(upbeat music) >> Welcome everyone to theCUBE's presentation of the AWS Startup Showcase AI Machine Learning Top Startups Building Generative AI on AWS. This is season 3, episode 1 of the ongoing series covering the exciting startup from the AWS ecosystem to talk about AI and machine learning. I'm your host, John Furrier. I'm joined by two great guests here, Adam Wenchel, who's the CEO of Arthur, and Chief Scientist of Arthur, John Dickerson. Talk about how they help people build better LLM AI systems to get them into the market faster. Gentlemen, thank you for coming on. >> Yeah, thanks for having us, John. >> Well, I got to say I got to temper my enthusiasm because the last few months explosion of interest in LLMs with ChatGPT, has opened the eyes to everybody around the reality of that this is going next gen, this is it, this is the moment, this is the the point we're going to look back and say, this is the time where AI really hit the scene for real applications. So, a lot of Large Language Models, also known as LLMs, foundational models, and generative AI is all booming. This is where all the alpha developers are going. This is where everyone's focusing their business model transformations on. This is where developers are seeing action. So it's all happening, the wave is here. So I got to ask you guys, what are you guys seeing right now? You're in the middle of it, it's hitting you guys right on. You're in the front end of this massive wave. >> Yeah, John, I don't think you have to temper your enthusiasm at all. I mean, what we're seeing every single day is, everything from existing enterprise customers coming in with new ways that they're rethinking, like business things that they've been doing for many years that they can now do an entirely different way, as well as all manner of new companies popping up, applying LLMs to everything from generating code and SQL statements to generating health transcripts and just legal briefs. Everything you can imagine. And when you actually sit down and look at these systems and the demos we get of them, the hype is definitely justified. It's pretty amazing what they're going to do. And even just internally, we built, about a month ago in January, we built an Arthur chatbot so customers could ask questions, technical questions from our, rather than read our product documentation, they could just ask this LLM a particular question and get an answer. And at the time it was like state of the art, but then just last week we decided to rebuild it because the tooling has changed so much that we, last week, we've completely rebuilt it. It's now way better, built on an entirely different stack. And the tooling has undergone a full generation worth of change in six weeks, which is crazy. So it just tells you how much energy is going into this and how fast it's evolving right now. >> John, weigh in as a chief scientist. I mean, you must be blown away. Talk about kid in the candy store. I mean, you must be looking like this saying, I mean, she must be super busy to begin with, but the change, the acceleration, can you scope the kind of change you're seeing and be specific around the areas you're seeing movement and highly accelerated change? >> Yeah, definitely. And it is very, very exciting actually, thinking back to when ChatGPT was announced, that was a night our company was throwing an event at NeurIPS, which is maybe the biggest machine learning conference out there. And the hype when that happened was palatable and it was just shocking to see how well that performed. And then obviously over the last few months since then, as LLMs have continued to enter the market, we've seen use cases for them, like Adam mentioned all over the place. And so, some things I'm excited about in this space are the use of LLMs and more generally, foundation models to redesign traditional operations, research style problems, logistics problems, like auctions, decisioning problems. So moving beyond the already amazing news cases, like creating marketing content into more core integration and a lot of the bread and butter companies and tasks that drive the American ecosystem. And I think we're just starting to see some of that. And in the next 12 months, I think we're going to see a lot more. If I had to make other predictions, I think we're going to continue seeing a lot of work being done on managing like inference time costs via shrinking models or distillation. And I don't know how to make this prediction, but at some point we're going to be seeing lots of these very large scale models operating on the edge as well. So the time scales are extremely compressed, like Adam mentioned, 12 months from now, hard to say. >> We were talking on theCUBE prior to this session here. We had theCUBE conversation here and then the Wall Street Journal just picked up on the same theme, which is the printing press moment created the enlightenment stage of the history. Here we're in the whole nother automating intellect efficiency, doing heavy lifting, the creative class coming back, a whole nother level of reality around the corner that's being hyped up. The question is, is this justified? Is there really a breakthrough here or is this just another result of continued progress with AI? Can you guys weigh in, because there's two schools of thought. There's the, "Oh my God, we're entering a new enlightenment tech phase, of the equivalent of the printing press in all areas. Then there's, Ah, it's just AI (indistinct) inch by inch. What's your guys' opinion? >> Yeah, I think on the one hand when you're down in the weeds of building AI systems all day, every day, like we are, it's easy to look at this as an incremental progress. Like we have customers who've been building on foundation models since we started the company four years ago, particular in computer vision for classification tasks, starting with pre-trained models, things like that. So that part of it doesn't feel real new, but what does feel new is just when you apply these things to language with all the breakthroughs and computational efficiency, algorithmic improvements, things like that, when you actually sit down and interact with ChatGPT or one of the other systems that's out there that's building on top of LLMs, it really is breathtaking, like, the level of understanding that they have and how quickly you can accelerate your development efforts and get an actual working system in place that solves a really important real world problem and makes people way faster, way more efficient. So I do think there's definitely something there. It's more than just incremental improvement. This feels like a real trajectory inflection point for the adoption of AI. >> John, what's your take on this? As people come into the field, I'm seeing a lot of people move from, hey, I've been coding in Python, I've been doing some development, I've been a software engineer, I'm a computer science student. I'm coding in C++ old school, OG systems person. Where do they come in? Where's the focus, where's the action? Where are the breakthroughs? Where are people jumping in and rolling up their sleeves and getting dirty with this stuff? >> Yeah, all over the place. And it's funny you mentioned students in a different life. I wore a university professor hat and so I'm very, very familiar with the teaching aspects of this. And I will say toward Adam's point, this really is a leap forward in that techniques like in a co-pilot for example, everybody's using them right now and they really do accelerate the way that we develop. When I think about the areas where people are really, really focusing right now, tooling is certainly one of them. Like you and I were chatting about LangChain right before this interview started, two or three people can sit down and create an amazing set of pipes that connect different aspects of the LLM ecosystem. Two, I would say is in engineering. So like distributed training might be one, or just understanding better ways to even be able to train large models, understanding better ways to then distill them or run them. So like this heavy interaction now between engineering and what I might call traditional machine learning from 10 years ago where you had to know a lot of math, you had to know calculus very well, things like that. Now you also need to be, again, a very strong engineer, which is exciting. >> I interviewed Swami when he talked about the news. He's ahead of Amazon's machine learning and AI when they announced Hugging Face announcement. And I reminded him how Amazon was easy to get into if you were developing a startup back in 2007,8, and that the language models had that similar problem. It's step up a lot of content and a lot of expense to get provisioned up, now it's easy. So this is the next wave of innovation. So how do you guys see that from where we are right now? Are we at that point where it's that moment where it's that cloud-like experience for LLMs and large language models? >> Yeah, go ahead John. >> I think the answer is yes. We see a number of large companies that are training these and serving these, some of which are being co-interviewed in this episode. I think we're at that. Like, you can hit one of these with a simple, single line of Python, hitting an API, you can boot this up in seconds if you want. It's easy. >> Got it. >> So I (audio cuts out). >> Well let's take a step back and talk about the company. You guys being featured here on the Showcase. Arthur, what drove you to start the company? How'd this all come together? What's the origination story? Obviously you got a big customers, how'd get started? What are you guys doing? How do you make money? Give a quick overview. >> Yeah, I think John and I come at it from slightly different angles, but for myself, I have been a part of a number of technology companies. I joined Capital One, they acquired my last company and shortly after I joined, they asked me to start their AI team. And so even though I've been doing AI for a long time, I started my career back in DARPA. It was the first time I was really working at scale in AI at an organization where there were hundreds of millions of dollars in revenue at stake with the operation of these models and that they were impacting millions of people's financial livelihoods. And so it just got me hyper-focused on these issues around making sure that your AI worked well and it worked well for your company and it worked well for the people who were being affected by it. At the time when I was doing this 2016, 2017, 2018, there just wasn't any tooling out there to support this production management model monitoring life phase of the life cycle. And so we basically left to start the company that I wanted. And John has a his own story. I'll let let you share that one, John. >> Go ahead John, you're up. >> Yeah, so I'm coming at this from a different world. So I'm on leave now from a tenured role in academia where I was leading a large lab focusing on the intersection of machine learning and economics. And so questions like fairness or the response to the dynamism on the underlying environment have been around for quite a long time in that space. And so I've been thinking very deeply about some of those more like R and D style questions as well as having deployed some automation code across a couple of different industries, some in online advertising, some in the healthcare space and so on, where concerns of, again, fairness come to bear. And so Adam and I connected to understand the space of what that might look like in the 2018 20 19 realm from a quantitative and from a human-centered point of view. And so booted things up from there. >> Yeah, bring that applied engineering R and D into the Capital One, DNA that he had at scale. I could see that fit. I got to ask you now, next step, as you guys move out and think about LLMs and the recent AI news around the generative models and the foundational models like ChatGPT, how should we be looking at that news and everyone watching might be thinking the same thing. I know at the board level companies like, we should refactor our business, this is the future. It's that kind of moment, and the tech team's like, okay, boss, how do we do this again? Or are they prepared? How should we be thinking? How should people watching be thinking about LLMs? >> Yeah, I think they really are transformative. And so, I mean, we're seeing companies all over the place. Everything from large tech companies to a lot of our large enterprise customers are launching significant projects at core parts of their business. And so, yeah, I would be surprised, if you're serious about becoming an AI native company, which most leading companies are, then this is a trend that you need to be taking seriously. And we're seeing the adoption rate. It's funny, I would say the AI adoption in the broader business world really started, let's call it four or five years ago, and it was a relatively slow adoption rate, but I think all that kind of investment in and scaling the maturity curve has paid off because the rate at which people are adopting and deploying systems based on this is tremendous. I mean, this has all just happened in the few months and we're already seeing people get systems into production. So, now there's a lot of things you have to guarantee in order to put these in production in a way that basically is added into your business and doesn't cause more headaches than it solves. And so that's where we help customers is where how do you put these out there in a way that they're going to represent your company well, they're going to perform well, they're going to do their job and do it properly. >> So in the use case, as a customer, as I think about this, there's workflows. They might have had an ML AI ops team that's around IT. Their inference engines are out there. They probably don't have a visibility on say how much it costs, they're kicking the tires. When you look at the deployment, there's a cost piece, there's a workflow piece, there's fairness you mentioned John, what should be, I should be thinking about if I'm going to be deploying stuff into production, I got to think about those things. What's your opinion? >> Yeah, I'm happy to dive in on that one. So monitoring in general is extremely important once you have one of these LLMs in production, and there have been some changes versus traditional monitoring that we can dive deeper into that LLMs are really accelerated. But a lot of that bread and butter style of things you should be looking out for remain just as important as they are for what you might call traditional machine learning models. So the underlying environment of data streams, the way users interact with these models, these are all changing over time. And so any performance metrics that you care about, traditional ones like an accuracy, if you can define that for an LLM, ones around, for example, fairness or bias. If that is a concern for your particular use case and so on. Those need to be tracked. Now there are some interesting changes that LLMs are bringing along as well. So most ML models in production that we see are relatively static in the sense that they're not getting flipped in more than maybe once a day or once a week or they're just set once and then not changed ever again. With LLMs, there's this ongoing value alignment or collection of preferences from users that is often constantly updating the model. And so that opens up all sorts of vectors for, I won't say attack, but for problems to arise in production. Like users might learn to use your system in a different way and thus change the way those preferences are getting collected and thus change your system in ways that you never intended. So maybe that went through governance already internally at the company and now it's totally, totally changed and it's through no fault of your own, but you need to be watching over that for sure. >> Talk about the reinforced learnings from human feedback. How's that factoring in to the LLMs? Is that part of it? Should people be thinking about that? Is that a component that's important? >> It certainly is, yeah. So this is one of the big tweaks that happened with InstructGPT, which is the basis model behind ChatGPT and has since gone on to be used all over the place. So value alignment I think is through RLHF like you mentioned is a very interesting space to get into and it's one that you need to watch over. Like, you're asking humans for feedback over outputs from a model and then you're updating the model with respect to that human feedback. And now you've thrown humans into the loop here in a way that is just going to complicate things. And it certainly helps in many ways. You can ask humans to, let's say that you're deploying an internal chat bot at an enterprise, you could ask humans to align that LLM behind the chatbot to, say company values. And so you're listening feedback about these company values and that's going to scoot that chatbot that you're running internally more toward the kind of language that you'd like to use internally on like a Slack channel or something like that. Watching over that model I think in that specific case, that's a compliance and HR issue as well. So while it is part of the greater LLM stack, you can also view that as an independent bit to watch over. >> Got it, and these are important factors. When people see the Bing news, they freak out how it's doing great. Then it goes off the rails, it goes big, fails big. (laughing) So these models people see that, is that human interaction or is that feedback, is that not accepting it or how do people understand how to take that input in and how to build the right apps around LLMs? This is a tough question. >> Yeah, for sure. So some of the examples that you'll see online where these chatbots go off the rails are obviously humans trying to break the system, but some of them clearly aren't. And that's because these are large statistical models and we don't know what's going to pop out of them all the time. And even if you're doing as much in-house testing at the big companies like the Go-HERE's and the OpenAI's of the world, to try to prevent things like toxicity or racism or other sorts of bad content that might lead to bad pr, you're never going to catch all of these possible holes in the model itself. And so, again, it's very, very important to keep watching over that while it's in production. >> On the business model side, how are you guys doing? What's the approach? How do you guys engage with customers? Take a minute to explain the customer engagement. What do they need? What do you need? How's that work? >> Yeah, I can talk a little bit about that. So it's really easy to get started. It's literally a matter of like just handing out an API key and people can get started. And so we also offer alternative, we also offer versions that can be installed on-prem for models that, we find a lot of our customers have models that deal with very sensitive data. So you can run it in your cloud account or use our cloud version. And so yeah, it's pretty easy to get started with this stuff. We find people start using it a lot of times during the validation phase 'cause that way they can start baselining performance models, they can do champion challenger, they can really kind of baseline the performance of, maybe they're considering different foundation models. And so it's a really helpful tool for understanding differences in the way these models perform. And then from there they can just flow that into their production inferencing, so that as these systems are out there, you have really kind of real time monitoring for anomalies and for all sorts of weird behaviors as well as that continuous feedback loop that helps you make make your product get better and observability and you can run all sorts of aggregated reports to really understand what's going on with these models when they're out there deciding. I should also add that we just today have another way to adopt Arthur and that is we are in the AWS marketplace, and so we are available there just to make it that much easier to use your cloud credits, skip the procurement process, and get up and running really quickly. >> And that's great 'cause Amazon's got SageMaker, which handles a lot of privacy stuff, all kinds of cool things, or you can get down and dirty. So I got to ask on the next one, production is a big deal, getting stuff into production. What have you guys learned that you could share to folks watching? Is there a cost issue? I got to monitor, obviously you brought that up, we talked about the even reinforcement issues, all these things are happening. What is the big learnings that you could share for people that are going to put these into production to watch out for, to plan for, or be prepared for, hope for the best plan for the worst? What's your advice? >> I can give a couple opinions there and I'm sure Adam has. Well, yeah, the big one from my side is, again, I had mentioned this earlier, it's just the input data streams because humans are also exploring how they can use these systems to begin with. It's really, really hard to predict the type of inputs you're going to be seeing in production. Especially, we always talk about chatbots, but then any generative text tasks like this, let's say you're taking in news articles and summarizing them or something like that, it's very hard to get a good sampling even of the set of news articles in such a way that you can really predict what's going to pop out of that model. So to me, it's, adversarial maybe isn't the word that I would use, but it's an unnatural shifting input distribution of like prompts that you might see for these models. That's certainly one. And then the second one that I would talk about is, it can be hard to understand the costs, the inference time costs behind these LLMs. So the pricing on these is always changing as the models change size, it might go up, it might go down based on model size, based on energy cost and so on, but your pricing per token or per a thousand tokens and that I think can be difficult for some clients to wrap their head around. Again, you don't know how these systems are going to be used after all so it can be tough. And so again that's another metric that really should be tracked. >> Yeah, and there's a lot of trade off choices in there with like, how many tokens do you want at each step and in the sequence and based on, you have (indistinct) and you reject these tokens and so based on how your system's operating, that can make the cost highly variable. And that's if you're using like an API version that you're paying per token. A lot of people also choose to run these internally and as John mentioned, the inference time on these is significantly higher than a traditional classifi, even NLP classification model or tabular data model, like orders of magnitude higher. And so you really need to understand how that, as you're constantly iterating on these models and putting out new versions and new features in these models, how that's affecting the overall scale of that inference cost because you can use a lot of computing power very quickly with these profits. >> Yeah, scale, performance, price all come together. I got to ask while we're here on the secret sauce of the company, if you had to describe to people out there watching, what's the secret sauce of the company? What's the key to your success? >> Yeah, so John leads our research team and they've had a number of really cool, I think AI as much as it's been hyped for a while, it's still commercial AI at least is really in its infancy. And so the way we're able to pioneer new ways to think about performance for computer vision NLP LLMs is probably the thing that I'm proudest about. John and his team publish papers all the time at Navs and other places. But I think it's really being able to define what performance means for basically any kind of model type and give people really powerful tools to understand that on an ongoing basis. >> John, secret sauce, how would you describe it? You got all the action happening all around you. >> Yeah, well I going to appreciate Adam talking me up like that. No, I. (all laughing) >> Furrier: Robs to you. >> I would also say a couple of other things here. So we have a very strong engineering team and so I think some early hires there really set the standard at a very high bar that we've maintained as we've grown. And I think that's really paid dividends as scalabilities become even more of a challenge in these spaces, right? And so that's not just scalability when it comes to LLMs, that's scalability when it comes to millions of inferences per day, that kind of thing as well in traditional ML models. And I think that's compared to potential competitors, that's really... Well, it's made us able to just operate more efficiently and pass that along to the client. >> Yeah, and I think the infancy comment is really important because it's the beginning. You really is a long journey ahead. A lot of change coming, like I said, it's a huge wave. So I'm sure you guys got a lot of plannings at the foundation even for your own company, so I appreciate the candid response there. Final question for you guys is, what should the top things be for a company in 2023? If I'm going to set the agenda and I'm a customer moving forward, putting the pedal to the metal, so to speak, what are the top things I should be prioritizing or I need to do to be successful with AI in 2023? >> Yeah, I think, so number one, as we talked about, we've been talking about this entire episode, the things are changing so quickly and the opportunities for business transformation and really disrupting different applications, different use cases, is almost, I don't think we've even fully comprehended how big it is. And so really digging in to your business and understanding where I can apply these new sets of foundation models is, that's a top priority. The interesting thing is I think there's another force at play, which is the macroeconomic conditions and a lot of places are, they're having to work harder to justify budgets. So in the past, couple years ago maybe, they had a blank check to spend on AI and AI development at a lot of large enterprises that was limited primarily by the amount of talent they could scoop up. Nowadays these expenditures are getting scrutinized more. And so one of the things that we really help our customers with is like really calculating the ROI on these things. And so if you have models out there performing and you have a new version that you can put out that lifts the performance by 3%, how many tens of millions of dollars does that mean in business benefit? Or if I want to go to get approval from the CFO to spend a few million dollars on this new project, how can I bake in from the beginning the tools to really show the ROI along the way? Because I think in these systems when done well for a software project, the ROI can be like pretty spectacular. Like we see over a hundred percent ROI in the first year on some of these projects. And so, I think in 2023, you just need to be able to show what you're getting for that spend. >> It's a needle moving moment. You see it all the time with some of these aha moments or like, whoa, blown away. John, I want to get your thoughts on this because one of the things that comes up a lot for companies that I talked to, that are on my second wave, I would say coming in, maybe not, maybe the front wave of adopters is talent and team building. You mentioned some of the hires you got were game changing for you guys and set the bar high. As you move the needle, new developers going to need to come in. What's your advice given that you've been a professor, you've seen students, I know a lot of computer science people want to shift, they might not be yet skilled in AI, but they're proficient in programming, is that's going to be another opportunity with open source when things are happening. How do you talk to that next level of talent that wants to come in to this market to supplement teams and be on teams, lead teams? Any advice you have for people who want to build their teams and people who are out there and want to be a coder in AI? >> Yeah, I've advice, and this actually works for what it would take to be a successful AI company in 2023 as well, which is, just don't be afraid to iterate really quickly with these tools. The space is still being explored on what they can be used for. A lot of the tasks that they're used for now right? like creating marketing content using a machine learning is not a new thing to do. It just works really well now. And so I'm excited to see what the next year brings in terms of folks from outside of core computer science who are, other engineers or physicists or chemists or whatever who are learning how to use these increasingly easy to use tools to leverage LLMs for tasks that I think none of us have really thought about before. So that's really, really exciting. And so toward that I would say iterate quickly. Build things on your own, build demos, show them the friends, host them online and you'll learn along the way and you'll have somebody to show for it. And also you'll help us explore that space. >> Guys, congratulations with Arthur. Great company, great picks and shovels opportunities out there for everybody. Iterate fast, get in quickly and don't be afraid to iterate. Great advice and thank you for coming on and being part of the AWS showcase, thanks. >> Yeah, thanks for having us on John. Always a pleasure. >> Yeah, great stuff. Adam Wenchel, John Dickerson with Arthur. Thanks for coming on theCUBE. I'm John Furrier, your host. Generative AI and AWS. Keep it right there for more action with theCUBE. Thanks for watching. (upbeat music)

(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)

>>Hey everyone. Welcome back to Chicago. The Cube is excited to be live on day two of Ansible Fest, 2022. Lisa Martin and John Fur. You're here having some great conversations, a lot of cube alumni, a lot of wisdom from the Ansible community coming at you on this program this week. You know, John, we've been, we've been hearing stories about the power and the capabilities and the collective wisdom of the Ansible community. You can feel it here. Yeah, there's no doubt about that. It's, Ansible is nothing, as Stephanie Chair said yesterday, if not a community and the significant contributions that it makes over and over again, or it's fuel. >>I mean the power of the community is what drives Ansible is gonna drive the future of, I think, cloud in our next generation modern application environment. And it's collective intelligence. It's a production system at the end of the day. And I think these guys have harnessed it. So it should be a really great segment to talk about all the contributor work that's been done. So I'm looking forward to it. >>We've got two great alumni here to talk about the contributor work, how you can get involved. Please welcome back to the cube. Carol Chen, principal community architect at Red Hat. Adam Miller joins us as well, fresh from the keynote stage senior principal software engineer at Red Hat. Guys, great to have you on the cube. Great to be here. Yeah, thank you. So we, we talked, we enjoyed your keynotes, Adam, and what you were talking about on stage, the Ansible contributor summit. That's, you guys have been doing what, this is the seven you've had seven so far in just a couple of years. >>Well, we had seven virtual contributor summits. >>Seven virtual. This is the first Monday was the first in person in. >>First in person since the pandemic and actually the 15th contributor summit overall >>15th overall. Talk about the contributor summits, what the contributors are able to do and the influence that it's having on Ansible Red Hat and what people are able to do with cloud. At the Edge automation. Yeah. >>So our community contributors have always had ways to influence and contribute to the project. But the contributor Summit is really a place where we can get people together, preferably in the same place so that we can, you know, have a really great dynamic conversations and interactions. But we also want to make sure that we don't leave out people who have been constantly online joining us. So this year we are so happy to be here in Chicago in person. We've had about 60 to 70 here joining us. And at first I thought maybe we'll have one third of the attendees joining online because about 30 to 40 people signed up to join online. But in the end, we have more than 100 per people watching our live stream. So that's more than half of the attendees overall, were joining us online. So that really shows where, you know, the contributors are interested in participating for >>Develop. Right. Yeah, it's been, it's been interesting. It's been since 2019, since the in-person Ansible Fest in Atlanta. Now we're in Chicago, we had the pandemic. Couple interesting observations from our side that I wanna get your reaction to Adam Carol. And that is one Ansible's relevance has grown significantly since then. Just from a cloud growth standpoint, developer open source standpoint, and how people work and collaborate has changed. So your contributor based in your community is getting more powerful in scope, in my opinion. Like in, as they become, have the keys to the kingdom in the, in their respective worlds as it gets bigger and larger. So the personas are changing, the makeup of the community's changing and also how you guys collaborate is changing. Can you share your, what's going on with those two dynamics? Cause I think that power dynamic is, is looking really good. How are you guys handling >>That? Yeah, so I mean, I, I had the opportunity to represent the community on stage yesterday as part of the keynote and talk to this point specifically is one of the things that we've seen is the project has had the opportunity to kind of grow and evolve. There's been certain elements that have had to kind of decompose from a technology perspective. We actually had to kind of break it apart and change the architecture a little bit and move things into what are called Ansible collections, which, you know, folks here are very familiar with No One Love. And we've seen a lot of community work in the form of working groups coalesce around those organically. However, they've done so in kind of different ways. They, they pick tools and collaboration platforms that are popular to their subject matter expertise audience and things like that. So we find ourselves in a place where kind of the, the community itself had more or less segmented naturally in a way. And we needed to find ways to, you know, kind of ke that >>Fragmentation by demographics or by expertise or both as >>A Mostly, mostly expertise. Yeah. And so there was a open source technology called Matrix. It is a open source, standardized, federated messaging platform that we're able to use to start to bridge back some of those communities that have kind of broken off and, and made their their own home elsewhere on the internet. So now we're able to, for example, the right, the docs organization, they had a, a group of people who was very interested in contributing to the Ansible documentation, but they'd already self-organized on Discord. And what was interesting there is the existing team for the Ansible documentation, they were already on internet Relay Chat, also known as irc. And Matrix allowed us to actually bring those two together and bridge that into the other matrix cha chat channels that we had. So now we're able to have people from all over the world in different areas and different platforms, coalesce and, and cross. It's like a festival cross pollinate. Yeah. >>And you're meeting the contributors exactly where they are and where they want to be, where they're comfortable. >>Yes. Yeah, we always say we, we reach out to where they are. So, >>And, and, and much in the way that Ansible has the capability to reach out to things in their own way, Right. And allow that subject matter expertise to, you know, cause the technology has the potential and possibility and capability to talk to anything over any protocol. We're able to do, you know, kind of the same thing with Matrix, allowing us to bridge into any chat platform that it has support for bridging and, and we're able to bring a lot of people >>Together. Yeah. And how's that, how's the feedback been on that so far? >>I, I think it has been very positive. For example, I want to highlight that the technical writers that we have contributing via Discord is actually a group from Nigeria. And Dave also participated in the contributor summit online virtually joining us in, in, you know, on the matrix platform. So that, that bridge that really helps to bring together people from different geographical regions and also different topics and arenas like that. >>So what were some of the outcomes of the contributor summit? The, the first in person in a while, great. That you guys were able to do seven virtually during the pandemic. That's hard. It's hard to get people together. You, there's so much greatness and innovation that comes when we're all together in person that just can't replicate by video. You can do a lot. Right. But talk about some of the outcomes from Monday. What were some of the feedback? What were some of the contributions that you think are really going to impact the community? >>I think for a lot of us, myself included, the fact that we are in person and meeting people face to face, it helps to really build the connections. And when we do talk about contribution, the connection is so important that you understand, well this person a little bit about their background, what they've done for the SPO project and or just generally what, what they're interested in that builds the rapport and connection that helps, you know, further, further collaboration in the future. Because maybe on that day we did not have any, you know, co contributions or anything, but the fact that we had a chance to sit together in the same place to discuss things and share new ideas, roadmaps is really the, the kind of a big step to the future for our community. Yes, >>Yes. And in a lot of ways we often online the project has various elements that are able to function asynchronously. So we work very well globally across many time zones. And now we were able to get a lot of people in the same place at the same time, synchronously in the same time zone. And then we had breakout sessions where the subject matter, you know, working groups were able to kind of go and focus on things that maybe have been taking a little while to discuss in, in that asynchronous form of communication and do it synchronously and, you know, be in the same room and work on things. It's been, it's been fantastic >>Developers there, like they, they take to asynchronous like fish to water. It's not a problem. But I do want to ask if there's any observations that you guys have had now that we're kind of coming out of that one way, but the pandemic, but the world's changed. It's hybrid, hybrid work environment, steady state. So we see that. Any observations on your end on what's new that you observed that people are gravitating to? Is there a pattern of styles is or same old self-governing, or what's new? What do you see that's coming out of the pandemic that might be a norm? >>I I think that even though people are excited to get back in person, there are, things have changed, like you said, and we have to be more aware of, there are people who think that not be in person, it's okay. And that's how they want to do it. And we have to make sure that they, they are included. So we, we did want to make a high priority for online participation in this event. And like I said, even though only 4 30, 40 people signed up to join us online initially, so that it was what we were expecting, but in the end, more than 100 people were watching us and, and joining participation in >>Actually on demand consumption be good too, >>Right? Yeah. So, you know, I think going forward that is probably the trend. And as, as much as we, we love being in person, we, we want this to continue that we, we take care of people who are, has been constantly participating online and contributing you >>Meaning again, meaning folks where they are, but also allowing the, the, those members that want to get together to, to collaborate in person. I can only imagine the innovation that's gonna come even from having part of the back, Right. >>And, and not to continue to harp on the matrix point, but it, it's been very cool because Matrix has the ability to do live video sessions using open source another to open source technology called jy. So we're able to actually use the same place that we normally find ourselves, you know, congregating and collaborating for the project itself in an asynchronous and, you know, somewhat synchronous way to also host these types of things that are, are now hybrid that used to be, you know, all one way or all the other. Yeah. And it's been, it's >>Been incredible. Integration is, the integration is have been fascinating to watch how you guys do that. And also, you know, with q we've been virtual too. It's like, it's like people don't want another microsite, but they want a more of a festival vibe, a hub, right? Like a place to kind of check in and have choice, not get absolutely jammed into a, you know, forum or, you know, or whatever. Hey, if you wanna be on Discord, be on Discord, right? Why >>Not? And we still, you know, we do still have our asynchronous forms of >>Work through >>Our get GitHub. We have our projects, we have our issues, we have our, you know, wiki, we have various elements there that everybody can continue to collaborate on. And it's all been, it's all been very good. >>Speaking of festivals, octoberfest that's going on, not to be confused with Octoberfest, that was last month. Talk about how the Ansible project and the Ansible community is involved in Octoberfest. Give us the dates, Carol. So >>YesTo Fest is a annual thing in October. So October Octoberfest, I think it's organized by Digital Ocean for the past eight or nine years. And it's really a, a way to kind of encourage people to contribute to open source projects. So it's not anal specific, but we as an Ansible project encourage people to take this opportunity to, you know, a lot of them doing their first contributions during this event. And when, when we first announced, we're participating in Octoberfest within the first four days of October, which is over a weekend actually. We've had 24 contributions, it, 24 issues fixed, which is like amazing, like, you know, just the interest and the, the momentum that we had. And so far until I just checked with my teammates this morning that we've had about 35 contributions so far during the month, which is, and I'm sorry, I forgot to mention this is only for Ansible documentation. So yeah, specifically. And, and that's also one thing we want to highlight, that contributions don't just come in code in, you know, kind of software side, but really there's many ways to contribute and documentation is such a, a great way for first time users, first time contributors to get involved. So it's really amazing to see these contributions from all over the world. And also partly thanks to the technical writers in Nigeria kind of promoting and sharing this initiative. And it's just great to see the, the results from that. Can >>You double click on the different ways of contribution? You mentioned a couple documentation being one, code being the other, but what is the breadth of opportunities that the contributors have to contribute to the project? >>Oh, there's, there's so many. So I actually take care more of outreach efforts in the community. So I helped to organize events and meetups from around the world. And now that we're slowly coming out of the pandemic, I've seen more and more in person meetups. I was just talking to someone from Minneapolis, they're trying to get, get people back together again. They have people in Singapore, in Netherlands from pretty much, you know, all corners of the globe wanting to form not just for the Ansible project, but the local kind of connection with the re people in the region, sometimes in their own language, in their local languages to really work together on the project and just, >>You know, you to create a global Yeah. Network, right? I mean it's like Ansible Global. >>Exactly. >>Create local subnets not to get all networking, >>Right? >>Yeah. >>Yeah. One, one quick thing I want to touch on Theto Fest. I think it's a great opportunity for existing contributors to mentor cause many people like to help bring in new contributors and this is kind of a focal point to be able to focus on that. And then to, to the the other point we, you know, it, it's been, it's been extremely powerful to see as we return these sub communities pop up and, and kind of work with themselves, so on different ways to contribute. So code is kind of the one that gets the most attention. I think documentation I think is a unsung hero, highly important, great way. The logistical component, which is invaluable because it allows us to continue with our adoption and evangelization and things like that. So specifically adoption and evangelize. Evangelization is another place that contributors can join and actually spawn a local meetup and then connect in with the existing community and try to, you know, help increase the network, create a new subject. Yeah. >>Yeah. Network affects huge. And I think the thing that you brought up about reuse is, is part of that whole things get documented properly. The leverage that comes out of that just feeds into the system that flywheel. Absolutely. I mean it's, that's how communities are supposed to work, right? Yep. Yes. >>That's what I was just gonna comment on is the flywheel effect that it's clearly present and very palpable. Thank you so much for joining John, me on the program, talking about the contributors summit, the ways of contribution, the impacts that are being made so far, what Octoberfest is already delivering. And we're, we still have about 10 days or so left in October, so there's still more time for contributors to get involved. We thank you so much for your insights and your time. Thank >>You. Thank you so much for having us. >>Our pleasure. For our guests and John Purer, I'm Lisa Martin. You're watching The Cube Live from Chicago, day two of our coverage of Red Hat Ansible Summit 22. We will see you right n after this short break with our next guest.

>> We're back at the ARIA Las Vegas. We're covering CrowdStrike's Fal.Con 22. First one since 2019. Dave Vellante and Dave Nicholson on theCUBE. Adam Meyers is here, he is the Senior Vice President of Intelligence at CrowdStrike. Adam, thanks for coming to theCUBE. >> Thanks for having me. >> Interesting times, isn't it? You're very welcome. Senior Vice President of Intelligence, tell us what your role is. >> So I run all of our intelligence offerings. All of our analysts, we have a couple hundred analysts that work at CrowdStrike tracking threat actors. There's 185 threat actors that we track today. We're constantly adding more of them and it requires us to really have that visibility and understand how they operate so that we can inform our other products: our XDR, our Cloud Workload Protections and really integrate all of this around the threat actor. >> So it's that threat hunting capability that CrowdStrike has. That's what you're sort of... >> Well, so think of it this way. When we launched the company 11 years ago yesterday, what we wanted to do was to tell customers, to tell people that, well, you don't have a malware problem, you have an adversary problem. There are humans that are out there conducting these attacks, and if you know who they are what they're up to, how they operate then you're better positioned to defend against them. And so that's really at the core, what CrowdStrike started with and all of our products are powered by intelligence. All of our services are our OverWatch and our Falcon complete, all powered by intelligence because we want to know who the threat actors are and what they're doing so we can stop them. >> So for instance like you can stop known malware. A lot of companies can stop known malware, but you also can stop unknown malware. And I infer that the intelligence is part of that equation, is that right? >> Absolutely. That that's the outcome. That's the output of the intelligence but I could also tell you who these threat actors are, where they're operating out of, show you pictures of some of them, that's the threat intel. We are tracking down to the individual persona in many cases, these various threats whether they be Chinese nation state, Russian threat actors, Iran, North Korea, we track as I said, quite a few of these threats. And over time, we develop a really robust deep knowledge about who they are and how they operate. >> Okay. And we're going to get into some of that, the big four and cyber. But before we do, I want to ask you about the eCrime index stats, the ECX you guys call it a little side joke for all your nerds out there. Maybe you could explain that Adam >> Assembly humor. >> Yeah right, right. So, but, what is that index? You guys, how often do you publish it? What are you learning from that? >> Yeah, so it was modeled off of the Dow Jones industrial average. So if you look at the Dow Jones it's a composite index that was started in the late 1800s. And they took a couple of different companies that were the industrial component of the economy back then, right. Textiles and railroads and coal and steel and things like that. And they use that to approximate the overall health of the economy. So if you take these different stocks together, swizzle 'em together, and figure out some sort of number you could say, look, it's up. The economy's doing good. It's down, not doing so good. So after World War II, everybody was exuberant and positive about the end of the war. The DGI goes up, the oil crisis in the seventies goes down, COVID hits goes up, sorry, goes down. And then everybody realizes that they can use Amazon still and they can still get the things they need goes back up with the eCrime index. We took that approach to say what is the health of the underground economy? When you read about any of these ransomware attacks or data extortion attacks there are criminal groups that are working together in order to get things spammed out or to buy credentials and things like that. And so what the eCrime index does is it takes 24 different observables, right? The price of a ransom, the number of ransom attacks, the fluctuation in cryptocurrency, how much stolen material is being sold for on the underground. And we're constantly computing this number to understand is the eCrime ecosystem healthy? Is it thriving or is it under pressure? And that lets us understand what's going on in the world and kind of contextualize it. Give an example, Microsoft on patch Tuesday releases 56 vulnerabilities. 11 of them are critical. Well guess what? After hack Tuesday. So after patch Tuesday is hack Wednesday. And so all of those 11 vulnerabilities are exploitable. And now you have threat actors that have a whole new array of weapons that they can deploy and bring to bear against their victims after that patch Tuesday. So that's hack Wednesday. Conversely we'll get something like the colonial pipeline. Colonial pipeline attack May of 21, I think it was, comes out and all of the various underground forums where these ransomware operators are doing their business. They freak out because they don't want law enforcement. President Biden is talking about them and he's putting pressure on them. They don't want this ransomware component of what they're doing to bring law enforcement, bring heat on them. So they deplatform them. They kick 'em off. And when they do that, the ransomware stops being as much of a factor at that point in time. And the eCrime index goes down. So we can look at holidays, and right around Thanksgiving, which is coming up pretty soon, it's going to go up because there's so much online commerce with cyber Monday and such, right? You're going to see this increase in online activity; eCrime actors want to take advantage of that. When Christmas comes, they take vacation too; they're going to spend time with their families, so it goes back down and it stays down till around the end of the Russian Orthodox Christmas, which you can probably extrapolate why that is. And then it goes back up. So as it's fluctuating, it gives us the ability to really just start tracking what that economy looks like. >> Realtime indicator of that crypto. >> I mean, you talked about, talked about hack Wednesday, and before that you mentioned, you know, the big four, and I think you said 185 threat actors that you're tracking, is 180, is number 185 on that list? Somebody living in their basement in their mom's basement or are the resources necessary to get on that list? Such that it's like, no, no, no, no. this is very, very organized, large groups of people. Hollywood would have you believe that it's guy with a laptop, hack Wednesday, (Dave Nicholson mimics keyboard clacking noises) and everything done. >> Right. >> Are there individuals who are doing things like that or are these typically very well organized? >> That's a great question. And I think it's an important one to ask and it's both it tends to be more, the bigger groups. There are some one-off ones where it's one or two people. Sometimes they get big. Sometimes they get small. One of the big challenges. Have you heard of ransomware as a service? >> Of course. Oh my God. Any knucklehead can be a ransomwarist. >> Exactly. So we don't track those knuckleheads as much unless they get onto our radar somehow, they're conducting a lot of operations against our customers or something like that. But what we do track is that ransomware as a service platform because the affiliates, the people that are using it they come, they go and, you know, it could be they're only there for a period of time. Sometimes they move between different ransomware services, right? They'll use the one that's most useful for them that that week or that month, they're getting the best rate because it's rev sharing. They get a percentage that platform gets percentage of the ransom. So, you know, they negotiate a better deal. They might move to a different ransomware platform. So that's really hard to track. And it's also, you know, I think more important for us to understand the platform and the technology that is being used than the individual that's doing it. >> Yeah. Makes sense. Alright, let's talk about the big four. China, Iran, North Korea, and Russia. Tell us about, you know, how you monitor these folks. Are there different signatures for each? Can you actually tell, you know based on the hack who's behind it? >> So yeah, it starts off, you know motivation is a huge factor. China conducts espionage, they do it for diplomatic purposes. They do it for military and political purposes. And they do it for economic espionage. All of these things map to known policies that they put out, the Five Year Plan, the Made in China 2025, the Belt and Road Initiative, it's all part of their efforts to become a regional and ultimately a global hegemon. >> They're not stealing nickels and dimes. >> No they're stealing intellectual property. They're stealing trade secrets. They're stealing negotiation points. When there's, you know a high speed rail or something like that. And they use a set of tools and they have a set of behaviors and they have a set of infrastructure and a set of targets that as we look at all of these things together we can derive who they are by motivation and the longer we observe them, the more data we get, the more we can get that attribution. I could tell you that there's X number of Chinese threat groups that we track under Panda, right? And they're associated with the Ministry of State Security. There's a whole other set. That's too associated with the People's Liberation Army Strategic Support Force. So, I mean, these are big operations. They're intelligence agencies that are operating out of China. Iran has a different set of targets. They have a different set of motives. They go after North American and Israeli businesses right now that's kind of their main operation. And they're doing something called hack and lock and leak. With a lock and leak, what they're doing is they're deploying ransomware. They don't care about getting a ransom payment. They're just doing it to disrupt the target. And then they're leaking information that they steal during that operation that brings embarrassment. It brings compliance, regulatory, legal impact for that particular entity. So it's disruptive >> The chaos creators that's.. >> Well, you know I think they're trying to create a they're trying to really impact the legitimacy of some of these targets and the trust that their customers and their partners and people have in them. And that is psychological warfare in a certain way. And it, you know is really part of their broader initiative. Look at some of the other things that they've done they've hacked into like the missile defense system in Israel, and they've turned on the sirens, right? Those are all things that they're doing for a specific purpose, and that's not China, right? Like as you start to look at this stuff, you can start to really understand what they're up to. Russia very much been busy targeting NATO and NATO countries and Ukraine. Obviously the conflict that started in February has been a huge focus for these threat actors. And then as we look at North Korea, totally different. They're doing, there was a major crypto attack today. They're going after these crypto platforms, they're going after DeFi platforms. They're going after all of this stuff that most people don't even understand and they're stealing the crypto currency and they're using it for revenue generation. These nuclear weapons don't pay for themselves, their research and development don't pay for themselves. And so they're using that cyber operation to either steal money or steal intelligence. >> They need the cash. Yeah. >> Yeah. And they also do economic targeting because Kim Jong Un had said back in 2016 that they need to improve the lives of North Koreans. They have this national economic development strategy. And that means that they need, you know, I think only 30% of North Korea has access to reliable power. So having access to clean energy sources and renewable energy sources, that's important to keep the people happy and stop them from rising up against the regime. So that's the type of economic espionage that they're conducting. >> Well, those are the big four. If there were big five or six, I would presume US and some Western European countries would be on there. Do you track, I mean, where United States obviously has you know, people that are capable of this we're out doing our thing, and- >> So I think- >> That defense or offense, where do we sit in this matrix? >> Well, I think the big five would probably include eCrime. We also track India, Pakistan. We track actors out of Columbia, out of Turkey, out of Syria. So there's a whole, you know this problem is getting worse over time. It's proliferating. And I think COVID was also, you know a driver there because so many of these countries couldn't move human assets around because everything was getting locked down. As machine learning and artificial intelligence and all of this makes its way into the cameras at border and transfer points, it's hard to get a human asset through there. And so cyber is a very attractive, cheap and deniable form of espionage and gives them operational capabilities, not, you know and to your question about US and other kind of five I friendly type countries we have not seen them targeting our customers. So we focus on the threats that target our customers. >> Right. >> And so, you know, if we were to find them at a customer environment sure. But you know, when you look at some of the public reporting that's out there, the malware that's associated with them is focused on, you know, real bad people, and it's, it's physically like crypted to their hard drive. So unless you have sensor on, you know, an Iranian or some other laptop that might be target or something like that. >> Well, like Stuxnet did. >> Yeah. >> Right so. >> You won't see it. Right. See, so yeah. >> Well Symantec saw it but way back when right? Back in the day. >> Well, I mean, if you want to go down that route I think it actually came from a company in the region that was doing the IR and they were working with Symantec. >> Oh, okay. So, okay. So it was a local >> Yeah. I think Crisis, I think was the company that first identified it. And then they worked with Symantec. >> It Was, they found it, I guess, a logic controller. I forget what it was. >> It was a long time ago, so I might not have that completely right. >> But it was a seminal moment in the industry. >> Oh. And it was a seminal moment for Iran because you know, that I think caused them to get into cyber operations. Right. When they realized that something like that could happen that bolstered, you know there was a lot of underground hacking forums in Iran. And, you know, after Stuxnet, we started seeing that those hackers were dropping their hacker names and they were starting businesses. They were starting to try to go after government contracts. And they were starting to build training offensive programs, things like that because, you know they realized that this is an opportunity there. >> Yeah. We were talking earlier about this with Shawn and, you know, in the nuclear war, you know the Cold War days, you had the mutually assured destruction. It's not as black and white in the cyber world. Right. Cause as, as Robert Gates told me, you know a few years ago, we have a lot more to lose. So we have to be somewhat, as the United States, careful as to how much of an offensive posture we take. >> Well here's a secret. So I have a background on political science. So mutually assured destruction, I think is a deterrent strategy where you have two kind of two, two entities that like they will destroy each other if they so they're disinclined to go down that route. >> Right. >> With cyber I really don't like that mutually assured destruction >> That doesn't fit right. >> I think it's deterrents by denial. Right? So raising the cost, if they were to conduct a cyber operation, raising that cost that they don't want to do it, they don't want to incur the impact of that. Right. And think about this in terms of a lot of people are asking about would China invade Taiwan. And so as you look at the cost that that would have on the Chinese military, the POA, the POA Navy et cetera, you know, that's that deterrents by denial, trying to, trying to make the costs so high that they don't want to do it. And I think that's a better fit for cyber to try to figure out how can we raise the cost to the adversary if they operate against our customers against our enterprises and that they'll go someplace else and do something else. >> Well, that's a retaliatory strike, isn't it? I mean, is that what you're saying? >> No, definitely not. >> It's more of reducing their return on investment essentially. >> Yeah. >> And incenting them- disincening them to do X and sending them off somewhere else. >> Right. And threat actors, whether they be criminals or nation states, you know, Bruce Lee had this great quote that was "be like water", right? Like take the path of least resistance, like water will. Threat actors do that too. So, I mean, unless you're super high value target that they absolutely have to get into by any means necessary, then if you become too hard of a target, they're going to move on to somebody that's a little easier. >> Makes sense. Awesome. Really appreciate your, I could, we'd love to have you back. >> Anytime. >> Go deeper. Adam Myers. We're here at Fal.Con 22, Dave Vellante, Dave Nicholson. We'll be right back right after this short break. (bouncy music plays)

>>Okay. We're here with the rest of the child who was the chief product officer at Altryx and Adam Wilson, the CEO of Trifacta. Now of course, part of Altryx just closed this quarter. Gentlemen. Welcome. >>Great to be here. >>Okay. So rest, let me start with you. In my opening remarks, I talked about Altrix is traditional position serving business analysts and how the hyper Anna acquisition brought you deeper into the business user space. What does Trifacta bring to your portfolio? Why'd you buy the company? >>Yeah. Thank you. Thank you for the question. Um, you know, we see, uh, we see a massive opportunity of helping, um, brands, um, democratize the use of analytics across their business. Um, every knowledge worker, every individual in the company should have access to analytics. It's no longer optional, um, as they navigate, uh, their businesses with that in mind, you know, we know designer and are the products that Ultrix has been selling the past decade or so do a really great job, um, addressing the business analysts, uh, with, um, hyperaware, um, now kind of renamed, um, Altrix auto insights. Uh, we even speak with the, uh, business owner of the line of business owner. Who's looking for insights that aren't real in traditional dashboards and so on. Um, but we see this opportunity of really helping the data engineering teams and it organizations, um, to also make better use of analytics. Um, and that's where the drive factor comes in for us. Um, drive factor has the best data engineering cloud in the planet. Um, they have an established track record of working across multiple cloud platforms and helping data engineers, um, do better data pipelining and work better with, uh, this massive kind of cloud transformation that's happening in every business. Um, and so Trifacta made so much sense for us. >>Yeah. Thank you for that. I mean, look, you could have built it yourself. Would've taken, you know, who knows how long, but, uh, so definitely a great time to market move, Adam. I wonder if we could dig into Trifacta some more, I mean, I remember interviewing Joe Hellerstein in the early days. You've talked about this as well, uh, on the cube coming at the problem of taking data from raw refined to an experience point of view. And Joe in the early days, talked about flipping the model and starting with data visualization, something Jeff, her was expert at. So maybe explain how we got here. We used to have this cumbersome process of ETL and you may be in some others changed that model with ELL and then T explain how Trifacta really changed the data engineering game. >>Yeah, that's exactly right. Uh, David, it's been a really interesting journey for us because I think the original hypothesis coming out of the campus research, uh, at Berkeley and Stanford that really birthed Trifacta was, you know, why is it that the people who know the data best can't do the work? You know, why is this become the exclusive purview of the highly technical and, you know, can we rethink this and make this a user experience, problem powered by machine learning that will take some of the more complicated things that people want to do with data and really helped to automate those. So, so a, a broader set of users can, um, can really see for themselves and help themselves. And, and I think that, um, there was a lot of pent up frustration out there because people have been told for, you know, for a decade now to be more data-driven and then the whole time they're saying, well, then give me the data, you know, in the shape that I can use it with the right level of quality and I'm happy to be, but don't tell me to be more data driven and then, and, and not empower me, um, to, to get in there and to actually start to work with the data in meaningful ways. >>And so, um, that was really, you know, what, you know, the origin story of the company. And I think as, as we, um, you know, saw over the course of the last 5, 6, 7 years that, um, you know, a real, uh, excitement to embrace this idea of, of trying to think about data engineering differently, trying to democratize the, the ETL process and to also leverage all of these exciting new, uh, engines and platforms that are out there that allow for processing, you know, ever more diverse data sets, ever larger data sets and new and interesting ways. And that's where a lot of the push down or the ELT approaches that, you know, I think it could really won the day. Um, and that, and that for us was a hallmark of the solution from the very beginning. >>Yeah, this is a huge point that you're making. This is first of all, there's a large business, it's probably about a hundred billion dollar Tam. Uh, and the, the point you're making is we've looked, we've contextualized most of our operational systems, but the big data pipelines hasn't gotten there. And maybe we could talk about that a little bit because democratizing data is Nirvana, but it's been historically very difficult. You've got a number of companies it's very fragmented and they're all trying to attack their little piece of the problem to achieve an outcome, but it's been hard. And so what's going to be different about Altryx as you bring these puzzle pieces together, how is this going to impact your customers who would like to take that one? >>Yeah, maybe, maybe I'll take a crack at it. And Adam will, um, add on, um, you know, there hasn't been a single platform, uh, for analytics automation in the enterprise, right? People have relied on, uh, different products, um, to solve kind of, uh, smaller problems, um, across this analytics, automation, data transformation domain. Um, and, um, I think uniquely altereds has that opportunity. Uh, we've got 7,000 plus customers who rely on analytics for, um, data management, for analytics or AI and ML, uh, for transformations, uh, for reporting and visualization for automated insights and so on. And so by bringing drive factor, we have the opportunity to scale this even further and solve for more use cases, expand the scenarios where it's gets applied and so multiple personas. Um, and now we just talked about the data engineers. They are really a growing stakeholder in this transformation of data and analytics. >>Yeah, good. Maybe we can stay on this for a minute cause you, you you're right. You bring it together. Now that at least 3% is the business analyst, the end user slash business user. And now the data engineer, which is really out of an it role in a lot of companies, and you've used this term, the data engineering cloud, what is that, how is it going to integrate in with, or support these other personas? And, and how's it going to integrate into the broader ecosystem of clouds and cloud data warehouses or any other data stores? >>Yeah, no, that's great. Uh, yeah, I think for us, we really looked at this and said, you know, we want to build an open and interactive cloud platform for data engineers, you know, to collaboratively profile pipeline, um, and prepare data for analysis. And that really meant collaborating with the analysts that were in the line of business. And so this is why a big reason why this combination is so magic because ultimately if we can get the data engineers that are creating the data products together with the analysts that are, uh, in the line of business that are driving a lot of the decision-making and allow for that, what I would describe as collaborative curation of the data together, so that you're starting to see, um, uh, you know, increasing returns to scale as this, uh, as this rolls out. I just think that is an incredibly powerful combination and, and frankly, something that the market has not cracked the code on yet. And so, um, I think when we, when I sat down with Suresh and with mark and the team at Ultrix, that was really part of the, the, the big idea, the big vision that that was painted and, and got us really energized about the acquisition and about the potential of the combination. >>Yeah. And you're really, you're obviously riding the cloud and the cloud native wave. Um, and, but specifically we're seeing, you know, I almost don't even want to call it a data warehouse anyway, because when you look at what's, for instance, snowflake is doing, of course their marketing is around the data cloud, but I actually think there's real justification for that because it's not like the traditional data warehouse, right. It's, it's simplified get there fast, don't necessarily have to go through the central organization to share data. Uh, and, and, and, but it's really all about simplification, right? Isn't that really what the democratization comes down to. >>Yeah. It's simplification and collaboration. Right. I don't want to, I want to kind of just, um, what Adam said resonates with me deeply, um, analytics is one of those, um, massive disciplines, an enterprise that's really had the weakest of tools. Um, and we just have interfaces to collaborate with, and I think truly this was Alteryx's and a superpower was helping the analysts get more out of their data, get more out of the analytics, like imagine a world where these people are collaborating and sharing insights in real time and sharing workflows and getting access to new data sources, um, understanding data models better, I think, um, uh, curating those insights. I boring Adam's phrase again. Um, I think that creates a real value inside the organization, uh, because frankly in scaling analytics and democratizing analytics and data, we're still in such early phases of this journey. >>So how should we think about designer cloud, which is from Altryx it's really been the on-prem and the server desktop offering. And of course Trifacta is with cloud cloud data warehouses. Right. Uh, how, how should we think about those two products? >>Yeah, I think, I think you should think about them and, uh, um, as, as very complimentary right design a cloud really shares a lot of DNA and heritage with, uh, designer desktop, um, the low code tooling and that interface, uh, that really appeals to the business analysts, um, and gets a lot of the things that they do well, we've also built it with interoperability in mind, right. So if you started building your workflows in designer desktop, you want to share that with design and cloud, we want to make it super easy for you to do that. Um, and I think over time now we're only a week into, um, this Alliance with, um, with Trifacta. Um, I think we have to get deeper inside to think about what does the data engineer really need what's business analysts really need and how to design a cloud, and Trifacta really support both of those requirements, uh, while kind of continue to build on the tri-factor on the amazing tri-factor cloud platform. >>You know, >>I was just going to say, I think that's one of the things that, um, you know, creates a lot of, uh, opportunity as we go forward, because ultimately, you know, Trifacta took a platform, uh, first mentality to everything that we built. So thinking about openness and extensibility and, um, and how over time people could build things on top of, by factor that are a variety of analytic tool chain, or analytic applications. And so, uh, when you think about, um, Ultrix now starting to, uh, to move some of its capabilities or to provide additional capabilities, uh, in the cloud, um, you know, Trifacta becomes a platform that can accelerate, you know, all of that work and create, uh, uh, a cohesive set of, of cloud-based services that, um, share a common platform. And that maintains independence because both companies, um, have been, uh, you know, fiercely independent, uh, and really giving people choice. >>Um, so making sure that whether you're, uh, you know, picking one cloud platform and other, whether you're running things on the desktop, uh, whether you're running in hybrid environments, that, um, no matter what your decision, um, you're always in a position to be able to get out your data. You're always in a position to be able to cleanse transform shape structure, that data, and ultimately to deliver, uh, the analytics that you need. And so I think in that sense, um, uh, you know, this, this again is another reason why the combination, you know, fits so well together, giving people, um, the choice. Um, and as they, as they think about their analytics strategy and their platform strategy going forward, >>Yeah. I make a chuckle, but I, one of the reasons I always liked Altryx is cause you kinda did the little end run on it. It can be a blocker sometimes, but that created problems, right? Because the current organization said, wow, there's big data stuff is taken off, but we need security. We need governance. And, and it was interesting because he got, you know, ETTL has been complex, whereas the visualization tools, they really, you know, really weren't great at governance and security. It took some time there. So that's not, not their heritage. You're bringing those worlds together. And I'm interested, you guys just had your sales kickoff, you know, what was their reaction like, uh, maybe Suresh, you could start off and maybe Adam, you could bring us home. >>Yeah. Um, thanks for asking about our sales kickoff. So we met for the first time and kind of two years, right. For, as, as it is for many of us, um, in person, uh, um, which I think was, uh, was a real breakthrough as Qualtrics has been on its transformation journey. Uh, we had a Trifacta to, um, the, the party such as the tour, um, and getting all of our sales teams and product organizations, um, to meet in person in one location. I thought that was very powerful for us, the company. Uh, but then I tell you, um, um, the reception for Trifacta was beyond anything I could have imagined. Uh, we were working Adam and I were working so hard on, on the deal and the core hypothesis and so on. And then you step back and you kind of share the vision, uh, with the field organization and it blows you away, the energy that it creates among our sellers, our partners, and I'm sure Adam will, and his team were mocked every single day with questions and opportunities to bring them in. >>But Adam, maybe he's chair. Yeah, I know it was, uh, it was through the roof. I mean, uh, uh, the, uh, the amount of energy, the, uh, certainly how welcoming everybody was, uh, uh, you know, just, I think the story makes so much sense together. I think culturally, the company is, are very aligned. Um, and, uh, it was a real, uh, real capstone moment, uh, to be able to complete the acquisition and to, and to close and announced, you know, at the kickoff event. And, um, I think, you know, for us, when we really thought about it, you know, when we ended the story, that we was just, you have this opportunity to really cater to what the end-users, you know, care about, which is a lot about interactivity and self-service, and at the same time. And that's, and that's a lot of the goodness that, um, that Ultrix has brought, you know, through, you know, you know, years and years of, of building a very vibrant community of, you know, thousands, hundreds of thousands of users. >>And on the other side, you know, Trifacta bringing in this data engineering focus, that's really about, uh, the governance things that you mentioned and the openness, um, that, that it cares deeply about. And all of a sudden, now you have a chance to put that together into a complete story where the data engineering cloud and analytics, automation, you know, coming together. And, um, and I just think, you know, the lights went on, um, you know, for people instantaneously and, you know, this is a story that, um, that I think the market is really hungry for. And certainly the reception we got from, uh, from the broader team at kickoff was, uh, was a great indication of that. >>Well, I think the story hangs together really well, you know, one of the better ones I've seen in, in this space, um, and, and you guys coming off a really, really strong quarter. So congratulations on that Jensen. We have to leave it there. I really appreciate your time today. Yeah. Take a look at this short video. And when we come back, we're going to dig into the ecosystem and the integration into cloud data warehouses and how leading organizations are creating modern data teams and accelerating their digital businesses. You're watching the cube, your leader in enterprise tech coverage.

[Music] okay we're here with ceres vitale who's the chief product officer at alteryx and adam wilson the ceo of trifacta now of course part of alteryx just closed this quarter gentlemen welcome great to be here okay so rush let me start with you in my opening remarks i talked about alteryx's traditional position serving business analysts and how the hyperanna acquisition brought you deeper into the business user space what does trifacta bring to your portfolio why'd you buy the company yeah thank you thank you for the question um you know we see a we see a massive opportunity of helping brands democratize the use of analytics across their business every knowledge worker every individual in the company should have access to analytics it's no longer optional as they navigate their businesses with that in mind you know we know designer and our the products that alteryx has been selling the past decade or so do a really great job addressing the business analysts with hyper rana now kind of renamed alteryx auto insights we even speak with the business owner the line of business owner who's looking for insights that aren't revealed in traditional dashboards and so on um but we see this opportunity of really helping the data engineering teams and i.t organizations to also make better use of analytics and that's where trifacta comes in for us trifacta has the best data engineering cloud in the planet they have an established track record of working across multiple cloud platforms and helping data engineers um do better data pipelining and work better with this massive kind of cloud transformation that's happening in every business um and so trifecta made so much sense for us yeah thank you for that i mean look you could have built it yourself would have taken you know who knows how long you know but uh so definitely a great time to market move adam i wonder if we could dig into trifacta some more i mean i remember interviewing joe hellerstein in the early days you've talked about this as well on thecube coming at the problem of taking data from raw refined to an experience point of view and joe in the early days talked about flipping the model and starting with data visualization something jeff herr was expert at so maybe explain how we got here we used to have this cumbersome process of etl and you maybe and some others change that model with you know el and then t explain how trifacta really changed the data engineering game yeah that's exactly right uh dave and it's been a really interesting journey for us because i think the original hypothesis coming out of the campus research at berkeley and stanford that really birthed trifacta was you know why is it that the people who know the data best can't do the work you know why is this become the exclusive purview the highly technical and you know can we rethink this and make this a user experience problem powered by machine learning that will take some of the more complicated things that people want to do with data and really help to automate those so so a broader set of users can can really see for themselves and help themselves and and i think that um there was a lot of pent up frustration out there because people have been told for you know for a decade now to be more data driven and then the whole time they're saying well then give me the data you know in the shape that i can use it with the right level of quality and i'm happy to be but don't tell me to be more data driven and they'll don't then and and not empower me um to to get in there and to actually start to work with the data in meaningful ways and so um that was really you know what you know the origin story of the company and i think as as we saw over the course of the last five six seven years that um you know a real uh excitement to embrace this idea of of trying to think about data engineering differently trying to democratize the the etl process and to also leverage all these exciting new uh engines and platforms that are out there that allow for you know processing you know ever more diverse data sets ever larger data sets and new and interesting ways and that's where a lot of the push down or the elt approaches uh you know i think it really won the day um and that and that for us was a hallmark of the solution from the very beginning yeah this is a huge point that you're making this is first of all there's a large business probably about a hundred billion dollar tam uh and and the the point you're making is we look we've contextualized most of our operational systems but the big data pipelines hasn't gotten there but and maybe we could talk about that a little bit because democratizing data is nirvana but it's been historically very difficult you've got a number of companies it's very fragmented and they're all trying to attack their little piece of the problem to achieve an outcome but it's been hard and so what's going to be different about alteryx as you bring these puzzle pieces together how is this going to impact your customers who would like to take that one yeah maybe maybe i'll take a crack at it and adam will add on um you know there hasn't been a single platform [Music] for analytics automation in the enterprise right people have relied on different products to solve kind of smaller problems across this analytics automation data transformation domain and i think uniquely alteryx has that opportunity we've got 7000 plus customers who rely on analytics for data management for analytics for ai and ml for transformations for reporting and visualization for automated insights and so on and so by bringing trifecta we have the opportunity to scale this even further and solve for more use cases expand the scenarios where angles gets applied and serve multiple personas um and now we just talked about the data engineers they are really a growing stakeholder in this transformation of data analytics yeah good maybe we can stay on this for a minute because you're right you bring it together now at least three personas the business analyst the end user size business user and now the data engineer which is really out of an i.t role in a lot of companies and you've used this term the data engineering cloud what is that how is it going to integrate in with or support these other personas and and how's it going to integrate into the broader ecosystem of clouds and cloud data warehouses or any other data stores yeah you know that's great uh you know i think for us we really looked at this and said you know we want to build an open and interactive you know cloud platform for data engineers you know to collaboratively profile pipeline um and prepare data for analysis and and that really meant collaborating with the analysts that were in the line of business and so this is why a big reason why this combination is so magic because ultimately if we can get the data engineers that are creating the data products together with the analysts that are in the line of business that are driving a lot of the decision making and allow for that what i would describe as collaborative curation you know of the data together so that you're starting to see um uh you know increasing returns to scale as this uh as this rolls out i just think that is an incredibly uh powerful combination and frankly something that the market has not cracked the code on yet and so um i think when we when i sat down with surash and with mark and and the team at ultrix that was really part of the the big idea the big vision that that was painted and and got us really energized um about the acquisition and about the the potential of the combination yeah and you're really you're obviously riding the cloud and the cloud native wave um and but specifically we're seeing you know i almost don't even want to call it a data warehouse anyway because when you look at what princeton snowflake is doing of course their marketing is around the data cloud but i i actually think there's real justification for that because it's not like the traditional data warehouse right it's it's simplified get there fast don't necessarily have to go through this central organization to share data uh and and but it's really all about simplification right isn't that really what the democratization comes down to yeah it's simplification and collaboration right i don't want to i want to kind of just uh what what adam said resonates with me deeply um analytics is one of those massive disciplines inside an enterprise that's really had the weakest of tools um and weakest of interfaces to collaborate with and i think truly this was alteryx's end of superpower was helping the analysts get more out of their data get more out of the analytics like imagine a world where these people are collaborating and sharing insights in real time and sharing workflows and getting access to new data sources understanding data models better i think curating those insights i borrowing adam's phrase again i think that creates a real value inside the organization because frankly in scaling analytics and democratizing analytics and data we're still in such early phases of this journey so how should we think about designer cloud which is from alteryx it's really been the on-prem the server or desktop you know offering and of course trifecta is about cloud cloud data warehouses right um how should we think about those two products yeah i think i think you should think about them and as very complementary right designer cloud really shares a lot of dna and heritage with designer desktop the low code tooling and the interface that really appeals to the business analysts and gets a lot of the things that they do well we've also built it with interoperability in mind right so if you started building your workflows in designer desktop you want to share that with designer cloud we want to make it super easy for you to do that and i think over time now we're only a week into this alliance with uh with trifacta i think we have to get deeper and start to think about what does the data engineer really need what business analysts really need and how to design a cloud and try factor really support both of those requirements uh while kind of continue to build on the trifecta on the amazing trifecta cloud platform you know and i think let's go ahead i'm just to say i think that's one of the things that um you know creates a lot of opportunity as we go forward because ultimately you know trifacta took a platform uh first mentality to everything that we built so thinking about openness and extensibility and um and how over time people could build things on top of trifacta that are a variety of analytic tool chain or analytic applications and so when you think about um alteryx now starting to uh to move some of its capabilities or to provide additional capabilities uh in the cloud um you know trifacta becomes uh a a platform that can accelerate you know all of that work and create a cohesive set of of cloud-based services that share a common platform and that maintains independence because both companies um have been uh you know fiercely independent uh in really giving people choice um so making sure that whether you're uh you know picking one cloud platform another whether you're running things on the desktop uh whether you're running in hybrid environments that no matter what your decision you're always in a position to be able to get out your data you're always in a position to be able to cleanse transform shape structure that data and ultimately to deliver uh the analytics that you need and so i think in in that sense um uh you know this this again is another reason why the combination you know fits so well together giving people um the choice um and as they as they think about their analytics strategy and and their platform strategy going forward you know i make a chuckle but one of the reasons i always liked alteryx is because you kind of did did a little end run on i.t i.t can be a blocker sometimes but that created problems right because the organization said wow this big data stuff is taken off but we need security we need governance and and it's interesting because you got you know etl has been complex whereas the visualization tools they really you know really weren't great at governance and security it took some time there so that's not not their heritage you're bringing those worlds together and i'm interested you guys just had your sales kickoff you know what was the reaction like uh maybe suresh you could start off and maybe adam you could bring us home yeah um thanks for asking about our sales kickoff so we met uh for the first time in kind of two years right for as it is for many of us um in person uh um which i think was a was a real breakthrough as alteryx has been on its transformation journey uh we had a try factor to um the the party such as it were um and getting all of our sales teams and product organizations um to meet in person in one location i thought that was very powerful for us as a company but then i tell you um the reception for trifecta was beyond anything i could have imagined uh we were working adam and i were working so hard on on the the deal and the core hypotheses and so on and then you step back and kind of share the vision with the field organization and it blows you away the energy that it creates among our sellers our partners and i'm sure adam and his team were mobbed every single day with questions and opportunities to bring them in but adam maybe you should share yeah no it was uh it was through the roof i mean uh the uh the amount of energy the uh when so certainly how welcoming everybody was uh you know just i think the story makes so much sense together i think culturally the companies are very aligned um and uh it was a real uh real capstone moment uh to be able to complete the acquisition and to and to close and announce you know at the kickoff event and um i think you know for us when we really thought about it you know when we and the story that we told was just you have this opportunity to really cater to what the end users you know care about which is a lot about interactivity and self-service and at the same time and that's and that's a lot of the goodness that um that alteryx is has brought you know through you know you know years and years of of building a very vibrant community of you know thousands hundreds of thousands of users and on the other side you know trifecta bringing in this data engineering focus that's really about uh the governance things that you mentioned and the openness that that it cares deeply about and all of a sudden now you have a chance to put that together into a complete story where the data engineering cloud and analytics automation you know come together and um and i just think you know the lights went on um you know for people instantaneously and you know this is a story that um that i think the market is really hungry for and and certainly the reception we got from from the broader team at kickoff was uh was a great indication of that well i think the story hangs together really well you know one of the better ones i've seen in this space um and and you guys coming off a really really strong quarter so congratulations on that gents we have to leave it there really appreciate your time today yeah take a look at this short video and when we come back we're going to dig into the ecosystem and the integration into cloud data warehouses and how leading organizations are creating modern data teams and accelerating their digital businesses you're watching the cube your leader in enterprise tech coverage [Music]

(upbeat music) >> We're back at AWS re:Invent 2021. You're watching theCUBE and we're really excited to have Adam Williams on, he's a senior director of engineering at Iron Mountain. Som Shahapurkar, who's the product engineering of vertical solutions at Iron Mountain. Guys, great to see you. Thanks for coming on. >> Thank you >> Thank you. All right Adam, we know Iron Mountain trucks, tapes, what's new? >> What's new. So we've developed a SaaS platform for digitizing, classifying and bringing out and unlocking the value of our customer's data and putting their data to work. The content services platform that we've developed, goes together with an IDP that we call an intelligent document processing capability to do basic content management, but also to do data extraction and to increase workflow capabilities for our customers. >> Yeah, so I was kind of joking before Iron Mountain, the legacy business of course, everybody's seeing the trucks, but $4 billion company, $13 billion market cap, the stock's been on fire. The pandemic obviously has been a tailwind for you guys, but Som, if you had to describe it to like my mother, what's the sound bite that you'd give. >> Well the sound bite, as everyone knows data is gold today, right? And we are sitting figuratively and literally on a mountain of data. And now we have the technology to take that data partner with AWS, the heavy machinery to convert that into value, into value that people can use to complete the human story of healthcare, of mortgage, finance. A lot of this sits in systems, but it also sits in paper. And we are bridging that paper to digital divide, the physical and digital divide to create one story. >> This has been a journey for you guys. I mean, I recall that when you kind of laid this vision out a number of years ago, I think he made some acquisitions. And so maybe take us through that amazing transformation that Iron Mountain has made, but help the audience understand that. >> Transformations really been going from the physical records management that we've built our business around to evolving with our customers, to be able to work with all of the digital documents and not just be a transportation and records management storage company, but to actually work with them, to put their data to work, allowing them to be able to digitize a lot of their content, but also to bring in already digitized content and rich media. >> One of the problems that always existed, especially if you go back to back of my brain, 2006, the federal rules of civil procedure, which said that emails could now be evidence in a case and everyone like, oh, I don't like, how do I find email. So one of the real problems was classifying the information for retention policies. The lawyers wanted to throw everything out after whatever six or seven years, the business people wanted to keep everything forever. Neither of those strategies work, so classification and you couldn't do it manually. So have you guys solved that problem? How do you solve that problem? Does the machine intelligence help? It used to be, I'll use support vector machines or math or probabilistic, latent, semantic, indexing, all kinds of funky stuff. And now we enter this cloud world, have you guys been able to solve that problem and how? >> So our customers already have 20 plus years of retention rules and guidelines that are built within our systems. And we've helped them define those over the years. So we're able to take those records, retention schedules that they have, and then apply them to the documents. But instead of doing that manually, we're able to do that using our classification capabilities with AI ML and that Som's expertise. >> Awesome, so lay it on me. How do you guys do that? It's a lot of math. >> Yeah, so it can get complicated real fast, but at a simple level, what's changed really from support beta machines of 2006 to today is the scale at which we can do it, right? The scale at which we are bringing those technologies. Plus the latest technologies of deep learning, your conventional neural networks going from a bag of characters and words to really the way humans look at it. You look at a document and you know this is an invoice or this is a prescription, you don't have to even know to read to know that, machines are now capable of having that vision, the computer vision to say prescription, invoice. So we train those models and have them do it at industrial scale. >> Yeah, because humans are actually pretty bad at classifying at scale. >> At scale like their back. >> You remember, we used to try to do, oh, it was just tag it, oh, what a nightmare. And then when something changes and so now machines and the cloud and Jane said, how about, I mean, I presume highly regulated industries are the target, but maybe you could talk about the industry solutions a little bit. >> Right. Regulated industries are a challenge, right. Especially when you talk about black box methodologies like AI, where we don't know, okay, why does it classify this as this and that is that? But that's where I think a combined approach of what we are trying to say, composite AI. So the human knowledge, plus AI knowledge combined together to say, okay, we know about these regulations and hey, AI, be cognizant of this regulations while you do our stuff, don't go blindly. So we keep the AI in the guardrails and guided to be within those lines. >> And other part of that is we know our customers really well. We spent a lot of time with them. And so now we're able to take a lot of the challenges they have and go meet those needs with the document classification. But we also go beyond that, allowing them to implement their own workflows within the system, allowing them to be able to define their own capabilities and to be able to take those records into the future and to use our content management system as a true content services platform. >> Okay, take me through the before and the after. So the workflow used to be, I'd ring you up, or maybe you come in and every week grab a box of records, put them in the truck and then stick them in the Iron Mountain. And that was the workflow. And you wanted them back, you'd go get it back and it take awhile. So you've digitized that whole and when you say I'm inferring that the customer can define their own workflow because it's now software defined, right. So that's what you guys have engineered. Some serious engineering work. So what's the tech behind that. Can you paint a picture? >> So the tech behind it is we've run all of our cloud systems and Kubernetes. So using Kubernetes, we can scale really, really large. All of our capabilities are obviously cloud-based, which allows us to be able to scale rapidly. With that we run elastic search is our search engine and MongoDB is our no SQL database. And that allows us to be able to run millions of documents per minute through our system. We have customers that we're doing eight million documents a day for the reel over the process. And they're able to do that with a known level of accuracy. And they can go look at the documents that have had any exceptions. And we can go back to what Som was talking about to go through and retrain models and relabel documents so that we can catch that extra percentage and get it as close to 100% accuracy as we would like, or they would like. >> So what happens? So take me through the customer experience. What is that like? I mean, do they still... we you know the joke, the paperless bathroom will occur before the paperless office, right? So there's still paper in the office, but so what's the workload? I presume a lot of this is digitized at the office, but there's still paper, so help us understand that. >> Customers can take a couple of different paths. One is that we already have the physical documents that they'd like us to scan. We call that backfile scanning. So we already have the documents, they're in a box they're in a record center. We can move them between different records centers and get them imaged in our high volume scanning operation centers. From there-- >> Sorry to interrupt. And at that point, you're auto classifying, right? It's not already classified, I mean, it kind of is manually, but you're going to reclassify it on creation. >> Correct. >> Is that electronic document? >> For some of our customers, we have base metadata that gives us some clues as to what documents may be. But for other documents, we're able to train the models to know if their invoices or if their contracts commonly formatted documents, but customers can also bring in their already digitized content. They can bring in basic PDFs or Word documents or Google Docs for instance, but they can also bring in rich media, such as video and audio. And from there, we also do a speech to text for video and audio, in addition to just basic OCR for documents. >> Public sector, financial services, health care, insurance, I got to imagine that those have got to be the sweet spots. >> Another sweet spot for us is the federal space in public sector. We achieved FedRAMP, which is a major certification to be able to work with, with the federal government. >> Now, how would he work with AWS? What's your relationship with them? How do you use the cloud? Maybe you could describe that a little bit. >> Well, yeah, at multiple levels, right? So of course we use their cloud infrastructure to run our computing because with the AI and machine learning, you need a lot of computing power, right. And AWS is the one who can reliably provide it, space to store the digital data, computing the processes, extract all the information, train our models, and then process these, like he's talking about, we are talking about eight, 12, 16 million documents a day. So now you need seconds and sub second processing times, right? So at different levels, at the company infrastructure level, also the AI and machine learning algorithms levels, AWS has great, like Tesseract is one the ones that everyone knows but there is others purpose-built model APIs that we utilize. And then we'll put our secret sauce on top of that to build that pathway up and make it really compelling. >> And the secret sauce is obviously there's a workflow and the flexibility of the workflow, there's the classification and the machine learning and intelligence and all the engineering that makes the cloud work you manage. What else is there? >> Knowledge graphs, like he was saying, right, the domain. So mortgage is not that a document that looks very similar in mortgage versus a bank stated mortgage and bank statement in healthcare have different meanings. You're looking at different things. So you have something called a knowledge graph that maintains the knowledge of a person working in that field. And then we have those created for different fields and within those fields, different applications and use cases. So that's unique and that's powerful. >> That provides the ability to prior to hierarchy for our customers, so they can trace a document back to the original box that was given to us some many years ago. >> You got that providence and that lineage, I know you're not go to market guys, but conceptually, how do you price? Is it that, it's SaaS? Is it licensed? Is it term? Is it is a consumption based, based on how much I ingest? >> We have varying different pricing models. So we first off we're in six major markets from EU, Latin America, North America and others that we serve. So within those markets, we offer different capabilities. We have an essentials offering on AWS that we've launched in the last two weeks that allows you to be able to bring in base content. And that has a per object pricing. And then from there, we go into our standard edition that has ability to bring in additional workflows and have some custom pricing. And then we have what we call the enterprise. And for enterprise, we look at the customer's problem. We look at custom AI and ML models who might be developing and the solution that we're having to build for them and we provide a custom price and capability for what they need. >> And then the nativists this week announced a new glacier tier. So you guys are all over that. That's where you use it, right? The cheapest and the deepest, right? >> Yeah, one of the major things that AWS provides us as well is the compliance capabilities for our customers. So our customers really require us to have highly secure, highly trusted environments in the cloud. And then the ability to do that with data sovereignty is really important. And so we're able to meet that with AWS as well. >> What do you do in situations where AWS might not have a region? Do you have to find your own data center to do that stuff or? >> Well, so data privacy laws can be really complex. When you work with the customer, we can often find that the nearest data center in their region works, but we also do, we've explored the ability to run cloud capabilities within data centers, within the region that allows us to be able to bridge that. We also do have offerings where we can run on-premise, but obviously our focus here is on the cloud. >> Awesome business. Does Iron Mountain have any competitors? I mean like... >> Yeah. >> You don't have to name them, but I mean, this is awesome business. You've been around for a long time. >> And we found that we have new competitors now that we're in a new business. >> They are trying to disrupt and okay. So you guys are transforming as an incumbent. You're the incumbent disruptor. >> Yes. >> Yes, it's self disruption to some extent, right. Saying, hey, let's broaden our horizon perspective offering value. But I think the key thing is, I want to focus more on the competitive advantage rather than the competitors is that we have the end to end flow, right? From the high volume scanning operations, trucking, the physical world, then up and about into the digital world, right? So you extract it, it's not just PDFs. And then you go into database, machine learnings, unstructured to structured extraction. And then about that value added models. It's not just about classification. Well, now that you have classified and you have all this documents and you have all this data, what can you glean from it? What can you learn about your customers, the customers, customers, and provide them better services. So we are adding value all throughout this chain. And think we are the only ones that can do that full stack. >> That's the real competitive advantage. Guys, really super exciting. Congratulations on getting there. I know it's been a lot of hard work and engineering and way to go. >> Thank you. >> It's fun. >> Dave: It's good, suppose to have you back. >> Thanks. >> All right and thank you for watching. This is Dave Vellante for theCUBE, the leader in live tech coverage. (upbeat music)

>>Hi, everyone. Welcome to the cubes coverage of Avis reinvent 2021 we're onsite in person. It's a virtual event, also hybrid events. I'm Jennifer and my host, David Dante ninth year, Dave, we've been doing Avis reinvent the cube and it's 11th season. We've seen a lot. Yeah, I'll say. >>And the show is pretty packed, John. I mean, I think it's surprised some folks over 25,000 people here. I mean, obviously a lot of sponsors, but >>Customers to a bad event for AWS in terms of attendance is like record-breaking for any other company, people are standing in line for sessions. It's definitely happening. People are here to learn. They're not just all employees. So definitely a successful event in person as well in the live stream. But so much news to talk about. Andy Jassy is now the CEO of Amazon. That's the top story Adam's Lipsky's taking over as CEO of AWS time, Amazonian who left Amazon to take the CEO job of Tableau sold that company to Salesforce under mark Benioff. Now back to take the helm from Andy Jassy and quite the pressure cooker here as he takes the stage, a lot of people are asking, is will he do well? Will he fumble on stage? Will he do the right things? And does he have what it takes to take the cloud to the next generation with AWS as their number one clear far and away, then the second competitor in Microsoft and then a look distant third and Google. So Amazon's are under a ton of competitive pressure. At least from an industry standpoint, everyone's still trying to catch up. It's the same theme, Dave, every year Amazon is out front and the lead just gets extended and extended. And again, here, no exception. Well, the Uber >>Of course there's you mentioned is Andy Jassy is now taking over a CEO of Amazon. And you know, history would suggest that a lot of times that companies falter when there's a CEO transition, but it feels like it's different this time. Andy Jassy was here since the beginning launched AWS versus a profit engine of Amazon brought back Adam sill Lipski who has a deep understand. He's not as technical as Andy, but obviously as a deep understanding of the business, yeah, he was comfortable up in the keynote. It wasn't John, a typical firehose of announcements. Even those, a lot of announcements, they didn't shove them down our throat and they didn't in the analyst session as well. Usually in the analyst session, it's hours and hours and hours of firehose Kool-Aid injection, not this year. Why do you think that is, is that a COVID thing? Is that a change in now? >>I think Adam's Leschi wants to be his own guy. As, as leader here, a lot of things were eliminated from the keynote that Andy Jasmine did, for instance, Andy Jesse loves music. So we always had the music walk up music like you see in sports, uh, which is very cool. That's an Andy Jassy kind of tweak. Andy is all about announcements and he was just, uh, pushing the envelope. Adam was much more laid back. He sees, I think, more of a holistic picture being more of an app guy being more of a data guy, less of a, I would say under the covers nerd like Jassy was, Andy was very deep on, on a lot of the tech stuff as is Adam. But I think Andy a little bit more proactive on that. So Adam was very much more about the impact of 80 of us culturally, as a society, as a company and kind of brought in this kind of think different apple vibe, which is, you know, the people who are Pathfinders, um, as he takes that Jassy kind of, um, approach of leaders, but be a builder, be a change agent, be a game changer. >>Adam took it to another level by saying, Hey, it's okay to be a Pathfinder because it's net new disruption with the cloud. And I think that's the story that I see coming out of this where, uh, in talking to Adam one-on-one Amazon absolutely has a secret weapon in it's chips, custom Silicon. They're absolutely crushing it with how they're thinking about SAS and platforms and they have a huge ecosystem. And I think at the end of the day, and we talked about this in our story on Silicon angle, Amazon could actually wipe out Microsoft. And I think Microsoft's core competitive advantage has always been their ecosystem and their developers. I think right now in the next few years, if Microsoft doesn't match Amazon, they will be decimated anyway, you know? >>Yeah, hold on. Okay. Amazon's not going to wipe out Microsoft. Microsoft has too much of a cash cow. Look at the hanging on to windows. Couldn't, you know, the mistake and missing mobile event initially missing the cloud. Didn't wipe out Microsoft. So they've just got too much of a software cashflow. That's not gonna happen maybe a little bit over the top. >>I thought, but Microsoft has done a great job and it's not going to tell it to kind of stay in the game and do more. But if you look at the major inflection points, Dave where's digital equipment corporation, where's prime computer. Well, >>I think this is the point is again, history would show that those companies, when they handed the reigns over to a new CEO failed, they faltered, it was self-inflicted wounds. It almost happened. You thought it would happen with Microsoft, whether it became irrelevant under bomber, but when Nadella came in, he reinvigorated because specifically they had the cashflow to be able to do that. Now. So the big question is, okay, w what's going to happen. We ran a survey to our community to see what could disrupt Amazon. You know, that the us government wants to break them apart or wants to regulate them. But our survey respondents said there's a 60% plus probability that Amazon will be disrupted by other factors. And that's what I was self-inflicted wound that's Jesse's that's right. And that's, Jessie's big challenge is how to not make those disruptions, how to fight those disruptions. >>The number one, uh, reason why they could be disrupted was self-inflicted wounds, which again, history would show what happened. But one of the things we talked about is that normally happens when companies stop innovating when they rest on their laurels. Right. And you kind of saw that with those companies that you mentioned, but you mentioned their secret weapon. We wrote about that in our article, the chips. So we heard no secret. Everybody knew graviton three was coming, right? And so that is Amazon secret up. And you know, I've been thinking about this. John Amazon makes a lot of money on x86 instances that they've deployed years ago and they charge a lot for, I was wondering, you know, is the, or the old X 86 instances actually more profitable than graviton, maybe at this point in time, but long-term graviton. They control their own destiny because they control the hardware and software stack. And I bet you allows them to get better negotiating leverage with >>M D and it's of course, I mean, pat, Kelsey, we should talk about this all the time, but as bad as Jason Intel, you, if you're not out in the next wave, your driftwood, I think Intel and AMD and others, they have purpose-built general purpose chips. They're probably going to be for the lift and shift stuff when you, but if you're actually seriously writing software as an owner on the cloud, and you want specific advantages of speed and performance, you're going to want the custom Silicon that's purpose-built for your application and write code to that stack. So, so I think there's a whole nother level of platform as a service. Dave, that's kind of coming out of this re-invent that I think could be a multi generational trend, which is, Hey, the cloud is of super cloud or platform. Look at the riser, snowflake and Databricks. Those guys are on Amazon. Like they're super clouds in and of themselves they're platforms. They're not appoint SAS solution. I think Microsoft in my, my analysis is, yeah, they got office 365, okay. Word processing stuff. But what other SAS apps do they have besides SQL server and other things that are actually being built on there? And if, if I'm a developer you're going to want to go to the platform. That's the highest performance for office 365. It's a cash cow. But how long is that going to last >>A long time? I mean, major momentum. We argue about that later, but I wanna, I want to touch on graviton three because I think that was the big announcement of the day 25% faster than graviton to at least twice the floating point performance twice the crypto graphic performance in three times for machine learning, learning workloads, and very importantly, 60% less power. So at Amazon scale, uh, Adam said this in our meeting, he said, the economics really favor us because of our scale. And so, and they've also announced new training them instances and, and, and what, what having custom Silicon allows Amazon to do is release on a much, much faster cadence than traditional x86. And they could do, and they could do really cool things. Nitro is there, Nick they're smart NEC, which it says the basis, their new hypervisor, if you will. So it allows them to bring in x86, uh, Nvidia NPUs some of their own or Nvidia GPU, some of their own Silicon. So optionality is really the key there. You heard them announce, uh, an SAP instance. So that's a memory intensive instance. They can dial things up, dial things down. They've got full control of the stack. And by the way, copying them Google's copy of Microsoft is copying them. And who's leading this charge in custom Silicon, AWS, obviously Tesla, apple. I mean, these are leading companies that I don't think they all got it wrong. I think >>The Silicon angle is to have your own custom Silicon. And that's the, that is the clearly the advantage as it's vertically integrated. But the other thing that's coming out of this reinvents, the purpose built software concept where, you know, they're not copying Microsoft playbook as the wall street journal was saying, and some are saying Microsoft copying Amazon, Amazon has always been this horizontally scalable resource that's cloud, but with machine learning and AI, you now have this purpose-built kind of capability from software into the app itself where data has to be addressable. And I think the people in the data business kind of know this, but as the rest of the world comes out, architecturally having that horizontal observation space and data that's vertically tied to machine learning is a huge architectural shift. This is a complete rethinking of how software is built and that's going to be a game changer. I think Amazon's well out on front of that. And I think that's going to be a huge architectural shift. >>Well, let's quantify this a little bit because you know, you're, you're making the point that Amazon is the number one cloud, which I would agree with. We're talking here about IAS infrastructure as a service in the past layer that sits on top of that. Microsoft defines the cloud is we'll put in an office 365, Google we'll put in its Google apps, Amazon pure infrastructure as a service. And if you just look at that space, that's about $120 billion business. When you add up AWS, Azure, Alibaba and GCP, which I would contend are the only four hyperscalers out there. I don't include Oracle as a hyperscale. I don't include IBM. I get a lot of crap for that sometimes. Yeah, but we're talking big scaler, $120 billion. So actually relatively small compared to the trillion dollar opportunity that they have, but it's growing at 35% a year. Amazon will do more than 60 billion this year, 62 billion, just to quantify it in that ISS space. Microsoft will be about 38, 30 9 billion. Okay. So pretty substantial. Those two are far ahead of the others. Everybody else's, you know, Google is still in, you know, under 10 billion, Alibaba is right around there. So those two, it's really a two horse race. And I asked Microsoft using its software estate. Amazon's gotta be the innovator and has to have the best cloud to win. And it does well >>Also a platform. Let's go back to the little history lesson for the younger folks out there. When Microsoft was had a monopoly, they had windows operating system, which has had DAS under the covers, but windows was the operating system. And office was a suite of applications. They encourage software developers to build on top of windows and they had other servers off SQL server all came out of that small history. So their bread and butter was to have developers build on top of windows. Hence the monopoly, of course they had the application and the system software, hence the monopoly, hence the Microsoft breakup by the government in 1997. Now today cloud is essentially one big kind of PC concept. It's like windows, it's windows equivalent. So cloud is essentially an environment platform that has apps that run on top of it. Okay. In that world, Amazon by far is the number one windows model at Amazon's. >>I mean, Microsoft is used to is okay, I got Azure and I got office 365 that keeps them in business that keeps them from losing. So it's a placeholder. So that what I'm looking at is what is Amazon? I mean, Amazon versus Azure, doing relative to ISV and uptake for developers. And I'm suggesting that this trend of Amazon will go, if it goes uncontested by Azure, they'll wipe the table on ISV and suffer developers. If you're an owner of a software, you're not gonna write software, that's gonna be sub-optimized for a platform. That's not going to be before, >>Unless you're, unless you're a Microsoft developer, nearly all.net days. And there are a lot of those. And that's what, that's what Microsoft is doing. They're they're, they're, they've, they've shifted to cloud, they've gone everything into cloud. So Azure is their platform for innovation and acceleration. >>So those developers are going to build a sub application versus going over here on AWS. >>Well, that's the, that's the story with Microsoft. Good enough. I know >>Again, this is we're speculating, but we're going to watch that, but that is, to me, will be the battlefield of what will determine Azure versus AWS. And I think everything else is smoke and mirrors Amazon Webster way ahead of Azure, but the TeleSign is going to be does 80 bus attract those developers on their cloud with the custom Silicon, with the integrated stack and with the purpose-built software. I mean, it's looking really good. I think they've got a really compelling story. >>I think it's less about Azure versus AWS. I mean, that's an interesting storyline and I love to talk about it, but I think they'll go back to 120 billion out of 4 trillion. That's really the, the larger opportunity for, for both Microsoft and AWS to continue to grow. Because you look at, you look at Dell with apex, you look at HPE with GreenLake, Lenovo, Cisco, they've all got their own clouds. One of the things that didn't get into our article, but Adam Lipski when, when you asked him about hybrid is that hybrid cloud. When we were talking about some of the stuff they're doing, he S he said, look, that's not cloud what those guys are doing. That's not what we did. And he talked today about edge has to be AWS, not like AWS. That was the quote to use. Talk about, you know, private 5g, bringing out posts. And he gave some examples of that. The point is they, AWS is bringing its system, its architecture to the edge it's programming model infrastructure as code to the edge. Now, Kubernetes, Kubernetes does moderate that a little bit, but his point was, that's not AWS. That's not the cloud. >>Yeah. I think in summary, Dave had to wrap up what's the big trend this week is that Amazon web services is a, is a heaven environment for a developer, for the elite people who want to roll their own for the folks in it. In these other environments, you can have prefabricated purpose-built software platform to build on top of. And I think that isn't going to address the whole ease of ease of rollout. So if I'm a SAS developer, I don't, I want, I don't want to rebuild that over again. I don't want to roll my own. I'll take what you got and connects a good example. If you want to call shedder, you can take it and use it and then build on top of it and iterate on it. So I think it's more of here's a platform for you and take it. So I think that to me is the big story and that's not and think about it. How many people out there, a role in their own Amazon, you've got to be pretty strong at Amazon, uh, familiar ups to roll your own gut >>Of other quick points that he barely emphasized the primitives, the API APIs, that multiple databases, right tool for the right job, took a shot at Oracle without mentioning Oracle because they had sort of one database, but I will say this is mission critical. Oracle still owns that. Uh, they talked about a mainframe migration, tooling and runtime from mainframe compatible runtime. That's going to allow them to nip at the edges of those mainframe workloads and Oracle workloads. It, they're not going to get to the core anytime soon. They also talked about role level and cell level security. We think that's the squirrel acquisition from years ago. And then he made a statement. We have three X with Redshift price performance better than any cloud data warehouse sort of interesting shot at, at, at, at a snowflake and Databricks Databricks. So, um, anyway, yeah, >>I mean, I think, I think overall, I thought Adam did a good job. I think he didn't, uh, he didn't disappoint. Okay. But that's comfortable. I think his goal was to get through this and not have people go well, it's not Andy Jassy. I thought he did an awesome job and he did a good job. And he, he got, he got what he needed to do >>Comfortable. And he obviously leaned on some of his Pathfinder customers. NASDAQ, I thought was very impressive. United airlines dish. So, >>Okay. Cutie coverage, ninth year of the cube here at ADP reinvent, uh, 2021 is the cube. You're watching the leader in high-tech coverage. The cube.

>> Welcome to today's session of theCUBE's presentation of the AWS Startup Showcase, The Next Big Thing in AI, Security and Life Sciences. Today featuring Lacework for the security track. I'm your host Natalie Erlich. Thank you for joining us. And we will discuss today how LendingTree automates AWS security for DevOps teams and stays compliant with Lacework. Now we're joined by Adam Leftik the VP of Product at Lacework as well as a Arun Sankaran, CISO of LendingTree. Thank you both very much for joining us today. >> Thank you for having us. >> Well, wonderful. Adam, let's start with you. Lacework positions itself as, "cloud security at the speed of cloud innovation." What does that mean to you and how are you helping your customers? >> Great question, Natalie. I think one of the things that's really important to understand about Lacework really comes back to essentially what's happening at cloud speed, which is customers are aggressively moving more and more of their applications to the cloud, but they're doing so with the same number of resources to secure that environment. And as the cloud continues to grow, both in terms of complexity, as well as overall ability to unlock new styles of applications that were never before even possible without this new technology landscape. Fundamentally, Lacework is designed to enable those builders to go faster without worrying about all the different intricacies and threats that they face out there on the internet. And so the core mission of Lacework is really about enabling builders to build those applications and leverage those cloud resources and new cloud technologies to move quicker and quicker. >> Natalie: Fascinating. >> Yeah, thanks. If you go back to the sort of foundation of the company there we took a very different approach to how we think about security. Often, you know, security approaches in the past have been a rules driven model where you try and think of all the different vectors that attacks can come at. And fundamentally, you end up writing a series of these rules that are impossible to maintain, they atrophy over time, and that you can't possibly think ahead of all these nefarious actors. So one of the things that Lacework did from the very beginning was take a very different approach which is leveraging security as a data problem. And the way we do this is through what we refer to as our polygraph. And the polygraph essentially looks at all the exhaust telemetry that we're able to ingest both from your cloud accounts as well as the underlying infrastructure. And we take that and we build a baseline and a behavioral model for how the application should behave when it's normal. And this baseline represents the state of normalcy. And so then we leverage modern data science techniques to essentially build a model that can identify potential threats without requiring our users to build rules and ultimately play catch up to all the different threats that they face. And this is a really, really powerful capability because it allows our customers both to identify misconfigurations and remediate them, monitor all the activity to reduce the overall overhead on their security organization, and of course help them build faster and identify threats as they come into the system. And we differentiate in lots of different ways as well. So one of the things we're looking to do as part of the overall cloud transformation is really meet the DevOps teams and the security teams where they are. And so all of the information that Lacework captures, synthesizes, and produce through our automation ultimately feed into the different channels that our users are really leveraging that skill today. Whether that's through their ChatOps windows or ultimately into their CICD pipeline so that we give broad coverage both at build time as well as run time and give them full visibility and insights and the ability to remediate those quickly. You know, one of the other things that we're really proud of and this is core to our product philosophy is building more and more partnerships with our customers and LendingTree is really at the forefront of that partnership and we're super excited to be partnering with them. And that's certainly something that we've done to differentiate our product offering and I'd love to hear from Arun, how have you been working with Lacework and how has that been going so far? >> Yeah, thank you, Adam. You know, frankly I think that's a huge differentiator for us. There's a lot of players that can solve technology problems but what we've really appreciated is that as a smaller shop and a smaller organization, the level of connectedness that we feel with the development teams at Lacework. We raise a opportunity. You know, this can make things more efficient for us or this can reduce our time to triage, or this visualization or this UI could be modified to support certain security operations center use cases, maybe that's not what it's designed for. And we've enjoyed just a lot of success in kind of shaping the product in order to meet all the different use cases. And as Adam mentioned, you know, as a CISO, my primary responsibility is security, but frankly there's a lot of DevOps and tech use cases within the polygraph visualization tool, and understanding our environment and troubleshooting has frankly it saved us quite a bit of time and we're looking forward to the partnership to continue to grow out the tool. As we, as a company, scale in today's world, it's very important that we're able to scale our capability 2-3X without a corresponding 2-3X in staff and resources. I think this is the kind of tool that's going to help us get there. >> Well, speaking to you Arun, Lacework has recently grown tremendously and gotten a lot of industry attention but you saw something before everyone else. Can you tell us what really caught your attention? What stood out to you and why you decided to become an early adopter? >> Yeah, great question. Honestly, I wish it was a super tricky kind of answer but the real honest answer is it was a very easy decision because we had a need. We knew that we needed robust monitoring capability and detection of threats within containerized environments. And, you know, there are other players in the space but we have a very diverse environment. We're a combination of multiple container technologies and multiple cloud platforms. And we needed something that had the greatest diversity of coverage across our environments. And this was really the only solution that would work for us. I'd love to be able to say that it was like an aggressive bake-off and there's all these different options. But really, from a capability, and scope, and coverage, it was a fairly easy decision for us. >> And how has your threat detection and investigation process changed since you brought on Lacework? >> Yeah, it certainly has. Our environment within 24 hour period, it might generate 300, 400 million events and that's process level data from hosts and network data access. It's just a very noisy amount of alerts. With the Lacework's platform, those 300, 400 million get reduced to about a hundred alerts a day that we see and of those, five are critical and those tend to all be very actionable. So from an alert fatigue perspective, we really rely on this to give us actionable data, actionable alerts that teams can really focus on and reduces that noise. So I would say that's probably the number one way that our detection process has changed and frankly, a lot of it is what Adam mentioned as far as the underlying self-learning, self-tuning engine. There's not a whole lot of active rules that we had to create or configuration that we had to do. It's kind of a learning system and I think it's really, probably, I would estimate maybe 50-60% reduction in triage and response time for alerts as well. >> And Adam, now going to you, while 2020 was a really rough year for a lot of people, a lot of businesses, Lacework realized 300% revenue growth. So now that the economy is bouncing back and seemingly so in full force, what are your expectations for Lacework in the next year? >> Great question. I think one of the things we're seeing broadly across the industry is an acceleration, a realization that companies that are going through digital transformations have accelerated their pace and so we anticipate even faster growth. Additionally, you know, the companies that may have not been on that trajectory are now realizing that they need to move to the cloud. There's not a lot of folks right now thinking that they're going to be racking and stacking in physical data centers going forward. So we fully expect a continuation of massive growth. And increasingly as customers are moving into the cloud, they're looking for tools to help them build a secure footprint but also enable them to go faster. So, we have a point of view that we're going to continue to see this massive growth and if not, how to accelerate from here. >> Well, you're also the man behind the product. So could you go behind some of the key features that it offers? >> Sure. So, if you think about our overall product portfolio, we really have both breadth and depth. So, first and foremost, most customers who are moving to the cloud or have a large cloud footprint, the first concern they have is, do I have a series of misconfigurations? We really help our customers both identify best practices with those configurations in the cloud, and then also help them move quickly towards potential compliance standards that they need to adhere to. Everyone's operating in a regulated environment these days. And then of course, once you've got that footprint to a place where it's healthy, you really, really want to be able to monitor and track the changes to the configurations over time to ensure you're continuing to maintain that footprint. And so we provide a polygraph based model that essentially identifies potential behavioral risks that we're observing through our data clustering algorithms to help you identify potential holes that you may have created over time and help you remediate those things. And then of course, you know, every customer faces a significant challenge when it comes to just keeping up with the overall landscape changes in terms of overall vulnerability footprint in their environments. And so we have a great capability with what we call vulnerability discovery, which enables our customers to understand where they're vulnerable and not simply tell them how many vulnerabilities they have, but help them isolate, leveraging all the run time and bill time contexts we have so that they can really prioritize what's important to them and what represents the highest risk. And then of course, lastly, you know, where the company really got started is in helping customers protect their cloud workloads. And we do this by identifying threats that we're able to leverage our machine learning and data clustering algorithms so that once we have those baseline behaviors identified and modeled, we can leverage all of our threat intelligence to identify anomalies in that system and help customers really identify those risks as they're coming into the system and deal with those in a really timely manner. So those are kind of the overall key capabilities that they really help teams scale and drive their overall cloud security programs. >> And Arun, really quickly from your perspective, what is a key feature that is really beneficial to LendingTree? >> It's kind of what Adam mentioned with the kind of the self-tuning capability, the reduction of alerts and data based on behavioral-based detection versus rule-based. A lot of people have, you have fancy words, they call AI and machine learning, this and that, but I've rarely seen it work effectively. I think this is a situation where it does work really effectively and does free up time and resources on our side that we can apply to other problems we're trying to solve so I think that's the number one. >> Okay, terrific. Well, I'm really curious Adam. Got to ask you this question. I mean, we saw a really big software IPO last year. What do you think is in store for Lacework? >> Yeah, well, you know, the IPO is just a point in time as opposed to it's part of the journey. Lacework's continuing to invest and really focus on fundamentally changing the security landscape. One of the reasons why I joined Lacework and continue to be really excited about the opportunity comes back to the fundamental challenge that all security tools have. We do not want to create a platform that drives wet blanket behavior, but really fundamentally enables teams like Arun's to move faster and enable the builders to build the applications that fundamentally drive great business outcomes for our customers. And so that's what gets me out of bed. And I think everyone at Lacework is really focused on helping drive great outcomes for our customers. >> Fascinating to hear how Lacework is securing cloud around the world. Lovely to have you on the show. Adam Leftik, the VP of Lacework, as well Arun Sankaran, the CISO of LendingTree. I'm your host for the AWS Startup Network here on theCUBE. Thank you very much for watching.

>>Welcome to the cubes coverage of Dell Technologies world 2021. The digital experience. I'm lisa martin. I've got two guests here with me today. Adam Glick is here. Senior Director of portfolio marketing for Apex at Dell Technologies. Adam welcome to the cube >>lisa. It's great to be here with you >>likewise. And Andrew Glinka is here VP of Competitive intelligence at Dell Technologies as well. Andrew welcome to you as well. >>Thank you. Glad to be here. >>So the last Dell Technologies world was only about six months or so ago and sadly I was sitting in the same room doing that. We're not in Vegas at the convention center but hopefully one day we will be soon. But a lot of news there um Adam was about Apex and this big transformation about what Dell wants to do, give us a little bit of a history and what's transpired in the last six months. >>Well, a lot of things have happened in the past six months with what we were calling Project Apex before probably the first most obvious one is we've removed project from the name as we've made the offering generally available. We've also added a lot to it. There's a lot of new pieces of technology that are part of Project Apex now, we've talked about bringing in the cloud, bringing the custom solutions, hear a lot about that at Dell Technologies where all this time and really practicing that all up together in a single experience for customers, giving them something that's super simple agile and gives them all the control that they want to use their infrastructure where they want it all of that as a service. >>Big changes Andrew. Let's go over to you now. Talk to me about some of the players in the market. >>Well, he has a service market is growing incredibly fast and will continue to grow over the next number of years. And what we're seeing is a lot of players trying to enter that market because it is growing so fast. So you have some of the traditional infrastructure players that are entering like HP has their offer out in the market and pure storage and that happened many others. And you also have the public cloud providers like amazon web services, google Microsoft azure that are starting to develop um on prem tech capabilities to kind of validate this hybrid cloud as a service, all things everywhere model. So uh rapidly growing market a lot changing in a lot of players entering this space very quickly. >>So a lot of acceleration we've seen with respect to digital transformation Andrew in the last year. So talk to me about how Apex compares to those infrastructure players, you mentioned peer storage, Netapp HP. Talk to me about the comparison there. >>Yeah, so one of the things is we continue to develop, Apex is we're going to offer the broadest portfolio of as a service solutions for customers, all with different consumption models. So we'll be offering outcome based meter based as well as custom solutions, which is a little bit different than what others can provide all delivered using market leading technology and all Dell supported. So we're not using third party to deliver any of the asset service, it's all Dell supported, um some other very tactical things like single rate, so we don't charge for over usage or charge extra, which is different than some um and also it's all self service. So through the console you can place an order for a new system or upgraded system and you're avoiding the lengthy sale cycles and all the back and forth. So just a couple of questions you can get the outcome that you're looking for. >>Adam. Talk to me about how apex compares to the public cloud providers, customers obviously have that choice as well. AWS google cloud platform. What's the comparison contrast there? >>So when we think about what's going on with public cloud providers, we really look at them as partners and people that we work with. There's a Venn diagram if you think about it and the reality is that although there is some overlap between, there's also a lot of differentiated value that we look at, that we bring their and it's how do we work together on those pieces? So the most obvious of those is when you're thinking about things like a hybrid cloud and how people work together to make sure that they've got a cloud that meets their needs, both on prem in their Coehlo locations out of the edge as well as whatever they're doing with public >>cloud. >>And so we're looking at how do we bring all those pieces together? And there are certain things that work better in certain places, certain ones that work better than others. We do a lot of things around the simplicity of billing to make that easy for customers, giving them really high performance ways to to work well that really meet the needs of a lot of workloads that might need regulatory needs or might have specific performance mapping, high performance computing, things like that. But it works together. And that's really the point is that what customers tell us is that they have needs for on premises, They have needs for things in their private cloud and follows. They also have needs in the public cloud. And how do they bring that together? And so we're working to say, how do we bridge that gap to make the best possible outcome for customers? We work on partnerships with the partnership that we announced with Equinix to bring together co location facilities around the world and bring apex services customers easily when they want to say reduce the latency between what they're running and what they control within their own hardware stacks and what might be running in the public cloud. It's kind of a merger of both that really helps customers get the best of all that they need because at the end of the day that's the goal is helping our customers get the best I. T. Outcomes for their businesses as possible. >>Right? And you mentioned Hybrid cloud and we talk about that so often customers are in that hybrid world for many reasons. So basically what you're saying is there is partnerships that Dell Technologies has with Apex and the other hyper scholars so that when customers come in, if they're most likely already using some of those other platforms, they actually could come in and work with Apex too, develop a solution that works very synergistically. >>Yeah, we're helping them pull together what they need. And if you take a look, 72 of organizations say that they're taking a hybrid cloud approach, they want to be able to bring the best of both worlds to what they're doing and really choose what's right for them. Where do they need to be able to really control what's happening with their data? Where do they want to be able to maintain and control the costs that they have and also be able to access the other services that might be out there that they would need. So how do they bring those together? And those ways that we work together for the benefit of customers? And we bridge those two pieces is really what we're aiming to do here. >>Excellent. So Andrew, let's go back over you. I want to talk about workloads here because you know when we look at some of the numbers, the 8020 rule with the cloud, 80 of those workloads still on prem customers needing to determine which workloads should go to the cloud. How does apex work with customers to facilitate making those decisions? Um about the workloads that are best suited for apex versus club? >>Well, I think that's the beauties, it's very flexible. And so some of those traditional workloads that are still on prem can be run as a service without a whole lot of change. So you don't have to re platform, you don't have to reengineer them and you can move them into an as a service model, continue to run them easily. But then there's a whole lot of new development like high performance computing and Ai And machine learning, particularly at an edge where Gartner says by 2025 75 of all data will be processed at the edge. So as these new capabilities are being built out, uh customers have been asking us to start to run that infrastructure in these new workloads and and at as a service model and so high performance computing ai. Ml these edge workloads are fantastic use cases just get started with as a service and can certainly extend back into some of the more traditional workloads that they've been running >>adam. Can you talk to us a little bit about what's transpired in the last six months from the customers lens as we talked a little bit about, we talked a lot in the last year about the acceleration of digital transformation and so many businesses having to pivot multiple times in the last year. A lot of acceleration of those getting to cloud for, for to survive. Talk to me about the customer experience, what you see in the last six months. >>So what we've heard a lot from our customers is that they're really looking for the benefits of consumption as a service that especially as you see the financial impacts that happened over the past year, People looking at ways to preserve capital and what are the ways that they can go and maintain what they want to do or perhaps even grow and accelerate. Take advantage of those new opportunities in ways that don't require large capital purchases and the ability to go in and purchase as a service is something we've heard from multiple customers is something that is really attractive to them as they look at. Hey, there's no opportunities they've opened up and how do they be able to expand on those as well as how do they be able to preserve the capital? They have, be able to continue with the projects that they're looking at but be able to take a more agile approach for those things. And so the as a service offerings that we've been talking to our customers about have been really something they've been excited about and they come to us kind of, hey, what do you have? What's the roadmap? How can we have more of those kinds of things? And that's why we're so excited Dell Technologies world to be talking about how we're bringing even more apex services as a service available to our customers. >>And I'm just curious in the last year since we've seen so many industries, every industry really rocked by the very dynamic market, but some of the things like healthcare and government, I'm just curious if you've seen any industries in particular really take a leading edge here and working with you in apex. >>one of the most >>interesting things that I've seen from the customers that I've been talking to is that it really is broad ranging that I've talked to customers who are governmental customers who are interested in expanding what they're doing with it but very concerned about things like data, locality and data sovereignty. That's very interesting to them. I've talked to manufacturing organizations, they're looking at how do they expand their operations in asian manufacturing for instance. And they're going from, how do they operate within the United States to how do they expand their operations? Be able to do that in a more quick fashion? What they're doing? Talk to healthcare organizations, they're looking at, how do they be able to bring digital healthcare and as you to think about what's happening more virtually that people are doing, What does that mean in terms of health care? Both from people who are actually doing virtual visits with their doctors as well as even things like digital surgery. So there's so many things that are happening really. I could talk to you about dozens of industries. But the takeaway that I've had is that there's no real one industry, it's really something that has impacted just operations globally and different folks. Look at different things in different ways. I talked to a company that does train that actually train company. They do logistics and they're looking at edge scenarios and how do they do train inspections faster to be able to provide better turnaround times for their trains because there's a limited amount of track and so if they miss a maintenance window like that's time that they not only have to wait for the next window, they have to wait for all the other trains to pass too. So it's really breathtaking, just the scope of all that's changing in it and all the opportunities that are coming up as people think about what consuming it services as a service can mean for them. >>Yeah, amazing opportunities. And you talked about, you know, the virtual and there's so much of it that's going to persist in in a good way, silver linings, right? Um and you want to go back over to you talk to me when we, when we talked about apex at Dell technologies world 2026 months ago, this was kind of revolutionary and really looking at it as a really big change to Dell's future strategy. Talk to me about that. >>Well, it's a change for the entire company, so having to rethink how we deliver all these services and outcomes to customers. So it's it's not just about the product. The product is now the service and the service is the product, so it's very different in how we approach it. Thinking more about how we can help our customers achieve these outcomes um and help deliver these services that get them there, which is a little different than just developing the products themselves. And so that's been a big thing that we've been taking on and making sure that we deliver these outcomes for our customers. >>Yeah. And then adam last question for you talk to me about kind of same perspective of looking at this as as how Dell intends to compete in the future and what customers can expect. Also how can they engage? Is this something that is available with Channel Partners? Dell Direct? >>So this is the beginning of a huge journey and transformation as Andrew spoke about, like this is a transformation of not only what we're providing, but a transformation across all of Dell. We're looking at how do we expand the X portfolio to bring a portfolio of options to our customers? You know, we're starting with with storage and cloud and some are custom solutions, but we really have a vision of how do we bring all of Dell's business products and into services for our customers? You know, it's a huge transformation, it's something I'm incredibly excited about because it really aligns what we do with what our customers do. We've never had an opportunity to be so closely connected with our customers and create great outcomes for them. So the transformation, like we're just at the beginning of this and it's an incredible path that we're on that's providing amazing value for the people that we've already started working with. For people that want to find out more about it. You can certainly come to our website, Dell technologies dot com slash apex. People who have a relationship with Dell already contact their sales representative will be more than happy to talk to them about what their current needs are and what effects can do to help them continue their digital transformation and create better outcomes for their organization. >>Excellent, Adam Andrew, Thank you for joining me today to talk about what's going on. Project apex to apex the tremendous amount of opportunities that it's helping customers in any industry uncover. We look forward to seeing down the road some of those great customer outcomes that come from this. I thank you both for joining me today. >>Thank you very much. Thank you >>for Adam Glick and Andrew Glinka. I'm lisa martin. You're watching the cubes coverage of Dell Technologies World 2021 The Digital Experience.

>> (soft upbeat music) >> Announcer: From around the globe, it's the cube with digital coverage of Ansible Fest 2020, brought to you by RedHat. >> Hello, welcome to the cubes coverage of Ansible Fest 2020. We're not in person, we're virtual. I'm John Furrier , your host of theCube. We've got a great power panel here of RedHat engineers. We have Brad Thorton, Senior Principle Software Engineer for Ansible networking. Adam Miller, Senior Principle Software Engineer for Security and Jill Rouleau, who's the Senior Software Engineer for Ansible Cloud. Thanks for joining me today. Appreciate it. Thanks for coming on. >> Thanks. >> Good to be here. >> We're not in person this year because of COVID, a lot going on but still a lot of great news coming out of Ansible Fest this year. Last year, you guys launched a lot since last year. It's been awesome. Launched the new platform. The automation platform, grown the collections, certified collections community from five supported platforms to over 50, launched a lot of automation services catalog. Brad let's start with you. Why are customers successful with Ansible in networking? >> Why are customers successful with Ansible in networking? Well, let's take a step back to a bit of classic network engineering, right? Lots of CLI interaction with the terminal, a real opportunity for human error there. Managing thousands of devices from the CLI becomes very difficult. I think one of the reasons why Ansible has done well in the networking space and why a lot of network engineers find it very easy to use is because you can still see an attack at the CLI. But what we have the ability to do is pull information from the same COI that you were using manually, and showed that as structured data and then let you return that structured data and push it back to the configuration. So what you get when you're using Ansible is a way to programmatically interface and do configuration management across your entire fleet. It brings consistency and stability, and speed really to network configuration management. >> You know, one of the big hottest areas is, you know, I always ask the folks in the cloud what's next after cloud and pretty much unanimously it's edge, and edge is super important around automation, Brad. What's your thoughts on, as people start thinking about, okay, I need to have edge devices. How does automation play into that? And cause networking, edge it's kind of hand in hand there. So what's your thought on that? >> Yeah, for sure. It really depends on what infrastructure you have at the edge. You might be deploying servers at the edge. You may be administering IOT devices and really how you're directing that traffic either into edge compute or back to your data center. I think one of the places Ansible is going to be really critical is administering the network devices along that path from the edge, from IOT back to the data center, or to the cloud. >> Jill, when you have a Cloud, what's your thoughts on that? Because when you think about Cloud and Multicloud, that's coming around the horizon, you're looking at kind of the operational model. We talked about this a lot last year around having Cloud ops on premises and in the Cloud. What should customers think about when they look at the engineering challenges and the development challenges around Cloud? >> So cloud gets used for a lot of different things, right? But if we step back Cloud just means any sort of distributed applications, whether it's on prem in your own data center, on the edge, in a public hosted environment, and automation is critical for making those things work, when you have these complex applications that are distributed across, whether it's a rack, a data center or globally. You need a tool that can help you make sense of all of that. You've got to... We can't manage things just with, Oh, everything is on one box anymore. Cloud really just means that things have been exploded out and broken up into a bunch of different pieces. And there's now a lot more architectural complexity, no matter where you're running that. And so I think if you step back and look at it from that perspective, you can actually apply a lot of the same approaches and philosophies to these new challenges as they come up without having to reinvent the wheel of how you think about these applications. Just because you're putting them in a new environment, like at the edge or in a public Cloud or on a new, private on premise solution. >> It's interesting, you know, I've been really loving the cloud native action lately, especially with COVID, we're seeing a lot of more modern apps come out of that. If I could follow up there, how do you guys look at tools like Terraform and how does Ansible compare to that? Because you guys are very popular in the cloud configuration, you look at cloud native, Jill, your thoughts. >> Yeah. So Terraform and tools like that. Things like cloud formation or heat in the OpenStack world, they do really, really great at things like deploying your apps and setting up your stack and getting them out there. And they're really focused on that problem space, which is a hard problem space that they do a fantastic job with where Ansible tends to come in and a tool like Ansible is what do you do on day two with that application? How do you run an update? How do you manage it in the longterm of something like 60% of the workloads or cloud spend at least on AWS is still just EC2 instances. What do you do with all of those EC2 instances once you've deployed them, once they're in a stack, whether you're managing it, whatever tool you're managing it with, Ansible is a phenomenal way of getting in there and saying, okay, I have these instances, I know about them, but maybe I just need to connect out and run an update or add a package or reconfigure a service that's running on there. And I think you can glue these things together and use Ansible with these other stack deployment based tools really, really effectively. >> Real quick, just a quick followup on that. what's the big pain point for developers right now when they're looking at these tools? Because they see the path, what are some of the pain points that they're living right now that they're trying to overcome? >> I think one of the problems kind of coincidentally is we have so many tools. We're in kind of a tool explosion in the cloud space, right now. You could piece together as as many tools to manage your stack, as you have components in your stack and just making sense of what that landscape looks like right now and figuring out what are the right tools for the job I'm trying to do, that can be flexible and that are not going to box me into having to spend half of my engineering time, just managing my tools and making sense of all of that is a significant effort and job on its own. >> Yes, too many may add, would choke in years ago in the big data search, the tools, the tool train, one we call the tool shed, after a while, you don't know what's in the back, what you're using every day. People get comfortable with the right tools, but the platform becomes a big part of that thinking holistically as a system. And Adam, this comes back to security. There's more tools in the security space than ever before. Talking about tool challenges, security is the biggest tool shed everyone's got tools they'd buy everything, but you got to look at, what a platform looks like and developers just want to have the truth. And when you look at the configuration management piece of it, security is critical. What's your thoughts on the source of truth when it comes into play for these security appliances? >> So these are... Source of truth piece is kind of an interesting one because this is going to be very dependent on the organization. What type of brownfield environment they've developed, what type of things that they rely on, and what types of data they store there. So we have the ability for various sources of truth to come in for your inventory source and the types of information you store with that. This could be tagged information on a series of cloud instances or series about resources. This could be something you store in a network management tool or a CMDB. This could even be something that you put into a privilege access management system, such as, CyberArk or hashivault. Like those are the things and because of Ansible flexibility and because of the way that everything is put together in a pluggable nature, we have the capability to actually bring in all of these components from anywhere in a brownfield environment, in a preexisting infrastructure, as well as new decisions that are being made for the enterprise as I move forward. And, and we can bring all that together and be that infrastructure glue, be that automation component that can tie all these disjoint loosely coupled, or complete disc couple pieces, together. And that's kind of part of that, that security posture, remediation various levels of introspection into your environment, these types of things, as we go forward, and that's kind of what we're focusing on doing with this. >> What kind of data is stored in the source of truth? >> I mean... So what type of data? This could be credential. It could be single use credential access. This could be your inventory data for your systems, what target systems you're trying to do. It could be, various attributes of different systems to be able to classify them ,and codify them in different ways. It's kind of kind of depending, be configuration data. You know, we have the ability with some of the work that Brad and his team are doing to actually take unstructured data, make it structured, bullet into whatever your chosen source of truth is, store it, and then utilize that to, kind of decompose it into different vendors, specific syntax representations and those types of things. So we have a lot of different capability there as well. >> Brad, you were mentioned, do you have a talk on parsing, can you elaborate on that? And why should network operators care about that? >> Yeah, welcome to 2020. We're still parsing network configuration and operational state. This is an interesting one. If you had asked me years ago, did I think that we would be investing development time into parsing with Ansible network configurations? I would have said, "Well, I certainly hope not. "I hope programmability of network devices and the vendors "really have their API's in order." But I think what we're seeing is network containers are still comfortable with the command line. They're still very familiar with the command line and when it comes time to do operational state assessment and health assessment of your network, engineers are comfortable going to the command line and running show commands. So really what we're trying to do in the parsing space is not author brand new parking and parsing engine ourselves, but really leverage a lot of the open source tools that are already out there bringing them into Ansible, so network engineers can now harvest the critical information from usher operational state commands on their network devices. And then once they've gotten to the structure data, things get really interesting because now you can do entrance criteria checks prior to doing configuration changes, right? So if you want to ensure a network device has a very particular operational state, all the BGP neighbors are, for example before pushing configuration changes, what we have the ability to do now is actually parse the command that you would have run from the command line. Use that within a decision tree in your Ansible playbook, and only move forward when the configuration changes. If the box is healthy. And then once the configuration changes are made at the end, you run those same health checks to ensure that you're in a speck can do a steady state and are production ready. So parsing is the mechanism. It's the data that you get from the parsing that's so critical. >> If I had to ask you real quick, just while it's on my mind. You know, people want to know about automation. It's top of mind use case. What are some of these things around automation and configuration parsing, whether it's parsing to other configuration manager, what are the big challenges around automation? Because it's the Holy grail. Everyone wants it now. What are the couches? where's the hotspots that needs to be jumped on and managed carefully? Or the easiest low hanging fruit? >> Well, there's really two pieces to it, right? There's the technology. And then there's the culture. And, and we talk really about a culture of automation, bringing the team with you as you move into automation, ensuring that everybody has the tools and they're familiar with how automation is going to work and how their day job is going to change because of automation. So I think once the organization embraces automation and the culture is in place. On the technology side, low hanging fruit automation can be as simple as just using Ansible to push the commands that you would have previously pushed to the device. And then as your organization matures, and you mature along this kind of path of network automation, you're dealing with larger pieces, larger sections of the configuration. And I think over time, network engineers will become data managers, right? Because they become less concerned about the network, the vendors specific configuration, and they're really managing the data that makes up the configuration. And I think once you hit that part, you've won at automation because you can move forward with Ansible resource modules. You're well positioned to do NETCONF for RESTCONF or... Right once you've kind of grown to that it's the data that we need to be concerned about and it could fit (indistinct) and the operational state management piece, you're going to go through a transformation on the networking side. >> So you mentioned-- >> And one thing to note there, if I may, I feel like a piece of this too, is you're able to actually bridge teams because of the capability of Ansible, the breadth of technologies that we've had integrations with and our ability to actually bridge that gap between different technologies, different teams. Once you have that culture of automation, you can start to realize these DevOps and DevSecOps workflow styles that are top of everybody's mind these days. And that's something that I think is very powerful. And I like to try to preach when I have the opportunity to talk to folks about what we can do, and the fact that we have so much capability and so many integrations across the entire industry. >> That's a great point. DevSecOps is totally a hop on. When you have software and hardware, it becomes interesting. There's a variety of different equipment, on the security automation. What kind of security appliances can you guys automate? >> As of today, we are able to do endpoint management systems, enterprise firewalls, security information, and event management systems. We're able to do security orchestration, automation, remediation systems, privileged access management systems. We're doing some threat intelligence platforms. And we've recently added to the I'm sorry, did I say intrusion detection? We have intrusion detection and prevention, and we recently added endpoint security management. >> Huge, huge value there. And I think everyone's wants that. Jill, I've got to ask you about the Cloud because the modules came up. What use cases do you see the Ansible modules in for the public cloud? Because you got a lot of cloud native folks in public cloud, you've got enterprises lifting and shifting, there's a hybrid and multicloud horizon here. What's some of the use cases where you see those Ansible modules fitting well with public level. >> The modules that we have in public cloud can work across all of those things, you know. In our public clouds, we have support for Amazon web services, Azure GCP, and they all support your main services. You can spin up a Lambda, you can deploy ECS clusters, build AMI, all of those things. And then once you get all of that up there, especially looking at AWS, which is where I spend the most time, you get all your EC2 instances up, you can now pull that back down into Ansible, build an inventory from that. And seamlessly then use Ansible to manage those instances, whether they're running Linux or windows or whatever distro you might have them running, we can go straight from having deployed all of those services and resources to managing them and going between your instances in your traditional operating system management or those instances and your cloud services. And if you've got multiple clouds or if you still have on prem, or if you need to, for some reason, add those remote cloud instances into some sort of on-prem hardware load balancer, security endpoint, we can go between all of those things and glue everything together, fairly seamlessly. You can put all of that into tower and have one kind of view of your cloud and your hardware and your on-prem and being able to move things between them. >> Just put some color commentary on what that means for the customer in terms of, is it pain reduction, time savings? How would you classify their value? >> I mean, both. Instead of having to go between a number of different tools and say, "Oh, well for my on-prem, I have to use this. "But as soon as I shift over to a cloud, "I have to use these tools. "And, Oh, I can't manage my Linux instances with this tool "that only knows how to speak to, the EC2 to API." You can use one tool for all of these things. So like we were saying, bring all of your different teams together, give them one tool and one view for managing everything end to end. I think that's, that's pretty killer. >> All right. Now I get to the fun part. I want you guys to weigh in on the Kubernetes. Adam, if you can start with you, we'll start with you go in and tell us why is Kubernetes more important now? What does it mean? A lot of hype continues to be out there. What's the real meet around Kubernetes what's going on? >> I think the big thing is the modernization of the application development delivery. When you talk about Kubernetes and OpenShift and the capabilities we have there, and you talk about the architecture, you can build a lot of the tooling that you used to have to maintain, to be able to deliver sophisticated resilient architectures in your application stack, are now baked into the actual platform, so the container platform itself takes care of that for you and removes that complexity from your operations team, from your development team. And then they can actually start to use these primitives and kind of achieve what the cloud native compute foundation keeps calling cloud native applications and the ability to develop and do this in a way that you are able to take yourself out of some of the components you used to have to babysit a lot. And that becomes in also with the OpenShift operator framework that came out of originally Coral S, and if you go to operator hub, you're able to see these full lifecycle management stacks of infrastructure components that you don't... You no longer have to actually, maintain a large portion of what you start to do. And so the operator SDK itself, are actually developing these operators. Ansible is one of the automation capabilities. So there's currently three supported there's Ansible, there's one that you just have full access to the Golang API and then helm charts. So Ansible's specifically obviously being where we focus. We have our collection content for the... carries that core, and then also ReHat to OpenShift certified collection's coming out in, I think, a month or so. Don't hold me to the timeline. I'm shoving in trouble for that one, but we have those things going to come out. Those will be baked into the operator's decay that we fully supported by our customer base. And then we can actually start utilizing the Ansible expertise of your operations team to container native of the infrastructure components that you want to put into this new platform. And then Ansible itself is able to build that capability of automating the entire Kubernetes or OpenShift cluster in a way that allows you to go into a brownfield environment and automate your existing infrastructure, along with your more container native, futuristic next generation, net structure. >> Jill this brings up the question. Why don't you just use native public cloud resources versus Kubernetes and Ansible? What's the... What should people know about where you use that, those resources? >> Well, and it's kind of what Adam was saying with all of those brownfield deployments and to the same point, how many workloads are still running just in EC2 instances or VMs on the cloud. There's still a lot of tech out there that is not ready to be made fully cloud native or containerized or broken up. And with OpenShift, it's one more layer that lets you put everything into a kind of single environment instead of having to break things up and say, "Oh, well, this application has to go here. "And this application has to be in this environment.' You can do that across a public cloud and use a little of this component and a little of that component. But if you can bring everything together in OpenShift and manage it all with the same tools on the same platform, it simplifies the landscape of, I need to care about all of these things and look at all of these different things and keep track of these and are my tools all going to work together and are my tools secure? Anytime you can simplify that part of your infrastructure, I think is a big win. >> John: You know, I think about-- >> The one thing, if I may, Jill spoke to this, I think in the way that a architectural, infrastructure person would, but I want to try to really quick take the business analyst component of it as the hybrid component. If you're trying to address multiple footprints, both on prem, off prem, multiple public clouds, if you're running OpenShift across all of them, you have that single, consistent deployment and development footprint for everywhere. So I don't disagree with anything they said, I just wanted to focus specifically on... That piece is something that I find personally unique, as that was a problem for me in a past life. And that kind of speaks to me. >> Well, speaking of past lives-- >> Having me as an infrastructure person, thank you. >> Yeah. >> Well, speaking of past lives, OpenStack, you look at Jill with OpenStack, we've been covering the Cuba thing when OpenStack was rolling out back in the day, but you can also have private cloud. Where you used to... There's a lot of private cloud out there. How do you talk about that? How do people understand using public cloud versus the private cloud aspect of Ansible? >> Yeah, and I think there is still a lot of private cloud out there and I don't think that's a bad thing. I've kind of moved over onto the public cloud side of things, but there are still a lot of use cases that a lot of different industries and companies have that don't make sense for putting into public cloud. So you still have a lot of these on-prem open shift and on-prem OpenStack deployments that make a ton of sense and that are solving a bunch of problems for these folks. And I think they can all work together. We have Ansible that can support both of those. If you're a telco, you're not going to put your network function, virtualization on USC as to one in spot instances, right? When you call nine one one, you don't want that going through the public cloud. You want that to be on dedicated infrastructure, that's reliable and well-managed and engineered for that use case. So I think we're going to see a lot of ongoing OpenStack and on-prem OpenShift, especially with edge, enabling those types of use cases for a long time. And I think that's great. >> I totally agree with you. I think private cloud is not a bad thing at all. Things that are only going to accelerate my opinion. You look at the VM world, they talked about the telco cloud and you mentioned edge when five G comes out, you're going to have basically have private clouds everywhere, I guess, in my opinion. But anyway, speaking of VMware, could you talk about the Ansible VMware module real quick? >> Yeah, so we have a new collection that we'll be debuting at Ansible Fest this year bore the VMware REST API. So the existing VMware modules that we have usually SOAP API for VMware, and they rely on an external Python library that VMware provides, but with these fare 6.0 and especially in vSphere 6.5, VMware has stepped up with a REST API end point that we find is a lot more performance and offers a lot of options. So we built a new collection of VMware modules that will take advantage of that. That's brand new, it's a lighter way. It's much faster, we'll get better performance out of it. You know, reduced external requirements. You can install it and get started faster. And especially with these sphere seven, continuing to build on this REST API, we're going to see more and more interfaces being exposed so that we can take advantage. We plan to expand it as new interfaces are being exposed in that API, it's compatible with all of the existing modules. You can go back and forth, use your existing playbooks and start introducing these. But I think especially on the performance side, and especially as we get these larger clouds and more cloud deployments, edge clouds, where you have these private clouds and lots and lots of different places, the performance benefits of this new collection that we're trying to build is going to be really, really powerful for a lot of folks. >> Awesome. Brad, we didn't forget about you. We're going to bring you back in. Network automation has moved towards the resource modules. Why should people care about them? >> Yeah. Resource modules, excuse me. Probably I think having been a network engineer for so long, I think some of the most exciting work that has gone into Ansible network over the past year and a half, what the resource modules really do for you is they will reach out to network devices. They will pull back that network native, that vendor native configuration. While the resource module actually does the parsing for you. So there's none of that with the resource modules. And we returned structured data back to the user that represents the configuration. Going back to your question about source of truth. You can take that structure data, maybe for your interface CONFIG, your OSPF CONFIG, your access list CONFIG, and you can store that data in your source of truth under source of truth. And then where you are moving forward, is you really spend time as every engineer managing the data that makes up the configuration, and you can share that data across different platforms. So if you were to look at a lot of the resource modules, the data model that they support, it's fairly consistent between vendors. As an example, I can pull OSPF configuration from one vendor and with very small changes, push that OSPF configuration to a different vendor's platform. So really what we've tried to do with the resource modules is normalize the data model across vendors. It'll never be a hundred percent because there's functionality that exists in one platform that doesn't exist and that's exposed through the configuration, but where we could, we have normalized the data model. So I think it's really introducing the concept of network configuration management through data management and not through CLI commands anymore. >> Yeah, that's a great point. It just expands the network automation vision. And one of the things that's interesting here in this panel is you're talking about, cloud holistically, public multicloud, private hybrid security network automation as a platform, not just a tool, we're still going to have all kind of tools out there. And then the importance of automating the edge. I mean, that's a network game Brad. I mean, it's a data problem, right? I mean, we all know about networking, moving packets from here to there, but automating the data is critical and you give have bad data and you don't have... If you have misinformation, it sounds like our current politics, but you know, bad information is bad automation. I mean, what's your thoughts? How do you share that concept to developers out there? What should they be thinking about in terms of the data quality? >> I think that's the next thing we have to tackle as network engineers. It's not, do I have access to the data? You can get the data now for resource modules, you can get the data from NETCONF, from RESTCONF, you can get it from OpenConfig, you can get it from parsing. The question really is, how do you ensure the integrity and the quality of the data that is making up your configurations and the consistency of the data that you're using to look at operational state. And I think this is where the source of truth really becomes important. If you look at Git as a viable source of truth, you've got all the tools and the mechanisms within Git to use that as your source of truth for network configuration. So network engineers are actually becoming developers in the sense that they're using Git ops to worklow to manage configuration moving forward. It's just really exciting to see that transformation happen. >> Great panel. Thanks for everyone coming on, I appreciate it. We'll just end this by saying, if you guys could just quickly summarize Ansible fast 2020 virtual, what should people walk away with? What should your customers walk away with this year? What's the key points. Jill, we'll start with you. >> Hopefully folks will walk away with the idea that the Ansible community includes so many different folks from all over, solving lots of different, interesting problems, and that we can all come together and work together to solve those problems in a way that is much more effective than if we were all trying to solve them individually ourselves, by bringing those problems out into the open and working together, we get a lot done. >> Awesome, Brad? >> I'm going to go with collections, collections, collections. We introduced in last year. This year, they are real. Ansible2.10 that just came out is made up of collections. We've got certified collections on automation. We've got cloud collections, network collections. So they are here. They're the real thing. And I think it just gets better and deeper and more content moving forward. All right, Adam? >> Going last is difficult. Especially following these two. They covered a lot of ground and I don't really know that I have much to add beyond the fact that when you think about Ansible, don't think about it in a single context. It is a complete automation solution. The capability that we have is very extensible. It's very pluggable, which has a standing ovation to the collections and the solutions that we can come up with collectively. Thanks to ourselves. Everybody in the community is almost infinite. A few years ago, one of the core engineers did a keynote speech using Ansible to automate Philips hue light bulbs. Like this is what we're capable of. We can automate the fortune 500 data centers and telco networks. And then we can also automate random IOT devices around your house. Like we have a lot of capability here and what we can do with the platform is very unique and something special. And it's very much thanks to the community, the team, the open source development way. I just, yeah-- >> (Indistinct) the open source of truth, being collaborative all is what it makes up and DevOps and Sec all happening together. Thanks for the insight. Appreciate the time. Thank you. >> Thank you. I'm John Furrier, you're watching theCube here for Ansible Fest, 2020 virtual. Thanks for watching. (soft upbeat music)

>>Welcome everyone. Good morning. Good evening to all of you around the world. I am so excited to welcome you to launch bad our annual conference for customers, for partners, for our own colleagues here at Mirandes. This is meant to be a forum for learning, for sharing for discovery. One of openness. We're incredibly excited. Do you have you here with us? I want to take a few minutes this morning and opened the conference and share with you first and foremost where we're going as a company. What is our vision then? I also want to share with you on update on what we have been up to you for the past year. Especially with two important acquisitions, Doc Enterprise and then container and lens. And what are some of the latest developments at Mirandes? And then I'll close also with an exciting announcement that we have today, which we hope is going to be interesting and valuable for all of you. But let me start with our mission. What are we here to Dio? It's very simple. We want to help you the ship code faster. This is something that we're very excited about, something that we have achieved for many of you around the world. And we just want thio double down on. We feel this is a mission that's very much worthwhile and relevant and important to you. Now, how do we do that? How do we help you ship code faster? There are three things we believe in. We believe in this world of cloud. Um, choice is incredibly important. We all know that developers want to use the latest tools. We all know that cloud technology is evolving very quickly and new innovations appear, um, very, very quickly, and we want to make them available to you. So choice is very important. At the same time, consuming choice can be difficult. So our mission is to make choice simple for you to give developers and operators simplicity and then finally underpinning everything that we dio is security. These are the three big things that we invest in and that we believe that choice, simplicity and security and the foundation technology that we're betting on to make that happen for you is kubernetes many of you, many of our customers use kubernetes from your aunties today and they use it at scale. And this is something we want to double down on the fundamental benefit. The our key promise we want to deliver for you is Speed. And we feel this is very relevant and important and and valuable in the world that we are in today. So you might also be interested in what have been our priorities since we acquired Doc Enterprise. What has happened for the past year at Miranda's And there are three very important things we focused on as a company. The first one is customer success. Um, when we acquired Doc Enterprise, the first thing we did is listen to you connect with the most important customers and find out what was your sentiment. What did you like? What were you concerned about? What needed to improve? How can we create more value and a better experience for you? So, customers success has been a top of our list of priorities ever since. And here is what we've heard here is what you've told us. You've told us that you very much appreciated the technology that you got a lot of value out of the technology, but that at the same time, there are some things that we can do better. Specifically, you wanted better. Sele's better support experience. You also wanted more clarity on the road map. You also wanted to have a deeper alignment and a deeper relationship between your needs and your requirements and our our technical development that keep people in our development organization are most important engineers. So those three things are were very, very important to you and they were very important to us here. So we've taken that to heart and over the past 12 months, we believe, as a team, we have dramatically improved the customer support experience. We introduced new SLS with prod care. We've rolled out a roadmap to many many of our customers. We've taken your requirements of the consideration and we've built better and deeper relationships with so many of you. And the evidence for that that we've actually made some progress is in a significant increase off the work clothes and in usage of all platforms. I was so fortunate that we were able to build better and stronger relationships and take you to the next level of growth for companies like Visa like soc T general, like nationwide, like Bosch, like Axa X l like GlaxoSmithKline, like standard and Poor's, like Apple A TNT. So many, many off you, Many of all customers around the world, I believe over the past 12 months have experienced better, better, better support strong s L. A s a deeper relationship and a lot more clarity on our roadmap and our vision forward. The second very big priority for us over the last year has been product innovation. This is something that we are very excited about that we've invested. Most of our resource is in, and we've delivered some strong proof points. Doc Enterprise 3.1 has been the first release that we have shipped. Um, as Mirant is as the unified company, Um, it's had some big innovative features or Windows support or a I and machine learning use cases and a significant number off improvements in stability and scalability earlier this year. We're very excited to have a quiet lens and container team, which is by far the most popular kubernetes. I'd, um, in the world today and every day, 600 new users are starting to use lens to manage the community's clusters to deploy applications on top of communities and to dramatically simplify the experience for communities for operators and developers alike. That is a very big step forward for us as a company. And then finally, this week at this conference, we announcing our latest product, which we believe is a huge step forward for Doc Enterprise and which we call Doc Enterprise, Container Cloud, and you will hear a lot more about that during this conference. The third vector of development, the third priority for us as a company over the past year was to become mawr and Mawr developer centric. As we've seen over the past 10 years, developers really move the world forward. They create innovation, they create new software. And while our platform is often managed and run and maybe even purchased by RT architects and operators and I T departments, the actual end users are developers. And we made it our mission a za company, to become closer and closer to developers to better understand their needs and to make our technology as easy and fast to consume as possible for developers. So as a company, we're becoming more and more developers centric, really. The two core products which fit together extremely well to make that happen, or lens, which is targeted squarely at a new breed off kubernetes developers sitting on the desktop and managing communities, environments and the applications on top on any cloud platform anywhere and then DACA enterprise contain a cloud which is a new and radically innovative, contain a platform which we're bringing to market this week. So with this a za background, what is the fundamental problem which we solve for you, for our customers? What is it that we feel are are your pain points that can help you resolve? We see too very, very big trends in the world today, which you are experiencing. On one side, we see the power of cloud emerging with more features mawr innovation, more capabilities coming to market every day. But with those new features and new innovations, there is also an exponential growth in cloud complexity and that cloud complexity is becoming increasingly difficult to navigate for developers and operators alike. And at the same time, we see the pace of change in the economy continuing to accelerate on bits in the economy and in the technology as well. So when you put these two things together on one hand, you have MAWR and Mawr complexity. On the other hand, you have fast and faster change. This makes for a very, very daunting task for enterprises, developers and operators to actually keep up and move with speed. And this is exactly the central problem that we want to solve for you. We want to empower you to move with speed in the middle off rising complexity and change and do it successfully and with confidence. So with that in mind, we are announcing this week at LAUNCHPAD a big and new concept to take the company forward and take you with us to create value for you. And we call this your cloud everywhere, which empowers you to ship code faster. Dr. Enterprise Container Cloud is a lynch bit off your cloud everywhere. It's a radical and new container platform, which gives you our customers a consistent experience on public clouds and private clouds alike, which enables you to ship code faster on any infrastructure, anywhere with a cohesive cloud fabric that meets your security standards that offers a choice or private and public clouds and offer you a offers you a simple, an extremely easy and powerful to use experience. for developers. All of this is, um, underpinned by kubernetes as the foundation technology we're betting on forward to help you achieve your goals at the same time. Lens kubernetes e. It's also very, very well into the real cloud. Every concept, and it's a second very strong linchpin to take us forward because it creates the developing experience. It supports developers directly on their desktop, enabling them Thio manage communities workloads to test, develop and run communities applications on any infrastructure anywhere. So Doc, Enterprise, Container, Cloud and Lens complement each other perfectly. So I'm very, very excited to share this with you today and opened the conference for you. And with this I want to turn it over to my colleague Adam Parker, who runs product development at Mirandes to share a lot more detail about Doc Enterprise Container Cloud. Why we're excited about it. Why we feel is a radical step forward to you and why we feel it can add so much value to your developers and operators who want to embrace the latest kubernetes technology and the latest container technology on any platform anywhere. I look forward to connecting with you during the conference and we should all the best. Bye bye. >>Thanks, Adrian. My name is Adam Parco, and I am vice president of engineering and product development at Mirant ISS. I'm extremely excited to be here today And to present to you Dr Enterprise Container Cloud Doc Enterprise Container Cloud is a major leap forward. It Turpal charges are platform. It is your cloud everywhere. It has been completely designed and built around helping you to ship code faster. The world is moving incredibly quick. We have seen unpredictable and rapid changes. It is the goal of Docker Enterprise Container Cloud to help navigate this insanity by focusing on speed and efficiency. To do this requires three major pillars choice, simplicity and security. The less time between a line of code being written and that line of code running in production the better. When you decrease that cycle, time developers are more productive, efficient and happy. The code is higher, quality contains less defects, and when bugs are found are fixed quicker and more easily. And in turn, your customers get more value sooner and more often. Increasing speed and improving developer efficiency is paramount. To do this, you need to be able to cycle through coding, running, testing, releasing and monitoring all without friction. We enabled us by offering containers as a service through a consistent, cloudlike experience. Developers can log into Dr Enterprise Container Cloud and, through self service, create a cluster No I T. Tickets. No industry specific experience required. Need a place to run. A workload simply created nothing quicker than that. The clusters air presented consistently no matter where they're created, integrate your pipelines and start deploying secure images everywhere. Instantly. You can't have cloud speed if you start to get bogged down by managing, so we offer fully automated lifecycle management. Let's jump into the details of how we achieve cloud speed. The first is cloud choice developers. Operators add mons users they all want. In fact, mandate choice choice is extremely important in efficiency, speed and ultimately the value created. You have cloud choice throughout the full stack. Choice allows developers and operators to use the tooling and services their most familiar with most efficient with or perhaps simply allows them to integrate with any existing tools and services already in use, allowing them to integrate and move on. Doc Enterprise Container Cloud isn't constructive. It's open and flexible. The next important choice we offer is an orchestration. We hear time and time again from our customers that they love swarm. That's simply enough for the majority of their applications. And that just works that they have skills and knowledge to effectively use it. They don't need to be or find coop experts to get immediate value, so we will absolutely continue to offer this choice and orchestration. Our existing customers could rest assure their workloads will continue to run. Great as always. On the other hand, we can't ignore the popularity that growth, the enthusiasm and community ecosystem that has exploded with communities. So we will also be including a fully conforming, tested and certified kubernetes going down the stock. You can't have choice or speed without your choice and operating system. This ties back to developer efficiency. We want developers to be able to leverage their operating system of choice, were initially supporting full stack lifecycle management for a bun, too, with other operating systems like red hat to follow shortly. Lastly, all the way down at the bottom of stack is your choice in infrastructure choice and infrastructure is in our DNA. We have always promoted no locking and flexibility to run where needed initially were supporting open stock AWS and full life cycle management of bare metal. We also have a road map for VM Ware and other public cloud providers. We know there's no single solution for the unique and complex requirements our customers have. This is why we're doubling down on being the most open platform. We want you to truly make this your cloud. If done wrong, all this choice at speed could have been extremely complex. This is where cloud simplification comes in. We offer a simple and consistent as a service cloud experience, from installation to day to ops clusters Air created using a single pane of glass no matter where they're created, giving a simple and consistent interface. Clusters can be created on bare metal and private data centers and, of course, on public cloud applications will always have specific operating requirements. For example, data protection, security, cost efficiency edge or leveraging specific services on public infrastructure. Being able to create a cluster on the infrastructure that makes the most sense while maintaining a consistent experience is incredibly powerful to developers and operators. This helps developers move quick by being able to leverage the infra and services of their choice and operators by leveraging, available, compute with the most efficient and for available. Now that we have users self creating clusters, we need centralized management to support this increase in scale. Doc Enterprise Container cloud use is the single pane of glass for observe ability and management of all your clusters. We have day to ops covered to keep things simple and new. Moving fast from this single pane of glass, you can manage the full stack lifecycle of your clusters from the infra up, including Dr Enterprise, as well as the fully automated deployment and management of all components deployed through it. What I'm most excited about is Doc Enterprise Container Cloud as a service. What do I mean by as a service doctor? Enterprise continue. Cloud is fully self managed and continuously delivered. It is always up to date, always security patched, always available new features and capabilities pushed often and directly to you truly as a service experience anywhere you want, it run. Security is of utmost importance to Miranda's and our customers. Security can't be an afterthought, and it can't be added later with Doctor and a price continued cloud, we're maintaining our leadership and security. We're doing this by leveraging the proven security and Dr Enterprise. Dr. Enterprise has the best and the most complete security certifications and compliance, such as Stig Oscar, How and Phipps 1 $40 to thes security certifications allows us to run in the world's most secure locations. We are proud and honored to have some of the most security conscious customers in the world from all industries into. She's like insurance, finance, health care as well as public, federal and government agencies. With Dr Enterprise Container Cloud. We put security as our top concern, but importantly, we do it with speed. You can't move fast with security in the way so they solve this. We've added what we're calling invisible security security enabled by default and configured for you as part of the platform. Dr Price Container Cloud is multi tenant with granular are back throughout. In conjunction with Doc Enterprise, Docker Trusted Registry and Dr Content Trust. We have a complete end to end secured software supply chain Onley run the images that have gone through the appropriate channels that you have authorized to run on the most secure container engine in the >>industry. >>Lastly, I want to quickly touch on scale. Today. Cluster sprawl is a very real thing. There are test clusters, staging clusters and, of course, production clusters. There's also different availability zones, different business units and so on. There's clusters everywhere. These clusters are also running all over the place. We have customers running Doc Enterprise on premise there, embracing public cloud and not just one cloud that might also have some bare metal. So cloud sprawl is also a very real thing. All these clusters on all these clouds is a maintenance and observe ability. Nightmare. This is a huge friction point to scaling Dr Price. Container Cloud solves these issues, lets you scale quicker and more easily. Little recap. What's new. We've added multi cluster management. Deploy and attach all your clusters wherever they are. Multi cloud, including public private and bare metal. Deploy your clusters to any infra self service cluster creation. No more I T. Tickets to get resources. Incredible speed. Automated Full stack Lifecycle management, including Dr Enterprise Container, cloud itself as a service from the in for up centralized observe ability with a single pane of glass for your clusters, their health, your APs and most importantly to our existing doc enterprise customers. You can, of course, add your existing D clusters to Dr Enterprise Container Cloud and start leveraging the many benefits it offers immediately. So that's it. Thank you so much for attending today's keynote. This was very much just a high level introduction to our exciting release. There is so much more to learn about and try out. I hope you are as excited as I am to get started today with Doc Enterprise. Continue, Cloud, please attend the tutorial tracks up Next is Miska, with the world's most popular Kubernetes E Lens. Thanks again, and I hope you enjoy the rest of our conference.

>>from around the globe. It's the Cube with digital coverage of data automated and event. Siri's brought to you by Iot. Tahoe. Okay, we're back with Adam Worthington. Who's the CTO and co founder of Ethos Adam. Good to see you. How are things across the pond? >>Thank you. I'm sure that a little bit on your side. >>Okay, so let's let's set it up. Tell us about yourself. What your role is a CTO and give us the low down on those. >>Sure, So we get automatic. As you said CTO and co founder of A were pretty young company ourselves that we're in our sixth year and we specialize in emerging disruptive technologies within the infrastructure Data center kind of cloud space. And my role is the technical lead. So it's kind of my job to be an expert in all of the technologies that we work with, which can be a bit of a challenge if you have a huge portfolio, is one of the reasons we deliberately focusing on on also kind of a validation and evaluation of new technologies. Yeah, >>so you guys are really technology experts, data experts and probably also expert in process and delivering customer outcomes. Right? >>That's a great word there, Dave Outcomes. That's a lot of what I like to speak to customers about on. Sometimes I get that gets lost, particularly with within highly technical field. I like the virtualization guy or a network like very quickly start talking about the nuts and bolts of technology on I'm a techie. I'm absolutely a nerd, like the best tech guitar but fundamentally reporting in technologies to meet. This is outcomes to solve business problems on on to enable a better way. >>Love it. We love tech, too, but really, it's all about the customer. So let's talk about smart data. You know, when you when you throw in terms like this is it kind of Canfield Buzz Wordy. But let's let's get into the meat on it. What does that mean to you? One of the critical aspects of so called smart data >>cool probably hoped to step back a little bit and set the scene a little bit more in in terms of kind of where I came from, the types of problems that I'm really an infrastructure solution architect trace on what I kind of benefits. We organically But over time my personal framework, I focused on three core design principles whatever it was I was designing. And obviously they need different things. Depending on what technology area is that we're working with. That's pretty good on. And what I realized that we realized we started with those principles could be it could be used more broadly in the the absolute best of breed of technologies. And those really disrupt, uh, significantly improve upon the status quo in one or more of those three areas. Ideally or more simple, more on if we look at the data of the challenges that organizations, enterprises organizations have criticized around data and smart fail over the best way. Maybe it's good to reflect on what the opposite end of the story is kind of why data is often quite dumb. The traditional approaches. We have limited visibility into the data that we're up to the story using within our infrastructure as what we kind of ended up with over time, through no fault of the organizations that have happened silos, everyone silos of expertise. So whether that be, that's going out. Specialized teams, socialization, networking. They have been, for example, silos of infrastructure, which trade state of fragmentation copies of data in different areas of the infrastructure on copies of replication in that data set or reputation in terms of application environments. I think that that's kind of what we tend to focus on, what it's becoming, um, resonating with more organizations. There's a survey that one of the vendors that we work with actually are launched vendor 5.5 years ago, a medical be gone. They work with any company called Phantom Born a first of a kind of global market, 900 respondents, all different vectors, a little different countries, the U. S. And Germany. And what they found was shocking. It was a recent survey so focused on secondary data, but the lessons learned the information taken out a survey applies right across the gamut of infrastructure data organizations. Just some stats just pull out the five minutes 85% off the organization surveyed store between two and five stores data in 3 to 5 clouds. 63% of organizations have between four and 16 coffees of exactly the same data. Nearly nine out of 10 respondents believe that organizations, secondly, data's fragmented across silos are touched on is would become nearly impossible to manage over the long term on. And 91% of the vast majority of organizations leadership were concerned about the level of visibility their teams. So they're the kind of areas that a smart approach to data will directly address. So reducing silos that comes from simplifying so moving away from complexity of infrastructure, reducing the amount of copies of data that we have across the infrastructure and reducing the amount of application environment. I mean, Harry, so smarter we get with data is in my eyes. Anyway, the further we moved away from this, >>there was a lot in that answer, but I want to kind of summarize it if I can talk. You started with simplicity, flexibility, efficiency. Of course, that's what customers want. And then I was gonna ask you about you know, what challenges customers are facing, and I think you laid it out here. But I want to I want to pick on a couple of some of the data that you talked about the public cloud treat that adds complexity and diversity in skill requirements. The copies of data is so true, like data is just like like if rebels, If you Star Trek franchise, they just expand and replicate. So that's an expense, and it adds complexity. Silo data means you spend a lot of time trying to figure out who's got the right data. What's the real truth with a lot of manual processes involved in the visibility is obviously critical. So those are the problems on. But course you talked about how you address those, But But how does it work? I mean, how do you know what's what's involved in injecting smarts into your data? Lifecycle >>that plane, Think about it. So insurance of the infrastructure and say they were very good reasons why customers are in situations they have been in this situation because of the limits are traditional prices. So you look at something is fundamental. So a great example, um on applications that utilize the biggest fundamentally back ups are now often what that typically required is completely separate infrastructure to everything else. But when we're talking about the data set, so what would be a perfect is if we could back up data on use it for other things, and that's where a, uh, a technology provider like So So although it better technology is incredibly simple, it's also incredibly powerful and allows identification, consolidation. And then, if you look at just getting insight out of that fundamentally tradition approaches to infrastructure, they're put in a point of putting a requirement. And therefore it wasn't really incumbent exposed any information out of the data that's stored within the division, which makes it really tricky to do anything else outside of the application. That that's where something like Iot how come in in terms of abstracting away the complexity more directly, I So these are the kind of the area. So I think one of my I did not ready, but generally one of my favorite quotes from the French philosopher and a mathematician, Blaise Pascal, he says, I get this right. I have written a short letter, but I didn't have time. But Israel. I love that quite for lots of reasons, that computation of what we're talking about, it is actually really complicated to develop a technology capability to make things simple, more directly meet the needs of the business. So you provide self service capabilities that they just need to stop driving. I mean making data on infrastructure makes sense for the business users. Music. It's My belief is that the technology shouldn't mean that the users of the technology has to be a technology expert what we really want them to be. And they should be a business experts in any technology that you should enable on demand for the types of technologies to get me excited. They're not necessarily from a ftt complicated technology perspective, but those are really focused on impressive the capability. >>Yeah. Okay, so you talked about back up, We're gonna hear from Kohi City a little bit later and beyond backup data protection, Data Management, That insight piece you talked earlier about visibility, and that's what the Iot Tahoe's bringing table with its software. So that's another component of the tech stack, if you will, Um, and then you talk about simplicity. We're gonna hear from pure storage. They're all about simple storage. They call it the modern data experience. I think so. So those are some of the aspects and your job. Correct me. If I'm wrong is to kind of put that all together in a solution and then help the customer realize that we talked about earlier that business out. >>Yeah, it's that they said, in understanding both sides so that it keeps us on our ability to be able to deliver on exactly what you just said. It's being experts in the capabilities and new and better ways to do things but also having the kind of business under. I found it to be able to ask the right questions, identify how new a better price is positions and you touched on. Yet three vendors that we work with that you have on the panel are very genuinely of. I think of the most exciting around storage and pure is a great one. So yes, a lot of the way that they've made their way. The market is through impressive C and through producing data redundancy. But another area that I really like is with that platform, you can do more with less. And that's not just about using data redundancy. That's about creating application environment, that conservative, then the infrastructure to service different requirements are able to do that the random Io thing without getting too kind of low level as well as a sequential. So what that means is that you don't necessarily have to move data from application environment a do one thing. They disseminate it and then move it to the application environment. Be that based environment three in terms of an analytics on the left to right work. So keep the data where it is, use it for different requirements within the infrastructure and again do more with less. And what that does is not just about simplicity and efficiency. It significantly reduces the time to value. Well at that again resonates that I want to pick up a soundbite that resonates with all of the vendors we have on the panel later. This is the way that they're able todo a better a better TCO better our alliance significantly reduce the value of data. But to answer your question, yeah, you're exactly right. So it's key to us to kind of position, understand? Customer climbs, position the right technology. >>Adam. I wonder if you could give us your insights based on your experience with customers in terms of what success looks like. I'm interested in what they're measuring. I'm big on and end cycle times and taking a systems view, but of course you know customers. They want to measure everything, whether it's the productivity of developers or, you know, time to insights, etcetera. What >>are >>they? One of the KP eyes that are driving success and outcomes? >>Those capabilities on historically in our space have always been a bit really. When you talk about total cost of ownership, talk about return on investment, you talk about time to value on. I've worked in many different companies, many different infrastructure, often quite complicated environments and infrastructure. I'm being able to put together anything Security realistic gets proven out. One solution gets turned around our alliance TCO is challenging. But now with these new, a better approach is that more efficient, enables you to really build a true story and on replicate whatever you want. Obviously ran kind of our life, and the key thing is to say from data, But now it's time to value. So what we what? We help in terms of the scoping on in terms of the understanding what the requirements are, we specifically called out business outcomes what organizations are looking to achieve and then back on those metrics, uh, to those outcomes. What that does is a few different things, but it provides a certain success criteria. Whether that's success criteria within a proof of concept of the mobile solutions on being able to speak that language on before, more directly meet the needs of the business kind of crystallized defined way is we're only really be able to do that. Now we work with >>Yeah, So when you think about the business case, they are a why benefit over cost benefit obviously lower tco you lower the denominator, you're going to increase the output in the value. And then I would I would really stress that I think the numerator, ultimately especially in a world of data, is the most important. And I think the TCO is fundamental. It's really becoming table stakes. You gotta have simple. You've gotta have efficient. You've got to be agile. But it enables that that numerator, whether that's new customer revenue, maybe, you know, maybe cost savings across the business. And again that comes from taking that systems view. Do you >>have >>examples that you can share with us even if they're anonymous, eyes the customers that you work with that or maybe a little further down on the journey, or maybe not things that you can share with us that are proof points here. >>Sure, it's quite easy and very gratifying when you've spoken to a customer. We know you've been doing this for 20 years, and this is the way that your infrastructure if you think about it like this, if we implemented that technology or this new approach, then we will enable you to get simple, often ready, populous. Reduce your back. I worked on a project where a customer accused that back book from I think it was. It was nine. Just under 10. It was nine fully loaded. Wraps back. We should just for the it you're providing the fundamental underlying storage architectures. And they were able to consolidate that that down on, provide additional capacity. Great performance. The less than half Uh huh. Looking at the you mentioned data protection earlier. So another organization. This is a project which is just kind of nearing completion of the moment. Huge organization. They're literally petabytes of data that was servicing their back up in archive. And what they have is not just the reams of data, they have the combined thing. I different backup. Yeah, that they have dependent on the what area of infrastructure they were backing up. So whether it was virtualization that was different, they were backing up. Pretty soon they're backing up another database environment using something else in the cloud. So a consolidated approach that we recommended to work with them on they were able to significantly reduce complexity and reduce the amount of time that it system what they were able to achieve. And this is again one of the clients have they've gone above the threshold of being able to back up. When they tried to do a CR, you been everything back up into in a second. They want people to achieve it. Within the timescales is a disaster recovery, business continuity. So with this, we're able to prove them with a proof up. Just before they went into production and the our test using the new approach. And they were able to recover everything the entire interest in minutes instead of a production production, workloads that this was in comparison to hours and that was those hours is just a handful of workloads. They were able to get up and running with the entire estate, and I think it was something like an hour on the core production systems. They were up and running practically instantaneously. So if you look at really stepping back what the customers are looking to the chief, they want to be able to if there is any issues recover from those issues, understand what they're dealing with. Yeah, On another, we have customers that we work with recently what they had huge challenges around and they were understandably very scared about GDP are. But this is a little while ago, actually, a bit still no up. A conversation has gone away. Just everybody are still speaks to issues and concerns around GDP are applying understanding whether they so put in them in us in a position to be able to effectively react. Subject That was something that was a key metric. A target for on infrastructure solution that we work with and we were able to provide them with the insight into their data on day enables them to react to compliance. And they're here to get a subject access request way created in significantly. I'm >>awesome. Thank you for that. I want to pick up on a little bit. So the first example you get your infrastructure in order to bust down those silos and what I've when I talk to customers. And I've talked to a number of banks, insurance companies, other financial services of manufacturers when they're able to sort of streamline that data lifecycle and bring in automation and intelligence, if you will. What they tell me is now they're able to obviously compress the time to value, but also they're loading up on way more initiatives and projects that they can deliver for the business. And you talk for about about the line of business having self served. The businesses feel like they actually are really invested in the data, that it's their data that it's not, you know, confusing and a lot of finger pointing. So so that's that's huge on. And I think that your other example is right on as well of really clear business value that organizations are seeing. So thanks for those you know. Now is the time really, t get these houses in order, if you will, because it really drives competitive advantage, especially take your second example in this isolation economy, you know, being able to respond things like privacy are just increasingly critical. Adam, give us the final thoughts. Bring us home in this segment, >>not the farm of built, something we didn't particularly touch on that I think it's It's fairly fairly hidden. It isn't spoken about as much as I think it is that digital approaches to infrastructure we've already touched on there could be complicated on lack of efficiency, impact, a user's ability to be agile, what you find with traditional approaches. And you already touched on some of the kind of benefits and new approaches that they're often very prescriptive, designed for a particular as the infrastructure environment, the way that it served up to the users in a kind of A packaged either way means that they need to use it in that whatever way, in places. So that kind of self service aspect that comes in from a flexibility standpoint that for me in this platform approach, which is the right way to address technology in my eyes enables it's the infrastructure to be used effectively so that the business uses of the data users what we find in this capability into their hand and start innovating in the way that they use that on the way that they bring benefits a platform to prescriptive, and they are able to do that. So what you're doing with these new approaches is all of the metrics that we touched on fantastic from a cost standpoint, from a visibility standpoint. But what it means is that the innovators in the business want to really, really understand what they're looking to achieve and now tools to innovate with us. Now, I think I've started to see that with projects that were completed, you could do it in the right way. You articulate the capability and empower the business users in the right way. Then very significantly better position. Take advantage of this on really match and significantly bigger than their competition. >>Super Adam in a really exciting space. And we spent the last 10 years gathering all this data, you know, trying to slog through it and figure it out. And now, with the tools that we have and the automation capabilities, it really is a new era of innovation and insights. So, Adam or they didn't thanks so much for coming on the Cube and participating in this program >>Exciting times. And thank you very much today. >>Alright, Stay safe and thank you. Everybody, this is Dave Volante for the Cube. Yeah, yeah, yeah, yeah

(upbeat music) >> Narrator: From around the globe, it's theCUBE with digital coverage of PegaWorld Inspire brought to you by Pegasystems. Everybody welcome back to PegaWorld Inspired 2020 this is theCUBE and I'm Dave Vellante, we're here with Adam Field who is the head of innovation and experience at Pegasystems. Adam thanks for coming on, how are you doing man? >> It's my pleasure Dave, I'm doing well how are you? >> Good thank you, I'm excited we're talking innovation, we're talking to innovation hub but to start with your role I love the title, what do you do? Give us the background story. >> Yeah I get that question quite a bit so, I've been with Pega a little over 15 years now and I've held many roles, but currently as head of innovation and experience we have a team I like to call them Creative Misfits, if you will, we sort of bridge that gap between technology and creative, we do research on emerging tech and try to understand how our clients might use it, how it's going to change the future of work, that's the innovation side, on the experience side, we do things like for these PegaWorld events, we match we where art meets tech and we build these experiential things that people come and see at our events, we build all the demos and all the production that you see on the main stage, so we kind of touch a lot of different things around the future of where technology is going. >> Well, I can see, obviously you're innovative, you've got the awesome set up there, the great mic and sound, (laughing) fantastic you look good So and now you've been involved in previous PegaWorld both from behind the scenes and out front speaking obviously this is completely different, how did you prep differently for PegaWorld 2020 virtual versus what you normally do? >> Yeah right so this will be my well 16th I guess PegaWorld and obviously this one stands out as the most different normally we'd be in Boston today, we would have been, you know, working on our stage production and on a floor that's 170,000 square feet big with dozens of booths and hundreds of demos, and obviously this was completely different, but as far as prep goes, I remember the day we learned that early March, this was going virtual and after a few moments of sadness, the team really came together, and I remember the first thing we talked about is we're not going to take a three day event and try to put it all online. Let's--we know people's time is valuable, let's figure out how to take just what's important and get it out to people so that they're inspired to move forward and engage with Pega, so I think that's really been the biggest change in how we've prepped. >> Well, I think that's a great point because obviously theCUBE has been very much involved in these virtual events and. >> Right. >> People send out the note, hey we've made the tough decision to go virtual that's easy decision you really had no choice. >> That's right. >> The tough decision is what do you want to preserve from the physical and understanding that you can't just pop physical into virtual and you got to create a whole new content program, I think Robert Scoble wrote a post on, if you saw it he talks about, hey you better go out and hire Beyonce, oh you can't afford to be for Beyonce? well you better make your content interesting. So to me Adam, that's the tough part, help us understand how you thought that through and what the outcome actually is. >> Yeah, that's right. We didn't have Beyonce, but we did have the Dropkick Murphys, so that was pretty cool and they did a concert for us, so that's been great. But again a lot of people talk about all this free time that they have and I know I have two young kids who are schooling at home now, a job that's busier than it's ever been. I've tried to join a lot of these virtual events and frankly I have gotten overwhelmed, so we took two days and we boiled it down in a two and a half hours, and what we decided to do is we looked at all the areas which we go to market and how people design and deliver their apps, and some of the tech like Pega cloud that they use. And we went to our, I went to my extended team and I said, normally you have 75 booths, we're going to boil that down to 25, let's work together to figure that out. Normally your demos might be 20 minutes when someone walks up, we want to make them seven. But I think the biggest thing that we did, we said what we don't want to lose is that interactivity, and so we had online dozens of Pega experts we could ask questions live, Alan was online doing answering questions live. We made sure that we included live components, our host, Don Sherman was live from his house. We didn't just pre-record everything because then, why would anyone come join when they could just go watch it, 30 minutes later on your YouTube channel? >> See that's innovation to me is having that combination of live. Obviously, you've got to do some stuff a prerecorded, but having a live component adds a dimension, it's challenging, but that's pushing the envelope and I love it. The other thing is, Adam is roles. The roles are different in a virtual event, are they? You're not doing site inspections Like you said, you're not dealing with 170,000 square feet. How did you guys rethink the roles for virtual? >> Yeah so, there were some teams whose world was completely upended. You know, when this all went virtual, the people that do exactly what you were just talking about, dealing with hotels and vendors and things like that, and I got to tell you, one of the most events called PegaWorld Inspire and not to sound too cheesy about it, but one of the things that was really inspiring was to see how everyone stepped up and said, truly, how can I help? And what was really neat about it is we saw different skill sets come out of people that, maybe they hadn't had the opportunity to flex before where they might've worked on one thing that was no longer needed because of the change in the format, and they jumped into become copywriters or liaisons between cause now we have new vendors in this tech world that we didn't have that we turned around in just a matter of weeks. We had people like on my team who normally last year, build this massive physical exhibit containing mirrors and lights, that became video producers, to produce some of these live videos that we did. And one of the things was really impressive, you asked earlier about how did we prep differently and what changed? We looked in the marketplace for different tech and how to bring our CEO and our host and our head of product and everyone together live in split screen, and when you're a big studio you know, and you have that equipment ready to go, that's easy, but when you're just getting average people in their homes and you want to put all that together, we're finding some of the tech in the marketplace just wasn't there. My team built some new video chat technologies that they actually use to produce this in real time, so that was really impressive to me how we turn that around and really innovated not only the things that everyone sees, but all the stuff behind the scenes to. >> See again I think this is what's amazing to me is as I learned more and more about Pega interview Alan earlier. >> Sure. >> Pega is all about being able to adapt to these changes. So a lot of the processes we are using in virtual events, they're unknown. In normally software right through the history of software is okay, here's how the software works. Figure out how to fit your process into it, very rigid. >> That's right. >> Today you know, the last three months with this lockdown in this coronavirus have been completely unknown, and so that's sort of one of the hallmarks of your company, isn't it? >> That's right and we've had the tagline Build For Change for really long time, and I will tell you, I remember in that first meeting again, when we learned this was going virtual and someone stood up and they said, guys we're about to live our tagline. And people really do believe in that, 'cause we go to our clients every single day and say, change is what's going to make you special changes is what's going to make you different, now's your opportunity, seize that change and run with it. And so we said, look, we can't change the world right now, we know we got to go virtual, all we can do is change the type of event that we do, we're not going to do the standard event that we think every one else is going to do, let's do it differently and today was a pretty good example, I think we achieved that. >> I think a couple of things from a challenge standpoint, you mentioned the chat, how do you get people to engage? You had to sort of invent something. >> Yeah. >> And then really think it through for virtual. And I think the other is tech people come to these events, they want to touch the tech. And so you've got you know the innovation hub, it's where people get to play with the technology. You got to take us through how you thought through that and. >> Right. >> What the outcome is. >> Yeah, so that is the toughest part, and I got to tell you, you know all of this being said, I'm looking forward to someday being able to get back and meeting my clients in person, and I'm the type, when I see you on the floor of the innovation hub, I run by a booth and high five you for all the great weeks of hard work, you know? And I love to see people's faces, they see the demos and that's tough not being able to see them smile and get that moment of wow. But what was interesting was it really helped us hone our messages. I think we really realized when I went to everybody and said you don't have 20 minutes, you have seven minutes, here's a template, to follow, to be able to tell your story better, and people started thinking in that mode of storytelling, and what was interesting was lot of people came back to me and said, actually you know what? I can tell that story in a much more crisp way and really show people what they need to see in a in a much faster timeframe. And what it really allowed us to do was find those bits that we thought were most important, find those demos that we think are most important and just, you know bubble those up. One of the things we also did too, we took the opportunity to say you know what, we're going to be online, I watch my kids. My kids are avid gamers whether I like it or not, and they. >> Yeah. >> Watch these Twitch streams, and we thought well, we should be able to do that with even corporate software. So we had these live build sessions where we took some of our developers and I said you're going to be put on the hot seat for 15 minutes on script and we're going to let people just guide and direct you. And they were a little nervous at first, but they went off great, and it was a new format we had never tried before. So if we keep doing these types of different things and we just embrace the moment that we're in I think people will really really come to it and get some value out of it. >> I mean that's awesome, you've got to keep your audience engaged, and so you do lose, you don't have a captive audience, so you lose some time in terms of how much you can you know? how much Kool-Aid injection you can give him. I mean take 20 minutes down to seven minutes. But so you do lose some of that, but what do you gain with virtual? >> Well, I think one of the things that you obviously gain is you can be more widespread, so yeah, you know this event reached tens of thousands of people in dozens of countries. I did an event first week of April, so you can imagine you know, we had two weeks to turn on and I was supposed to be in London and Amsterdam presenting in soccer stadiums. And instead we made that a one hour virtual event and we thought, well, we're just going to get people from the London market and from the Netherlands market, and it turned out, we got people from all over the world to join. So one of the benefits to this is the reach, so we're able to reach a lot more people. I'd say one of the other just things that we realized after tours we're creating a lot of content, we filmed all of this as we were rehearsing, and we're going to put it up online later, so now we have all this great content that anyone can use and go view later, so that was sort of you know, unexpected outcome as well. >> Right yeah, you lose the airline miles, but you gain. (laughing) as I want to going to say you gian the post. >> I don't mind not traveling as well. >> Yeah I here you but, but you do gain that post and I think with physical events, people always at the end of it, it's like, I've never given birth, but I've witnessed that many times. but people feel like, okay, I got to just chill out now for a couple of weeks, and then when they come back, now they're swamped, they've got to catch up. And I think people are realizing, wow, there's a real opportunity maximize the post event here, post nurturing peep streaming out content and continue that engagement, that is a plus of these virtual event. >> Oh, for sure, and you know we started early on deciding how are we going to do, what are we going to do is follow ups you know? That European event that I talked about once again instead of taking all these different markets and trying to replicate it, we did one one hour event. But then because we were in the early days of COVID and some of our clients weren't able to get recorded and speak, we did subsequent webinars in the weeks following them, and the attendance was fantastic. So it allowed us to plan ahead and you know, have a lot of followup activities that we're starting to launch right now as soon as the event ended. >> How do you feel about the outcome for Pega? Do you think it was better, worse, the same or just different? >> I'm going to go with different you know, like I said I get energy I love being up on stage in front of 5,000 people, I love meeting my clients in person, I love the energy of being with my colleagues, but you know it is what it is, We had to do it, and I think what we really embraced it, so I'll say it's just a different way of doing things, but you know I do look forward to the day that I'm able to go meet my clients again and get back on stage and produce some really great things and once again being able to physically see our attendees go oh, when they actually see the software in person, that's the most rewarding thing for me. >> It's going to be interesting as we come out of this I mean, very clearly things are going to be different probably going to have hybrid for some time. Maybe even indefinitely but I'm interested in some of the learnings, some of the things that you think will be permanent, some of the advice. And one of the things I always say to people is don't start with what software are we going to use in there? Your software platform, think about the experience that you want to work backwards from there but what are other advice would you give for given your experiences? >> Right. >> You're so right about that point, I remember interviewing a lot of vendors that we were going to use to bring this online and we were telling them what we wanted to do, and some of them said no one's ever asked about that before we can't do that, so you're a hundred percent right about that. The advice I will say, and the thing I do worry about a little bit is, at first people were a little bit more accepting if maybe the video quality wasn't as good, or you know the content was like any old webinar. As months ago on expectations are going to be higher, people are going to have attended a lot of these things so you're going to have to keep upping the game. And I think the advice I would give is try to take what's great about an in person event and put it online but don't try to replicate the event and put it online. And some of the best things about in person events are just the live nature of it, take the risks, do some live stuff. People will really appreciate that, you'll get a lot of credit for that. The interactivity is what's important about a live event, so as best you can, figure out how to make sure there's some interactivity. Now in the early days I think it's going to be some live Q and A as we move on, it'll be real private rooms with experts that you're able to have one-on-one chats and go through and bounce around and be able to talk to people you know, just like you would accept, between two cameras instead of in person. So I think everyone is months go on. they just going to have to up their game. I think that's great advice, you're absolutely right up your game, up your brand, get a good camera, get good sound, and it's going to just, help your personal brand and your company's brand. Adam. >> We learned what it was like to try to ship microphone and camera equipment around the world (laughing) overnight so we're experts at that, if you you've got any questions. >> Well, I mean what a difference it made, so Adam, thanks so much for coming on theCUBE and sharing your experiences. You guys, have one of the best that we've seen at the Virtual Event Platform so congratulations on that and really appreciate your contribution (mumbles). >> Thanks it's my pleasure, great to talk to you today (mumbles). All right, keep it right there buddy, this is theCUBES coverage of PegaWorld Inspire 2020 the virtual event, will be right back after a short break. (upbeat music)

>> Announcer: Live from Las Vegas, it's theCUBE! Covering AWS Executive Summit. Brought to you by Accenture. >> Everyone to theCUBE's live coverage of the Accenture Executive Summit here at the Venetian part of the AWS re:Invent show, I'm your host, Rebecca Knight. We're joined by Adam Burden, he is the chief software engineer at Accenture, thank you so much for coming back on theCUBE, Adam. >> It's great to be here again, Rebecca, thanks a lot for inviting me. >> So I want to talk to you about some research that you conducted about the future, about future systems. We're going to get into what future systems are in a little bit, but I first want to hear about this research itself, what was the genesis of it, what were you trying to understand? >> It was really interesting. First of all, we actually followed the scientific method for this, starting with a real hypothesis, and then conducted a really big research study to find out, was that hypothesis true? And what we were trying to understand is, we see this thing called an innovation achievement gap at many of our clients, where they're investing heavily in new disruptive technologies, but they're not seeing the benefit out of it that they expect, and others, their peers often are. And why is that? And we thought that was really important to understand for our clients who are trying to compete in the digital era. >> So you had this hypothesis, so what did you go in thinking? >> First of all, we went in and we said, we believe that there's a number of barriers out there that people have to really preventing them from embracing and adapting in the digital age in the right way. A lot of it has to do with what I call the inertia of legacy, or the handicap of legacy. So the things that, the way that they used to build systems, like the methods can be a really serious drawback, like if they're using waterfall techniques. Maybe their legacy systems, for example, that they are not really open, they don't provide the ability to interface with them properly. Another great example of the challenges of legacy systems can really be that they're built in a more monolithic nature, and because they're built in that fashion, it's really hard to maintain them in an Agile way, with lots of different teams working on components, because they need them all to be assembled together at once. So it forces you into this release schedule which can be months long or even years long to do things, and that type of speed, that just doesn't work in the digital age, so it's holding them back, and that's some of the diagnostic that we went into this research study with, saying these are the challenges that are out there. >> So before we get talking about the results, I want you to just define for us what these future systems are. >> Great, and this is where we really were trying to say that we think it's time for a hard reset around a lot of the way that business systems and applications are built today. And the reason that we believe that is that, companies who are very large enterprises that really should be dominating in their industry, that there are so many examples of where small startups have come in and disrupted them, things that you think should never have happened. So the democratization of technology, the introduction of cloud, et cetera, the capabilities that AWS is talking to us here at this conference about, that's what's enabling them to do it. But enterprises have so many advantages, the wealth of data that they've got, the enormous investment capacity in others, how is that possible? And we really believe a lot of it comes down to the way that they're using, and the way they're embracing these future systems. And there's three characteristics of these things that we look at, first we say that they're boundaryless, and they really break down the traditional stack of IT, so that it's more open and it's able to connect with services outside of their enterprise, and they embrace the way that that works, so the traditional layers of application and data, and compute and storage, those are really going away, and everything's becoming code and much more components. Another one is adaptable, I'm a really big believer in this space, because I've seen so many things come in, that just makes you really kind of rethink the way that you may have built some things in the past, so that might be like blockchain, or it could be DevOps or other things, and are there ways to build systems that are much more flexible and evolutionary in nature, so they don't have to be completely disrupted and changed, in order to embrace some new technology, so adaptable is another one. And the third one is radically human, this is my favorite one, I think if I had one, it's about building systems for people, rather than building the people around the technology that you're using, in fact I'll give you an example, that keyboard right in front of you today, that keyboard, you know when that keyboard was designed? >> Rebecca: Oh my god, when? >> 1887, or 1880s, about. And basically, that keyboard was designed to slow you down, to keep you from typing too fast. And that was because people were typesetting newspapers, and they were crossing the little bars in their typewriter. Yet, today, what's the date today, 2019, we're still using that, right? Isn't it time for us to have more of a radically human approach to technology, and instead of having people design themselves around how technology works, having the technology best designed for them, so taking better advantage of artificial intelligence, maybe making AI the new UI, those types of things are really going to change it, and we think that future systems will exhibit this key characteristic of radically human in the way that they're built and organized. >> Okay, so I like it. Adaptable, and boundaryless, and radically human. What did you find, so how did you go about this survey, and then what did you find? >> Okay, so first, this was the single biggest survey of enterprise systems that Accenture's ever conducted. And we surveyed more than 8300 companies, c-level, across 20 industries and 20 different geographies. And the survey was looking at more than 100 data points from each one of them, as well as other demographic data, we collected 1.6 million pieces of data about this. We ran machine learning on the data to find patterns that surprised us, we looked at the data in terms of our hypothesis to say, what is it about these future systems, are there some companies that are starting to do things like this boundaryless, adaptable, and radically human space that we could learn something from? And we found some really interesting things. So when I dug into the data, maybe the biggest headline out of it was, the companies that have begun to adapt or to use these future systems type of approaches for things, we'll call them the top 10% of this group. Their revenues are growing at twice the speed of anyone else in their peer group. So think about that, if their revenues are growing faster, and everything else about their peers is the same, they're competitors, they're in the same geography, even the same industry, but the revenues of this group is changing faster, isn't that great evidence that adapting these characteristics of future systems is super important to the business performance that you've got there? It's a huge difference. >> Right, so that's compelling me, so what are they doing differently, what is this 10% of companies, how are they leading the pack? >> Yeah, so it boils down to a couple of key things that they're really doing differently, and I'll start by saying that they look at, instead of just looking at things as applications, they look at them more as systems of interconnected solutions, and they are treating components in a way that allows them to reassemble things in different and unique ways much faster than others can do. Sometimes they're using API solutions, a lot of times they're using outside functions outside of their enterprise to do that, and it's giving them remarkable flexibility. Another thing is the methods, the way that they build systems and what they're embracing, but it goes beyond just using Agile, it's almost like a different culture altogether. I think about some clients that I visited that really are getting this right, and the way that they look at failure, for example, is success, and the conservative nature of a lot of enterprises as it pertains to technology, to carefully study it before they invest, before they move forward, it's holding them back, and maybe that paid dividends for a long time when things were done in a much more waterfall nature, but in the digital age, you can't afford to take that kind of time to embrace or to try and leverage new technologies. I think another one that really stands out for me too, is the breadth of disruptive technologies that they tried, and so it wasn't just that they experimented with everything that worked, they've experimented with a lot of things that maybe haven't produced the kind of results or outcomes that conventional wisdom said that they were going to. Augmented reality is a good example, right, I think it's taken time for augmented reality to really start producing value in the enterprise, but it's been around for a while now. We found that the leaders had all experimented with augmented reality, it didn't necessarily mean that they'd adopted it and begun to use it, but that was actually something that separated them from the laggards, what a surprise, right? Because you had thought, "Okay, well maybe the leaders "are just smarter, they only choose the things "that are really going to make a difference." But it's the fact that they were trying lots of different things, and they weren't afraid to experiment, that really made a difference for them. >> And not afraid to fail, too, as you said. >> Or maybe shelve it and say, "Not quite ready yet. "Maybe in a few years we'll get there." So I thought that was fascinating, and it really helped us sort of confirm that there are definitely things different that these leaders are doing than laggards, and it goes beyond just their adoption of future systems, it's the way that they were building them too, and the culture that they've embraced as a result. >> So we had a dizzying number of announcements on the main stage this morning from Andy Jassy, so many different mainframe legacy migrations, so many different areas that AWS is moving into, and starting new services, how does what you heard today from Andy Jassy translate to the research that you're doing? >> Well it's actually great, and I think it's a great microcosm of what is truly different about these leaders, and laggards. All of them, in some ways, have said, "We're adopting cloud." Okay, great, everybody's doing cloud, all 8300 companies, I can't think of one that said they were doing nothing with cloud. They were doing something with SaaS, or maybe they've got public cloud or others. But here's the difference, here's the difference. When the leaders do cloud, they think about it differently. The laggards look at cloud as a cheaper data center. They say "Okay, we can just move our compute and storage "into cloud, great, awesome." The leaders look at cloud as an innovation catalyst, they're taking advantage of the cloud native services, the things Andy was talking about today, fraud detect, private VPNs, all of the things that he was introducing and describing today, they can't wait to get their hands on that capability. And it's more than that, though, because you could do this on-premise, but it's too expensive, and it takes too long to do that. When you've got a cloud service provider that's making things like Rekognition or SageMaker available at your fingertips, to do amazing things with artificial intelligence, that is what an innovation catalyst is all about, and the leaders are taking advantage of that at every turn, and that's why, that's why they can do things so fast. >> So for the 90% that are not in this leading category, it sounds as though it will require a real change in mindset. Are there any other, what's your advice to help these laggards improve? >> Yeah, so I would say it really boils down to two things, I would give them, if you're in that laggard category, first of all, you can definitely move out of it, and the other thing is, is that you're in strange company. There's digital natives, like the most successful born-in-the-cloud kind of companies, that have this problem too, so it's kind of surprising, right, that you wouldn't expect that, but that's definitely the case, and we see lots of examples of that. The good news is, though, is that you can move from A to B, and I would say it starts with doing two things. The first is, is embracing more fast and flexible technologies, so the things that I really like to see companies embrace, or the things that we observed in this research that they're doing is, looking at Agile at scale, embracing product-based operating models, doing things that allow them, like DevOps, to increase automation and the way that they're building and deploying systems, that type of change is a significant adjustment to the way that you think about technology, and how quickly it can be deployed for use, and if you look at the difference between these digital born-in-the-cloud, digital companies that are the succeeding companies in this space, that's the way that they do it, and they really, it is really kind of part of the secret sauce, so that's one thing, embracing these solutions that make them fast and flexible. And the other one gets back to what I was describing earlier about cloud, recognize that cloud is an innovation catalyst. It is not going to be successful for you to think about cloud as just a cheaper data center. It might very well be lower cost for you to do that, but if you're not taking advantage of the cloud native services, whether that's AWS databases like Aurora, it's the new features that they introduced around the low latency application development, those are the things that will really allow you to do stuff much faster than you could've ever imagined on-premise, so I'd start there, if I was a company that's one of those laggards, and then I'd look at, what is my blueprint for future systems, and how do I embrace those characteristics of boundaryless, adaptable, and radically human. >> Cloud as an innovation engine, I love it. Adam, thank you so much for coming back on theCUBE, it was a pleasure. >> It's great to be here, Rebecca, thank you again for inviting me. >> I'm Rebecca Knight, stay tuned for more of theCUBE's live coverage from the Accenture Executive Summit. (techno music)