this is where you know, kind of helped We're going to be talking don't do it again. I stole that from you, yeah. Steve, great to have you Dave: We talked about Was that year ago, that was a year ago. We're, now the maturity's starting to say, and apply that to the cloud? 1992, do you remember And that is going to alter in the move to the cloud. That's the main barrier being developed in the cloud. like the digital economy, Get out of the middle man. covers to make all this happen? And all the way from the That's part of it. the people going to into the cloud, I get that. I mean, you know, there You look at the numbers. It tells the story. and in order to get that agility, going to happen with AWS? of the higher level stuff, does the cloud get commoditized? a lot of the CSPs have, that is going to get How pure is the lone survivor in Flash? and abstract the way 'cause all the margin went to Intel. But the cloud guys, you capture the workload. of the super cloud, this And that's the opportunity that they're getting. It's the app teams that say, to support four clouds the decision was made without them, Did you see the Berkeley paper, No, and that you think really that the infrastructure guy's That is the business. coming down the pike from Aviatrix? It's not about connecting the cloud to And Amazon's going to And that means, end to end. Because they want and any of the keep Arista, the Cisco, But you'll take the data in. And he who wins the core, clouds going to win everywhere. You got to come back, there's so, we've- This is new for you guys. the leader in live enterprise

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Anand Birje & Prabhakar Appana, HCLTech | AWS re:Invent 2022


 

>>Hey everyone. Welcome back to Las Vegas. The cube is live at the Venetian Expo Center for AWS Reinvent 2022. There are thousands and thousands and thousands of people here joining myself, Lisa Martin at Dave Valante. David, it's great to see the energy of day one alone. People are back, they're ready to be back. They're ready to hear from AWS and what it's gonna announce to. >>Yeah, all through the pandemic. Of course, we've talked about digital transformation, but the conversation is evolving beyond that to business transformation now, deeper integration of the cloud to really transform fundamental business operations and And that's a new era. >>It is a new era. It's exciting. We've got a couple of guests that we're gonna unpack that with. Anan. Beji joins us, the President Digital Business Services at HCL Tech and Prar, SVP and Global head of AWS business unit. Also from HCL Tech. Guys, welcome. Thank >>You. Thank you, >>Thank you. >>Let's talk about some of the latest trends anon. We'll start with you. What are some of the latest trends in digitalization, especially as it relates to cloud adoption? What are you hearing out in the marketplace? >>Yeah, I think you said it right. The post pandemic, every industry, every enterprise and every industry realize that for resilience, for their ability to change and adapt change and their ability to increase, you know, velocity of change so that they can move fast and keep up the expectations of their consumers, their partners, their employees, they need to have composability at the core and resilience at the core. And so, digital transformation became all about the ability to change, an ability to pivot faster. Now, it's easier said than done, right? Larger enterprises, especially as you move into complex regulated industries, you know, oil and gas, manufacturing, life sciences, healthcare, utilities, these are industries that are not easy to change. They're not adaptable to change, and yet they had to really become more adaptable. And they saw cloud as an enabler to, to all of that, right? So they started looking at every area of their business, business processes that make up their value chains and really look at how can they increase the adaptability and the ability to change these value chains so that they can engage with their customers better, their partners, better their employees better, and also build some of the composability. >>And what might mean that is that just kind of like Lego blocks, they don't have to make changes that are sweeping and big that are difficult to make, but make them in parts so that they can make them again and again. So velocity of change becomes important. Clouds become an enabler to all of this. And so if I look at the last four years, every industry, whether regulated or not b2c, B2B to C, B2B is adopting cloud for digital acceleration. >>I'm curious to what you're seeing on the front lines, given the macro headwinds. You mentioned business resilience and during the pandemic, it was a lot of CIOs told us, wow, we were, we were kind of focused on disaster recovery, but our business wasn't resilient. We were really optimizing for efficiency. And then they started to okay, build in that business resilience. But now you got the economic headwinds. Yes. People are tapping their brakes a little bit. There's some uncertainty, a longer sales cycle, even the cloud's not immune. Yeah. Even though it's still growing at 30% plus per year. What are you guys seeing in the field with the AWS partnership? How are customers, you know, dealing with some of those more strategic transformation projects? Yeah, >>Yeah. So you know, first off, one thing that's changed and is different is every industry realizes that there is no choice. They don't have a choice to not be resilient. They don't have a choice to not be adaptable. The pandemic has taught them that the markets and the macros are increasingly changing supply chains. It's changing customer behavior for their own industries. It's changing their pricing and their cost models. And for all of that, they need to continue on their digital journeys. Now, what's different though is they wanna prioritize. They wanna prioritize and do more with less. They want to adapt faster, but also make sure that they don't, they don't just try to do everything together. And so there's a lot of focus on what do we prioritize? How do we leverage cloud to move faster, you know, and cheaper in terms of our change. >>And also to decide where do we consume and where do we compose? We'll talk a little bit more about that. There are certain things that you don't want to invent yourself. You can consume from cloud providers, whether it's business features, whether it is cloud capabilities. And so it's, there is a shift from adopting cloud just for cost takeout and just for resilience, but also for composability, which means let's consume what I can consume from the cloud and really build those features faster. So squeeze the go to market time, squeeze the time to market and squeeze the price to market, right? So that's the >>Change and really driving those business outcomes. As we talked about Absolut ard, talk to us about how hcl tech and AWS are working together. How are you enabling customers to achieve what an was talking about? >>Oh, absolutely. I mean, our partnership has started almost 10 years back, but over the last one year, we have created what we call as AWS dedicated business unit to look at end to end stock from an AWS perspective. So what we see in the market as a explained is more drive from clients for optimization, driving, app modernization, driving consolidation, looking at the cost, sustainability angles, looking at the IOT angle, manufacturing platforms, the industry adoption. All this is actually igniting the way the industry would look at AWS and as well as the partnership. So from an HCL tech and AWS partnership, we're actually accelerating most of these conversations by building bespoke accelerated industry solutions. So what I mean is, for example, there is an issue with a manufacturing plant and take Covid situation, people can't get into a a manufacturing plant. So how can AWS help put it in the cloud, accelerate those conversations. So we are building those industry specific solutions so that it can be everybody from a manufacturing sector can adopt and actually go to market. As well as you can access all this applications once it is in the cloud from anywhere, any device with a scalable options. That's where our partnership is actually igniting lot of cloud conversations and playing conversations in the market. So we see a lot of traction there. Lisa, on >>That, incredibly important during the last couple of years alone. >>Absolutely. I mean, last couple of years have been groundbreaking, right? Especially with the covid, for example, Amazon Connect, we use, we used Amazon Connect to roll out, you know, call center at the cloud, right? So you don't have to walk into an office, for example. People are working in the banking sector, especially in the trading platform. They were, they were not able to get there. So, but they need to make calls. How do you do the customer service? So Amazon Connect came right at the junction, so call center in the cloud and you can access, dial the number so the customer don't feel the pain of, you know, somebody not answering. It's accessible. That's where the partnership or the HCL tech partnership and AWS comes into play because we bring the scale, the skill set capability with the services of, you know, aws, Amazon, and that forms a concrete story for the client, right? That's one such example. And you know, many such examples are in the market that we are accelerating in the, in the discussions. >>And connect is a good example. Lisa, we were talking earlier about Amazon doubling down on the primitives, but also moving up up market as well, up chain up the value chain. And it needs partners like HCL to be able to go into various industries and apply that effectively. Absolutely. And that's where business transformation comes >>In. Absolutely. Absolutely. I think some of the aspects that we are looking at is, you know, while we do most of this cloud transformation initiatives from an tech perspective, what we are doing is we are encompassing them into a story, which we call it as cloud smart, right? So we are calling it as cloud smart, which is a go-to market offering from Atcl Tech, where the client doesn't have to look at each of these services from various vendors. So it's a one stop shop, right? From strategy consulting, look, implementation, underpinned by app modernization, consolidation, and the operational. So we do that as end to end service with our offerings, which is why helping us actually accelerate conversations on the crowd. What happen is the clients are also building these capabilities more and more often. You see a lot of new services are being added to aws, so not many clients are aware of it. So it is the responsibility of system integrator like us to make them aware and bring it into a shape where the client can consume in a low cost option, in an optimized way. That's where I think it's, it's, it's working out very well for us. With the partnership of, so >>You curate those services that you know will fit the customer's business. You, you know, the ingredients that you could put together, the, the dinner. >>Absolutely. You're preparing a dish, right? So you're preparing a dish, you know where the ingredients are. So the ingredients are supplied by aws. So you need to prepare a pasta dish, right? So you, you how spicy you want to make it howland, you want to make it, you know what source you want to use. How do you bring all those elements together? That's what, you know, tech has been focusing on. >>And you use the word curation, right? Curation is really industry process down, depending on your industry, every industry, every enterprise, there are things that are differentiating them. There's a business processes that differentiate you and there are business processes that don't necessarily differentiate you but are core to you. For example, if you're a retailer, you know, you're retailing, you're merchandising, how you price your products, how you market your products, your supply chains, those differentiate you. How you run your general ledger, your accounting, your payables. HR is core to your business but doesn't differentiate you. And the choices you make in the cloud for each of these areas are different. What differentiates you? You compose what doesn't differentiate you consume because you don't want to try and compose what >>Telco Exactly. Oh my gosh. >>Our biggest examples are in Telco, right? Right. Their omnichannel marketing, you know, how they connect with their consumers, how they do their billing systems, how they do their pricing systems. Those are their differentiations and things that don't they want to consume. And that's where cloud adoption needs to come with really a curation framework. We call it the Phoenix framework, which defines what differentiates you versus not. And based on that, what are the architectural choices you make at the applications layer, the integration layer, the data layer, and the infrastructure layer all from aws and how do you make those choices? >>Talk about a customer example anon that really articulates that value. >>Yeah, I'll give you an example that sort of, everybody can relate to a very large tools company that manufactures tools that we all use at home for, you know, remodeling our houses, building stuff, building furniture. Their business post pandemic dramatically shifted in every way possible. Nobody was going anymore to Home Depot and Lowe's to buy their tools, their online business surge by 200%. Their supply chains were changing because their manufacturers originally were in China and Malaysia. They were shifting a lot of that base to Taiwan and Germany and Latin America. Their pricing model was changing. Their last mile deliveries were changing cuz they were not used to delivering you and me last mile deliveries. So every aspect of their business was changing. They hadn't thought of their business in the same way, but guess what? That business was growing, but the needs were changing and they needed to rethink every value chain in their business. >>And so they had to adopt cloud. They leverage AWS at their core to rethink every part of their business. Rebuilding their supply chain applications, modernizing their warehouse management systems, modernizing their pricing systems, modernizing their sales and marketing platforms, every aspect you can think of and all of that within 24 months. Cuz otherwise they would lose market share, you know, in any given market. And all of this, while they were, you know, delivering their day to day business, they were manufacturing the goods and they were shipping products. So that was quite a lot to achieve in 24 months. And that's not just one example is across industries, examples like that that we have. That's >>One of the best business transformation examples I think I've heard. >>Absolutely. Absolutely. And so cloud does need to start with a business transformation objective. And that's what's happening to the cloud. It's changing away from an infrastructure consolidation discussion to business task. >>Because I know you guys have a theater session tomorrow on, on continuous modern, it was experiencing cloud transformation and continuous modernization. That's the theme. Pre-cloud. It was just a, you'd, you'd live, you'd rip and replace your infrastructure and it was a big application portfolio assessment and rationalization. It was just, it just became this years long, you know, like an SAP installation. Yes. How has cloud changed that and what's, tell us more about that session and that continuous modernization. Yeah, >>So, so we are doing a John session with a client on how HCL Tech helped the client in terms of transforming the landscape and adopting cloud much faster, you know, into the ecosystem. So what we are currently doing is, so it's a continuous process. So when we talk about cloud adoption transformation, it doesn't stop there. So it, it needs to keep evolving. So what we came up with a framework for the all such clients who are on the cloud transformation part need to look at which we call it a smart waste cloud, cloud smart. Where once it is in the clouds, smart waste to cloud for cloud and in the cloud. So what happens is, when it is to cloud, what do you do? What are the accelerators? What are the frameworks? Smart waste for clouds? How do you look at the governance of it? >>Okay? Consolidation activities of it, once it is in the cloud, how do we optimize, what do you look at? Security aspects, et cetera. So the client doesn't have to go to multiple ecosystem partners to look at it. So he is looking at one such service provider who can actually encompass and give all this onto the plate in a much more granular fashion with accelerated approach. So we build accelerated solutions frameworks, which helps the client to actually pick and choose in a much lower cost, I think. And it has to be a continuous modernization for the client. So why we are calling it as a continuous modernization is we are also also creating what we call cloud foundries and factories. What happens is the client can look at not only in a transformation journey, but also futuristic when there are new services are adapted, how this transformation and factories helping them in a lower cost option and driving that a acceleration story. So we are addressing it in multiple ways. One on the transformation front, one on the TCO front, one on the AX accelerated front, one on the operational front. So all this combined into one single framework, which is what is a continuous modernization of clouded option from xgl tech. >>When you apply this framework with customers, how do you deal with technical debt? Can you avoid technical debt? Can you hide technical debt? Or is it like debt and taxes? We're always gonna have technical debt because Amazon, you know, they'll talk about, they don't ever deprecate anything. Yeah. You know, are they gonna, are we gonna see Amazon take on tech? How do you avoid that? Or at least shield the customer for that technical debt. >>So every cio, right? Key ambitions are digital cloud, TCO optimization, sustainability. So we have a framework for that. So every CIO will look at, okay, I wanna spend, but I want to be optimized. My TCO should not go up. So that's where a system integrator like us comes. We have AOP story where, which does the complete financial analysis of your cloud adoption as to what estate and what technical client already has. How can we optimize that and how can we, how can we overlay on top of that our own services to make it much more optimized solution for the client? And there are several frameworks that we have defined for the CIO organizations where the CIO can actually look at some of these elements and adopt it internally within the system. You wanna pick it from there? >>Yeah, I think, I think it's, it's, it's a great question. First of all, there's a generational shift in the last three years where nobody's doing lift and shift of traditional applications or traditional data systems to the cloud. As you said, nobody's taking their technical debt to the cloud anymore. >>Business value's not there. >>There's no business value, right? The value is really being cloud native, which means you want to continuously modernize your value chains, which means your applications, your integration, your data to leverage the cloud and continuously modernize. Now you will still make priority decisions, right? Things that really differentiate you. You will modernize them through composition things that don't, you'll rather consume them, but in both factors, you're modernizing, I use the word surround and drown enterprises are surrounding their traditional, you know, environments and drowning them over a period of time. So over the next five years, you'll see more and more irrelevant legacy because the relevance is being built in the cloud, cloud for the future. That's the way I see it. >>Speaking of, take us out here, speaking of business value and on, we're almost outta time here. If there's a billboard on 1 0 1 in Palo Alto regarding HCL tech, what's the value prop? What does it say? >>It's a simple billboard. We say we are super charging our customers, our partners, our employees. We are super charging progress. And we believe that the strength that we bring from learnings of over 200,000 professionals that work at hcl working with over half of, you know, 500 of the, the largest Fortune thousands in the world is, is really bringing those learnings that we continuously look at every day that we live with, every day across all kind of regulations, all kind of industries, in adopting new technologies, in modernizing their business strategies and achieving their business transformation goals with the velocity they want. That's kind of the supercharging progress mantra, >>Super charging progress. Love it. Guys, thank you so much for joining. David, me on the program talking about, thank you for having a conversation. Our pleasure. What's going on with HCL Tech, aws, the value that you're delivering for customers. Thank you so much for your time. Thank >>You. Thank you. Thanks. Have a great time. >>Take care for our guests. I'm Lisa Martin, he's Dave Valante. You're watching The Cube, the leader in live enterprise and emerging tech coverage.

Published Date : Nov 29 2022

SUMMARY :

The cube is live at the Venetian Expo Center for AWS beyond that to business transformation now, deeper integration of the cloud to really transform We've got a couple of guests that we're gonna unpack that with. What are you hearing out in the marketplace? and their ability to increase, you know, velocity of change so that they can move fast and keep And so if I look at the last four years, every industry, How are customers, you know, dealing with some of those more And for all of that, they need to continue on their digital journeys. So squeeze the go to market How are you enabling customers to achieve what an was talking about? once it is in the cloud from anywhere, any device with a scalable options. so call center in the cloud and you can access, dial the number so the customer don't And it needs partners like HCL to be able to go into various industries and apply that effectively. So it is the responsibility of system integrator like us to make them You, you know, the ingredients that you could put together, the, the dinner. So you need to prepare a pasta dish, And the choices you make in the cloud for each of these We call it the Phoenix framework, which defines what differentiates you versus not. company that manufactures tools that we all use at home for, you know, remodeling our houses, And all of this, while they were, you know, And so cloud does need to start with a business transformation objective. you know, like an SAP installation. So what happens is, when it is to cloud, what do you do? So the client doesn't have to go to multiple We're always gonna have technical debt because Amazon, you know, they'll talk about, they don't ever deprecate anything. So we have a framework for that. As you said, nobody's taking their technical debt to the cloud anymore. So over the next five years, you'll see more What does it say? the strength that we bring from learnings of over 200,000 professionals that work at Thank you so much for your time. Have a great time. the leader in live enterprise and emerging tech coverage.

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Evan Kaplan, InfluxData | AWS re:invent 2022


 

>>Hey everyone. Welcome to Las Vegas. The Cube is here, live at the Venetian Expo Center for AWS Reinvent 2022. Amazing attendance. This is day one of our coverage. Lisa Martin here with Day Ante. David is great to see so many people back. We're gonna be talk, we've been having great conversations already. We have a wall to wall coverage for the next three and a half days. When we talk to companies, customers, every company has to be a data company. And one of the things I think we learned in the pandemic is that access to real time data and real time analytics, no longer a nice to have that is a differentiator and a competitive all >>About data. I mean, you know, I love the topic and it's, it's got so many dimensions and such texture, can't get enough of data. >>I know we have a great guest joining us. One of our alumni is back, Evan Kaplan, the CEO of Influx Data. Evan, thank you so much for joining us. Welcome back to the Cube. >>Thanks for having me. It's great to be here. So here >>We are, day one. I was telling you before we went live, we're nice and fresh hosts. Talk to us about what's new at Influxed since the last time we saw you at Reinvent. >>That's great. So first of all, we should acknowledge what's going on here. This is pretty exciting. Yeah, that does really feel like, I know there was a show last year, but this feels like the first post Covid shows a lot of energy, a lot of attention despite a difficult economy. In terms of, you know, you guys were commenting in the lead into Big data. I think, you know, if we were to talk about Big Data five, six years ago, what would we be talking about? We'd been talking about Hadoop, we were talking about Cloudera, we were talking about Hortonworks, we were talking about Big Data Lakes, data stores. I think what's happened is, is this this interesting dynamic of, let's call it if you will, the, the secularization of data in which it breaks into different fields, different, almost a taxonomy. You've got this set of search data, you've got this observability data, you've got graph data, you've got document data and what you're seeing in the market and now you have time series data. >>And what you're seeing in the market is this incredible capability by developers as well and mostly open source dynamic driving this, this incredible capability of developers to assemble data platforms that aren't unicellular, that aren't just built on Hado or Oracle or Postgres or MySQL, but in fact represent different data types. So for us, what we care about his time series, we care about anything that happens in time, where time can be the primary measurement, which if you think about it, is a huge proportion of real data. Cuz when you think about what drives ai, you think about what happened, what happened, what happened, what happened, what's going to happen. That's the functional thing. But what happened is always defined by a period, a measurement, a time. And so what's new for us is we've developed this new open source engine called IOx. And so it's basically a refresh of the whole database, a kilo database that uses Apache Arrow, par K and data fusion and turns it into a super powerful real time analytics platform. It was already pretty real time before, but it's increasingly now and it adds SQL capability and infinite cardinality. And so it handles bigger data sets, but importantly, not just bigger but faster, faster data. So that's primarily what we're talking about to show. >>So how does that affect where you can play in the marketplace? Is it, I mean, how does it affect your total available market? Your great question. Your, your customer opportunities. >>I think it's, it's really an interesting market in that you've got all of these different approaches to database. Whether you take data warehouses from Snowflake or, or arguably data bricks also. And you take these individual database companies like Mongo Influx, Neo Forge, elastic, and people like that. I think the commonality you see across the volume is, is many of 'em, if not all of them, are based on some sort of open source dynamic. So I think that is an in an untractable trend that will continue for on. But in terms of the broader, the broader database market, our total expand, total available tam, lots of these things are coming together in interesting ways. And so the, the, the wave that will ride that we wanna ride, because it's all big data and it's all increasingly fast data and it's all machine learning and AI is really around that measurement issue. That instrumentation the idea that if you're gonna build any sophisticated system, it starts with instrumentation and the journey is defined by instrumentation. So we view ourselves as that instrumentation tooling for understanding complex systems. And how, >>I have to follow quick follow up. Why did you say arguably data bricks? I mean open source ethos? >>Well, I was saying arguably data bricks cuz Spark, I mean it's a great company and it's based on Spark, but there's quite a gap between Spark and what Data Bricks is today. And in some ways data bricks from the outside looking in looks a lot like Snowflake to me looks a lot like a really sophisticated data warehouse with a lot of post-processing capabilities >>And, and with an open source less >>Than a >>Core database. Yeah. Right, right, right. Yeah, I totally agree. Okay, thank you for that >>Part that that was not arguably like they're, they're not a good company or >>No, no. They got great momentum and I'm just curious. Absolutely. You know, so, >>So talk a little bit about IOx and, and what it is enabling you guys to achieve from a competitive advantage perspective. The key differentiators give us that scoop. >>So if you think about, so our old storage engine was called tsm, also open sourced, right? And IOx is open sourced and the old storage engine was really built around this time series measurements, particularly metrics, lots of metrics and handling those at scale and making it super easy for developers to use. But, but our old data engine only supported either a custom graphical UI that you'd build yourself on top of it or a dashboarding tool like Grafana or Chronograph or things like that. With IOCs. Two or three interventions were important. One is we now support, we'll support things like Tableau, Microsoft, bi, and so you're taking that same data that was available for instrumentation and now you're using it for business intelligence also. So that became super important and it kind of answers your question about the expanded market expands the market. The second thing is, when you're dealing with time series data, you're dealing with this concept of cardinality, which is, and I don't know if you're familiar with it, but the idea that that it's a multiplication of measurements in a table. And so the more measurements you want over the more series you have, you have this really expanding exponential set that can choke a database off. And the way we've designed IIS to handle what we call infinite cardinality, where you don't even have to think about that design point of view. And then lastly, it's just query performance is dramatically better. And so it's pretty exciting. >>So the unlimited cardinality, basically you could identify relationships between data and different databases. Is that right? Between >>The same database but different measurements, different tables, yeah. Yeah. Right. Yeah, yeah. So you can handle, so you could say, I wanna look at the way, the way the noise levels are performed in this room according to 400 different locations on 25 different days, over seven months of the year. And that each one is a measurement. Each one adds to cardinality. And you can say, I wanna search on Tuesdays in December, what the noise level is at 2:21 PM and you get a very quick response. That kind of instrumentation is critical to smarter systems. How are >>You able to process that data at at, in a performance level that doesn't bring the database to its knees? What's the secret sauce behind that? >>It's AUM database. It's built on Parque and Apache Arrow. But it's, but to say it's nice to say without a much longer conversation, it's an architecture that's really built for pulling that kind of data. If you know the data is time series and you're looking for a time measurement, you already have the ability to optimize pretty dramatically. >>So it's, it's that purpose built aspect of it. It's the >>Purpose built aspect. You couldn't take Postgres and do the same >>Thing. Right? Because a lot of vendors say, oh yeah, we have time series now. Yeah. Right. So yeah. Yeah. Right. >>And they >>Do. Yeah. But >>It's not, it's not, the founding of the company came because Paul Dicks was working on Wall Street building time series databases on H base, on MyQ, on other platforms and realize every time we do it, we have to rewrite the code. We build a bunch of application logic to handle all these. We're talking about, we have customers that are adding hundreds of millions to billions of points a second. So you're talking about an ingest level. You know, you think about all those data points, you're talking about ingest level that just doesn't, you know, it just databases aren't designed for that. Right? And so it's not just us, our competitors also build good time series databases. And so the category is really emergent. Yeah, >>Sure. Talk about a favorite customer story they think really articulates the value of what Influx is doing, especially with IOx. >>Yeah, sure. And I love this, I love this story because you know, Tesla may not be in favor because of the latest Elon Musker aids, but, but, but so we've had about a four year relationship with Tesla where they built their power wall technology around recording that, seeing your device, seeing the stuff, seeing the charging on your car. It's all captured in influx databases that are reporting from power walls and mega power packs all over the world. And they report to a central place at, at, at Tesla's headquarters and it reports out to your phone and so you can see it. And what's really cool about this to me is I've got two Tesla cars and I've got a Tesla solar roof tiles. So I watch this date all the time. So it's a great customer story. And actually if you go on our website, you can see I did an hour interview with the engineer that designed the system cuz the system is super impressive and I just think it's really cool. Plus it's, you know, it's all the good green stuff that we really appreciate supporting sustainability, right? Yeah. >>Right, right. Talk about from a, what's in it for me as a customer, what you guys have done, the change to IOCs, what, what are some of the key features of it and the key values in it for customers like Tesla, like other industry customers as well? >>Well, so it's relatively new. It just arrived in our cloud product. So Tesla's not using it today. We have a first set of customers starting to use it. We, the, it's in open source. So it's a very popular project in the open source world. But the key issues are, are really the stuff that we've kind of covered here, which is that a broad SQL environment. So accessing all those SQL developers, the same people who code against Snowflake's data warehouse or data bricks or Postgres, can now can code that data against influx, open up the BI market. It's the cardinality, it's the performance. It's really an architecture. It's the next gen. We've been doing this for six years, it's the next generation of everything. We've seen how you make time series be super performing. And that's only relevant because more and more things are becoming real time as we develop smarter and smarter systems. The journey is pretty clear. You instrument the system, you, you let it run, you watch for anomalies, you correct those anomalies, you re instrument the system. You do that 4 billion times, you have a self-driving car, you do that 55 times, you have a better podcast that is, that is handling its audio better, right? So everything is on that journey of getting smarter and smarter. So >>You guys, you guys the big committers to IOCs, right? Yes. And how, talk about how you support the, develop the surrounding developer community, how you get that flywheel effect going >>First. I mean it's actually actually a really kind of, let's call it, it's more art than science. Yeah. First of all, you you, you come up with an architecture that really resonates for developers. And Paul Ds our founder, really is a developer's developer. And so he started talking about this in the community about an architecture that uses Apache Arrow Parque, which is, you know, the standard now becoming for file formats that uses Apache Arrow for directing queries and things like that and uses data fusion and said what this thing needs is a Columbia database that sits behind all of this stuff and integrates it. And he started talking about it two years ago and then he started publishing in IOCs that commits in the, in GitHub commits. And slowly, but over time in Hacker News and other, and other people go, oh yeah, this is fundamentally right. >>It addresses the problems that people have with things like click cows or plain databases or Coast and they go, okay, this is the right architecture at the right time. Not different than original influx, not different than what Elastic hit on, not different than what Confluent with Kafka hit on and their time is you build an audience of people who are committed to understanding this kind of stuff and they become committers and they become the core. Yeah. And you build out from it. And so super. And so we chose to have an MIT open source license. Yeah. It's not some secondary license competitors can use it and, and competitors can use it against us. Yeah. >>One of the things I know that Influx data talks about is the time to awesome, which I love that, but what does that mean? What is the time to Awesome. Yeah. For developer, >>It comes from that original story where, where Paul would have to write six months of application logic and stuff to build a time series based applications. And so Paul's notion was, and this was based on the original Mongo, which was very successful because it was very easy to use relative to most databases. So Paul developed this commitment, this idea that I quickly joined on, which was, hey, it should be relatively quickly for a developer to build something of import to solve a problem, it should be able to happen very quickly. So it's got a schemaless background so you don't have to know the schema beforehand. It does some things that make it really easy to feel powerful as a developer quickly. And if you think about that journey, if you feel powerful with a tool quickly, then you'll go deeper and deeper and deeper and pretty soon you're taking that tool with you wherever you go, it becomes the tool of choice as you go to that next job or you go to that next application. And so that's a fundamental way we think about it. To be honest with you, we haven't always delivered perfectly on that. It's generally in our dna. So we do pretty well, but I always feel like we can do better. >>So if you were to put a bumper sticker on one of your Teslas about influx data, what would it >>Say? By the way, I'm not rich. It just happened to be that we have two Teslas and we have for a while, we just committed to that. The, the, so ask the question again. Sorry. >>Bumper sticker on influx data. What would it say? How, how would I >>Understand it be time to Awesome. It would be that that phrase his time to Awesome. Right. >>Love that. >>Yeah, I'd love it. >>Excellent time to. Awesome. Evan, thank you so much for joining David, the >>Program. It's really fun. Great thing >>On Evan. Great to, you're on. Haven't Well, great to have you back talking about what you guys are doing and helping organizations like Tesla and others really transform their businesses, which is all about business transformation these days. We appreciate your insights. >>That's great. Thank >>You for our guest and Dave Ante. I'm Lisa Martin, you're watching The Cube, the leader in emerging and enterprise tech coverage. We'll be right back with our next guest.

Published Date : Nov 29 2022

SUMMARY :

And one of the things I think we learned in the pandemic is that access to real time data and real time analytics, I mean, you know, I love the topic and it's, it's got so many dimensions and such Evan, thank you so much for joining us. It's great to be here. Influxed since the last time we saw you at Reinvent. terms of, you know, you guys were commenting in the lead into Big data. And so it's basically a refresh of the whole database, a kilo database that uses So how does that affect where you can play in the marketplace? And you take these individual database companies like Mongo Influx, Why did you say arguably data bricks? And in some ways data bricks from the outside looking in looks a lot like Snowflake to me looks a lot Okay, thank you for that You know, so, So talk a little bit about IOx and, and what it is enabling you guys to achieve from a And the way we've designed IIS to handle what we call infinite cardinality, where you don't even have to So the unlimited cardinality, basically you could identify relationships between data And you can say, time measurement, you already have the ability to optimize pretty dramatically. So it's, it's that purpose built aspect of it. You couldn't take Postgres and do the same So yeah. And so the category is really emergent. especially with IOx. And I love this, I love this story because you know, what you guys have done, the change to IOCs, what, what are some of the key features of it and the key values in it for customers you have a self-driving car, you do that 55 times, you have a better podcast that And how, talk about how you support architecture that uses Apache Arrow Parque, which is, you know, the standard now becoming for file And you build out from it. One of the things I know that Influx data talks about is the time to awesome, which I love that, So it's got a schemaless background so you don't have to know the schema beforehand. It just happened to be that we have two Teslas and we have for a while, What would it say? Understand it be time to Awesome. Evan, thank you so much for joining David, the Great thing Haven't Well, great to have you back talking about what you guys are doing and helping organizations like Tesla and others really That's great. You for our guest and Dave Ante.

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Dhabaleswar “DK” Panda, Ohio State State University | SuperComputing 22


 

>>Welcome back to The Cube's coverage of Supercomputing Conference 2022, otherwise known as SC 22 here in Dallas, Texas. This is day three of our coverage, the final day of coverage here on the exhibition floor. I'm Dave Nicholson, and I'm here with my co-host, tech journalist extraordinaire, Paul Gillum. How's it going, >>Paul? Hi, Dave. It's going good. >>And we have a wonderful guest with us this morning, Dr. Panda from the Ohio State University. Welcome Dr. Panda to the Cube. >>Thanks a lot. Thanks a lot to >>Paul. I know you're, you're chopping at >>The bit, you have incredible credentials, over 500 papers published. The, the impact that you've had on HPC is truly remarkable. But I wanted to talk to you specifically about a product project you've been working on for over 20 years now called mva, high Performance Computing platform that's used by more than 32 organ, 3,200 organizations across 90 countries. You've shepherded this from, its, its infancy. What is the vision for what MVA will be and and how is it a proof of concept that others can learn from? >>Yeah, Paul, that's a great question to start with. I mean, I, I started with this conference in 2001. That was the first time I came. It's very coincidental. If you remember the Finman Networking Technology, it was introduced in October of 2000. Okay. So in my group, we were working on NPI for Marinette Quadrics. Those are the old technology, if you can recollect when Finman was there, we were the very first one in the world to really jump in. Nobody knew how to use Infin van in an HPC system. So that's how the Happy Project was born. And in fact, in super computing 2002 on this exhibition floor in Baltimore, we had the first demonstration, the open source happy, actually is running on an eight node infinite van clusters, eight no zeros. And that was a big challenge. But now over the years, I means we have continuously worked with all infinite van vendors, MPI Forum. >>We are a member of the MPI Forum and also all other network interconnect. So we have steadily evolved this project over the last 21 years. I'm very proud of my team members working nonstop, continuously bringing not only performance, but scalability. If you see now INFIN event are being deployed in 8,000, 10,000 node clusters, and many of these clusters actually use our software, stack them rapid. So, so we have done a lot of, like our focuses, like we first do research because we are in academia. We come up with good designs, we publish, and in six to nine months, we actually bring it to the open source version and people can just download and then use it. And that's how currently it's been used by more than 3000 orange in 90 countries. And, but the interesting thing is happening, your second part of the question. Now, as you know, the field is moving into not just hvc, but ai, big data, and we have those support. This is where like we look at the vision for the next 20 years, we want to design this MPI library so that not only HPC but also all other workloads can take advantage of it. >>Oh, we have seen libraries that become a critical develop platform supporting ai, TensorFlow, and, and the pie torch and, and the emergence of, of, of some sort of default languages that are, that are driving the community. How, how important are these frameworks to the, the development of the progress making progress in the HPC world? >>Yeah, no, those are great. I mean, spite our stencil flow, I mean, those are the, the now the bread and butter of deep learning machine learning. Am I right? But the challenge is that people use these frameworks, but continuously models are becoming larger. You need very first turnaround time. So how do you train faster? How do you do influencing faster? So this is where HPC comes in and what exactly what we have done is actually we have linked floor fighters to our happy page because now you see the MPI library is running on a million core system. Now your fighters and tenor four clan also be scaled to to, to those number of, large number of course and gps. So we have actually done that kind of a tight coupling and that helps the research to really take advantage of hpc. >>So if, if a high school student is thinking in terms of interesting computer science, looking for a place, looking for a university, Ohio State University, bruns, world renowned, widely known, but talk about what that looks like from a day on a day to day basis in terms of the opportunity for undergrad and graduate students to participate in, in the kind of work that you do. What is, what does that look like? And is, and is that, and is that a good pitch to for, for people to consider the university? >>Yes. I mean, we continuously, from a university perspective, by the way, the Ohio State University is one of the largest single campus in, in us, one of the top three, top four. We have 65,000 students. Wow. It's one of the very largest campus. And especially within computer science where I am located, high performance computing is a very big focus. And we are one of the, again, the top schools all over the world for high performance computing. And we also have very strength in ai. So we always encourage, like the new students who like to really work on top of the art solutions, get exposed to the concepts, principles, and also practice. Okay. So, so we encourage those people that wish you can really bring you those kind of experience. And many of my past students, staff, they're all in top companies now, have become all big managers. >>How, how long, how long did you say you've been >>At 31 >>Years? 31 years. 31 years. So, so you, you've had people who weren't alive when you were already doing this stuff? That's correct. They then were born. Yes. They then grew up, yes. Went to university graduate school, and now they're on, >>Now they're in many top companies, national labs, all over the universities, all over the world. So they have been trained very well. Well, >>You've, you've touched a lot of lives, sir. >>Yes, thank you. Thank >>You. We've seen really a, a burgeoning of AI specific hardware emerge over the last five years or so. And, and architectures going beyond just CPUs and GPUs, but to Asics and f PGAs and, and accelerators, does this excite you? I mean, are there innovations that you're seeing in this area that you think have, have great promise? >>Yeah, there is a lot of promise. I think every time you see now supercomputing technology, you see there is sometime a big barrier comes barrier jump. Rather I'll say, new technology comes some disruptive technology, then you move to the next level. So that's what we are seeing now. A lot of these AI chips and AI systems are coming up, which takes you to the next level. But the bigger challenge is whether it is cost effective or not, can that be sustained longer? And this is where commodity technology comes in, which commodity technology tries to take you far longer. So we might see like all these likes, Gaudi, a lot of new chips are coming up, can they really bring down the cost? If that cost can be reduced, you will see a much more bigger push for AI solutions, which are cost effective. >>What, what about on the interconnect side of things, obvi, you, you, your, your start sort of coincided with the initial standards for Infin band, you know, Intel was very, very, was really big in that, in that architecture originally. Do you see interconnects like RDMA over converged ethernet playing a part in that sort of democratization or commoditization of things? Yes. Yes. What, what are your thoughts >>There for internet? No, this is a great thing. So, so we saw the infinite man coming. Of course, infinite Man is, commod is available. But then over the years people have been trying to see how those RDMA mechanisms can be used for ethernet. And then Rocky has been born. So Rocky has been also being deployed. But besides these, I mean now you talk about Slingshot, the gray slingshot, it is also an ethernet based systems. And a lot of those RMA principles are actually being used under the hood. Okay. So any modern networks you see, whether it is a Infin and Rocky Links art network, rock board network, you name any of these networks, they are using all the very latest principles. And of course everybody wants to make it commodity. And this is what you see on the, on the slow floor. Everybody's trying to compete against each other to give you the best performance with the lowest cost, and we'll see whoever wins over the years. >>Sort of a macroeconomic question, Japan, the US and China have been leapfrogging each other for a number of years in terms of the fastest supercomputer performance. How important do you think it is for the US to maintain leadership in this area? >>Big, big thing, significantly, right? We are saying that I think for the last five to seven years, I think we lost that lead. But now with the frontier being the number one, starting from the June ranking, I think we are getting that leadership back. And I think it is very critical not only for fundamental research, but for national security trying to really move the US to the leading edge. So I hope us will continue to lead the trend for the next few years until another new system comes out. >>And one of the gating factors, there is a shortage of people with data science skills. Obviously you're doing what you can at the university level. What do you think can change at the secondary school level to prepare students better to, for data science careers? >>Yeah, I mean that is also very important. I mean, we, we always call like a pipeline, you know, that means when PhD levels we are expecting like this even we want to students to get exposed to, to, to many of these concerts from the high school level. And, and things are actually changing. I mean, these days I see a lot of high school students, they, they know Python, how to program in Python, how to program in sea object oriented things. Even they're being exposed to AI at that level. So I think that is a very healthy sign. And in fact we, even from Ohio State side, we are always engaged with all this K to 12 in many different programs and then gradually trying to take them to the next level. And I think we need to accelerate also that in a very significant manner because we need those kind of a workforce. It is not just like a building a system number one, but how do we really utilize it? How do we utilize that science? How do we propagate that to the community? Then we need all these trained personal. So in fact in my group, we are also involved in a lot of cyber training activities for HPC professionals. So in fact, today there is a bar at 1 1 15 I, yeah, I think 1215 to one 15. We'll be talking more about that. >>About education. >>Yeah. Cyber training, how do we do for professionals? So we had a funding together with my co-pi, Dr. Karen Tom Cook from Ohio Super Center. We have a grant from NASA Science Foundation to really educate HPT professionals about cyber infrastructure and ai. Even though they work on some of these things, they don't have the complete knowledge. They don't get the time to, to learn. And the field is moving so fast. So this is how it has been. We got the initial funding, and in fact, the first time we advertised in 24 hours, we got 120 application, 24 hours. We couldn't even take all of them. So, so we are trying to offer that in multiple phases. So, so there is a big need for those kind of training sessions to take place. I also offer a lot of tutorials at all. Different conference. We had a high performance networking tutorial. Here we have a high performance deep learning tutorial, high performance, big data tutorial. So I've been offering tutorials at, even at this conference since 2001. Good. So, >>So in the last 31 years, the Ohio State University, as my friends remind me, it is properly >>Called, >>You've seen the world get a lot smaller. Yes. Because 31 years ago, Ohio, in this, you know, of roughly in the, in the middle of North America and the United States was not as connected as it was to everywhere else in the globe. So that's, that's pro that's, I i it kind of boggles the mind when you think of that progression over 31 years, but globally, and we talk about the world getting smaller, we're sort of in the thick of, of the celebratory seasons where, where many, many groups of people exchange gifts for varieties of reasons. If I were to offer you a holiday gift, that is the result of what AI can deliver the world. Yes. What would that be? What would, what would, what would the first thing be? This is, this is, this is like, it's, it's like the genie, but you only get one wish. >>I know, I know. >>So what would the first one be? >>Yeah, it's very hard to answer one way, but let me bring a little bit different context and I can answer this. I, I talked about the happy project and all, but recently last year actually we got awarded an S f I institute award. It's a 20 million award. I am the overall pi, but there are 14 universities involved. >>And who is that in that institute? >>What does that Oh, the I ici. C e. Okay. I cycle. You can just do I cycle.ai. Okay. And that lies with what exactly what you are trying to do, how to bring lot of AI for masses, democratizing ai. That's what is the overall goal of this, this institute, think of like a, we have three verticals we are working think of like one is digital agriculture. So I'll be, that will be my like the first ways. How do you take HPC and AI to agriculture the world as though we just crossed 8 billion people. Yeah, that's right. We need continuous food and food security. How do we grow food with the lowest cost and with the highest yield? >>Water >>Consumption. Water consumption. Can we minimize or minimize the water consumption or the fertilization? Don't do blindly. Technologies are out there. Like, let's say there is a weak field, A traditional farmer see that, yeah, there is some disease, they will just go and spray pesticides. It is not good for the environment. Now I can fly it drone, get images of the field in the real time, check it against the models, and then it'll tell that, okay, this part of the field has disease. One, this part of the field has disease. Two, I indicate to the, to the tractor or the sprayer saying, okay, spray only pesticide one, you have pesticide two here. That has a big impact. So this is what we are developing in that NSF A I institute I cycle ai. We also have, we have chosen two additional verticals. One is animal ecology, because that is very much related to wildlife conservation, climate change, how do you understand how the animals move? Can we learn from them? And then see how human beings need to act in future. And the third one is the food insecurity and logistics. Smart food distribution. So these are our three broad goals in that institute. How do we develop cyber infrastructure from below? Combining HP c AI security? We have, we have a large team, like as I said, there are 40 PIs there, 60 students. We are a hundred members team. We are working together. So, so that will be my wish. How do we really democratize ai? >>Fantastic. I think that's a great place to wrap the conversation here On day three at Supercomputing conference 2022 on the cube, it was an honor, Dr. Panda working tirelessly at the Ohio State University with his team for 31 years toiling in the field of computer science and the end result, improving the lives of everyone on Earth. That's not a stretch. If you're in high school thinking about a career in computer science, keep that in mind. It isn't just about the bits and the bobs and the speeds and the feeds. It's about serving humanity. Maybe, maybe a little, little, little too profound a statement, I would argue not even close. I'm Dave Nicholson with the Queue, with my cohost Paul Gillin. Thank you again, Dr. Panda. Stay tuned for more coverage from the Cube at Super Compute 2022 coming up shortly. >>Thanks a lot.

Published Date : Nov 17 2022

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Welcome back to The Cube's coverage of Supercomputing Conference 2022, And we have a wonderful guest with us this morning, Dr. Thanks a lot to But I wanted to talk to you specifically about a product project you've So in my group, we were working on NPI for So we have steadily evolved this project over the last 21 years. that are driving the community. So we have actually done that kind of a tight coupling and that helps the research And is, and is that, and is that a good pitch to for, So, so we encourage those people that wish you can really bring you those kind of experience. you were already doing this stuff? all over the world. Thank this area that you think have, have great promise? I think every time you see now supercomputing technology, with the initial standards for Infin band, you know, Intel was very, very, was really big in that, And this is what you see on the, Sort of a macroeconomic question, Japan, the US and China have been leapfrogging each other for a number the number one, starting from the June ranking, I think we are getting that leadership back. And one of the gating factors, there is a shortage of people with data science skills. And I think we need to accelerate also that in a very significant and in fact, the first time we advertised in 24 hours, we got 120 application, that's pro that's, I i it kind of boggles the mind when you think of that progression over 31 years, I am the overall pi, And that lies with what exactly what you are trying to do, to the tractor or the sprayer saying, okay, spray only pesticide one, you have pesticide two here. I think that's a great place to wrap the conversation here On

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Kelly Gaither, University of Texas | SuperComputing 22


 

>>Good afternoon everyone, and thank you so much for joining us. My name is Savannah Peterson, joined by my co-host Paul for the afternoon. Very excited. Oh, Savannah. Hello. I'm, I'm pumped for this. This is our first bit together. Exactly. >>It's gonna be fun. Yes. We have a great guest to kick off with. >>We absolutely do. We're at Supercomputing 2022 today, and very excited to talk to our next guest. We're gonna be talking about data at scale and data that really matters to us joining us. Kelly Gayer, thank you so much for being here and you are with tech. Tell everyone what TAC is. >>Tech is the Texas Advanced Computing Center at the University of Texas at Austin. And thank you so much for having me here. >>It is wonderful to have you. Your smile's contagious. And one of the themes that's come up a lot with all of our guests, and we just talked about it, is how good it is to be back in person, how good it is to be around our hardware, community tech. You did some very interesting research during the pandemic. Can you tell us about that? >>I can. I did. So when we realized sort of mid-March, we realized that, that this was really not normal times and the pandemic was statement. Yes. That pandemic was really gonna touch everyone. I think a lot of us at the center and me personally, we dropped everything to plug in and that's what we do. So UT's tagline is what starts here changes the world and tax tagline is powering discoveries that change the world. So we're all about impact, but I plugged in with the research group there at UT Austin, Dr. Lauren Myers, who's an epidemiologist, and just we figured out how to plug in and compute so that we could predict the spread of, of Covid 19. >>And you did that through the use of mobility data, cell phone signals. Tell us more about what exactly you were choreographing. >>Yeah, so that was really interesting. Safe graph during the pandemic made their mobility data. Typically it was used for marketing purposes to know who was going into Walmart. The offenses >>For advertising. >>Absolutely, yeah. They made all of their mobility data available for free to people who were doing research and plugging in trying to understand Covid. 19, I picked that data up and we used it as a proxy for human behavior. So we knew we had some idea, we got weekly mobility updates, but it was really mobility all day long, you know, anonymized. I didn't know who they were by cell phones across the US by census block group or zip code if we wanted to look at it that way. And we could see how people were moving around. We knew what their neighbor, their home neighborhoods were. We knew how they were traveling or not traveling. We knew where people were congregating, and we could get some idea of, of how people were behaving. Were they really, were they really locking down or were they moving in their neighborhoods or were they going outside of their neighborhoods? >>What a, what a fascinating window into our pandemic lives. So now that you were able to do this for this pandemic, as we look forward, what have you learned? How quickly could we forecast? What's the prognosis? >>Yeah, so we, we learned a tremendous amount. I think during the pandemic we were reacting, we were really trying. It was a, it was an interesting time as a scientist, we were reacting to things almost as if the earth was moving underneath us every single day. So it was something new every day. And I've told people since I've, I haven't, I haven't worked that hard since I was a graduate student. So it was really daylight to dark 24 7 for a long period of time because it was so important. And we knew, we, we knew we were, we were being a part of history and affecting something that was gonna make a difference for a really long time. And, and I think what we've learned is that indeed there is a lot of data being collected that we can use for good. We can really understand if we get organized and we get set up, we can use this data as a means of perhaps predicting our next pandemic or our next outbreak of whatever. It is almost like using it as a canary in the coal mine. There's a lot in human behavior we can use, given >>All the politicization of, of this last pandemic, knowing what we know now, making us better prepared in theory for the next one. How confident are you that at least in the US we will respond proactively and, and effectively when the next one comes around? >>Yeah, I mean, that's a, that's a great question and, and I certainly understand why you ask. I think in my experience as a scientist, certainly at tech, the more transparent you are with what you do and the more you explain things. Again, during the pandemic, things were shifting so rapidly we were reacting and doing the best that we could. And I think one thing we did right was we admitted where we felt uncertain. And that's important. You have to really be transparent to the general public. I, I don't know how well people are gonna react. I think if we have time to prepare, to communicate and always be really transparent about it. I think those are three factors that go into really increasing people's trust. >>I think you nailed it. And, and especially during times of chaos and disaster, you don't know who to trust or what to believe. And it sounds like, you know, providing a transparent source of truth is, is so critical. How do you protect the sensitive data that you're working with? I know it's a top priority for you and the team. >>It is, it is. And we, we've adopted the medical mantra, do no harm. So we have, we feel a great responsibility there. There's, you know, two things that you have to really keep in mind when you've got sensitive data. One is the physical protection of it. And so that's, that's governed by rule, federal rules, hipaa, ferpa, whatever, whatever kind of data that you have. So we certainly focus on the physical protection of it, but there's also sort of the ethical protection of it. What, what is the quote? There's lies, damn lies and statistics. >>Yes. Twain. >>Yeah. So you, you really have to be responsible with what you're doing with the data, how you're portraying the results. And again, I think it comes back to transparency is is basically if people are gonna reproduce what I did, I have to be really transparent with what I did. >>I, yeah, I think that's super important. And one of the themes with, with HPC that we've been talking about a lot too is, you know, do people trust ai? Do they trust all the data that's going into these systems? And I love that you just talked about the storytelling aspect of that, because there is a duty, it's not, you can cut data kind of however you want. I mean, I come from marketing background and we can massage it to, to do whatever we want. So in addition to being the deputy director at Tech, you are also the DEI officer. And diversity I know is important to you probably both as an individual, but also in the work that you're doing. Talk to us about that. >>Yeah, I mean, I, I very passionate about diversity, equity and inclusion in a sense of belongingness. I think that's one of the key aspects of it. Core >>Of community too. >>I got a computer science degree back in the eighties. I was akin to a unicorn in a, in an engineering computer science department. And, but I was really lucky in a couple of respects. I had a, I had a father that was into science that told me I could do anything I, I wanted to set my mind to do. So that was my whole life, was really having that support system. >>He was cheers to dad. >>Yeah. Oh yeah. And my mom as well, actually, you know, they were educators. I grew up, you know, in that respect, very, very privileged, but it was still really hard to make it. And I couldn't have told you back in that time why I made it and, and others didn't, why they dropped out. But I made it a mission probably back, gosh, maybe 10, 15 years ago, that I was really gonna do all that I could to change the needle. And it turns out that there are a number of things that you can do grassroots. There are certainly best practices. There are rules and there are things that you really, you know, best practices to follow to make people feel more included in an organization, to feel like they belong it, shared mission. But there are also clever things that you can do with programming to really engage students, to meet people and students where they are interested and where they are engaged. And I think that's what, that's what we've done over, you know, the course of our programming over the course of about maybe since 2016. We have built a lot of programming ATAC that really focuses on that as well, because I'm determined the needle is gonna change before it's all said and done. It just really has to. >>So what, what progress have you made and what goals have you set in this area? >>Yeah, that, that's a great question. So, you know, at first I was a little bit reluctant to set concrete goals because I really didn't know what we could accomplish. I really wasn't sure what grassroots efforts was gonna be able to, you're >>So honest, you can tell how transparent you are with the data as well. That's >>Great. Yeah, I mean, if I really, most of the successful work that I've done is both a scientist and in the education and outreach space is really trust relationships. If I break that trust, I'm done. I'm no longer effective. So yeah, I am really transparent about it. But, but what we did was, you know, the first thing we did was we counted, you know, to the extent that we could, what does the current picture look like? Let's be honest about it. Start where we are. Yep. It was not a pretty picture. I mean, we knew that anecdotally it was not gonna be a great picture, but we put it out there and we leaned into it. We said, this is what it is. We, you know, I hesitated to say we're gonna look 10% better next year because I'm, I'm gonna be honest, I don't always know we're gonna do our best. >>The things that I think we did really well was that we stopped to take time to talk and find out what people were interested in. It's almost like being present and listening. My grandmother had a saying, you have two errors in one mouth for a reason, just respect the ratio. Oh, I love that. Yeah. And I think it's just been building relationships, building trust, really focusing on making a difference, making it a priority. And I think now what we're doing is we've been successful in pockets of people in the center and we are, we are getting everybody on board. There's, there's something everyone can do, >>But the problem you're addressing doesn't begin in college. It begins much, much, that's right. And there's been a lot of talk about STEM education, particularly for girls, how they're pushed out of the system early on. Also for, for people of color. Do you see meaningful progress being made there now after years of, of lip service? >>I do. I do. But it is, again, grassroots. We do have a, a, a researcher who was a former teacher at the center, Carol Fletcher, who is doing research and for CS for all we know that the workforce, so if you work from the current workforce, her projected workforce backwards, we know that digital skills of some kind are gonna be needed. We also know we have a, a, a shortage. There's debate on how large that shortage is, but about roughly about 1 million unmet jobs was projected in 2020. It hasn't gotten a lot better. We can work that problem backwards. So what we do there is a little, like a scatter shot approach. We know that people come in all forms, all shapes, all sizes. They get interested for all different kinds of reasons. We expanded our set of pathways so that we can get them where they can get on to the path all the way back K through 12, that's Carol's work. Rosie Gomez at the center is doing sort of the undergraduate space. We've got Don Hunter that does it, middle school, high school space. So we are working all parts of the problem. I am pretty passionate about what we consider opportunity youth people who never had the opportunity to go to college. Is there a way that we can skill them and get, get them engaged in some aspect and perhaps get them into this workforce. >>I love that you're starting off so young. So give us an example of one of those programs. What are you talking to kindergartners about when it comes to CS education? >>You know, I mean, gaming. Yes. Right. It's what everybody can wrap their head around. So most kids have had some sort of gaming device. You talk in the context, in the context of something they understand. I'm not gonna talk to them about high performance computing. It, it would go right over their heads. And I think, yeah, you know, I, I'll go back to something that you said Paul, about, you know, girls were pushed out. I don't know that girls are being pushed out. I think girls aren't interested and things that are being presented and I think they, I >>Think you're generous. >>Yeah. I mean, I was a young girl and I don't know why I stayed. Well, I do know why I stayed with it because I had a father that saw something in me and I had people at critical points in my life that saw something in me that I didn't see. But I think if we ch, if we change the way we teach it, maybe in your words they don't get pushed out or they, or they won't lose interest. There's, there's some sort of computing in everything we do. Well, >>Absolutely. There's also the bro culture, which begins at a very early >>Age. Yeah, that's a different problem. Yeah. That's just having boys in the classroom. Absolutely. You got >>It. That's a whole nother case. >>That's a whole other thing. >>Last question for you, when we are sitting here, well actually I've got, it's two parter, let's put it that way. Is there a tool or something you wish you could flick a magic wand that would make your job easier? Where you, you know, is there, can you identify the, the linchpin in the DEI challenge? Or is it all still prototyping and iterating to figure out the best fit? >>Yeah, that is a, that's a wonderful question. I can tell you what I get frustrated with is that, that >>Counts >>Is that I, I feel like a lot of people don't fully understand the level of effort and engagement it takes to do something meaningful. The >>Commitment to a program, >>The commitment to a program. Totally agree. It's, there is no one and done. No. And in fact, if I do that, I will lose them forever. They'll be, they will, they will be lost in the space forever. Rather. The engagement is really sort of time intensive. It's relationship intensive, but there's a lot of follow up too. And the, the amount of funding that goes into this space really is not, it, it, it's not equal to the amount of time and effort that it really takes. And I think, you know, I think what you work in this space, you realize that what you gain is, is really more of, it's, it really feels good to make a difference in somebody's life, but it's really hard to do on a shoer budget. So if I could kind of wave a magic wand, yes, I would increase understanding. I would get people to understand that it's all of our responsibility. Yes, everybody is needed to make the difference and I would increase the funding that goes to the programs. >>I think that's awesome, Kelly, thank you for that. You all heard that. More funding for diversity, equity, and inclusion. Please Paul, thank you for a fantastic interview, Kelly. Hopefully everyone is now inspired to check out tac perhaps become a, a Longhorn, hook 'em and, and come deal with some of the most important data that we have going through our systems and predicting the future of our pandemics. Ladies and gentlemen, thank you for joining us online. We are here in Dallas, Texas at Supercomputing. My name is Savannah Peterson and I look forward to seeing you for our next segment.

Published Date : Nov 16 2022

SUMMARY :

Good afternoon everyone, and thank you so much for joining us. It's gonna be fun. Kelly Gayer, thank you so much for being here and you are with tech. And thank you so much for having me here. And one of the themes that's come up a to plug in and compute so that we could predict the spread of, And you did that through the use of mobility data, cell phone signals. Yeah, so that was really interesting. but it was really mobility all day long, you know, So now that you were able to do this for this pandemic, as we look forward, I think during the pandemic we were reacting, in the US we will respond proactively and, and effectively when And I think one thing we did right was we I think you nailed it. There's, you know, two things that you have to really keep And again, I think it comes back to transparency is is basically And I love that you just talked about the storytelling aspect of I think that's one of the key aspects of it. I had a, I had a father that was into science I grew up, you know, in that respect, very, very privileged, I really wasn't sure what grassroots efforts was gonna be able to, you're So honest, you can tell how transparent you are with the data as well. but what we did was, you know, the first thing we did was we counted, you And I think now what we're doing is we've been successful in Do you see meaningful progress being all we know that the workforce, so if you work from the current workforce, I love that you're starting off so young. And I think, yeah, you know, I, I'll go back to something that But I think if we ch, There's also the bro culture, which begins at a very early That's just having boys in the classroom. you know, is there, can you identify the, the linchpin in the DEI challenge? I can tell you what I get frustrated with of effort and engagement it takes to do something meaningful. you know, I think what you work in this space, you realize that what I look forward to seeing you for our next segment.

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Dr. Dan Duffy and Dr. Bill Putman | SuperComputing 22


 

>>Hello >>Everyone and welcome back to Dallas where we're live from, Super computing. My name is Savannah Peterson, joined with my co-host David, and we have a rocket of a show for you this afternoon. The doctors are in the house and we are joined by nasa, ladies and gentlemen. So excited. Please welcome Dr. Dan Duffy and Dr. Bill Putman. Thank you so much for being here, guys. I know this is kind of last minute. How's it to be on the show floor? What's it like being NASA here? >>What's exciting? We haven't, we haven't been here for three years, so this is actually really exciting to come back and see everybody, to see the showroom floor, see the innovations that have happened over the last three years. It's pretty exciting. >>Yeah, it's great. And, and so, because your jobs are so cool, and I don't wanna even remotely give even too little of the picture or, or not do it justice, could you give the audience a little bit of background on what you do as I think you have one of the coolest jobs ever. YouTube bill. >>I, I appreciate that. I, I, I run high Performance Computing Center at NASA Goddard for science. It's high performance information technology. So we do everything from networking to security, to high performance computing, to data sciences, artificial intelligence and machine learning is huge for us now. Yeah, large amounts of data, big data sets, but we also do scientific visualizations and then cloud and commercial cloud computing, as well as on premises cloud computing. And quite frankly, we support a lot of what Bill and his team does. >>Bill, why don't you tell us what your team >>Does? Yeah, so I'm a, I'm an earth scientist. I work as the associate chief at the global modeling assimilation office. And our job is to really, you know, maximize the use of all the observations that NASA takes from space and build that into a coherent, consistent physical system of the earth. Right? And we're focused on utilizing the HC that, that Dan and the folks at the nccs provide to us, to the best of our abilities to integrate those observations, you know, on time scales from hours, days to, to seasonal to to monthly time scales. That's, that's the essence of our focus at the GMA o >>Casual modeling, all of NASA's earth data. That, that in itself as a sentence is pretty wild. I imagine you're dealing with a ton of data. >>Oh, massive amounts of data. Yes, >>Probably, I mean, as much as one probably could, now that I'm thinking about it. I mean, and especially with how far things have to travel. Bill, sticking with you, just to open us up, what technology here excites you the most about the future and that will make your job easier? Let's put it that way. >>To me, it's the accelerator technologies, right? So there's the limited, the limiting factor for, for us as scientists is how fast we can get an answer. And if we can get our answer faster through accelerated technologies, you know, with the support of the, of the nccs and the computing centers, but also the software engineers enabling that for us, then we can do more, right. And push the questions even further, you know, so once we've gotten fast enough to do what we want to do, there's always something next that we wanna look for. So, >>I mean, at nasa you have to exercise such patience, whether that be data, coming back, images from a rover, doesn't matter what it is. Sometimes there's a lot of time, days, hours, years, depending on the situation. Right? I really, I really admire that. What about you, Dan? What's got you really excited about the future here? So >>Bill talked about the, the accelerated technology, which is absolutely true and, and, and is needed to get us not to only to the point where we have the compute resources to do the simulations that Bill wants to do, and also do it in a energy efficient way. But it's really the software frameworks that go around that and the software frameworks, the technology that dealing with how to use those in an energy efficient and and most efficient way is extremely important. And that's some of the, you know, that's what I'm really here to try to understand better about is how can I support these scientists with not just the hardware, but the software frameworks by which they can be successful. >>Yeah. We've, we've had a lot of kind of philosophical discussion about this, the difference between the quantitative increases in power in computing that we're seeing versus the question of whether or not we need truly qualitative changes moving forward. Where do you see the limits of, of, of, you know, if you, if you're looking at the ability to gather more data and process more data more quickly, what you can do with that data changes when you're getting updates every second versus every month seems pretty obvious. Is there a, is there, but is there, is there a near term target that you have specifically where once you reach that target, if you weren't thinking ahead of that target, you'd kind of be going, Okay, well we solved that problem, we're getting the data in so fast that you can, you can ask me, what is the temperature in this area? And you can go, Oh, well, huh, an hour ago the data said this. Beyond that, do you need a qualitative change in our ability to process information and tease insight into out of chaos? Or do you just need more quantity to be able to get to the point where you can do things like predict weather six months in advance? What are, what are your thoughts on that? Yeah, >>It's an interesting question, right? And, and you ended it with predicting whether six months in advance, and actually I was thinking the other way, right? I was thinking going to finer and finer scales and shorter time scales when you talk about having data more frequently, right? So one of the things that I'm excited about as a modeler is going to hire resolution and representing smaller scale processes at nasa, we're, we're interested in observations that are global. So our models are global and we'd like to push those to as fine a resolution as possible to do things like severe storm predictions and so forth. So the faster we can get the data, the more data we can have, and that area would improve our ability to do that as well. So, >>And your background is in meteorology, right? >>Yes, I'm a meteorologist. >>Excellent. Okay. Yeah, yeah, >>Yeah. So, so I have to ask a question, and I'm sure all the audience cares about this. And I went through this when I was talking about the ghost satellites as well. What, what is it about weather that makes it so hard to predict? >>Oh, it's the classic chaos problem. The, the butterfly effects problem, and it's just true. You know, you always hear the story of a butterfly in Africa flaps, its rings and wings, and the weather changes in, in New York City, and it's just, computers are an excellent example of that, right? So we have a model of the earth, we can run it two times in a row and get the exact same answer, but if we flip a bit somewhere, then the answer changes 10 days later significantly. So it's a, it's a really interesting problem. So, >>Yeah. So do you have any issue with the fact that your colleague believes that butterflies are responsible for weather? No, I does that, does that, is it responsible for climate? Does that bother you at all? >>No, it doesn't. As a matter of fact, they actually run those butterfly like experi experiments within the systems where they do actually flip some bits and see what the uncertainties are that happen out 7, 8, 9 days out in advance to understand exactly what he's saying, to understand the uncertainties, but also the sensitivity with respect to the observations that they're taking. So >>Yeah, it's fascinating. It is. >>That is fascinating. Sticking with you for a second, Dan. So you're at the Center for Climate Simulation. Is that the center that's gonna help us navigate what happens over the next decade? >>Okay, so I, no one center is gonna help us navigate what's gonna happen over the next decade or the next 50 or a hundred years, right. It's gonna be everybody together. And I think NASA's role in that is really to pioneer the, the, the models that that bill and others are doing to understand what's gonna happen in not just the seasonal sub, but we also work with G, which is the God Institute for Space Studies. Yeah. Which does the decatal and, and the century long studies. Our, our job is to really help that research, understand what's happening with the client, but then feed that back into what observations we need to make next in order to better understand and better quantify the risks that we have to better quantify the mitigations that we can make to understand how and, and, and affect how the climate is gonna go for the future. So that's really what we trying to do. We're trying to do that research to understand the climate, understand what mitigations we can have, but also feedback into what observations we can make for the future. >>Yeah. And and what's the partnership ecosystem around that? You mentioned that it's gonna take all of us, I assume you work with a lot of >>Partners, Probably both of you. I mean, obviously the, the, the federal agencies work huge amounts together. Nasa, Noah is our huge partnerships. Sgs, a huge partnerships doe we've talked to doe several times this, so this, this this week already. So there's huge partnerships that go across the federal agency. We, we work also with Europeans as much as we can given the, the, the, you know, sort of the barriers of the countries and the financials. But we do collaborate as much as we can with, And the nice thing about NASA, I would say is the, all the observations that we take are public, they're paid for by the public. They're public, everybody can down them, anybody can down around the world. So that's also, and they're global measurements as Bill said, they're not just regional. >>Do you have, do you have specific, when you think about improving your ability to gain insights from data that that's being gathered? Yeah. Do you set out specific milestones that you're looking for? Like, you know, I hope by June of next year we will have achieved a place where we are able to accomplish X. Yeah. Do you, do you, Yeah. Bill, do you put, what, >>What milestones do we have here? So, yeah, I mean, do you have >>Yeah. Are, are you, are you sort of kept track of that way? Do you think of things like that? Like very specific things? Or is it just so fluid that as long as you're making progress towards the future, you feel okay? >>No, I would say we absolutely have milestones that we like to keep in track, especially from the modeling side of things, right? So whether it's observations that exist now that we want to use in our system, milestones to getting those observations integrated in, but also thinking even further ahead to the observations that we don't have yet. So we can use the models that we have today to simulate those kind of observations that we might want in the future that can help us do things that we can do right now. So those missions are, are aided by the work that we do at the GBO and, and the nccs, but, >>Okay, so if we, if we extrapolate really to the, to the what if future is really trying to understand the entire earth system as best as we can. So all the observations coming in, like you said, in in near real time, feeding that into an earth system model and to be able to predict short term, midterm or even long term predictions with, with some degree of certainty. And that may be things like climate change or it may be even more important, shorter term effects of, of severe weather. Yeah. Which is very important. And so we are trying to work towards that high resolution, immediate impact model that we can, that we can, you know, really share with the world and share those results as best, as best we can. >>Yeah. I, I have a quick, I have a quick follow up on that. I I bet we both did. >>So, so if you think about AI and ml, artificial intelligence and machine learning, something that, you know, people, people talk about a lot. Yeah. There's the concept of teaching a machine to go look for things, call it machine learning. A lot of it's machine teaching we're saying, you know, hit, you know, hit the rack on this side with a stick or the other side with the stick to get it to, to kind of go back and forth. Do you think that humans will be able to guide these systems moving forward enough to tease out the insights that we want? Or do you think we're gonna have to rely on what people think of as artificial intelligence to be able to go in with this massive amount of information with an almost infinite amount of variables and have the AI figure out that, you know what, it was the butterfly, It really was the butterfly. We all did models with it, but, but you understand the nuance that I'm saying. It's like we, we, we think we know what all the variables are and that it's chaotic because there's so many variables and there's so much data, but maybe there's something we're not taking into >>A account. Yeah, I I, I'm, I'm, I'm sure that's absolutely the case. And I'll, I'll start and let Bill, Bill jump in here. Yeah, there's a lot of nuances with a aiml. And so the, the, the, the real approach to get to where we want to be with this earth system model approach is a combination of both AI ML train models as best as we can and as unbiased way as we can. And there's a, there's a big conversation we have around that, but also with a physics or physical based model as well, Those two combined with the humans or the experts in the loop, we're not just gonna ask the artificial intelligence to predict anything and everything. The experts need to be in the loop to guide the training in as best as we, as, as we can in an unbiased, equitable way, but also interpret the results and not just give over to the ai. But that's the combination of that earth system model that we really wanna see. The future's a combination of AI l with physics based, >>But there's, there's a, there's an obvious place for a AI and ML in the modeling world that is in the parameterizations of the estimations that we have to do in our systems, right? So when we think about the earth system and modeling the earth system, there are many things like the equations of motions and thermodynamics that have fixed equations that we know how to solve on a computer. But there's a lot of things that happen physically in the atmosphere that we don't have equations for, and we have to estimate them. And machine learning through the use of high resolution models or observations in training the models to understand and, and represent that, yeah, that that's the place where it's really useful >>For us. There's so many factors, but >>We have to, but we have to make sure that we have the physics in that machine learning in those, in those training. So physics informed training isn't very important. So we're not just gonna go and let a model go off and do whatever it wants. It has to be constrained within physical constraints that the, that the experts know. >>Yeah. And with the wild amount of variables that affect our, our earth, quite frankly. Yeah, yeah. Which is geez. Which is insane. My god. So what's, what, what technology or what advancement needs to happen for your jobs to get easier, faster for our ability to predict to be even more successful than it is currently? >>You know, I think for me, the vision that I have for the future is that at some point, you know, all data is centrally located, essentially shared. We have our applications are then services that sit around all that data. I don't have to sit as a user and worry about, oh, is this all this data in place before I run my application? It's already there, it's already ready for me. My service is prepared and I just launch it out on that service. But that coupled with the performance that I need to get the result that I want in time. And I don't know when that's gonna happen, but at some point it might, you know, I don't know rooting for you, but that's, >>So there are, there are a lot of technologies we can talk about. What I'd like to mention is, is open science. So NASA is really trying to make a push and transformation towards open science. 2023 is gonna be the year of open science for nasa. And what does that mean? It means a lot of what Bill just said is that we have equity and fairness and accessibility and you can find the data, it's findability, it's fair data, you know, a fair findability accessibility reproducibility, and I forget what the eye stands for, but these are, these are tools and, and, and things that we need to, as, as a computing centers and including all the HC centers here, as well as the scientists need to support, to be as transparent as possible with the data sets and the, and the research that we're doing. And that's where I think is gonna be the best thing is if we can get this data out there that anybody can use in an equitable way and as transparent as possible, that's gonna eliminate, in my opinion, the bias over time because mistakes will be found and mistakes will be corrected over time. >>I love that. Yeah. The open source science end of this. No, it's great. And the more people that have access people I find in the academic world, especially people don't know what's going on in the private sector and vice versa. And so I love that you just brought that up. Closing question for you, because I suspect there might be some members of our audience who maybe have fantasized about working at nasa. You've both been working there for over a decade. Is it as cool as we all think of it? It is on the outside. >>I mean, it's, it's definitely pretty cool. >>You don't have to be modest about it, you know, >>I mean, just being at Goddard and being at the center where they build the James web web telescope and you can go to that clean room and see it, it's just fascinating. So it, it's really an amazing opportunity. >>Yeah. So NASA Goddard as a, as a center has, you know, information technologist, It has engineers, it has scientists, it has support staff, support team members. We have built more things, more instruments that have flown in this space than any other place in the world. The James Lab, we were part of that, part of a huge group of people that worked on James. We and James, we came through and was assembled in our, our, our clean room. It's one of the biggest clean rooms in, in, in the world. And we all took opportunities to go over and take selfies with this as they put those loveness mirrors on them. Yeah, it was awesome. It was amazing. And to see what the James we has done in such a short amount of time, the successes that they've gone through is just incredible. Now, I'm not a, I'm not a part of the James web team, but to be a, to be at the same center, to to listen to scientists like Bill talk about their work, to listen to scientists that, that talk about James, we, that's what's inspiring. And, and we get that all the time. >>And to have the opportunity to work with the astronauts that service the, the Hubble Telescope, you know, these things are, >>That's literally giving me goosebumps right now. I'm sitting over >>Here just, just an amazing opportunity. And woo. >>Well, Dan, Bill, thank you both so much for being on the show. I know it was a bit last minute, but I can guarantee we all got a lot out of it. David and I both, I know I speak for us in the whole cube audience, so thank you. We'll have you, anytime you wanna come talk science on the cube. Thank you all for tuning into our supercomputing footage here, live in Dallas. My name is Savannah Peterson. I feel cooler having sat next to these two gentlemen for the last 15 minutes and I hope you did too. We'll see you again soon.

Published Date : Nov 16 2022

SUMMARY :

The doctors are in the house and we are joined by We haven't, we haven't been here for three years, so this is actually really could you give the audience a little bit of background on what you do as I think you And quite frankly, we support a lot of what Bill and his And our job is to really, you know, maximize the use of all the observations I imagine you're dealing with a ton of data. Oh, massive amounts of data. what technology here excites you the most about the future and that will make your job easier? And push the questions even further, you know, I mean, at nasa you have to exercise such patience, whether that be data, coming back, images from a rover, And that's some of the, you know, be able to get to the point where you can do things like predict weather six months in advance? So the faster we can get the data, the more data we can have, and that area would improve our ability And I went through this when I was talking about the ghost satellites So we have a model of the earth, we can run it two times Does that bother you at all? what he's saying, to understand the uncertainties, but also the sensitivity with respect to the observations that they're taking. Yeah, it's fascinating. Is that the center that's gonna help us navigate what happens over the next decade? just the seasonal sub, but we also work with G, which is the God Institute for I assume you work with a lot of the, the, you know, sort of the barriers of the countries and the financials. Like, you know, I hope by Do you think of things like that? So we can use the models that we have today to simulate those kind of observations that we can, that we can, you know, really share with the world and share those results as best, I I bet we both did. We all did models with it, but, but you understand the nuance that I'm saying. And there's a, there's a big conversation we have around that, but also with a physics or physical based model as is in the parameterizations of the estimations that we have to do in our systems, right? There's so many factors, but We have to, but we have to make sure that we have the physics in that machine learning in those, in those training. to get easier, faster for our ability to predict to be even more successful you know, I don't know rooting for you, but that's, it's findability, it's fair data, you know, a fair findability accessibility reproducibility, And so I love that you just brought telescope and you can go to that clean room and see it, it's just fascinating. And to see what the James we has done in such a short amount of time, the successes that they've gone through is I'm sitting over And woo. next to these two gentlemen for the last 15 minutes and I hope you did too.

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David Cardenas, County of Los Angeles Department of Public Health | UiPath Forward 5


 

(upbeat music) >> TheCUBE presents UiPath Forward 5. Brought to you by UiPath. >> Hello and welcome back to TheCUBE's coverage of UiPath Forward 5. We're here in Las Vegas at the Venetian Convention Center. This is day two. We're wrapping up Dave Nicholson and Dave Vellante. This is the fourth time theCUBE has been at UiPath Forward. And we've seen the transformation of the company from, essentially, what was a really interesting and easy to adopt point product to now one through acquisitions, IPO, has made a number of enhancements to its platform. David Cardenas is here. Deputy Director of Operations for County of Los Angeles, the Department of Public Health. David, good to see you. Thanks for coming on theCUBE. >> Thanks for having me on guys. Appreciate it. >> So what is your role? What does it have to do with automation? >> So I had been, actually started off in the IT space within the public health. Had served as a CIO previously, but now been moving into broader operations. And I basically manage all of the back office operations for the department, HR, IT, finance, all that. >> So you've had a wild ride in the last couple of years. >> Yeah, I think, like I've been talking earlier, it's just been, the last two years have just been horrendous. It's been a really difficult experience for us. >> Yeah, and I mean, the scars are there, and maybe permanently. But it also had major effects on organizations, on operations that, again, seem to be permanent. How would you describe the situation in your organization? >> So I think it, the urgency that came along with the pandemic response, kind of required us to look at things, you know, differently. We had to be, realize we had to be a lot more nimble than when we were and try to figure out how to enhance our operations. But really look at the core of what we're doing and figure out how it is to be more efficient. So I think we've kind of seen it as an opportunity to really examine ourselves a little bit more deeply and see what things we need to do to kind of, to fix our operations and get things on a better path. >> You know, I think a lot of organizations we talked to say that. But I want to understand how you handle this is, you didn't have time to sit back in the middle of the pandemic. >> Yeah. >> And then as you exit, what I call the isolation economy, people are so burned out, you know? So how do you deal with that organizational trauma? Say, okay now, let's sit back and think about this. Do people, are they eager to do so? Do they have the appetite for it? What's that dynamic like? >> So I think certainly there's a level of exhaustion inside the organization. I can't say that there isn't because it's just been, you know, two years of 24/7/365 kind of work. And that's tough on any organization. But I think what we realize is that there's, you know, we need to move into action quickly 'cause we don't know what's going to come next, right? And we're expecting that this is just a sign of what's to come and that we're just at the start of that stage of, we're just going to see a lot more outbreaks, we're going to see a lot more conditions kind of hitting us. And if we're not prepared for that, we're not going to be able to respond for the, and preserve the health and safety of our citizens, right? So I think we're taking a very active, like, look at these opportunities and see what we've done and say how do we now make the changes that we made in response to the pandemic permanent so that the next time this comes at us, we won't have to be struggling the way that we were to try to figure things out because we'll have such a better foundation in place to be able to move things forward. >> I mean, I've never served in the military, but I imagine that when you're in the military, you're always prepared for some kind of, you know, in your world, code red, right? >> Yeah. >> So it's like this code red culture. And that seems to have carried through, right? People are, you know, constantly aware that, wow. We got caught off guard and we don't want that to happen again. Because that was a big part of the trauma was just the unknown- >> Right. >> and the lack of preparedness. So thinking about technology and its role in helping you to prepare for that type of uncertainty. Can you describe how you're applying technology to prepare for the next unknown? >> So I think, so that first part of what you said, I think the difficulty we've always had in the public health side is that there's the, generally the approach to healthcare is very reactionary, right? Your first interface with the healthcare system is, "I'm going to go see my doctor; I'm going to go to the hospital." The work that we do in public health is to try to do everything we can to keep you out of that, right? So it's broad-based messaging, social media now is going to put us out there. But also, to be able to surveil disease in a different way. And so the holy grail for us in healthcare has always been, at least on the public health side, has been to try to see how can we tap in more actively that when you go see the doctor or when you go to the hospital, how can I get access to that information very, very quickly so that I know, and can see, and surveil my entire county in my jurisdiction and know, oh, there's an outbreak of disease happening in this section of the county. We're 10 million people with, you know, hundreds of square miles inside of LA. There are places where we can see very, you know, specific targets that we know we have to hit. But the data's a little stale and we find out several months after. We need to figure out a way to do that more actively. Technology's going to be our path to be able to capture that information more actively and come up on something a little bit, so we can track things faster and be able to respond more quickly. So that's our focus for all our technology implementations, automation like UiPath has offered us and other things, is around how to gather that information more quickly and put that into action so we can do quick interventions. >> People have notoriously short memories. Please tell me (chuckles) any of the friction that you may have experienced in years past before the pandemic. That those friction points where people are thinking, "Eh, what are the odds?" >> Yeah. "Eh, I've got finite budget, I think I'm going to spend it on this thing over here." Do you, are you able to still ride sort of the wave of mind share at this point when putting programs together for the future? >> So whatever friction was there during the pandemic wiped away. I mean, we had amazing collaboration with the medical provider community, our hospital partners. The healthcare system in LA was working very closely with us to make sure that we were responding. And there is that wave that we are trying to make sure that we use this as an opportunity to kind of ride it so that we can implement all the things that we want. 'Cause we don't know how long that's going to last us. The last time that I saw anything this large was after the anthrax attacks and the bioterrorism attacks that we had after 9/11. >> How interesting. >> Public health was really in lens at that point. And we had a huge infusion of funding, a lot of support from stakeholders, both politically and within the healthcare system. And we were able to make some large steps in movement at that point. This feels the same but in a larger scale because now it touched every part of the infrastructure. And we saw how society really had to react to what was going on in a different way than anyone has ever prepared for. And so now is we think is a time where we know that people are making more investments. And our success is going to be their success in the longterm. >> And you have to know that expectations are now set- >> Extremely high. >> at a completely different level, right? >> Yes, absolutely. >> There is no, "Oh, we don't have enough PPE." >> Correct. >> Right? >> David: Correct. >> The the expectation level is, hey, you should have learned from all of- >> We should have it; we can deliver it, We'll have it at the ready when we need to provide it. Yes, absolutely. >> Okay, so I sort of mentioned, we're, David cubed on theCUBE (all laughing). So three Daves. You spoke today at the conference? >> Actually I'm speaking later actually in the session in an hour or so. >> Oh Okay. My understanding is that you've got this concept of putting humans at the center of the automation. What does that mean? Why is that important? Help us understand that. >> So I think what we found in the crisis is that the high demand for information was something we hadn't seen before, right? We're one of the largest media markets in the United States. And what we really had trouble with is trying to figure out how to serve the residents, to provide them the information that we needed to provide to them. And so what we had traditionally done is press releases, you know, just general marketing campaigns, billboards, trying to send our message out. And when you're talking about a pandemic where on a daily basis, hour-by-hour people wanted to know what was going on in their local communities. Like, we had to change the way that we focused on. So we started thinking about, what is the information that the residents of our county need? And how can we set up an infrastructure to sustain the feeding of that? Because if we can provide more information, people will make their own personal decisions around their personal risk, their personal safety measures they need to take, and do so more actively. More so than, you know, one of us going on camera to say, "This is what you should do." They can look for themselves and look at the data that's in front of them and be able to make those choices for themselves, right? And so we needed to make sure that everything that we were doing wasn't built around feeding it to our political stakeholders, which are important stakeholders. We needed to make sure that they're aware and are messaging out, and our leadership are aware. But it's what could we give the public to be able to make them have access to information that we were collecting on an every single day basis to be able to make the decisions for their lives. And so the automation was key to that. We were at the beginning of the pandemic just had tons and tons of resources that we were throwing at the problem that was, our systems were slow, we didn't have good ability to move data back and forth between our systems, and we needed a stop-gap solution to really fill that need and be able to make the data cycles to meet the data cycles. We had basically every day had to deliver reports and analytics and dashboards by like 10 o'clock in the morning because we knew that the 12 an hour and the five-hour news cycles were going to hit and the press were going to then take those and message out. And the public started to kind of come in at that same time and look at 10 and 11 o'clock and 12 o'clock. >> Yeah. >> We could see it from how many hits were hitting our website, looking for that information. So when we failed and had a cycle where that data cycle didn't work and we couldn't deliver, the public would let us know, the press would let us know, the stakeholders would let us know. We had never experienced anything like that before, right. Where people had like this voracious appetite for the information. So we needed to have a very bulletproof process to make sure that every single 24 hours we were delivering that data, making it available at the ready. >> Software robots enabled that. >> Exactly. >> Okay. And so how were you able to implement that so quickly within such a traumatic environment? >> So I think, I guess necessity is always the mother of invention. It kind of drove us to go real quickly to look at what we had. We had data entry operations set up where we had dozens and dozens of people whose sole job in life on a 24-hour cycle was to receive medical reports that we we're getting, interview data that's coming from our case interviews, hospitalization data that was coming in through all these different channels. And it was all coming in in various forms. And they were entering that into our systems of record. And that's what we were using, extracts from that system of record, what was using to generate the data analyses in our systems and our dashboards. And so we couldn't rely on those after a while because the data was coming in at such high volume. There wasn't enough data entry staff to be able to fit the need, right? And so we needed to replace those humans and take them out of that data entry cycle, pop in the bots. And so what we started to look at is, let's pick off the, where it is that that data entry cycle starts and see what we could do to kind of replace that cycle. And we started off with a very discreet workload that was focused on some of our case interview data that was being turned into PDFs that somebody was using to enter into our systems. And we said, "Well before you do that," since we can't import into the systems 'cause it wasn't working, the import utilities weren't working. We got 'em into simple Excel spreadsheets, mapped those to the fields in our systems and let the bots do that over and over again. And we just started off with that one-use case and just tuned it and went cycle after cycle. The bots just got better and better to the point where we had almost like 95% success rates on each submission of data transactions that we did every day. >> Okay, and you applied that automation, I don't know, how many bots was it roughly? >> We're now at like 30; we started with about five. >> Okay, oh, interesting. So you started with five and you applied 'em to this specific use case to handle the velocity and volume of data- >> Correct. >> that was coming in. But that's obviously dynamic and it's changed. >> Absolutely. >> I presume it's shifted to other areas now. So how did you take what you learned there and then apply it to other use cases in other parts of the organization? >> So, fortunately for us, the process that was being used to capture the information to generate the dashboards and the analyses for the case interview data, which is what we started with- >> Yeah. >> Was essentially being used the same for the hospitalization data that we were getting and for tracking deaths as they were coming in as well. And so the bots essentially were just, we just took one process, take the same bots, copy them over essentially, and had them follow the very same process. We didn't try to introduce any different workflow than what was being done for the first one so we could replicate quickly. So I think it was lucky for us a lot- >> Dave V.: I was going to say, was that luck or by design? >> It was the same people doing the same analyses, right? So in the end they were thinking about how to be efficient themselves. So they kind of had coalesced around a similar process. And so it was kind of like fortunate, but it was by design in terms of how they- >> Dave V.: It was logical to them. >> Logical to them to make it. >> Interesting. >> So for us to be able to insert the bots became pretty easy on the front end. It's just now as we're trying to now expand to other areas that were now encountering like unique processes that we just can't replicate that quickly. We're having to like now dig into. >> So how are you handling that? First of all, how are you determining which processes? Is it sort of process driven? Is it data driven? How do you determine that? >> So obviously right now the focus still is COVID. So the the priorities scale that we've set internally for analyzing those opportunities really is centered around, you know, which things are really going to help our pandemic response, right? We're expecting another surge that's going to happen probably in the next couple of weeks. That'll probably take us through December. Hopefully, at that point, things start to calm down. But that means high-data volume again; these same process. So we're looking at optimizing the processes that we have, what can we do to make those cycles better, faster, you know, what else can we add? The data teams haven't stopped to try to figure out how else can they turn out new data reports, new data analysis, to give us a different perspective on the new variants and the new different outbreaks and hotspots that are popping up. And so we also have to kind of keep up with where they're going on these data dashboards. So they're adding more data into these reports so we know we have to optimize that. And then there's these kind of tangential work. So for example, COVID brought about, unfortunately, a lot of domestic violence reports. And so we have a lot of domestic violence agencies that we work with and that we have interactions with and to monitor their work, we have certain processes. So that's kind of like COVID-adjacent. But it's because it's such a very critical task, we're looking at how we can kind of help in those processes and areas. Same thing in like in our substance abuse area. We have substance use disorder treatment services that we provide. And we're delivering those at a higher rate because COVID kind of created more of a crisis than we would've liked. And so that's how we're prioritizing. It's really about what is the social need, what does the community need, and how can we put the technology work in those areas? >> So how do you envision the future of automation in your organization and the future of your organization? What does that look like? Paint a picture for us. >> So I'm hoping that it really does, you know, so we're going to take everything that's COVID related in the disease control areas, both in terms of our laboratory operations, in terms of our clinic operations, the way we respond, vaccination campaigns, things of that nature. And we're going to look at it to see what can efficiencies can we do there because it's a natural outgrowth of everything we've done on COVID up to this point. So, you know, it's almost like it's as simple as you're just replicating it with another disease. The disease might have different characteristics, but the work process that we follow is very similar. It's not like we're going to change everything and do something completely different for a respiratory condition as we would for some other type of foodborne condition or something else that might happen. So we certainly see very easy opportunities to just to grow out what we've already done in terms of the processes is to do that. So that's wave one, is really focus on that grow out. The second piece I think is to look at these kind of other general kind of community-based type of operations and see what operations we can do there to kind of implement some improvements there. And then I'm certainly in my new role of, in Deputy Director of Operation, I'm a CIO before. Now that I'm in this operations role, I have access to the full administrative apparatus for the department. And believe me, there's enough to keep me busy there. (Dave V. Laughing) And so that's going to be kind of my third prong is to kind of look at the implement there. >> Awesome. Go ahead, Dave. >> Yeah, so, this is going to be taking a step back, kind of a higher level view. If we could direct the same level of rigor and attention towards some other thing that we've directed towards COVID, if you could snap your fingers and make that happen, what would that thing be in the arena of public health in LA County in particular, or if you want California, United States. What is something that you feel maybe needs more attention that it's getting right now? >> So I think I touched on it a little bit earlier, but I think it's the thing we've been always been trying to get to is how to really become just very intentional about how we share data more actively, right? I don't have to know everything about you, but there are certain things I care about when you go to the doctor for that doctor and that physician to tell me. Our physicians, our healthcare system as you know, is always under a lot of pressure. Doctors don't have the time to sit down and write a form out for me and tell me everything that's going on. During COVID they did because they were, they cared about their patients so much and knew, I need to know what's going on at every single moment. And if I don't tell you what's going on in my office, you'll never know and can't tell us what's going on in the community. So they had a vested interest in telling us. But on a normal day-to-day, they don't have the time for that. I got to replace that. We got to make sure that when we get to, not me only, but everyone in this public health community has to be focused and working with our healthcare partners to automate the dissemination and the distribution of information so that I have the information at my fingers, that I can then tell you, "Here's what's going on in your local community," down to your neighborhood, down to your zip code, your census tracked, down to your neighbors' homes. We'll be able to tell you, "This is your risk. Here are the things that are going on. This is what you have to watch out for." And the more that we can be more that focused and laser-focused on meeting that goal, we will be able to do our job more effectively. >> And you can do that while preserving people's privacy. >> Privacy, absolutely. >> Yeah, absolutely. But if people are informed then they can make their own decisions. >> Correct. >> And they're not frustrated at the systems. David, we got to wrap. >> Sure. >> But maybe you can help us. What's your impression of the, first of all, is this your first Forward? You've been to others? >> This is my first time. >> Okay. >> My first time. >> What's your sort of takeaway when you go back to the office or home and people say, "Hey, how was the show? What, what'd you learn?" What are you going to say? >> Well, from just seeing all the partners here and kind of seeing all the different events I've been able to go to and the sessions there's, you don't know many times I've gone to and say, "We've got to be doing that." And so there's certainly these opportunities for, you know, more AI, more automation opportunities that we have not, we just haven't even touched on really. I think that we really need to do that. I have to be able to, as a public institution at some point our budgets get capped. We only have so much that we're going to receive. Even riding this wave, there's only so much we're going to be able to get. So we have to be very efficient and use our resources more. There's a lot more that we can do with AI, a lot more with the tools that we saw, some of the work product that are coming out at this conference that we think we can directly apply to kind of take the humans out of that, their traditional roles, get them doing higher level work so I can get the most out of them and have this other more mundane type of work, just have the systems just do it. I don't need anybody doing that necessarily, that work. I need to be able to leverage them for other higher level capabilities. >> Well thank you for that. Thanks for coming on theCUBE and really appreciate. Dave- >> It's been great talking to you guys, thank you. >> Dave, you know, I love software shows because the business impact is so enormous and I especially love cool software shows. You know, this first of all, the venue. 3,500 people here. Very cool venue. I like the fact that it's not like booth in your face, booth competition. I mean I love VMware, VMworld, VMware Explore. But it's like, "My booth is bigger than your booth." This is really nice and clean, and it's all about the experience. >> A lot of steak, not as much sizzle. >> Yeah, definitely. >> A lot of steak. >> And the customer content at the UiPath events is always outstanding. But we are entering a new era for UiPath, and we're talking. We heard a lot about the Enterprise platform. You know, the big thing is this company's been in this quarterly shock-lock since last April when it went public. And it hasn't all been pretty. And so new co-CEO comes in, they've got, you know, resetting priorities around financials, go to market, they've got to have profitable growth. So watching that that closely. But also product innovation so the co-CEOs will be able to split that up, split their duties up. Daniel Dines the product visionary, product guru. Rob Enslin, you know- making the operations work. >> Operations execution business, yeah. >> We heard that Carl Eschenbach did the introduction. Carl's a major operator, wanted that DNA into the company. 'Cause they got to keep product innovation. And I want to, I want to see R&D spending, stay relatively high. >> Product innovation, but under the heading of platform. And that's the key thing is just not being that tool set. The positioning has been, I think, accurate that, you know, over history, we started with these RPA tools and now we've moved into business process automation and now we're moving into new frontiers where, where truly, AI and ML are being leveraged. I love the re-infer story about going in and using natural national (chuckles) national, natural language processing. I can't even say it, to go through messaging. That's sort of a next-level of intelligence to be able to automate things that couldn't be automated before. So that whole platform story is key. And they seem to have made a pretty good case for their journey into platform as far as I'm concerned. >> Well, yeah, to me again. So it's always about the customers, want to come to an event like this, you listen to what they say in the keynotes and then you listen to what the customers say. And there's a very strong alignment in the UiPath community between, you know, the marketing and the actual implementation. You know, marketing's always going to be ahead. But, we saw this a couple of years ago with platform. And now we're seeing it, you know, throughout the customer base, 10,000+ customers. I think this company could have, you know, easily double, tripled, maybe even 10x that. All right, we got to wrap. Dave Nicholson, thank you. Two weeks in a row. Good job. And let's see. Check out siliconangle.com for all the news. Check out thecube.net; wikibon.com has the research. We'll be on the road as usual. theCUBE, you can follow us. UiPath Forward 5, Dave Vellante for Dave Nicholson. We're out and we'll see you next time. Thanks for watching. (gentle music)

Published Date : Sep 30 2022

SUMMARY :

Brought to you by UiPath. and easy to adopt point product Thanks for having me on guys. of the back office operations in the last couple of years. the last two years have Yeah, and I mean, the scars are there, is to be more efficient. in the middle of the pandemic. I call the isolation economy, so that the next time this comes at us, And that seems to have and the lack of preparedness. is to try to do everything we can any of the friction that I think I'm going to spend to make sure that we were responding. And our success is going to be "Oh, we don't have enough PPE." We'll have it at the ready So three Daves. in the session in an hour or so. center of the automation. And the public started to kind So we needed to have a And so how were you able to And we said, "Well before you do that," we started with about five. to handle the velocity that was coming in. and then apply it to other use cases And so the bots essentially were just, Dave V.: I was going to say, So in the end they were thinking about that we just can't replicate that quickly. the processes that we have, the future of automation in terms of the processes is to do that. What is something that you And the more that we can be more And you can do that while preserving But if people are informed at the systems. You've been to others? There's a lot more that we can do with AI, Well thank you for that. talking to you guys, thank you. and it's all about the experience. And the customer content that DNA into the company. And they seem to have made So it's always about the customers,

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Dr. Edward Challis, UiPath & Ted Kummert, UiPath | UiPath Forward 5


 

(upbeat music) >> Announcer: theCUBE presents UiPath Forward5. Brought to you by UiPath. >> Hi everybody, we're back in Las Vegas. We're live with Cube's coverage of Forward 5 2022. Dave Vellante with Dave Nicholson Ted Kumer this year is the Executive Vice President, product and engineering at UiPath. Brought on to do a lot of the integration and bring on new capabilities for the platform and we've seen that over the last several years. And he's joined by Dr. Edward Challis, who's the co-founder of the recent acquisition that UiPath made, company called Re:infer. We're going to learn about those guys. Gents, welcome to theCUBE. Ted, good to see you again. Ed, welcome. >> Good to be here. >> First time. >> Thank you. >> Yeah, great to be here with you. >> Yeah, so we have seen, as I said, this platform expanding. I think you used the term business automation platform. It's kind of a new term you guys introduced at the conference. Where'd that come from? What is that? What are the characteristics that are salient to the platform? >> Well, I see the, the evolution of our platform in three chapters. You understand the first chapter, we call that the RPA chapter. And that's where we saw the power of UI automation applied to the old problems of how do I integrate apps? How do I automate processes? That was chapter one. You know, chapter two gets us to Forward3 in 2019, and the definition of this end-to-end automation platform you know, with the capabilities from discover to measure, and building out that core platform. And as the platform's progressed, what we've seen happen with our customers is the use of it goes from being very heavy in automating the repetitive and routine to being more balanced, to now where they're implementing new brought business process, new capability for their organization. So that's where the name, Business Automation Platform, came from. Reflecting now that it's got this central role, as a strategic tool, sitting between their application landscape, their processes, their people, helping that move forward at the rate that it needs to. >> And process mining and task mining, that was sort of the enabler of chapter two, is that right? >> Well, I'd say chapter two was, you know, first the robots got bigger in terms of what they could cover and do. API integration, long running workflows, AI and ML skills integrated document processing, citizen development in addition to professional development, engaging end users with things like user interfaces built with UiPath apps. And then the discovery. >> So, more robustness of the? Yeah, okay. >> Yeah. Just an expansion of the whole surface area which opened up a lot of things for our customers to do. That went much broader than where core RPA started. And so, and the other thing about this progression to the business automation platform is, you know, we see customers now talking more about outcomes. Early on they talk a lot about hours saved and that's great, but then what about the business outcomes it's enabling? The transformations in their business. And the other thing we're doing in the platform is thinking about, well, where can we land with solutions capabilities that more directly land on business, measurable business outcomes? And so we had started, for example, offering an email automation solution, big business problem for a lot of our customers last year. And we'd started encountering this company Re:infer as we were working with customers. And then, and we encountered Re:infer being used with our platform together. And we saw we can accelerate this. And what that is giving us now is a solution now that aligns with a very defined business outcome. And this way, you know, we can help you process communications and do it efficiently and provide better service for your customers. And that's beginning of another important progression for us in our platform. >> So that's a nice segue, Ed. Tell about Re:infer. Why did you start the company? >> Right, yeah, so my whole career has been in machine learning and AI and I finished my PhD around 2013, it was a very exciting time in AI. And me and my co-founders come from UCL, this university in London, and Deep Mind, this company which Google acquired a few years later, came from our same university. So very exciting time amongst the people that really knew about machine learning and AI. And everyone was thinking, you know, how do we, these are just really big breakthroughs. And you could just see there was going to be a whole bunch of subsequent breakthroughs and we thought NLP would be the next breakthrough. So we were really focused on machine reading problems. And, but we also knew as people that had like built machine learning production systems. 'Cause I'd also worked in industry that built that journey from having a hypothesis that machine learning can solve a problem to getting machine learning into production. That journey is of painful, painful journey and that, you know, you can see that you've got these advances, but getting into broad is just way too hard. >> So where do you fit in the platform? >> Yeah, so I think when you look in the enterprise just so many processes start with a message start with a no, start with a case ticket or, you know, some other kind of request from a colleague or a customer. And so it's super exciting to be able to, you know, take automation one step higher in that process chain. So, you could automatically read that request, interpret it, get all the structured data you need to drive that process forward. So it's about bringing automation into these human channels. >> So I want to give the audience a sense here. So we do a lot of events at the Venetian Conference Center, and it's usually very booth heavy, you know, brands and big giant booths. And here the booths are all very small. They're like kiosks, and they're all pretty much the same size. So it's not like one vendor trying to compete with the other. And there are all these elements, you know I feel like there's clouds and there's, you know, of course orange is the color here. And one of the spots is, it has this really kind of cool sitting area around customer stories. And I was in there last night reading about Deutsche Bank. Deutsche Bank was also up on stage. Deutsche Bank, you guys were talking about a Re:infer. So share with our audience what Deutsche Bank are doing with UiPath and Re:infer. >> Yeah, so I mean, you know, before we automate something, we often like to do what we call communications mining. Which is really understanding what all of these messages are about that might be hitting a part of the business. And at Deutsche Bank and in many, you know, like many large financial services businesses, huge volumes of messages coming in from the clients. We analyze those, interpret the high volume query types and then it's about automating against those to free up capacity. Which ultimately means you can provide faster, higher quality service because you've got more time to do it. And you're not dealing with all of those mundane tasks. So it's that whole journey of mining to automation of the coms that come into the corporate bank. >> So how do I invoke the service? So is it mother module or what's the customer onboarding experience like? >> So, I think the first thing that we do is we generate some understanding of actually the communications data they want to observe, right? And we call it mining, but you know, what we're trying to understand is like what are these communications about? What's the intent? What are they trying to accomplish? Tone can be interesting, like what's the sentiment of this customer? And once you understand that, you essentially then understand categories of conversations you're having and then you apply automations to that. And so then essentially those individual automations can be pointed to sets of emails for them to automate the processing of. And so what we've seen is customers go from things they're handling a hundred percent manual to now 95% of them are handled basically with completely automated processing. The other thing I think is super interesting here and why communications mining and automation are so powerful together is communications about your business can be very, very dynamic. So like, new conversations can emerge, something happens right in your business, you have an outage, whatever, and the automation platform, being a very rapid development platform, can help you adapt quickly to that in an automated way. Which is another reason why this is such a powerful thing to put the two things together. >> So, you can build that event into the automation very quickly you're saying? >> Speaker 1: Yeah. >> Speaker 2: That's totally right. >> Cool. >> So Ed, on the subject of natural language processing and machine learning versus machine teaching. If I text my wife and ask her would you like to go to an Italian restaurant tonight? And she replies, fine. Okay, how smart is your machine? And, of course, context usually literally denotes things within the text, and a short response like that's very difficult to do this. But how do you go through this process? Let's say you're implementing this for a given customer. And we were just talking about, you know, the specific customer requirements that they might have. What does that process look like? Do you have an auditor that goes through? And I mean do you get like 20% accuracy, and then you do a pass, and now you're at 80% accuracy, and you do a pass? What does that look? >> Yeah, so I mean, you know when I was talking about the pain of getting a machine learning model into production one of the principle drivers of that is this process of training the machine learning model. And so what we use is a technique called active learning which is effectively where the AI and ML model queries the user to say, teach me about this data point, teach me about this sentence. And that's a dynamic iterative process. And by doing it in that way you make that training process much, much faster. But critically that means that the user has, when you train the model the user defines how you want to encode that interpretation. So when you were training it you would say fine from my wife is not good, right? >> Sure, so it might be fine, do you have a better suggestion? >> Yeah, but that's actually a very serious point because one of the things we do is track the quality of service. Our customers use us to attract the quality of service they deliver to their clients. And in many industries people don't use flowery language, like, thank you so much, or you know, I'm upset with you, you know. What they might say is fine, and you know, the person that manages that client, that is not good, right? Or they might say I'd like to remind you that we've been late the last three times, you know. >> This is urgent. >> Yeah, you know, so it's important that the client, our client, the user of Re:infer, can encode what their notions of good and bad are. >> Sorry, quick follow up on that. Differences between British English and American English. In the U.K., if you're thinking about becoming an elected politician, you stand for office, right? Here in the U.S., you run for office. That's just the beginning of the vagaries and differences. >> Yeah, well, I've now got a lot more American colleagues and I realize my English phrasing often goes amiss. So I'm really aware of the problem. We have customers that have contact centers, some of them are in the U.K., some of them are in America, and they see big differences in the way that the customers get treated based on where the customer is based. So we've actually done analysis in Re:infer to look at how agents and customers interact and how you should route customers to the contact centers to be culturally matched. Because sometimes there can be a little bit of friction just for that cultural mapping. >> Ted, what's the what's the general philosophy when you make an acquisition like this and you bring in new features? Do you just wake up one day and all of a sudden there's this new capability? Is it a separate sort of for pay module? Does it depend? >> I think it depends. You know, in this case we were really led here by customers. We saw a very high value opportunity and the beginnings of a strategy and really being able to mine all forms of communication and drive automated processing of all forms of communication. And in this case we found a fantastic team and a fantastic piece of software that we can move very quickly to get in the hands of our customer's via UiPath. We're in private preview now, we're going to be GA in the cloud right after the first of the year and it's going to continue forward from there. But it's definitely not one size fits all. Every single one of 'em is different and it's important to approach 'em that way. >> Right, right. So some announcements, StudioWeb was one that I think you could. So I think it came out today. Can't remember what was today. I think we talked about it yesterday on the keynotes anyway. Why is that important? What is it all about? >> Well we talked, you know, at a very top level. I think every development platform thinks about two things for developers. They think, how do I make it more expressive so you can do other things, richer scenarios. And how do I make it simpler? 'Cause fast is always better, and lower learning curves is always better, and those sorts of things. So, Re:infer's a great example of look the runtime is becoming more and more expressive and now you can buy in communications state as part of your automation, which is super cool. And then, you know StudioWeb is about kind of that second point and Studios and Studio X are already low code visual, but they're desktop. And part of our strategy here is to elevate all of that experience into the web. Now we didn't elevate all of studio there, it's a subset. It is API integration and web based application automation, Which is a great foundation for a lot of apps. It's a complete reimagining of the studio user interface and most importantly it's our first cross-platform developer strategy. And so that's been another piece of our strategy, is to say to the customers we want to be everywhere you need us to be. We did cross-platform deployment with the automation suite. We got cross-platform robots with linear robots, serverless robots, Mac support and now we got a cross-platform devs story. So we're starting out with a subset of capabilities maybe oriented toward what you would associate with citizen scenarios. But you're going to see more roadmap, bringing more and more of that. But it's pretty exciting for us. We've been working on this thing for a couple years now and like this is a huge milestone for the team to get to this, this point. >> I think my first conversation on theCUBE with a customer was six years ago maybe at one of the earlier Forwards, I think Forward2. And the pattern that I saw was basically people taking existing processes and making them better, you know taking the mundane away. I remember asking customers, yeah, aren't you kind of paving the cow path? Aren't there sort of new things that you can do, new process? And they're like, yeah, that's sort of the next wave. So what are you seeing in terms of automating existing processes versus new processes? I would see Re:infer is going to open up a whole new vector of new processes. How should we think about that? >> Yeah, I think, you know, I mean in some ways RPA has this reputation because there's so much value that's been provided in the automating of the repetitive and routine. But I'd say in my whole time, I've been at the company now for two and a half years, I've seen lots of new novel stuff stood up. I mean just in Covid we saw the platform being used in PPP loan processing. We saw it in new clinical workflows for COVID testing. We see it and we've just seen more and more progression and it's been exciting that the conference, to see customers now talking about things they built with UiPath apps. So app experiences they've been delivering, you know. I talked about one in healthcare yesterday and basically how they've improved their patient intake processing and that sort of thing. And I think this is just the front end. I truly believe that we are seeing the convergence happen and it's happening already of categories we've talked about separately, iPass, BPM, low-code, RPA. It's happening and it's good for customers 'cause they want one thing to cover more stuff and you know, I think it just creates more opportunity for developers to do more things. >> Your background at Microsoft probably well prepared you for a company that you know, was born on-prem and then went all in on the cloud and had, you know, multiple code bases to deal with. UiPath has gone through a similar transformation and we talked to Daniel last night about this and you're now cloud first. So how is that going just in terms of managing multiple code bases? >> Well it's actually not multiple Code bases. >> Oh, it's the same one, Right, deployment models I should say. >> Is the first thing, Yeah, the deployment models. Another thing we did along the way was basically replatform at an infrastructure level. So we now can deploy into a Kubernetes Docker world, what you'd call the cloud native platform. And that allows us to have much more of a shared infrastructure layer as we look to deliver to the automation cloud. The same workload to the automation cloud that we now deliver in the automation suite for deployment on-prem or deploying a public cloud for a customer to manage. Interesting and enough, that's how Re:infer was built, which is it was built also in the cloud native platform. So it's going to be pretty easy. Well, pretty easy, there's some work to do, but it's going to be pretty easy for us to then bring that into the platform 'cause they're already working on that same platform and provide those same services both on premises and in the cloud without having your developers have to think too much about both. >> Okay, I got to ask you, so I could wrap my stack in a container and put it into AWS or Azure or Google and it'll run great. As well, I could tap some of the underlying primitives of those respective clouds, which are different and I could run them just fine. Or/and I could create an abstraction layer that could hide those underlying primitives and then take the best of each and create an automation cloud, my own cloud. Does that resonate? Is that what you're doing architecturally? Is that a roadmap, or? >> Certainly going forward, you know, in the automation cloud. The automation cloud, we announced a great partnership or a continued partnership with Microsoft. And just Azure and our platform. We obviously take advantage of anything we can to make that great and native capabilities. And I think you're going to see in the Automation Suite us doing more and more to be in a deployment model on Azure, be more and more optimized to using those infrastructure services. So if you deploy automation suite on-prem we'll use our embedded distro then when we deploy it say on Azure, we'll use some of their higher level managed services instead of our embedded distro. And that will just give customers a better optimized experience. >> Interesting to see how that'll develop. Last question is, you know what should we expect going forward? Can you show us a little leg on on the future? >> Well, we've talked about a number of directions. This idea of semantic automation is a place where you know, you're going to, I think, continue to see things, shoots, green shoots, come up in our platform. And you know, it's somewhat of an abstract idea but the idea that the platform is just going to become semantically smarter. You know, I had to serve Re:infer as a way, we're semantically smarter now about communications data and forms of communications data. We're getting semantically smarter about documents, screens you know, so developers aren't dealing with, like, this low level stuff. They can focus on business problem and get out of having to deal with all this lower level mechanism. That is one of many areas I'm excited about, but I think that's an area you're going to see a lot from us in the next coming years. >> All right guys, hey, thanks so much for coming to theCUBE. Really appreciate you taking us through this. Awesome >> Yeah Always a pleasure. >> Platform extension. Ed. All right, keep it right there, everybody. Dave Nicholson, I will be back right after this short break from UiPath Forward5, Las Vegas. (upbeat music)

Published Date : Sep 30 2022

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Brought to you by UiPath. Ted, good to see you again. Yeah, great to be here I think you used the term and the definition of this two was, you know, So, more robustness of the? And this way, you know, Why did you start the company? And everyone was thinking, you know, to be able to, you know, and there's, you know, and in many, you know, And we call it mining, but you know, And we were just talking about, you know, the user defines how you want and you know, the person Yeah, you know, so it's Here in the U.S., you run for office. and how you should route and the beginnings of a strategy StudioWeb was one that I think you could. and now you can buy in and making them better, you that the conference, for a company that you know, Well it's actually not multiple Oh, it's the same one, that into the platform of the underlying primitives So if you deploy automation suite on-prem Last question is, you know And you know, it's somewhat Really appreciate you Always a pleasure. right after this short break

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Keynote Analysis | UiPath Forward5


 

>>The Cube presents UI Path Forward five, brought to you by UI Path. >>Hi everybody. Welcome to Las Vegas. We're here in the Venetian, formerly the Sans Convention Center covering UI Path Forward five. This is the fourth time the Cube has covered forward, not counting the years during Covid, but UiPath was one of the first companies last year to bring back physical events. We did it at the Bellagio last year, Lisa Martin and myself. Today, my co-host is David Nicholson, coming off of last week's awesome CrowdStrike show back here in Vegas. David talking about UI path. UI path is a company that had a very strange path, as I wrote one time to IPO this company that was founded in 2005 and was basically a development shop. And then they realized they got lightning in a bottle with this RPA thing. Yeah. And Daniel Deez, the founder of the company, just really drove it hard and they really didn't do any big kind of VC raise for several years. >>And then all of a sudden, boom, the rocket ship took off, kind of really got out over their skis a little bit, but then got to IPO and, and has had a very successful sort of penetration into the market. The IPO obviously has not gone as well. We can talk about that, but, but they've hit a billion dollars in arr. There aren't a lot of companies that, you know, have hit a billion dollars in ARR that quickly. These guys had massive valuations that were cut back, obviously with the, with the downturn, but also some execution misuses. But the one thing about UiPath, Dave, is they've been very successful at penetrating customers. And that's the thing you always get at forward customer stories. And the other thing I'll, I'll, I'll add is that it started out with the narrative was, oh, automation software, robots, they're gonna take away jobs. The opposite has happened, the zero unemployment. Now basically we're heading into a recession, we're actually probably in a recession. And so how do you combat a recession? You put automation to work and gain if, if, if, if inflation is five to 7% and you can get 20% from automation. Well, it's a good roi. But you sat in the keynotes, it was really your first exposure to the company. What were your thoughts? >>Yeah, I think the whole subject is interesting. I think if you've been involved in tech for a while, the first thing you think of is, well, hold on a second. Isn't this just high tech scripting? Aren't you essentially just automating stuff? How, how cool can that possibly be? >>Well, it kinda was in the >>Beginning. Yeah, yeah. But, but, but when you dig into it, to your, to your point about the concern about displacing human beings, the first things that can automate it are the mundane and the repetitive tasks, which then frees individuals up frontline individuals who are doing those tasks to do more strategic things for the business. So when you, when we, you know, one of the things that was talked about in the keynote was this idea of an army of citizen developers within an organization. Not, you know, not just folks who are innovating and automating at the core of enterprise applications, but also folks out on the front line automating the tasks that are interfering with their productivity. So it seems like it's a win-win for, for everybody throughout the enterprise. >>Yeah. So let's take a, let's take folks through the, the keynote to, basically we learned there are 3,500 people here, roughly, you know, we're in the Venetian and we do a lot of shows at, at the Venetian, formerly the San Convention Center. The one thing about UiPath, they, they are a cool company. Yeah, they are orange colors, kinda like pure storage, but they got the robots moving around. The setup is very nice, it's very welcoming and very cool, but 300 3500 attendees, including partners and UiPath employees, 250 sessions. They've got a CIO, automation council and a pickleball court inside this hall, which pickleball is, you know, all the rage. So Bobby, Patrick and Mary Telo kicked it off. Bobby's the cmo, Mary's the head of branding, and Bobby raised four themes. It it, this is a tool that it's, this is RPA is going from a tool to a way of operating and innovating. >>The second thing is, the big news here is the UI path business platform, something like that. They're calling, but they're talking about about platform and they're really super gluing that to digital transformation. The third is really outcomes shifting from tactical. I have a robot, a software robot on my desk doing, you know, mimicking what I do with the script to something that's transformative. We're seeing this operationalized very deeply. We'll go into some examples. And then the fourth theme is automation is being featured as a strategic line item in annual reports. Bobby Patrick, as he left the stage, I think he was commenting on my piece where I said that RPA automation is more discretionary than some other things. He said, this is not discretionary, it's strategic. You know, unfortunately when you're heading into a recession, you can, you can put off some of the more strategic items. However, the flip side of that, Dave, is as they were saying before, if you're gonna, if if you're, if you're looking at five to 7% inflation may be a way to attack that is with automation. Yeah. >>There's no question, there's no question that automation is a way to attack that. There's no question that automation is critical moving forward. There's no question that we have moved. We're in the, you know, we're, we're still in the age of cloud, but automation is gonna be absolutely critical. The question is, what will UI path's role be in that market? And, and, and when you hear, when you hear UI path talk about platform versus tool sets and things like that, that's a critical differentiator because if they are just a tool, then why wouldn't someone exploit a tool that is within an application environment instead of exploiting a platform? So what I'm gonna be looking for in terms of the, the folks we talked to over the next few days is this question of, you know, make the case that this is actually a platform that extends across all kinds of application environments. If they can't seize that high ground moving forward, it's it's gonna be, it's gonna be tough for them. >>Well, they're betting the company on >>That, that's Rob Ensslin coming in. That's why he's part of the, the equation. But >>That platform play is they are betting the company. And, and the reason is, so the, the, the history here is in the early days of this sort of RPA craze, Automation Anywhere and UI path went out, they both raised a ton of money. UI Path rocketed out to the lead. They had a much e easier to install, you know, Automation Anywhere, Blue Prism, some of the other legacy business process folks, you know, kind of had on-prem, Big Stacks, UiPath came in a really simple self-serve platform and took off and really got a foothold in the market. And then started building or or making some of these acquisitions like Process Gold, like cloud elements, which is API automation. More recently Reiner, We, which is natural language processing. We heard them up on the stage today and they've been putting that together to do not just rpa but process mining, task mining, you know, document automation, et cetera. >>And so Rob Ins insulin was brought in from Google, formerly Google and SAP, to really provide that sort of financial and go to market expertise as well as Shim Gupta who's, who's the cfo. So they, they, and they were kinda late with that. They sort of did all this post ipo. I wish they had done it, you know, somewhat beforehand, but they're sort of bringing in that adult supervision supervision that's necessary. Rob Sland, I thought was very cogent. He was assertive on stage, he was really clear, he was energetic. He talked about the phases, e r p, Internet cloud and the now automation is a new S-curve. He quoted a Forester analyst talking about that. He also had a great quote. He said, you know, the old adage better, faster, cheaper, pick two. He said, You don't have to do that anymore with automation. He cited reports from analysts, 50% efficiency improvement, 40% productivity improvement, 40% improvement in customer satisfaction. >>And then what I always, again, love about UiPath is they're no shortage of customers. They do as good a job as anybody, and I think I would say the best of, of, of getting customers to talk about their experiences. You'll see that on the cube all this week, talked about Changi airport from Singapore. They're adding 50 able to service 50 million new customers, new travelers with no new headcount company called Vital or retail. And how you say that a hundred thousand employees having access to it. Uber, 150% ROI in one year. New York state getting 1.2 million relief checks out in two weeks and identifying potentially 12 billion in fraud. They also talk about 25% of the, of the UI path finance team is digital. And they've, they've only incremented headcount, you know, very slightly one and a half times their revenue's grown. What a 10 x? And really he talked about how to, for how to turn automation into a force multiplier for growth. And to your point, I think that's their challenge. What were your thoughts on Rob ens insulin's keynote? >>First of all, in addition to his background, Rob brings a brand with him. Rob Ensslin is a brand, and that brand is enterprise overarching platform. Someone you go to for that platform play, not for a tool set. And again, I'll, I'll say it again. It's critically important that they, that they demonstrate this to the marketplace, that they are a platform worth embracing as opposed to simply a tool set. Because the large enterprise software providers are going to provide their own tool sets within their platforms. And if you can't convince someone that it's worth doing two things instead of one thing, you're, you're, you're never gonna make it. So I've had experiences with Rob when he was at Google. He's, he's, he's the right person for the job and I, and I I I buy into his strategy and narrative about where we are and the critical nature of automation question remains, will you I path to be able to benefit from that trend. >>So a couple things on that. So your point about sap, you know, is right on EY was up on stage. They, EY is a huge SAP customer and they chose UI path to automate their SAP installation, right? And they're going all in with UI path as a partner. Of course. I I often like to say that the global system integrators, they like to eat at the trough, right? When you see GSIs like EY and others coming into the ecosystem, that means there's business being done. We saw Orange up on stage, which was really interesting. >>Javier from Spain. Yeah. Yep. >>Talking about he had this really cool dashboard and then Ted Coomer was talking about the business automation platform and all the different chapters and the evolution. They've gotta get to a platform play because the thing I failed to mention is Microsoft a couple years ago made a tuck in acquisition and got it to this market really providing individual automations and making it, you know, it's Microsoft, they're gonna make it really easy to add it really >>Cheaply. SAP would tell you that they have the same thing and, >>And then, and then just grow from that. So UiPath has to pivot to a platform play. They started this back in 2019, but as you know, it takes a long time to integrate stuff. Okay. So they're, they're, they're working through that. But this is, you know, Rob ends and put up on the, the slide go big, I, I tweeted, took a page outta Michael Dell. Go big or go home. Final thoughts before we break? >>I think go big or go home is pretty much sums it up. I mean this is, this is an existential mission that UiPath is on right now, starting to stay forward. They need to seize that high ground of platform versus tool set. Otherwise they will never get beyond where they are now. I I I, I do wanna mention too, to folks in the audience, there's a huge difference between a billion dollar valuation and a billion dollars in revenue every year. So, so, you know, these, these guys have reached a milestone, there's no question about that. But to get to that next level platform, platform, platform, and I know we'll be, we'll be probing our guests on that question over the next couple years. >>Yeah. And the key is obviously gonna be keep servicing the customers, you know, all the financial machinations and you know, they reduced yesterday their guidance from the high end being 25% ARR growth down to roughly 20% when you, when you factor out currency conversions. UiPath has a lot of business overseas. They're taking that overseas revenue and converting it back to dollars though dollars are appreciated. So they're less of them. I know this is kind of the inside baseball, but, but we're gonna get into that over the next two days. Dave Ante and Dave, you're watching the Cubes coverage of UI path forward, five from Las Vegas. We'll be right back, right after this short break.

Published Date : Sep 29 2022

SUMMARY :

The Cube presents UI Path Forward five, brought to you by And Daniel Deez, the founder of the company, And that's the thing you always Aren't you essentially just automating stuff? when we, you know, one of the things that was talked about in the keynote was this idea of an army of you know, all the rage. a software robot on my desk doing, you know, mimicking what I do with the script to this question of, you know, make the case that this is actually a platform But They had a much e easier to install, you know, Automation Anywhere, He said, you know, the old adage better, And how you say that a hundred thousand employees important that they, that they demonstrate this to the marketplace, that they are a and they chose UI path to automate their SAP installation, play because the thing I failed to mention is Microsoft a couple years ago made a tuck in acquisition and SAP would tell you that they have the same thing and, They started this back in 2019, but as you know, it takes a long time to integrate stuff. So, so, you know, you know, they reduced yesterday their guidance from the high end being 25% ARR growth

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Kate Hall Slade, dentsu & Flo Ye, dentsu | UiPath Forward5


 

>>The Cube Presents UI Path Forward five. Brought to you by UI Path. >>Welcome back to the Cube's Coverage of Forward five UI Path Customer event. This is the fourth forward that we've been at. We started in Miami, had some great events. It's all about the customer stories. Dave Valante with Dave Nicholson, Flow Yees here. She's the director of engineering and development at dsu and Kate Hall is to her right. And Kate is the director of Automation Solutions at dsu. Ladies, welcome to the Cube. Thanks so much. Thanks >>You to >>Be here. Tell us about dsu. You guys are huge company, but but give us the focus. >>Yeah, absolutely. Dentsu, it's one of the largest advertising networks out there. One of the largest in the world with over 66,000 employees and we're operating in a hundred plus countries. We're really proud to serve 95% of the Fortune 100 companies. Household names like Microsoft Factor and Gamble. If you seen the Super Bowls ads last year, Larry, Larry Davids ads for the crypto brand. That's a hilarious one for anyone who haven't seen it. So we're just really proud to be here and we really respect the creatives of our company. >>That was the best commercial, the Super Bowl by far. For sure. I, I said at the top of saying that Dave and I were talking UI pass, a cool company. You guys kinda look like cool people. You got cool jobs. Tell, tell us about your respective roles. What do you guys do? Yeah, >>Absolutely, absolutely. Well, I'm the director of engineering and automation, so what I really do is to implement the automation operating model and connecting developers across five continents together, making sure that we're delivering and deploying automation projects up to our best standards setting by the operating model. So it's a really, really great job. And when we get to see all these brilliant minds across the world >>And, And Kate, what's your role? Yeah, >>And the Automation Solutions vertical that I head up, the focus is really on converting business requirements into technical designs for flows, developers to deliver. So making sure that we are managing our pipeline, sourcing the right ideas, prioritizing them according to the business businesses objectives and making sure that we route them to the right place. So is it, does it need to be an automation first? Do we need to optimize the process? Does this make sense for citizen developers or do we need to bring in the professional resources on flow's >>Team? So you're bilingual, you speak, you're like the translator, you speak geek and wall, right? Is that fair? Okay. So take me back to the, let's, let's do a little mini case study here. How did you guys get started? I'm always interested, was this a top down? Is, is is top down required to be successful? Cuz it does feel like you can have bottom up bottoms up with rpa, but, but how did you guys get started? What was the journey like? >>Yeah, we started back in 2017, very traditional top down approach. So we delivered a couple POCs working directly with UiPath. You know, going back those five years, delivered those really highly scalable top down solutions that drove hundreds of thousands of hours of ROI for the business. However, as people kind of began to embrace automation and they learned that this is something that they could, that could help them, it's not something that they should be afraid of to take away their jobs. You know, DSU is a young company with a lot of young, young creatives. They wanna make their lives better. So we were absolutely inundated with all of these use cases of, hey I, I need a bot to do this. I need a bot to do that i's gonna save me, you know, 10 hours a week. It's gonna save my team a hundred hours a month, et cetera, et cetera. All of these smaller use cases that were gonna be hugely impactful for the individuals, their teams, even in entire department, but didn't have that scalable ROI for us to put professional development resources against it. So starting in 2020 we really introduced the citizen development program to put the power into those people's hands so that they could create their own solutions. And that was really just a snowball effect to tackle it from the bottom up as well as the top down. >>So a lot of young people, Dave, they not not threatened by robots that racing it. So >>They've grown up with the technology, they know that they can order an Uber from their phone, right? Why am I, you know, sitting here at MITs typing data from Excel into a program that might be older than some of our youngest employees. >>Yeah. Now, now the way you described it, correct me if I'm wrong, the way you described it, it sounds like there's sort of a gating function though. You're not just putting these tools in the hands of people sitting, especially creatives who are there to create. You're not saying, Oh you want things automated, here are the tools. Go ahead. Automated. We'll we, for those of you who want to learn how to use the tools, we'll have you automate that there. Did I hear that right? You're, you're sort of making decisions about what things will be developed even by citizen developers. >>Let me, Do you wanna talk to them about governance? Yeah, absolutely. >>Yeah, so I think we started out with assistant development program, obviously the huge success, right? Last year we're also here at the Cubes. We're very happy to be back again. But I think a lot, a lot had changed and we've grown a lot since last year. One, I have the joy being a part of this team. And then the other thing is that we really expanded and implemented an automation operating model that I mentioned briefly just earlier. So what that enabled us to do is to unite developers from five continents together organically and we're now able to tap into their talent at a global scale. So we are really using this operating model to grow our automation practice in a scalable and also controlled manner. Okay. What I mean by that is that these developer originally were sitting in 18 plus markets, right? There's not much communication collaboration between them. >>And then we went in and bridged them together. What happened is that originally they were only delivering projects and use cases within their region and sometimes these use cases could be very, very much, you know, small scale and not really maximizing their talent. What we are now able to do is tap into a global automation pipeline. So we connecting these highly skilled people to the pipeline elsewhere, the use cases elsewhere that might not be within their regions because one of our focus, a lot of change I mentioned, right? One thing that will never change with our team, it's used automation to elevate people's potential. Now it's really a win-win situation cuz we are connecting the use cases from different pipelines. So the business is happy cuz we are delivering these high scalable solutions. We also utilizing these developers and they're happy because their skills are being maximized and then at the same time growing our automation program. So then that way the citizen development program so that the lower complexities projects are being delivered at a local level and we are able to innovate at a local level. >>I, I have so many questions flow based on what you just said. It's blowing my mind >>Here. It's a whole cycle. >>So let me start with how do you, you know, one of the, one of the concerns I had initially with RPA, cuz just you're talking about some very narrow use cases and your goal is to expand that to realize the potential of each individual, right? But early days I saw a lot of what I call paving the cow path, taking a process that was not a great process and then automating it, right? And that was limiting the potential. So how do you guys prioritize which processes to focus on and maybe which processes should be rethought, >>Right? Exactly. A lot of time when we do automation, right, we talk about innovations and all that stuff, but innovation doesn't happen with the same people sitting in the same room doing the same thing. So what we are doing now, able to connect all these people, different developers from different groups, we really bring the diversity together. That's diversity D diverse diversity in the mindset, diversity in the skill. So what are we really able to do and we see how we tackle this problem is to, and that's a problem for a lot of business out there is the short-termism. So there's something, what we do is that we take two approaches. One, before we, you know, for example, when we used to receive a use case, right? Maybe it's for the China market involving a specific tool and we just go right into development and start coding and all that good stuff, which is great. >>But what we do with this automation framework, which we think it's a really great service for any company out there that want to grow and mature their automation practice, it's to take a step back, think about, okay, so the China market would be beneficial from this automation. Can we also look at the Philippine market? Can we also look at the Thailand market? Because we also know that they have similar processes and similar auto tools that they use. So we are really able to make our automation in a more meaningful way by scaling a project just beyond one market. Now it's impacting the entire region and benefiting people in the entire region. That is what we say, you know, putting automation for good and then that's what we talked about at dsu, Teaming without limits. And that's a, so >>By taking, we wanna make sure that we're really like taking a step back, connecting all of the dots, building the one thing the right way, the first time. Exactly. And what's really integral into being able to have that transparency, that visibility is that now we're all working on the same platform. So you know, Brian spoke to you last year about our migration into automation cloud, having everything that single pipeline in the cloud. Anybody at DSU can often join the automation community and get access to automation hub, see what's out there, submit their own ideas, use the launchpad to go and take training. Yeah. And get started on their own automation journey as a citizen developer and you know, see the different paths that are available to them from that one central space. >>So by taking us a breath, stepping back, pausing just a bit, the business impact at the tail end is much, much higher. Now you start in 2017 really before you UI path made it's big enterprise play, it acquired process gold, you know, cloud elements now most recently referenced some others. How much of what you guys are, are, are doing is platform versus kind of the initial sort of robot installation? Yeah, >>I mean platforms power people and that's what we're here to do as the global automation team. Whether it's powering the citizen developers, the professional developers, anybody who's interacting with our automations at dsu, we wanna make sure that we're connecting the docs for them on a platform basis so that developers can develop and they don't need to develop those simple use cases that could be done by a citizen developer. You know, they're super smart technical people, they wanna do the cool shit with the new stuff. They wanna branch into, you know, using AI center and doing document understanding. That's, you know, the nature of human curiosity. Citizen developers, they're thrilled that we're making an investment to upscale them, to give them a new capability so that they can automate their own work. And they don't, they, they're the process experts. They don't need to spend a month talking to us when they could spend that time taking the training, learning how to create something themselves. >>How, how much sort of use case runway when you guys step back and look at your business, do you see a limit to the use cases? I mean where are you, if you had on a spectrum of, you know, maturity, how much more opportunity is there for DSU to automate? >>There's so much I think the, you feel >>Like it's limitless? >>No, I absolutely feel like it's limitless because there one thing, it's, there's the use cases and I think it's all about connecting the talent and making sure that something we do really, you know, making sure that we deliver these use cases, invest the time in our people so we make sure our professional developers part of our team spending 10 to 20% of the time to do learning and development because only limitless if our people are getting the latest and the greatest technology and we want to invest the time and we see this as an investment in the people making sure that we deliver the promise of putting people first. And the second thing, it's also investment in our company's growth. And that's a long term goal. And overcoming just focusing on things our short term. So that is something we really focus to do. And not only the use cases we are doing what we are doing as an operating model for automation. That is also something that we really value because then this is a kind of a playbook and a success model for many companies out there to grow their automation practice. So that's another angle that we are also focusing >>On. Well that, that's a relief because you guys are both seem really cool and, and I'm sitting here thinking they don't realize they're working themselves out of a job once they get everything automated, what are they gonna do? Right? But, but so, so it sounds like it's a never ending process, but because you guys are, are such a large global organization, it seems like you might have a luxury of being able to benchmark automations from one region and then benchmark them against other regions that aren't using that automation to be able to see very, very quickly not only realize ROI really quickly from the region where it's been implemented, but to be able to compare it to almost a control. Is that, is that part of your process? Yeah, >>Absolutely. Because we are such a global brand and with the automation, automation operating model, what we are able to do, not only focusing on the talent and the people, but also focusing on the infrastructure. So for example, right, maybe there's a first use case developing in Argentina and they have never done these automation before. And when they go to their security team and asking for an Okta bypass service account and the security team Argentina, like we never heard of automation, we don't know what UiPath is, why would I give you a service account for good reason, right? They're doing their job right. But what we able to do with automation model, it's to establish trust between the developers and the security team. So now we have a set up standing infrastructure that we are ready to go whenever an automation's ready to deploy and we're able to get the set up standing infrastructure because we have the governance to make sure the quality would delivered and making sure anything that we deployed, automation that we deploy are developed and governed by the best practice. So that's how we able to kind of get this automation expand globally in a very control and scalable manner because the people that we have build a relationship with. What are >>The governors to how fast you can adopt? Is it just expertise or bandwidth of that expertise or what's the bottleneck? >>Yeah, >>If >>You wanna talk more about, >>So in terms of the pipeline, we really wanna make sure that we are taking that step back and instead of just going, let's develop, develop, develop, here are the requirements like get started and go, we've prove the value of automation at Densu. We wanna make sure we are taking that step back and observing the pipeline. And it's, it's up to us to work with the business to really establish their priorities and the priorities. It's a, it's a big global organization. There might be different priorities in APAC than there are in EM for a good reason. APAC may not be adopted on the same, you know, e r P system for example. So they might have those smaller scale ROI use cases, but that's where we wanna work with them to identify, you know, maybe this is a legitimate need, the ROI is not there, let's upscale some citizen developers so that they can start, you know, working for themselves and get those results faster for those simpler use cases. >>Does, does the funding come from the line of business or IT or a combination? I mean there are obviously budget constraints are very concerned about the macro and the recession. You guys have some global brands, you know, as, as things ebb and flow in the economy, you're competing with other budgets. But where are the budgets coming from inside of dsu? Is it the business, is it the tech >>Group? Yeah, we really consider our automation group is the cause of doing business because we are here connecting people with bridging people together and really elevating. And the reason why we structure it that way, it's people, we do automation at dsu not to reduce head count, not to, you know, not, not just those matrix number that we measure, but really it's to giving time back to the people, giving time back to our business. So then that way they can focus on their wellbeing and that way they can focus on the work-life balance, right? So that's what we say. We are forced for good and by using automation for good as one really great example. So I think because of this agenda and because DSU do prioritize people, you know, so that's why we're getting the funding, we're getting the budget and we are seeing as a cause of doing business. So then we can get these time back using innovation to make people more fulfilling and applying automation in meaningful ways. >>Kate and Flo, congratulations. Your energy is palpable and really great success, wonderful story. Really appreciate you sharing. Thank you so >>Much for having us today. >>You're very welcome. All keep it right there. Dave Nicholson and Dave Ante. We're live from UI path forward at five from Las Vegas. We're in the Venetian Consent Convention Center. Will be right back, right for the short break.

Published Date : Sep 29 2022

SUMMARY :

Brought to you by And Kate is the director You guys are huge company, but but give us the focus. we really respect the creatives of our company. What do you guys do? Well, I'm the director of engineering and automation, So making sure that we are managing our pipeline, sourcing the right ideas, up with rpa, but, but how did you guys get started? So we were absolutely inundated with all of these use cases So a lot of young people, Dave, they not not threatened by robots that racing it. Why am I, you know, sitting here at MITs typing data from Excel into to use the tools, we'll have you automate that there. Let me, Do you wanna talk to them about governance? So we are really using So we connecting these highly skilled people to I, I have so many questions flow based on what you just said. So how do you guys prioritize which processes to focus on and Maybe it's for the China market involving a specific tool and we just go right into So we are really able to So you know, of what you guys are, are, are doing is platform versus kind of the initial sort They wanna branch into, you know, using AI center and doing document understanding. And not only the use cases we are doing what On. Well that, that's a relief because you guys are both seem really cool and, and the security team Argentina, like we never heard of automation, we don't know what UiPath So in terms of the pipeline, we really wanna make sure that we are taking that step back You guys have some global brands, you know, as, as things ebb and flow in the So then we can get these time back using innovation to Thank you so We're in the Venetian Consent Convention Center.

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