(chill electronic music) >> Hello everyone, welcome to theCUBE's presentation of the AWS Startups Showcase, AI and machine learning, the top startups building generative AI on AWS. This is the season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talk about AI and machine learning. Can't believe it's three years and season one. I'm your host, John Furrier. Got a great guest today, we're joined by Joseph Nelson, the co-founder and CEO of Roboflow, doing some cutting edge stuff around computer vision and really at the front end of this massive wave coming around, large language models, computer vision. The next gen AI is here, and it's just getting started. We haven't even scratched a service. Thanks for joining us today. >> Thanks for having me. >> So you got to love the large language model, foundation models, really educating the mainstream world. ChatGPT has got everyone in the frenzy. This is educating the world around this next gen AI capabilities, enterprise, image and video data, all a big part of it. I mean the edge of the network, Mobile World Conference is happening right now, this month, and it's just ending up, it's just continue to explode. Video is huge. So take us through the company, do a quick explanation of what you guys are doing, when you were founded. Talk about what the company's mission is, and what's your North Star, why do you exist? >> Yeah, Roboflow exists to really kind of make the world programmable. I like to say make the world be read and write access. And our North Star is enabling developers, predominantly, to build that future. If you look around, anything that you see will have software related to it, and can kind of be turned into software. The limiting reactant though, is how to enable computers and machines to understand things as well as people can. And in a lot of ways, computer vision is that missing element that enables anything that you see to become software. So in the virtue of, if software is eating the world, computer vision kind of makes the aperture infinitely wide. It's something that I kind of like, the way I like to frame it. And the capabilities are there, the open source models are there, the amount of data is there, the computer capabilities are only improving annually, but there's a pretty big dearth of tooling, and an early but promising sign of the explosion of use cases, models, and data sets that companies, developers, hobbyists alike will need to bring these capabilities to bear. So Roboflow is in the game of building the community around that capability, building the use cases that allow developers and enterprises to use computer vision, and providing the tooling for companies and developers to be able to add computer vision, create better data sets, and deploy to production, quickly, easily, safely, invaluably. >> You know, Joseph, the word in production is actually real now. You're seeing a lot more people doing in production activities. That's a real hot one and usually it's slower, but it's gone faster, and I think that's going to be more the same. And I think the parallel between what we're seeing on the large language models coming into computer vision, and as you mentioned, video's data, right? I mean we're doing video right now, we're transcribing it into a transcript, linking up to your linguistics, times and the timestamp, I mean everything's data and that really kind of feeds. So this connection between what we're seeing, the large language and computer vision are coming together kind of cousins, brothers. I mean, how would you compare, how would you explain to someone, because everyone's like on this wave of watching people bang out their homework assignments, and you know, write some hacks on code with some of the open AI technologies, there is a corollary directly related to to the vision side. Can you explain? >> Yeah, the rise of large language models are showing what's possible, especially with text, and I think increasingly will get multimodal as the images and video become ingested. Though there's kind of this still core missing element of basically like understanding. So the rise of large language models kind of create this new area of generative AI, and generative AI in the context of computer vision is a lot of, you know, creating video and image assets and content. There's also this whole surface area to understanding what's already created. Basically digitizing physical, real world things. I mean the Metaverse can't be built if we don't know how to mirror or create or identify the objects that we want to interact with in our everyday lives. And where computer vision comes to play in, especially what we've seen at Roboflow is, you know, a little over a hundred thousand developers now have built with our tools. That's to the tune of a hundred million labeled open source images, over 10,000 pre-trained models. And they've kind of showcased to us all of the ways that computer vision is impacting and bringing the world to life. And these are things that, you know, even before large language models and generative AI, you had pretty impressive capabilities, and when you add the two together, it actually unlocks these kind of new capabilities. So for example, you know, one of our users actually powers the broadcast feeds at Wimbledon. So here we're talking about video, we're streaming, we're doing things live, we've got folks that are cropping and making sure we look good, and audio/visual all plugged in correctly. When you broadcast Wimbledon, you'll notice that the camera controllers need to do things like track the ball, which is moving at extremely high speeds and zoom crop, pan tilt, as well as determine if the ball bounced in or out. The very controversial but critical key to a lot of tennis matches. And a lot of that has been historically done with the trained, but fallible human eye and computer vision is, you know, well suited for this task to say, how do we track, pan, tilt, zoom, and see, track the tennis ball in real time, run at 30 plus frames per second, and do it all on the edge. And those are capabilities that, you know, were kind of like science fiction, maybe even a decade ago, and certainly five years ago. Now the interesting thing, is that with the advent of of generative AI, you can start to do things like create your own training data sets, or kind of create logic around once you have this visual input. And teams at Tesla have actually been speaking about, of course the autopilot team's focused on doing vision tasks, but they've combined large language models to add reasoning and logic. So given that you see, let's say the tennis ball, what do you want to do? And being able to combine the capabilities of what LLM's represent, which is really a lot of basically, core human reasoning and logic, with computer vision for the inputs of what's possible, creates these new capabilities, let alone multimodality, which I'm sure we'll talk more about. >> Yeah, and it's really, I mean it's almost intoxicating. It's amazing that this is so capable because the cloud scales here, you got the edge developing, you can decouple compute power, and let Moore's law and all the new silicone and the processors and the GPUs do their thing, and you got open source booming. You're kind of getting at this next segment I wanted to get into, which is the, how people should be thinking about these advances of the computer vision. So this is now a next wave, it's here. I mean I'd love to have that for baseball because I'm always like, "Oh, it should have been a strike." I'm sure that's going to be coming soon, but what is the computer vision capable of doing today? I guess that's my first question. You hit some of it, unpack that a little bit. What does general AI mean in computer vision? What's the new thing? Because there are old technology's been around, proprietary, bolted onto hardware, but hardware advances at a different pace, but now you got new capabilities, generative AI for vision, what does that mean? >> Yeah, so computer vision, you know, at its core is basically enabling machines, computers, to understand, process, and act on visual data as effective or more effective than people can. Traditionally this has been, you know, task types like classification, which you know, identifying if a given image belongs in a certain category of goods on maybe a retail site, is the shoes or is it clothing? Or object detection, which is, you know, creating bounding boxes, which allows you to do things like count how many things are present, or maybe measure the speed of something, or trigger an alert when something becomes visible in frame that wasn't previously visible in frame, or instant segmentation where you're creating pixel wise segmentations for both instance and semantic segmentation, where you often see these kind of beautiful visuals of the polygon surrounding objects that you see. Then you have key point detection, which is where you see, you know, athletes, and each of their joints are kind of outlined is another more traditional type problem in signal processing and computer vision. With generative AI, you kind of get a whole new class of problem types that are opened up. So in a lot of ways I think about generative AI in computer vision as some of the, you know, problems that you aimed to tackle, might still be better suited for one of the previous task types we were discussing. Some of those problem types may be better suited for using a generative technique, and some are problem types that just previously wouldn't have been possible absent generative AI. And so if you make that kind of Venn diagram in your head, you can think about, okay, you know, visual question answering is a task type where if I give you an image and I say, you know, "How many people are in this image?" We could either build an object detection model that might count all those people, or maybe a visual question answering system would sufficiently answer this type of problem. Let alone generative AI being able to create new training data for old systems. And that's something that we've seen be an increasingly prominent use case for our users, as much as things that we advise our customers and the community writ large to take advantage of. So ultimately those are kind of the traditional task types. I can give you some insight, maybe, into how I think about what's possible today, or five years or ten years as you sort go back. >> Yes, definitely. Let's get into that vision. >> So I kind of think about the types of use cases in terms of what's possible. If you just imagine a very simple bell curve, your normal distribution, for the longest time, the types of things that are in the center of that bell curve are identifying objects that are very common or common objects in context. Microsoft published the COCO Dataset in 2014 of common objects and contexts, of hundreds of thousands of images of chairs, forks, food, person, these sorts of things. And you know, the challenge of the day had always been, how do you identify just those 80 objects? So if we think about the bell curve, that'd be maybe the like dead center of the curve, where there's a lot of those objects present, and it's a very common thing that needs to be identified. But it's a very, very, very small sliver of the distribution. Now if you go out to the way long tail, let's go like deep into the tail of this imagined visual normal distribution, you're going to have a problem like one of our customers, Rivian, in tandem with AWS, is tackling, to do visual quality assurance and manufacturing in production processes. Now only Rivian knows what a Rivian is supposed to look like. Only they know the imagery of what their goods that are going to be produced are. And then between those long tails of proprietary data of highly specific things that need to be understood, in the center of the curve, you have a whole kind of messy middle, type of problems I like to say. The way I think about computer vision advancing, is it's basically you have larger and larger and more capable models that eat from the center out, right? So if you have a model that, you know, understands the 80 classes in COCO, well, pretty soon you have advances like Clip, which was trained on 400 million image text pairs, and has a greater understanding of a wider array of objects than just 80 classes in context. And over time you'll get more and more of these larger models that kind of eat outwards from that center of the distribution. And so the question becomes for companies, when can you rely on maybe a model that just already exists? How do you use your data to get what may be capable off the shelf, so to speak, into something that is usable for you? Or, if you're in those long tails and you have proprietary data, how do you take advantage of the greatest asset you have, which is observed visual information that you want to put to work for your customers, and you're kind of living in the long tails, and you need to adapt state of the art for your capabilities. So my mental model for like how computer vision advances is you have that bell curve, and you have increasingly powerful models that eat outward. And multimodality has a role to play in that, larger models have a role to play in that, more compute, more data generally has a role to play in that. But it will be a messy and I think long condition. >> Well, the thing I want to get, first of all, it's great, great mental model, I appreciate that, 'cause I think that makes a lot of sense. The question is, it seems now more than ever, with the scale and compute that's available, that not only can you eat out to the middle in your example, but there's other models you can integrate with. In the past there was siloed, static, almost bespoke. Now you're looking at larger models eating into the bell curve, as you said, but also integrating in with other stuff. So this seems to be part of that interaction. How does, first of all, is that really happening? Is that true? And then two, what does that mean for companies who want to take advantage of this? Because the old model was operational, you know? I have my cameras, they're watching stuff, whatever, and like now you're in this more of a, distributed computing, computer science mindset, not, you know, put the camera on the wall kind of- I'm oversimplifying, but you know what I'm saying. What's your take on that? >> Well, to the first point of, how are these advances happening? What I was kind of describing was, you know, almost uni-dimensional in that you have like, you're only thinking about vision, but the rise of generative techniques and multi-modality, like Clip is a multi-modal model, it has 400 million image text pairs. That will advance the generalizability at a faster rate than just treating everything as only vision. And that's kind of where LLMs and vision will intersect in a really nice and powerful way. Now in terms of like companies, how should they be thinking about taking advantage of these trends? The biggest thing that, and I think it's different, obviously, on the size of business, if you're an enterprise versus a startup. The biggest thing that I think if you're an enterprise, and you have an established scaled business model that is working for your customers, the question becomes, how do you take advantage of that established data moat, potentially, resource moats, and certainly, of course, establish a way of providing value to an end user. So for example, one of our customers, Walmart, has the advantage of one of the largest inventory and stock of any company in the world. And they also of course have substantial visual data, both from like their online catalogs, or understanding what's in stock or out of stock, or understanding, you know, the quality of things that they're going from the start of their supply chain to making it inside stores, for delivery of fulfillments. All these are are visual challenges. Now they already have a substantial trove of useful imagery to understand and teach and train large models to understand each of the individual SKUs and products that are in their stores. And so if I'm a Walmart, what I'm thinking is, how do I make sure that my petabytes of visual information is utilized in a way where I capture the proprietary benefit of the models that I can train to do tasks like, what item was this? Or maybe I'm going to create AmazonGo-like technology, or maybe I'm going to build like delivery robots, or I want to automatically know what's in and out of stock from visual input fees that I have across my in-store traffic. And that becomes the question and flavor of the day for enterprises. I've got this large amount of data, I've got an established way that I can provide more value to my own customers. How do I ensure I take advantage of the data advantage I'm already sitting on? If you're a startup, I think it's a pretty different question, and I'm happy to talk about. >> Yeah, what's startup angle on this? Because you know, they're going to want to take advantage. It's like cloud startups, cloud native startups, they were born in the cloud, they never had an IT department. So if you're a startup, is there a similar role here? And if I'm a computer vision startup, what's that mean? So can you share your your take on that, because there'll be a lot of people starting up from this. >> So the startup on the opposite advantage and disadvantage, right? Like a startup doesn't have an proven way of delivering repeatable value in the same way that a scaled enterprise does. But it does have the nimbleness to identify and take advantage of techniques that you can start from a blank slate. And I think the thing that startups need to be wary of in the generative AI enlarged language model, in multimodal world, is building what I like to call, kind of like sandcastles. A sandcastle is maybe a business model or a capability that's built on top of an assumption that is going to be pretty quickly wiped away by improving underlying model technology. So almost like if you imagine like the ocean, the waves are coming in, and they're going to wipe away your progress. You don't want to be in the position of building sandcastle business where, you don't want to bet on the fact that models aren't going to get good enough to solve the task type that you might be solving. In other words, don't take a screenshot of what's capable today. Assume that what's capable today is only going to continue to become possible. And so for a startup, what you can do, that like enterprises are quite comparatively less good at, is embedding these capabilities deeply within your products and delivering maybe a vertical based experience, where AI kind of exists in the background. >> Yeah. >> And we might not think of companies as, you know, even AI companies, it's just so embedded in the experience they provide, but that's like the vertical application example of taking AI and making it be immediately usable. Or, of course there's tons of picks and shovels businesses to be built like Roboflow, where you're enabling these enterprises to take advantage of something that they have, whether that's their data sets, their computes, or their intellect. >> Okay, so if I hear that right, by the way, I love, that's horizontally scalable, that's the large language models, go up and build them the apps, hence your developer focus. I'm sure that's probably the reason that the tsunami of developer's action. So you're saying picks and shovels tools, don't try to replicate the platform of what could be the platform. Oh, go to a VC, I'm going to build a platform. No, no, no, no, those are going to get wiped away by the large language models. Is there one large language model that will rule the world, or do you see many coming? >> Yeah, so to be clear, I think there will be useful platforms. I just think a lot of people think that they're building, let's say, you know, if we put this in the cloud context, you're building a specific type of EC2 instance. Well, it turns out that Amazon can offer that type of EC2 instance, and immediately distribute it to all of their customers. So you don't want to be in the position of just providing something that actually ends up looking like a feature, which in the context of AI, might be like a small incremental improvement on the model. If that's all you're doing, you're a sandcastle business. Now there's a lot of platform businesses that need to be built that enable businesses to get to value and do things like, how do I monitor my models? How do I create better models with my given data sets? How do I ensure that my models are doing what I want them to do? How do I find the right models to use? There's all these sorts of platform wide problems that certainly exist for businesses. I just think a lot of startups that I'm seeing right now are making the mistake of assuming the advances we're seeing are not going to accelerate or even get better. >> So if I'm a customer, if I'm a company, say I'm a startup or an enterprise, either one, same question. And I want to stand up, and I have developers working on stuff, I want to start standing up an environment to start doing stuff. Is that a service provider? Is that a managed service? Is that you guys? So how do you guys fit into your customers leaning in? Is it just for developers? Are you targeting with a specific like managed service? What's the product consumption? How do you talk to customers when they come to you? >> The thing that we do is enable, we give developers superpowers to build automated inventory tracking, self-checkout systems, identify if this image is malignant cancer or benign cancer, ensure that these products that I've produced are correct. Make sure that that the defect that might exist on this electric vehicle makes its way back for review. All these sorts of problems are immediately able to be solved and tackled. In terms of the managed services element, we have solutions as integrators that will often build on top of our tools, or we'll have companies that look to us for guidance, but ultimately the company is in control of developing and building and creating these capabilities in house. I really think the distinction is maybe less around managed service and tool, and more around ownership in the era of AI. So for example, if I'm using a managed service, in that managed service, part of their benefit is that they are learning across their customer sets, then it's a very different relationship than using a managed service where I'm developing some amount of proprietary advantages for my data sets. And I think that's a really important thing that companies are becoming attuned to, just the value of the data that they have. And so that's what we do. We tell companies that you have this proprietary, immense treasure trove of data, use that to your advantage, and think about us more like a set of tools that enable you to get value from that capability. You know, the HashiCorp's and GitLab's of the world have proven like what these businesses look like at scale. >> And you're targeting developers. When you go into a company, do you target developers with freemium, is there a paid service? Talk about the business model real quick. >> Sure, yeah. The tools are free to use and get started. When someone signs up for Roboflow, they may elect to make their work open source, in which case we're able to provide even more generous usage limits to basically move the computer vision community forward. If you elect to make your data private, you can use our hosted data set managing, data set training, model deployment, annotation tooling up to some limits. And then usually when someone validates that what they're doing gets them value, they purchase a subscription license to be able to scale up those capabilities. So like most developer centric products, it's free to get started, free to prove, free to poke around, develop what you think is possible. And then once you're getting to value, then we're able to capture the commercial upside in the value that's being provided. >> Love the business model. It's right in line with where the market is. There's kind of no standards bodies these days. The developers are the ones who are deciding kind of what the standards are by their adoption. I think making that easy for developers to get value as the model open sources continuing to grow, you can see more of that. Great perspective Joseph, thanks for sharing that. Put a plug in for the company. What are you guys doing right now? Where are you in your growth? What are you looking for? How should people engage? Give the quick commercial for the company. >> So as I mentioned, Roboflow is I think one of the largest, if not the largest collections of computer vision models and data sets that are open source, available on the web today, and have a private set of tools that over half the Fortune 100 now rely on those tools. So we're at the stage now where we know people want what we're working on, and we're continuing to drive that type of adoption. So companies that are looking to make better models, improve their data sets, train and deploy, often will get a lot of value from our tools, and certainly reach out to talk. I'm sure there's a lot of talented engineers that are tuning in too, we're aggressively hiring. So if you are interested in being a part of making the world programmable, and being at the ground floor of the company that's creating these capabilities to be writ large, we'd love to hear from you. >> Amazing, Joseph, thanks so much for coming on and being part of the AWS Startup Showcase. Man, if I was in my twenties, I'd be knocking on your door, because it's the hottest trend right now, it's super exciting. Generative AI is just the beginning of massive sea change. Congratulations on all your success, and we'll be following you guys. Thanks for spending the time, really appreciate it. >> Thanks for having me. >> Okay, this is season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talking about the hottest things in tech. I'm John Furrier, your host. Thanks for watching. (chill electronic music)

(upbeat music) >> Hello everyone. Welcome to theCube's presentation of the "AWS Startup Showcase." The topic this episode is AI and machine learning, top startups building foundational model infrastructure. This is season three, episode one of the ongoing series covering exciting startups from the AWS ecosystem. And this time we're talking about AI and machine learning. I'm your host, John Furrier. I'm excited I'm joined today by Robert Nishihara, who's the co-founder and CEO of a hot startup called Anyscale. He's here to talk about Ray, the open source project, Anyscale's infrastructure for foundation as well. Robert, thank you for joining us today. >> Yeah, thanks so much as well. >> I've been following your company since the founding pre pandemic and you guys really had a great vision scaled up and in a perfect position for this big wave that we all see with ChatGPT and OpenAI that's gone mainstream. Finally, AI has broken out through the ropes and now gone mainstream, so I think you guys are really well positioned. I'm looking forward to to talking with you today. But before we get into it, introduce the core mission for Anyscale. Why do you guys exist? What is the North Star for Anyscale? >> Yeah, like you mentioned, there's a tremendous amount of excitement about AI right now. You know, I think a lot of us believe that AI can transform just every different industry. So one of the things that was clear to us when we started this company was that the amount of compute needed to do AI was just exploding. Like to actually succeed with AI, companies like OpenAI or Google or you know, these companies getting a lot of value from AI, were not just running these machine learning models on their laptops or on a single machine. They were scaling these applications across hundreds or thousands or more machines and GPUs and other resources in the Cloud. And so to actually succeed with AI, and this has been one of the biggest trends in computing, maybe the biggest trend in computing in, you know, in recent history, the amount of compute has been exploding. And so to actually succeed with that AI, to actually build these scalable applications and scale the AI applications, there's a tremendous software engineering lift to build the infrastructure to actually run these scalable applications. And that's very hard to do. So one of the reasons many AI projects and initiatives fail is that, or don't make it to production, is the need for this scale, the infrastructure lift, to actually make it happen. So our goal here with Anyscale and Ray, is to make that easy, is to make scalable computing easy. So that as a developer or as a business, if you want to do AI, if you want to get value out of AI, all you need to know is how to program on your laptop. Like, all you need to know is how to program in Python. And if you can do that, then you're good to go. Then you can do what companies like OpenAI or Google do and get value out of machine learning. >> That programming example of how easy it is with Python reminds me of the early days of Cloud, when infrastructure as code was talked about was, it was just code the infrastructure programmable. That's super important. That's what AI people wanted, first program AI. That's the new trend. And I want to understand, if you don't mind explaining, the relationship that Anyscale has to these foundational models and particular the large language models, also called LLMs, was seen with like OpenAI and ChatGPT. Before you get into the relationship that you have with them, can you explain why the hype around foundational models? Why are people going crazy over foundational models? What is it and why is it so important? >> Yeah, so foundational models and foundation models are incredibly important because they enable businesses and developers to get value out of machine learning, to use machine learning off the shelf with these large models that have been trained on tons of data and that are useful out of the box. And then, of course, you know, as a business or as a developer, you can take those foundational models and repurpose them or fine tune them or adapt them to your specific use case and what you want to achieve. But it's much easier to do that than to train them from scratch. And I think there are three, for people to actually use foundation models, there are three main types of workloads or problems that need to be solved. One is training these foundation models in the first place, like actually creating them. The second is fine tuning them and adapting them to your use case. And the third is serving them and actually deploying them. Okay, so Ray and Anyscale are used for all of these three different workloads. Companies like OpenAI or Cohere that train large language models. Or open source versions like GPTJ are done on top of Ray. There are many startups and other businesses that fine tune, that, you know, don't want to train the large underlying foundation models, but that do want to fine tune them, do want to adapt them to their purposes, and build products around them and serve them, those are also using Ray and Anyscale for that fine tuning and that serving. And so the reason that Ray and Anyscale are important here is that, you know, building and using foundation models requires a huge scale. It requires a lot of data. It requires a lot of compute, GPUs, TPUs, other resources. And to actually take advantage of that and actually build these scalable applications, there's a lot of infrastructure that needs to happen under the hood. And so you can either use Ray and Anyscale to take care of that and manage the infrastructure and solve those infrastructure problems. Or you can build the infrastructure and manage the infrastructure yourself, which you can do, but it's going to slow your team down. It's going to, you know, many of the businesses we work with simply don't want to be in the business of managing infrastructure and building infrastructure. They want to focus on product development and move faster. >> I know you got a keynote presentation we're going to go to in a second, but I think you hit on something I think is the real tipping point, doing it yourself, hard to do. These are things where opportunities are and the Cloud did that with data centers. Turned a data center and made it an API. The heavy lifting went away and went to the Cloud so people could be more creative and build their product. In this case, build their creativity. Is that kind of what's the big deal? Is that kind of a big deal happening that you guys are taking the learnings and making that available so people don't have to do that? >> That's exactly right. So today, if you want to succeed with AI, if you want to use AI in your business, infrastructure work is on the critical path for doing that. To do AI, you have to build infrastructure. You have to figure out how to scale your applications. That's going to change. We're going to get to the point, and you know, with Ray and Anyscale, we're going to remove the infrastructure from the critical path so that as a developer or as a business, all you need to focus on is your application logic, what you want the the program to do, what you want your application to do, how you want the AI to actually interface with the rest of your product. Now the way that will happen is that Ray and Anyscale will still, the infrastructure work will still happen. It'll just be under the hood and taken care of by Ray in Anyscale. And so I think something like this is really necessary for AI to reach its potential, for AI to have the impact and the reach that we think it will, you have to make it easier to do. >> And just for clarification to point out, if you don't mind explaining the relationship of Ray and Anyscale real quick just before we get into the presentation. >> So Ray is an open source project. We created it. We were at Berkeley doing machine learning. We started Ray so that, in order to provide an easy, a simple open source tool for building and running scalable applications. And Anyscale is the managed version of Ray, basically we will run Ray for you in the Cloud, provide a lot of tools around the developer experience and managing the infrastructure and providing more performance and superior infrastructure. >> Awesome. I know you got a presentation on Ray and Anyscale and you guys are positioning as the infrastructure for foundational models. So I'll let you take it away and then when you're done presenting, we'll come back, I'll probably grill you with a few questions and then we'll close it out so take it away. >> Robert: Sounds great. So I'll say a little bit about how companies are using Ray and Anyscale for foundation models. The first thing I want to mention is just why we're doing this in the first place. And the underlying observation, the underlying trend here, and this is a plot from OpenAI, is that the amount of compute needed to do machine learning has been exploding. It's been growing at something like 35 times every 18 months. This is absolutely enormous. And other people have written papers measuring this trend and you get different numbers. But the point is, no matter how you slice and dice it, it' a astronomical rate. Now if you compare that to something we're all familiar with, like Moore's Law, which says that, you know, the processor performance doubles every roughly 18 months, you can see that there's just a tremendous gap between the needs, the compute needs of machine learning applications, and what you can do with a single chip, right. So even if Moore's Law were continuing strong and you know, doing what it used to be doing, even if that were the case, there would still be a tremendous gap between what you can do with the chip and what you need in order to do machine learning. And so given this graph, what we've seen, and what has been clear to us since we started this company, is that doing AI requires scaling. There's no way around it. It's not a nice to have, it's really a requirement. And so that led us to start Ray, which is the open source project that we started to make it easy to build these scalable Python applications and scalable machine learning applications. And since we started the project, it's been adopted by a tremendous number of companies. Companies like OpenAI, which use Ray to train their large models like ChatGPT, companies like Uber, which run all of their deep learning and classical machine learning on top of Ray, companies like Shopify or Spotify or Instacart or Lyft or Netflix, ByteDance, which use Ray for their machine learning infrastructure. Companies like Ant Group, which makes Alipay, you know, they use Ray across the board for fraud detection, for online learning, for detecting money laundering, you know, for graph processing, stream processing. Companies like Amazon, you know, run Ray at a tremendous scale and just petabytes of data every single day. And so the project has seen just enormous adoption since, over the past few years. And one of the most exciting use cases is really providing the infrastructure for building training, fine tuning, and serving foundation models. So I'll say a little bit about, you know, here are some examples of companies using Ray for foundation models. Cohere trains large language models. OpenAI also trains large language models. You can think about the workloads required there are things like supervised pre-training, also reinforcement learning from human feedback. So this is not only the regular supervised learning, but actually more complex reinforcement learning workloads that take human input about what response to a particular question, you know is better than a certain other response. And incorporating that into the learning. There's open source versions as well, like GPTJ also built on top of Ray as well as projects like Alpa coming out of UC Berkeley. So these are some of the examples of exciting projects in organizations, training and creating these large language models and serving them using Ray. Okay, so what actually is Ray? Well, there are two layers to Ray. At the lowest level, there's the core Ray system. This is essentially low level primitives for building scalable Python applications. Things like taking a Python function or a Python class and executing them in the cluster setting. So Ray core is extremely flexible and you can build arbitrary scalable applications on top of Ray. So on top of Ray, on top of the core system, what really gives Ray a lot of its power is this ecosystem of scalable libraries. So on top of the core system you have libraries, scalable libraries for ingesting and pre-processing data, for training your models, for fine tuning those models, for hyper parameter tuning, for doing batch processing and batch inference, for doing model serving and deployment, right. And a lot of the Ray users, the reason they like Ray is that they want to run multiple workloads. They want to train and serve their models, right. They want to load their data and feed that into training. And Ray provides common infrastructure for all of these different workloads. So this is a little overview of what Ray, the different components of Ray. So why do people choose to go with Ray? I think there are three main reasons. The first is the unified nature. The fact that it is common infrastructure for scaling arbitrary workloads, from data ingest to pre-processing to training to inference and serving, right. This also includes the fact that it's future proof. AI is incredibly fast moving. And so many people, many companies that have built their own machine learning infrastructure and standardized on particular workflows for doing machine learning have found that their workflows are too rigid to enable new capabilities. If they want to do reinforcement learning, if they want to use graph neural networks, they don't have a way of doing that with their standard tooling. And so Ray, being future proof and being flexible and general gives them that ability. Another reason people choose Ray in Anyscale is the scalability. This is really our bread and butter. This is the reason, the whole point of Ray, you know, making it easy to go from your laptop to running on thousands of GPUs, making it easy to scale your development workloads and run them in production, making it easy to scale, you know, training to scale data ingest, pre-processing and so on. So scalability and performance, you know, are critical for doing machine learning and that is something that Ray provides out of the box. And lastly, Ray is an open ecosystem. You can run it anywhere. You can run it on any Cloud provider. Google, you know, Google Cloud, AWS, Asure. You can run it on your Kubernetes cluster. You can run it on your laptop. It's extremely portable. And not only that, it's framework agnostic. You can use Ray to scale arbitrary Python workloads. You can use it to scale and it integrates with libraries like TensorFlow or PyTorch or JAX or XG Boost or Hugging Face or PyTorch Lightning, right, or Scikit-learn or just your own arbitrary Python code. It's open source. And in addition to integrating with the rest of the machine learning ecosystem and these machine learning frameworks, you can use Ray along with all of the other tooling in the machine learning ecosystem. That's things like weights and biases or ML flow, right. Or you know, different data platforms like Databricks, you know, Delta Lake or Snowflake or tools for model monitoring for feature stores, all of these integrate with Ray. And that's, you know, Ray provides that kind of flexibility so that you can integrate it into the rest of your workflow. And then Anyscale is the scalable compute platform that's built on top, you know, that provides Ray. So Anyscale is a managed Ray service that runs in the Cloud. And what Anyscale does is it offers the best way to run Ray. And if you think about what you get with Anyscale, there are fundamentally two things. One is about moving faster, accelerating the time to market. And you get that by having the managed service so that as a developer you don't have to worry about managing infrastructure, you don't have to worry about configuring infrastructure. You also, it provides, you know, optimized developer workflows. Things like easily moving from development to production, things like having the observability tooling, the debug ability to actually easily diagnose what's going wrong in a distributed application. So things like the dashboards and the other other kinds of tooling for collaboration, for monitoring and so on. And then on top of that, so that's the first bucket, developer productivity, moving faster, faster experimentation and iteration. The second reason that people choose Anyscale is superior infrastructure. So this is things like, you know, cost deficiency, being able to easily take advantage of spot instances, being able to get higher GPU utilization, things like faster cluster startup times and auto scaling. Things like just overall better performance and faster scheduling. And so these are the kinds of things that Anyscale provides on top of Ray. It's the managed infrastructure. It's fast, it's like the developer productivity and velocity as well as performance. So this is what I wanted to share about Ray in Anyscale. >> John: Awesome. >> Provide that context. But John, I'm curious what you think. >> I love it. I love the, so first of all, it's a platform because that's the platform architecture right there. So just to clarify, this is an Anyscale platform, not- >> That's right. >> Tools. So you got tools in the platform. Okay, that's key. Love that managed service. Just curious, you mentioned Python multiple times, is that because of PyTorch and TensorFlow or Python's the most friendly with machine learning or it's because it's very common amongst all developers? >> That's a great question. Python is the language that people are using to do machine learning. So it's the natural starting point. Now, of course, Ray is actually designed in a language agnostic way and there are companies out there that use Ray to build scalable Java applications. But for the most part right now we're focused on Python and being the best way to build these scalable Python and machine learning applications. But, of course, down the road there always is that potential. >> So if you're slinging Python code out there and you're watching that, you're watching this video, get on Anyscale bus quickly. Also, I just, while you were giving the presentation, I couldn't help, since you mentioned OpenAI, which by the way, congratulations 'cause they've had great scale, I've noticed in their rapid growth 'cause they were the fastest company to the number of users than anyone in the history of the computer industry, so major successor, OpenAI and ChatGPT, huge fan. I'm not a skeptic at all. I think it's just the beginning, so congratulations. But I actually typed into ChatGPT, what are the top three benefits of Anyscale and came up with scalability, flexibility, and ease of use. Obviously, scalability is what you guys are called. >> That's pretty good. >> So that's what they came up with. So they nailed it. Did you have an inside prompt training, buy it there? Only kidding. (Robert laughs) >> Yeah, we hard coded that one. >> But that's the kind of thing that came up really, really quickly if I asked it to write a sales document, it probably will, but this is the future interface. This is why people are getting excited about the foundational models and the large language models because it's allowing the interface with the user, the consumer, to be more human, more natural. And this is clearly will be in every application in the future. >> Absolutely. This is how people are going to interface with software, how they're going to interface with products in the future. It's not just something, you know, not just a chat bot that you talk to. This is going to be how you get things done, right. How you use your web browser or how you use, you know, how you use Photoshop or how you use other products. Like you're not going to spend hours learning all the APIs and how to use them. You're going to talk to it and tell it what you want it to do. And of course, you know, if it doesn't understand it, it's going to ask clarifying questions. You're going to have a conversation and then it'll figure it out. >> This is going to be one of those things, we're going to look back at this time Robert and saying, "Yeah, from that company, that was the beginning of that wave." And just like AWS and Cloud Computing, the folks who got in early really were in position when say the pandemic came. So getting in early is a good thing and that's what everyone's talking about is getting in early and playing around, maybe replatforming or even picking one or few apps to refactor with some staff and managed services. So people are definitely jumping in. So I have to ask you the ROI cost question. You mentioned some of those, Moore's Law versus what's going on in the industry. When you look at that kind of scale, the first thing that jumps out at people is, "Okay, I love it. Let's go play around." But what's it going to cost me? Am I going to be tied to certain GPUs? What's the landscape look like from an operational standpoint, from the customer? Are they locked in and the benefit was flexibility, are you flexible to handle any Cloud? What is the customers, what are they looking at? Basically, that's my question. What's the customer looking at? >> Cost is super important here and many of the companies, I mean, companies are spending a huge amount on their Cloud computing, on AWS, and on doing AI, right. And I think a lot of the advantage of Anyscale, what we can provide here is not only better performance, but cost efficiency. Because if we can run something faster and more efficiently, it can also use less resources and you can lower your Cloud spending, right. We've seen companies go from, you know, 20% GPU utilization with their current setup and the current tools they're using to running on Anyscale and getting more like 95, you know, 100% GPU utilization. That's something like a five x improvement right there. So depending on the kind of application you're running, you know, it's a significant cost savings. We've seen companies that have, you know, processing petabytes of data every single day with Ray going from, you know, getting order of magnitude cost savings by switching from what they were previously doing to running their application on Ray. And when you have applications that are spending, you know, potentially $100 million a year and getting a 10 X cost savings is just absolutely enormous. So these are some of the kinds of- >> Data infrastructure is super important. Again, if the customer, if you're a prospect to this and thinking about going in here, just like the Cloud, you got infrastructure, you got the platform, you got SaaS, same kind of thing's going to go on in AI. So I want to get into that, you know, ROI discussion and some of the impact with your customers that are leveraging the platform. But first I hear you got a demo. >> Robert: Yeah, so let me show you, let me give you a quick run through here. So what I have open here is the Anyscale UI. I've started a little Anyscale Workspace. So Workspaces are the Anyscale concept for interactive developments, right. So here, imagine I'm just, you want to have a familiar experience like you're developing on your laptop. And here I have a terminal. It's not on my laptop. It's actually in the cloud running on Anyscale. And I'm just going to kick this off. This is going to train a large language model, so OPT. And it's doing this on 32 GPUs. We've got a cluster here with a bunch of CPU cores, bunch of memory. And as that's running, and by the way, if I wanted to run this on instead of 32 GPUs, 64, 128, this is just a one line change when I launch the Workspace. And what I can do is I can pull up VS code, right. Remember this is the interactive development experience. I can look at the actual code. Here it's using Ray train to train the torch model. We've got the training loop and we're saying that each worker gets access to one GPU and four CPU cores. And, of course, as I make the model larger, this is using deep speed, as I make the model larger, I could increase the number of GPUs that each worker gets access to, right. And how that is distributed across the cluster. And if I wanted to run on CPUs instead of GPUs or a different, you know, accelerator type, again, this is just a one line change. And here we're using Ray train to train the models, just taking my vanilla PyTorch model using Hugging Face and then scaling that across a bunch of GPUs. And, of course, if I want to look at the dashboard, I can go to the Ray dashboard. There are a bunch of different visualizations I can look at. I can look at the GPU utilization. I can look at, you know, the CPU utilization here where I think we're currently loading the model and running that actual application to start the training. And some of the things that are really convenient here about Anyscale, both I can get that interactive development experience with VS code. You know, I can look at the dashboards. I can monitor what's going on. It feels, I have a terminal, it feels like my laptop, but it's actually running on a large cluster. And I can, with however many GPUs or other resources that I want. And so it's really trying to combine the best of having the familiar experience of programming on your laptop, but with the benefits, you know, being able to take advantage of all the resources in the Cloud to scale. And it's like when, you know, you're talking about cost efficiency. One of the biggest reasons that people waste money, one of the silly reasons for wasting money is just forgetting to turn off your GPUs. And what you can do here is, of course, things will auto terminate if they're idle. But imagine you go to sleep, I have this big cluster. You can turn it off, shut off the cluster, come back tomorrow, restart the Workspace, and you know, your big cluster is back up and all of your code changes are still there. All of your local file edits. It's like you just closed your laptop and came back and opened it up again. And so this is the kind of experience we want to provide for our users. So that's what I wanted to share with you. >> Well, I think that whole, couple of things, lines of code change, single line of code change, that's game changing. And then the cost thing, I mean human error is a big deal. People pass out at their computer. They've been coding all night or they just forget about it. I mean, and then it's just like leaving the lights on or your water running in your house. It's just, at the scale that it is, the numbers will add up. That's a huge deal. So I think, you know, compute back in the old days, there's no compute. Okay, it's just compute sitting there idle. But you know, data cranking the models is doing, that's a big point. >> Another thing I want to add there about cost efficiency is that we make it really easy to use, if you're running on Anyscale, to use spot instances and these preemptable instances that can just be significantly cheaper than the on-demand instances. And so when we see our customers go from what they're doing before to using Anyscale and they go from not using these spot instances 'cause they don't have the infrastructure around it, the fault tolerance to handle the preemption and things like that, to being able to just check a box and use spot instances and save a bunch of money. >> You know, this was my whole, my feature article at Reinvent last year when I met with Adam Selipsky, this next gen Cloud is here. I mean, it's not auto scale, it's infrastructure scale. It's agility. It's flexibility. I think this is where the world needs to go. Almost what DevOps did for Cloud and what you were showing me that demo had this whole SRE vibe. And remember Google had site reliability engines to manage all those servers. This is kind of like an SRE vibe for data at scale. I mean, a similar kind of order of magnitude. I mean, I might be a little bit off base there, but how would you explain it? >> It's a nice analogy. I mean, what we are trying to do here is get to the point where developers don't think about infrastructure. Where developers only think about their application logic. And where businesses can do AI, can succeed with AI, and build these scalable applications, but they don't have to build, you know, an infrastructure team. They don't have to develop that expertise. They don't have to invest years in building their internal machine learning infrastructure. They can just focus on the Python code, on their application logic, and run the stuff out of the box. >> Awesome. Well, I appreciate the time. Before we wrap up here, give a plug for the company. I know you got a couple websites. Again, go, Ray's got its own website. You got Anyscale. You got an event coming up. Give a plug for the company looking to hire. Put a plug in for the company. >> Yeah, absolutely. Thank you. So first of all, you know, we think AI is really going to transform every industry and the opportunity is there, right. We can be the infrastructure that enables all of that to happen, that makes it easy for companies to succeed with AI, and get value out of AI. Now we have, if you're interested in learning more about Ray, Ray has been emerging as the standard way to build scalable applications. Our adoption has been exploding. I mentioned companies like OpenAI using Ray to train their models. But really across the board companies like Netflix and Cruise and Instacart and Lyft and Uber, you know, just among tech companies. It's across every industry. You know, gaming companies, agriculture, you know, farming, robotics, drug discovery, you know, FinTech, we see it across the board. And all of these companies can get value out of AI, can really use AI to improve their businesses. So if you're interested in learning more about Ray and Anyscale, we have our Ray Summit coming up in September. This is going to highlight a lot of the most impressive use cases and stories across the industry. And if your business, if you want to use LLMs, you want to train these LLMs, these large language models, you want to fine tune them with your data, you want to deploy them, serve them, and build applications and products around them, give us a call, talk to us. You know, we can really take the infrastructure piece, you know, off the critical path and make that easy for you. So that's what I would say. And, you know, like you mentioned, we're hiring across the board, you know, engineering, product, go-to-market, and it's an exciting time. >> Robert Nishihara, co-founder and CEO of Anyscale, congratulations on a great company you've built and continuing to iterate on and you got growth ahead of you, you got a tailwind. I mean, the AI wave is here. I think OpenAI and ChatGPT, a customer of yours, have really opened up the mainstream visibility into this new generation of applications, user interface, roll of data, large scale, how to make that programmable so we're going to need that infrastructure. So thanks for coming on this season three, episode one of the ongoing series of the hot startups. In this case, this episode is the top startups building foundational model infrastructure for AI and ML. I'm John Furrier, your host. Thanks for watching. (upbeat music)

(upbeat music) >> From theCube Studios in Palo Alto in Boston, bringing you data driven insights from theCube and ETR. This is Breaking Analysis with Dave Vellante. >> While by no means a safe haven, the cybersecurity sector has outpaced the broader tech market by a meaningful margin, that is up until very recently. Cybersecurity remains the number one technology priority for the C-suite, but as we've previously reported the CISO's budget has constraints just like other technology investments. Recent trends show that economic headwinds have elongated sales cycles, pushed deals into future quarters, and just like other tech initiatives, are pacing cybersecurity investments and breaking them into smaller chunks. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis we explain how cybersecurity trends are reverting to the mean and tracking more closely with other technology investments. We'll make a couple of valuation comparisons to show the magnitude of the challenge and which cyber firms are feeling the heat, which aren't. There are some exceptions. We'll then show the latest survey data from ETR to quantify the contraction in spending momentum and close with a glimpse of the landscape of emerging cybersecurity companies, the private companies that could be ripe for acquisition, consolidation, or disruptive to the broader market. First, let's take a look at the recent patterns for cyber stocks relative to the broader tech market as a benchmark, as an indicator. Here's a year to date comparison of the bug ETF, which comprises a basket of cyber security names, and we compare that with the tech heavy NASDAQ composite. Notice that on April 13th of this year the cyber ETF was actually in positive territory while the NAS was down nearly 14%. Now by August 16th, the green turned red for cyber stocks but they still meaningfully outpaced the broader tech market by more than 950 basis points as of December 2nd that Delta had contracted. As you can see, the cyber ETF is now down nearly 25%, year to date, while the NASDAQ is down 27% and change. Now take a look at just how far a few of the high profile cybersecurity names have fallen. Here are six security firms that we've been tracking closely since before the pandemic. We've been, you know, tracking dozens but let's just take a look at this data and the subset. We show for comparison the S&P 500 and the NASDAQ, again, just for reference, they're both up since right before the pandemic. They're up relative to right before the pandemic, and then during the pandemic the S&P shot up more than 40%, relative to its pre pandemic level, around February is what we're using for the pre pandemic level, and the NASDAQ peaked at around 65% higher than that February level. They're now down 85% and 71% of their previous. So they're at 85% and 71% respectively from their pandemic highs. You compare that to these six companies, Splunk, which was and still is working through a transition is well below its pre pandemic market value and 44, it's 44% of its pre pandemic high as of last Friday. Palo Alto Networks is the most interesting here, in that it had been facing challenges prior to the pandemic related to a pivot to the Cloud which we reported on at the time. But as we said at that time we believe the company would sort out its Cloud transition, and its go to market challenges, and sales compensation issues, which it did as you can see. And its valuation jumped from 24 billion prior to Covid to 56 billion, and it's holding 93% of its peak value. Its revenue run rate is now over 6 billion with a healthy growth rate of 24% expected for the next quarter. Similarly, Fortinet has done relatively well holding 71% of its peak Covid value, with a healthy 34% revenue guide for the coming quarter. Now, Okta has been the biggest disappointment, a darling of the pandemic Okta's communication snafu, with what was actually a pretty benign hack combined with difficulty absorbing its 7 billion off zero acquisition, knocked the company off track. Its valuation has dropped by 35 billion since its peak during the pandemic, and that's after a nice beat and bounce back quarter just announced by Okta. Now, in our view Okta remains a viable long-term leader in identity. However, its recent fiscal 24 revenue guide was exceedingly conservative at around 16% growth. So either the company is sandbagging, or has such poor visibility that it wants to be like super cautious or maybe it's actually seeing a dramatic slowdown in its business momentum. After all, this is a company that not long ago was putting up 50% plus revenue growth rates. So it's one that bears close watching. CrowdStrike is another big name that we've been talking about on Breaking Analysis for quite some time. It like Okta has led the industry in a key ETR performance indicator that measures customer spending momentum. Just last week, CrowdStrike announced revenue increased more than 50% but new ARR was soft and the company guided conservatively. Not surprisingly, the stock got absolutely crushed as CrowdStrike blamed tepid demand from smaller and midsize firms. Many analysts believe that competition from Microsoft was one factor along with cautious spending amongst those midsize and smaller customers. Notably, large customers remain active. So we'll see if this is a longer term trend or an anomaly. Zscaler is another company in the space that we've reported having great customer spending momentum from the ETR data. But even though the company beat expectations for its recent quarter, like other companies its Outlook was conservative. So other than Palo Alto, and to a lesser extent Fortinet, these companies and others that we're not showing here are feeling the economic pinch and it shows in the compression of value. CrowdStrike, for example, had a 70 billion valuation at one point during the pandemic Zscaler top 50 billion, Okta 45 billion. Now, having said that Palo Alto Networks, Fortinet, CrowdStrike, and Zscaler are all still trading well above their pre pandemic levels that we tracked back in February of 2020. All right, let's go now back to ETR'S January survey and take a look at how much things have changed since the beginning of the year. Remember, this is obviously pre Ukraine, and pre all the concerns about the economic headwinds but here's an X Y graph that shows a net score, or spending momentum on the y-axis, and market presence on the x-axis. The red dotted line at 40% on the vertical indicates a highly elevated net score. Anything above that we think is, you know, super elevated. Now, we filtered the data here to show only those companies with more than 50 responses in the ETR survey. Still really crowded. Note that there were around 20 companies above that red 40% mark, which is a very, you know, high number. It's a, it's a crowded market, but lots of companies with, you know, positive momentum. Now let's jump ahead to the most recent October survey and take a look at what, what's happening. Same graphic plotting, spending momentum, and market presence, and look at the number of companies above that red line and how it's been squashed. It's really compressing, it's still a crowded market, it's still, you know, plenty of green, but the number of companies above 40% that, that key mark has gone from around 20 firms down to about five or six. And it speaks to that compression and IT spending, and of course the elongated sales cycles pushing deals out, taking them in smaller chunks. I can't tell you how many conversations with customers I had, at last week at Reinvent underscoring this exact same trend. The buyers are getting pressure from their CFOs to slow things down, do more with less and, and, and prioritize projects to those that absolutely are critical to driving revenue or cutting costs. And that's rippling through all sectors, including cyber. Now, let's do a bit more playing around with the ETR data and take a look at those companies with more than a hundred citations in the survey this quarter. So N, greater than or equal to a hundred. Now remember the followers of Breaking Analysis know that each quarter we take a look at those, what we call four star security firms. That is, those are the, that are in, that hit the top 10 for both spending momentum, net score, and the N, the mentions in the survey, the presence, the pervasiveness in the survey, and that's what we show here. The left most chart is sorted by spending momentum or net score, and the right hand chart by shared N, or the number of mentions in the survey, that pervasiveness metric. that solid red line denotes the cutoff point at the top 10. And you'll note we've actually cut it off at 11 to account for Auth 0, which is now part of Okta, and is going through a go to market transition, you know, with the company, they're kind of restructuring sales so they can take advantage of that. So starting on the left with spending momentum, again, net score, Microsoft leads all vendors, typical Microsoft, very prominent, although it hadn't always done so, it, for a while, CrowdStrike and Okta were, were taking the top spot, now it's Microsoft. CrowdStrike, still always near the top, but note that CyberArk and Cloudflare have cracked the top five in Okta, which as I just said was consistently at the top, has dropped well off its previous highs. You'll notice that Palo Alto Network Palo Alto Networks with a 38% net score, just below that magic 40% number, is healthy, especially as you look over to the right hand chart. Take a look at Palo Alto with an N of 395. It is the largest of the independent pure play security firms, and has a very healthy net score, although one caution is that net score has dropped considerably since the beginning of the year, which is the case for most of the top 10 names. The only exception is Fortinet, they're the only ones that saw an increase since January in spending momentum as ETR measures it. Now this brings us to the four star security firms, that is those that hit the top 10 in both net score on the left hand side and market presence on the right hand side. So it's Microsoft, Palo Alto, CrowdStrike, Okta, still there even not accounting for a Auth 0, just Okta on its own. If you put in Auth 0, it's, it's even stronger. Adding then in Fortinet and Zscaler. So Microsoft, Palo Alto, CrowdStrike, Okta, Fortinet, and Zscaler. And as we've mentioned since January, only Fortinet has shown an increase in net score since, since that time, again, since the January survey. Now again, this talks to the compression in spending. Now one of the big themes we hear constantly in cybersecurity is the market is overcrowded. Everybody talks about that, me included. The implication there, is there's a lot of room for consolidation and that consolidation can come in the form of M&A, or it can come in the form of people consolidating onto a single platform, and retiring some other vendors, and getting rid of duplicate vendors. We're hearing that as a big theme as well. Now, as we saw in the previous, previous chart, this is a very crowded market and we've seen lots of consolidation in 2022, in the form of M&A. Literally hundreds of M&A deals, with some of the largest companies going private. SailPoint, KnowBe4, Barracuda, Mandiant, Fedora, these are multi billion dollar acquisitions, or at least billion dollars and up, and many of them multi-billion, for these companies, and hundreds more acquisitions in the cyberspace, now less you think the pond is overfished, here's a chart from ETR of emerging tech companies in the cyber security industry. This data comes from ETR's Emerging Technologies Survey, ETS, which is this diamond in a rough that I found a couple quarters ago, and it's ripe with companies that are candidates for M&A. Many would've liked, many of these companies would've liked to, gotten to the public markets during the pandemic, but they, you know, couldn't get there. They weren't ready. So the graph, you know, similar to the previous one, but different, it shows net sentiment on the vertical axis and that's a measurement of, of, of intent to adopt against a mind share on the X axis, which measures, measures the awareness of the vendor in the community. So this is specifically a survey that ETR goes out and, and, and fields only to track those emerging tech companies that are private companies. Now, some of the standouts in Mindshare, are OneTrust, BeyondTrust, Tanium and Endpoint, Net Scope, which we've talked about in previous Breaking Analysis. 1Password, which has been acquisitive on its own. In identity, the managed security service provider, Arctic Wolf Network, a company we've also covered, we've had their CEO on. We've talked about MSSPs as a real trend, particularly in small and medium sized business, we'll come back to that, Sneek, you know, kind of high flyer in both app security and containers, and you can just see the number of companies in the space this huge and it just keeps growing. Now, just to make it a bit easier on the eyes we filtered the data on these companies with with those, and isolated on those with more than a hundred responses only within the survey. And that's what we show here. Some of the names that we just mentioned are a bit easier to see, but these are the ones that really stand out in ERT, ETS, survey of private companies, OneTrust, BeyondTrust, Taniam, Netscope, which is in Cloud, 1Password, Arctic Wolf, Sneek, BitSight, SecurityScorecard, HackerOne, Code42, and Exabeam, and Sim. All of these hit the ETS survey with more than a hundred responses by, by the IT practitioners. Okay, so these firms, you know, maybe they do some M&A on their own. We've seen that with Sneek, as I said, with 1Password has been inquisitive, as have others. Now these companies with the larger footprint, these private companies, will likely be candidate for both buying companies and eventually going public when the markets settle down a bit. So again, no shortage of players to affect consolidation, both buyers and sellers. Okay, so let's finish with some key questions that we're watching. CrowdStrike in particular on its earnings calls cited softness from smaller buyers. Is that because these smaller buyers have stopped adopting? If so, are they more at risk, or are they tactically moving toward the easy button, aka, Microsoft's good enough approach. What does that mean for the market if smaller company cohorts continue to soften? How about MSSPs? Will companies continue to outsource, or pause on on that, as well as try to free up, to try to free up some budget? Adam Celiski at Reinvent last week said, "If you want to save money the Cloud's the best place to do it." Is the cloud the best place to save money in cyber? Well, it would seem that way from the standpoint of controlling budgets with lots of, lots of optionality. You could dial up and dial down services, you know, or does the Cloud add another layer of complexity that has to be understood and managed by Devs, for example? Now, consolidation should favor the likes of Palo Alto and CrowdStrike, cause they're platform players, and some of the larger players as well, like Cisco, how about IBM and of course Microsoft. Will that happen? And how will economic uncertainty impact the risk equation, a particular concern is increase of tax on vulnerable sectors of the population, like the elderly. How will companies and governments protect them from scams? And finally, how many cybersecurity companies can actually remain independent in the slingshot economy? In so many ways the market is still strong, it's just that expectations got ahead of themselves, and now as earnings forecast come, come, come down and come down to earth, it's going to basically come down to who can execute, generate cash, and keep enough runway to get through the knothole. And the one certainty is nobody really knows how tight that knothole really is. All right, let's call it a wrap. Next week we dive deeper into Palo Alto Networks, and take a look at how and why that company has held up so well and what to expect at Ignite, Palo Alto's big user conference coming up later this month in Las Vegas. We'll be there with theCube. Okay, many thanks to Alex Myerson on production and manages the podcast, Ken Schiffman as well, as our newest edition to our Boston studio. Great to have you Ken. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our EIC over at Silicon Angle. He does some great editing for us. Thank you to all. Remember these episodes are all available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibond.com and siliconangle.com, or you can email me directly David.vellante@siliconangle.com or DM me @DVellante, or comment on our LinkedIn posts. Please do checkout etr.ai, they got the best survey data in the enterprise tech business. This is Dave Vellante for theCube Insights powered by ETR. Thanks for watching, and we'll see you next time on Breaking Analysis. (upbeat music)

>>The Cube presents UI Path Forward five. Brought to you by UI Path. >>Hi everybody. We're back at UI path forward to five. This is Dave Ante with Dave Nicholson. Derek Mu is here. He's automation product line lead for Merck. Thank you, by the way, for, you know, all you guys do, and thank you Dave for having in the, in the, in the vaccine area, saving our butts. And Bill Engel is back on the cube. He's the director at cgi. Guys, good to see you again. >>Good to see you. Thank >>You. So Merrick, Wow, it's been quite a few years for you guys. Take us through Derek, what's happening in sort of your world that's informing your automation strategy? >>Well, Dave, I mean as you know, we just came out of the pandemic. We actually have quite a few products like Gabriel Antiviral Pill. Obviously we worked, you know, continue to drive our products through a difficult time. But, you know, is during these can last few years that, you know, we've accelerated our journey in automation. We're about four years plus in our journey, you know, so just like the theme of this conference we're we're trying to move towards, you know, bigger automations, transformational change, continue to drive digital transformation in our company. >>Now Bill, you've been on before, but CGI tell people about the firm. It's not computer graphics imaging. >>Sure. No, it's, it's definitely not. So cgi, we're a global consultancy about 90,000 folks across the world. We're a, we're both a product company and a services company. So we have a lot of different, you know, software products that we deliver to our clients, such as CGI Advantage, which is a state local government EER P platform. And so outside of that, we, my team does automation and so we wrap automation around R IP and deliver that to our clients. >>So you guys are automation pros, implementation partners, right? So, so let's go back. Yep. Derek said four years I think. Yep. Right, You're in. So take us through what was the catalyst, how did you get started? Obviously it was pre pandemic, so it's interesting, a lot of companies pre pandemic gave lip service to digital transformation. Sounds like you guys already started your journey, but I'll come back to that. But take us back to the Catalyst four years ago. Why automation? We'll get into why UI path, >>Right. So I, I would say it started pretty niche in our company. Started first in our finance area. Of course, you know, we were looking in technology evaluating different companies, Blue Prism, ui P. Ultimately we chose UI p did it on-prem to start to use automation in sort of our invoice processing, sort of our financial processes, right? And then from there, after it was really when the pandemic hit, that's when sort of we all went to remote work. That's when the team, the COE continued to scale up, especially during pandemic. We were trying to automate more and more processes given the fact that more and more of our workers are remote, they reprocesses. How, how do you do events? You know, part of our livelihood is, is meeting with engaging with customers. Customers in this case is, are doctors and physicians, right? How do you engage with them digitally? How do you, you know, you know, a lot of the face to face contact now have to kind of shift to more digital, digital way. And so automation was a way to kind of help accelerate that, help facilitate that. >>You, you, I think you mentioned COE as in center of excellence. Yep. So, so describe your approach to implementing automation. It's, that sounds like when you say center, it sounds like something is centralized as, as opposed to a bunch of what we've been hearing a lot about citizen developers. What does that interaction >>Look like? We do have both. I would say in the beginning was more decentralized, but over time we, over the few years as, as we built more and more bots, we're now at maybe somewhere between four to 500 bots. We now have sort of internal to the company functional verticals, right? So there's an animal health, we have an animal health function. So there's, there's a team building engaging with the animal health business to build animal health box. There's human health, which is what I work on as well as hr, finance, manufacturing, research. And so internally there's engagement leads, one of the engagement leads that interact with the business. Then when there's an engineering squads that help build and design, develop and support and maintain those as well as sort of a DevOps team that supports the platform and maintains all the bot infrastructure. >>So you started in finance common story, right? I'm sure you hear this a lot Belt, How did you decide what to target? Was it, was it process driven decision? Was it, was it data oriented? Like some kind of combination? How did you decide, Do you remember? Or do you, could you take >>Us back to Oh yeah. So for, for cgi how we started to engage with MER is, you know, we, we do a lot of other business with Merck. We work on all their different business lines and we, we understand the business process. So we, we knew where there was potential for automation. So we brought those ideas to Merck and, and really kind of landed there and helped them realize the value from automation from that standpoint. And then from there the journey just continued to expand, you know, looking for those use cases that, that, you know, fit the mold for, for, for RPA to start. And now the evolution is to go to broader hyper automation. >>And, and was it CFO led into the finance department and then, or was it sort of more bottoms >>Up? Yeah, so, so I think it started in, in finance and, and, but we actually really started out in the business line. So out in regulatory clinical, that's, that's where we, we have the life science expertise that are embedded. And so I partnered with them to come up with, hey, here's a real solution we could do to help streamline, say submission archiving. So when, when submissions come back from the fda, they need to be archived into, you know, the, their system of record. So that's, those are the types of use cases that, that we helped automate. >>Okay. Cause you're saying a human had to sort physically archive that and you were able to sort of replicate that. Okay. And you started with software robots, obviously rpa and now you're expanding into, we we're hearing from UI this the platform message. How does that coincide Derek, with what you guys are doing? Are you sort of adding platform? What aspects of the platform are, are you adding? >>Yeah, no, I mean we are, we are on-premise, right? So we have the platform, but some of the cool things we just had, another colleague of mine presented earlier today. Some of the cool things we're, we're doing ephemeral infrastructure. So infrastructure as code, which essentially means instead of having all these dedicated bot machines, that that, you know, cuz these bots only in some cases run 10 minutes and they're done. So we're, we're soon of doing all on demand, you know, start up a server, run the bot when it's finished, you know, kill the server. So we only pay for the servers that we use, which allows us to save a whole >>Lot of money. Serverless bots. So you, but you're doing that OnPrem, so you >>No, >>No, but >>That's >>Cloud. We, >>We, we we're doing it OnPrem, but our, our bot machines that actually run the, let's say SAP process, right? We spin that machine up, it's on the cloud, it runs it finish, Let's say it's processed in one hour and then when it's done, we kill that machine. So we only play for that one hour usage of that bot machine. >>Okay. So you mentioned SAP earlier you mentioned Blue Prism when you probably looked at other competitors too. You pull the Gartner Magic quadrant, blah, blah, you know, with the way people, you know, evaluate technology, but SAP's got a product. Why UI path mean? Is it that a company like SAP two narrow for their only sap you wanted to apply it other ways? Maybe they weren't even in the business that back then four years ago they probably weren't. Right? But I'm curious as to how the decision was made for UiPath. >>Well, I think you hit it right on the nail. You know, SAP sort of came on a little later and they're specific to sort of their function, right? So UiPath for us is the most flexible tool can interact by UI to our sales and marketing systems, to, to workday, to service Now. It's, it cuts across every function that we have in the company as well as you're the most mature. I mean, you're the market leader, right? So Right. Definitely you, you continue to build upon those capabilities and we are exploring the new capabilities, especially being announced today. >>And what do you see Bill in the marketplace? Are you, are you kind of automation tool agnostic? Are you more sort of all in on? I >>Would say we are, we are agnostic as a company, but obviously as part of a, as an automation practice lead, you know, I want to deliver solutions to my clients that are gonna benefit them as a whole. So looking at UI path, you know, that this platform is, it covers the end to end spectrum of, of automation. So I can go really into any use case and be able to provide a solution that, that delivers value. And so that's, that's where I see the value in UI path and that's why CGI is, is a customer as well. We automate our internal processes. We actually have, we just launched probably SALT in the, in the market last week, expanded partnership with UiPath. We launched CGI, Excel 360. That's our fully managed service around automation. We host our clients whole UI path infrastructure and bots. It's completely hands off to them and they just get the value outta >>Automation. Nice, nice. Love >>It. Derek, you mentioned, you mentioned this ephemeral infrastructure. Yeah. Sounds like it's also ethereal possibility possibly you're saying, you, you're saying you have processes that are running on premises, right? But then you reach out to have an automation process run that's happening off pre and you're, and you're sort of, >>It's on the cloud, so, so yeah, so we have a in-house orchestrator, so we don't, we're not using your sort of on the cloud orchestrator. So, so we brought it in-house for security reasons. Okay. But we use, you know, so inside the vpn, you know, we have these cloud machines that run these automations. So, so that's, that's the ephemeral side of the, of the >>Infrastructure. But is there a financial angle to that in terms of when you're spinning these things up, are you, is it a, is it a pay by the drink or by the, by the CPU >>Hours, if you can imagine like we, you know, like I mentioned where somewhere between four to 500 bots and every bot has a time slot to run and takes a certain amount of time. And so that's hundreds and hundreds of bot machines that we in the old days have to have to buy and procure and, you know, staff and support and maintain. So in this new model, and we're just beginning to kind of move from pilot into implementation, we're moving all, all of bots this in ephemeral infrastructure, right? So these, okay, these machines, these bot machines are, you know, spun up. They run the, they, they run their automation and then they spin >>Down. But just to be clear, they're being spun up on physical infrastructure that is in your >>Purview and they spun up on aws. Yeah. Okay. And then they spin down. Okay, got >>It. Got it. Interesting. Four >>To 500 bots. You know, Daniel one point play out this vision of a bot chicken in every pot, I called it a bot for every employee. Is that where you're headed or is that kind of in this new ephemeral world, not necessary, it's like maybe every employee has access to an ephemeral bot. How, how are you thinking about that? >>That's a good question. So obviously the, the four to 500 is a mix of unattended bonds versus attended bonds, right? That, that we also have a citizen developer, sort of a group team. We support that as well from a coe. So, you know, we see the future as a mix. There's, there's a spectrum of, we are the professional development team. There's also, we support and nurture the personal automation and we provide the resources to help them build smaller scale automations that help, you know, reduce the, you know, the mundaneness and the hours of their own tasks. But you know, for us, we want to focus more and more on building bigger and bigger transfer transformational automations that really drive process efficiencies and, and savings. >>And what's the, what's the business impact been? You mentioned savings and maybe there's other sort of productivity. How do you measure the benefit, the ROI and, and >>Quantify that we, you know, I, I don't, I don't profess I don't think we have all the right answers, but yeah, simple metrics like number of hours saved or other sort of excitement sort of in like an nps, internal NPS between the different groups that we engage. But we definitely see automation demand coming from our, our functional teams going up, driving up. So it's, it's continued to be a hot area and hopefully we, we can, you know, like, like what the key message and theme of this, of this conference. Essentially we want to take and build upon the, the good work that we've done in terms of rpa and we want to drive it more towards digital transformation. >>So Bill, what are you seeing across the, your customer base in terms of, of, of roi? I'm not looking for percentages there. I'm sure they're off the charts, but in terms of, you know, you can optimize for fast payback, you know, maybe lower the denominator, you know, or you can optimize for, you know, net benefit over time, right? You know, what are you seeing? What are customers after they want fast payback and little quick hits? Or are they looking for sort of a bigger enterprise wide impact? >>Yeah, I think it's, it's the latter. It's that larger impact, right? Obviously they, you know, they want an roi and just depending upon the use case, that's gonna vary in terms of the, the benefits delivered. And a lot of our clients, depending on the industry, so in in life sciences it may be around, you know, compliance like GXP compliance is huge. And so that may may not be much of a time saver, but it ensures that they're, they're running their processes and they're being compliant with, you know, federal standards. So that's, that's one aspect to it. But you know, to, you know, a bank, they're looking to reduce their overall costs and and so on. But yeah, I think, I think the other, the other part of it is, you know, impacting broader business processes. So taking that top down approach versus kind of bottom up, you know, doing ta you know, the ones you choose the tasks is not as impactful as looking at broader across the entire business process and seeing how we can impact >>It. Now, Derek, when you guys support a citizen developer, how does that work? So, hey, I got this task I want to automate, I'm gonna go write a, you know, software robot. I'm gonna go do an automation. Do I just do it and then throw her to the defense? You guys, you guys send me a video on how to do it. Hold my hand. How's that work? >>Yeah, I mean, good question. So, so we obviously direct them to the UI path Academy, get some training. We also have some internal training materials to how to build a bot sort of internal inside Merck. We, we go through, we have writeups and SOPs on using the right framework for automations, using the right documentation, PDD kind of materials, and then ultimately how do we deploy bot inside the MER ecosystem. But I, I, maybe I'll just add, I think you asked the point about ROI before. Yeah. I'll also say because we're, we're a pharmaceutical company. I think one of the other key metrics is actually time saved, right? So if, if, if we have a bot that helps us get through the clinical process or even the getting a, a label approved faster, even if it's eight days saved, that's eight days of a product that can get out to the market faster to, to our patients and, and healthcare professionals. And that's, that, that's immeasurable benefit. >>Yeah, I bet if you compress that ELAP time of, of getting approval and so forth. All right guys, we've gotta go. Thanks so much. Congratulations on all the success and appreciate you sharing your story. Thank >>You so much. Appreciate it. You're welcome. >>Appreciate it. All right. Thank you for watching this Dave Ante for Dave Nicholson, The cubes coverage, two day coverage. We're here in day one, UI path forward, five. We'll be right back right after the short break. Awesome. >>Great.

>>Welcome back to Boston. Everybody watching the cubes coverage, OFS reinforce 22 from Boston, Atlanta chow lobster, the SOS a ruin in my summer, Andy and Smith is here is the CMO of laminar. Andy. Good to see you. Good >>To see you. Great to be >>Here. So laminar came outta stealth last year, 2021, sort of, as we were exiting the isolation economy. Yeah. Why was laminar started >>Really about there's there's two mega trends in the industry that, that created a problem that wasn't being addressed. Right? So the two mega trends was cloud transformation. Obviously that's been going on for a while, but what most people doesn't don't realize is it really accelerated with COVID right? Being all, everybody having to be remote, et cetera, various stats I've read like increased five times, right? So cloud transformation are now you are now problem, right? That's going on? And then the other next big mega trend is data democratization. So more data in the cloud than ever before. And this is, this is just going and going and going. And the result of those two things, more data in the cloud, how am I securing that data? You know, the, the, the breach culture we're in like every day, a new, a new data breach coming up, et cetera, just one Twitter, one yesterday, et cetera. The, those two things have caused a gap with data security teams and, and that's what he >>Heard at attract. Yeah. So, you know, to your point and we track this stuff pretty carefully quarterly, and you saw, it was really interesting trend. You actually saw AWS's growth rate accelerate during the pandemic. Absolutely. You know? Absolutely. So you're talking about, you know, a couple of hundred billion dollars for the big four clouds. If you, if you include Alibaba and it's still growing at 35, you know, 40% a year, which is astounding, so, okay. So more cloud, more data. Explain why that's a, a problem for practitioners. >>Yeah, exactly. The reality is in, in the security, what, what are we doing? What all the security it's about protecting your data in the end, right? Like, like we're here at this at, at reinforce all these security vendors here really it's about protecting your data, your sensitive data. And, but what, what had been happening is all the focus was on the infrastructure, the network, et cetera, et cetera, and not as much focus, particularly on the data and, and the move to the cloud gave the developers and the data scientists, way more power. They don't longer have to ask for permission. And so they can just do what they want. And it's actually wonderful for the business. The business is moving faster, you spin up applications sooner, you get new, new insights. So all those things are really great, but because the developer has so much power, they can just copy data over here, make a backup over here, new et cetera. And, and security has no idea about all these copies of the, of the data that are out there. And they're typically not as well protected as that main production source. And that's the gap that >>Exists. Okay. So there was this shift from sort of perimeter hardening the perimeter, hardening the infrastructure and, and now your premises, it's moving to the data we saw when, when there was during the pandemic, there was definitely a shift to end point security. There was a shift to cloud security rethinking the network, but it was still a lot of, you know, kind of cha chasing the whackamole and people have talked about this is a data problem for years. Yeah. But it was, it's taken a while for, for companies, for the technology industry to, to come at it. You guys are one of the first, if not the first. Yeah. Why do you think it took so long? Is this cuz it's really hard. >>Yeah. I mean, it, it's hard. You need to focus on it. The, the traditional security has been around the network and the box, right. And those are still necessary. It's important to, you know, your use identity to cover the edge, to, to make sure people can't get into the box, but you also have to have data. So what, what happens is there's really good solutions for enterprise data security, looking at database, you know, technology, et cetera. There are good solutions for cloud infrastructure security. So the CSP of the world and the CWPP are protecting containers, you know, protecting the infrastructure. But there really wasn't much for cloud everything you build and run in the cloud. So basically your custom application, your custom applications in the IAS and PAs environments, there really wasn't anything solving that. And that's really where laminar is focused. >>Okay. So you guys use this term shadow data. We talk about shadow. It what's shadow data. >>Yeah. So what we're finding at a hundred percent of our customer environments and our POVs and talking to CISOs out there is that they have these shadow data assets and shadow data elements that they have no clue that existed. So here's the example. Everybody knows the main RDS database that is in production. And this is where, you know, our, our data is taken from. But what people don't realize is there's a copy of that. You know, in a dev environment, somebody went to run a test and they was supposed to be there for two weeks. But then that developer left forgot, left it there. They left the company, oh, now it's been there for two years that there was an original SQL database left over from a lift and shift project. They got moved to RDS, but nobody deleted that thing there, you know, it's a database connected to an application, the application left, but that database, that abandoned database is still sitting. These are all real life customer examples of shadow data that we run into. And there's, and what the problem is that main production data store is secured pretty well. It's following all your policies, et cetera. But all these shadow data resources are typically less well protected unmonitored. And that is what the attackers are after. >>So you're, you know, the old, the, the Watergate follow the money, you're following the data, >>Following the data. >>How do you follow that data if there's so much of it, it, and it's, you know, sometimes, you know, not really well understood where it is. How do you know where >>It is? Yeah. It's the beauty of partnering with somebody like AWS, right? So with each of the cloud providers, we actually take a role in your cloud account and use the APIs from the cloud provider to see all the changes in all the instances are going on. Like it is, the problem is way more complicated in the cloud because I mean, AWS has over 200 services, dozens of ways to store data, right. It's wonderful for the developer, but it's very hard for the security practitioner. And so, because we have that visibility through the cloud provider's APIs, we can see all those changes that are happening. We can then say, ah, that's a data store. Let me go analyze, make a copy, have a snapshot of that and do the analyzing of that data right inside our customer's account without pulling the data out. And we have complete visibility to everything. And then we can give that data catalog over to the customer. >>All right. I gotta ask you a couple Colombo questions. So if you know, we talk about encryption, everything's encrypted everything. If, if the data is encrypted, why then would I need laminar? >>Because I mean, we'll make sure that the data's encrypted okay. Right. Often. So it's not supposed to be and not right. Two is, we're gonna tell you what type of data is inside there. Oh, is this, is this health information? Is it personal identify information? Is it credit cards? You know, et cetera, C so we'll classify the data for you. We will also, then there's things like retention, period. How long should we, I hold onto that data, all the things about what are, who has access, what's the exposure level for that data. And so when you, when you think about data security posture, what's the posture of that data you're looking at at those data policies. It's something that has been very well defined and written down. But in the past, there was just no way to go verify that those, that, that, that policy is actually being followed. And so we're doing that verification automatically. >>So without the context, you can't answer those other questions. So you make sure it's encrypted. If it's not, or you can at least notify me that it's not, you don't do the encryption. Right. Or do you, >>We don't do it ourselves, but we can give you here. Here's the command in and the Amazon to go encrypt it >>Right. Then I can automate that. And then the classification is key because now you're telling me the context. So I can say, okay, apply this policy to that data, retain it for this long, get rid of it after X number of years, or if it's work, process, get rid of it now. Yeah. And then who should have access to that data. And so you can help at least inform how to enforce those policies. >>Exactly. And so we, we, we call it guided remediation because what we're, you know, talking to a CISO, they're like, I need 400 more alerts, like a hole in the head like that. Doesn't do me any good. If you can't tell me how to resolve the, the, the, this security gap that I have or this, then it doesn't do any good. And, and the first, first it starts with who do I need to go talk to? Right. So they have hundreds, if not thousands of developers. Oh, great. You found this issue. I, I, I don't know who to go. Like, I can't just delete it myself, but I need to go talk to somebody really, should this be deleted? We need, do we really, really need to hold onto this? So we, we help guide who the data owner is. So we give you who to talk to. You, give you all the context. Here's the data, here's the data asset that it's in. Here's our suggestion. Here's the problem. Here's our suggestion for >>Solution. And you started the company on AWS >>Started on AWS. Absolutely. >>So what's of course it's best cloud and why not start there? So what's the relationship like, I mean, how'd you get started? You said, okay, Hey, we're we got an idea for a company. We're gonna build it on AWS. We're gonna become a customer. We're gonna, you know, >>We actually, so insight partners is our main investor. Yeah. And they were very helpful in giving us access to literally hundreds of CSOs, who we had conversations with before we actually launched the company. And so we did some shifting and to, to figure out our exact use case. But by the time we came to market, it was in February this year, we actually GAed the product that, where like product market fit nailed because we'd had so many conversations that we knew the problem in the market that we needed to solve. And we knew where we needed to solve it first. And, and the, the, the relationship we AWS is great. We just got on the marketplace, just became a, a partner. So really good. Good >>Start. So I gotta ask you, so I always ask this question. So how do you actually know when you have product market fit? >>You it's about those conversations. Right. You know, so like, I I've been to lots of startups and sometimes you're you're, you, you each have a conversation and then they, they saying, oh, well kind of want this. And we kind of like that. And so it, the more conversations you have, the more, you know, you're solving a real problem. Right. And, and, and, and, and you re react to what that, what that prospect is telling you back and, or that advisor or that whoever we're talking to. And, and every single one of the CISO conversations we had was I don't have a good inventory of my data in the cloud. >>The reason I asked that, cause I always ask the startups, like, when do you scale? Cause I think startups sometimes scale too fast. They try to scale too fast, they'll hire 50 sales people. And then they, you know, churn, you know, they, they got a 50% churn, but they're trying to optimize their go to market when they got 50% of their customers are gonna leave. So it's, it's gotta be the sequential thing. So, so you got product market fit. So are, are you in the scaling phase >>Now? We are. Yeah. Yeah, yeah. So now it's about how quickly can we deliver? We, we we're ramping customer base significantly. And, and you know, we've got a whole go to market team in, you know, sales and marketing in the us and, and often off to the races >>And you just run on AWS or you run another clouds. >>It's multi-cloud so AWS, Azure, GCP, et cetera. >>Okay. So then my least my next question is it sort of, you can do this within each of the individual clouds today. Do you see a day and maybe it's here today is where you can create a single experience across those clouds >>Today. It's a single experience across cloud. So our SaaS, we have our SaaS portion runs in AWS, but the actual data analysis runs in each cloud provider. So AWS, Azure, GCP and snowflake too, actually. >>Ah, okay. So I come through your whatever portal, like if I can use that term. Yep. And that's running on AWS. Yes. You're SAS, as you say, and then you go out to these other environments, GCP, Azure, AWS itself, and snowflake. Yep. And I see laminar, is that right? Or >>There's a piece running inside our customer's environment. Okay. So, so we have a customer, they, the, we have, we get a role inside of their cloud account or read only role inside of their cloud account. And we spin up serverless functions in that cloud account. That's where all the analysis happens. And that's why we don't take any data out of the environment. So it all stays there. And, and therefore we don't, we don't actually see the data outside of the environment. Like, I, I can tell you there's a metadata comes out. I can tell you, there are credit cards inside that data store, but I can't tell you exactly which credit card it is cuz I don't know. So all the important actions happens are there and just the metadata metadata comes out. So we can give you a cross cloud dashboard of all your sensitive data. >>And of course, so take the example of snowflake. They're going across clouds, they're building what we call super cloud sort of, of a layer that floats on top. You're just sort of going wherever that data goes. >>Yeah, exactly. So, so each of there's a component that lives in the customer's environment in the, in those multi-cloud environments and then a single view of the world dashboard that is our SaaS component that runs an AWS. So >>You guys are, is, am I correct? You're series a funded >>Series, a funded yeah, exactly. >>And, and already scaling to go to market. Yeah. Which is, which is early to scale. Right. I mean you've got startup experience. Right? >>Absolutely. >>How does it compare? >>Well, what was amazing here was access. I mean, really it was through the relationship with insight. It was access to the CISOs that I had never had at any of the other startups I was with. You're trying to get meetings, you're meeting with a lot of practitioners, you know, et cetera. But getting all those conversations with buyers was, was super valuable for us to say, ah, I know I'm solving a real problem that has value that they will pay for. Right. And, and, and so that, that was a year and a half probably still of all that work going on. We just, just waited to GA until we understood the market >>Better. Yeah. Insight. They're amazing. The way to talk about scaling. I mean, they've just the last 10 years that comp that, that PE firm has just gone wild in terms of just their, their philosophy, their approach, their cadence, their consistency. And now of course their portfolio. >>Yeah. And, and they started doing a little bit earlier and earlier stage. I mean, I, I always think of them as PE too, but you know, they, they did our seed round. Right. They did our a round and, and they're doing earlier stages, but particularly what they saw in Laar was exactly what we started this conversation with. They saw cloud transformation speeding up, they saw data democratization happening. They're like, we need to invest in this now because this is a now a problem to solve. >>Yeah. It's interesting. Cuz when you go back even pre 2010, you talk to, you know, look at insight, they would wait. They would invest in companies unless there was, you know, on the way to five plus million dollar ARR, they weren't doing seed deals. Totally. Like they saw, wow, these actually can be pretty lucrative and we can play and we have a point of view and yeah. So cool. Well, congratulations. I'll give you the final word. What, what should we be watching for from, from Laar as sort of, you know, milestones that you guys want to hit and, and indicators of success. >>Yeah. Now it's all about growth partnerships, you know, integrations with, with other of the players out here. Right. And so, you know, like scaling our AWS partnership is one of the key aspects for us. And so, you know, just look for, look for the name out there and, and you'll start, you'll start to see it a lot more. And, and if, if you have the need, you know, come look us up. Laar security.com. >>Awesome. Well thanks very much for coming to Cuban. Good luck. Appreciate it. All right. >>Wonderful. Thanks. You're >>Welcome. All right. Keep it right there, everybody. This is Dave ante. We'll be back right after this short break from AWS reinvent 2022 in Boston. You're watching the cue.

(upbeat music) >> Welcome back to Boston, everybody. This is the birthplace of theCUBE. In 2010, May of 2010 at EMC World, right in this very venue, John Furrier called it the chowder and lobster post. I'm Dave Vellante. We're here at RE:INFORCE 2022, Ed Walsh, CEO of ChaosSearch. Doing a drive by Ed. Thanks so much for stopping in. You're going to help me wrap up in our final editorial segment. >> Looking forward to it. >> I really appreciate it. >> Thank you for including me. >> How about that? 2010. >> That's amazing. It was really in this-- >> Really in this building. Yeah, we had to sort of bury our way in, tunnel our way into the Blogger Lounge. We did four days. >> Weekends, yeah. >> It was epic. It was really epic. But I'm glad they're back in Boston. AWS was going to do June in Houston. >> Okay. >> Which would've been awful. >> Yeah, yeah. No, this is perfect. >> Yeah. Thank God they came back. You saw Boston in summer is great. I know it's been hot, And of course you and I are from this area. >> Yeah. >> So how you been? What's going on? I mean, it's a little crazy out there. The stock market's going crazy. >> Sure. >> Having the tech lash, what are you seeing? >> So it's an interesting time. So I ran a company in 2008. So we've been through this before. By the way, the world's not ending, we'll get through this. But it is an interesting conversation as an investor, but also even the customers. There's some hesitation but you have to basically have the right value prop, otherwise things are going to get sold. So we are seeing longer sales cycles. But it's nothing that you can't overcome. But it has to be something not nice to have, has to be a need to have. But I think we all get through it. And then there is some, on the VC side, it's now buckle down, let's figure out what to do which is always a challenge for startup plans. >> In pre 2000 you, maybe you weren't a CEO but you were definitely an executive. And so now it's different and a lot of younger people haven't seen this. You've got interest rates now rising. Okay, we've seen that before but it looks like you've got inflation, you got interest rates rising. >> Yep. >> The consumer spending patterns are changing. You had 6$, $7 gas at one point. So you have these weird crosscurrents, >> Yup. >> And people are thinking, "Okay post-September now, maybe because of the recession, the Fed won't have to keep raising interest rates and tightening. But I don't know what to root for. It's like half full, half empty. (Ed laughing) >> But we haven't been in an environment with high inflation. At least not in my career. >> Right. Right. >> I mean, I got into 92, like that was long gone, right?. >> Yeah. >> So it is a interesting regime change that we're going to have to deal with, but there's a lot of analogies between 2008 and now that you still have to work through too, right?. So, anyway, I don't think the world's ending. I do think you have to run a tight shop. So I think the grow all costs is gone. I do think discipline's back in which, for most of us, discipline never left, right?. So, to me that's the name of the game. >> What do you tell just generally, I mean you've been the CEO of a lot of private companies. And of course one of the things that you do to retain people and attract people is you give 'em stock and it's great and everybody's excited. >> Yeah. >> I'm sure they're excited cause you guys are a rocket ship. But so what's the message now that, Okay the market's down, valuations are down, the trees don't grow to the moon, we all know that. But what are you telling your people? What's their reaction? How do you keep 'em motivated? >> So like anything, you want over communicate during these times. So I actually over communicate, you get all these you know, the Sequoia decks, 2008 and the recent... >> (chuckles) Rest in peace good times, that one right? >> I literally share it. Why? It's like, Hey, this is what's going on in the real world. It's going to affect us. It has almost nothing to do with us specifically, but it will affect us. Now we can't not pay attention to it. It does change how you're going to raise money, so you got to make sure you have the right runway to be there. So it does change what you do, but I think you over communicate. So that's what I've been doing and I think it's more like a student of the game, so I try to share it, and I say some appreciate it others, I'm just saying, this is normal, we'll get through this and this is what happened in 2008 and trust me, once the market hits bottom, give it another month afterwards. Then everyone says, oh, the bottom's in and we're back to business. Valuations don't go immediately back up, but right now, no one knows where the bottom is and that's where kind of the world's ending type of things. >> Well, it's interesting because you talked about, I said rest in peace good times >> Yeah >> that was the Sequoia deck, and the message was tighten up. Okay, and I'm not saying you shouldn't tighten up now, but the difference is, there was this period of two years of easy money and even before that, it was pretty easy money. >> Yeah. >> And so companies are well capitalized, they have runway so it's like, okay, I was talking to Frank Slootman about this now of course there are public companies, like we're not taking the foot off the gas. We're inherently profitable, >> Yeah. >> we're growing like crazy, we're going for it. You know? So that's a little bit of a different dynamic. There's a lot of good runway out there, isn't there? >> But also you look at the different companies that were either born or were able to power through those environments are actually better off. You come out stronger in a more dominant position. So Frank, listen, if you see what Frank's done, it's been unbelievable to watch his career, right?. In fact, he was at Data Domain, I was Avamar so, but look at what he's done since, he's crushed it. Right? >> Yeah. >> So for him to say, Hey, I'm going to literally hit the gas and keep going. I think that's the right thing for Snowflake and a right thing for a lot of people. But for people in different roles, I literally say that you have to take it seriously. What you can't be is, well, Frank's in a different situation. What is it...? How many billion does he have in the bank? So it's... >> He's over a billion, you know, over a billion. Well, you're on your way Ed. >> No, no, no, it's good. (Dave chuckles) Okay, I want to ask you about this concept that we've sort of we coined this term called Supercloud. >> Sure. >> You could think of it as the next generation of multi-cloud. The basic premises that multi-cloud was largely a symptom of multi-vendor. Okay. I've done some M&A, I've got some Shadow IT, spinning up, you know, Shadow clouds, projects. But it really wasn't a strategy to have a continuum across clouds. And now we're starting to see ecosystems really build, you know, you've used the term before, standing on the shoulders of giants, you've used that a lot. >> Yep. >> And so we're seeing that. Jerry Chen wrote a seminal piece on Castles in The Cloud, so we coined this term SuperCloud to connote this abstraction layer that hides the underlying complexities and primitives of the individual clouds and then adds value on top of it and can adjudicate and manage, irrespective of physical location, Supercloud. >> Yeah. >> Okay. What do you think about that concept?. How does it maybe relate to some of the things that you're seeing in the industry? >> So, standing on shoulders of giants, right? So I always like to do hard tech either at big company, small companies. So we're probably your definition of a Supercloud. We had a big vision, how to literally solve the core challenge of analytics at scale. How are you going to do that? You're not going to build on your own. So literally we're leveraging the primitives, everything you can get out of the Amazon cloud, everything get out of Google cloud. In fact, we're even looking at what it can get out of this Snowflake cloud, and how do we abstract that out, add value to it? That's where all our patents are. But it becomes a simplified approach. The customers don't care. Well, they care where their data is. But they don't care how you got there, they just want to know the end result. So you simplify, but you gain the advantages. One thing's interesting is, in this particular company, ChaosSearch, people try to always say, at some point the sales cycle they say, no way, hold on, no way that can be fast no way, or whatever the different issue. And initially we used to try to explain our technology, and I would say 60% was explaining the public, cloud capabilities and then how we, harvest those I guess, make them better add value on top and what you're able to get is something you couldn't get from the public clouds themselves and then how we did that across public clouds and then extracted it. So if you think about that like, it's the Shoulders of giants. But what we now do, literally to avoid that conversation because it became a lengthy conversation. So, how do you have a platform for analytics that you can't possibly overwhelm for ingest. All your messy data, no pipelines. Well, you leverage things like S3 and EC2, and you do the different security things. You can go to environments say, you can't possibly overrun me, I could not say that. If I didn't literally build on the shoulders giants of all these public clouds. But the value. So if you're going to do hard tech as a startup, you're going to build, you're going to be the principles of Supercloud. Maybe they're not the same size of Supercloud just looking at Snowflake, but basically, you're going to leverage all that, you abstract it out and that's where you're able to have a lot of values at that. >> So let me ask you, so I don't know if there's a strict definition of Supercloud, We sort of put it out to the community and said, help us define it. So you got to span multiple clouds. It's not just running in each cloud. There's a metadata layer that kind of understands where you're pulling data from. Like you said you can pull data from Snowflake, it sounds like we're not running on Snowflake, correct? >> No, complimentary to them in their different customers. >> Yeah. Okay. >> They want to build on top of a data platform, data apps. >> Right. And of course they're going cross cloud. >> Right. >> Is there a PaaS layer in there? We've said there's probably a Super PaaS layer. You're probably not doing that, but you're allowing people to bring their own, bring your own PaaS sort of thing maybe. >> So we're a little bit different but basically we publish open APIs. We don't have a user interface. We say, keep the user interface. Again, we're solving the challenge of analytics at scale, we're not trying to retrain your analytics, either analysts or your DevOps or your SOV or your Secop team. They use the tools they already use. Elastic search APIs, SQL APIs. So really they program, they build applications on top of us, Equifax is a good example. Case said it coming out later on this week, after 18 months in production but, basically they're building, we provide the abstraction layer, the quote, I'm going to kill it, Jeff Tincher, who owns all of SREs worldwide, said to the effect of, Hey I'm able to rethink what I do for my data pipelines. But then he also talked about how, that he really doesn't have to worry about the data he puts in it. We deal with that. And he just has to, just query on the other side. That simplicity. We couldn't have done that without that. So anyway, what I like about the definition is, if you were going to do something harder in the world, why would you try to rebuild what Amazon, Google and Azure or Snowflake did? You're going to add things on top. We can still do intellectual property. We're still doing patents. So five grand patents all in this. But literally the abstraction layer is the simplification. The end users do not want to know that complexity, even though they ask the questions. >> And I think too, the other attribute is it's ecosystem enablement. Whereas I think, >> Absolutely >> in general, in the Multicloud 1.0 era, the ecosystem wasn't thinking about, okay, how do I build on top and abstract that. So maybe it is Multicloud 2.0, We chose to use Supercloud. So I'm wondering, we're at the security conference, >> RE: INFORCE is there a security Supercloud? Maybe Snyk has the developer Supercloud or maybe Okta has the identity Supercloud. I think CrowdStrike maybe not. Cause CrowdStrike competes with Microsoft. So maybe, because Microsoft, what's interesting, Merritt Bear was just saying, look, we don't show up in the spending data for security because we're not charging for most of our security. We're not trying to make a big business. So that's kind of interesting, but is there a potential for the security Supercloud? >> So, I think so. But also, I'll give you one thing I talked to, just today, at least three different conversations where everyone wants to log data. It's a little bit specific to us, but basically they want to do the security data lake. The idea of, and Snowflake talks about this too. But the idea of putting all the data in one repository and then how do you abstract out and get value from it? Maybe not the perfect, but it becomes simple to do but hard to get value out. So the different players are going to do that. That's what we do. We're able to, once you land it in your S3 or it doesn't matter, cloud of choice, simple storage, we allow you to get after that data, but we take the primitives and hide them from you. And all you do is query the data and we're spinning up stateless computer to go after it. So then if I look around the floor. There's going to be a bunch of these players. I don't think, why would someone in this floor try to recreate what Amazon or Google or Azure had. They're going to build on top of it. And now the key thing is, do you leave it in standard? And now we're open APIs. People are building on top of my open APIs or do you try to put 'em in a walled garden? And they're in, now your Supercloud. Our belief is, part of it is, it needs to be open access and let you go after it. >> Well. And build your applications on top of it openly. >> They come back to snowflake. That's what Snowflake's doing. And they're basically saying, Hey come into our proprietary environment. And the benefit is, and I think both can win. There's a big market. >> I agree. But I think the benefit of Snowflake's is, okay, we're going to have federated governance, we're going to have data sharing, you're going to have access to all the ecosystem players. >> Yep. >> And as everything's going to be controlled and you know what you're getting. The flip side of that is, Databricks is the other end >> Yeah. >> of that spectrum, which is no, no, you got to be open. >> Yeah. >> So what's going to happen, well what's happening clearly, is Snowflake's saying, okay we've got Snowpark. we're going to allow Python, we're going to have an Apache Iceberg. We're going to have open source tooling that you can access. By the way, it's not going to be as good as our waled garden where the flip side of that is you get Databricks coming at it from a data science and data engineering perspective. And there's a lot of gaps in between, aren't there? >> And I think they both win. Like for instance, so we didn't do Snowpark integration. But we work with people building data apps on top of Snowflake or data bricks. And what we do is, we can add value to that, or what we've done, again, using all the Supercloud stuff we're done. But we deal with the unstructured data, the four V's coming at you. You can't pipeline that to save. So we actually could be additive. As they're trying to do like a security data cloud inside of Snowflake or do the same thing in Databricks. That's where we can play. Now, we play with them at the application level that they get some data from them and some data for us. But I believe there's a partnership there that will do it inside their environment. To us they're just another large scaler environment that my customers want to get after data. And they want me to abstract it out and give value. >> So it's another repository to you. >> Yeah. >> Okay. So I think Snowflake recently added support for unstructured data. You chose not to do Snowpark because why? >> Well, so the way they're doing the unstructured data is not bad. It's JSON data. Basically, This is the dilemma. Everyone wants their application developers to be flexible, move fast, securely but just productivity. So you get, give 'em flexibility. The problem with that is analytics on the end want to be structured to be performant. And this is where Snowflake, they have to somehow get that raw data. And it's changing every day because you just let the developers do what they want now, in some structured base, but do what you need to do your business fast and securely. So it completely destroys. So they have large customers trying to do big integrations for this messy data. And it doesn't quite work, cause you literally just can't make the pipelines work. So that's where we're complimentary do it. So now, the particular integration wasn't, we need a little bit deeper integration to do that. So we're integrating, actually, at the data app layer. But we could, see us and I don't, listen. I think Snowflake's a good actor. They're trying to figure out what's best for the customers. And I think we just participate in that. >> Yeah. And I think they're trying to figure out >> Yeah. >> how to grow their ecosystem. Because they know they can't do it all, in fact, >> And we solve the key thing, they just can't do certain things. And we do that well. Yeah, I have SQL but that's where it ends. >> Yeah. >> I do the messy data and how to play with them. >> And when you talk to one of their founders, anyway, Benoit, he comes on the cube and he's like, we start with simple. >> Yeah. >> It reminds me of the guy's some Pure Storage, that guy Coz, he's always like, no, if it starts to get too complicated. So that's why they said all right, we're not going to start out trying to figure out how to do complex joins and workload management. And they turn that into a feature. So like you say, I think both can win. It's a big market. >> I think it's a good model. And I love to see Frank, you know, move. >> Yeah. I forgot So you AVMAR... >> In the day. >> You guys used to hate each other, right? >> No, no, no >> No. I mean, it's all good. >> But the thing is, look what he's done. Like I wouldn't bet against Frank. I think it's a good message. You can see clients trying to do it. Same thing with Databricks, same thing with BigQuery. We get a lot of same dynamic in BigQuery. It's good for a lot of things, but it's not everything you need to do. And there's ways for the ecosystem to play together. >> Well, what's interesting about BigQuery is, it is truly cloud native, as is Snowflake. You know, whereas Amazon Redshift was sort of Parexel, it's cobbled together now. It's great engineering, but BigQuery gets a lot of high marks. But again, there's limitations to everything. That's why companies like yours can exist. >> And that's why.. so back to the Supercloud. It allows me as a company to participate in that because I'm leveraging all the underlying pieces. Which we couldn't be doing what we're doing now, without leveraging the Supercloud concepts right, so... >> Ed, I really appreciate you coming by, help me wrap up today in RE:INFORCE. Always a pleasure seeing you, my friend. >> Thank you. >> All right. Okay, this is a wrap on day one. We'll be back tomorrow. I'll be solo. John Furrier had to fly out but we'll be following what he's doing. This is RE:INFORCE 2022. You're watching theCUBE. I'll see you tomorrow.

>>the Cube presents H p E discover 2022. Brought to you by H P E. >>Okay, we're back at HPD. Discovered 2022 This is Day Three. We're kind of in the mid point of day three. John Furry and Dave Volonte Wall to wall coverage. I think there are 14th hp slash hp Discover we've sort of documented the history of the company over the last decade. Plus, I'm not a is here is executive vice president at Infosys and Cejudo. Sankaran Kutty is the CEO and vice president of Infosys. Infosys doing some amazing work in the field with clients. Guys, Thanks for coming on the Cube. Thank >>you for the opportunity. >>Yeah, absolutely so. Digital transformation. It's all the buzz word kind of pre pandemic. It was sort of Yeah, you know, we'll get there a lot of lip service to it. Some Some started the journey and then, of course, pandemic. If you weren't digital business, you are out of business. What are the trends that you're seeing now that we're exiting the isolation economy? >>Yeah, um, again, as you rightly called out pre pandemic, it was all about using sort of you know innovation at scale as one of the levers for digital transformation. But if you look at now, post Pandemic, one of the things that we see it's a big trend is at a broad level, right? Digital transformation is not about cost. Take out. Uh, it's all about growth, right? So essentially, uh, like, uh, what we hear from most of the CEO s and most of the customers and most of the executives in the tech company, Digital transformation should be used for business growth. And essentially, it means three things that we see three trends in that space. One is how can you build better products and solutions as part of your transformation strategy? How can you basically use digital transformation to expand into new markets and new new territories and new regions? And the third is, how can you better the experience for your customers? Right. So I think that is broadly what we see as, uh, some other things. And essentially, if you have better customer experience, they will buy more. If you expand into new markets, your revenue will increase. If you actually build better products and solutions, consumers will buy it right, so It's basically like a sort of an economy that goes hand in hand. So I would say the trend is clearly going towards business growth than anything else when it comes to the, >>you know, follow up on that. We had I d. C on yesterday and they were sharing with some of their high level numbers. We've looked at this and and and it seems like I t spending is pretty consistent despite the fact that, for example, you know, the to see the consumer businesses sort of tanking right now. Are you seeing any pullback or any evidence that people are pulling the reins back on the digital transformation Or they just going because if they don't keep keep moving fast, they're gonna fall behind. What are you seeing there? Absolutely. >>In fact, you know what? What we call them as the secular headwinds, right? I mean, if you look at the headwinds here, we see digital transformation is in the minds of everybody, every customer, right. So while there are budget constraints, where are all these macro tailwinds as we call with respect to inflation, with respect to what's happening with Russia and Ukraine with respect to everything that's happening with respect to supply chain right. I think we see some of those tail headwinds. But essentially, digital transformation is not stopping. Everybody is going after that because essentially they want to be relevant in the market. And if they want to be relevant in the market, they have to transform. And if they have to transform, they have to adopt digital transformation. >>Basically, there's no hiding anymore. You know, hiding and you can't hide the projects and give lip service because there's evidence of what the consequences are. And it can be quantified. Yes, you go out of business, you lose money. You mentioned some of the the cost takeouts growth is yes. So I got given the trends and the headwinds and the tail winds. What are you guys seeing as the pattern of companies that came out of the pandemic with growth? And what's going on with that growth driver? What are the elements that are powering companies to grow? Is that machine learning? Is that cloud scales and integration? What are some of the key areas that's given that extra up into the right? >>Yes, I I would say there are six technologies that are defining how growth is being enabled, right? So I think we call it as cloud ai edge five g, Iot and of course, everything to do with a And so these are six technologies that are powering digital transformation. And, uh, one of the things that we are saying is more and more customers are now coming and saying that we want to use these six technologies to drive business outcomes. Uh, for example, uh, we have a very large oil and gas customer of ours who says that, you know, we want to basically use cloud as a lever to Dr Decarbonization. E S G is such a big initiative for everybody in the SGS in the minds of everybody. So their outcome of using technology is to drive decarbonization. And they don't make sure that, you know, they achieve the goals of E. S G. Right There is another customer of ours in the retail space. They are saying we want to use cloud to drive experience for our employees. So I would say that you know, there is pretty much, you know, all these drivers which are helping not just growing their business, but also bettering the experience and meeting some of the organisation goals that they have set up with respect to cloud. So I would say Cloud is playing a big role in every digital transformation initiative of the company. >>How do you spend your time? What's the role of the CEO inside of a large organisation like Infosys? >>So, um, one is in terms of bringing in an outside in view of how technology is making an impact to our customers. And I'm looking at How do we actually start liberating some of these technologies in building solutions, you know, which can actually drive value for our customers? That's one of the focus areas. You know what I do? Um, And if you look at some of the trends, you know what we have seen in the past years as well as what we're seeing now? Uh, there's been a huge spend around cloud which is happening with our customers and predominantly around the cloud Native application development, leveraging some of the services. What's available from the cloud providers like eh? I am l in Hyoty. Um, and and there's also a new trend. You know what we are seeing off late now, which is, um, in terms of improving the experience overall experience liberating some of the technologies, like technologies like block, block, chain as well as we are, we are right, and and this is actually creating new set of solutions. Um, new demands, you know, for our customers in terms of leveraging technologies like matadors leveraging technologies like factory photo. Um, and these are all opportunities for us to build solutions, you know, which can, you know, improve the time to market for our customers in terms of adopting some of these things. Because there has been a huge focus on the improved end user experience or improve experience improved, uh, productivity of, uh, employees, you know, which is which has been a focus. Uh, post pandemic. Right? You know, it has been something which is happening pre pandemic, but it's been accelerated Post pandemic. So this is giving an opportunity for for my role right now in terms of liberating these technologies, building solutions, building value propositions, taking it to our customers, working with partners and then trying to see how we can have this tightly integrated with partners like HP E in this case, and then take it jointly to the market and and find out you know, what's what's the best we can actually give back to our customers? >>You know, you guys have been we've been following you guys for for a long, long time. You've seen many cycles, uh, in the industry. Um, and what's interesting to get your reaction to what we're seeing? A lot of acceleration points, whether it's cloud needed applications. But one is the software business is no longer there. It's open source now, but cloud scale integrations, new hybrid environment kind of brings and changes the game, so there's definitely software plentiful. You guys are doing a lot of stuff with the software. How are customers integrated? Because seeing more and more customers participating in the open source community uh, so what? Red hat's done. They're transforming the open shift. So as cloud native applications come in and get scale and open source software, cloud scale performance and integrations are big. You guys agree with that? >>Absolutely. Absolutely. So if you if you look at it, um, right from the way we can't socialise those solutions, um, open source is something What we have embedded big way right into the solution. Footprint. What we have one is, uh, the ability for us to scale the second is the ability for us to bring in a level of portability, right? And the third is, uh, ensuring that there is absolutely no locking into something. What we're building. We're seeing this this being resonated by our customers to because one is they want to build a child and scalable applications. Uh, it's something where the whole, I would say, the whole dependency on the large software stacks. Uh, you know, the large software providers is likely diminishing now, right? Uh, it's all about how can I simplify my application portfolio Liberating some of the open source technologies. Um, how can I deploy them on a multi cloud world liberating open standards so that I'm not locked into any of these providers? Um, how can I build cloud native applications, which can actually enable portability? And how can I work with providers who doesn't have a lock in, you know, into their solutions, >>And security is gonna be embedded in everything. Absolutely. >>So security is, uh, emperor, right from, uh, design phase. Right? You know, we call it a secure by design And that's something What? We drive for our customers right from our solutions as well as for developing their own solutions >>as opposed to secure by bolt on after the fact. What is the cobalt go to market strategy? How does that affect or how you do business within the HP ecosystem? Absolutely. >>I think you know what we did in, uh, in 2000 and 20. We were the first ones, uh, to come out with an integrated cloud brand called Cobalt. So essentially, our thought process was to make sure that, you know, we talk one consistent language with the customer. There is a consistent narrative. There is a consistent value proposition that we take right. So, essentially, if you look at the Cobalt gold market, it is based on three pillars. The first pillar is all about technology solutions. Getting out of data centres migrating were close to cloud E r. P on Cloud Cloud, Native Development, legacy modernisation. So we'll continue to do that because that's the most important pillar. And that's where our bread and butter businesses right. The second pillar is, uh, more and more customers are asking industry cloud. So what are you specifically doing for my industry. So, for example, if you look at banking, uh, they would say we are focused on Modernising our payment systems. We want to reduce the financial risk that we have because of anti money laundering and those kind of solutions that they're expecting. They want to better the security portion. And of course, they want to improve the experience, right? So they are asking for each of these imperatives that we have in banking. What are some of those specific industry solutions that you are bringing to the table? Right. So that's the second pillar of our global go to market. And the third pillar of our go to market as soon as I was saying is looking at what we call us Horizon three offerings, whether it is metal wars, whether it is 13.0, whether it is looking at something else that will come in the future. And how do we build those solutions which can become mainstream the next 18 to 24 months? So that's essentially the global >>market. That's interesting. Okay, so take the banking example where you've got a core app, it's probably on Prem, and it's not gonna have somebody shoved into the cloud necessarily. But they have to do things like anti money, money laundering and know your ky. See? How are they handling that? Are they building micro services? Are you building for them microservices layers around that that actually might be in the cloud or cloud Native on Prem and Greenway. How is that? How are customers Modernising? >>Absolutely brilliant question. In fact, what we have done is, uh, as part of cobalt, we have something called a reference. Architecture are basically a blueprint. So if you go to a bank and you're engaging a banking executive, uh, the language that we speak with them is not about, uh, private cloud or public cloud or AWS or HP or zero, right? I mean, we talk the language that they understand, which is the banking language. So we take this reference architecture, and we say here is what your core architecture should look like. And, as you rightly called out, there is K. I see there is retail banking. There is anti money laundering. There is security experience. Uh, there are some kpi s and those kind of things banking a PSR open banking as we call, How do we actually bring our solutions, which we have built on open source and something that are specific to cloud and something that our cloud neutral and that's what we take them. So we built this array of solutions around each of those reference architectures that we take to our customers. >>Final question for you guys. How are you guys leveraging the H, P E and new Green Lake and all the new stuff they got here to accelerate the customers journey to edge the cloud? >>So I would say it on three areas right now. This is one is Obviously we are working very closely with HP in terms of taking out solutions jointly to the market and, um, leveraging the whole green late model and providing what I call it as a hyper scale of like experience for our customers in a hybrid, multi cloud world. That's the first thing. The second thing is Onion talked about the cobalt, right? It's an important, I would say, an offering from, uh, you know and offering around cloud from our side. So what we've done is we've closely integrated the assets. You know what I was referring to what we have in our cobalt, uh, under other Kobold umbrella very closely with the HP ecosystem, right? You know, it can be tools like the Emphasis Polly Cloud Platform or the Emphasis pollinate platform very tightly integrated with the HP stack, so that we could actually offer the value proposition right across the value chain. The thought of you know we have actually taken the industry period, like what again mentioned right in terms of rather than talking about a public cloud or a private cloud solution or an edge computing solution. We actually talk about what exactly are the problem statements? What is there in manufacturing today? Or it's there in financial industries today? Or or it's in a bank today or whatever it's relevant to the industry. That's an industry people. So we talk right from an industry problem and and and and and and build that industry, industry people solutions, leveraging the assets, what we have in the and the framework that we have within the couple, plus the integrated solutions. What we bring along with HB. That's that's Those are the three things, what we do along with >>it and that that industry pieces do. There's a whole data layer emerging those industries learning cos they're building their own clouds. Look, working with companies like you because they want to monetise. That's a big part of their digital strategy, guys. Thanks so much for coming on the cue. Thank you. Appreciate your time. Thank >>you. Thank you very much. Really appreciate. >>Thank you. Thank you for watching John and I will be back. John Ferrier, Development at HPD Discovered 2022. You're watching the queue? >>Yeah. >>Mm.

>>To the Seaport in Boston, Massachusetts, everybody's buzzing. The Bruins are playing tonight. They tied it up. The Celtics tied it up last night. We're excited. We don't talk about the red Sox. Red Sox are getting struggles, but you know, we have good distractions. Paul goer is here. He's the president and chief executive officer at red hat and also a Boston fan of great to see, of course, you too. >>Nice to see you guys, you know, it's been a, it's been a while. >><laugh> yeah, we saw you, you know, online and virtually for a couple of years there, but, uh, you know, we've been doing red hat summit for a long, long time. Yeah, of course we were talking earlier. It's just much more intimate, kind of a VIP event, a few more suit jackets here. You know, I got my tie on, so I don't get too much grief. I usually get grief when I wear a tie of red hat summit, but it's a different format this year. Compressed keynotes. Your keynote was great. The new normal, sometimes we call it the new abnormal <laugh>, uh, but you know, how do you feel? >>I, I, I, I feel great. First of all, you know, combination today, virtual audience in, in house audience here today. I think we're gonna see a lot of that in the future. I mean, we designed the event around that and I, I think it, I think it played pretty well. Kudos, kudos to our team. You're right. It's, it's, it's a bit more intimate even the way it was set up, but those are the conversations we like having with our customers and our partners, much more partner centric, uh, as well right now, as well. >>You know, we were talking about, you know, hybrid cloud. It was kind of, you know, it was a good marketing term. And, but now it's, it's, it's become the real thing. I've said many times the, the definition of cloud is changing. It's expanding it's no, the cloud is no longer this remote set of services, you know, somewhere up in the cloud, it's on prem connecting to a cloud across clouds, out to the edge and you need capabilities that work everywhere. And that's what red hat did. The market's just swimming toward you. >>Yeah. I mean, you look at it, you know, I was, uh, you know, if you look at it, you know, the clouds are powerful unto themselves, right? The clouds are powerful unto themselves. They're all different. Right? And that that's, I mean, hardware vendors were, were similar, but different, same thing. You need that connective tissue across, across the whole thing. I mean, as I said, in my keynote today, I remember talking to some of our CIOs and customers 10 years ago and they said, we're going 90% of our apps tomorrow to one cloud. And we knew that wasn't practical because of course the clouds are built from Linux. So we knew it was underneath the hood and, and what's happened. It's taken some time, but as they started to get into that, they started to see, well, maybe one cloud's more suited for one application than the other, these apps. You may have to keep on premise, but you know, what really exploded at the, the, the hybrid thing, the edge. Now they're putting things at the edge, the GM announcement tell you, I know you're gonna talk to Francis. Yeah, yeah. Later. I mean, that's, that's a mini data center in, in every cloud, but that's still under the purview of the CIO, you know? So, so, so that's what hybrid's all about is tying all those pieces together, cuz it got more powerful, but it also more complex. >>You mentioned being the connective tissue, but we don't hear as much talk about multi-cloud seems to me, as we used to this conference has been all about hybrid cloud. You don't really talk about multi-cloud. How important is that to the red hat strategy, being that consistent layer? >>It's probably my mistake or our mistake because multi's more prevalent and more important than just hybrid alone. I mean, hybrid hybrid started from on-premise to one part to any one particular cloud. That was the, the first thought of hybrid. But as I said, as, as, as um, some of the cloud providers became so big, um, every, every CIO I talked to, whether they know whether they know it or not most do are in a multi environment for a whole bunch of reasons, right. You know, one cloud provider might be better in a different part of the world. And another one cloud provider might have a better service than another. Some just don't like to be stuck to one it's it's really hybrid multi. We should, we should train ourselves to every time we say hybrid, say multi, because that's really, that's really what it is. It, I think that happened overnight with, with Microsoft, you know, with Microsoft they've, they've, they've really grown over the last few years, so has Amazon for that matter. But Microsoft really coming up is what really made it a, a high, a multi world. >>Microsoft's remarkable what, what they're doing. But I, I, I have a different thinking on this. I, I heard Chuck Whitten last week at, at the Dell conference he used, he said used the phrase a multicloud, uh, by default versus multi-cloud by design. And I thought that was pretty interesting because I've said that multi-cloud is largely multi-vendor, you know? And so hybrid has implications, right? We, we bring and a shesh came up with a new term today. Metacloud I use Supercloud I like Metacloud better because something's happening, Paul. It feels like there's this layer abstraction layer that the underlying complexity is hidden. Think about OpenShift. Yeah. I could buy, I could get OpenShift for free. Yeah. I mean, I could, and I could cobble together and stitch together at 13, 15 dozens of different services and replicate, but I don't, I don't want that complexity. I want you to hide that complexity. I want, I'd rather spend money on your R and D than my engineering. So something's changing. It feels like >>You buy that. I totally buy that. I mean, you know, I, I, I'm gonna try to not make this sound like a marketing thing because it's not, not fair enough. Right. I mean, I'm engineer at heart, you know that, so, >>Okay. >>I really look to what we're trying to do is we're building a hybrid multi cloud. I mean that we, I look at us as a cloud provider spanning the hybrid multi all the way out to the edge world, but we don't have the data centers in the back. Like the cloud providers do in and by that is you're seeing our products being consumed more like cloud services because that's what our customers are demanding. Our, our products now can be bought out of the various marketplaces, et cetera. You're seeing different business models from us. So, uh, you're seeing, uh, committed spend, for example, like the cloud providers where a customer will buy so much up front and sort of just work it down. You're seeing different models on how they're consumed, consumption, based pricing. These, these are all things that came from the cloud providers and customers buying like that. >>They now want that across their entire environment. They don't wanna buy differently on premise or in one cloud and they don't wanna develop differently. They don't wanna operate differently. They don't wanna have to secure it differently. Security's the biggest thing with, with our, with our customers, because hybrid's powerful, but you no longer have the, you know, your security per perimeter, no longer the walls of your data center. You know, you're, you're responsible as a CIO. You're responsible for every app. Yeah. No matter where it's running, if that's the break in point, you're responsible for that. So that's why we've done things like, you know, we cried stack rocks. We've, we've built it into the container Kubernetes platform that spans those various footprints because you no longer can just do perimeter security because the perimeter is, is very, very, very large right now >>Diffuse. One of the thing on the multi-cloud hyper skills, I, I, red hat's never been defensive about public cloud. You, I think you look at the a hundred billion dollars a year in CapEx spend that's a gift to the industry. Not only the entire it industry, but, but the financial services companies and healthcare companies, they can build their own hybrid clouds. Metacloud super clouds taking advantage of that, but they still need that connective tissue. And that's where >>We products come in. We welcome our customers to go to, to the public cloud. Um, uh, look, it's it's. I said a long time ago, we said a long time it was gonna be a hybrid. Well, I should have said multi anybody said hybrid, then it's gonna be a hybrid world. It is. And it doesn't matter if it's a 20, 80, 80, 20, 40, 60, 60, 40. It's not gonna be a hundred percent anywhere. Yeah. And, and so in that, in that definition, it's a hybrid multi world. >>I wanna change the tune a little bit because I've been covering IBM for 40 years and seen a lot of acquisitions and see how they work. And usually it follows the same path. There's a commitment to leaving the acquire company alone. And then over time that fades, the company just becomes absorbed. Same thing with red hat. It seems like they're very much committed to, to, to leaving you alone. At least they said that upon the acquisition, have they followed through on that promise? >>I have to tell you IBM has followed through on every commitment they've made, made to us. I mean, I, I owe it, I owe a lot of it to Arvin. Um, he was the architect of the deal, right. Um, we've known each other for a long time. Um, he's a great guy. Um, he, uh, he, he believes in it. It's not, he's not just doing it that way because he thinks, um, something bad will happen if he doesn't, he's doing it that way. Cuz he believes in that our ecosystem is what made us. I mean, I mean, even here it's about the partners in the ecosystem. If you look at what made REL people think what made red hat as a company was support, right. Support's really important. Small piece of the value proposition life cycle supports certainly their life cycle a 10 year life cycle just came out of a, a, a customer conference asking about the life cycle and could we extend it to 15 years? You know? Um, the ecosystem is probably the most important part of, of, of, of the, of the overall value proposition. And Arvin knows in IBM knows that, you know, we have to be neutral to be able to do everything the same for all of our ecosystem partners. Some that are IBM's competitors, even. So, >>So we were noticing this morning, I mean, aside from a brief mention of power PC and the IBM logo during, at one point, there was no mention of IBM during the keynote sessions this morning. Is that intentional? Or is that just >>No, no, it it's, it's not intentional. I mean, I think that's part of, we have our strategy to drive and we're, we're driving our, our strategy. We, we, we IBM great partner. We look at them as a partner just as we do our, our many other partners and we won't, you know, we wouldn't, we wouldn't do something with our products, um, for I with IBM that we wouldn't offer to our, our entire ecosystem. >>But there is a difference now, right? I don't know these numbers. Exactly. You would know though, but, but pre 2019 acquisition red hat was just, I think north of 3 billion in revenue growing at maybe 12% a year. Something like that, AR I mean, we hear on the earnings calls, 21% growth. I think he's publicly said you're north of 5 billion or now I don't know how much of that consulting gets thrown in. IBM likes to, you know, IBM math, but still it's a much bigger business. And, and I wonder if you could share with us, obviously you can't dig into the numbers, but have you hired more people? I would imagine. I mean, sure. Like what's been different from that standpoint in terms of the accelerant to your >>Business. Yeah. We've been on the same hiring cycle percentage wise as, as we, we always were. I mean, I think the best way to characterize the relationship and where they've helped is, um, Arvin, Arvin will say, IBM can be opinionated on red hat, but not the other way around <laugh>. So, so what that, what that means is they had a lot of, they had, they had a container based Linux platform. Yeah, right, right. They, they had all their, they were their way of moving to the cloud was that when we came in, they actually stopped that. And they standardized on OpenShift across all of their products. We're now the vehicle that brings the blue software products to the hybrid cloud. We are that vehicle that does it. So I think that's, that's how, that's how they, they look about it. I mean, I know, I mean in IBM consulting, I know, I know they have a great relationship with Microsoft of course. >>Right. And so, so that's, that's how to really look at it. They they're opinionated on us where we not the other way around, but that, but they're a great partner. And even if we're at two separate companies, we'd do be doing all the same things we're doing with them. Now, what they do do for us can do for us is they open a lot of doors in many cases. I mean, IBM's been around for over a hundred years. So in many cases, they're in, in, in the C-suite, we, we may be in the C suite, but we may be one layer down, one, two layers down or something. They, they can, they help us get access. And I think that's been a, a part of the growth as well as is them talking into their, into, into their >>Constituents. Their consulting's one of the FA if not the fastest growing part of their business. So that's kind of the tip of the spear for application modernization, but enough on IBM you said something in your keynote. That was really interesting to me. You said, you, you, you didn't use the word hardware Renaissance, but that my interpretation was you're expecting the next, you know, several years to be a hardware Renaissance. We, we certainly have done relationships with arm. You mentioned Nvidia and Intel. Of course, you've had relationships with Intel for a long time. And we're seeing just the spate of new hardware developments, you know, does hardware matter? I'll ask you, >>Oh, oh, I mean the edge, as I said, you're gonna see hardware innovation out in the edge, software innovation as well. You know, the interesting part about the edge is that, you know, obviously remade red hat. What we did with REL was we did a lot of engineering work to make every hardware architecture when, when it was, when, when the world was just standalone servers, we made every hardware architecture just work out of the box. Right? And we did that in such, because with an open source development model. So embedded in our psyche, in our development processes is working upstream, bringing it downstream 10 years, support all of that kind of thing. So we lit up all that hardware. Now we go out to the edge, it's a whole new, different set of hardware innovation out at the edge. We know how to do that. >>We know how to, we know how to make hardware, innovation safe for the customer. And so we're bringing full circle and you have containers embedded in, in Linux and REL right now as well. So we're actually with the edge, bringing it all full circle back to what we've been doing for 20 plus years. Um, on, on the hardware side, even as a big part of the world, goes to containers and hybrid in, in multi-cloud. So that's why we're so excited about, about, about the edge, you know, opportunity here. That's, that's a big part of where hybrid's going. >>And when you guys talk about edge, I mean, I, I know a lot of companies will talk about edge in the context of your retail location. Okay. That's fine. That's cool. That's edge or telco that that's edge. But when you talk about, um, an in vehicle operating system, right. You know, that's to me the far edge, and that's where it gets really interesting, massive volumes, different architectures, both hardware and software. And a lot of the data may stay. Maybe it doesn't even get persisted. May maybe some comes back to the club, but that's a new >>Ballgame. Well, think about it, right? I mean, you, if you listen, I think you, right. My talk this morning, how many changes are made in the Linux kernel? Right? You're running in a car now, right? From a safety perspective. You wanna update that? I mean, look, Francis talked about it. You'll talk to Francis later as well. I mean, you know, how many, how many in, in your iPhone world Francis talked about this this morning, you know, they can, they can bring you a whole new world with software updates, the same in the car, but you have to do it in such a way that you still stay with the safety protocols. You're able to back things out, things like that. So it's open source, but getting raw upstream, open source and managing itself yourself, I just, I'm sorry. It takes a lot of experience to be able to be able to do those kinds of things. So it's secure, that's insecure. And that's what that's, what's exciting about it. You look at E the telco world look where the telco world came from in the telco world. It was a hardware stack from the hardware firmware operating system, every service, whether it was 9 1, 1 or 4, 1, 1 was its own stack. Yep. In the 4g, 3g, >>4g >>Virtualized. Now, now it's all software. Yeah. Now it's all software all the way out to the cell tower. So now, so, so now you see vendors out there, right? As an application, as a container based application, running out, running in the base of a cell tower, >>Cell tower is gonna be a little mini data >>Center. Yeah, exactly. Because we're in our time here asking quickly, because you've been at red hat a long time. You, you, you, uh, architected a lot of the reason they're successful is, is your responsibility. A lot of companies have tried to duplicate the red hat model, the, the service and support model. Nobody has succeeded. Do you think anybody ever will or will red hat continue to be a unicorn in that respect? >>No, I, I, I think, I think it will. I think open source is making it into all different parts of technology. Now I have to tell you the, the reason why we were able to do it is we stayed. We stayed true to our roots. We made a decision a long time ago that we weren't gonna put a line, say everything below the line was open and above the line was closed. Sometimes it's hard sometimes to get a differentiation with the competition, it can be hard, but we've stayed true to that. And I, to this day, I think that's the thing that's made us is never a confusion on if it's open or not. So that forces us to build our business models around that as well. But >>Do you have a differentiated strategy? Talk about that. What's your what's your differentiation >>Are, are, well, I mean, with the cloud, a differentiation is that common cloud platform across I differentiate strategy from an open source perspective is to, to sort make open source consumable. And, and it's even more important now because as Linux Linux is the base of everything, there's not enough skills out there. So even, even a container platform like open source op like OpenShift, could you build your own? Certainly. Could you keep it updated? Could you keep it updated without breaking all the applications on top? Do you have an ecosystem around it? It's all of those things. It was, it was the support, the, the, the hardening the 10 year to predictability the ecosystem. That was, that was, that is the secret. I mean, we even put the secret out as open. >>Yeah, <laugh> right. Free, like a puppy, as they say. All right, Paul, thanks so much for coming back in the cubes. Great to see you face to face. Nice to see you guys get it. All right. Keep it right there. Dave Valante for Paul Gill, you're watching the cubes coverage of red hat summit, 2022 from Boston. Be right back.

>> the CUBE presents Dell Technologies World brought to you by Dell. >> Hey everyone, welcome back to the CUBE'S coverage day one, Dell Technologies World 2022 live from The Venetian in Las Vegas. Lisa Martin, with Dave Vellante. We've been here the last couple of hours. You can hear probably the buzz behind me. Lots of folks here, we're think around seven to eight thousand folks in this solution expo, the vibe is awesome. We've got two guests helping to round out our day one coverage. Ryan Fournier joins us, senior director of product management Edge Solutions at Dell Technologies. And MuneyB Minttazuddin vice president of Edge Computing at VMware. Guys, welcome to the program. >> Oh, glad to be here. >> Yeah. >> Isn't it great to be here in person? >> Oh man, yes. >> The vibe, the vibe of day one is awesome. >> Yes. >> Oh yeah. >> I think it's fantastic. >> Like people give energy off to each other, right? >> Absolutely. So lots of some good news coming out today so far on day one. Let's talk about, Ryan let's start with you. With Edge, it's not new. We've been talking about it for a while, but what are some of the things that are new? What are some of the key trends that you are seeing that are driving changes at the Edge? >> Great, good question. We've been talking to a lot of customers. Okay, a lot of the customers you know, the different verticals really find that is a common theme happening around a massive digital transformation and really based on the pandemic, okay. Which caused some acceleration in some, but also not, but many are kind of laggers left behind. And one primary reason is the culture of the OT, IT, you know, lack of barriers or something like that. The OT is obviously the business outcomes, okay. Focused where the IT is more enabling the function and it'll take retail. For example, that's accelerated a significant usage of an in-store frictionless experience, okay. As well as supply chain automation, warehousing logistics, connected inventory, a lot of the new use cases in this new normal post that pandemic. It's really that new retail operating landscape. >> Consumers we are so demanding, we want the same experience that we have online and we want that in the store and that's really driving a lot of this out of consumer demand. >> Oh yeah, no. I think, you know, retail you know, the way you shop for milk and bread change during the pandemic, right? There was pre-pandemic. The online shopping in the United States was only 5%, but during the pandemic and afterwards that's going to caught up to 25, 30%. That's huge. How do you bring new processes in? How do you create omnichannel consumer experiences where online well as physical are blended together? Becomes a massive challenge for the retailers. So yes, Edge has been there for a long time. Innovation hasn't happened, but a simple credit card swipe When you used to pre-pandemic, just to go do your checkout now has become into a curbside pickup. Integration with like, it's just simple payment card processing is not complicated like, you know, crazy. So people are forced to go in a way and that's happening in manufacturing because they're supply chain issues, could be not. So a lot of that has accelerated this investment and what's kind of driving Edge Computing is if everything ran out of the cloud, then you almost need infinite bandwidth. So suddenly people are realizing that everything runs out of cloud. I can't process my video analytics in a store. That's a lot of video, right? >> So we often ask ourselves, okay, who's going to win the edge? You know, we have that conversation. The cloud guys? VMware? You know, Dell? How are they going to go at it? And so to your point, you're not going to do a round trip to the cloud too expensive, too slow. Now cloud guys will try to bring their cloud basically on prem or out to the edge. You're kind of bringing it from the data center. So how do you see that evolution? >> No, great question. As the edge market happens, right? So there's market data now which says enterprise edge workloads in the next five years are going to be the fastest growing workloads. But then you have different communities coming to solve that problem. Like you just said, John is, you know, hyperscalers are going, Hey, all of the new workloads were built on us, let's bring them to the edge. Data center workloads move to the edge. >> Now important community here are, you know, Telcos and Service Providers because they have assets that are highly distributed at the edge. However, they're networking assets like cell towers and stuff like that. There's opportunity to convert them into computer and storage assets. So you can provide edge computing POPs. So you're seeing a convergence of lot of industry segments, traditional IT, hyperscalers, telcos, and then OT like Ryan pointed out is naturally transforming itself. There's almost this confluence of this pot where all these different technologies need to come together. From VMware and Dell perspective, our mission is a multi-cloud edge. We want to be able to support multi-cloud services because you've heard this multiple times, is at the edge consumers and customers will require services from all the hyperscalers. They don't want buy a one hyperscaler suit to suit solution. They want to mix and match. So not bound. We want be multi-cloud south bound to support IT and OT environments. So that becomes our value proposition in the middle. >> Yep. >> So Ryan, you were talking about that IT, OT schism. And we talk about that a lot. I wonder if you could help us parse that a little bit, because you were using, for instance retail, as an example. Sometimes I think about in the industrial. >> And I think the OT people are kind of like having an engineering mindset. Don't touch my stuff. Kind of like the IT guys too, but different, you know. So there's so much opportunity at the edge. I wonder how you guys think about that? How you segment it? How you prioritize it? Obviously retail telco are big enough. >> Yep. >> That you can get your hands around them, but then there's to your point about all this data that's going to going to compute. It's going to come in pockets. And I wonder how you guys think about that schism and the other opportunity. >> Yeah, out there. It's also a great question, you know, in manufacturing. There's the true OT persona. >> Yeah. >> Okay, and that really is focused on the business outcomes. Things like predictive maintenance use cases, operational equipment effectiveness, like that's really around bottleneck analysis, and the process that go through that. If the plant goes down, they're fine, okay. They can still work on their own systems, but they're not needing that high availability solution. But they're also the decision makers and where to buy the Edge Computing, okay. So we need to talk more to the OT persona from a Dell perspective, okay. And to add on to Ryan, right. So industrial is an interesting challenge, right? So one of the things we did, and this is VMware and Dell working together at vMware it was virtual. We announced something called edge compute stack. And for the first time in 23 years of vMware history, we made the hypervisor layer real-time. >> Yep. >> What that means is in order to capture some of these OT workloads, you need to get in and operate it between the industrial PC and the program of logical controllers at a sub millisecond performance level, because now you're controlling robotic arms that you cannot miss a beat. So we actually created this real time functionality. With that functionality in the last six months, we've been able to virtualize PLCs, IPCs. So what I'm getting at is we're opening up an entire wide space of operational technology workloads, which we was not accessible to our market for the last 20 plus years. >> Now we're talking. >> Yeah. And that allows us that control plane infrastructure to edge compute. There's purpose built for edge allows us to pivot and do other solutions like analytics with the adoption of AI Analytics with our recent announcement of Deep North, okay. That provides that in store video analytics functionality. And then we also partner with PTC based on a manufacturing solution, working with that same edge compute stack. Think of that as that control plane, where again, like I said, you can pivot off a different solutions. Okay, so we leverage PTCs thing works. >> So, okay, great. So I wanted to go to that. So real-time's really interesting. >> 'Cause most of much of AI today is modeling done in the cloud. >> Yes. >> The real opportunity is real time inferencing at the edge. >> You got it. >> Okay, now this is why this gets so interesting. And I wonder if Project Monterey fits into this at all. because I feel like so why did Intel win? Intel won, it crushed all the Unix systems out there because it had PC volumes. And the edge volume's going to dwarf anything we've ever seen before. >> Yeah. >> So I feel like there's this new cocktail, you guys describe this convergence and this mixture and it's unknown. What's going to happen? That's why Project Monterey is so interesting. >> Of course. >> Yeah. >> Right? Because you're bringing together kind of hedging a lot of bets and serving a lot of different use cases. Maybe you could talk about where that might fit here. >> Oh absolutely. So the edge compute stack is made up of vSphere, Tanzu, which is vSphere's you know, VM container and Tanzu's our container technology and vSphere contains Monterey in it, right. And we've added vSAN a for storage at the edge. And connectivity is SD-WAN because a lot of the times it's far location. So you're not having a large footprint, you have one or two hoses, it's more wide area, narrow area. So the edge compute stack supports real-time, non-real-time time workloads. VMs and containers, CPU GPU, right. >> NPU, accelerators, >> NPU, DPU all of them, right. Because what you're dealing with here is that inferencing real time, because to Ryan's point, when you're doing predictive maintenance, you got to pick these signals up in like milliseconds. >> Yes. >> So we've gone our stack down to microseconds and we pick up and inform because if I can save this predictive maintenance in two seconds, I save millions of dollars in you know, wastage of product, right? >> And you may not even persist that data, right? You might just let it go, I mean, how much data does Tesla save? Right? I mean. >> You're absolutely right. A lot of the times, all you're doing is this volume of data coming at you. You're matching it to an inferencing pattern. If it doesn't match, you just drop, right. It's not persistent, but the moment you hit a trigger, immediately everything lights go off, you're login, you're applying outcome. So like super interesting at the edge. >> And the compute is going to go through the roof. So yeah, my premise is that, you know, general purpose x86 running SAP is not going to be the architecture for the edge. >> You're absolutely right. >> Going to be low cost, low power, super performance. 'Cause when you combine the CPU, GPU, NPU, you're going to blow away the performance that we've ever seen on the curves. >> There's also a new application pattern. I've called out something called edge-native applications. We went through this client-server architecture era. We built all this, you know, a very clear in architecture. We went through cloud native where everything was hyperscaled in the cloud. Both of the times we optimize our own compute. >> Yeah. >> At the edge, we got to optimize our owns data because it's not ephemeral compute that you have in hyperscale compute space, you have ephemeral data you got to deal with. So a new nature of application workloads are emerging. We call it edge-native apps. >> Yep. >> And those have very different characteristics, you know, to client server apps or you know, cloud native apps, which is amazing. It's driven by data analysts like developers, not like dot net Java developers. It's actually data analysts who are trying to mine this with fast patents and come out with outcomes, right? >> Yeah, I love that edge-native apps Lisa, that's a new term for me. >> Right, just trademark it on me. I made made it up. (panel laughing) >> Can you guys talk about a joint customer that you've really helped to dramatically transform in the last six months? >> You want to shout or I can go-- >> I think my industry is fine. >> Yeah, yeah. So, you know, at VMworld we talked about Oshkosh, which is again, like in the manufacturing space, we have retailers and manufacturers and we also brought in, you know, Proctor and Gamble and et cetera, et cetera, right? So the customers look at us jointly because you know edge doesn't happen in its own silo. It's a continuum from the data center to the cloud, to the edge, right. There's the continuum exists. So if only edge was in its own silo, you would do things. But the key thing about all of this, there's no right place, it's about that workload placement. Where do I place the workload for the most optimal business outcome? Now for real-time applications, it's at the edge. For non-real-time stuff it could be in the data center, it could be in a cloud. It doesn't really matter, where VMware and Dell strengths comes in with Oshkosh or all of those folks. We have the end-to-end. From you want place it in the data center, You want to place it in your charge to public cloud, You want to derive some of these applications. You want to place it at the far edge, which is a customer prem or a near edge, which is a telco. We've done joint announcements with telcos, like South Dakota Telecom, where we've taken their cell towers and converted them into compute and storage. So they can actually store it at the near edge, right. So this is 5G solutions. I also own the 5G part of the vMware business, but doesn't matter. Compute network storage, we got to find the right mix for placing the workload at the right place. >> You call that the near edge. I think of it as the far edge, but that's what you mean, right? >> Yeah, yeah. >> Way out there in the (mumbles), okay. >> It's all about just optimizing operations, reducing cost, increasing profitability for the customer. >> So you said edge, not its own silo. And I agree. >> it's not a silo. Is mobile a valid sort of example or a little test case because when we developed mobile apps, it drove a lot of things in the data center and in the cloud. Is that a way to think of about it as opposed to like PCs work under their own silo? Yeah, we connect to the internet, but is mobile a reasonable proxy or no? >> Mobile is an interesting proxy. When you think about the application again, you know, you got a platform by the way, you'll get excited by this. We've got mobile developers, mobile device manufacturers. You can count them in your fingers. They want to now have these devices sitting in factory floors because now these devices are so smart. They have sensors, temperature controls. They can act like these multisensory device at the edge, but the app landscape is quite interesting. I think John, where you were going was they have a very thin shim app layer that can be pushed from anywhere. The, the notion of these edge-native applications could be virtual machines, could be containers, could be, you know, this new thing called Web Assembly Wasm, which is a new type of technology, very thin shim layer which is mobile like app layer. But you know, all of these are combination of how these applications may get expressed. The target platforms could be anywhere from mobile devices to IOT gateways, to IOT devices, to servers, to, you know, massive data centers. So what's amazing is this thing can just go everywhere. And our goal is consistent infrastructure, consistent operations across the board. That's where VMware and Dell win together. >> Yeah. >> Yeah, excellent. And I was just talking to a customer today, a major airline manufacturer, okay. About their airport and the future with the mobile device just being frictionless, okay, no one wants to touch anything anymore. You can use your mobile device to do your check-in and you've got to you avoid kiosks, okay. So they're trying to figure out how to get rid of the kiosk. Now you need a kiosk for like checking baggage, okay. You can't get in the way of that, but at least that frictionless experience, for that airport in the future, but it brings in some other issues. >> It does, but I like the sound of that. Last question guys, where can customers go to learn more information about the joint solutions? >> So you can go to like our public websites obviously search on edge. And if you hear at the show, there's a lot of hands on labs, okay. There's a booth over there. A lot of Edge Solutions that we offer. >> Yeah, no, this is I guess as Ryan pointed our websites have these. We've had a lot of partnership in announcements together because you know, one of the things as we've expressed, manufacturing, retail, you know, when you get in the use cases, they involve ISPs, right? So they you know, they bring the value of you know, not just having a horizontal AI platform. We like opinionated models of fraud detection. So we're actually working with ecosystem of partners to make this real. >> So we may even hear more. >> The rich vertical solution, I call it the ISVs. They enrich our vertical solutions. >> Right. >> Oh, WeMo is going to be revolutionary. >> All right, can't wait. Guys thank you so much for joining David and me today and talking about what Dell and vMware are doing together and helping retailers manufacturers really convert the edge to incredible success. We appreciate your time. >> Thank you very much. Thanks Lisa, thanks John for having us. >> For Dave Vellante, I'm Lisa Martin. You're watching the CUBE. We are wrapping up day one of our coverage of Dell Technologies World 2022. We'll be back tomorrow, John Farrer and Dave Nicholson will join us. We'll see you then. (soft music)

>>Alteryx is a company with a long history that goes all the way back to the late 1990s. Now the one consistent theme over 20 plus years has been that Ultrix has always been a data company early in the big data and Hadoop cycle. It saw the need to combine and prep different data types so that organizations could analyze data and take action Altrix and similar companies played a critical role in helping companies become data-driven. The problem was the decade of big data, brought a lot of complexities and required immense skills just to get the technology to work as advertised this in turn limited, the pace of adoption and the number of companies that could really lean in and take advantage of the cloud began to change all that and set the foundation for today's theme to Zuora of digital transformation. We hear that phrase a ton digital transformation. >>People used to think it was a buzzword, but of course we learned from the pandemic that if you're not a digital business, you're out of business and a key tenant of digital transformation is democratizing data, meaning enabling, not just hypo hyper specialized experts, but anyone business users to put data to work. Now back to Ultrix, the company has embarked on a major transformation of its own. Over the past couple of years, brought in new management, they've changed the way in which it engaged with customers with the new subscription model and it's topgraded its talent pool. 2021 was even more significant because of two acquisitions that Altrix made hyper Ana and trifecta. Why are these acquisitions important? Well, traditionally Altryx sold to business analysts that were part of the data pipeline. These were fairly technical people who had certain skills and were trained in things like writing Python code with hyper Ana Altryx has added a new persona, the business user, anyone in the business who wanted to gain insights from data and, or let's say use AI without having to be a deep technical expert. >>And then Trifacta a company started in the early days of big data by cube alum, Joe Hellerstein and his colleagues at Berkeley. They knocked down the data engineering persona, and this gives Altryx a complimentary extension into it where things like governance and security are paramount. So as we enter 2022, the post isolation economy is here and we do so with a digital foundation built on the confluence of cloud native technologies, data democratization and machine intelligence or AI, if you prefer. And Altryx is entering that new era with an expanded portfolio, new go-to market vectors, a recurring revenue business model, and a brand new outlook on how to solve customer problems and scale a company. My name is Dave Vellante with the cube and I'll be your host today. And the next hour, we're going to explore the opportunities in this new data market. And we have three segments where we dig into these trends and themes. First we'll talk to Jay Henderson, vice president of product management at Ultrix about cloud acceleration and simplifying complex data operations. Then we'll bring in Suresh Vetol who's the chief product officer at Altrix and Adam Wilson, the CEO of Trifacta, which of course is now part of Altrix. And finally, we'll hear about how Altryx is partnering with snowflake and the ecosystem and how they're integrating with data platforms like snowflake and what this means for customers. And we may have a few surprises sprinkled in as well into the conversation let's get started. >>We're kicking off the program with our first segment. Jay Henderson is the vice president of product management Altryx and we're going to talk about the trends and data, where we came from, how we got here, where we're going. We get some launch news. Well, Jay, welcome to the cube. >>Great to be here, really excited to share some of the things we're working on. >>Yeah. Thank you. So look, you have a deep product background, product management, product marketing, you've done strategy work. You've been around software and data, your entire career, and we're seeing the collision of software data cloud machine intelligence. Let's start with the customer and maybe we can work back from there. So if you're an analytics or data executive in an organization, w J what's your north star, where are you trying to take your company from a data and analytics point of view? >>Yeah, I mean, you know, look, I think all organizations are really struggling to get insights out of their data. I think one of the things that we see is you've got digital exhaust, creating large volumes of data storage is really cheap, so it doesn't cost them much to keep it. And that results in a situation where the organization's, you know, drowning in data, but somehow still starving for insights. And so I think, uh, you know, when I talk to customers, they're really excited to figure out how they can put analytics in the hands of every single person in their organization, and really start to democratize the analytics, um, and, you know, let the, the business users and the whole organization get value out of all that data they have. >>And we're going to dig into that throughout this program data, I like to say is plentiful insights, not always so much. Tell us about your launch today, Jay, and thinking about the trends that you just highlighted, the direction that your customers want to go and the problems that you're solving, what role does the cloud play in? What is what you're launching? How does that fit in? >>Yeah, we're, we're really excited today. We're launching the Altryx analytics cloud. That's really a portfolio of cloud-based solutions that have all been built from the ground up to be cloud native, um, and to take advantage of things like based access. So that it's really easy to give anyone access, including folks on a Mac. Um, it, you know, it also lets you take advantage of elastic compute so that you can do, you know, in database processing and cloud native, um, solutions that are gonna scale to solve the most complex problems. So we've got a portfolio of solutions, things like designer cloud, which is our flagship designer product in a browser and on the cloud, but we've got ultra to machine learning, which helps up-skill regular old analysts with advanced machine learning capabilities. We've got auto insights, which brings a business users into the fold and automatically unearths insights using AI and machine learning. And we've got our latest edition, which is Trifacta that helps data engineers do data pipelining and really, um, you know, create a lot of the underlying data sets that are used in some of this, uh, downstream analytics. >>Let's dig into some of those roles if we could a little bit, I mean, you've traditionally Altryx has served the business analysts and that's what designer cloud is fit for, I believe. And you've explained, you know, kind of the scope, sorry, you've expanded that scope into the, to the business user with hyper Anna. And we're in a moment we're going to talk to Adam Wilson and Suresh, uh, about Trifacta and that recent acquisition takes you, as you said, into the data engineering space in it. But in thinking about the business analyst role, what's unique about designer cloud cloud, and how does it help these individuals? >>Yeah, I mean, you know, really, I go back to some of the feedback we've had from our customers, which is, um, you know, they oftentimes have dozens or hundreds of seats of our designer desktop product, you know, really, as they look to take the next step, they're trying to figure out how do I give access to that? Those types of analytics to thousands of people within the organization and designer cloud is, is really great for that. You've got the browser-based interface. So if folks are on a Mac, they can really easily just pop, open the browser and get access to all of those, uh, prep and blend capabilities to a lot of the analysis we're doing. Um, it's a great way to scale up access to the analytics and then start to put it in the hands of really anyone in the organization, not just those highly skilled power users. >>Okay, great. So now then you add in the hyper Anna acquisition. So now you're targeting the business user Trifacta comes into the mix that deeper it angle that we talked about, how does this all fit together? How should we be thinking about the new Altryx portfolio? >>Yeah, I mean, I think it's pretty exciting. Um, you know, when you think about democratizing analytics and providing access to all these different groups of people, um, you've not been able to do it through one platform before. Um, you know, it's not going to be one interface that meets the, of all these different groups within the organization. You really do need purpose built specialized capabilities for each group. And finally, today with the announcement of the alternates analytics cloud, we brought together all of those different capabilities, all of those different interfaces into a single in the end application. So really finally delivering on the promise of providing analytics to all, >>How much of this you've been able to share with your customers and maybe your partners. I mean, I know OD is fairly new, but if you've been able to get any feedback from them, what are they saying about it? >>Uh, I mean, it's, it's pretty amazing. Um, we ran a early access, limited availability program that led us put a lot of this technology in the hands of over 600 customers, um, over the last few months. So we have gotten a lot of feedback. I tell you, um, it's been overwhelmingly positive. I think organizations are really excited to unlock the insights that have been hidden in all this data. They've got, they're excited to be able to use analytics in every decision that they're making so that the decisions they have or more informed and produce better business outcomes. Um, and, and this idea that they're going to move from, you know, dozens to hundreds or thousands of people who have access to these kinds of capabilities, I think has been a really exciting thing that is going to accelerate the transformation that these customers are on. >>Yeah, those are good. Good, good numbers for, for preview mode. Let's, let's talk a little bit about vision. So it's democratizing data is the ultimate goal, which frankly has been elusive for most organizations over time. How's your cloud going to address the challenges of putting data to work across the entire enterprise? >>Yeah, I mean, I tend to think about the future and some of the investments we're making in our products and our roadmap across four big themes, you know, in the, and these are really kind of enduring themes that you're going to see us making investments in over the next few years, the first is having cloud centricity. You know, the data gravity has been moving to the cloud. We need to be able to provide access, to be able to ingest and manipulate that data, to be able to write back to it, to provide cloud solution. So the first one is really around cloud centricity. The second is around big data fluency. Once you have all of the data, you need to be able to manipulate it in a performant manner. So having the elastic cloud infrastructure and in database processing is so important, the third is around making AI a strategic advantage. >>So, uh, you know, getting everyone involved and accessing AI and machine learning to unlock those insights, getting it out of the hands of the small group of data scientists, putting it in the hands of analysts and business users. Um, and then the fourth thing is really providing access across the entire organization. You know, it and data engineers, uh, as well as business owners and analysts. So, um, cloud centricity, big data fluency, um, AI is a strategic advantage and, uh, personas across the organization are really the four big themes you're going to see us, uh, working on over the next few months and, uh, coming coming year. >>That's good. Thank you for that. So, so on a related question, how do you see the data organizations evolving? I mean, traditionally you've had, you know, monolithic organizations, uh, very specialized or I might even say hyper specialized roles and, and your, your mission of course is the customer. You, you, you, you and your customers, they want to democratize the data. And so it seems logical that domain leaders are going to take more responsibility for data, life cycles, data ownerships, low code becomes more important. And perhaps this kind of challenges, the historically highly centralized and really specialized roles that I just talked about. How do you see that evolving and, and, and what role will Altryx play? >>Yeah. Um, you know, I think we'll see sort of a more federated systems start to emerge. Those centralized groups are going to continue to exist. Um, but they're going to start to empower, you know, in a much more de-centralized way, the people who are closer to the business problems and have better business understanding. I think that's going to let the centralized highly skilled teams work on, uh, problems that are of higher value to the organization. The kinds of problems where one or 2% lift in the model results in millions of dollars a day for the business. And then by pushing some of the analytics out to, uh, closer to the edge and closer to the business, you'll be able to apply those analytics in every single decision. So I think you're going to see, you know, both the decentralized and centralized models start to work in harmony and a little bit more about almost a federated sort of a way. And I think, you know, the exciting thing for us at Altryx is, you know, we want to facilitate that. We want to give analytic capabilities and solutions to both groups and types of people. We want to help them collaborate better, um, and drive business outcomes with the analytics they're using. >>Yeah. I mean, I think my take on another one, if you could comment is to me, the technology should be an operational detail and it has been the, the, the dog that wags the tail, or maybe the other way around, you mentioned digital exhaust before. I mean, essentially it's digital exhaust coming out of operationals systems that then somehow, eventually end up in the hand of the domain users. And I wonder if increasingly we're going to see those domain users, users, those, those line of business experts get more access. That's your goal. And then even go beyond analytics, start to build data products that could be monetized, and that maybe it's going to take a decade to play out, but that is sort of a new era of data. Do you see it that way? >>Absolutely. We're actually making big investments in our products and capabilities to be able to create analytic applications and to enable somebody who's an analyst or business user to create an application on top of the data and analytics layers that they have, um, really to help democratize the analytics, to help prepackage some of the analytics that can drive more insights. So I think that's definitely a trend we're going to see more. >>Yeah. And to your point, if you can federate the governance and automate that, then that can happen. I mean, that's a key part of it, obviously. So, all right, Jay, we have to leave it there up next. We take a deep dive into the Altryx recent acquisition of Trifacta with Adam Wilson who led Trifacta for more than seven years. It's the recipe. Tyler is the chief product officer at Altryx to explain the rationale behind the acquisition and how it's going to impact customers. Keep it right there. You're watching the cube. You're a leader in enterprise tech coverage. >>It's go time, get ready to accelerate your data analytics journey with a unified cloud native platform. That's accessible for everyone on the go from home to office and everywhere in between effortless analytics to help you go from ideas to outcomes and no time. It's your time to shine. It's Altryx analytics cloud time. >>Okay. We're here with. Who's 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 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 their businesses with that in mind, you know, we know designer and are the products that Altrix has been selling the past decade or so do a really great job, um, addressing the business analysts, uh, with, um, hyper Rana now kind of renamed, um, Altrix auto. We even speak with the business owner and 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 fact made so much sense for us. >>Yeah. Thank you for that. I mean, you, 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, 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 birth 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 help to automate those. So, so a broader set of, 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 could 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 is, as we, um, saw over the course of the last 5, 6, 7 years that, um, you know, uh, 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 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 is, 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, because we've looked, we've contextualized most of our operational systems, but the big data pipeline is 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 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 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 Alcon's has that opportunity. Uh, we've got 7,000 plus customers who rely on analytics for, um, data management, for analytics, for AI and ML, uh, for transformations, uh, for reporting and visualization for automated insights and so on. Um, 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 applied and so multiple personas. Um, and 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 at least three personas 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 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 is not crack 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 was painted and got us really energized about the acquisition and about the potential of the combination. >>And you're really, you're obviously writing 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's 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 what Adam said resonates with me deeply. Um, analytics is one of those, um, massive disciplines inside 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 all drinks 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 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 Altrix 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 designer cloud really shares a lot of DNA and heritage with, uh, designer desktop, um, the low code tooling and that interface, uh, the 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, um, Trifacta, um, I think we have to get deeper inside to think about what does the data engineer really need? What's the 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 trifecta on the amazing Trifacta cloud platform. >>You know, >>I think we're 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 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, 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 one of the reasons I always liked Altrix is cause you kinda did the little end run on it. It can be a blocker sometimes, but that created problems, right? Because the organization said, wow, this big data stuff has taken off, but we need security. We need governance. And it's interesting because you've 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 their reaction like? Uh, maybe Suresh, you could start off and maybe Adam, you could bring us home. >>Um, thanks for asking about our sales kickoff. So we met for the first time and you've got a two years, right. For, as, as it is for many of us, um, in person, uh, um, which I think was a, was a real breakthrough as Qualtrics has been on its transformation journey. Uh, we added a Trifacta to, um, the, the potty 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 other 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 out him and I will, when he's so hot on, on the deal and the core hypotheses and so on. And then you step back and you're going to share the vision with the field organization, and it blows you away, the energy that it creates among our sellers out of partners. >>And I'm sure Madam will and his team were mocked, um, every single day, uh, 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, 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 told was just, you have this opportunity to really cater to what the end users 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 Altryx 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, 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. >>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 jets. 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 you're leader in enterprise tech coverage. >>This is your data housed neatly insecurely in the snowflake data cloud. And all of it has potential the potential to solve complex business problems, deliver personalized financial offerings, protect supply chains from disruption, cut costs, forecast, grow and innovate. All you need to do is put your data in the hands of the right people and give it an opportunity. Luckily for you. That's the easy part because snowflake works with Alteryx and Alteryx turns data into breakthroughs with just a click. Your organization can automate analytics with drag and drop building blocks, easily access snowflake data with both sequel and no SQL options, share insights, powered by Alteryx data science and push processing to snowflake for lightning, fast performance, you get answers you can put to work in your teams, get repeatable processes they can share in that's exciting because not only is your data no longer sitting around in silos, it's also mobilized for the next opportunity. Turn your data into a breakthrough Alteryx and snowflake >>Okay. We're back here in the queue, focusing on the business promise of the cloud democratizing data, making it accessible and enabling everyone to get value from analytics, insights, and data. We're now moving into the eco systems segment the power of many versus the resources of one. And we're pleased to welcome. Barb Hills camp was the senior vice president partners and alliances at Ultrix and a special guest Terek do week head of technology alliances at snowflake folks. Welcome. Good to see you. >>Thank you. Thanks for having me. Good to see >>Dave. Great to see you guys. So cloud migration, it's one of the hottest topics. It's the top one of the top initiatives of senior technology leaders. We have survey data with our partner ETR it's number two behind security, and just ahead of analytics. So we're hovering around all the hot topics here. Barb, what are you seeing with respect to customer, you know, cloud migration momentum, and how does the Ultrix partner strategy fit? >>Yeah, sure. Partners are central company's strategy. They always have been. We recognize that our partners have deep customer relationships. And when you connect that with their domain expertise, they're really helping customers on their cloud and business transformation journey. We've been helping customers achieve their desired outcomes with our partner community for quite some time. And our partner base has been growing an average of 30% year over year, that partner community and strategy now addresses several kinds of partners, spanning solution providers to global SIS and technology partners, such as snowflake and together, we help our customers realize the business promise of their journey to the cloud. Snowflake provides a scalable storage system altereds provides the business user friendly front end. So for example, it departments depend on snowflake to consolidate data across systems into one data cloud with Altryx business users can easily unlock that data in snowflake solving real business outcomes. Our GSI and solution provider partners are instrumental in providing that end to end benefit of a modern analytic stack in the cloud providing platform, guidance, deployment, support, and other professional services. >>Great. Let's get a little bit more into the relationship between Altrix and S in snowflake, the partnership, maybe a little bit about the history, you know, what are the critical aspects that we should really focus on? Barb? Maybe you could start an Interra kindly way in as well. >>Yeah, so the relationship started in 2020 and all shirts made a big bag deep with snowflake co-innovating and optimizing cloud use cases together. We are supporting customers who are looking for that modern analytic stack to replace an old one or to implement their first analytic strategy. And our joint customers want to self-serve with data-driven analytics, leveraging all the benefits of the cloud, scalability, accessibility, governance, and optimizing their costs. Um, Altrix proudly achieved. Snowflake's highest elite tier in their partner program last year. And to do that, we completed a rigorous third party testing process, which also helped us make some recommended improvements to our joint stack. We wanted customers to have confidence. They would benefit from high quality and performance in their investment with us then to help customers get the most value out of the destroyed solution. We developed two great assets. One is the officer starter kit for snowflake, and we coauthored a joint best practices guide. >>The starter kit contains documentation, business workflows, and videos, helping customers to get going more easily with an altered since snowflake solution. And the best practices guide is more of a technical document, bringing together experiences and guidance on how Altryx and snowflake can be deployed together. Internally. We also built a full enablement catalog resources, right? We wanted to provide our account executives more about the value of the snowflake relationship. How do we engage and some best practices. And now we have hundreds of joint customers such as Juniper and Sainsbury who are actively using our joint solution, solving big business problems much faster. >>Cool. Kara, can you give us your perspective on the partnership? >>Yeah, definitely. Dave, so as Barb mentioned, we've got this standing very successful partnership going back years with hundreds of happy joint customers. And when I look at the beginning, Altrix has helped pioneer the concept of self-service analytics, especially with use cases that we worked on with for, for data prep for BI users like Tableau and as Altryx has evolved to now becoming from data prep to now becoming a full end to end data science platform. It's really opened up a lot more opportunities for our partnership. Altryx has invested heavily over the last two years in areas of deep integration for customers to fully be able to expand their investment, both technologies. And those investments include things like in database pushed down, right? So customers can, can leverage that elastic platform, that being the snowflake data cloud, uh, with Alteryx orchestrating the end to end machine learning workflows Alteryx also invested heavily in snow park, a feature we released last year around this concept of data programmability. So all users were regardless of their business analysts, regardless of their data, scientists can use their tools of choice in order to consume and get at data. And now with Altryx cloud, we think it's going to open up even more opportunities. It's going to be a big year for the partnership. >>Yeah. So, you know, Terike, we we've covered snowflake pretty extensively and you initially solve what I used to call the, I still call the snake swallowing the basketball problem and cloud data warehouse changed all that because you had virtually infinite resources, but so that's obviously one of the problems that you guys solved early on, but what are some of the common challenges or patterns or trends that you see with snowflake customers and where does Altryx come in? >>Sure. Dave there's there's handful, um, that I can come up with today, the big challenges or trends for us, and Altrix really helps us across all of them. Um, there are three particular ones I'm going to talk about the first one being self-service analytics. If we think about it, every organization is trying to democratize data. Every organization wants to empower all their users, business users, um, you know, the, the technology users, but the business users, right? I think every organization has realized that if everyone has access to data and everyone can do something with data, it's going to make them competitively, give them a competitive advantage with Altrix is something we share that vision of putting that power in the hands of everyday users, regardless of the skillsets. So, um, with self-service analytics, with Ultrix designer they've they started out with self-service analytics as the forefront, and we're just scratching the surface. >>I think there was an analyst, um, report that shows that less than 20% of organizations are truly getting self-service analytics to their end users. Now, with Altryx going to Ultrix cloud, we think that's going to be a huge opportunity for us. Um, and then that opens up the second challenge, which is machine learning and AI, every organization is trying to get predictive analytics into every application that they have in order to be competitive in order to be competitive. Um, and with Altryx creating this platform so they can cater to both the everyday business user, the quote unquote, citizen data scientists, and making a code friendly for data scientists to be able to get at their notebooks and all the different tools that they want to use. Um, they fully integrated in our snow park platform, which I talked about before, so that now we get an end to end solution caring to all, all lines of business. >>And then finally this concept of data marketplaces, right? We, we created snowflake from the ground up to be able to solve the data sharing problem, the big data problem, the data sharing problem. And Altryx um, if we look at mobilizing your data, getting access to third-party datasets, to enrich with your own data sets, to enrich with, um, with your suppliers and with your partners, data sets, that's what all customers are trying to do in order to get a more comprehensive 360 view, um, within their, their data applications. And so with Altryx alterations, we're working on third-party data sets and marketplaces for quite some time. Now we're working on how do we integrate what Altrix is providing with the snowflake data marketplace so that we can enrich these workflows, these great, great workflows that Altrix writing provides. Now we can add third party data into that workflow. So that opens up a ton of opportunities, Dave. So those are three I see, uh, easily that we're going to be able to solve a lot of customer challenges with. >>So thank you for that. Terrick so let's stay on cloud a little bit. I mean, Altrix is undergoing a major transformation, big focus on the cloud. How does this cloud launch impact the partnership Terike from snowflakes perspective and then Barb, maybe, please add some color. >>Yeah, sure. Dave snowflake started as a cloud data platform. We saw our founders really saw the challenges that customers are having with becoming data-driven. And the biggest challenge was the complexity of having imagine infrastructure to even be able to do it, to get applications off the ground. And so we created something to be cloud-native. We created to be a SAS managed service. So now that that Altrix is moving to the same model, right? A cloud platform, a SAS managed service, we're just, we're just removing more of the friction. So we're going to be able to start to package these end to end solutions that are SAS based that are fully managed. So customers can, can go faster and they don't have to worry about all of the underlying complexities of, of, of stitching things together. Right? So, um, so that's, what's exciting from my viewpoint >>And I'll follow up. So as you said, we're investing heavily in the cloud a year ago, we had two pre desktop products, and today we have four cloud products with cloud. We can provide our users with more flexibility. We want to make it easier for the users to leverage their snowflake data in the Alteryx platform, whether they're using our beloved on-premise solution or the new cloud products were committed to that continued investment in the cloud, enabling our joint partner solutions to meet customer requirements, wherever they store their data. And we're working with snowflake, we're doing just that. So as customers look for a modern analytic stack, they expect that data to be easily accessible, right within a fast, secure and scalable platform. And the launch of our cloud strategy is a huge leap forward in making Altrix more widely accessible to all users in all types of roles, our GSI and our solution provider partners have asked for these cloud capabilities at scale, and they're excited to better support our customers, cloud and analytic >>Are. How about you go to market strategy? How would you describe your joint go to market strategy with snowflake? >>Sure. It's simple. We've got to work backwards from our customer's challenges, right? Driving transformation to solve problems, gain efficiencies, or help them save money. So whether it's with snowflake or other GSI, other partner types, we've outlined a joint journey together from recruit solution development, activation enablement, and then strengthening our go to market strategies to optimize our results together. We launched an updated partner program and within that framework, we've created new benefits for our partners around opportunity registration, new role based enablement and training, basically extending everything we do internally for our own go-to-market teams to our partners. We're offering partner, marketing resources and funding to reach new customers together. And as a matter of fact, we recently launched a fantastic video with snowflake. I love this video that very simply describes the path to insights starting with your snowflake data. Right? We do joint customer webinars. We're working on joint hands-on labs and have a wonderful landing page with a lot of assets for our customers. Once we have an interested customer, we engage our respective account managers, collaborating through discovery questions, proof of concepts really showcasing the desired outcome. And when you combine that with our partners technology or domain expertise, it's quite powerful, >>Dark. How do you see it? You'll go to market strategy. >>Yeah. Dave we've. Um, so we initially started selling, we initially sold snowflake as technology, right? Uh, looking at positioning the diff the architectural differentiators and the scale and concurrency. And we noticed as we got up into the larger enterprise customers, we're starting to see how do they solve their business problems using the technology, as well as them coming to us and saying, look, we want to also know how do you, how do you continue to map back to the specific prescriptive business problems we're having? And so we shifted to an industry focus last year, and this is an area where Altrix has been mature for probably since their inception selling to the line of business, right? Having prescriptive use cases that are particular to an industry like financial services, like retail, like healthcare and life sciences. And so, um, Barb talked about these, these starter kits where it's prescriptive, you've got a demo and, um, a way that customers can get off the ground and running, right? >>Cause we want to be able to shrink that time to market, the time to value that customers can watch these applications. And we want to be able to, to tell them specifically how we can map back to their business initiatives. So I see a huge opportunity to align on these industry solutions. As BARR mentioned, we're already doing that where we've released a few around financial services working in healthcare and retail as well. So that is going to be a way for us to allow customers to go even faster and start to map two lines of business with Alteryx. >>Great. Thanks Derek. Bob, what can we expect if we're observing this relationship? What should we look for in the coming year? >>A lot specifically with snowflake, we'll continue to invest in the partnership. Uh, we're co innovators in this journey, including snow park extensibility efforts, which Derek will tell you more about shortly. We're also launching these great news strategic solution blueprints, and extending that at no charge to our partners with snowflake, we're already collaborating with their retail and CPG team for industry blueprints. We're working with their data marketplace team to highlight solutions, working with that data in their marketplace. More broadly, as I mentioned, we're relaunching the ultra partner program designed to really better support the unique partner types in our global ecosystem, introducing new benefits so that with every partner, achievement or investment with ultra score, providing our partners with earlier access to benefits, um, I could talk about our program for 30 minutes. I know we don't have time. The key message here Alteryx is investing in our partner community across the business, recognizing the incredible value that they bring to our customers every day. >>Tarik will give you the last word. What should we be looking for from, >>Yeah, thanks. Thanks, Dave. As BARR mentioned, Altrix has been the forefront of innovating with us. They've been integrating into, uh, making sure again, that customers get the full investment out of snowflake things like in database push down that I talked about before that extensibility is really what we're excited about. Um, the ability for Ultrix to plug into this extensibility framework that we call snow park and to be able to extend out, um, ways that the end users can consume snowflake through, through sequel, which has traditionally been the way that you consume snowflake as well as Java and Scala, not Python. So we're excited about those, those capabilities. And then we're also excited about the ability to plug into the data marketplace to provide third party data sets, right there probably day sets in, in financial services, third party, data sets and retail. So now customers can build their data applications from end to end using ultrasound snowflake when the comprehensive 360 view of their customers, of their partners, of even their employees. Right? I think it's exciting to see what we're going to be able to do together with these upcoming innovations. Great >>Barb Tara, thanks so much for coming on the program, got to leave it right there in a moment, I'll be back with some closing thoughts in a summary, don't go away. >>1200 hours of wind tunnel testing, 30 million race simulations, 2.4 second pit stops make that 2.3. The sector times out the wazoo, whites are much of this velocity's pressures, temperatures, 80,000 components generating 11.8 billion data points and one analytics platform to make sense of it all. When McLaren needs to turn complex data into insights, they turn to Altryx Qualtrics analytics, automation, >>Okay, let's summarize and wrap up the session. We can pretty much agree the data is plentiful, but organizations continue to struggle to get maximum value out of their data investments. The ROI has been elusive. There are many reasons for that complexity data, trust silos, lack of talent and the like, but the opportunity to transform data operations and drive tangible value is immense collaboration across various roles. And disciplines is part of the answer as is democratizing data. This means putting data in the hands of those domain experts that are closest to the customer and really understand where the opportunity exists and how to best address them. We heard from Jay Henderson that we have all this data exhaust and cheap storage. It allows us to keep it for a long time. It's true, but as he pointed out that doesn't solve the fundamental problem. Data is spewing out from our operational systems, but much of it lacks business context for the data teams chartered with analyzing that data. >>So we heard about the trend toward low code development and federating data access. The reason this is important is because the business lines have the context and the more responsibility they take for data, the more quickly and effectively organizations are going to be able to put data to work. We also talked about the harmonization between centralized teams and enabling decentralized data flows. I mean, after all data by its very nature is distributed. And importantly, as we heard from Adam Wilson and Suresh Vittol to support this model, you have to have strong governance and service the needs of it and engineering teams. And that's where the trifecta acquisition fits into the equation. Finally, we heard about a key partnership between Altrix and snowflake and how the migration to cloud data warehouses is evolving into a global data cloud. This enables data sharing across teams and ecosystems and vertical markets at massive scale all while maintaining the governance required to protect the organizations and individuals alike. >>This is a new and emerging business model that is very exciting and points the way to the next generation of data innovation in the coming decade. We're decentralized domain teams get more facile access to data. Self-service take more responsibility for quality value and data innovation. While at the same time, the governance security and privacy edicts of an organization are centralized in programmatically enforced throughout an enterprise and an external ecosystem. This is Dave Volante. All these videos are available on demand@theqm.net altrix.com. Thanks for watching accelerating automated analytics in the cloud made possible by Altryx. And thanks for watching the queue, your leader in enterprise tech coverage. We'll see you next time.

>> From theCUBE studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> The pandemic precipitated what is shaping up to be a permanent shift in cybersecurity spending patterns. As a direct result of hybrid work, CSOs have vested heavily in endpoint security, identity access management, cloud security, and further hardening the network beyond the headquarters. We've reported on this extensively in this Breaking Analysis series. Moreover, the need to build security into applications from the start rather than bolting protection on as an afterthought has led to vastly high heightened awareness around DevSecOps. Finally, attacking security as a data problem with automation and AI is fueling new innovations in cyber products and services and startups. Hello and welcome to this week's Wikibon CUBE Insights powered by ETR. In this Breaking Analysis, we present our quarterly findings in the security industry, and share the latest ETR survey data on the spending momentum and market movers. Let's start with the most recent news in cybersecurity. Nary a week goes by without more concerning news. The latest focus in the headlines is, of course, Russia's relentless cyber attacks on critical infrastructure in the Ukraine, including banking, government websites, weaponizing information. The hacker group, BlackByte, put a double whammy on the San Francisco 49ers, meaning they exfiltrated data and they encrypted the organization's files as part of its ransomware attack. Then there's the best Super Bowl ad last Sunday, the Coinbase floating QR code. Did you catch that? As people rushed to scan the code and participate in the Coinbase Bitcoin giveaway, it highlights yet another exposure, meaning we're always told not to click on links that we don't trust or we've never seen, but so many people activated this random QR code on their smartphones that it crashed Coinbase's website. What does that tell you? In other news, Securonix raised a billion dollars. They did this raise on top of Lacework's massive $1.3 billion raise last November. Both of these companies are attacking security with data automation and APIs that can engage machine intelligence. Securonix, specifically in the announcement, mentioned the uptake from MSSPs, managed security service providers, something we've talked about in this series. And that's a trend that we see as increasingly gaining traction as customers are just drawing in and drowning in security incidents. Peter McKay's company, Snyk, acquired Fugue, a company focused on making sure security policies are consistent throughout the software development life cycle. It's a really an example of a developer-defined security approach where policy can be checked at the dev, deployment, and production phases to ensure the same policies are in place at all stages, including monitoring at runtime. Fugue, according to Crunchbase, had raised $85 million to date. In some other company news, Cisco was rumored to be acquiring Splunk for not much more than Splunk is worth today. And the talks reportedly broke down. This would be a major move in security by Cisco and underscores the pressure to consolidate. Cisco would get an extremely strong customer base and through efficiencies could improve Splunk's profitability, but it seems like the premium Cisco was willing to pay was not enough to entice board to act. Splunk board, that is. Datadog blew away its earnings, and the stock was up 12%. It's pulled back now, thanks to Putin, but it's one of those companies that is disrupting Splunk. Datadog is less than half the size of Splunk, revenue-wise, but its valuation is more than 2 1/2 times greater. Finally, Elastic, another Splunk disruptor, settled its trademark dispute with AWS, and now AWS will now stop using the name Elasticsearch. All right, let's take a high level look at how cyber companies have performed in the stock market over time. Here's a graph of the Cyber ETF, and you can see the March 1st crosshairs of 2020 signifying the start of the lockdown. The trajectory of cybersecurity stocks is shown by the orange and blue lines, and it surely has steepened post March of 2020. And, of course, it's been down with the market lately, but the run up, as you can see, was substantial and eclipsed the trajectory of the previous cycles over the last couple of years, owing much of the momentum to the spending dynamics that we talked about at our open. Let's now drill into some of the names that we've been following over the last few years and take a look at the firm level. This chart shows some data that we've been tracking since before the pandemic. The top rows show the S&P 500 and the NASDAQ prices, and the bottom rows show specific stocks. The first column is the index price or the market cap of the company just before the pandemic, then the same data one year later. Then the next column shows the peak value during the pandemic, and then the current value. Then it shows in the next column where it is today, in percentage terms, i.e., how far has it pulled back from the peak, then the delta from pre-pandemic, in other words, how much did the issue earn or lose during the pandemic for investors? We then compare the pre-pandemic revenue multiple using a trailing 12-month revenue metric. Sorry, that's what we used. It's easy to get. (laughs) And that's the revenue multiple compared to the August in 2020, when multiples were really high, and where they are today, and then a recent quarterly growth rate guide based on the last earnings report. That's the last column. Okay, so I'm throwing a lot of data at you here, but what does it tell us? First, the S&P and the NAS are well up from pre-pandemic levels, yet they're off 9% and 15%, respectively, from their peaks today. That was earlier on Friday morning. Now let's look at the names more closely. Splunk has been struggling. It definitely had a tailwind from the pandemic as all boats seem to rise, but its execution has been lacking. It's now 30% off from its pre-pandemic levels. (groans) And it's multiple is compressing, and perhaps Cisco thought it could pick up the company for a discount. Now let's talk about Palo Alto Networks. We had reported on some of the challenges the company faced moving into a cloud-friendly model. that was before the pandemic. And we talked about the divergence between Palo Alto's stock price and the valuations relative to Fortinet, and we said at the time, we fully expected Palo Alto to rebound, and that's exactly what happened. It rode the tailwinds of the last two years. It's up over 100% from its pre-COVID levels, and its revenue multiple is expanding, owing to the nice growth rates. Now Fortinet had been doing well coming into the pandemic. In fact, we said it was executing on a cloud strategy better than Palo Alto Networks, hence that divergence in valuations at the time. So it didn't get as much of a boost from the pandemic. Didn't get that momentum at first, but the company's been executing very well. And as you can see, with 155% increase in valuation since just before the pandemic, it's going more than okay for Fortinet. Now, Okta is a name that we've really followed closely, the identity access management specialist that rocketed. But since it's Auth0 acquisition, it's pulled back. Investors are concerned about its guidance and its profitability. And several analyst have downgraded their price targets on Okta. We still really like the company. The Auth0 acquisition gives Okta a developer vector, and we think the company is going hard after market presence and is willing to sacrifice short-term profitability. We actually like that posture. It's very Frank Slupin-like. This company spends a lot of money on R&D and go-to-market. The question is, does Okta have inherent profitability? The company, as they say, spends a ton in some really key areas but it looks to us like it's going to establish a footprint. It's guiding revenue CAGR in the mid-30s over the mid to long-term and near term should beat that benchmark handily. But you can see the red highlights on Okta. And even though Okta is up 59% from its pre-pandemic levels, it's far behind its peers shown in the chart, especially CrowdStrike and Zscaler, the latter being somewhat less impacted by the pullback in stocks recently, of course, due to the fears of inflation and interest rates, and, of course, Russian invasion escalation. But these high flyers, they were bound to pull back. The question is can they maintain their category leadership? And for the most part, we think they can. All right, let's get into some of the ETR data. Here's our favorite XY view with net score, or spending momentum on the Y-axis, and market share or pervasiveness in the data center on the horizontal axis. That red 40% line, that indicates a highly elevated spending level. And the chart inserts to the right, that shows how the data is plotted with net score and shared N in each of the columns by each company. Okay, so this is an eye chart, but there really are three main takeaways. One is that it's a crowded market. And this shows only the companies ETR captures in its survey. We filtered on those that had more than 50 mentions. So there's others in the ETR survey that we're not showing here, and there are many more out there which don't get reported in the spending data in the ETR survey. Secondly, there are a lot of companies above the 40% mark, and plenty with respectable net scores just below. Third, check out SentinelOne, Elastic, Tanium, Datadog, Netskope, and Darktrace. Each has under 100 N's but we're watching these companies closely. They're popping up in the survey, and they're catching our attention, especially SentinelOne, post-IPO. So we wanted to pare this back a bit and filter the data some more. So let's look at companies with more than 100 mentions in the same chart. It gets a little cleaner this picture, but it's still crowded. Auth0 leads everyone in net score. Okta is also up there, so that's very positive sign since they had just acquired Auth0. CrowdStrike SalePoint, Cyberark, CloudFlare, and Zscaler are all right up there as well. And then there's the bigger security companies. Palo Alto Network, very impressive because it's well above the 40% mark, and it has a big presence in the survey, and, of course, in the market. And Microsoft as well. They're such a big whale. They skew the data for everybody else to kind of mess up these charts. And the position of Cisco and Splunk make for an interesting combination. They get both decent net scores, not above the 40% line but they got a good presence in the survey as well. Thinking about the acquisition, Al Shugart was the CEO of of Seagate, and founder. Brilliant Silicon valley icon and engineer. Great business person. I was asking him one time, hey, you thinking about buying this company or that company? And of course, he's not going to tell me who he's thinking about buying or acquiring. He said, let me just tell you this. If you want to know what I'm thinking, ask yourself if it were free, would you take it? And he said the answer's not always obviously yes, because acquisitions can be messy and disruptive. In the case of Cisco and Splunk, I think the answer would be a definitive yes It would expand Cisco's portfolio and make it the leader in security, with an opportunity to bring greater operating leverage to Splunk. Cisco's just got to pay more if it wants that asset. It's got to pay more than the supposed $20 billion offer that it made. It's going to have to get kind of probably north of 23 billion. I pinged my ETR colleague, Erik Bradley, on this, and he generally agreed. He's very close to the security space. He said, Splunk isn't growing the customer base but the customers are sticky. I totally agree. Cisco could roll Splunk into its security suite. Splunk is the leader in that space, security information and event management, and Cisco really is missing that piece of the pie. All right, let's filter the data even more and look at some of the companies that have moved in the survey over the past year and a half. We'll go back here to July 2020. Same two-dimensional chart. And we're isolating here Auth0, Okta, SalePoint CrowdStrike, Zscaler, Cyberark, Fortinet, and Cisco. No Microsoft. That cleans up the chart. Okay, why these firms? Because they've made some major moves to the right, and some even up since last July. And that's what this next chart shows. Here's the data from the January 2022 survey. The arrow start points show the position that we just showed you earlier in July 2020, and all these players have made major moves to the right. How come? Well, it's likely a combination of strong execution, and the fact that security is on the radar of every CEO, CIO, of course, CSOs, business heads, boards of directors. Everyone is thinking about security. The market momentum is there, especially for the leaders. And it's quite tremendous. All right, let's now look at what's become a bit of a tradition with Breaking Analysis, and look at the firms that have earned four stars. Four-star firms are leaders in the ETR survey that demonstrate both a large presence, that's that X-axis that we showed you, and elevated spending momentum. Now in this chart, we filter the N's. Has to be greater than 100. And we isolate on those companies. So more than 100 responses in the survey. On the left-hand side of the chart, we sort by net score or spending velocity. On the right-hand side, we sort by shared N's or presence in the dataset. We show the top 20 for each of the categories. And the red line shows the top 10 cutoffs. Companies that show up in the top 10 for both spending momentum and presence in the data set earn four stars. If they show up in one, and make the top 10 in one, and make the top 20 in the other, they get two stars. And we've added a one-star category as honorable mention for those companies that make the top 20 in both categories. Microsoft, Palo Alto Networks, CrowdStrike, and Okta make the four-star grade. Okta makes it even without Auth0, which has the number one net score in this data set with 115 shared N to boot. So you can add that to Okta. The weighted average would pull Okta's net score to just above Cyberark's into fourth place. And its shared N would bump Okta up to third place on the right-hand side of the chart Cisco, Splunk, Proofpoint, KnowBe4, Zscaler, and Cyberark get two stars. And then you can see the honorable mentions with one star. Now thinking about a Cisco, Splunk combination. You'd get an entity with a net score in the mid-20s. Yeah, not too bad, definitely respectable. But they'd be number one on the right-hand side of this chart, with the largest market presence in the survey by far. Okay, let's wrap. The trends around hybrid work, cloud migration and the attacker escalation that continue to drive cybersecurity momentum and they're going to do so indefinitely. And we've got some bullet points here that you're seeing private companies, (laughs) they're picking up gobs of money, which really speaks to the fact that there's no silver bullet in this market. It's complex, chaotic, and cash-rich. This idea of MSSPs on the rise is going to continue, we think. About half the mid-size and large organization in the US don't have a SecOps, a security operation center, and outsourcing to one that can be tapped on a consumption basis, cloud-like, as a service just makes sense to us. We see the momentum that companies that we've highlighted over the many quarters of Breaking Analysis are forming. They're forming a strong base in the market. They're going for market share and footprint, and they're focusing on growth, at bringing in new talent. They have good balance sheets and strong management teams and we think they'll be leading companies in the future, Zscaler, CrowdStrike, Okta, SentinelOne, Cyberark, SalePoint, over time, joining the ranks of billion dollar cyber firms, when I say billion dollar, billion dollar revenue like Palo Alto Networks, Fortinet, and Splunk, if it doesn't get acquired. These independent firms that really focus on security. Which underscores the pressure and consolidation and M&A in the whole space. It's almost assured with the fragmentation of companies and so many new entrants fighting for escape velocity that this market is going to continue with robust M&A and consolidation. Okay, that's it for today. Thanks to my colleague, Stephanie Chan, who helped research this week's topics, and Alex Myerson on the production team. He also manages the Breaking Analysis podcast. Kristen Martin and Cheryl Knight, who get the word out. Thank you to all. Remember these episodes are all available as podcasts wherever you listen. All you do is search Breaking Analysis podcast. Check out ETR's website at etr.ai. We also publish a full report every week on wikibon.com and siliconangle.com. You can email me at david.vellante@siliconangle.com. @dvellante is my DM. Comment on our LinkedIn posts. This is Dave Vellante for theCUBE Insights powered by ETR. Have a great week. Be safe, be well, and we'll see you next time. (upbeat music)

(soft upbeat music) (crowd chattering) >> Over the past 18 to 24 months, chief information security officers have dramatically changed their priorities. They had to, to support the remote work trend. So things like endpoint security, cloud security, and in particular identity and access management became top of mind. And a whole shift occurred. And we're going to talk about that today. Hi everybody, this is Dave Vellante and you're watching theCUBE. We're here at AWS re:Invent 2021. Katie Curtin-Mestre is here. She's the vice president of marketing at CyberArk and Bar Lavie senior product manager at Cloud Identity and Security. Bar, sorry for botching your name, but folks welcome to theCUBE, great to see you. >> Glad to be here. >> Great to hear. >> So Katie, upfront I talked about some of those trends. It's been a hugely dramatic shift away from this kind of traditional approaches to cyber. What are some of the trends that CyberArk has seen? >> Well, Bar is going to take the first part of this. >> Great, just go on. (Bar laughing) >> Yeah, so one trait that we are seeing is that cloud migration projects accelerate as organization turbocharged digital transformation. Is they're a looking to take advantage off the agility and operational efficiency of the cloud providers. Some of the concerns that I can think about one of those is the reducing the potential loss of data that is caused due to the excessive access to resources. And the other one is provision secure and scalable access to resources. And the third one would be implementing least privilege for all type of identity whether if it's a human identity or non-human identity. >> And on that end Dave, we recently commissioned a survey with the Cloud Security Alliance. We co-sponsored a survey and found that 94% of respondents said that securing human permissions was a top security challenge and machine identities weren't far behind at 77%. Another challenge that we're hearing from our customers is the need to secure the secrets used by applications. So we're really excited by today's news from AWS. They announced some new capabilities with a code guru called Secret Detector that helps to find unsecured secrets in applications. And the other concern that we're hearing from our customers is the need to monitor and audit the activity of all of their cloud identities. This is really important to help their security operation teams with their investigations and also to meet audit and compliance requirements. >> So the definition of identity is now more encompassing and includes like you say machines, right? It's not just people anymore. Of course we've seen, you know, phishing has always been problematic. It's escalated daily, right? We get phished. I mean, are we going to see the day where we finally get rid of passwords? Is that even possible? But maybe we could talk a little bit about sort of identity, how identity is evolving, this notion of zero trust. Zero trust used to be a Password. So, maybe Bar you could talk a little bit about what you're seeing in terms of identity access management. Maybe privileged access management are those things coming together? How does CyberArk think about those things? >> You going to take this one Katie >> Well, what CyberArk sees is we definitely see a trend where access management and privileged access management are coming together. Security teams are struggling too many security tools and they're really looking to standardize on a small handful of vendors and get more bank for their buck from their security investment. So we're definitely seeing that trends of unified platforms across access and privileged access management to secure any identity, whether human or machine from kind of like your standard workforce identity, to those who have highly privileged access. >> I don't know if you've ever, ever seen that chart. I think Optiv puts it out. It's consultancy. And it's this eye chart. It's a taxonomy of all the different security I have published at a number of times. it's mind boggling. So CSOs, SecOps teams they have to manage all this complexity, all these different tools and you ask CSOs what's your biggest challenge? They'll tell you lack of skills. We just can't find people. We can't train them fast enough. So what's CyberArk working on? What are some of the key initiatives that you guys are focused on that people should know about? >> Well, one of the things that we're working on is actually, and we see a greater adoption of it is something that was actually started as an initiative within our innovation lab. It's a CyberArk Clouding Titles Manager, which help to detect and remediate excessive permissions to cloud resources for any type of identity. I mentioned before the both human and non-human. Which are the something that you were looking to to secure. Another solution that we see a great adoption is our circuit ranger which helps organization to re remove the necessity of having a hard-coded credentials within application. It can be either traditional applications for their own premise or even cloud native applications. And peg this also into your CI CD pipeline. And we are actually innovating in these type of area with AWS as well. So this is one of the great things that we were doing. Also we're investing on a new solution for just-in-time access for cloud VMs and cloud consoles. And all of these solutions that I've mentioned and more to that are part of our identity security platform which came to provide you with the suite of solution to apply least privilege and secure access to any type of resource from any device for any type of identity. >> So is that best practice? I mean, if you had to, you know, advise a customer on best practice in identity, how should they think about that? Where should they start? >> Well, on the best practices front we recently published an ebook with AWS. And it's focused on the shared responsibility model and foundational best practices for securing cloud access. And it's all part of an initiative that CyberArk has, which is our identity security blueprint. Which guides customers on how best to move forward with their identity security initiatives. >> So where do they start? First of all how do they get that is it a security website or? >> It's available on our website and we detailed some of the steps that that customers can take. For example, one of the steps that we recommend to our customers is to limit the use of the root account and also to very much lock down the root account to use federated identities whenever possible. And Bar already alluded to some of the other best practices that we recommend. Such as removing hard-coded credentials from secrets. Another best practice that we really recommend to our customers is to have a consistent set of controls across their entire estate. Both from on-premises to the cloud. And this really helps to reduce complexity by having a unified and consistent set of security controls. And in fact one of our customers who is one of the world's largest convenience chains. They're using CyberArk to secure the credentials both for their on-premise servers and their AWS EC2 instances. And they're also using us as well to secure the credentials used by applications in the CI CD pipeline. So getting to those consistent controls is another best practice we highly recommend. >> So, consistent identity across your state, whether it's on-prem or in the cloud. And then also you've referenced CI CD a couple of times. So it's it's developer friendly? Are you're designing security in as opposed to a bolt on after the fact? And then you mentioned root accounts access. Is that where privilege access management comes in? Are we going to treat everybody as privileged access? Or how do you deal with machines? You mentioned hard-coded? Like some machines are hard-coded. Like I would imagine a lot of these internet cameras are exposures. How do you deal with all that? I mean, do you just have to cycle through and modernize your fleet of machines? Are there ways in which CyberArk can help sort of anticipate that or defend against that? >> Well, CyberArk can help on, on multiple fronts. Of course you need to secure the root account but that's just only one example of needing to secure a privilege access. And one thing that customers need to understand is that now going forward, any identity can have privilege access at any point in time, because at any point and time, you yourself could have access to a highly sensitive system or have access to highly sensitive data. So with CyberArk we help our customers understand which of their applications and infrastructure have the most sensitive data and then work with them to secure the access to that data whether that access be a human access or machine or programmatic access. >> So what are the customer implications of all this? I mean pre pandemic, you know, this whole zero trust thing with password. Now it's like fundamental premise. You don't trust to verify. What are the customer implications as we enter this new era ransomware through the roof, the adversaries are well funded highly capable. They're living off the land, they're island hopping. They're, doing self forming malware. It's a new world, right? So what are the customer implications? What should they be thinking about? You know, they don't have unlimited budget. So what's the advice? >> Well, eventually at the end of the day, there are all kinds of best practices of how to applies security. I think that both AWS have their own best practices and CyberArk has also our own best practices calling the blueprint which help organization to focus on to crown jewel on the most important stuff. And then going deeper and lower within each and every initiative. And on each and every level, try to investigate what you're trying to protect and what kind of security mechanisms can be applied in order to protect both access and maintaining that no one whether if it's internal or external attacker can gain access to it. >> Yup, I think the other implication for customers and you already alluded to it is really to continue to move forward with their zero trust initiatives. I think that that is a foundational going forward. Now that remote work is kind of the defacto norm and we can no longer rely on the traditional network perimeter. And so in this new environment securing your identities is the new perimeter. So that's an important implication for customers. And then another one that I would mention is that security teams need to work more closely with their dev and dev ops counterparts to bacon security earlier. It really can't be that security is brought in after the fact. Security very much needs to shift left and be included in the very early stages of application development before an application comes to production. >> I mean, I think it's that last point but all good points. The last point was a huge theme at CubeCon this year. That notion of shift left developers, you've mentioned the CI CD pipeline several times. I mean I think that is, you know, especially when you think about machines and the edge and IoT. I used to say all the time, you know that you used to put a moat around the castle, build a wall, protect the queen. Well, the queen has left the castle. But now with the pandemic, we've seen the effects of that. And as I say, the adversaries are seeing huge opportunities. Well-funded super sophisticated. It's like it makes Stuxnet look like a kindergarten. I know that was still >> That's scary. still pretty sophisticated. But I mean, look at what we saw with the government hack and solar winds, you know huge huge. But if we can talk to CSOs about that, they're like, you know, that's, we have to move fast. But they don't have unlimited budget, right? Cybersecurity is their number one initiative in terms of priorities. But then they have all these other things to fund. They have to fund a forced march to digital transformation, machine learning and AI, they're migrating to the cloud. They're driving automation. They're modernizing their application portfolio. So, security is still number one, isn't it? So it's a good business that you're in. >> Yes, and we really want to work with our CSOs so they can get the most investment out of what they're putting into CyberArk and the rest of their strategic security vendors. Because as you mentioned there's a talent shortage. So anything that we can do as vendors to make it easier for them to use our products and get more value from our solutions, is something that's really important. >> And automation is part of the answer but it's not the only answer, right? You got to follow the NIST framework and follow these best practices and keep fighting the fight. Guys. Thanks so much for coming on theCUBE. It was great to have you. I'd love to have you back. >> Thanks for having us. >> Thank you for having us. >> All right. Our pleasure. All right, this is Dave Vellante for theCUBE. You're watching our coverage of AWS re:Invent 2021. (gentle upbeat music)

(upbeat music) >> Hello, welcome back to theCUBE's coverage of AWS re:Invent 2021. We're here in the studios in Palo Alto, California. Two great guests Linda Tong, general manager of Cisco AppDynamics and Garrick Linn, architect of operations at Match.com. Thanks for joining us. We're talking about AppDynamics, Match.com and customer experience. Mainly around cloud migration. So Linda, great to see you and Garrick, thanks for coming on theCUBE. >> Great to see you again. Thank you for having us. >> Same here. >> Linda, you're a CUBE alumni. we've talked about cloud migration application performance, modern application development, all powered by the Cloud, right? So this is really key and people are relying on the cloud and cloud scale and data to drive the digital transformation, the digital services and applications right now. How has the pandemic affected your customers and their expectations for digital experiences? >> Oh boy, I mean the pandemic has been, it has been rough for our customers, you know, and part of that is what Garrick's going to tell you a little bit more about today, but folks are seeing this increase in expectancy of accelerated speed and delivering innovation, building great applications and iterating on them quickly. And frankly, their customers' demands we're engaging with them through digital services. And that has led to this massive increase in, one, the types of technologies that they're consuming to build and deliver these applications. And two the complexity upon how they actually wrap their arms around it and understand what's going on and deliver these great experiences. And so it's been a rough road for our customers and what we find with AppDynamics and Cisco is our ability to partner with our customers to help them wrap their arms around that complexity. >> John: Garrick, I'd love to get your commentary on this because I'll say, Match.com has been at large-scale for many, many years, and now the pandemic comes in now a new user experience, more accelerated, more action, more things are happening, right? So this is truly the hybrid world coming together. I mean, it is kind of the same game, but kind of new patterns are emerging. What have you seen in the pandemic around the expectations and the services and you guys are providing in the digital experiences? >> Yeah, sure. So as you mentioned, Match has been around for quite some time. We've been here for over 25 years. We have an interesting mix, heterogeneous, technology, some old stuff, some new stuff. A lot of the mentality that we try to bring is to innovate. The pandemic was, it brought a lot of uncertainty. We weren't really sure how people were going to react. Was it going to be everybody kind of hunkers down on dating definitely is something that requires human interaction in multiple levels. And it turned out that people were still very much interested in getting to a place where they can find human connections and you know Match as a premium product tries to make that delightful. And so we had our hands full, especially at the beginning, things like, by checking the video features, how does that work? What are the expectations? Is that going to creep people out? If we try to offer that, are they going to use it? How are they going to date? How are they going to talk? How can we make sure that they're safe? All these kinds of things went into it. And so when we have been using AppDynamics for you know, years now, well before the pandemic, and we use that in order to get a gauge, not just on the type of traffic and load, but also, "Hey, you've got these new features, "how do they fit into this huge complex environment?" And so some of those timelines that maybe were a little bit more relaxed were very much accelerated, And like a lot of companies, we had to figure out how to deliver on that. >> John: Yeah, Linda, I want to get your thoughts. We've talked about in the past, AppDynamics has been a leader in really accelerating the value for customers. Now with the pandemic, you mentioned these new experiences are being pulled in from the physical world, right? So you have things that were happening on digital in the application space. Now you have more experiences coming in because there's no places to meet face to face. Now it's coming together, but people have been seeing the value. Well, if I can't meet in person Match.com are going to do some things, new things, online chat, whatever. This dynamic of old way, new way is changing and cloud is powering that. What are you seeing in terms of your customers' journeys around what was once pre-pandemic and now post-pandemic? >> Well, a big part of that is more and more of these experiences rely on digital services and these amazing sort of ways to connect with each other and in a very digital space, expectations of customers have changed. So not only do you experience applications and you want it to be simple, easy to use, delightful, and it delivers on the needs that you want. But on top of that, you expect it to be performant. You expect it to be secure. You expect there to be frankly, no hiccups whatsoever, because now this is your way to connect with others. This is your way to find dates or go on dates. And the last thing you want, is watching your screen pixelate, as you're trying to have an important conversation. And these kinds of experiences and these challenges as people build more and more of these digital services to build these connections, frankly, require a lot more of folks like Garrick and his team. They now have to deliver amazing experiences with perfect performance, no security risks, no bumps in the night. And that's really tough, right? Expectations have gone through the roof. >> John: Yeah, the whole story on that one point, just to kind of add live in this was that that whole concept of moving fast used to take months, right? I mean, weeks, months, now it's days and hours. So months to weeks, days and hours but Garrick, this is the challenge. This is the opportunity with the cloud. Can you just take us through your cloud journey and your goals and some of the impacts that has had on your transition to the cloud? What does that look like? >> Yeah, so we've had our on-prem data centers for quite some time, and we started putting our toe in, I guess, although it was a kind of intense at the beginning, just trying to get people on board and to say, "Hey, this is possible." We started out with a fairly small SWAT team then managed within a couple of months, working closely with our developers. We have a lot of smart people, you know, with background or overall, just security folks over devs to just demonstrate that we could do it. So we managed to take something like 80% of our front end traffic for most of the day, just kind of spinning that up, learning lessons from that, knowing what we didn't know. AppDynamics, if we didn't have that would have been almost impossible to get a read if for no other reason, then just one little tidbit. We used to have a data center in Virginia. And so physics being what it is, you know, there's just been a flight that we have to contend with. And for a couple, few years, we hadn't had the 30 millisecond or so round trip latency on there. So all of a sudden we're going back to the cloud that reintroduced this latency. So what does that mean? Will you be asked to sort of glide by and absorb it? How do we track it? How can we figure out what the Delta is between, you know, here's how we've done things on-prem. Here's how it looks out here. If you are the cross, you know, calls and, you know, AppDynamics was what we used to be able to get a read and say, "Hey, look, it isn't as good as we know we can make it, but it's something, it's a starting point. Here's why, we can show you the graphs. We can show you the data. Let's do this thing." So we then pulled back and we have focused this year on actually our affinity apps, which is a collection of applications that are also going to be okay just in, and so we've been asked to get those completely migrated over. We're going to be running in hybrid mode for a while. We're going to need to be able to compare apples to apples, apples to orangutans, all that. And this is one of the main things for you, we describe. >> {John] If I can just follow up on that just real quick, because I think this is a good point. You got the data points, you double down on that. You're looking at real data, and then you look at success and you double down, that's the playbook. So, and the other thing is that you guys actually have a real operation that's running full throttled, right? (John laughs) So, yeah, so I can see that nice balance. What does the future look like beyond that? Because when you got a business that's scaling, it's running, it's like changing the airplane engine out at 30,000 feet. You got to continue to push the envelope. >> Yup, so, and no, exactly right. Again, we're a premium product. And so we've got to back that up. And that means, maintaining high availability. And so over the next few years, we're going to be looking at what have we already do? What can we move in piecemeal kind of way where it makes sense? What are the things that we can rethink? We're also using AppDynamics as part of our containerization initiative. You know, we've got lots of virtual infrastructure, but what is it, again, what does it look like on-prem, in a container, go down the list of different things that might be different. And then to be able to compare that to what it looks like, in the cloud. So it's going to be a while yet, but like a lot of companies, when we got into this, we didn't think it was going to be done in six months. Even if we have to deliver those features at a much faster rate, we know that the long haul, we got to make smart decisions and plan the capacity, and, you know, get there. (chuckles) >> John: That's a real pragmatic approach. Linda, you and I both are sports fans. We've talked in the past about sports, and the old adage, what inning are we in growth? It's to use that baseball metaphor. I would say it's a double header, game one won by the cloud, game two is happening now. And the trend is this end-to-end mature, operationally focused customer base. And IT, where IT has shifted to the cloud right now. And they're having this new view of what modern is. End-to-end, understanding different stacks relative to applications. It's not as simple as it was before, but it's relevant. Can you share your views on how that's playing out because, or do you agree with that? And do you see that as an important part of the customer? >> Yeah, I mean, I think it's, that complexity that the IT organizations are seeing now, as they fully adopt the cloud for all their new applications and start to migrate some of their existing applications over. That world is only increasing in complexity. The way that you can virtualize your applications, break them out into millions of services, the dependencies you have on third party applications or SaaS services. These things only add that many more data points that you now have to cover and think about and make sure that those things deliver upon their SLAs, right? And wrapping your arms around that requires a partner to help you separate signal from noise. Because now you're going into a world without simplicity that you just mentioned has gotten to some point where it's beyond what you can actually sort of keep in your mind. Beyond what you can just look at data and sift through and understand, you really need tools and systems that come together, and understand that data for you and start to represent your business to you in a new way and abstract away those layers of complexity. While you do that, because I think, as you talk about those innings, that first inning, second inning, or rather first game, second game in the series, it's not a full migration to the cloud, right? There are going to be some applications that stay on-prem that stay in their traditional environments and may never move. And then some of them are going to go hybrid. Some will keep parts of the applications on-prem, and they're going to start to modularize components of it. And so it's not going to be sort of a mass scale migration. And then we're all in the promised land. And we deal with the cloud complexity. It's going to be ever increasing complexity. As we now introduce so many variants of applications, so many variants of technology, and what people are going to need is someone who can help them cover that entire estate and understand it at scale. >> John: Yeah, I mean, I think it's the enterprise conversion, if you will of cloud operations on-premises because of the reasons. And now you've got the edge. Garrick, this is the whole kind of end-to-end stack conversation view. And by the way, there isn't one tech stack to rule them all because you have different use cases. You might have an application that needs a financial gateway or have other capabilities. So integration's huge. This only increases the point Linda was making about complexity behind the scenes. How does AppDynamics help you with this for Match.com? >> So we have quite a bit of infrastructure, you know, a lot of it is shared, well, most of all, maintaining, sandboxes for user data and that sort of thing. And so now the navigating that space is always interesting. So for instance, one of the new things that we have coming out is Star.com It's out there right now. It's a dating site that's geared towards single parents. It does share some of the infrastructure, but we're realizing what that means, how is that different, how our registration flow is different, how our subscription flow is different. Where are the things that DevOps are actively trying to improve on and rethink? That's one of the things that we try to focus on when we're trying to kind of pick out, like, is this a good candidate to move over to the cloud sooner or later? Is this a good candidate for something that needs to be maybe bake a little bit more? And having established those baselines with the shared infrastructure, and having a pretty good understanding of how they react, how they work really helps us, you know, tee up these new initiatives and in front of those needs in a more efficient way. So yeah, absolutely. >> John: What's some of the activity you guys seen? And what's the peak activity on Match.com these days? >> Yeah, so dating apps in general, but not so particular we use a nested or breast fractal peak, and it's a pattern that, from what they told me back in the old days, took a little while to realize was a thing. And not just like, oh we changed something and then did this and produced that. So every evening is our peak basically. So with taking time zones into account, obviously, in the United States from about five to 10 o'clock at night or so, we get this, growing, burst of traffic. So that can be anywhere from 23% sometimes. It kind of varies. Then we have a weekly peak where every, you know, Sunday and Monday we expect a higher amount of traffic than we would other days. And it kind of makes sense from an Archer psychology kind of standpoint where, you know, you're coming off of dates, you're trying to set dates up. That's where a lot of that activity is. And then we have a yearly peak, which goes from around Christmas to President's day. Believe it or not, it's President's day, it's not Valentine's day. And so the sort of thing where when we're trying to plan for capacity and we do a lot of, what cost squeeze tests, were not quite as I guess, engineering, but hey, what does it look like if we go down in capacity by 50%, what happens? where are the weak points? A January, Monday night is very different from a May, Thursday in June (chuckles). So we have to predict, we can anticipate some of that, but we don't know for sure, a lot can change in a year. So when we're preparing for a yearly peak, we really have to pay attention. We have to prep. We have to plan for that and work with that to figure out how we can get through it and maintain that level of service. >> That's awesome, and AppDynamics to help you to do that. I'd love to get a bot to give me the optimal dating times, to share with my single friends. Great stuff. Linda, thank you for coming. Great to see you. Congratulations on a great case study. Great story. How large-scale applications and are working in the modern cloud. So congratulations on your success. Thanks for coming on theCUBE. Appreciate it. >> Awesome, thank you, so good to be here. >> Okay, CUBE coverage of re:Invent 2021. I'm John Furrier with theCUBE. Thanks for watching. (upbeat music)

>> Welcome back to Los Angeles. The cube is live. It feels so good to say that. I'm going to say that again. The cube is alive in Los Angeles. We are a coop con cloud native con 21. Lisa Martin with Dave Nicholson. We're talking to storm forge next. Cool name, right? We're going to get to the bottom of that. Please welcome Matt Provo, the founder and CEO of storm forge and Tom Ellery, the SVP of revenue storm forge, guys, welcome to the program. Thanks for having us. So storm forge, you have to say it like that. Like I feel like do you guys wear Storm trooper outfits on Halloween. >> Sometimes Storm trooper? The colors are black. You know, we hit anvils from time to time. >> I thought I, I thought they, that I saw >> Or may not be a heavy metal band that might be infringing on our name. It's all good. That's where we come from. >> I see. So you, so you started the company in 2015. Talk to me about the Genesis of the company. What were some of the gaps in the market that you saw that said we got to come in here and solve this? >> Yeah, so I was fortunate to always know. I think when you start a company, sometimes you, you know exactly the set of problems that you want to go after and potentially why you might be uniquely set up to solve it. What we knew at the beginning was we had a number of really talented data scientists. I was frustrated by the buzzwords around AI and machine learning when under the hood, this really a lot of vaporware. And so at the outset, really the, the point was build something real at the core, connect that to a set of problems that could drive value. And when we looked at really the beginnings of Kubernetes and containerization five, six years ago at its Genesis, we saw just a bunch of opportunity for machine learning, to play the right kind of role if we could build it correctly. And so at the outset it was what's going on. Why are people are people moving content workloads over to containers in the first place? And, you know, because of the flexibility and the portability around Kubernetes, we then ran into quickly its complexity. And within that complexity was really the foundation to set up the company and the solution for prob a set of problems uniquely and most beneficially solved by using machine learning. And so when we sort of brought that together and designed out some ideas, we, we did what any, any founder with a product background would do. We went and talked to a bunch of potential users and kind of tried to validate the problems themselves and, and got a really positive response. So. >> So Tom, from a business perspective, what, what attracted you to this? >> Well, initially I wasn't attracted just, I'll say that just from a startup standpoint. So I've been in the industry for 30 years, I've done six or seven pre IPO companies. I was exiting a private company. I did not want to go do another startup company, but being in the largest enterprise companies for the last 20 years, you see Kubernetes like wildfire in these places. And you knew there was huge amount of complexity and sophistication when they deployed it. So I started talking to Matt early on. He explained what they were doing and how unique the offer was around machine learning. I already knew the problems that customers had at scale with Kubernetes. So it was for me, I said, all right, I'm going to take one more run at this with Matt. I think we're, we're in a great position to differentiate ourselves. So that was really the launch pad for me, was really the technology and the market space. Those, those two things in combination are very exciting for us as a business. >> And, you know, a couple of bottles of amazing wine and a number of dinners that. >> Helps as well. >> That definitely helped twist his arm? >> Now tell us, just really kind of get into the technology. What does it do? How does it help facilitate the Kubernetes environment? >> Yeah, absolutely. So when organizations start moving workloads over to Kubernetes and get their applications up and running, there's a number of amazing organizations, whether it's through cloud providers or otherwise that that sort of solved that day one problem, those challenges. And as I was mentioning, you know, they moved because of flexibility and so developers love it and it starts to create a great experience, but there's these set of expectations. >> Where, where typically are these moving from? What you, what, what are the, what are the top three environments these are, that these are moving out of? >> Yeah. I mean, of course, non containerized environments, more generally. They could be coming from, you know, bare metal environment and it could be coming from kind of a VM driven environment. >> Okay. >> So when you look back at kind of the, the growth and Genesis and of VMs, you see a lot of parallels to what we're seeing now with, with containerization. And so as you move, it's, it's exciting. And then you get smacked in the face with the complexity, for all of the knobs that are able to be turned within a Kubernetes environment. It gives developers a lot of flexibility. These knobs, as you turn them, you have no visibility into how into the impact on the application itself. And so often organizations are become, you know, becoming more agile shipping, you know, shipping code more quickly, but then all of a sudden the, the cloud bill comes and they've, over-provisioned by 80, 90%, the, they didn't need nearly as many resources. And so what we do is we help understand the unique goals and requirements for each of the applications that are running in Kubernetes. And we have machine learning capabilities that can predict very accurately what organizations will need from a resource standpoint, in order to meet their goals, not just from a cost standpoint, but also from a performance standpoint. And so we allow organizations to typically save usually between 40 and 60% off their cloud bill and usually increased performance between 30 and 50%. Historically developers had to choose between cost and performance and their worldview on the application environment was very limited to a small set of what we would call parameters or metrics that they could choose from. And machine learning allows that world to just be blown open and not many humans are, are sophisticated in the way we think about multidimensional math to be able to make those kinds of predictions. You're talking about billions and billions of combinations, not just in a static environment, but an ongoing basis. So our technology sits in the middle of all that chaos and, and allows it to allows organizations just to re reap a whole lot of benefits that they otherwise may not ever find. >> Those numbers that you mentioned were, were big from a cost savings perspective than a performance increased perspective, which is so critical these days is in the last 18 months, we've seen so much change. We've seen massive pivots from companies in every industry to survive first of all, and then to be able to thrive and be able to iterate quickly enough to develop new products and services and get them to market to be competitive. >> Yeah. >> Yeah. Sorry. I mean, the thing that's interesting, there was an article by Andreessen Horowitz. I don't know if you've taken to the cloud paradox. So we actually, if you start looking at that great example would be some of these cloud companies that are growing like astronomical rates, snowflakes, like phenomenal what they're doing, but go look at their cogs and what it's doing. Also, it's growing almost proportionately as the revenues growing. So you need to be able to solve that problem in a way that is sophisticated enough with machine learning algorithms, that people don't have to be in the loop to do it. And that the math can prove out the solution as you go out and scale your environments. And a lot of companies now are all transitioning over SAS based platforms, and they're going to start running into these problems that they go as they go to scale. And those are the areas that we're really focused and concentrating on as an organization. >> As the leader of sales, talk to me about the voice of the customer. What are some- you've been there six months or so we heard, we heard about the wine and the dinners is obvious. >> We haven't done a lot of that over the last 18 months. >> You'll have to make for lost time then >> As soon as he closes more business. >> Oh, oh there we go, we got that on camera! >> There's, there's been three, a market spaces that we've had some really good success in that. So we talked about a SAS marketplace. So there's a company that does Drupal and Matt knows very well up in Boston, Aquia. And they have every customer is a unique snowflake customer. So they need to optimize each of their customers in order to ensure the cost as well as performance for that customer on their site works appropriately. So that's one example of a SAS based company that where we can go in and help them optimize without humans doing the optimization and the math and the machine learning from storm forge doing that. So that's an area, the other area that we've seen some really good traction Cantonese with GSI. So part of our go to market model is with GSI. So if you think about what a GSI does, a lot of times customers are struggling either initially deploying Kubernetes or putting it in for 12, 18 months and realizing we're starting to scale, we got all kinds of performance issues. How do I solve it? A lot of these people go to the Accentures, the cognizance and other ones, and start flying their ninjas into kind of solve the problem. So we're getting a lot of traction with them because they're using our tool as a way to help solve the customer's problems. And they're in the largest enterprise customers as possible. >> So if I'm hearing what you're saying correctly, you're saying that when I deploy server less applications, I may in fact, get a bill for servers that are being used? Is it, is that what you're telling us? >> They're there in fact may be a bill for what was coined as server less. That is very difficult to understand, by the way, >> That's crazy talk, Matt. >> And connect back. >> Yeah. But absolutely we deal with that all the time. It's a, it's a painful process from time to time. >> Have you, have you, have you seen the statistics that's going on with how people, I mean, there was huge inertia from every CIO that you had have a cloud strategy in place. Everyone ran out and had a cloud strategy in place. And then they started deploying on Kubernetes. Now they're realizing, oh wow, we can run it, but it's costing us more than it ever costs us on prem and the operational complexity associated with that. So there's not enough people in the industry to help solve that problem, especially at the grass roots, that's where you need sophisticated solutions like storm forge and machine learning to help solve this at scale problem in a way that humans could never solve. >> And I would, I would just add to that, that the, the same humans managing the Kubernetes application environments today are likely the same humans that we're managing it in a, in a BM world. So there's a huge skills gap. I love what Castin announced at KU KU con this year around their learning environment where it's free. Come learn Kubernetes and this, and we need more of that. There's an enormous skills gap and, and the problems are complex enough in and of themselves. But when we have, when you add that to the skills gap, it it's, it presents a lot of challenges for organizations. >> What are some the ways in which you think that gap can start to be made smaller. >> Yeah. I mean, I think as more workloads get moved over, over, you know, over time, you see, you see more and more people becoming comfortable in an environment where scale is a part of what they have to manage and take care of. I love what the Linux foundation and the CNCF are doing around Kubernetes certifications, you know, more and more training. I think you're going to see training, you know, availability for more and more developers and practitioners be adopted more widely. You know, and I think that, you know, as the tool chain itself hardens within a CCD world in a containerized world, as that hardens, you're going to, you're going to start seeing more and more individuals who are comfortable across all these different tools. If you look at the CNCF landscape, I mean, today compared to four or five years ago, it's growing like crazy. And so, but, but there's also consolidation taking place within the tools. And people have an opportunity to, to learn and gain expertise within us. Which is very marketable by the way, >> Absolutely >> My employees often show me their LinkedIn profiles and remind me of how , how much they're getting recruited, but they've been loyal. So it's been a fantastic. >> Are there are so many parallels when you look at a VM in virtualization and what's happening with covers, obviously all the abstractions and stuff, but there was this whole concept of VM sprawl, you know, maybe 10 years in, if you think about the Kubernetes environment, that is exponentially bigger problem because of how many they're spitting up versus how, how many you spun up in VM. So those things ultimately need to be solved. It's not just going to be solved with people. It needs to be solved with sophisticated software. That's the only way you're going to solve a problem at scale like that. No matter how many people you have in the industry, it's just never going to solve the problem. >> So when you're in customer conversations, Tom, what are you say are like the top three differentiators that really set storm forage apart? >> Well, so the first one is we're very focused on Kubernetes only. So that's all we do is just Kubernetes environment. So we understand not just the applications that run in Kubernetes, but we understand the underlying architectures and techniques, which we think is really important. From a solution standpoint, >> So you're specialists? >> We are absolutely specialists. The other areas obviously are machine learning and the sophistication of our machine learning. And Matt said this really well, early on, I mean, the buzzwords are all out there. You can read them all up, all over the place for the last five to seven year AI and ML. And a lot of them are very hollow, but our whole foundation was based on machine learning and PhDs from Harvard. That's where we came out of from a technology background. So we were solving more, we weren't just solving the Kubernetes problems. We were solving machine learning problems. And so that's another really big area of differential for us. And I think the ability to actually scale and not just deal with small problems, but very large problems, because our focus is the fortune 2000 companies. And most of them have been deploying like financial services and stuff, Kubernetes for three, four or five years. And so they have had scale challenges that they're trying to solve. >> Yeah. It's Lisa and I talk about this concept of machine learning and looking under the covers and trying to find out is the machine really learning? Is it really learning or is it people are telling the machine, you need to do this. If you see that Where's the machine actually making those correlations and doing something intelligently. So can you give us an example of something that is actually happening that's intelligent? >> Well, so the, the, if this, then that problem is actually a huge source of my original frustration for starting the company, because you, you, you tag AI as a buzzword onto a lot of stuff. And we see that growing like crazy. And so I literally at the beginning said, if we can't actually build something real, that solves problems, like we're going to hang it up. And, you know, as Tom said, we came out of Harvard and, you know, there was a challenge initially of, are we just going to build like a really amazing algorithm? That's so heavy, it can never be productized or commercialized and it really should have just stayed in academia. And, you know, I the I, I will say a couple of things. One is I do not believe that that black box AI is a thing. We believe in what we would call human, augmented AI. So we want to empower practitioners and developers into the process instead of automate them out. We just want to give them the information and we want to save time for them and make their lives easier. But there's a kill switch on the technology. They can intervene at any point in time. They can direct the technology as they see fit. And what's really, really interesting is because their worldview of this application environment gets opened up by all the predictions and all of the learning that actually is taking place and, you know, give it because that worldview is open, they then get into a kind of a tinkering or experimental mindset with the technology. And they start thinking about all these other scenarios that they never were able to explore previously with the application. And, and so the machine learning itself is on an ongoing basis. Understanding changes in traffic, understanding and changes, changes in workloads for the application or demand. If you thought about like surge pricing for Uber, you know, because of a, a big game that took place. And you know, that, that change in peaks and valleys in demand, our, our technology not only understands those reactively, but it starts to build models and predict proactively in advance of the events that are going to take place on, on what ne- what kind of resources need to be allocated. And why that's the other piece around it is often solutions are giving you a little bit of a what, but they certainly are not giving you any explanation of the why. So the holy grail really like in our world is kind of truly explainable AI, which we're not there yet. Nobody's there yet. But human augmented AI with, with actual intelligence that's taking place that also is relevant to business outcomes is, is pretty exciting. So that's why where try to operate. >> Very exciting guys. Thanks for joining us, talking to us about storm forage, to feel like we need some store in forge. T-shirts what do you think? >> (unintelligible) >> See, I'm not even asking for the bottle of wine. I liked that idea. I thank Matt and Tom, thank you so much for joining us exciting company. Congratulations on your success. And we look forward to seeing what great things are to come from storm forage. >> Thanks so much for the time. >> Our pleasure. For Dave Nicholson. I'm Lisa Martin. We are alive in Los Angeles, the cube covering Kube con and cloud native con 21 stick around. Dave and I will be right back with our next guest.

>>Have you ever wondered how we sequence the human genome, how your smartphone is so well smart, how we will ever analyze all the patient data for the new vaccines or even how we plan to send humans to Mars? Well, at Cloudera, we believe that data can make what is impossible today possible tomorrow we are the enterprise data cloud company. In fact, we provide analytics and machine learning technology that does everything from making your smartphone smarter, to helping scientists ensure that new vaccines are both safe and effective, big data, no problem out era, the enterprise data cloud company. >>So I think for a long time in this country, we've known that there's a great disparity between minority populations and the majority of population in terms of disease burden. And depending on where you live, your zip code has more to do with your health than almost anything else. But there are a lot of smaller, um, safety net facilities, as well as small academic medical colleges within the United States. And those in those smaller environments don't have the access, you know, to the technologies that the larger ones have. And, you know, I call that, uh, digital disparity. So I'm, Harry's in academic scientist center and our mission is to train diverse health care providers and researchers, but also provide services to underserved populations. As part of the reason that I think is so important for me hearing medical college, to do data science. One of the things that, you know, both Cloudera and Claire sensor very passionate about is bringing those height in technologies to, um, to the smaller organizations. >>It's very expensive to go to the cloud for these small organizations. So now with the partnership with Cloudera and Claire sets a clear sense, clients now enjoy those same technologies and really honestly have a technological advantage over some of the larger organizations. The reason being is they can move fast. So we were able to do this on our own without having to, um, hire data scientists. Uh, we probably cut three to five years off of our studies. I grew up in a small town in Arkansas and is one of those towns where the railroad tracks divided the blacks and the whites. My father died without getting much healthcare at all. And as an 11 year old, I did not understand why my father could not get medical attention because he was very sick. >>Since we come at my Harry are looking to serve populations that reflect themselves or affect the population. He came from. A lot of the data you find or research you find health is usually based on white men. And obviously not everybody who needs a medical provider is going to be a white male. >>One of the things that we're concerned about in healthcare is that there's bias in treatment already. We want to make sure those same biases do not enter into the algorithms. >>The issue is how do we get ahead of them to try to prevent these disparities? >>One of the great things about our dataset is that it contains a very diverse group of patients. >>Instead of just saying, everyone will have these results. You can break it down by race, class, cholesterol, level, other kinds of factors that play a role. So you can make the treatments in the long run. More specifically, >>Researchers are now able to use these technologies and really take those hypotheses from, from bench to bedside. >>We're able to overall improve the health of not just the person in front of you, but the population that, yeah, >>Well, the future is now. I love a quote by William Gibson who said the future is already here. 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Consumers are always on and didn't control. It's the era of smart retail, smart shelves, digital signage, and smart mirrors offer an immersive customer experience while delivering product information, personalized offers and recommendations, video analytics, capture customer emotions and gestures to better understand and respond to in-store shopping experiences. Beacons sensors, and streaming video provide valuable data into in-store traffic patterns, hotspots and dwell times. This helps retailers build visual heat maps to better understand custom journeys, conversion rates, and promotional effectiveness in our robots automate routine tasks like capturing inventory levels, identifying out of stocks and alerting in store personnel to replenish shelves. When it comes to checking out automated e-commerce pickup stations and frictionless checkouts will soon be the norm making standing in line. A thing of the past data and analytics are truly reshaping. >>The everyday shopping experience outside the store, smart trucks connect the supply chain, providing new levels of inventory visibility, not just into the precise location, but also the condition of those goods. All in real time, convenience is key and customers today have the power to get their goods delivered at the curbside to their doorstep, or even to their refrigerators. Smart retail is indeed here. And Cloudera makes all of this possible using Cloudera data can be captured from a variety of sources, then stored, processed, and analyzed to drive insights and action. In real time, data scientists can continuously build and train new machine learning models and put these models back to the edge for delivering those moment of truth customer experiences. This is the enterprise data cloud powered by Cloudera enabling smart retail from the edge to AI. The future has arrived >>For is a global automotive supplier. We have three business groups, automotive seating in studios, and then emission control technologies or biggest automotive customers are Volkswagen for the NPSA. And we have, uh, more than 300 sites. And in 75 countries >>Today, we are generating tons of data, more and more data on the manufacturing intelligence. We are trying to reduce the, the defective parts or anticipate the detection of the, of the defective part. And this is where we can get savings. I would say our goal in manufacturing is zero defects. The cost of downtime in a plant could be around the a hundred thousand euros. So with predictive maintenance, we are identifying correlations and patterns and try to anticipate, and maybe to replace a component before the machine is broken. We are in the range of about 2000 machines and we can have up to 300 different variables from pressure from vibration and temperatures. And the real-time data collection is key, and this is something we cannot achieve in a classical data warehouse approach. So with the be data and with clouded approach, what we are able to use really to put all the data, all the sources together in the classical way of working with that at our house, we need to spend weeks or months to set up the model with the Cloudera data lake. We can start working on from days to weeks. We think that predictive or machine learning could also improve on the estimation or NTC patient forecasting of what we'll need to brilliance with all this knowledge around internet of things and data collection. We are applying into the predictive convene and the cockpit of the future. So we can work in the self driving car and provide a better experience for the driver in the car. >>The Cloudera data platform makes it easy to say yes to any analytic workload from the edge to AI, yes. To enterprise grade security and governance, yes. To the analytics your people want to use yes. To operating on any cloud. Your business requires yes to the future with a cloud native platform that flexes to meet your needs today and tomorrow say yes to CDP and say goodbye to shadow it, take a tour of CDP and see how it's an easier, faster and safer enterprise analytics and data management platform with a new approach to data. Finally, a data platform that lets you say yes, >>Welcome to transforming ideas into insights, presented with the cube and made possible by cloud era. My name is Dave Volante from the cube, and I'll be your host for today. And the next hundred minutes, you're going to hear how to turn your best ideas into action using data. And we're going to share the real world examples and 12 industry use cases that apply modern data techniques to improve customer experience, reduce fraud, drive manufacturing, efficiencies, better forecast, retail demand, transform analytics, improve public sector service, and so much more how we use data is rapidly evolving as is the language that we use to describe data. I mean, for example, we don't really use the term big data as often as we used to rather we use terms like digital transformation and digital business, but you think about it. What is a digital business? How is that different from just a business? >>Well, digital business is a data business and it differentiates itself by the way, it uses data to compete. So whether we call it data, big data or digital, our belief is we're entering the next decade of a world that puts data at the core of our organizations. And as such the way we use insights is also rapidly evolving. You know, of course we get value from enabling humans to act with confidence on let's call it near perfect information or capitalize on non-intuitive findings. But increasingly insights are leading to the development of data, products and services that can be monetized, or as you'll hear in our industry, examples, data is enabling machines to take cognitive actions on our behalf. Examples are everywhere in the forms of apps and products and services, all built on data. Think about a real-time fraud detection, know your customer and finance, personal health apps that monitor our heart rates. >>Self-service investing, filing insurance claims and our smart phones. And so many examples, IOT systems that communicate and act machine and machine real-time pricing actions. These are all examples of products and services that drive revenue cut costs or create other value. And they all rely on data. Now while many business leaders sometimes express frustration that their investments in data, people, and process and technologies haven't delivered the full results they desire. The truth is that the investments that they've made over the past several years should be thought of as a step on the data journey. Key learnings and expertise from these efforts are now part of the organizational DNA that can catapult us into this next era of data, transformation and leadership. One thing is certain the next 10 years of data and digital transformation, won't be like the last 10. So let's get into it. Please join us in the chat. >>You can ask questions. You can share your comments, hit us up on Twitter right now. It's my pleasure to welcome Mick Holliston in he's the president of Cloudera mic. Great to see you. Great to see you as well, Dave, Hey, so I call it the new abnormal, right? The world is kind of out of whack offices are reopening again. We're seeing travel coming back. There's all this pent up demand for cars and vacations line cooks at restaurants. Everything that we consumers have missed, but here's the one thing. It seems like the algorithms are off. Whether it's retail's fulfillment capabilities, airline scheduling their pricing algorithms, you know, commodity prices we don't know is inflation. Transitory. Is it a long-term threat trying to forecast GDP? It's just seems like we have to reset all of our assumptions and make a feel a quality data is going to be a key here. How do you see the current state of the industry and the role data plays to get us into a more predictable and stable future? Well, I >>Can sure tell you this, Dave, uh, out of whack is definitely right. I don't know if you know or not, but I happen to be coming to you live today from Atlanta and, uh, as a native of Atlanta, I can, I can tell you there's a lot to be known about the airport here. It's often said that, uh, whether you're going to heaven or hell, you got to change planes in Atlanta and, uh, after 40 minutes waiting on algorithm to be right for baggage claim when I was not, I finally managed to get some bag and to be able to show up dressed appropriately for you today. Um, here's one thing that I know for sure though, Dave, clean, consistent, and safe data will be essential to getting the world and businesses as we know it back on track again, um, without well-managed data, we're certain to get very inconsistent outcomes, quality data will the normalizing factor because one thing really hasn't changed about computing since the Dawn of time. Back when I was taking computer classes at Georgia tech here in Atlanta, and that's what we used to refer to as garbage in garbage out. In other words, you'll never get quality data-driven insights from a poor data set. This is especially important today for machine learning and AI, you can build the most amazing models and algorithms, but none of it will matter if the underlying data isn't rock solid as AI is increasingly used in every business app, you must build a solid data foundation mic. Let's >>Talk about hybrid. Every CXO that I talked to, they're trying to get hybrid, right? Whether it's hybrid work hybrid events, which is our business hybrid cloud, how are you thinking about the hybrid? Everything, what's your point of view with >>All those descriptions of hybrid? Everything there, one item you might not have quite hit on Dave and that's hybrid data. >>Oh yeah, you're right. Mick. I did miss that. What, what do you mean by hybrid data? Well, >>David in cloud era, we think hybrid data is all about the juxtaposition of two things, freedom and security. Now every business wants to be more agile. They want the freedom to work with their data, wherever it happens to work best for them, whether that's on premises in a private cloud and public cloud, or perhaps even in a new open data exchange. Now this matters to businesses because not all data applications are created equal. Some apps are best suited to be run in the cloud because of their transitory nature. Others may be more economical if they're running a private cloud, but either way security, regulatory compliance and increasingly data sovereignty are playing a bigger and more important role in every industry. If you don't believe me, just watch her read a recent news story. Data breaches are at an all time high. And the ethics of AI applications are being called into question every day and understanding the lineage of machine learning algorithms is now paramount for every business. So how in the heck do you get both the freedom and security that you're looking for? Well, the answer is actually pretty straightforward. The key is developing a hybrid data strategy. And what do you know Dave? That's the business cloud era? Is it on a serious note from cloud era's perspective? Adopting a hybrid data strategy is central to every business's digital transformation. It will enable rapid adoption of new technologies and optimize economic models while ensuring the security and privacy of every bit of data. What can >>Make, I'm glad you brought in that notion of hybrid data, because when you think about things, especially remote work, it really changes a lot of the assumptions. You talked about security, the data flows are going to change. You've got the economics, the physics, the local laws come into play. So what about the rest of hybrid? Yeah, >>It's a great question, Dave and certainly cloud era itself as a business and all of our customers are feeling this in a big way. We now have the overwhelming majority of our workforce working from home. And in other words, we've got a much larger surface area from a security perspective to keep in mind the rate and pace of data, just generating a report that might've happened very quickly and rapidly on the office. Uh, ether net may not be happening quite so fast in somebody's rural home in, uh, in, in the middle of Nebraska somewhere. Right? So it doesn't really matter whether you're talking about the speed of business or securing data, any way you look at it. Uh, hybrid I think is going to play a more important role in how work is conducted and what percentage of people are working in the office and are not, I know our plans, Dave, uh, involve us kind of slowly coming back to work, begin in this fall. And we're looking forward to being able to shake hands and see one another again for the first time in many cases for more than a year and a half, but, uh, yes, hybrid work, uh, and hybrid data are playing an increasingly important role for every kind of business. >>Thanks for that. I wonder if we could talk about industry transformation for a moment because it's a major theme of course, of this event. So, and the case. Here's how I think about it. It makes, I mean, some industries have transformed. You think about retail, for example, it's pretty clear, although although every physical retail brand I know has, you know, not only peaked up its online presence, but they also have an Amazon war room strategy because they're trying to take greater advantage of that physical presence, uh, and ended up reverse. We see Amazon building out physical assets so that there's more hybrid going on. But when you look at healthcare, for example, it's just starting, you know, with such highly regulated industry. It seems that there's some hurdles there. Financial services is always been data savvy, but you're seeing the emergence of FinTech and some other challenges there in terms of control, mint control of payment systems in manufacturing, you know, the pandemic highlighted America's reliance on China as a manufacturing partner and, and supply chain. Uh it's so my point is it seems that different industries they're in different stages of transformation, but two things look really clear. One, you've got to put data at the core of the business model that's compulsory. It seems like embedding AI into the applications, the data, the business process that's going to become increasingly important. So how do you see that? >>Wow, there's a lot packed into that question there, Dave, but, uh, yeah, we, we, uh, you know, at Cloudera I happened to be leading our own digital transformation as a technology company and what I would, what I would tell you there that's been arresting for us is the shift from being largely a subscription-based, uh, model to a consumption-based model requires a completely different level of instrumentation and our products and data collection that takes place in real, both for billing, for our, uh, for our customers. And to be able to check on the health and wellness, if you will, of their cloud era implementations. But it's clearly not just impacting the technology industry. You mentioned healthcare and we've been helping a number of different organizations in the life sciences realm, either speed, the rate and pace of getting vaccines, uh, to market, uh, or we've been assisting with testing process. >>That's taken place because you can imagine the quantity of data that's been generated as we've tried to study the efficacy of these vaccines on millions of people and try to ensure that they were going to deliver great outcomes and, and healthy and safe outcomes for everyone. And cloud era has been underneath a great deal of that type of work and the financial services industry you pointed out. Uh, we continue to be central to the large banks, meeting their compliance and regulatory requirements around the globe. And in many parts of the world, those are becoming more stringent than ever. And Cloudera solutions are really helping those kinds of organizations get through those difficult challenges. You, you also happened to mention, uh, you know, public sector and in public sector. We're also playing a key role in working with government entities around the world and applying AI to some of the most challenging missions that those organizations face. >>Um, and while I've made the kind of pivot between the industry conversation and the AI conversation, what I'll share with you about AI, I touched upon a little bit earlier. You can't build great AI, can't grow, build great ML apps, unless you've got a strong data foundation underneath is back to that garbage in garbage out comment that I made previously. And so in order to do that, you've got to have a great hybrid dated management platform at your disposal to ensure that your data is clean and organized and up to date. Uh, just as importantly from that, that's kind of the freedom side of things on the security side of things. You've got to ensure that you can see who just touched, not just the data itself, Dave, but actually the machine learning models and organizations around the globe are now being challenged. It's kind of on the topic of the ethics of AI to produce model lineage. >>In addition to data lineage. In other words, who's had access to the machine learning models when and where, and at what time and what decisions were made perhaps by the humans, perhaps by the machines that may have led to a particular outcome. So every kind of business that is deploying AI applications should be thinking long and hard about whether or not they can track the full lineage of those machine learning models just as they can track the lineage of data. So lots going on there across industries, lots going on as those various industries think about how AI can be applied to their businesses. Pretty >>Interesting concepts. You bring it into the discussion, the hybrid data, uh, sort of new, I think, new to a lot of people. And th this idea of model lineage is a great point because people want to talk about AI, ethics, transparency of AI. When you start putting those models into, into machines to do real time inferencing at the edge, it starts to get really complicated. I wonder if we could talk about you still on that theme of industry transformation? I felt like coming into the pandemic pre pandemic, there was just a lot of complacency. Yeah. Digital transformation and a lot of buzz words. And then we had this forced March to digital, um, and it's, but, but people are now being more planful, but there's still a lot of sort of POC limbo going on. How do you see that? Can you help accelerate that and get people out of that state? It definitely >>Is a lot of a POC limbo or a, I think some of us internally have referred to as POC purgatory, just getting stuck in that phase, not being able to get from point a to point B in digital transformation and, um, you know, for every industry transformation, uh, change in general is difficult and it takes time and money and thoughtfulness, but like with all things, what we found is small wins work best and done quickly. So trying to get to quick, easy successes where you can identify a clear goal and a clear objective and then accomplish it in rapid fashion is sort of the way to build your way towards those larger transformative efforts set. Another way, Dave, it's not wise to try to boil the ocean with your digital transformation efforts as it relates to the underlying technology here. And to bring it home a little bit more practically, I guess I would say at cloud era, we tend to recommend that companies begin to adopt cloud infrastructure, for example, containerization. >>And they begin to deploy that on-prem and then they start to look at how they may move those containerized workloads into the public cloud. That'll give them an opportunity to work with the data and the underlying applications themselves, uh, right close to home in place. They can kind of experiment a little bit more safely and economically, and then determine which workloads are best suited for the public cloud and which ones should remain on prem. That's a way in which a hybrid data strategy can help get a digital transformation accomplish, but kind of starting small and then drawing fast from there on customer's journey to the we'll make we've >>Covered a lot of ground. Uh, last question. Uh, w what, what do you want people to leave this event, the session with, and thinking about sort of the next era of data that we're entering? >>Well, it's a great question, but, uh, you know, I think it could be summed up in, uh, in two words. I want them to think about a hybrid data, uh, strategy. So, uh, you know, really hybrid data is a concept that we're bringing forward on this show really for the, for the first time, arguably, and we really do think that it enables customers to experience what we refer to Dave as the power of, and that is freedom, uh, and security, and in a world where we're all still trying to decide whether each day when we walk out each building, we walk into, uh, whether we're free to come in and out with a mask without a mask, that sort of thing, we all want freedom, but we also also want to be safe and feel safe, uh, for ourselves and for others. And the same is true of organizations. It strategies. They want the freedom to choose, to run workloads and applications and the best and most economical place possible. But they also want to do that with certainty, that they're going to be able to deploy those applications in a safe and secure way that meets the regulatory requirements of their particular industry. So hybrid data we think is key to accomplishing both freedom and security for your data and for your business as a whole, >>Nick, thanks so much great conversation and really appreciate the insights that you're bringing to this event into the industry. Really thank you for your time. >>You bet Dave pleasure being with you. Okay. >>We want to pick up on a couple of themes that Mick discussed, you know, supercharging your business with AI, for example, and this notion of getting hybrid, right? So right now we're going to turn the program over to Rob Bearden, the CEO of Cloudera and Manny veer, DAS. Who's the head of enterprise computing at Nvidia. And before I hand it off to Robin, I just want to say for those of you who follow me at the cube, we've extensively covered the transformation of the semiconductor industry. We are entering an entirely new era of computing in the enterprise, and it's being driven by the emergence of data, intensive applications and workloads no longer will conventional methods of processing data suffice to handle this work. Rather, we need new thinking around architectures and ecosystems. And one of the keys to success in this new era is collaboration between software companies like Cloudera and semiconductor designers like Nvidia. So let's learn more about this collaboration and what it means to your data business. Rob, thanks, >>Mick and Dave, that was a great conversation on how speed and agility is everything in a hyper competitive hybrid world. You touched on AI as essential to a data first strategy and accelerating the path to value and hybrid environments. And I want to drill down on this aspect today. Every business is facing accelerating everything from face-to-face meetings to buying groceries has gone digital. As a result, businesses are generating more data than ever. There are more digital transactions to track and monitor. Now, every engagement with coworkers, customers and partners is virtual from website metrics to customer service records, and even onsite sensors. Enterprises are accumulating tremendous amounts of data and unlocking insights from it is key to our enterprises success. And with data flooding every enterprise, what should the businesses do? A cloud era? We believe this onslaught of data offers an opportunity to make better business decisions faster. >>And we want to make that easier for everyone, whether it's fraud, detection, demand, forecasting, preventative maintenance, or customer churn, whether the goal is to save money or produce income every day that companies don't gain deep insight from their data is money they've lost. And the reason we're talking about speed and why speed is everything in a hybrid world and in a hyper competitive climate, is that the faster we get insights from all of our data, the faster we grow and the more competitive we are. So those faster insights are also combined with the scalability and cost benefit they cloud provides and with security and edge to AI data intimacy. That's why the partnership between cloud air and Nvidia together means so much. And it starts with the shared vision making data-driven, decision-making a reality for every business and our customers will now be able to leverage virtually unlimited quantities of varieties, of data, to power, an order of magnitude faster decision-making and together we turbo charge the enterprise data cloud to enable our customers to work faster and better, and to make integration of AI approaches a reality for companies of all sizes in the cloud. >>We're joined today by NVIDIA's Mandy veer dos, and to talk more about how our technologies will deliver the speed companies need for innovation in our hyper competitive environment. Okay, man, you're veer. Thank you for joining us over the unit. >>Thank you, Rob, for having me. It's a pleasure to be here on behalf of Nvidia. We are so excited about this partnership with Cloudera. Uh, you know, when, when, uh, when Nvidia started many years ago, we started as a chip company focused on graphics, but as you know, over the last decade, we've really become a full stack accelerated computing company where we've been using the power of GPU hardware and software to accelerate a variety of workloads, uh, AI being a prime example. And when we think about Cloudera, uh, and your company, a great company, there's three things we see Rob. Uh, the first one is that for the companies that will already transforming themselves by the use of data, Cloudera has been a trusted partner for them. The second thing seen is that when it comes to using your data, you want to use it in a variety of ways with a powerful platform, which of course you have built over time. >>And finally, as we've heard already, you believe in the power of hybrid, that data exists in different places and the compute needs to follow the data. Now, if you think about in various mission, going forward to democratize accelerated computing for all companies, our mission actually aligns very well with exactly those three things. Firstly, you know, we've really worked with a variety of companies today who have been the early adopters, uh, using the power acceleration by changing the technology in their stacks. But more and more, we see the opportunity of meeting customers, where they are with tools that they're familiar with with partners that they trust. And of course, Cloudera being a great example of that. Uh, the second, uh, part of NVIDIA's mission is we focused a lot in the beginning on deep learning where the power of GPU is really shown through, but as we've gone forward, we found that GPU's can accelerate a variety of different workloads from machine learning to inference. >>And so again, the power of your platform, uh, is very appealing. And finally, we know that AI is all about data, more and more data. We believe very strongly in the idea that customers put their data, where they need to put it. And the compute, the AI compute the machine learning compute needs to meet the customer where their data is. And so that matches really well with your philosophy, right? And Rob, that's why we were so excited to do this partnership with you. It's come to fruition. We have a great combined stack now for the customer and we already see people using it. I think the IRS is a fantastic example where literally they took the workflow. They had, they took the servers, they had, they added GPS into those servers. They did not change anything. And they got an eight times performance improvement for their fraud detection workflows, right? And that's the kind of success we're looking forward to with all customers. So the team has actually put together a great video to show us what the IRS is doing with this technology. Let's take a look. >>My name's Joanne salty. I'm the branch chief of the technical branch and RAs. It's actually the research division research and statistical division of the IRS. Basically the mission that RAs has is we do statistical and research on all things related to taxes, compliance issues, uh, fraud issues, you know, anything that you can think of. Basically we do research on that. We're running into issues now that we have a lot of ideas to actually do data mining on our big troves of data, but we don't necessarily have the infrastructure or horsepower to do it. So it's our biggest challenge is definitely the, the infrastructure to support all the ideas that the subject matter experts are coming up with in terms of all the algorithms they would like to create. And the diving deeper within the algorithm space, the actual training of those Agra algorithms, the of parameters each of those algorithms have. >>So that's, that's really been our challenge. Now the expectation was that with Nvidia in cloud, there is help. And with the cluster, we actually build out the test this on the actual fraud, a fraud detection algorithm on our expectation was we were definitely going to see some speed up in prom, computational processing times. And just to give you context, the size of the data set that we were, uh, the SMI was actually working, um, the algorithm against Liz around four terabytes. If I recall correctly, we'd had a 22 to 48 times speed up after we started tweaking the original algorithm. My expectations, quite honestly, in that sphere, in terms of the timeframe to get results, was it that you guys actually exceeded them? It was really, really quick. Uh, the definite now term short term what's next is going to be the subject matter expert is actually going to take our algorithm run with that. >>So that's definitely the now term thing we want to do going down, go looking forward, maybe out a couple of months, we're also looking at curing some, a 100 cards to actually test those out. As you guys can guess our datasets are just getting bigger and bigger and bigger, and it demands, um, to actually do something when we get more value added out of those data sets is just putting more and more demands on our infrastructure. So, you know, with the pilot, now we have an idea with the infrastructure, the infrastructure we need going forward. And then also just our in terms of thinking of the algorithms and how we can approach these problems to actually code out solutions to them. Now we're kind of like the shackles are off and we can just run them, you know, come onto our art's desire, wherever imagination takes our skis to actually develop solutions, know how the platforms to run them on just kind of the close out. >>I rarely would be very missed. I've worked with a lot of, you know, companies through the year and most of them been spectacular. And, uh, you guys are definitely in that category. The, the whole partnership, as I said, a little bit early, it was really, really well, very responsive. I would be remiss if I didn't. Thank you guys. So thank you for the opportunity to, and fantastic. And I'd have to also, I want to thank my guys. My, uh, my staff, David worked on this Richie worked on this Lex and Tony just, they did a fantastic job and I want to publicly thank him for all the work they did with you guys and Chev, obviously also. Who's fantastic. So thank you everyone. >>Okay. That's a real great example of speed and action. Now let's get into some follow up questions guys, if I may, Rob, can you talk about the specific nature of the relationship between Cloudera and Nvidia? Is it primarily go to market or you do an engineering work? What's the story there? >>It's really both. It's both go to market and engineering and engineering focus is to optimize and take advantage of invidious platform to drive better price performance, lower cost, faster speeds, and better support for today's emerging data intensive applications. So it's really both >>Great. Thank you. Many of Eric, maybe you could talk a little bit more about why can't we just existing general purpose platforms that are, that are running all this ERP and CRM and HCM and you know, all the, all the Microsoft apps that are out there. What, what do Nvidia and cloud era bring to the table that goes beyond the conventional systems that we've known for many years? >>Yeah. I think Dave, as we've talked about the asset that the customer has is really the data, right? And the same data can be utilized in many different ways. Some machine learning, some AI, some traditional data analytics. So the first step here was really to take a general platform for data processing, Cloudera data platform, and integrate with that. Now Nvidia has a software stack called rapids, which has all of the primitives that make different kinds of data processing go fast on GPU's. And so the integration here has really been taking rapids and integrating it into a Cloudera data platform. So that regardless of the technique, the customer's using to get insight from that data, the acceleration will apply in all cases. And that's why it was important to start with a platform like Cloudera rather than a specific application. >>So I think this is really important because if you think about, you know, the software defined data center brought in, you know, some great efficiencies, but at the same time, a lot of the compute power is now going toward doing things like networking and storage and security offloads. So the good news, the reason this is important is because when you think about these data intensive workloads, we can now put more processing power to work for those, you know, AI intensive, uh, things. And so that's what I want to talk about a little bit, maybe a question for both of you, maybe Rob, you could start, you think about the AI that's done today in the enterprise. A lot of it is modeling in the cloud, but when we look at a lot of the exciting use cases, bringing real-time systems together, transaction systems and analytics systems and real time, AI inference, at least even at the edge, huge potential for business value and a consumer, you're seeing a lot of applications with AI biometrics and voice recognition and autonomous vehicles and the like, and so you're putting AI into these data intensive apps within the enterprise. >>The potential there is enormous. So what can we learn from sort of where we've come from, maybe these consumer examples and Rob, how are you thinking about enterprise AI in the coming years? >>Yeah, you're right. The opportunity is huge here, but you know, 90% of the cost of AI applications is the inference. And it's been a blocker in terms of adoption because it's just been too expensive and difficult from a performance standpoint and new platforms like these being developed by cloud air and Nvidia will dramatically lower the cost, uh, of enabling this type of workload to be done. Um, and what we're going to see the most improvements will be in the speed and accuracy for existing enterprise AI apps like fraud detection, recommendation, engine chain management, drug province, and increasingly the consumer led technologies will be bleeding into the enterprise in the form of autonomous factory operations. An example of that would be robots that AR VR and manufacturing. So driving quality, better quality in the power grid management, automated retail IOT, you know, the intelligent call centers, all of these will be powered by AI, but really the list of potential use cases now are going to be virtually endless. >>I mean, this is like your wheelhouse. Maybe you could add something to that. >>Yeah. I mean, I agree with Rob. I mean he listed some really good use cases. You know, the way we see this at Nvidia, this journey is in three phases or three steps, right? The first phase was for the early adopters. You know, the builders who assembled, uh, use cases, particular use cases like a chat bot, uh, uh, from the ground up with the hardware and the software almost like going to your local hardware store and buying piece parts and constructing a table yourself right now. I think we are in the first phase of the democratization, uh, for example, the work we did with Cloudera, which is, uh, for a broader base of customers, still building for a particular use case, but starting from a much higher baseline. So think about, for example, going to Ikea now and buying a table in a box, right. >>And you still come home and assemble it, but all the parts are there. The instructions are there, there's a recipe you just follow and it's easy to do, right? So that's sort of the phase we're in now. And then going forward, the opportunity we really look forward to for the democratization, you talked about applications like CRM, et cetera. I think the next wave of democratization is when customers just adopt and deploy the next version of an application they already have. And what's happening is that under the covers, the application is infused by AI and it's become more intelligent because of AI and the customer just thinks they went to the store and bought, bought a table and it showed up and somebody placed it in the right spot. Right. And they didn't really have to learn, uh, how to do AI. So these are the phases. And I think they're very excited to be going there. Yeah. You know, >>Rob, the great thing about for, for your customers is they don't have to build out the AI. They can, they can buy it. And, and just in thinking about this, it seems like there are a lot of really great and even sometimes narrow use cases. So I want to ask you, you know, staying with AI for a minute, one of the frustrations and Mick and I talked about this, the guy go problem that we've all studied in college, uh, you know, garbage in, garbage out. Uh, but, but the frustrations that users have had is really getting fast access to quality data that they can use to drive business results. So do you see, and how do you see AI maybe changing the game in that regard, Rob over the next several years? >>So yeah, the combination of massive amounts of data that have been gathered across the enterprise in the past 10 years with an open API APIs are dramatically lowering the processing costs that perform at much greater speed and efficiency, you know, and that's allowing us as an industry to democratize the data access while at the same time, delivering the federated governance and security models and hybrid technologies are playing a key role in making this a reality and enabling data access to be hybridized, meaning access and treated in a substantially similar way, your respect to the physical location of where that data actually resides. >>That's great. That is really the value layer that you guys are building out on top of that, all this great infrastructure that the hyperscalers have have given us, I mean, a hundred billion dollars a year that you can build value on top of, for your customers. Last question, and maybe Rob, you could, you can go first and then manufacture. You could bring us home. Where do you guys want to see the relationship go between cloud era and Nvidia? In other words, how should we, as outside observers be, be thinking about and measuring your project specifically and in the industry's progress generally? >>Yeah, I think we're very aligned on this and for cloud era, it's all about helping companies move forward, leverage every bit of their data and all the places that it may, uh, be hosted and partnering with our customers, working closely with our technology ecosystem of partners means innovation in every industry and that's inspiring for us. And that's what keeps us moving forward. >>Yeah. And I agree with Robin and for us at Nvidia, you know, we, this partnership started, uh, with data analytics, um, as you know, a spark is a very powerful technology for data analytics, uh, people who use spark rely on Cloudera for that. And the first thing we did together was to really accelerate spark in a seamless manner, but we're accelerating machine learning. We accelerating artificial intelligence together. And I think for Nvidia it's about democratization. We've seen what machine learning and AI have done for the early adopters and help them make their businesses, their products, their customer experience better. And we'd like every company to have the same opportunity. >>Okay. Now we're going to dig into the data landscape and cloud of course. And talk a little bit more about that with drew Allen. He's a managing director at Accenture drew. Welcome. Great to see you. Thank you. So let's talk a little bit about, you know, you've been in this game for a number of years. Uh, you've got particular expertise in, in data and finance and insurance. I mean, you know, you think about it within the data and analytics world, even our language is changing. You know, we don't say talk about big data so much anymore. We talk more about digital, you know, or, or, or data driven when you think about sort of where we've come from and where we're going. What are the puts and takes that you have with regard to what's going on in the business today? >>Well, thanks for having me. Um, you know, I think some of the trends we're seeing in terms of challenges and puts some takes are that a lot of companies are already on this digital journey. Um, they focused on customer experience is kind of table stakes. Everyone wants to focus on that and kind of digitizing their channels. But a lot of them are seeing that, you know, a lot of them don't even own their, their channels necessarily. So like we're working with a big cruise line, right. And yes, they've invested in digitizing what they own, but a lot of the channels that they sell through, they don't even own, right. It's the travel agencies or third party, real sellers. So having the data to know where, you know, where those agencies are, that that's something that they've discovered. And so there's a lot of big focus on not just digitizing, but also really understanding your customers and going across products because a lot of the data has built, been built up in individual channels and in digital products. >>And so bringing that data together is something that customers that have really figured out in the last few years is a big differentiator. And what we're seeing too, is that a big trend that the data rich are getting richer. So companies that have really invested in data, um, are having, uh, an outside market share and outside earnings per share and outside revenue growth. And it's really being a big differentiator. And I think for companies just getting started in this, the thing to think about is one of the missteps is to not try to capture all the data at once. The average company has, you know, 10,000, 20,000 data elements individually, when you want to start out, you know, 500, 300 critical data elements, about 5% of the data of a company drives 90% of the business value. So focusing on those key critical data elements is really what you need to govern first and really invest in first. And so that's something we, we tell companies at the beginning of their data strategy is first focus on those critical data elements, really get a handle on governing that data, organizing that data and building data products around >>That day. You can't boil the ocean. Right. And so, and I, I feel like pre pandemic, there was a lot of complacency. Oh yeah, we'll get to that. You know, not on my watch, I'll be retired before that, you know, is it becomes a minute. And then of course the pandemic was, I call it sometimes a forced March to digital. So in many respects, it wasn't planned. It just ha you know, you had to do it. And so now I feel like people are stepping back and saying, okay, let's now really rethink this and do it right. But is there, is there a sense of urgency, do you think? Absolutely. >>I think with COVID, you know, we were working with, um, a retailer where they had 12,000 stores across the U S and they had didn't have the insights where they could drill down and understand, you know, with the riots and with COVID was the store operational, you know, with the supply chain of the, having multiple distributors, what did they have in stock? So there are millions of data points that you need to drill down at the cell level, at the store level to really understand how's my business performing. And we like to think about it for like a CEO and his leadership team of it, like, think of it as a digital cockpit, right? You think about a pilot, they have a cockpit with all these dials and, um, dashboards, essentially understanding the performance of their business. And they should be able to drill down and understand for each individual, you know, unit of their work, how are they performing? That's really what we want to see for businesses. Can they get down to that individual performance to really understand how their business >>Is performing good, the ability to connect those dots and traverse those data points and not have to go in and come back out and go into a new system and come back out. And that's really been a lot of the frustration. W where does machine intelligence and AI fit in? Is that sort of a dot connector, if you will, and an enabler, I mean, we saw, you know, decades of the, the AI winter, and then, you know, there's been a lot of talk about it, but it feels like with the amount of data that we've collected over the last decade and the, the, the low costs of processing that data now, it feels like it's, it's real. Where do you see AI fitting? Yeah, >>I mean, I think there's been a lot of innovation in the last 10 years with, um, the low cost of storage and computing and these algorithms in non-linear, um, you know, knowledge graphs, and, um, um, a whole bunch of opportunities in cloud where what I think the, the big opportunity is, you know, you can apply AI in areas where a human just couldn't have the scale to do that alone. So back to the example of a cruise lines, you know, you may have a ship being built that has 4,000 cabins on the single cruise line, and it's going to multiple deaths that destinations over its 30 year life cycle. Each one of those cabins is being priced individually for each individual destination. It's physically impossible for a human to calculate the dynamic pricing across all those destinations. You need a machine to actually do that pricing. And so really what a machine is leveraging is all that data to really calculate and assist the human, essentially with all these opportunities where you wouldn't have a human being able to scale up to that amount of data >>Alone. You know, it's interesting. One of the things we talked to Nicolson about earlier was just the everybody's algorithms are out of whack. You know, you look at the airline pricing, you look at hotels it's as a consumer, you would be able to kind of game the system and predict that they can't even predict these days. And I feel as though that the data and AI are actually going to bring us back into some kind of normalcy and predictability, uh, what do you see in that regard? Yeah, I think it's, >>I mean, we're definitely not at a point where, when I talked to, you know, the top AI engineers and data scientists, we're not at a point where we have what they call broad AI, right? You can get machines to solve general knowledge problems, where they can solve one problem and then a distinctly different problem, right? That's still many years away, but narrow why AI, there's still tons of use cases out there that can really drive tons of business performance challenges, tons of accuracy challenges. So for example, in the insurance industry, commercial lines, where I work a lot of the time, the biggest leakage of loss experience in pricing for commercial insurers is, um, people will go in as an agent and they'll select an industry to say, you know what, I'm a restaurant business. Um, I'll select this industry code to quote out a policy, but there's, let's say, you know, 12 dozen permutations, you could be an outdoor restaurant. >>You could be a bar, you could be a caterer and all of that leads to different loss experience. So what this does is they built a machine learning algorithm. We've helped them do this, that actually at the time that they're putting in their name and address, it's crawling across the web and predicting in real time, you know, is this a address actually, you know, a business that's a restaurant with indoor dining, does it have a bar? Is it outdoor dining? And it's that that's able to accurately more price the policy and reduce the loss experience. So there's a lot of that you can do even with narrow AI that can really drive top line of business results. >>Yeah. I liked that term, narrow AI, because getting things done is important. Let's talk about cloud a little bit because people talk about cloud first public cloud first doesn't necessarily mean public cloud only, of course. So where do you see things like what's the right operating model, the right regime hybrid cloud. We talked earlier about hybrid data help us squint through the cloud landscape. Yeah. I mean, I think for most right, most >>Fortune 500 companies, they can't just snap their fingers and say, let's move all of our data centers to the cloud. They've got to move, you know, gradually. And it's usually a journey that's taking more than two to three plus years, even more than that in some cases. So they're have, they have to move their data, uh, incrementally to the cloud. And what that means is that, that they have to move to a hybrid perspective where some of their data is on premise and some of it is publicly on the cloud. And so that's the term hybrid cloud essentially. And so what they've had to think about is from an intelligence perspective, the privacy of that data, where is it being moved? Can they reduce the replication of that data? Because ultimately you like, uh, replicating the data from on-premise to the cloud that introduces, you know, errors and data quality issues. So thinking about how do you manage, uh, you know, uh on-premise and, um, public as a transition is something that Accenture thinks, thinks, and helps our clients do quite a bit. And how do you move them in a manner that's well-organized and well thought of? >>Yeah. So I've been a big proponent of sort of line of business lines of business becoming much more involved in, in the data pipeline, if you will, the data process, if you think about our major operational systems, they all have sort of line of business context in them. And then the salespeople, they know the CRM data and, you know, logistics folks there they're very much in tune with ERP, almost feel like for the past decade, the lines of business have been somewhat removed from the, the data team, if you will. And that, that seems to be changing. What are you seeing in terms of the line of line of business being much more involved in sort of end to end ownership, if you will, if I can use that term of, uh, of the data and sort of determining things like helping determine anyway, the data quality and things of that nature. Yeah. I >>Mean, I think this is where thinking about your data operating model and thinking about ideas of a chief data officer and having data on the CEO agenda, that's really important to get the lines of business, to really think about data sharing and reuse, and really getting them to, you know, kind of unlock the data because they do think about their data as a fiefdom data has value, but you've got to really get organizations in their silos to open it up and bring that data together because that's where the value is. You know, data doesn't operate. When you think about a customer, they don't operate in their journey across the business in silo channels. They don't think about, you know, I use only the web and then I use the call center, right? They think about that as just one experience and that data is a single journey. >>So we like to think about data as a product. You know, you should think about a data in the same way. You think about your products as, as products, you know, data as a product, you should have the idea of like every two weeks you have releases to it. You have an operational resiliency to it. So thinking about that, where you can have a very product mindset to delivering your data, I think is very important for the success. And that's where kind of, there's not just the things about critical data elements and having the right platform architecture, but there's a soft stuff as well, like a, a product mindset to data, having the right data, culture, and business adoption and having the right value set mindset for, for data, I think is really >>Important. I think data as a product is a very powerful concept and I think it maybe is uncomfortable to some people sometimes. And I think in the early days of big data, if you will, people thought, okay, data is a product going to sell my data and that's not necessarily what you mean, thinking about products or data that can fuel products that you can then monetize maybe as a product or as a, as, as a service. And I like to think about a new metric in the industry, which is how long does it take me to get from idea I'm a business person. I have an idea for a data product. How long does it take me to get from idea to monetization? And that's going to be something that ultimately as a business person, I'm going to use to determine the success of my data team and my data architecture. Is that kind of thinking starting to really hit the marketplace? Absolutely. >>I mean, I insurers now are working, partnering with, you know, auto manufacturers to monetize, um, driver usage data, you know, on telematics to see, you know, driver behavior on how, you know, how auto manufacturers are using that data. That's very important to insurers, you know, so how an auto manufacturer can monetize that data is very important and also an insurance, you know, cyber insurance, um, are there news new ways we can look at how companies are being attacked with viruses and malware. And is there a way we can somehow monetize that information? So companies that are able to agily, you know, think about how can we collect this data, bring it together, think about it as a product, and then potentially, you know, sell it as a service is something that, um, company, successful companies, you're doing great examples >>Of data products, and it might be revenue generating, or it might be in the case of, you know, cyber, maybe it reduces my expected loss and exactly. Then it drops right to my bottom line. What's the relationship between Accenture and cloud era? Do you, I presume you guys meet at the customer, but maybe you could give us some insight. >>Yeah. So, um, I, I'm in the executive sponsor for, um, the Accenture Cloudera partnership on the Accenture side. Uh, we do quite a lot of business together and, um, you know, Cloudera has been a great partner for us. Um, and they've got a great product in terms of the Cloudera data platform where, you know, what we do is as a big systems integrator for them, we help, um, you know, configure and we have a number of engineers across the world that come in and help in terms of, um, engineer architects and install, uh, cloud errors, data platform, and think about what are some of those, you know, value cases where you can really think about organizing data and bringing it together for all these different types of use cases. And really just as the examples we thought about. So the telematics, you know, um, in order to realize something like that, you're bringing in petabytes and huge scales of data that, you know, you just couldn't bring on a normal, uh, platform. You need to think about cloud. You need to think about speed of, of data and real-time insights and cloud era is the right data platform for that. So, um, >>Having a cloud Cloudera ushered in the modern big data era, we kind of all know that, and it was, which of course early on, it was very services intensive. You guys were right there helping people think through there weren't enough data scientists. We've sort of all, all been through that. And of course in your wheelhouse industries, you know, financial services and insurance, they were some of the early adopters, weren't they? Yeah, absolutely. >>Um, so, you know, an insurance, you've got huge amounts of data with loss history and, um, a lot with IOT. So in insurance, there's a whole thing of like sensorized thing in, uh, you know, taking the physical world and digitizing it. So, um, there's a big thing in insurance where, um, it's not just about, um, pricing out the risk of a loss experience, but actual reducing the loss before it even happens. So it's called risk control or loss control, you know, can we actually put sensors on oil pipelines or on elevators and, you know, reduce, um, you know, accidents before they happen. So we're, you know, working with an insurer to actually, um, listen to elevators as they move up and down and are there signals in just listening to the audio of an elevator over time that says, you know what, this elevator is going to need maintenance, you know, before a critical accident could happen. So there's huge applications, not just in structured data, but in unstructured data like voice and audio and video where a partner like Cloudera has a huge role to play. >>Great example of it. So again, narrow sort of use case for machine intelligence, but, but real value. True. We'll leave it like that. Thanks so much for taking some time. Yes. Thank you so much. Okay. We continue now with the theme of turning ideas into insights. So ultimately you can take action. We heard earlier that public cloud first doesn't mean public cloud only, and a winning strategy comprises data, irrespective of physical location on prem, across multiple clouds at the edge where real time inference is going to drive a lot of incremental value. Data is going to help the world come back to normal. We heard, or at least semi normal as we begin to better understand and forecast demand and supply and balances and economic forces. AI is becoming embedded into every aspect of our business, our people, our processes, and applications. And now we're going to get into some of the foundational principles that support the data and insights centric processes, which are fundamental to digital transformation initiatives. And it's my pleasure to welcome two great guests, Michelle Goetz. Who's a Kuba woman, VP and principal analyst at Forrester, and doing some groundbreaking work in this area. And Cindy, Mikey, who is the vice president of industry solutions and value management at Cloudera. Welcome to both of >>You. Welcome. Thank you. Thanks Dave. >>All right, Michelle, let's get into it. Maybe you could talk about your foundational core principles. You start with data. What are the important aspects of this first principle that are achievable today? >>It's really about democratization. If you can't make your data accessible, um, it's not usable. Nobody's able to understand what's happening in the business and they don't understand, um, what insights can be gained or what are the signals that are occurring that are going to help them with decisions, create stronger value or create deeper relationships, their customers, um, due to their experiences. So it really begins with how do you make data available and bring it to where the consumer of the data is rather than trying to hunt and Peck around within your ecosystem to find what it is that's important. Great. >>Thank you for that. So, Cindy, I wonder in hearing what Michelle just said, what are your thoughts on this? And when you work with customers at Cloudera, does, are there any that stand out that perhaps embody the fundamentals that Michelle just shared? >>Yeah, there's, there's quite a few. And especially as we look across, um, all the industries that we're actually working with customers in, you know, a few that stand out in top of mind for me is one is IQ via and what they're doing with real-world evidence and bringing together data across the entire, um, healthcare and life sciences ecosystems, bringing it together in different shapes and formats, making the ed accessible by both internally, as well as for their, um, the entire extended ecosystem. And then for SIA, who's working to solve some predictive maintenance issues within, there are a European car manufacturer and how do they make sure that they have, you know, efficient and effective processes when it comes to, uh, fixing equipment and so forth. And then also, um, there's, uh, an Indonesian based, um, uh, telecommunications company tech, the smell, um, who's bringing together, um, over the last five years, all their data about their customers and how do they enhance our customer experience? How do they make information accessible, especially in these pandemic and post pandemic times, um, uh, you know, just getting better insights into what customers need and when do they need it? >>Cindy platform is another core principle. How should we be thinking about data platforms in this day and age? I mean, where does, where do things like hybrid fit in? Um, what's cloud era's point >>Of view platforms are truly an enabler, um, and data needs to be accessible in many different fashions. Um, and also what's right for the business. When, you know, I want it in a cost and efficient and effective manner. So, you know, data needs to be, um, data resides everywhere. Data is developed and it's brought together. So you need to be able to balance both real time, you know, our batch historical information. It all depends upon what your analytical workloads are. Um, and what types of analytical methods you're going to use to drive those business insights. So putting and placing data, um, landing it, making it accessible, analyzing it needs to be done in any accessible platform, whether it be, you know, a public cloud doing it on-prem or a hybrid of the two is typically what we're seeing, being the most successful. >>Great. Thank you, Michelle. Let's move on a little bit and talk about practices and practices and processes as the next core principles. Maybe you could provide some insight as to how you think about balancing practices and processes while at the same time managing agility. >>Yeah, it's a really great question because it's pretty complex. When you have to start to connect your data to your business, the first thing to really gravitate towards is what are you trying to do? And what Cindy was describing with those customer examples is that they're all based off of business goals off of very specific use cases that helps kind of set the agenda about what is the data and what are the data domains that are important to really understanding and recognizing what's happening within that business activity and the way that you can affect that either in, you know, near time or real time, or later on, as you're doing your strategic planning, what that's balancing against is also being able to not only see how that business is evolving, but also be able to go back and say, well, can I also measure the outcomes from those processes and using data and using insight? >>Can I also get intelligence about the data to know that it's actually satisfying my objectives to influence my customers in my market? Or is there some sort of data drift or detraction in my, um, analytic capabilities that are allowing me to be effective in those environments, but everything else revolves around that and really thinking succinctly about a strategy that isn't just data aware, what data do I have and how do I use it, but coming in more from that business perspective to then start to be, data-driven recognizing that every activity you do from a business perspective leads to thinking about information that supports that and supports your decisions, and ultimately getting to the point of being insight driven, where you're able to both, uh, describe what you want your business to be with your data, using analytics, to then execute on that fluidly and in real time. And then ultimately bringing that back with linking to business outcomes and doing that in a continuous cycle where you can test and you can learn, you can improve, you can optimize, and you can innovate because you can see your business as it's happening. And you have the right signals and intelligence that allow you to make great decisions. >>I like how you said near time or real time, because it is a spectrum. And you know, one of the spectrum, autonomous vehicles, you've got to make a decision in real time, but, but, but near real-time, or real-time, it's, it's in the eyes of the holder, if you will, it's it might be before you lose the customer before the market changes. So it's really defined on a case by case basis. Um, I wonder Michelle, if you could talk about in working with a number of organizations, I see folks, they sometimes get twisted up and understanding the dependencies that technology generally, and the technologies around data specifically can have on critical business processes. Can you maybe give some guidance as to where customers should start, where, you know, where can we find some of the quick wins and high return, it >>Comes first down to how does your business operate? So you're going to take a look at the business processes and value stream itself. And if you can understand how people and customers, partners, and automation are driving that step by step approach to your business activities, to realize those business outcomes, it's way easier to start thinking about what is the information necessary to see that particular step in the process, and then take the next step of saying what information is necessary to make a decision at that current point in the process, or are you collecting information asking for information that is going to help satisfy a downstream process step or a downstream decision. So constantly making sure that you are mapping out your business processes and activities, aligning your data process to that helps you now rationalize. Do you need that real time near real time, or do you want to start grading greater consistency by bringing all of those signals together, um, in a centralized area to eventually oversee the entire operations and outcomes as they happen? It's the process and the decision points and acting on those decision points for the best outcome that really determines are you going to move in more of a real-time, uh, streaming capacity, or are you going to push back into more of a batch oriented approach? Because it depends on the amount of information and the aggregate of which provides the best insight from that. >>Got it. Let's, let's bring Cindy back into the conversation in your city. We often talk about people process and technology and the roles they play in creating a data strategy. That's that's logical and sound. Can you speak to the broader ecosystem and the importance of creating both internal and external partners within an organization? Yeah. >>And that's, uh, you know, kind of building upon what Michelle was talking about. If you think about datas and I hate to use the phrase almost, but you know, the fuel behind the process, um, and how do you actually become insight-driven? And, you know, you look at the capabilities that you're needing to enable from that business process, that insight process, um, you're extended ecosystem on, on how do I make that happen? You know, partners, um, and, and picking the right partner is important because a partner is one that actually helps under or helps you implement what your decisions are. Um, so, um, looking for a partner that has the capability that believes in being insight-driven and making sure that when you're leveraging data, um, you know, for within process on that, if you need to do it in a time fashion, that they can actually meet those needs of the business, um, and enabling on those, those process activities. So the ecosystem looking at how you, um, look at, you know, your vendors are, and fundamentally they need to be that trusted partner. Um, do they bring those same principles of value of being insight driven? So they have to have those core values themselves in order to help you as a, um, an end of business person enable those capabilities. So, so yeah, I'm >>Cool with fuel, but it's like super fuel when you talk about data, cause it's not scarce, right? You're never going to run out. So Michelle, let's talk about leadership. W w who leads, what does so-called leadership look like in an organization that's insight driven? >>So I think the really interesting thing that is starting to evolve as late is that organizations enterprises are really recognizing that not just that data is an asset and data has value, but exactly what we're talking about here, data really does drive what your business outcomes are going to be data driving into the insight or the raw data itself has the ability to set in motion. What's going to happen in your business processes and your customer experiences. And so, as you kind of think about that, you're now starting to see your CEO, your CMO, um, your CRO coming back and saying, I need better data. I need information. That's representative of what's happening in my business. I need to be better adaptive to what's going on with my customers. And ultimately that means I need to be smarter and have clearer forecasting into what's about ready to come, not just, you know, one month, two months, three months or a year from now, but in a week or tomorrow. >>And so that's, how is having a trickle down effect to then looking at two other types of roles that are elevating from technical capacity to more business capacity, you have your chief data officer that is shaping the exp the experiences, uh, with data and with insight and reconciling, what type of information is necessary with it within the context of answering these questions and creating a future fit organization that is adaptive and resilient to things that are happening. And you also have a chief digital officer who is participating because they're providing the experience and shaping the information and the way that you're going to interact and execute on those business activities, and either running that autonomously or as part of an assistance for your employees and for your customers. So really to go from not just data aware to data driven, but ultimately to be insight driven, you're seeing way more, um, participation, uh, and leadership at that C-suite level. And just underneath, because that's where the subject matter expertise is coming in to know how to create a data strategy that is tightly connected to your business strategy. >>Right. Thank you. Let's wrap. And I've got a question for both of you, maybe Cindy, you could start and then Michelle bring us home. You know, a lot of customers, they want to understand what's achievable. So it's helpful to paint a picture of a, of a maturity model. Uh, you know, I'd love to go there, but I'm not going to get there anytime soon, but I want to take some baby steps. So when you're performing an analysis on, on insight driven organization, city, what do you see as the major characteristics that define the differences between sort of the, the early, you know, beginners, the sort of fat middle, if you will, and then the more advanced, uh, constituents. >>Yeah, I'm going to build upon, you know, what Michelle was talking about as data as an asset. And I think, you know, also being data where, and, you know, trying to actually become, you know, insight driven, um, companies can also have data and they can have data as a liability. And so when you're data aware, sometimes data can still be a liability to your organization. If you're not making business decisions on the most recent and relevant data, um, you know, you're not going to be insight driven. So you've got to move beyond that, that data awareness, where you're looking at data just from an operational reporting, but data's fundamentally driving the decisions that you make. Um, as a business, you're using data in real time. You're, um, you're, you know, leveraging data to actually help you make and drive those decisions. So when we use the term you're, data-driven, you can't just use the term, you know, tongue in cheek. It actually means that I'm using the recent, the relevant and the accuracy of data to actually make the decisions for me, because we're all advancing upon. We're talking about, you know, artificial intelligence and so forth. Being able to do that, if you're just data where I would not be embracing on leveraging artificial intelligence, because that means I probably haven't embedded data into my processes. It's data could very well still be a liability in your organization. So how do you actually make it an asset? Yeah, I think data >>Where it's like cable ready. So, so Michelle, maybe you could, you could, you could, uh, add to what Cindy just said and maybe add as well, any advice that you have around creating and defining a data strategy. >>So every data strategy has a component of being data aware. This is like building the data museum. How do you capture everything that's available to you? How do you maintain that memory of your business? You know, bringing in data from your applications, your partners, third parties, wherever that information is available, you want to ensure that you're capturing and you're managing and you're maintaining it. And this is really where you're starting to think about the fact that it is an asset. It has value, but you may not necessarily know what that value is. Yet. If you move into a category of data driven, what starts to shift and change there is you're starting to classify label, organize the information in context of how you're making decisions and how you do business. It could start from being more, um, proficient from an analytic purpose. You also might start to introduce some early stages of data science in there. >>So you can do some predictions and some data mining to start to weed out some of those signals. And you might have some simple types of algorithms that you're deploying to do a next next best action for example. And that's what data-driven is really about. You're starting to get value out of it. The data itself is starting to make sense in context of your business, but what you haven't done quite yet, which is what insight driven businesses are, is really starting to take away. Um, the gap between when you see it, know it and then get the most value and really exploit what that insight is at the time when it's right. So in the moment we talk about this in terms of perishable insights, data and insights are ephemeral. And we want to ensure that the way that we're managing that and delivering on that data and insights is in time with our decisions and the highest value outcome we're going to have, that that insight can provide us. >>So are we just introducing it as data-driven organizations where we could see, you know, spreadsheets and PowerPoint presentations and lots of mapping to help make sort of longer strategic decisions, or are those insights coming up and being activated in an automated fashion within our business processes that are either assisting those human decisions at the point when they're needed, or an automated decisions for the types of digital experiences and capabilities that we're driving in our organization. So it's going from, I'm a data hoarder. If I'm data aware to I'm interested in what's happening as a data-driven organization and understanding my data. And then lastly being insight driven is really where light between business, data and insight. There is none it's all coming together for the best outcomes, >>Right? So people are acting on perfect or near perfect information or machines or, or, uh, doing so with a high degree of confidence, great advice and insights. And thank you both for sharing your thoughts with our audience today. It's great to have you. Thank you. Thank you. Okay. Now we're going to go into our industry. Deep dives. There are six industry breakouts, financial services, insurance, manufacturing, retail communications, and public sector. Now each breakout is going to cover two distinct use cases for a total of essentially 12 really detailed segments that each of these is going to be available on demand, but you can scan the calendar on the homepage and navigate to your breakout session for choice of choice or for more information, click on the agenda page and take a look to see which session is the best fit for you. And then dive in, join the chat and feel free to ask questions or contribute your knowledge, opinions, and data. Thanks so much for being part of the community and enjoy the rest of the day.