(mellow music) >> Hello everyone, welcome to theCUBE's coverage of AWS Startup Showcase. This is the closing panel session on AI machine learning, the top startups generating generative AI on AWS. It's a great panel. This is going to be the experts talking about riding the wave in generative AI. We got Ankur Mehrotra, who's the director and general manager of AI and machine learning at AWS, and Clem Delangue, co-founder and CEO of Hugging Face, and Ori Goshen, who's the co-founder and CEO of AI21 Labs. Ori from Tel Aviv dialing in, and rest coming in here on theCUBE. Appreciate you coming on for this closing session for the Startup Showcase. >> Thanks for having us. >> Thank you for having us. >> Thank you. >> I'm super excited to have you all on. Hugging Face was recently in the news with the AWS relationship, so congratulations. Open source, open science, really driving the machine learning. And we got the AI21 Labs access to the LLMs, generating huge scale live applications, commercial applications, coming to the market, all powered by AWS. So everyone, congratulations on all your success, and thank you for headlining this panel. Let's get right into it. AWS is powering this wave here. We're seeing a lot of push here from applications. Ankur, set the table for us on the AI machine learning. It's not new, it's been goin' on for a while. Past three years have been significant advancements, but there's been a lot of work done in AI machine learning. Now it's released to the public. Everybody's super excited and now says, "Oh, the future's here!" It's kind of been going on for a while and baking. Now it's kind of coming out. What's your view here? Let's get it started. >> Yes, thank you. So, yeah, as you may be aware, Amazon has been in investing in machine learning research and development since quite some time now. And we've used machine learning to innovate and improve user experiences across different Amazon products, whether it's Alexa or Amazon.com. But we've also brought in our expertise to extend what we are doing in the space and add more generative AI technology to our AWS products and services, starting with CodeWhisperer, which is an AWS service that we announced a few months ago, which is, you can think of it as a coding companion as a service, which uses generative AI models underneath. And so this is a service that customers who have no machine learning expertise can just use. And we also are talking to customers, and we see a lot of excitement about generative AI, and customers who want to build these models themselves, who have the talent and the expertise and resources. For them, AWS has a number of different options and capabilities they can leverage, such as our custom silicon, such as Trainium and Inferentia, as well as distributed machine learning capabilities that we offer as part of SageMaker, which is an end-to-end machine learning development service. At the same time, many of our customers tell us that they're interested in not training and building these generative AI models from scratch, given they can be expensive and can require specialized talent and skills to build. And so for those customers, we are also making it super easy to bring in existing generative AI models into their machine learning development environment within SageMaker for them to use. So we recently announced our partnership with Hugging Face, where we are making it super easy for customers to bring in those models into their SageMaker development environment for fine tuning and deployment. And then we are also partnering with other proprietary model providers such as AI21 and others, where we making these generative AI models available within SageMaker for our customers to use. So our approach here is to really provide customers options and choices and help them accelerate their generative AI journey. >> Ankur, thank you for setting the table there. Clem and Ori, I want to get your take, because the riding the waves, the theme of this session, and to me being in California, I imagine the big surf, the big waves, the big talent out there. This is like alpha geeks, alpha coders, developers are really leaning into this. You're seeing massive uptake from the smartest people. Whether they're young or around, they're coming in with their kind of surfboards, (chuckles) if you will. These early adopters, they've been on this for a while; Now the waves are hitting. This is a big wave, everyone sees it. What are some of those early adopter devs doing? What are some of the use cases you're seeing right out of the gate? And what does this mean for the folks that are going to come in and get on this wave? Can you guys share your perspective on this? Because you're seeing the best talent now leaning into this. >> Yeah, absolutely. I mean, from Hugging Face vantage points, it's not even a a wave, it's a tidal wave, or maybe even the tide itself. Because actually what we are seeing is that AI and machine learning is not something that you add to your products. It's very much a new paradigm to do all technology. It's this idea that we had in the past 15, 20 years, one way to build software and to build technology, which was writing a million lines of code, very rule-based, and then you get your product. Now what we are seeing is that every single product, every single feature, every single company is starting to adopt AI to build the next generation of technology. And that works both to make the existing use cases better, if you think of search, if you think of social network, if you think of SaaS, but also it's creating completely new capabilities that weren't possible with the previous paradigm. Now AI can generate text, it can generate image, it can describe your image, it can do so many new things that weren't possible before. >> It's going to really make the developers really productive, right? I mean, you're seeing the developer uptake strong, right? >> Yes, we have over 15,000 companies using Hugging Face now, and it keeps accelerating. I really think that maybe in like three, five years, there's not going to be any company not using AI. It's going to be really kind of the default to build all technology. >> Ori, weigh in on this. APIs, the cloud. Now I'm a developer, I want to have live applications, I want the commercial applications on this. What's your take? Weigh in here. >> Yeah, first, I absolutely agree. I mean, we're in the midst of a technology shift here. I think not a lot of people realize how big this is going to be. Just the number of possibilities is endless, and I think hard to imagine. And I don't think it's just the use cases. I think we can think of it as two separate categories. We'll see companies and products enhancing their offerings with these new AI capabilities, but we'll also see new companies that are AI first, that kind of reimagine certain experiences. They build something that wasn't possible before. And that's why I think it's actually extremely exciting times. And maybe more philosophically, I think now these large language models and large transformer based models are helping us people to express our thoughts and kind of making the bridge from our thinking to a creative digital asset in a speed we've never imagined before. I can write something down and get a piece of text, or an image, or a code. So I'll start by saying it's hard to imagine all the possibilities right now, but it's certainly big. And if I had to bet, I would say it's probably at least as big as the mobile revolution we've seen in the last 20 years. >> Yeah, this is the biggest. I mean, it's been compared to the Enlightenment Age. I saw the Wall Street Journal had a recent story on this. We've been saying that this is probably going to be bigger than all inflection points combined in the tech industry, given what transformation is coming. I guess I want to ask you guys, on the early adopters, we've been hearing on these interviews and throughout the industry that there's already a set of big companies, a set of companies out there that have a lot of data and they're already there, they're kind of tinkering. Kind of reminds me of the old hyper scaler days where they were building their own scale, and they're eatin' glass, spittin' nails out, you know, they're hardcore. Then you got everybody else kind of saying board level, "Hey team, how do I leverage this?" How do you see those two things coming together? You got the fast followers coming in behind the early adopters. What's it like for the second wave coming in? What are those conversations for those developers like? >> I mean, I think for me, the important switch for companies is to change their mindset from being kind of like a traditional software company to being an AI or machine learning company. And that means investing, hiring machine learning engineers, machine learning scientists, infrastructure in members who are working on how to put these models in production, team members who are able to optimize models, specialized models, customized models for the company's specific use cases. So it's really changing this mindset of how you build technology and optimize your company building around that. Things are moving so fast that I think now it's kind of like too late for low hanging fruits or small, small adjustments. I think it's important to realize that if you want to be good at that, and if you really want to surf this wave, you need massive investments. If there are like some surfers listening with this analogy of the wave, right, when there are waves, it's not enough just to stand and make a little bit of adjustments. You need to position yourself aggressively, paddle like crazy, and that's how you get into the waves. So that's what companies, in my opinion, need to do right now. >> Ori, what's your take on the generative models out there? We hear a lot about foundation models. What's your experience running end-to-end applications for large foundation models? Any insights you can share with the app developers out there who are looking to get in? >> Yeah, I think first of all, it's start create an economy, where it probably doesn't make sense for every company to create their own foundation models. You can basically start by using an existing foundation model, either open source or a proprietary one, and start deploying it for your needs. And then comes the second round when you are starting the optimization process. You bootstrap, whether it's a demo, or a small feature, or introducing new capability within your product, and then start collecting data. That data, and particularly the human feedback data, helps you to constantly improve the model, so you create this data flywheel. And I think we're now entering an era where customers have a lot of different choice of how they want to start their generative AI endeavor. And it's a good thing that there's a variety of choices. And the really amazing thing here is that every industry, any company you speak with, it could be something very traditional like industrial or financial, medical, really any company. I think peoples now start to imagine what are the possibilities, and seriously think what's their strategy for adopting this generative AI technology. And I think in that sense, the foundation model actually enabled this to become scalable. So the barrier to entry became lower; Now the adoption could actually accelerate. >> There's a lot of integration aspects here in this new wave that's a little bit different. Before it was like very monolithic, hardcore, very brittle. A lot more integration, you see a lot more data coming together. I have to ask you guys, as developers come in and grow, I mean, when I went to college and you were a software engineer, I mean, I got a degree in computer science, and software engineering, that's all you did was code, (chuckles) you coded. Now, isn't it like everyone's a machine learning engineer at this point? Because that will be ultimately the science. So, (chuckles) you got open source, you got open software, you got the communities. Swami called you guys the GitHub of machine learning, Hugging Face is the GitHub of machine learning, mainly because that's where people are going to code. So this is essentially, machine learning is computer science. What's your reaction to that? >> Yes, my co-founder Julien at Hugging Face have been having this thing for quite a while now, for over three years, which was saying that actually software engineering as we know it today is a subset of machine learning, instead of the other way around. People would call us crazy a few years ago when we're seeing that. But now we are realizing that you can actually code with machine learning. So machine learning is generating code. And we are starting to see that every software engineer can leverage machine learning through open models, through APIs, through different technology stack. So yeah, it's not crazy anymore to think that maybe in a few years, there's going to be more people doing AI and machine learning. However you call it, right? Maybe you'll still call them software engineers, maybe you'll call them machine learning engineers. But there might be more of these people in a couple of years than there is software engineers today. >> I bring this up as more tongue in cheek as well, because Ankur, infrastructure's co is what made Cloud great, right? That's kind of the DevOps movement. But here the shift is so massive, there will be a game-changing philosophy around coding. Machine learning as code, you're starting to see CodeWhisperer, you guys have had coding companions for a while on AWS. So this is a paradigm shift. How is the cloud playing into this for you guys? Because to me, I've been riffing on some interviews where it's like, okay, you got the cloud going next level. This is an example of that, where there is a DevOps-like moment happening with machine learning, whether you call it coding or whatever. It's writing code on its own. Can you guys comment on what this means on top of the cloud? What comes out of the scale? What comes out of the benefit here? >> Absolutely, so- >> Well first- >> Oh, go ahead. >> Yeah, so I think as far as scale is concerned, I think customers are really relying on cloud to make sure that the applications that they build can scale along with the needs of their business. But there's another aspect to it, which is that until a few years ago, John, what we saw was that machine learning was a data scientist heavy activity. They were data scientists who were taking the data and training models. And then as machine learning found its way more and more into production and actual usage, we saw the MLOps become a thing, and MLOps engineers become more involved into the process. And then we now are seeing, as machine learning is being used to solve more business critical problems, we're seeing even legal and compliance teams get involved. We are seeing business stakeholders more engaged. So, more and more machine learning is becoming an activity that's not just performed by data scientists, but is performed by a team and a group of people with different skills. And for them, we as AWS are focused on providing the best tools and services for these different personas to be able to do their job and really complete that end-to-end machine learning story. So that's where, whether it's tools related to MLOps or even for folks who cannot code or don't know any machine learning. For example, we launched SageMaker Canvas as a tool last year, which is a UI-based tool which data analysts and business analysts can use to build machine learning models. So overall, the spectrum in terms of persona and who can get involved in the machine learning process is expanding, and the cloud is playing a big role in that process. >> Ori, Clem, can you guys weigh in too? 'Cause this is just another abstraction layer of scale. What's it mean for you guys as you look forward to your customers and the use cases that you're enabling? >> Yes, I think what's important is that the AI companies and providers and the cloud kind of work together. That's how you make a seamless experience and you actually reduce the barrier to entry for this technology. So that's what we've been super happy to do with AWS for the past few years. We actually announced not too long ago that we are doubling down on our partnership with AWS. We're excited to have many, many customers on our shared product, the Hugging Face deep learning container on SageMaker. And we are working really closely with the Inferentia team and the Trainium team to release some more exciting stuff in the coming weeks and coming months. So I think when you have an ecosystem and a system where the AWS and the AI providers, AI startups can work hand in hand, it's to the benefit of the customers and the companies, because it makes it orders of magnitude easier for them to adopt this new paradigm to build technology AI. >> Ori, this is a scale on reasoning too. The data's out there and making sense out of it, making it reason, getting comprehension, having it make decisions is next, isn't it? And you need scale for that. >> Yes. Just a comment about the infrastructure side. So I think really the purpose is to streamline and make these technologies much more accessible. And I think we'll see, I predict that we'll see in the next few years more and more tooling that make this technology much more simple to consume. And I think it plays a very important role. There's so many aspects, like the monitoring the models and their kind of outputs they produce, and kind of containing and running them in a production environment. There's so much there to build on, the infrastructure side will play a very significant role. >> All right, that's awesome stuff. I'd love to change gears a little bit and get a little philosophy here around AI and how it's going to transform, if you guys don't mind. There's been a lot of conversations around, on theCUBE here as well as in some industry areas, where it's like, okay, all the heavy lifting is automated away with machine learning and AI, the complexity, there's some efficiencies, it's horizontal and scalable across all industries. Ankur, good point there. Everyone's going to use it for something. And a lot of stuff gets brought to the table with large language models and other things. But the key ingredient will be proprietary data or human input, or some sort of AI whisperer kind of role, or prompt engineering, people are saying. So with that being said, some are saying it's automating intelligence. And that creativity will be unleashed from this. If the heavy lifting goes away and AI can fill the void, that shifts the value to the intellect or the input. And so that means data's got to come together, interact, fuse, and understand each other. This is kind of new. I mean, old school AI was, okay, got a big model, I provisioned it long time, very expensive. Now it's all free flowing. Can you guys comment on where you see this going with this freeform, data flowing everywhere, heavy lifting, and then specialization? >> Yeah, I think- >> Go ahead. >> Yeah, I think, so what we are seeing with these large language models or generative models is that they're really good at creating stuff. But I think it's also important to recognize their limitations. They're not as good at reasoning and logic. And I think now we're seeing great enthusiasm, I think, which is justified. And the next phase would be how to make these systems more reliable. How to inject more reasoning capabilities into these models, or augment with other mechanisms that actually perform more reasoning so we can achieve more reliable results. And we can count on these models to perform for critical tasks, whether it's medical tasks, legal tasks. We really want to kind of offload a lot of the intelligence to these systems. And then we'll have to get back, we'll have to make sure these are reliable, we'll have to make sure we get some sort of explainability that we can understand the process behind the generated results that we received. So I think this is kind of the next phase of systems that are based on these generated models. >> Clem, what's your view on this? Obviously you're at open community, open source has been around, it's been a great track record, proven model. I'm assuming creativity's going to come out of the woodwork, and if we can automate open source contribution, and relationships, and onboarding more developers, there's going to be unleashing of creativity. >> Yes, it's been so exciting on the open source front. We all know Bert, Bloom, GPT-J, T5, Stable Diffusion, that work up. The previous or the current generation of open source models that are on Hugging Face. It has been accelerating in the past few months. So I'm super excited about ControlNet right now that is really having a lot of impact, which is kind of like a way to control the generation of images. Super excited about Flan UL2, which is like a new model that has been recently released and is open source. So yeah, it's really fun to see the ecosystem coming together. Open source has been the basis for traditional software, with like open source programming languages, of course, but also all the great open source that we've gotten over the years. So we're happy to see that the same thing is happening for machine learning and AI, and hopefully can help a lot of companies reduce a little bit the barrier to entry. So yeah, it's going to be exciting to see how it evolves in the next few years in that respect. >> I think the developer productivity angle that's been talked about a lot in the industry will be accelerated significantly. I think security will be enhanced by this. I think in general, applications are going to transform at a radical rate, accelerated, incredible rate. So I think it's not a big wave, it's the water, right? I mean, (chuckles) it's the new thing. My final question for you guys, if you don't mind, I'd love to get each of you to answer the question I'm going to ask you, which is, a lot of conversations around data. Data infrastructure's obviously involved in this. And the common thread that I'm hearing is that every company that looks at this is asking themselves, if we don't rebuild our company, start thinking about rebuilding our business model around AI, we might be dinosaurs, we might be extinct. And it reminds me that scene in Moneyball when, at the end, it's like, if we're not building the model around your model, every company will be out of business. What's your advice to companies out there that are having those kind of moments where it's like, okay, this is real, this is next gen, this is happening. I better start thinking and putting into motion plans to refactor my business, 'cause it's happening, business transformation is happening on the cloud. This kind of puts an exclamation point on, with the AI, as a next step function. Big increase in value. So it's an opportunity for leaders. Ankur, we'll start with you. What's your advice for folks out there thinking about this? Do they put their toe in the water? Do they jump right into the deep end? What's your advice? >> Yeah, John, so we talk to a lot of customers, and customers are excited about what's happening in the space, but they often ask us like, "Hey, where do we start?" So we always advise our customers to do a lot of proof of concepts, understand where they can drive the biggest ROI. And then also leverage existing tools and services to move fast and scale, and try and not reinvent the wheel where it doesn't need to be. That's basically our advice to customers. >> Get it. Ori, what's your advice to folks who are scratching their head going, "I better jump in here. "How do I get started?" What's your advice? >> So I actually think that need to think about it really economically. Both on the opportunity side and the challenges. So there's a lot of opportunities for many companies to actually gain revenue upside by building these new generative features and capabilities. On the other hand, of course, this would probably affect the cogs, and incorporating these capabilities could probably affect the cogs. So I think we really need to think carefully about both of these sides, and also understand clearly if this is a project or an F word towards cost reduction, then the ROI is pretty clear, or revenue amplifier, where there's, again, a lot of different opportunities. So I think once you think about this in a structured way, I think, and map the different initiatives, then it's probably a good way to start and a good way to start thinking about these endeavors. >> Awesome. Clem, what's your take on this? What's your advice, folks out there? >> Yes, all of these are very good advice already. Something that you said before, John, that I disagreed a little bit, a lot of people are talking about the data mode and proprietary data. Actually, when you look at some of the organizations that have been building the best models, they don't have specialized or unique access to data. So I'm not sure that's so important today. I think what's important for companies, and it's been the same for the previous generation of technology, is their ability to build better technology faster than others. And in this new paradigm, that means being able to build machine learning faster than others, and better. So that's how, in my opinion, you should approach this. And kind of like how can you evolve your company, your teams, your products, so that you are able in the long run to build machine learning better and faster than your competitors. And if you manage to put yourself in that situation, then that's when you'll be able to differentiate yourself to really kind of be impactful and get results. That's really hard to do. It's something really different, because machine learning and AI is a different paradigm than traditional software. So this is going to be challenging, but I think if you manage to nail that, then the future is going to be very interesting for your company. >> That's a great point. Thanks for calling that out. I think this all reminds me of the cloud days early on. If you went to the cloud early, you took advantage of it when the pandemic hit. If you weren't native in the cloud, you got hamstrung by that, you were flatfooted. So just get in there. (laughs) Get in the cloud, get into AI, you're going to be good. Thanks for for calling that. Final parting comments, what's your most exciting thing going on right now for you guys? Ori, Clem, what's the most exciting thing on your plate right now that you'd like to share with folks? >> I mean, for me it's just the diversity of use cases and really creative ways of companies leveraging this technology. Every day I speak with about two, three customers, and I'm continuously being surprised by the creative ideas. And the future is really exciting of what can be achieved here. And also I'm amazed by the pace that things move in this industry. It's just, there's not at dull moment. So, definitely exciting times. >> Clem, what are you most excited about right now? >> For me, it's all the new open source models that have been released in the past few weeks, and that they'll keep being released in the next few weeks. I'm also super excited about more and more companies getting into this capability of chaining different models and different APIs. I think that's a very, very interesting development, because it creates new capabilities, new possibilities, new functionalities that weren't possible before. You can plug an API with an open source embedding model, with like a no-geo transcription model. So that's also very exciting. This capability of having more interoperable machine learning will also, I think, open a lot of interesting things in the future. >> Clem, congratulations on your success at Hugging Face. Please pass that on to your team. Ori, congratulations on your success, and continue to, just day one. I mean, it's just the beginning. It's not even scratching the service. Ankur, I'll give you the last word. What are you excited for at AWS? More cloud goodness coming here with AI. Give you the final word. >> Yeah, so as both Clem and Ori said, I think the research in the space is moving really, really fast, so we are excited about that. But we are also excited to see the speed at which enterprises and other AWS customers are applying machine learning to solve real business problems, and the kind of results they're seeing. So when they come back to us and tell us the kind of improvement in their business metrics and overall customer experience that they're driving and they're seeing real business results, that's what keeps us going and inspires us to continue inventing on their behalf. >> Gentlemen, thank you so much for this awesome high impact panel. Ankur, Clem, Ori, congratulations on all your success. We'll see you around. Thanks for coming on. Generative AI, riding the wave, it's a tidal wave, it's the water, it's all happening. All great stuff. This is season three, episode one of AWS Startup Showcase closing panel. This is the AI ML episode, the top startups building generative AI on AWS. I'm John Furrier, your host. Thanks for watching. (mellow music)

(light airy music) >> Hello, everyone, welcome to theCUBE's presentation of the AWS Startup Showcase, AI and machine learning. "Top Startups Building Generative AI on AWS." This is season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talking about AI machine learning. We have three great guests Bratin Saha, VP, Vice President of Machine Learning and AI Services at Amazon Web Services. Tom Mason, the CTO of Stability AI, and Aidan Gomez, CEO and co-founder of Cohere. Two practitioners doing startups and AWS. Gentlemen, thank you for opening up this session, this episode. Thanks for coming on. >> Thank you. >> Thank you. >> Thank you. >> So the topic is hype versus reality. So I think we're all on the reality is great, hype is great, but the reality's here. I want to get into it. Generative AI's got all the momentum, it's going mainstream, it's kind of come out of the behind the ropes, it's now mainstream. We saw the success of ChatGPT, opens up everyone's eyes, but there's so much more going on. Let's jump in and get your early perspectives on what should people be talking about right now? What are you guys working on? We'll start with AWS. What's the big focus right now for you guys as you come into this market that's highly active, highly hyped up, but people see value right out of the gate? >> You know, we have been working on generative AI for some time. In fact, last year we released Code Whisperer, which is about using generative AI for software development and a number of customers are using it and getting real value out of it. So generative AI is now something that's mainstream that can be used by enterprise users. And we have also been partnering with a number of other companies. So, you know, stability.ai, we've been partnering with them a lot. We want to be partnering with other companies as well. In seeing how we do three things, you know, first is providing the most efficient infrastructure for generative AI. And that is where, you know, things like Trainium, things like Inferentia, things like SageMaker come in. And then next is the set of models and then the third is the kind of applications like Code Whisperer and so on. So, you know, it's early days yet, but clearly there's a lot of amazing capabilities that will come out and something that, you know, our customers are starting to pay a lot of attention to. >> Tom, talk about your company and what your focus is and why the Amazon Web Services relationship's important for you? >> So yeah, we're primarily committed to making incredible open source foundation models and obviously stable effusions been our kind of first big model there, which we trained all on AWS. We've been working with them over the last year and a half to develop, obviously a big cluster, and bring all that compute to training these models at scale, which has been a really successful partnership. And we're excited to take it further this year as we develop commercial strategy of the business and build out, you know, the ability for enterprise customers to come and get all the value from these models that we think they can get. So we're really excited about the future. We got hugely exciting pipeline for this year with new modalities and video models and wonderful things and trying to solve images for once and for all and get the kind of general value and value proposition correct for customers. So it's a really exciting time and very honored to be part of it. >> It's great to see some of your customers doing so well out there. Congratulations to your team. Appreciate that. Aidan, let's get into what you guys do. What does Cohere do? What are you excited about right now? >> Yeah, so Cohere builds large language models, which are the backbone of applications like ChatGPT and GPT-3. We're extremely focused on solving the issues with adoption for enterprise. So it's great that you can make a super flashy demo for consumers, but it takes a lot to actually get it into billion user products and large global enterprises. So about six months ago, we released our command models, which are some of the best that exist for large language models. And in December, we released our multilingual text understanding models and that's on over a hundred different languages and it's trained on, you know, authentic data directly from native speakers. And so we're super excited to continue pushing this into enterprise and solving those barriers for adoption, making this transformation a reality. >> Just real quick, while I got you there on the new products coming out. Where are we in the progress? People see some of the new stuff out there right now. There's so much more headroom. Can you just scope out in your mind what that looks like? Like from a headroom standpoint? Okay, we see ChatGPT. "Oh yeah, it writes my papers for me, does some homework for me." I mean okay, yawn, maybe people say that, (Aidan chuckles) people excited or people are blown away. I mean, it's helped theCUBE out, it helps me, you know, feed up a little bit from my write-ups but it's not always perfect. >> Yeah, at the moment it's like a writing assistant, right? And it's still super early in the technologies trajectory. I think it's fascinating and it's interesting but its impact is still really limited. I think in the next year, like within the next eight months, we're going to see some major changes. You've already seen the very first hints of that with stuff like Bing Chat, where you augment these dialogue models with an external knowledge base. So now the models can be kept up to date to the millisecond, right? Because they can search the web and they can see events that happened a millisecond ago. But that's still limited in the sense that when you ask the question, what can these models actually do? Well they can just write text back at you. That's the extent of what they can do. And so the real project, the real effort, that I think we're all working towards is actually taking action. So what happens when you give these models the ability to use tools, to use APIs? What can they do when they can actually affect change out in the real world, beyond just streaming text back at the user? I think that's the really exciting piece. >> Okay, so I wanted to tee that up early in the segment 'cause I want to get into the customer applications. We're seeing early adopters come in, using the technology because they have a lot of data, they have a lot of large language model opportunities and then there's a big fast follower wave coming behind it. I call that the people who are going to jump in the pool early and get into it. They might not be advanced. Can you guys share what customer applications are being used with large language and vision models today and how they're using it to transform on the early adopter side, and how is that a tell sign of what's to come? >> You know, one of the things we have been seeing both with the text models that Aidan talked about as well as the vision models that stability.ai does, Tom, is customers are really using it to change the way you interact with information. You know, one example of a customer that we have, is someone who's kind of using that to query customer conversations and ask questions like, you know, "What was the customer issue? How did we solve it?" And trying to get those kinds of insights that was previously much harder to do. And then of course software is a big area. You know, generating software, making that, you know, just deploying it in production. Those have been really big areas that we have seen customers start to do. You know, looking at documentation, like instead of you know, searching for stuff and so on, you know, you just have an interactive way, in which you can just look at the documentation for a product. You know, all of this goes to where we need to take the technology. One of which is, you know, the models have to be there but they have to work reliably in a production setting at scale, with privacy, with security, and you know, making sure all of this is happening, is going to be really key. That is what, you know, we at AWS are looking to do, which is work with partners like stability and others and in the open source and really take all of these and make them available at scale to customers, where they work reliably. >> Tom, Aidan, what's your thoughts on this? Where are customers landing on this first use cases or set of low-hanging fruit use cases or applications? >> Yeah, so I think like the first group of adopters that really found product market fit were the copywriting companies. So one great example of that is HyperWrite. Another one is Jasper. And so for Cohere, that's the tip of the iceberg, like there's a very long tail of usage from a bunch of different applications. HyperWrite is one of our customers, they help beat writer's block by drafting blog posts, emails, and marketing copy. We also have a global audio streaming platform, which is using us the power of search engine that can comb through podcast transcripts, in a bunch of different languages. Then a global apparel brand, which is using us to transform how they interact with their customers through a virtual assistant, two dozen global news outlets who are using us for news summarization. So really like, these large language models, they can be deployed all over the place into every single industry sector, language is everywhere. It's hard to think of any company on Earth that doesn't use language. So it's, very, very- >> We're doing it right now. We got the language coming in. >> Exactly. >> We'll transcribe this puppy. All right. Tom, on your side, what do you see the- >> Yeah, we're seeing some amazing applications of it and you know, I guess that's partly been, because of the growth in the open source community and some of these applications have come from there that are then triggering this secondary wave of innovation, which is coming a lot from, you know, controllability and explainability of the model. But we've got companies like, you know, Jasper, which Aidan mentioned, who are using stable diffusion for image generation in block creation, content creation. We've got Lensa, you know, which exploded, and is built on top of stable diffusion for fine tuning so people can bring themselves and their pets and you know, everything into the models. So we've now got fine tuned stable diffusion at scale, which is democratized, you know, that process, which is really fun to see your Lensa, you know, exploded. You know, I think it was the largest growing app in the App Store at one point. And lots of other examples like NightCafe and Lexica and Playground. So seeing lots of cool applications. >> So much applications, we'll probably be a customer for all you guys. We'll definitely talk after. But the challenges are there for people adopting, they want to get into what you guys see as the challenges that turn into opportunities. How do you see the customers adopting generative AI applications? For example, we have massive amounts of transcripts, timed up to all the videos. I don't even know what to do. Do I just, do I code my API there. So, everyone has this problem, every vertical has these use cases. What are the challenges for people getting into this and adopting these applications? Is it figuring out what to do first? Or is it a technical setup? Do they stand up stuff, they just go to Amazon? What do you guys see as the challenges? >> I think, you know, the first thing is coming up with where you think you're going to reimagine your customer experience by using generative AI. You know, we talked about Ada, and Tom talked about a number of these ones and you know, you pick up one or two of these, to get that robust. And then once you have them, you know, we have models and we'll have more models on AWS, these large language models that Aidan was talking about. Then you go in and start using these models and testing them out and seeing whether they fit in use case or not. In many situations, like you said, John, our customers want to say, "You know, I know you've trained these models on a lot of publicly available data, but I want to be able to customize it for my use cases. Because, you know, there's some knowledge that I have created and I want to be able to use that." And then in many cases, and I think Aidan mentioned this. You know, you need these models to be up to date. Like you can't have it staying. And in those cases, you augmented with a knowledge base, you know you have to make sure that these models are not hallucinating. And so you need to be able to do the right kind of responsible AI checks. So, you know, you start with a particular use case, and there are a lot of them. Then, you know, you can come to AWS, and then look at one of the many models we have and you know, we are going to have more models for other modalities as well. And then, you know, play around with the models. We have a playground kind of thing where you can test these models on some data and then you can probably, you will probably want to bring your own data, customize it to your own needs, do some of the testing to make sure that the model is giving the right output and then just deploy it. And you know, we have a lot of tools. >> Yeah. >> To make this easy for our customers. >> How should people think about large language models? Because do they think about it as something that they tap into with their IP or their data? Or is it a large language model that they apply into their system? Is the interface that way? What's the interaction look like? >> In many situations, you can use these models out of the box. But in typical, in most of the other situations, you will want to customize it with your own data or with your own expectations. So the typical use case would be, you know, these are models are exposed through APIs. So the typical use case would be, you know you're using these APIs a little bit for testing and getting familiar and then there will be an API that will allow you to train this model further on your data. So you use that AI, you know, make sure you augmented the knowledge base. So then you use those APIs to customize the model and then just deploy it in an application. You know, like Tom was mentioning, a number of companies that are using these models. So once you have it, then you know, you again, use an endpoint API and use it in an application. >> All right, I love the example. I want to ask Tom and Aidan, because like most my experience with Amazon Web Service in 2007, I would stand up in EC2, put my code on there, play around, if it didn't work out, I'd shut it down. Is that a similar dynamic we're going to see with the machine learning where developers just kind of log in and stand up infrastructure and play around and then have a cloud-like experience? >> So I can go first. So I mean, we obviously, with AWS working really closely with the SageMaker team, do fantastic platform there for ML training and inference. And you know, going back to your point earlier, you know, where the data is, is hugely important for companies. Many companies bringing their models to their data in AWS on-premise for them is hugely important. Having the models to be, you know, open sources, makes them explainable and transparent to the adopters of those models. So, you know, we are really excited to work with the SageMaker team over the coming year to bring companies to that platform and make the most of our models. >> Aidan, what's your take on developers? Do they just need to have a team in place, if we want to interface with you guys? Let's say, can they start learning? What do they got to do to set up? >> Yeah, so I think for Cohere, our product makes it much, much easier to people, for people to get started and start building, it solves a lot of the productionization problems. But of course with SageMaker, like Tom was saying, I think that lowers a barrier even further because it solves problems like data privacy. So I want to underline what Bratin was saying earlier around when you're fine tuning or when you're using these models, you don't want your data being incorporated into someone else's model. You don't want it being used for training elsewhere. And so the ability to solve for enterprises, that data privacy and that security guarantee has been hugely important for Cohere, and that's very easy to do through SageMaker. >> Yeah. >> But the barriers for using this technology are coming down super quickly. And so for developers, it's just becoming completely intuitive. I love this, there's this quote from Andrej Karpathy. He was saying like, "It really wasn't on my 2022 list of things to happen that English would become, you know, the most popular programming language." And so the barrier is coming down- >> Yeah. >> Super quickly and it's exciting to see. >> It's going to be awesome for all the companies here, and then we'll do more, we're probably going to see explosion of startups, already seeing that, the maps, ecosystem maps, the landscape maps are happening. So this is happening and I'm convinced it's not yesterday's chat bot, it's not yesterday's AI Ops. It's a whole another ballgame. So I have to ask you guys for the final question before we kick off the company's showcasing here. How do you guys gauge success of generative AI applications? Is there a lens to look through and say, okay, how do I see success? It could be just getting a win or is it a bigger picture? Bratin we'll start with you. How do you gauge success for generative AI? >> You know, ultimately it's about bringing business value to our customers. And making sure that those customers are able to reimagine their experiences by using generative AI. Now the way to get their ease, of course to deploy those models in a safe, effective manner, and ensuring that all of the robustness and the security guarantees and the privacy guarantees are all there. And we want to make sure that this transitions from something that's great demos to actual at scale products, which means making them work reliably all of the time not just some of the time. >> Tom, what's your gauge for success? >> Look, I think this, we're seeing a completely new form of ways to interact with data, to make data intelligent, and directly to bring in new revenue streams into business. So if businesses can use our models to leverage that and generate completely new revenue streams and ultimately bring incredible new value to their customers, then that's fantastic. And we hope we can power that revolution. >> Aidan, what's your take? >> Yeah, reiterating Bratin and Tom's point, I think that value in the enterprise and value in market is like a huge, you know, it's the goal that we're striving towards. I also think that, you know, the value to consumers and actual users and the transformation of the surface area of technology to create experiences like ChatGPT that are magical and it's the first time in human history we've been able to talk to something compelling that's not a human. I think that in itself is just extraordinary and so exciting to see. >> It really brings up a whole another category of markets. B2B, B2C, it's B2D, business to developer. Because I think this is kind of the big trend the consumers have to win. The developers coding the apps, it's a whole another sea change. Reminds me everyone use the "Moneyball" movie as example during the big data wave. Then you know, the value of data. There's a scene in "Moneyball" at the end, where Billy Beane's getting the offer from the Red Sox, then the owner says to the Red Sox, "If every team's not rebuilding their teams based upon your model, there'll be dinosaurs." I think that's the same with AI here. Every company will have to need to think about their business model and how they operate with AI. So it'll be a great run. >> Completely Agree >> It'll be a great run. >> Yeah. >> Aidan, Tom, thank you so much for sharing about your experiences at your companies and congratulations on your success and it's just the beginning. And Bratin, thanks for coming on representing AWS. And thank you, appreciate for what you do. Thank you. >> Thank you, John. Thank you, Aidan. >> Thank you John. >> Thanks so much. >> Okay, let's kick off season three, episode one. I'm John Furrier, your host. Thanks for watching. (light airy music)

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

(upbeat music) >> Hello, everyone, welcome to theCUBE. I'm John Furrier, the host of theCUBE in Palo Alto, California. Also it's SiliconANGLE News. Got two great guests here to talk about AI, the impact of the future of the internet, the applications, the people. Amr Awadallah, the founder and CEO, Ed Alban is the CEO of Vectara, a new startup that emerged out of the original Cloudera, I would say, 'cause Amr's known, famous for the Cloudera founding, which was really the beginning of the big data movement. And now as AI goes mainstream, there's so much to talk about, so much to go on. And plus the new company is one of the, now what I call the wave, this next big wave, I call it the fifth wave in the industry. You know, you had PCs, you had the internet, you had mobile. This generative AI thing is real. And you're starting to see startups come out in droves. Amr obviously was founder of Cloudera, Big Data, and now Vectara. And Ed Albanese, you guys have a new company. Welcome to the show. >> Thank you. It's great to be here. >> So great to see you. Now the story is theCUBE started in the Cloudera office. Thanks to you, and your friendly entrepreneurship views that you have. We got to know each other over the years. But Cloudera had Hadoop, which was the beginning of what I call the big data wave, which then became what we now call data lakes, data oceans, and data infrastructure that's developed from that. It's almost interesting to look back 12 plus years, and see that what AI is doing now, right now, is opening up the eyes to the mainstream, and the application's almost mind blowing. You know, Sati Natel called it the Mosaic Moment, didn't say Netscape, he built Netscape (laughing) but called it the Mosaic Moment. You're seeing companies in startups, kind of the alpha geeks running here, because this is the new frontier, and there's real meat on the bone, in terms of like things to do. Why? Why is this happening now? What's is the confluence of the forces happening, that are making this happen? >> Yeah, I mean if you go back to the Cloudera days, with big data, and so on, that was more about data processing. Like how can we process data, so we can extract numbers from it, and do reporting, and maybe take some actions, like this is a fraud transaction, or this is not. And in the meanwhile, many of the researchers working in the neural network, and deep neural network space, were trying to focus on data understanding, like how can I understand the data, and learn from it, so I can take actual actions, based on the data directly, just like a human does. And we were only good at doing that at the level of somebody who was five years old, or seven years old, all the way until about 2013. And starting in 2013, which is only 10 years ago, a number of key innovations started taking place, and each one added on. It was no major innovation that just took place. It was a couple of really incremental ones, but they added on top of each other, in a very exponentially additive way, that led to, by the end of 2019, we now have models, deep neural network models, that can read and understand human text just like we do. Right? And they can reason about it, and argue with you, and explain it to you. And I think that's what is unlocking this whole new wave of innovation that we're seeing right now. So data understanding would be the essence of it. >> So it's not a Big Bang kind of theory, it's been evolving over time, and I think that the tipping point has been the advancements and other things. I mean look at cloud computing, and look how fast it just crept up on AWS. I mean AWS you back three, five years ago, I was talking to Swami yesterday, and their big news about AI, expanding the Hugging Face's relationship with AWS. And just three, five years ago, there wasn't a model training models out there. But as compute comes out, and you got more horsepower,, these large language models, these foundational models, they're flexible, they're not monolithic silos, they're interacting. There's a whole new, almost fusion of data happening. Do you see that? I mean is that part of this? >> Of course, of course. I mean this wave is building on all the previous waves. We wouldn't be at this point if we did not have hardware that can scale, in a very efficient way. We wouldn't be at this point, if we don't have data that we're collecting about everything we do, that we're able to process in this way. So this, this movement, this motion, this phase we're in, absolutely builds on the shoulders of all the previous phases. For some of the observers from the outside, when they see chatGPT for the first time, for them was like, "Oh my god, this just happened overnight." Like it didn't happen overnight. (laughing) GPT itself, like GPT3, which is what chatGPT is based on, was released a year ahead of chatGPT, and many of us were seeing the power it can provide, and what it can do. I don't know if Ed agrees with that. >> Yeah, Ed? >> I do. Although I would acknowledge that the possibilities now, because of what we've hit from a maturity standpoint, have just opened up in an incredible way, that just wasn't tenable even three years ago. And that's what makes it, it's true that it developed incrementally, in the same way that, you know, the possibilities of a mobile handheld device, you know, in 2006 were there, but when the iPhone came out, the possibilities just exploded. And that's the moment we're in. >> Well, I've had many conversations over the past couple months around this area with chatGPT. John Markoff told me the other day, that he calls it, "The five dollar toy," because it's not that big of a deal, in context to what AI's doing behind the scenes, and all the work that's done on ethics, that's happened over the years, but it has woken up the mainstream, so everyone immediately jumps to ethics. "Does it work? "It's not factual," And everyone who's inside the industry is like, "This is amazing." 'Cause you have two schools of thought there. One's like, people that think this is now the beginning of next gen, this is now we're here, this ain't your grandfather's chatbot, okay?" With NLP, it's got reasoning, it's got other things. >> I'm in that camp for sure. >> Yeah. Well I mean, everyone who knows what's going on is in that camp. And as the naysayers start to get through this, and they go, "Wow, it's not just plagiarizing homework, "it's helping me be better. "Like it could rewrite my memo, "bring the lead to the top." It's so the format of the user interface is interesting, but it's still a data-driven app. >> Absolutely. >> So where does it go from here? 'Cause I'm not even calling this the first ending. This is like pregame, in my opinion. What do you guys see this going, in terms of scratching the surface to what happens next? >> I mean, I'll start with, I just don't see how an application is going to look the same in the next three years. Who's going to want to input data manually, in a form field? Who is going to want, or expect, to have to put in some text in a search box, and then read through 15 different possibilities, and try to figure out which one of them actually most closely resembles the question they asked? You know, I don't see that happening. Who's going to start with an absolute blank sheet of paper, and expect no help? That is not how an application will work in the next three years, and it's going to fundamentally change how people interact and spend time with opening any element on their mobile phone, or on their computer, to get something done. >> Yes. I agree with that. Like every single application, over the next five years, will be rewritten, to fit within this model. So imagine an HR application, I don't want to name companies, but imagine an HR application, and you go into application and you clicking on buttons, because you want to take two weeks of vacation, and menus, and clicking here and there, reasons and managers, versus just telling the system, "I'm taking two weeks of vacation, going to Las Vegas," book it, done. >> Yeah. >> And the system just does it for you. If you weren't completing in your input, in your description, for what you want, then the system asks you back, "Did you mean this? "Did you mean that? "Were you trying to also do this as well?" >> Yeah. >> "What was the reason?" And that will fit it for you, and just do it for you. So I think the user interface that we have with apps, is going to change to be very similar to the user interface that we have with each other. And that's why all these apps will need to evolve. >> I know we don't have a lot of time, 'cause you guys are very busy, but I want to definitely have multiple segments with you guys, on this topic, because there's so much to talk about. There's a lot of parallels going on here. I was talking again with Swami who runs all the AI database at AWS, and I asked him, I go, "This feels a lot like the original AWS. "You don't have to provision a data center." A lot of this heavy lifting on the back end, is these large language models, with these foundational models. So the bottleneck in the past, was the energy, and cost to actually do it. Now you're seeing it being stood up faster. So there's definitely going to be a tsunami of apps. I would see that clearly. What is it? We don't know yet. But also people who are going to leverage the fact that I can get started building value. So I see a startup boom coming, and I see an application tsunami of refactoring things. >> Yes. >> So the replatforming is already kind of happening. >> Yes, >> OpenAI, chatGPT, whatever. So that's going to be a developer environment. I mean if Amazon turns this into an API, or a Microsoft, what you guys are doing. >> We're turning it into API as well. That's part of what we're doing as well, yes. >> This is why this is exciting. Amr, you've lived the big data dream, and and we used to talk, if you didn't have a big data problem, if you weren't full of data, you weren't really getting it. Now people have all the data, and they got to stand this up. >> Yeah. >> So the analogy is again, the mobile, I like the mobile movement, and using mobile as an analogy, most companies were not building for a mobile environment, right? They were just building for the web, and legacy way of doing apps. And as soon as the user expectations shifted, that my expectation now, I need to be able to do my job on this small screen, on the mobile device with a touchscreen. Everybody had to invest in re-architecting, and re-implementing every single app, to fit within that model, and that model of interaction. And we are seeing the exact same thing happen now. And one of the core things we're focused on at Vectara, is how to simplify that for organizations, because a lot of them are overwhelmed by large language models, and ML. >> They don't have the staff. >> Yeah, yeah, yeah. They're understaffed, they don't have the skills. >> But they got developers, they've got DevOps, right? >> Yes. >> So they have the DevSecOps going on. >> Exactly, yes. >> So our goal is to simplify it enough for them that they can start leveraging this technology effectively, within their applications. >> Ed, you're the COO of the company, obviously a startup. You guys are growing. You got great backup, and good team. You've also done a lot of business development, and technical business development in this area. If you look at the landscape right now, and I agree the apps are coming, every company I talk to, that has that jet chatGPT of, you know, epiphany, "Oh my God, look how cool this is. "Like magic." Like okay, it's code, settle down. >> Mm hmm. >> But everyone I talk to is using it in a very horizontal way. I talk to a very senior person, very tech alpha geek, very senior person in the industry, technically. they're using it for log data, they're using it for configuration of routers. And in other areas, they're using it for, every vertical has a use case. So this is horizontally scalable from a use case standpoint. When you hear horizontally scalable, first thing I chose in my mind is cloud, right? >> Mm hmm. >> So cloud, and scalability that way. And the data is very specialized. So now you have this vertical specialization, horizontally scalable, everyone will be refactoring. What do you see, and what are you seeing from customers, that you talk to, and prospects? >> Yeah, I mean put yourself in the shoes of an application developer, who is actually trying to make their application a bit more like magic. And to have that soon-to-be, honestly, expected experience. They've got to think about things like performance, and how efficiently that they can actually execute a query, or a question. They've got to think about cost. Generative isn't cheap, like the inference of it. And so you've got to be thoughtful about how and when you take advantage of it, you can't use it as a, you know, everything looks like a nail, and I've got a hammer, and I'm going to hit everything with it, because that will be wasteful. Developers also need to think about how they're going to take advantage of, but not lose their own data. So there has to be some controls around what they feed into the large language model, if anything. Like, should they fine tune a large language model with their own data? Can they keep it logically separated, but still take advantage of the powers of a large language model? And they've also got to take advantage, and be aware of the fact that when data is generated, that it is a different class of data. It might not fully be their own. >> Yeah. >> And it may not even be fully verified. And so when the logical cycle starts, of someone making a request, the relationship between that request, and the output, those things have to be stored safely, logically, and identified as such. >> Yeah. >> And taken advantage of in an ongoing fashion. So these are mega problems, each one of them independently, that, you know, you can think of it as middleware companies need to take advantage of, and think about, to help the next wave of application development be logical, sensible, and effective. It's not just calling some raw API on the cloud, like openAI, and then just, you know, you get your answer and you're done, because that is a very brute force approach. >> Well also I will point, first of all, I agree with your statement about the apps experience, that's going to be expected, form filling. Great point. The interesting about chatGPT. >> Sorry, it's not just form filling, it's any action you would like to take. >> Yeah. >> Instead of clicking, and dragging, and dropping, and doing it on a menu, or on a touch screen, you just say it, and it's and it happens perfectly. >> Yeah. It's a different interface. And that's why I love that UIUX experiences, that's the people falling out of their chair moment with chatGPT, right? But a lot of the things with chatGPT, if you feed it right, it works great. If you feed it wrong and it goes off the rails, it goes off the rails big. >> Yes, yes. >> So the the Bing catastrophes. >> Yeah. >> And that's an example of garbage in, garbage out, classic old school kind of comp-side phrase that we all use. >> Yep. >> Yes. >> This is about data in injection, right? It reminds me the old SQL days, if you had to, if you can sling some SQL, you were a magician, you know, to get the right answer, it's pretty much there. So you got to feed the AI. >> You do, Some people call this, the early word to describe this as prompt engineering. You know, old school, you know, search, or, you know, engagement with data would be, I'm going to, I have a question or I have a query. New school is, I have, I have to issue it a prompt, because I'm trying to get, you know, an action or a reaction, from the system. And the active engineering, there are a lot of different ways you could do it, all the way from, you know, raw, just I'm going to send you whatever I'm thinking. >> Yeah. >> And you get the unintended outcomes, to more constrained, where I'm going to just use my own data, and I'm going to constrain the initial inputs, the data I already know that's first party, and I trust, to, you know, hyper constrain, where the application is actually, it's looking for certain elements to respond to. >> It's interesting Amr, this is why I love this, because one we are in the media, we're recording this video now, we'll stream it. But we got all your linguistics, we're talking. >> Yes. >> This is data. >> Yep. >> So the data quality becomes now the new intellectual property, because, if you have that prompt source data, it makes data or content, in our case, the original content, intellectual property. >> Absolutely. >> Because that's the value. And that's where you see chatGPT fall down, is because they're trying to scroll the web, and people think it's search. It's not necessarily search, it's giving you something that you wanted. It is a lot of that, I remember in Cloudera, you said, "Ask the right questions." Remember that phrase you guys had, that slogan? >> Mm hmm. And that's prompt engineering. So that's exactly, that's the reinvention of "Ask the right question," is prompt engineering is, if you don't give these models the question in the right way, and very few people know how to frame it in the right way with the right context, then you will get garbage out. Right? That is the garbage in, garbage out. But if you specify the question correctly, and you provide with it the metadata that constrain what that question is going to be acted upon or answered upon, then you'll get much better answers. And that's exactly what we solved Vectara. >> Okay. So before we get into the last couple minutes we have left, I want to make sure we get a plug in for the opportunity, and the profile of Vectara, your new company. Can you guys both share with me what you think the current situation is? So for the folks who are now having those moments of, "Ah, AI's bullshit," or, "It's not real, it's a lot of stuff," from, "Oh my god, this is magic," to, "Okay, this is the future." >> Yes. >> What would you say to that person, if you're at a cocktail party, or in the elevator say, "Calm down, this is the first inning." How do you explain the dynamics going on right now, to someone who's either in the industry, but not in the ropes? How would you explain like, what this wave's about? How would you describe it, and how would you prepare them for how to change their life around this? >> Yeah, so I'll go first and then I'll let Ed go. Efficiency, efficiency is the description. So we figured that a way to be a lot more efficient, a way where you can write a lot more emails, create way more content, create way more presentations. Developers can develop 10 times faster than they normally would. And that is very similar to what happened during the Industrial Revolution. I always like to look at examples from the past, to read what will happen now, and what will happen in the future. So during the Industrial Revolution, it was about efficiency with our hands, right? So I had to make a piece of cloth, like this piece of cloth for this shirt I'm wearing. Our ancestors, they had to spend month taking the cotton, making it into threads, taking the threads, making them into pieces of cloth, and then cutting it. And now a machine makes it just like that, right? And the ancestors now turned from the people that do the thing, to manage the machines that do the thing. And I think the same thing is going to happen now, is our efficiency will be multiplied extremely, as human beings, and we'll be able to do a lot more. And many of us will be able to do things they couldn't do before. So another great example I always like to use is the example of Google Maps, and GPS. Very few of us knew how to drive a car from one location to another, and read a map, and get there correctly. But once that efficiency of an AI, by the way, behind these things is very, very complex AI, that figures out how to do that for us. All of us now became amazing navigators that can go from any point to any point. So that's kind of how I look at the future. >> And that's a great real example of impact. Ed, your take on how you would talk to a friend, or colleague, or anyone who asks like, "How do I make sense of the current situation? "Is it real? "What's in it for me, and what do I do?" I mean every company's rethinking their business right now, around this. What would you say to them? >> You know, I usually like to show, rather than describe. And so, you know, the other day I just got access, I've been using an application for a long time, called Notion, and it's super popular. There's like 30 or 40 million users. And the new version of Notion came out, which has AI embedded within it. And it's AI that allows you primarily to create. So if you could break down the world of AI into find and create, for a minute, just kind of logically separate those two things, find is certainly going to be massively impacted in our experiences as consumers on, you know, Google and Bing, and I can't believe I just said the word Bing in the same sentence as Google, but that's what's happening now (all laughing), because it's a good example of change. >> Yes. >> But also inside the business. But on the crate side, you know, Notion is a wiki product, where you try to, you know, note down things that you are thinking about, or you want to share and memorialize. But sometimes you do need help to get it down fast. And just in the first day of using this new product, like my experience has really fundamentally changed. And I think that anybody who would, you know, anybody say for example, that is using an existing app, I would show them, open up the app. Now imagine the possibility of getting a starting point right off the bat, in five seconds of, instead of having to whole cloth draft this thing, imagine getting a starting point then you can modify and edit, or just dispose of and retry again. And that's the potential for me. I can't imagine a scenario where, in a few years from now, I'm going to be satisfied if I don't have a little bit of help, in the same way that I don't manually spell check every email that I send. I automatically spell check it. I love when I'm getting type ahead support inside of Google, or anything. Doesn't mean I always take it, or when texting. >> That's efficiency too. I mean the cloud was about developers getting stuff up quick. >> Exactly. >> All that heavy lifting is there for you, so you don't have to do it. >> Right? >> And you get to the value faster. >> Exactly. I mean, if history taught us one thing, it's, you have to always embrace efficiency, and if you don't fast enough, you will fall behind. Again, looking at the industrial revolution, the companies that embraced the industrial revolution, they became the leaders in the world, and the ones who did not, they all like. >> Well the AI thing that we got to watch out for, is watching how it goes off the rails. If it doesn't have the right prompt engineering, or data architecture, infrastructure. >> Yes. >> It's a big part. So this comes back down to your startup, real quick, I know we got a couple minutes left. Talk about the company, the motivation, and we'll do a deeper dive on on the company. But what's the motivation? What are you targeting for the market, business model? The tech, let's go. >> Actually, I would like Ed to go first. Go ahead. >> Sure, I mean, we're a developer-first, API-first platform. So the product is oriented around allowing developers who may not be superstars, in being able to either leverage, or choose, or select their own large language models for appropriate use cases. But they that want to be able to instantly add the power of large language models into their application set. We started with search, because we think it's going to be one of the first places that people try to take advantage of large language models, to help find information within an application context. And we've built our own large language models, focused on making it very efficient, and elegant, to find information more quickly. So what a developer can do is, within minutes, go up, register for an account, and get access to a set of APIs, that allow them to send data, to be converted into a format that's easy to understand for large language models, vectors. And then secondarily, they can issue queries, ask questions. And they can ask them very, the questions that can be asked, are very natural language questions. So we're talking about long form sentences, you know, drill down types of questions, and they can get answers that either come back in depending upon the form factor of the user interface, in list form, or summarized form, where summarized equals the opportunity to kind of see a condensed, singular answer. >> All right. I have a. >> Oh okay, go ahead, you go. >> I was just going to say, I'm going to be a customer for you, because I want, my dream was to have a hologram of theCUBE host, me and Dave, and have questions be generated in the metaverse. So you know. (all laughing) >> There'll be no longer any guests here. They'll all be talking to you guys. >> Give a couple bullets, I'll spit out 10 good questions. Publish a story. This brings the automation, I'm sorry to interrupt you. >> No, no. No, no, I was just going to follow on on the same. So another way to look at exactly what Ed described is, we want to offer you chatGPT for your own data, right? So imagine taking all of the recordings of all of the interviews you have done, and having all of the content of that being ingested by a system, where you can now have a conversation with your own data and say, "Oh, last time when I met Amr, "which video games did we talk about? "Which movie or book did we use as an analogy "for how we should be embracing data science, "and big data, which is moneyball," I know you use moneyball all the time. And you start having that conversation. So, now the data doesn't become a passive asset that you just have in your organization. No. It's an active participant that's sitting with you, on the table, helping you make decisions. >> One of my favorite things to do with customers, is to go to their site or application, and show them me using it. So for example, one of the customers I talked to was one of the biggest property management companies in the world, that lets people go and rent homes, and houses, and things like that. And you know, I went and I showed them me searching through reviews, looking for information, and trying different words, and trying to find out like, you know, is this place quiet? Is it comfortable? And then I put all the same data into our platform, and I showed them the world of difference you can have when you start asking that question wholeheartedly, and getting real information that doesn't have anything to do with the words you asked, but is really focused on the meaning. You know, when I asked like, "Is it quiet?" You know, answers would come back like, "The wind whispered through the trees peacefully," and you know, it's like nothing to do with quiet in the literal word sense, but in the meaning sense, everything to do with it. And that that was magical even for them, to see that. >> Well you guys are the front end of this big wave. Congratulations on the startup, Amr. I know you guys got great pedigree in big data, and you've got a great team, and congratulations. Vectara is the name of the company, check 'em out. Again, the startup boom is coming. This will be one of the major waves, generative AI is here. I think we'll look back, and it will be pointed out as a major inflection point in the industry. >> Absolutely. >> There's not a lot of hype behind that. People are are seeing it, experts are. So it's going to be fun, thanks for watching. >> Thanks John. (soft music)

>> From theCUBE studios in Palo Alto in Boston, bringing you data-driven insights from the cube and ETR, this is Breaking Analysis with Dave Vellante. >> Organizations have considerable room to improve their performance without making expensive changes to their talent, their structure, or their fundamental business model. You don't need a slew of consultants to tell you what to do. You already know. What you need is to immediately ratchet up expectations, energy, urgency, and intensity. You have to fight mediocrity every step of the way. Amp it up and the results will follow. This is the fundamental premise of a hard-hitting new book written by Frank Slootman, CEO of Snowflake, and published earlier this year. It's called "Amp It Up, Leading for Hypergrowth "by Raising Expectations, Increasing Urgency, "and Elevating Intensity." Hello and welcome to this week's Wikibon CUBE Insights, powered by ETR. At Snowflake Summit last month, I was asked to interview Frank on stage about his new book. I've read it several times. And if you haven't read it, you should. Even if you have read it, in this Breaking Analysis, we'll dig deeper into the book and share some clarifying insights and nuances directly from Slootman himself from my one-on-one conversation with him. My first question to Slootman was why do you write this book? Okay, it's kind of a common throwaway question. And how the heck did you find time to do it? It's fairly well-known that a few years ago, Slootman put up a post on LinkedIn with the title Amp It Up. It generated so much buzz and so many requests for Frank's time that he decided that the best way to efficiently scale and share his thoughts on how to create high-performing companies and organizations was to publish a book. Now, he wrote the book during the pandemic. And I joked that they must not have Netflix in Montana where he resides. In a pretty funny moment, he said that writing the book was easier than promoting it. Take a listen. >> Denise, our CMO, you know, she just made sure that this process wasn't going to. It was more work for me to promote this book with all these damn podcasts and other crap, than actually writing the book, you know. And after a while, I was like I'm not doing another podcast. >> Now, the book gives a lot of interesting background information on Slootman's career and what he learned at various companies that he led and participated in. Now, I'm not going to go into most of that today, which is why you should read the book yourself. But Slootman, he's become somewhat of a business hero to many people, myself included. Leaders like Frank, Scott McNealy, Jayshree Ullal, and my old boss, Pat McGovern at IDG, have inspired me over the years. And each has applied his or her own approach to building cultures and companies. Now, when Slootman first took over the reins at Snowflake, I published a Breaking Analysis talking about Snowflake and what we could expect from the company now that Slootman and CFO Mike Scarpelli were back together. In that post, buried toward the end, I referenced the playbook that Frank used at Data Domain and ServiceNow, two companies that I followed quite closely as an analyst, and how it would be applied at Snowflake, that playbook if you will. Frank reached out to me afterwards and said something to the effect of, "I don't use playbooks. "I am a situational leader. "Playbooks, you know, they work in football games. "But in the military, they teach you "situational leadership." Pretty interesting learning moment for me. So I asked Frank on the stage about this. Here's what he said. >> The older you get, the more experience that you have, the more you become a prisoner of your own background because you sort of think in terms of what you know as opposed to, you know, getting outside of what you know and trying to sort of look at things like a five-year-old that has never seen this before. And then how would you, you know, deal with it? And I really try to force myself into I've never seen this before and how do I think about it? Because at least they're very different, you know, interpretations. And be open-minded, just really avoid that rinse and repeat mentality. And you know, I've brought people in from who have worked with me before. Some of them come with me from company to company. And they were falling prey to, you know, rinse and repeat. I would just literally go like that's not what we want. >> So think about that for a moment. I mean, imagine coming in to lead a new company and forcing yourself and your people to forget what they know that works and has worked in the past, put that aside and assess the current situation with an open mind, essentially start over. Now, that doesn't mean you don't apply what has worked in the past. Slootman talked to me about bringing back Scarpelli and the synergistic relationship that they have and how they build cultures and the no BS and hard truth mentality they bring to companies. But he bristles when people ask him, "What type of CEO are you?" He says, "Do we have to put a label on it? "It really depends on the situation." Now, one of the other really hard-hitting parts of the book was the way Frank deals with who to keep and who to let go. He uses the Volkswagen tagline of drivers wanted. He says in his book, in companies there are passengers and there are drivers, and we want drivers. He said, "You have to figure out really quickly "who the drivers are and basically throw the wrong people "off the bus, keep the right people, bring in new people "that fit the culture and put them "in the right seats on the bus." Now, these are not easy decisions to make. But as it pertains to getting rid of people, I'm reminded of the movie "Moneyball." Art Howe, the manager of the Oakland As, he refused to play Scott Hatteberg at first base. So the GM, Billy Bean played by Brad Pitt says to Peter Brand who was played by Jonah Hill, "You have to fire Carlos Pena." Don't learn how to fire people. Billy Bean says, "Just keep it quick. "Tell him he's been traded and that's it." So I asked Frank, "Okay, I get it. "Like the movie, when you have the wrong person "on the bus, you just have to make the decision, "be straightforward, and do it." But I asked him, "What if you're on the fence? "What if you're not completely sure if this person "is a driver or a passenger, if he or she "should be on the bus or not on the bus? "How do you handle that?" Listen to what he said. >> I have a very simple way to break ties. And when there's doubt, there's no doubt, okay? >> When there's doubt, there's no doubt. Slootman's philosophy is you have to be emphatic and have high conviction. You know, back to the baseball analogy, if you're thinking about taking the pitcher out of the game, take 'em out. Confrontation is the single hardest thing in business according to Slootman but you have to be intellectually honest and do what's best for the organization, period. Okay, so wow, that may sound harsh but that's how Slootman approaches it, very Belichickian if you will. But how can you amp it up on a daily basis? What's the approach that Slootman takes? We got into this conversation with a discussion about MBOs, management by objective. Slootman in his book says he's killed MBOs at every company he's led. And I asked him to explain why. His rationale was that individual MBOs invariably end up in a discussion about relief of the MBO if the person is not hitting his or her targets. And that detracts from the organizational alignment. He said at Snowflake everyone gets paid the same way, from the execs on down. It's a key way he creates focus and energy in an organization, by creating alignment, urgency, and putting more resources into the most important things. This is especially hard, Slootman says, as the organization gets bigger. But if you do approach it this way, everything gets easier. The cadence changes, the tempo accelerates, and it works. Now, and to emphasize that point, he said the following. Play the clip. >> Every meeting that you have, every email, every encounter in the hallway, whatever it is, is an opportunity to amp things up. That's why I use that title. But do you take that opportunity? >> And according to Slootman, if you don't take that opportunity, if you're not in the moment, amping it up, then you're thinking about your golf game or the tennis match that's going on this weekend or being out on your boat. And to the point, this approach is not for everyone. You're either built for it or you're not. But if you can bring people into the organization that can handle this type of dynamic, it creates energy. It becomes fun. Everything moves faster. The conversations are exciting. They're inspiring. And it becomes addictive. Now let's talk about priorities. I said to Frank that for me anyway, his book was an uncomfortable read. And he was somewhat surprised by that. "Really," he said. I said, "Yeah. "I mean, it was an easy read but uncomfortable "because over my career, I've managed thousands of people, "not tens of thousands but thousands, "enough to have to take this stuff very seriously." And I found myself throughout the book, oh, you know, on the one hand saying to myself, "Oh, I got that right, good job, Dave." And then other times, I was thinking to myself, "Oh wow, I probably need to rethink that. "I need to amp it up on that front." And the point is to Frank's leadership philosophy, there's no one correct way to approach all situations. You have to figure it out for yourself. But the one thing in the book that I found the hardest was Slootman challenged the reader. If you had to drop everything and focus on one thing, just one thing, for the rest of the year, what would that one thing be? Think about that for a moment. Were you able to come up with that one thing? What would happen to all the other things on your priority list? Are they all necessary? If so, how would you delegate those? Do you have someone in your organization who can take those off your plate? What would happen if you only focused on that one thing? These are hard questions. But Slootman really forces you to think about them and do that mental exercise. Look at Frank's body language in this screenshot. Imagine going into a management meeting with Frank and being prepared to share all the things you're working on that you're so proud of and all the priorities you have for the coming year. Listen to Frank in this clip and tell me it doesn't really make you think. >> I've been in, you know, on other boards and stuff. And I got a PowerPoint back from the CEO and there's like 15 things. They're our priorities for the year. I'm like you got 15, you got none, right? It's like you just can't decide, you know, what's important. So I'll tell you everything because I just can't figure out. And the thing is it's very hard to just say one thing. But it's really the mental exercise that matters. >> Going through that mental exercise is really important according to Slootman. Let's have a conversation about what really matters at this point in time. Why does it need to happen? And does it take priority over other things? Slootman says you have to pull apart the hairball and drive extraordinary clarity. You could be wrong, he says. And he admits he's been wrong on many things before. He, like everyone, is fearful of being wrong. But if you don't have the conversation according to Slootman, you're already defeated. And one of the most important things Slootman emphasizes in the book is execution. He said that's one of the reasons he wrote "Amp It Up." In our discussion, he referenced Pat Gelsinger, his former boss, who bought Data Domain when he was working for Joe Tucci at EMC. Listen to Frank describe the interaction with Gelsinger. >> Well, one of my prior bosses, you know, Pat Gelsinger, when they acquired Data Domain through EMC, Pat was CEO of Intel. And he quoted Andy Grove as saying, 'cause he was Intel for a long time when he was younger man. And he said no strategy is better than its execution, which if I find one of the most brilliant things. >> Now, before you go changing your strategy, says Slootman, you have to eliminate execution as a potential point of failure. All too often, he says, Silicon Valley wants to change strategy without really understanding whether the execution is right. All too often companies don't consider that maybe the product isn't that great. They will frequently, for example, make a change to sales leadership without questioning whether or not there's a product fit. According to Slootman, you have to drive hardcore intellectual honesty. And as uncomfortable as that may be, it's incredibly important and powerful. Okay, one of the other contrarian points in the book was whether or not to have a customer success department. Slootman says this became really fashionable in Silicon Valley with the SaaS craze. Everyone was following and pattern matching the lead of salesforce.com. He says he's eliminated the customer service department at every company he's led which had a customer success department. Listen to Frank Slootman in his own words talk about the customer success department. >> I view the whole company as a customer success function. Okay, I'm customer success, you know. I said it in my presentation yesterday. We're a customer-first organization. I don't need a department. >> Now, he went on to say that sales owns the commercial relationship with the customer. Engineering owns the technical relationship. And oh, by the way, he always puts support inside of the engineering department because engineering has to back up support. And rather than having a separate department for customer success, he focuses on making sure that the existing departments are functioning properly. Slootman also has always been big on net promoter score, NPS. And Snowflake's is very high at 72. And according to Slootman, it's not just the product. It's the people that drive that type of loyalty. Now, Slootman stresses amping up the big things and even the little things too. He told a story about someone who came into his office to ask his opinion about a tee shirt. And he turned it around on her and said, "Well, what do you think?" And she said, "Well, it's okay." So Frank made the point by flipping the situation. Why are you coming to me with something that's just okay? If we're going to do something, let's do it. Let's do it all out. Let's do it right and get excited about it, not just check the box and get something off your desk. Amp it up, all aspects of our business. Listen to Slootman talk about Steve Jobs and the relevance of demanding excellence and shunning mediocrity. >> He was incredibly intolerant of anything that he didn't think of as great. You know, he was immediately done with it and with the person. You know, I'm not that aggressive, you know, in that way. I'm a little bit nicer, you know, about it. But I still, you know, I don't want to give into expediency and mediocrity. I just don't, I'm just going to fight it, you know, every step of the way. >> Now, that story was about a little thing like some swag. But Slootman talked about some big things too. And one of the major ways Snowflake was making big, sweeping changes to amp up its business was reorganizing its go-to-market around industries like financial services, media, and healthcare. Here's some ETR data that shows Snowflake's net score or spending momentum for key industry segments over time. The red dotted line at 40% is an indicator of highly elevated spending momentum. And you can see for the key areas shown, Snowflake is well above that level. And we cut this data where responses were greater, the response numbers were greater than 15. So not huge ends but large enough to have meaning. Most were in the 20s. Now, it's relatively uncommon to see a company that's having the success of Snowflake make this kind of non-trivial change in the middle of steep S-curve growth. Why did they make this move? Well, I think it's because Snowflake realizes that its data cloud is going to increasingly have industry diversity and unique value by industry, that ecosystems and data marketplaces are forming around industries. So the more industry affinity Snowflake can create, the stronger its moat will be. It also aligns with how the largest and most prominent global system integrators, global SIs, go to market. This is important because as companies are transforming, they are radically changing their data architecture, how they think about data, how they approach data as a competitive advantage, and they're looking at data as specifically a monetization opportunity. So having industry expertise and knowledge and aligning with those customer objectives is going to serve Snowflake and its ecosystems well in my view. Slootman even said he joined the board of Instacart not because he needed another board seat but because he wanted to get out of his comfort zone and expose himself to other industries as a way to learn. So look, we're just barely scratching the surface of Slootman's book and I've pulled some highlights from our conversation. There's so much more that I can share just even from our conversation. And I will as the opportunity arises. But for now, I'll just give you the kind of bumper sticker of "Amp It Up." Raise your standards by taking every opportunity, every interaction, to increase your intensity. Get your people aligned and moving in the same direction. If it's the wrong direction, figure it out and course correct quickly. Prioritize and sharpen your focus on things that will really make a difference. If you do these things and increase the urgency in your organization, you'll naturally pick up the pace and accelerate your company. Do these things and you'll be able to transform, better identify adjacent opportunities and go attack them, and create a lasting and meaningful experience for your employees, customers, and partners. Okay, that's it for today. Thanks for watching. And thank you to Alex Myerson who's on production and he manages the podcast for Breaking Analysis. Kristin Martin and Cheryl Knight help get the word out on social and in our newsletters. And Rob Hove is our EIC over at Silicon Angle who does some wonderful and tremendous editing. Thank you all. Remember, all these episodes are available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. And you can email me at david.vellante@siliconangle.com or DM me @dvellante or comment on my LinkedIn posts. And please do check out etr.ai for the best survey data in enterprise tech. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching. Be well. And we'll see you next time on Breaking Analysis. (upbeat music)

>>From around the globe, it's the cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. >>Oh, welcome back to the cube. As we continue our coverage of AWS reinvent 2020, you know, I know you're familiar with Moneyball, the movie, Brad Pitt, starting as Billy Bean, the Oakland A's general manager, where the A's were all over data, right. With the Billy Bean approach, it was a very, uh, data driven approach to building his team and a very successful team. Well, AWS is taking that to an extraordinary level and with us to talk about that as Matt Hearst, who was the head of global sports marketing and communications at AWS and Matt, thanks for joining us here on the queue. >>John is my pleasure. Thanks so much for having me. You >>Bet. Um, now we've already heard from a couple of folks, NFL folks, uh, at re-invent, uh, about the virtual draft. Um, but for those of our viewers who maybe aren't up to speed on that, or having a chance to see, uh, what those folks had to say, uh, let's just talk about that as an opener, um, about your involvement with the NFL and particularly with, with the draft and, and what that announcement was all about. >>Sure. We, we saw, we've seen a great evolution with our work with the NFL over the past few years. And you mentioned during the infrastructure keynote where Michelle McKenna who's, the CIO for the NFL talks about how they were able to stage the 2020 virtual draft, which was the NFL is much most watched ever, uh, you know, over 55 million viewers over three days and how they were unable to do it without the help and the power of AWS, you know, utilizing AWS is reliability, scalability, security, and network connectivity, where they were able to manage thousands of live feeds to flow to the internet and go to ESPN, to airline. Um, but additionally, Jennifer LinkedIn, who's the SVP of player health and innovation at the NFL spoke during the machine learning keynote during reinvent. And she talked about how we're working with the NFL, uh, to co-develop the digital athlete, which is a computer simulation model of a football player that can replicate infinite scenarios in a game environment to help better foster and understanding of how to treat and rehabilitate injuries in the short term and in the long-term in the future, ultimately prevent, prevent and predict injuries. >>And they're using machine learning to be able to do that. So there's, those are just a couple of examples of, uh, what the NFL talked about during re-invent at a couple of keynotes, but we've seen this work with the NFL really evolve over the past few years, you know, starting with next gen stats. Those are the advanced statistics that, uh, brings a new level of entertainment to football fans. And what we really like to do, uh, with the NFL is to excite, educate, and innovate. And those stats really bring fans closer to the game to allow the broadcasters to go a little bit deeper, to educate the fans better. And we've seen some of those come to life through some of our ads, uh, featuring Deshaun Watson, Christian McCaffrey, um, these visually compelling statistics that, that come to life on screen. Um, and it's not just the NFL. AWS is doing this with some of the top sports leagues around the world, you know, powering F1 insights, Buddhist league, and match facts, six nations, rugby match stats, all of which utilize AWS technology to uncover advanced stats and really help educate and engage fans around the world in the sports that they love. >>Let's talk about that engagement with your different partners then, because you just touched on it. This is a wide array of avenues that you're exploring. You're in football, you're in soccer, you're in sailing, uh, you're uh, racing formula one and NASCAR, for example, all very different animals, right? In terms of their statistics and their data and of their fan interest, what fans ultimately want. So, um, maybe on a holistic basis first, how are you, uh, kind of filtering through your partner's needs and their fans needs and your capabilities and providing that kind of merger of capabilities with desires >>Sports, uh, for AWS and for Amazon are no different than any other industry. And we work backwards from the customer and what their needs are. You know, when we look at the sports partners and customers that we work with and why they're looking to AWS to help innovate and transform their sports, it's really the innovative technologies like machine learning, artificial intelligence, high performance computing, internet of things, for example, that are really transforming the sports world and some of the best teams and leagues that we've talked about, that you touched on, you know, formula one, NASCAR, NFL, Buena, Sligo, six nations, rugby, and so on and so forth are using AWS to really improve the athlete and the team performance transform how fans view and engage with sports and deliver these real-time advanced statistics to give fans, uh, more of that excitement that we're talking about. >>Let me give you a couple of examples on some of these innovative technologies that our customers are using. So the Seattle Seahawks, I built a data Lake on AWS to use it for talent, evaluation and acquisition to improve player health and recovery times, and also for their game planning. And another example is, you know, formula and we talk about the F1 insights, those advanced statistics, but they're also using AWS high-performance computing that helped develop the next generation race car, which will be introduced in the 2022 season. And by using AWS F1 was able to reduce the average time to run simulations by 70% to improve the car's aerodynamics, reducing the downforce loss and create more wheel to wheel racing, to bring about more excitement on the track. And a third example, similar to, uh, F1 using HPC is any of those team UK. So they compete in the America's cup, which is the oldest trophy in international sports. And endosteum UK is using an HPC environment running on Amazon, easy to spot instances to design its boat for the upcoming competition. And they're depending on this computational power on AWS needing 2000 to 3000 simulations to design the dimension of just a single boat. Um, and so the power of the cloud and the power of the AWS innovative technologies are really helping, uh, these teams and leagues and sports organizations around the world transform their sport. >>Well, let's go back. Uh, you mentioned the Seahawks, um, just as, uh, an example of maybe, uh, the kind of insights that that you're providing. Uh, let's pretend I'm there, there's an outstanding running back and his name's Matt Hearst and, uh, and he's at a, you know, a college let's just pretend in California someplace. Um, what kind of inputs, uh, are you now helping them? Uh, and what kind of insights are you trying to, are you helping them glean from those inputs that maybe they didn't have before? And how are they actually applying that then in terms of their player acquisition and thinking about draft, right player development, deciding whether Matt Hertz is a good fit for them, maybe John Wallace is a good fit for them. Um, but what are the kinds of, of, uh, what's that process look like? >>So the way that the Seahawks have built the data Lake, they built it on AWFs to really, as you talk about this talent, evaluation and acquisition, to understand how a player, you know, for example, a John Walls could fit into their scheme, you know, that, that taking this data and putting it in the data Lake and figuring out how it fits into their schemes is really important because you could find out that maybe you played, uh, two different positions in high school or college, and then that could transform into, into the schematics that they're running. Um, and try to find, I don't want to say a diamond in the rough, but maybe somebody that could fit better into their scheme than, uh, maybe the analysts or others could figure out. And that's all based on the power of data that they're using, not only for the talent evaluation and acquisition, but for game planning as well. >>And so the Seahawks building that data Lake is just one of those examples. Um, you know, when, when you talk about a player, health and safety, as well, just using the NFL as the example, too, with that digital athlete, working with them to co-develop that for that composite NFL player, um, where they're able to run those infinite scenarios to ultimately predict and prevent injury and using Amazon SageMaker and AWS machine learning to do so, it's super important, obviously with the Seahawks, for the future of that organization and the success that they, that they see and continue to see, and also for the future of football with the NFL, >>You know, um, Roger Goodell talks about innovation in the national football league. We hear other commissioners talking about the same thing. It's kind of a very popular buzz word right now is, is leagues look to, uh, ways to broaden their, their technological footprint in innovative ways. Again, popular to say, how exactly though, do you see AWS role in that with the national football league, for example, again, or maybe any other league in terms of inspiring innovation and getting them to perhaps look at things differently through different prisms than they might have before? >>I think, again, it's, it's working backwards from the customer and understanding their needs, right? We couldn't have predicted at the beginning of 2020, uh, that, you know, the NFL draft will be virtual. And so working closely with the NFL, how do we bring that to life? How do we make that successful, um, you know, working backwards from the NFL saying, Hey, we'd love to utilize your technology to improve Clare health and safety. How are we able to do that? Right. And using machine learning to do so. So the pace of innovation, these innovative technologies are very important, not only for us, but also for these, uh, leagues and teams that we work with, you know, using F1 is another example. Um, we talked about HPC and how they were able to, uh, run these simulations in the cloud to improve, uh, the race car and redesign the race car for the upcoming seasons. >>But, uh, F1 is also using Amazon SageMaker, um, to develop new F1 insights, to bring fans closer to the action on the track, and really understand through technology, these split-second decisions that these drivers are taking in every lap, every turn, when to pit, when not to pit things of that nature and using the power of the cloud and machine learning to really bring that to life. And one example of that, that we introduced this year with, with F1 was, um, the fastest driver insight and working F1, worked with the Amazon machine learning solutions lab to bring that to life and use a data-driven approach to determine the fastest driver, uh, over the last 40 years, relying on the years of historical data that they store in S3 and the ML algorithms that, that built between AWS and F1 data scientists to produce this result. So John, you and I could sit here and argue, you know, like, like two guys that really love F1 and say, I think Michael Schumacher is the fastest drivers. It's Lewis, Hamilton. Who's great. Well, it turned out it was a arts incentive, you know, and Schumacher was second. And, um, Hamilton's third and it's the power of this data and the technology that brings this to life. So we could still have a fun argument as fans around this, but we actually have a data-driven results through that to say, Hey, this is actually how it, how it ranked based on how everything works. >>You know, this being such a strange year, right? With COVID, uh, being rampant and, and the major influence that it has been in every walk of global life, but certainly in the American sports. Um, how has that factored into, in terms of the kinds of services that you're looking to provide or to help your partners provide in order to increase that fan engagement? Because as you've pointed out, ultimately at the end of the day, it's, it's about the consumer, right? The fan, and giving them info, they need at the time they want it, that they find useful. Um, but has this year been, um, put a different point on that for you? Just because so many eyeballs have been on the screen and not necessarily in person >>Yeah. T 20, 20 as, you know, a year, unlike any other, um, you know, in our lifetimes and hopefully going forward, you know, it's, it's not like that. Um, but we're able to understand that we can still bring fans closer to the sports that they love and working with, uh, these leagues, you know, we talk about NFL draft, but with formula one, we, uh, in the month of may developed the F1 Pro-Am deep racer event that featured F1 driver, uh, Daniel Ricardo, and test driver TA Sianna Calderon in this deep racer league and deep racers, a one 18th scale, fully autonomous car, um, that uses reinforcement learning, learning a type of machine learning. And so we had actual F1 driver and test driver racing against developers from all over the world. And technology is really playing a role in that evolution of F1. Um, but also giving fans a chance to go head to head against the Daniel Ricardo, which I don't know that anyone else could ever say that. >>Yeah, I raced against an F1 driver for head to head, you know, and doing that in the month of may really brought forth, not only an appreciation, I think for the drivers that were involved on the machine learning and the technology involved, but also for the developers on these split second decisions, these drivers have to make through an event like that. You know, it was, it was great and well received. And the drivers had a lot of fun there. Um, you know, and that is the national basketball association. The NBA played in the bubble, uh, down in Orlando, Florida, and we work with second spectrum. They run on AWS. And second spectrum is the official optical provider of the NBA and they provide Clippers court vision. So, uh, it's a mobile live streaming experience for LA Clippers fans that uses artificial intelligence and machine learning to visualize data through on-screen graphic overlays. >>And second spectrum was able to rely on, uh, AWS is reliability, connectivity, scalability, and move all of their equipment to the bubble in Orlando and still produce a great experience for the fans, um, by reducing any latency tied to video and data processing, um, they needed that low latency to encode and compress the media to transfer an edit with the overlays in seconds without losing quality. And they were able to rely on AWS to do that. So a couple of examples that even though 2020 was, uh, was a little different than we all expected it to be, um, of how we worked closely with our sports partners to still deliver, uh, an exceptional fan experience. >>So, um, I mean, first off you have probably the coolest job at AWS. I think it's so, uh, congratulations. I mean, it's just, it's fascinating. What's on your want to do less than in terms of 20, 21 and beyond and about what you don't do now, or, or what you would like to do better down the road, any one area in particular that you're looking at, >>You know, our, our strategy in sports is no different than any other industry. We want to work backwards from our customers to help solve business problems through innovation. Um, and I know we've talked about the NFL a few times, but taking them for, for another example, with the NFL draft, improving player health and safety, working closely with them, we're able to help the NFL advance the game both on and off the field. And that's how we look at doing that with all of our sports partners and really helping them transform their sport, uh, through our innovative technologies. And we're doing this in a variety of ways, uh, with a bunch of engaging content that people can really enjoy with the sports that they love, whether it's, you know, quick explainer videos, um, that are short two minute or less videos explaining what these insights are, these advanced stats. >>So when you see them on the screening and say, Oh yeah, I understand what that is at a, at a conceptual level or having blog posts from a will, Carlin who, uh, has a long storied history in six nations and in rugby or Rob Smedley, along story history and F1 writing blog posts to give fans deeper perspective as subject matter experts, or even for those that want to go deeper under the hood. We've worked with our teams to take a deeper look@howsomeofthesecometolifedetailingthetechnologyjourneyoftheseadvancedstatsthroughsomedeepdiveblogsandallofthiscanbefoundataws.com slash sports. So a lot of great rich content for, uh, for people to dig into >>Great stuff, indeed. Um, congratulations to you and your team, because you really are enriching the fan experience, which I am. One of, you know, hundreds of millions are enjoying that. So thanks for that great work. And we wish you all the continued success down the road here in 2021 and beyond. Thanks, Matt. Thanks so much, Sean.

>>from around the globe. It's the Cube with digital coverage of Dell Technologies. World Digital experience brought to you by Dell Technologies. Hello, everyone. And welcome back to the cubes coverage of Del Tech World 2020 the virtual del tech world. Of course, the virtual queue with me is Alison Do. She's the CMO and a member of the executive leadership team at Dell Technologies. Hey there, Alison. Good to see you. >>Hi, David. Good to see you too. I'm gonna see you alive, but it's so good to see on the feed. >>Yeah, I miss you, too. You know, it's been it's been tough, but we're getting through it and, you know, it's a least with technology. We're able to meet this way and, you know, for us continue the cube for you to continue del Tech world, reaching out to your to your customers. But, you know, maybe we could start there. It's like I said the other day else into somebody. I feel like everybody I know in the technology industry has also become a covert expert in the last six months. But but, you know, it changed so much. But I'm interested in well, first of all, you're a great communicator. I have met many, many members of your team. They're really motivated group. How did you handle the pandemic? Your communications. Uh, did you increase that? Did you? Did you have to change anything? Or maybe not. Because like I say, you've always been a great communicator with a strong team. What was your first move? >>Eso There's obviously there's many audiences that we serve through communications, but in this instance, the two most important our customers and our team members. So I'll take the customers first. You have likely seen the spoof Real's Going Around the Internet of Here's How Not to Talk to Customers, Right? So you saw early in February and March in April, all of these communications that started with in these troubled times We are here to help you and, you know, we're already in a crisis every single day, all day long. I don't think people needed to be reminded that there was a crisis happening. So you've got this one end where it's over crisis mongering and the other side where it was just ignoring the crisis. And so what we did was we really looked at all of our communications a new So, for example, in our small business space, we were just about I mean days away from launching a campaign that was about celebrating the success of small businesses. It's a beautiful piece of creative. I love it, and we made the very tough decision to put that work on the shelf and not launch it. Why? Because it would have been incredibly tone deaf in a moment where small businesses were going out of business and under incredible struggle to have a campaign that was celebrating their success. It just wouldn't have worked. And what we did very quickly was a new piece of creative that had our own small business advisers, lower production values, them working from home and talking about how they were helping customers. But frankly, even that then has a shelf life, because ultimately you have to get back to your original story. So as we thought about our own communications, my own leadership team and I went through every single piece of creative toe. Look for what's appropriate now what's tone deaf, and that was a very heavy lift and something that we had to continue to do and I'm really proud of the work. We did pivot quickly, then on the employee side. If you'd asked me in January, was Team member Communications the most important thing I was doing? I would have said It's an important thing I'm doing and I care deeply about it, But it's not the most important thing I'm doing. Where there was a period from probably February to June where I would have said it became the most important thing that I was doing because we had 120,000 people pivot over a weekend toe. Working from home, you had all of the demands of home schooling, the chaos that stress whilst also were obviously trying to keep a business running. So this engagement with our employees and connecting the connecting with them through more informal means, like zoom meetings with Michael and his leadership team, where once upon a time we would have had a more high value production became a key piece of what we did. So it sounds so easy, but this increase of the frequency with our own employees, while also being really honest with ourselves about the tone of those communications, so that's what we did and continue to dio >>Well, you've done a good job and you struck a nice balance. I mean, you weren't did see some folks ambulance chasing and it was a real turn off. Or like you said, sometimes tone deaf. And we can all look back over history and see, you know, so many communications disasters like you say, people being tone deaf or ignoring something. It was sloughing it off, and then it really comes back to bite them. Sometimes security breaches air like that. So it seems like Dell has I don't know, there's a methodology. I don't know if you use data or it's just a lot of good good experience. How have you been able to sort of nail it? I guess I would say is it is. >>But there's some secret method that I'm cautiously optimistic. And the superstitious part of me is like, Don't say that, Okay, I'm not gonna would alright eso so that it's it's both it z experience, obviously. And then what? I What I talk a lot about is this intersection of data versus did data and creativity, and you spend a lot of time in marketing circles. Those two things can be sometimes pitched is competing with each other. Oh, it's all about the creativity, or it's all about the data. And I think that's a silly non argument. And it should be both things And this this time like this. This point that I make about ambulance chasing and not re traumatizing people every single day by talking about in these troubled times is actually from a piece of research that we did, if you believe it or not. In 2008 during the middle of the global financial crisis, when we started to research some of our creative, we found that some of the people who have seen our creative were actually less inclined to buy Dell and less positive about Dell. Why? Because we started with those really hackneyed lines of in these troubled times. And then we went on to talk about how we could take out I t costs and were targeted at I T makers, who basically we first played to their fear function and they said, and now we're going to put you out of a job, right? So there's this years of learning around where you get this sweet spot from a messaging perspective to talk about customer outcomes while also talking about what you do is a company, and keeping the institutional knowledge is knowledge of those lessons and building and refining over time. And so that's why I think we've been able to pivot as quickly as we have is because we've been data driven and had a creative voice for a very long time. The other piece that has helped us be fast is that we've spent the last 2.5 3 years working on bringing our own data, our own customer data internally after many, many years of having that with the third party agency. So all the work we had to do to retarget to re pivot based on which verticals were being successful in this time and which were not we were able to now due in a matter of hours, something that would have taken us weeks before. So there's places where it's about the voice of who we are as a brand, and that's a lot of that is creative judgment. And then there's places about institutional knowledge of the data, and then riel getting too real time data analysis where we're on the cusp of doing that. >>Yeah, so I like the way you phrase that it's not just looking at the data and going with some robotic fashion. It reminds me of, you know the book. Michael Lewis, Moneyball, the famous movie, You know, it's like for a while it was it was in baseball, like whoever had the best nerds they thought we were gonna win. But it really is a balance of art and science, and it seems like you're on this journey with your customers together. I mean, how much how much? I mean, I know there's a lot of interaction, but but it seems like you guys are all learning together and evolving together in that regard. >>Absolutely. David, One of the things that has been really interesting to watch is we have had a connected workplace program for 10 years, so we've had flexible work arrangements for a very long time, and one of the things that we have learned from that is a combination of three key factors. The technology, obviously, can you do it? The three culture, and then the process is right. So when you have a the ability to work from home doesn't mean you should work from home 22 out of 24 hours. And that's where culture comes in. And I frankly, that's where this moment of cumulative global stress is so important to realize as a leader and to bring out to the Open and to talk about it. I mean, Michael's talked a lot about this is a marathon. This is not a sprint. We've done a lot of things to support our employees. And so if you think about those three factors and what we've learned, one of the things that we found as we got into the pipe pandemic was on the technology side. Even customers who thought they had business continuity plans in place or thought that they had worked from home infrastructure in place found that they didn't really so there was actually a very quick move to help our customers get the technology that would enable them to keep their businesses running and then on the other two fronts around processes and culture and leadership. We've been ableto have smaller, more intimate conversations with our customers than we would have historically, because frankly, we can bring Michael, Jeff. Other parts of the leadership team me together to have a conversation and one of the benefits of the fact that those of us who've been road warriors for many, many, many years as I know you have a swell suddenly found yourself actually staying in one place. You have time to have that conversation so that we continue to obviously help our customers on the technology front, but also have been able to lean in in a different way on what we've learned over 10 years and what we've learned over this incredibly dramatic eight months, >>you know, and you guys actually have some work from Home Street cred? I think, Del, you're the percentage of folks that were working from home Pre Koven was higher than the norm, significantly higher than normal. Wasn't that long ago that there were a couple of really high profile companies that were mandating come into the office and clear that they were on the wrong side of history? I mean, that surprised me actually on. Do you know what also surprised me? I don't know. I'm just gonna say it is There were two companies run by women, and I would have thought there was more empathy there. Uh, but Dal has always had this culture of Yeah, we were, You know, we could work. We could be productive no matter where. Maybe that's because of the the heritage or your founders. Still still chairman and CEO. I don't know. >>You know those companies and obviously we know who they are. Even at the time, what I thought about them was You don't have a location problem. You have a culture problem and you have a productivity problem and you a trust problem with your employees. And so, yes, I think they are going to be proven to be on the wrong side of history. And I think in those instances they've been on the wrong side of history on many things, sadly, and I hope that will never be us. I don't wanna be mean about that, but but the truth of the matter is one of the other benefits of being more flexible about where and how you work is. It opens up access to different talent pools who may or may not want to live in Austin, Texas, as an example, and that gives you a different way to get a more diverse workforce to get a younger workforce. And I think lots of companies are starting to have that really ization. And, you know, as I said, we've been doing this for 10 years. Even with that context, this is a quantum leap in. Now we're all basically not 100% but mainly all working from home, and we're still learning. So there's an interesting, ongoing lifelong learning that I think is very, very court of the Dell culture. >>I want to ask you about the virtual events you had you had a choice to make. You could have done what many did and said, Okay, we're going to run the event as scheduled, and you would have got a covert Mulligan. I mean, we saw Cem some pretty bad productions, frankly, but that was okay because they had to move fast and they got it done. So in a way, you kind of put more pressure on your yourselves. Andi, I guess you know, we saw this with VM Ware. I guess Was, you know, just recently last >>few >>weeks. Yeah, and so but they kind of raise the bar had great, you know, action with John Legend. So that was really kind of interesting, but, you know, kind of what went into that decision? A Zeiss A. You put more pressure on yourself because now you But you also had compares what? Your thoughts on >>that. So there was a moment in about March where I felt like I was making a multimillion dollar decision every single day. And that was on a personal note, somewhat stressful to kind of wake up and think, What? What? Not just on the events front. But as I said on the creative front, What work that my team has been working on for the last two years? I am I going to destroy today was sort of. I mean, I'm kind of joking, but not entirely how that felt for me personally at the moment. And we had about we made the decision early on to cancel events. We also made the decision quite early on that when we call that, we said we're not going to do any in person events until the end of this calendar year. So I felt good about the definitiveness there. We had about a week where we were still planning to do the virtual world in May and what I did together with my head of communications and head of event is we really sat and looked at the trajectory in the United States, and we thought, this is not gonna be a great moment for the U. S. The week we were supposed to run in May, if you looked at the trajectory of diseases, you would have news be dominated by the fact that we had an increasing spike in number of cases and subsequent deaths. And we just thought that don't just gonna care about our launches. So we had to really, very quickly re pivot that and what I was trying to do was not turn my own organization. So make the decisions start to plan and move on. And at the same time, though, what that then meant is we still have to get product launches out the door. So we did nine virtual launches in nine weeks. That was a big learning learning her for my team. I feel really good about that, and hopefully it helps us. And what I think will be a hybrid future going forward. >>Yeah, so not to generalize, but I've been generalizing about the following. So I've been saying for a while now that a lot >>of the >>marketing people have always wanted to have a greater component of virtual. But, you know, sales guys love the belly. The belly closed the deals, you know? But so where do you land on that? How do you see? You know, the future of events we do, you expect to continue to have ah, strong virtual component. >>I think it's gonna be a hybrid. I think we will never go back to what we did before. I think the same time people do need that human connection. Honestly, I miss seeing the people that I work with face to face. I said at the beginning of this conversation, I would like to be having this discussion with you live and I hate Las Vegas. So I never thought I'd be that interested in, like, let's go to Las Vegas, you know, who knew? But but so I think you'll see a hybrid future going forward. And then we will figure out what those smaller, more direct personal relationship moments are that over the next couple of years you could do more safely and then also frankly give you the opportunity to have those conversations that are more meaningful. So I'm not entirely sure what that looks like. Obviously, we're gonna learn a lot this year with this event, and we're going to continue to build on it. But there's places in the world if you look at what we've done in China for many, many, many years, we have held on over abundance of digital events because of frankly, just the size of the population and the the geographic complexity. And so there are places that even early into this, we could say, Well, we've already done this in China. How do we take that and apply it to the rest of the world? So that's what we're working through now. That's actually really exciting, >>You know, when you look at startups, it's like two things matter the engineering and sales and that's all anything else is a waste of money in their minds when you and and all they talk about is Legion Legion Legion. You don't hear that from a company like Dell because you have so many other channels on ways Thio communicate with your customers and engage with your customers. But of course, legions important demand. Gen. Is important. Do you feel like virtual events can be a Z effective? Maybe it's a longer tail, but can they be as productive as the physical events? >>So one thing that I've always been a little bit cantankerous on within marketing circles is I refuse to talk about it in terms of Brand versus Li Jen, because I think that's a false argument. And the way I've talked about it with my own team is there are things that we do that yield short term business results, maybe even in corridor in half for a year. And there are things that we do that lead to long term business results. First one is demand, and the second one is more traditional brand. But we have to do both. We have to think about our legacy as a known primarily for many, many years as a PC maker. In order for us to be successful in the business businesses that we are in now, we love our PC heritage. I grew up in that business, but we also want to embrace the other parts of their business and educate people about the things that we do that they may not even know, right? So that's a little bit of context in terms of you got to do both. You got to tell your story. You've got to change perceptions and you got to drive demand in quarter. So the interesting things about digital events is we can actually reach more people than we ever could in an in person world. So I think that expands the pie for both the perceptions and long term and short term. And I hope what we are more able to do effectively because of that point that I made about our own internal marketing digital transformation is connect those opportunities to lead and pass them off to sales more effectively. We've done a lot of work on the plumbing on the back end of that for the last couple of years, and I feel really fortunate that we did that because I don't think we'd be able to do what we're doing now. If we hadn't invested there, >>Well, it's interesting. You're right. I mean, Del of course, renowned during the PC era and rode that wave. And then, of course, the AMC acquisition one of the most amazing transformations, if not the most amazing transformation in the history of the computer industry. But when you when you look to the future and of course, we're hearing this week about as a service and you new pricing models, just new mindsets I look at and I wonder if you could comment, I look at Dell's futures, you know, not really a product company. You're becoming a platform. Essentially, for for digital transformation is how I look atyou. Well, how do you see the brand message going forward? >>Absolutely. I think that one of the things that's really interesting about Dell is that we have proven our ability to constantly and consistently reinvent ourselves, and I won't go through the whole thing. But if you look at started as a direct to consumer company, then went into servers then and started to go into small business meeting business a little bit about when private acquired e. M. C. I mean, we are a company who is always moving forward and always thinking about what's next. Oftentimes, people don't even realize the breadth and depth of what we do and who we are now so as even with all of that context in place, the horizon that we're facing into now is, I believe, the most important transformation that we've done, which is, as you see, historical, I t models change and it becomes, yes, about customer choice. We know that many of our customers will continue to want to buy hardware the way they always have. But we also know that we're going to see a very significant change in consumption models. And the way we stay on top of our game going forward is we lean into that huge transformation. And that's what we're announcing this week with Project Apex, which is that commitment to the entire company's transformation around as a service. And that's super exciting for us. >>Well, I was saying Before, you're sort of in lockstep with your customers. Or maybe you could we could. We could close by talking a little bit about Dell's digital transformation and what you guys have going on internally, and maybe some of the cultural impacts that you've seen. >>So you, you you touched on it. It's so easy to make it about just the I t. Work, and in fact, you actually have to make it about the i t. The business process. Change in the culture change. So if you look at what we did with the AMC acquisition and the fact that you know that there's a lot of skepticism about that at the time, they're not gonna be able to absorb that. Keep the business running. And in fact, we have really shown huge strides forward in the business. One of the reasons we've been able to do that is because we've been so thoughtful about all of those things. The technology, the culture and the business process change, and you'll see us continue to do that. As I said in my own organization, just to use the data driven transformation of marketing. Historically, we would have hired a certain type of person who was more of a creative Brett bent. Well, now, increasingly, we're hiring quants who are going to come into a career in marketing, and they never would have seen themselves doing that a couple of years ago. And so my team has to think about okay, these don't look like our historical marketing profile. How do we hire them? How do we do performance evaluations for them. And how do we make sure that we're not putting the parameters of old on a very new type of talent? And so when we talk about diversity, it's not just age, gender, etcetera. It's also of skills. And that's where I think the future of digital transformation is so interesting. There has been so much hype on this topic, and I think now is when we're really starting to see those big leaps forward and peoples in companies. Riel transformation. That's the benefit of this cookie year we got here, Dave. >>Well, I think I do think the culture comes through, especially in conversations like this. I mean, you're obviously a very clear thinker and good communicator, but I think your executive team is in lockstep. It gets down, toe the middle management into the into the field and and, you know, congratulations on how far you've come. And, uh, and and also I'm really impressed that you guys have such a huge ambitions in so many ways. Changing society obviously focused on customers and building great companies. So, Alison, thanks so much for >>thank you, Dave. You virtually I'm very >>great to see it. Hopefully hopefully see Assumes. Hopefully next year we could be together. Until then, virtually you'll >>see virtual, >>huh? Thank you for watching everybody. This is Dave Volonte for the Cube. Keep it right there. Our coverage of Del Tech World 2020. We'll be right back right after this short break.

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

>>From Miami beach, Florida. It's the queue covering a chronics global cyber summit 2019 brought to you by Acronis. >>Okay, welcome back. Everyone's the cubes covers two days here in Miami beach. The Fontainebleau hotel for the Kronos has global cyber summit 2019. It's inaugural event around a new category emerging called cyber protection. Um, this isn't a wave that's going to be part of the modernization a week we've been calling cloud 2.0 or whatever you want to call it. A complete modernization of the it technology stack and development environment includes core data center to the edge and beyond. Our next guest is Dan havens, chief growth officer per Chronis. Dan, thanks for coming on. Appreciate it. And thank you for having me, Dan. So, uh, what does chief growth officer mean? You guys obviously are growing, so obviously we see some growth there. Yeah, numbers are there. What she, what she, we have a couple of divisions in the company where we see we can really accelerate the business. >>So we came in and we wanted to make some large investments here. One of those areas was sports. You're seeing race cars out here on the floor, you're seeing all kinds of baseball teams, soccer teams, and we're talking to everybody. We have 40 teams now that are using our technology for competitive advantage on the field. Uh, the other areas, OEM, so, uh, original equipment manufacturers, everybody from making a camera to a server somewhere, having a Cronus be embedded, that's a big angle for us and we just didn't have a lot of focus. So I came into to build those divisions. I've actually joined the CEO before in a prior life in his last company and did something similar for him on a similar, uh, back there and we had violent success. So yeah, it's been a lot of fun. I've been here a year and a half and we're killing it. >>We got triple digit growth in the sporting category and similar in the OEM. It's interesting, you know, I look at a lot of these growth companies and the kind of a formula. You see, you guys have a very efficient and strong product platform engineering group. A lot of developers, a lot of smart people in the company, and a strong customer facing for the lack of a better word, field. The group you're in, you're involved, this is not, and you got marketing supporting it in the middle. Yep. So nice, efficient organizational structure on a massive way. But cyber, because this isn't your grandfather's data projection, this is a platform. What's the pitch? So the key here for us is we have to always say, and, and it, it's, it's hard to simplify and we're easy. In fact, we're cost-effective. Sometimes I'll even say I'm cheap and I'm easy. >>And that does not go out of style for an enterprise, right? So our ability to take good old fashioned backup and these things that other people need and basically extend that across. Now I can have one window where I can control, keep 'em out. If somebody gets in or from the inside or a disaster happens. I from this one place can recover my data. I'm secure with my data. I have the ability to notarize my data. So this one, and by the way, key simple interface. Customers love simple. This one simple interface to be able to do that. Now it takes a lot of engineering that goes behind that. I have plenty of, I have fancy engineering degrees and all that, but I try forget that when I'm talking to a customer because at the end of the day it's gotta make sense. A mind that doesn't know, says no. >>And I think we do a pretty good job of simplifying the message, but as they get under the covers and they roll it out, they recognize that there's, you know, we, we, we have more engineers per employee capita than any company that would have 1600 employees. Simple, easy to use. It reduces the steps it takes to do something as a winning business model. You kind of come from that school you mentioned, you know, cheap and easy. That's what is key. Yeah. But we're in a world where complexity is increasing and costs are increasing. Yep. These are two dynamics that are facing every enterprise, cyber it everywhere. What's your story when you want to educate that person so they can get to that? Yes. I want to work with you guys. What's that? What's that getting to? Yes. Processed motion look like. So the beautiful part is is we sell software right now. >>Software can be purchased complex. You install it, you can figure, you do everything yourself. We also can sell that from a cloud standpoint. So now you consume it like a service. Just like you consume Netflix at home, right? I can now consume this protection as a service. You have bolts spectrums covered. Most enterprises are somewhere in the middle. We call that hybrid. So the idea here is that there's going to be components where this data's not leaving these four walls. It might be government agency, it might be some compliance factor, but the ability to be able to say yes anywhere on that spectrum, it makes it very easy for an executive to say, okay, but we have a very, as you leverage the cloud, the OnRamp for this can be as simple as turning on the surface and pointing it at a data source. I mean, you're a student of history, obviously even in this business for awhile, you've done been there longer than you'd think. >>Data protection was kind of like that. Afterthought, backup data recovery all based upon, yeah, we might have an outage or a flood or hurricane Sandy who knows what's going to happen. You know, some force majority out there might happen, but security is a constant disrupter of business continuity. The data's being hijacked and ransomware to malware attacks. This is a major disruption point of a world that was supposed to be a non disruptive operational value proposition. Yeah, so the world has changed. They went from a niche, well, we've got their architecture of throwing back up. You've got to think about it from day one at the beginning. This seems to be your, your story for the company. You think about security from the beginning with data protection. There's only one club in the bag, so to speak. Talk about that dynamic and how's that translating into your customer's storytelling customer engagements to show you, you used an interesting word at the beginning, disaster recovery years ago, I started my tech industry in 1992 right? >>Disaster recovery is when we're going to have a flood or a hurricane and the building's going to burn down. What we find is most of our customers, that's certainly happens, but that's not the driver. The driver now is somebody after my data because the world has changed. Not only has the amount of data we're collecting change, but the ability to illegally monetize somebody else's data has become reality and you have social media that is socializes if you get breached and so forth. So there's a number of drivers. Number one, I don't want to be turned out of business. Number two, I don't want to be ransom. Then number three, I certainly don't want to do the cover of the wall street journal tomorrow morning as a top executive who looked past data. We literally watch brands, I won't mention the brand now, but a very large fortune 1000 what's called out yesterday. >>We see it every few days and we watched the carnage of their brand get deluded because they weren't protected. So I think it's the perfect storm up. I've got a ton of data, so it's coming in from all directions. Secondly, I I'm concerned about, you know, my brand and been able to protect that data and then you know, what do I do? And the disaster in this case is not necessarily flood or fire. It's that somebody from the inside or outside got in the gym. Pretend that I'm a decision maker. I'm like, my head's exploding. I'm got all this carnage going on. I don't want to get fired yet. I know I'm exposed. Nothing's yet happened yet. Maybe I settled the ransomware thing, but I know I'm not in a good place. What's your story to any, what's your pitch to me? What's in it for me? Tell me. >>Tell me the posture and the, well, we're halfway home. If you say, I know I'm not in a good place, right? Cause oftentimes somebody has to get bit first or they have to see their neighbor get bit first and then they say, Hey come in. One of my first plays would be let's find out what place you really are. I can do that very quickly and an assessment, we can gather your systems, we can get a sense for our, where's your data? Where it's flowing from. What are you doing? What are you doing to protect it? We typically will come back and there's going to be spots where there's blind spots. Sometimes they're fully naked, right? But the good news is is now we know the problem, so let's not waste any time, but you can get onboard and baby steps or you know, we can bandaid it or we can really go into full surgery however you want to move forward. >>But the idea is recognizing this has to be addressed because it's a beast. Every single device that's out there on the floor, in any enterprise, any company is a way in and a POC are critical for your business model. You want to get them certainly candy taste, show the value quickly has a POC, gets structured unit assessment. You come in on a narrow entry nail something quick, get a win. What's the, what's the playbook? Love PLCs because we're so fast and easy meaning oftentimes you do PLCs cause you're complex software and you're trying to prove your point and so forth. I love to push a POC cause I can do it inside of days, but I get the customer to take the drive. It's just on the car lot. If I get you to drive it down the block, you're not bringing it back. You're bringing it home to the neighbors. >>Right. That is the case with our software and our hit rate is key. But again it's because it's straightforward and it's easy. So though most sales cycles don't push for pilot. I can't wait to get a pilot but we don't need 30 days to do it in a couple of days. They're going to recognize I can do this too. You have a good track record of POC. If I get, this is going to be the most conceding. You might have to edit this out. If I get an audience, I will win. That is the most conceited statement on the planet. And if I get the audience and they will look, and this is why we use the sports teams. Sports teams are the cool kids using this. And if I get an executive to say, what are you guys doing with the red Sox? If I could get him or her to look, it's game over. >>Hey being bad ass and having some swagger. It's actually a good thing if you got the goods to back it up. That's not fun. Piece here is that the product works well and it's not this massive mountain to hurdle. It is. We can get started today and take bites as we go, but you mentioned sports. Let's get into that talk track. As we have been covering sports data for now six years on the cube in San Francisco. We were briefly talking about it last night at the reception, but I think sports teams encapsulates probably the most acute use case of digital transformation because they have multiple theaters that are exploding. They got to run their business, they got a team to manage and they got fan experience and their consumers, so you've got consumerization of it. You got security of your customers either in a physical venue from a potential terrorist disaster could happen to just using analytics to competitive venture from the Moneyball model to whatever sports really encapsulates what I call the poster child of using digital into a business model that works. >>You've been successful with sports. We interviewed Brian shield yesterday. Yup. Red Sox, vice-president technology. He was very candid. He's like, look it, we use analytics. It helps us get a competitive, not going to tell you the secrets, but we have other issues that people not thinking about drone strikes while the games going on, potential terrorist attacks, gathering the people, you know, adding on East sports stadium to Fenway park. They have a digital business model integrating in real time with a very successful consumer product and business in sports. This has been a good market for you guys. What's been the secret to success? >> Explosive market? Couple things. First off, you summarized well, sports teams are looking for competitive advantage, so anything that can come in under that guys is gonna get some attention plus data, fan data, system data, ticket data. Um, in baseball, they're studying every single pitch of pictures ever thrown. >>They have video on everything. This is heavy lift data, right? So a place to put it saved money, a place to protect it, a pace to access it so that all of my Scouts that are out in the field with a mobile device have the ability to upload or evaluate a player while they're out still on them and on the field somewhere maybe in another country. And then add the added caveat in our sexiest piece. And that's artificial intelligence. You mentioned Moneyball, right? Uh, the, the entire concept of, of stat of statistics came out in the Moneyball concept and you know, we all saw the movie and we all read the book, but at the end of the day, this is the next step to that, which is not just written down statistics. Now we can analyze data with machine learning and we have very, we have unique baseball examples where there's absolutely no doubt they have the data. >>It's the ability to, how do I turn that to where I can be more competitive on our racing team. So we're actually working with teams improving, changing the car on the track during the race, using our software fact. We always look forward to opportunities where somebody says, Hey, come in and talk about that because it's incredibly sexy to see. Um, but sports are fun because first off they're the cool kids. Secondly, they're early adopters. If it's gonna give competitive advantage, uh, and third, they hit all the vectors. Tons of data have to protect it. >> It's our life in the business models digital too. So the digital transformation is in prime time. We cannot ignore the fact that people want wifi. They got Instagram, Facebook, all of these, they're all conscious of social media. There are all kinds of listening sports club, they have to be, they have to be hip, right? >>And being out front like that, think about the data they have come in at. And so not just to be smart on the field, they have to be smart with our customer. They're competing with that customer for four of their major sports or whatever. Major sports in the, in the, in the, in our case in this fashionable to be hip is cool for the product, but now you think about how they run their business. They've got suppliers, um, that have data and trusting suppliers with data's. There's a difficult protection formula. They've got national secure security issues. They have to protect, well they have to protect as a big part, but they have to protect, well first off these, these archives of data that are of 20 races ago or of this pitcher pitched three years ago and I have a thousand of his pitches and I'm looking for towels. >>That is, that's mission critical. But also, uh, to boot you have just business functions where I'm a, I'm a team and I have a huge telco sponsor and we are shifting back and forth and designing what their actual collateral is going to be in the stadium. They're actually using a Chronis to be able to do that up in the cloud where they can both collaborate on that. Not only doing it, but being able to protect it that way. It's, it's more efficient for them. It's interesting. I asked Brian shield this question, I asked her how does baseball flex and digital with the business model of digital with the success of the physical product or their actual product baseball. And he said an interesting thing. He's like the ROI models just get whacked out because what's the ROI of an investment in technology? It used to be total cost of ownership. >>The class that's right under the under the iceberg to sharpen whatever you use, you use that. We don't use that. We think about other consequences like a terrorist attack. That's right. So so the business model, ROI calculation shifting, do you have those kinds of conversations with some of these big teams and these sports teams? Because you know they win the world series, their brand franchise goes up if they win the national championship, but whatever their goal is has real franchise value. There's numbers on that. There's also the risk of say an attack or some sort of breach. >> Well, I won't mention the names, I won't mention the teams by name, but I have a half a dozen teams right now and two that are actually rolling out that are doing facial recognition just for security, a fan's entering their stadium. So they are taking the ownership of the safety of their fan to the level of doing visual or facial recognition coming into their stadium. >>Obviously the archive to measure against is important and we can archive that, but they're also using artificial intelligence for that. So you're absolutely right. They owe their fan a safe experience, not only a safe experience with good experience and so forth. And we love to be associated whenever we can with wins and losses. But to your point, how do you get, or how do you show a TCO on a disaster and nobody wants to, and by the way, we've seen enough of that to know it's looming. And there's also the supply chain too. I can buy a hotdog and a beer from Aramark, which is the red socks. They say supplier that's not owned by the red Sox. They have a relationship. But my data's in, I'm a consumer of the red Sox. I'm procuring a, you know, some food or service from a vendor. Yeah, yeah. My data's out there. >>So who protects that? Well, these are unique questions that come up all the time. Again, that's a business decision for the customer. The idea is with cloud collaboration, it's technically quite easy, but again, they have to decide where they're gonna commingle their data, how they're going to share. But the idea here is, again, back to the spectrum, fully cloud and accessible and locked down airtight government's scenario where we have a, you know, a lock bottom line is you get to pick where you want to be on there and there's going to be times where my example of talking to the, uh, the telco vendor, we're, we're actually going to share our data together and we're going to make us faster, make a quicker return and design this collateral for our stadium faster. Those are business decisions, but they're allowed because it, Coronas can be as hybrid as you need to be along the site. >>And again, that resonates with an executive. They never want to be wearing handcuffs and they don't want to pay overpay for stuff to not use our stuff. And if you decide to consume cloud, you, you just pay as you go. It's like your electricity bill. All right. So the red Sox are a customer of you guys. You have or they use your service. What other sports teams have you guys engaged with who you're talking to? Give a taste of some of the samples. So European, we have a couple of formula one teams. We have a racing point. We have the Williams team and formula E we have to cheetah the dragon team. We have a adventury, we also have Neo. So we have a good presence in the racing clubs. We have a ton of a world rally cars and, and, and motorcycle motorcross and so forth. >>Then you step over into European football. So we, we, we started in cars and recognize this is hot. So then we got our first, uh, European team, uh, and we had arsenal. As a matter of fact, we have one of the legends here signing with us today. And you know, I mean, they're rock stars, right? People follow them. Anyway, so we have arsenal and we did man city. Um, and we just landed, uh, Liverpool just did that this quarter, two weeks ago. I literally just, the ink is still drying. Um, and then you move into the United States, which I brought the, you know, I brought the circus to town on January one, 2019. First when was the Boston red Sox. We quickly followed that up. You'll see us on the home run fence at San Diego Padres. Volts bought for different reasons, but both very sexy reasons. So it's the reason. >>What were the main drivers? So in the case of the Boston red Sox, it was, it was a heavy lift on video. A lot of on the protection side. Um, the, uh, San Diego was file sync and share. So the example I was giving of, um, being able to share with your largest telco vendor or with your largest investors slash sponsor for your stadium, um, that was the driver. Now what's funny about both is as they get started, he's always expanding, right? So we have the baseball teams, we did land this quarter, the Dallas stars. So that's our first hockey club. I really want. And my goal is to try to get a couple in each of the main four categories and then some of the subs, um, just cause you get the cool kids, you get a tipping point. Everybody then wants to know what's going on. I have a hundred and play. >>And so we, we typically try to qualify regional where it makes sense. Um, uh, we're, you know, we're very close with a team here in the region. So, you know, they, in the feedback from, from the, from the successes you had implementations, why, what's uh, what's been the feedback from the customers. So here's the file in this. Sounds like I'm just tripping with sales guy and I apologize. Warning signs. Okay. If they use it, we're home free. So when you get Brian or any one of these guys that are using it, all I have to do is make sure that a new customer hears this person who has no reason to say anything else and just expose them to it. Because it's this unknown, scary thing that we're trying to protect against and being able to do that and have the freedom of how aggressive or you know, what metaphor am I going to cover that? >>And then also, uh, you know, the, obviously the economics work is you pay as you go. Um, it's, you know, it's a good story. Well, Dan, congratulations on the success. Um, great to see you guys really digging in and getting those PLCs and being successful. We watching your growth. Final question for you yes. Is all the data and the patterns that you see and all of customers. What's the number one reason why a Cronus is selected and why you women? I think that's an interesting question and I think that it's a couple of reasons. Number one, we work, we're easy. We have an enormous footprint. So there's a lot to reference from. Many people have already used us on the consumer side, so we're safe. So that's one reason I would also tell you however, that we have a great ecosystem because a Kronos is different than most software companies. >>Most software companies have a huge outside sales force that sells direct to customer a Chronis. Everybody here is a partner. We sell through a service provider to a channel member through a, through a, a, a, an ISV. Um, and then we have some direct enterprise. But the idea is there's a variety of solutions that can be baked on this foundation. And I think people like that variety. I, they, they like the, like the freedom of I'm not just trapped with this one thing. I can buy it and all options are available and I will tell you an it, nobody wants to be locked down. Everybody wants options, safety in numbers. They want their data protected with the whole cyber land lens. And they know everything's changing every six months. Something's different. And I don't want to be handcuffed in my desk. I want all options available. I think that's our best value from all right, Dan, thanks for coming on. Dan havens, chief growth officer, but Krohn is weird. The Chronis global cyber summit. I'm John Ford. Stay tuned for more cube coverage after this short break.

>> Announcer: From Miami Beach, Florida, it's The Cube, covering Acronis Global Cyber Summit 2019. Brought to you by Acronis. >> Welcome back everyone. We are here with The Cube coverage for two days. We're wrapping up, getting down on day one in the books for the Acronis Global Cyber Summit 2019. I'm John Furrier, your host of The Cube. We are in Miami Beach, the Fontainebleau Hotel. I'm personally excited for this next guest because I'm a huge Red Sox fan, even though I got moved out to California. Giants is in a different area. National League is different than American League, still my heart with the Red Sox. And we're here with an industry veteran, seasoned professional in IT and data, Brian Shield. Boston Red Sox Vice President of Technology and IT. Welcome to The Cube, thanks for joining us. >> Thank you. It's great to be here. >> John: So congratulations on the rings. Since I moved out of town, Red sox win their World Series, break the curse of the Bambino. >> Hey we appreciate that. Thank you. >> My family doesn't want me back. You got to show >> Yeah, maybe I'll put this one up for the, maybe someone can zoom in on this. Which camera is the good one? This one here? So, there ya go. So, World Series champs for at least for another week. (laughter) >> Bummer about this year. Pitching just couldn't get it done. But, good team. >> Happens. >> Again, things move on, but you know. New regime, new GM going to come on board. >> Yup. >> So, but in general, Red Sox, storied franchise. Love it there. Fenway Park, the cathedral of baseball parks. >> Brian: Defnitely. >> And you're seeing that just play out now, standard. So just a great place to go. We have tickets there. So, I got to ask you. Technology, sports, really is modernized faster than I think any category. And certainly cyber security forced to modernize because of the threats. But sports, you got a business to run, not just IT and making the planes run on time. >> Sure. >> Scouts, money, whatever. >> Fans. >> You got fan experience. >> Stadium opportunities. >> Club management, scouts are out there. So you got business, team, fans. And data's a big part of it. That's part of your career. Tell us what the cutting edge innovation is at the Red Sox these days. >> I think baseball in general, as you indicated, it's a very evolving kind of environment. I mean historically I think people really sort of relish the nostalgia of sports and Fenway Park being as historic as it is, was probably the pinnacle of that, in some respects. But Red Sox have always been leaders and baseball analytics, you know. And everyone's pretty familiar with "Moneyball" and Brad Pitt. >> John: Is that a true story, he turned down the GM job? >> I'm told it is. (laughter) I don't know if I fully vetted that question. But over the last six, seven years, you know we've really turned our attention to sort of leveraging sort of technology across the businesses, right? Not just baseball and analytics and how we do scouting, which continues to evolve at a very rapid pace. But also as you pointed out, running a better business, understanding our fans, understanding fan behavior, understanding stadiums. There's a lot of challenges around running an effective stadium. First and foremost to all of us is really ensuring it's a great fan experience. Whether it's artificial intelligence, or IoT technologies or 5G or the latest Wifi, all those things are coming up at Fenway Park. You and I talked earlier about we're about to break ground for a new theater, so a live theater on the outside, beyond the bleachers type of thing. So that'll be a 5,400-seat arena, 200 live performances a year, and with e-sports, you know, complementing it. It just gives you an example of just how fast baseball is sort of transitioning. >> And the theater, is that going to be blown out from where that parking garage is, structure and going towards >> So the corner of Landsdown and Ipswich, if you think of that sort of corner back there, for those that are familiar with the Fenway area. So it's going to be a very big change and you'll see the difference too from within the ballpark. I think we'll lose a couple of rows of the bleachers. That'll be replaced with another gathering area for fans and things like that, on the back end of that theater. So build a great experience and I think it really speaks to sort of our ability to think of Fenway as more of a destination, as a venue, as a complementary experience. We want people to come to the area to enjoy sports and to enjoy entertainment and things. >> You know Brian, the consumerization of IT has been kicked around. Last decade, that was a big buzzword. Now the blending of a physical event and digital has certainly consumed the world. >> Absolutely. >> And we're starting to see that dynamic. You speak to a theater. That's a physical space. But digital is also a big part of kind of that complementary. It's not mutually exclusive for each other. They're integrated business models. >> Absolutely. >> So therefore, the technology has to be seamless. The data has to be available. >> Yup. >> And it's got to be secure. >> Well the data's got to be ubiquitous, right? I mean you don't want to, if we're going to have fans attending theater and then you're going to go to Fenway Park or they leave a game and then go to some other event or they attend a tour of Fenway Park, and beyond maybe the traditional what people might think about, is certainly when you think about baseball and Fenway Park. You know we have ten to twelve concerts a year. We'll host Spartan games, you know. This Christmas, I'm sorry, Christmas 2020 we now have sort of the Fenway Bowl. So we'll be hosting the AAC ACC championship games there with ESPN. >> John: Hockey games? >> Hockey games. Obviously we have Liverpool soccer being held there so it's much more of a destination, a venue for us. How we leverage all the wonderful things about Fenway Park and how we modernize, how we get basically the best of what makes Fenway Park as great as it is, yet as modern as we can make it, where appropriate to create a great fan experience. >> It's a tough balance between balancing the brand and having things on brand as well. >> Sure. >> Does that come into your job a lot around IT? Saying being on brand, not kind of tearing down the old. >> Yeah absolutely. I think our CEOs and leadership team, I mean it's not success for us if you pan to the audience and everyone is looking at their phone, right? That's not what we aspire to. We aspire to leverage technology to simplify people's experience of how do you get to the ballpark, how do I park, how do I get if I want to buy concessions or merchandise, how do I do it easily and simply? How do we supplement that experience with maybe additional data that you may not have had before. Things like that, so we're doing a lot of different testing right now whether it's 4D technologies or how we can understand, watch a play from different dimensions or AI and be able to perhaps see sort of the skyline of Boston since 1912, when Fenway Park launched... And so we sort of see all these technologies as supplemental materials, really kind of making it a holistic experience for fans. >> In Las Vegas, they have a section of Las Vegas where they have all their test beds. 5G, they call it 5G, it's really, you know, evolution, fake 5G but it's a sandbox. One of the challenges that you guys have in Boston, I know from a constraint standpoint physically, you don't have a lot of space. How do you sandbox new technologies and what are some of the things that are cool that people might not know about that are being sandboxed? So, one, how do you do it? >> Yeah. >> Effectively. And then what are some of the cool things that you guys are looking at or things they might not know about that would be interesting. >> Sure. Yeah so Fenway Park, we struggle as you know, a little bit with our footprint. You know, honestly, I walk into some of these large stadiums and I get instant jealousy, relative to just the amount of space that people have to work with and things. But we have a great relationship with our partners so we really partner, I think, particularly well with key partners like Verizon and others. So we now have 5G partially implemented at Fenway Park. We expect to have it sort of fully live come opening day next year. So we're really excited about that. We hope to have a new version of Wifi, the latest version of Wifi available, for the second half of the year. After the All-Star Break, probably after the season's over. But before our bowl game hopefully. We're looking at some really interesting ways that we can tease that out. That bowl game, we're really trying to use that as an opportunity, the Fenway Bowl, as an opportunity to make it kind of a high-tech bowl. So we're looking at ways of maybe doing everything from hack-a-thons to a pre-egaming sort of event to some interesting fan experiential opportunities and things like that. >> Got a lot of nerds at MIT, Northeastern, BU, Bentley, Babson, all the schools in the area. >> Yeah, so we'll be reaching out to colleges and we'll be reaching out to our, the ACC and AACs as well, and see what we can do to kind of create sort of a really fun experience and capitalize on the evolving role of e-sports and the role that technology can play in the future. >> I want to get to the e-sports in a second but I want to just get the plug in for Acronis. We're here at their Global Cyber Summit. You flew down for it, giving some keynote speeches and talks around security. It's a security company, data protection, to cyber protection. It is a data problem, not a storage appliance problem. It's a data problem holistically. You get that. >> Sure. Sure. >> You've been in the business for a long time. What is the security kind of posture that you guys have? Obviously you want to protect the data, protect privacy. But you got to business. You have people that work with you, supply chain, complex but yet dynamic, always on environment. >> That's a great question. It's evolving as you indicated. Major League Baseball, first and foremost, does an outstanding job. So the last, probably last four plus years, Major League Baseball has had a cyber security program that all the clubs partake in. So all 30 clubs are active participants in the program. They basically help build out a suite of tools as well as the ability to kind of monitor, help participate in the monitoring, sort of a lot of our cyber security assets and logs And that's really elevated significantly our posture in terms of security. We supplement that quite a bit and a good example of that is like Acronis. Acronis, for us, represents the ability for us to be able to respond to certain potential threats like ransom-ware and other things. As well as frankly, what's wonderful about a tool like this is that it allows us to also solve other problems. Making our scouts more efficient. We've got these 125 scouts scattered around the globe. These guys are the lifeblood of our, you know, the success of our business. When they have a problem, if they're in Venezuela or the Dominican or someplace else, in southeast Asia, getting them up and running as quickly as we can, being able to consume their video assets and other things as they're scouting prospects. We use Acronis for those solutions. It's great to kind of have a partner who can both double down as a cyber partner as well as someone who helps drive a more efficient business. >> People bring their phone into the stadiums too so those are end points now connecting to your network. >> Definitely. And as you pointed out before, we've got great partnerships. We've got a great concession relationship with Aramark and they operate, in the future they'll be operating off our infrastructure. So we're in the point of rolling out all new point-of-sale terminals this off-season. We're excited about that 'cause we think for the first time it really allows us to build a very comprehensive, very secure environment for both ourselves and for all the touchpoints to fans. >> You have a very stellar career. I noticed you were at Scudder Investments back in the '80s, very cutting-edge firm. FTD that set the whole standard for connecting retailers. Again, huge scale play. Can see the data kind of coming out, they way you've been a CIO, CTO. The EVP CIO at The Weather Channel and the weather.com again, first mover, kind of pioneer. And then now the Red Sox, pioneering. So I got to ask you the modernization question. Red Sox certainly have been cutting-edge, certainly under the last few owners, and the previous Henry is a good one, doing more and more, Has the business model of baseball evolved, 'cause you guys a franchise. >> Sure. >> You operate under the franchisor, Major League Baseball, and you have jurisdictions. So has digital blurred the lines between what Advanced Media unit can do. You got communities developing outside. I watch the games in California. I'm not in there but I'm present digitally. >> Sure. Sure. >> So how has the business model flexed with the innovation of baseball? >> That's a great question. So I mean, first off, the relationship between clubs like ours and MLB continue to evolve. We have a new commissioner, relatively new commissioner, and I think the whole one-baseball model that he's been promoting I think has been great. The boundaries sometimes between digital assets and how we innovate and things like that continues to evolve. Major League Baseball and technology groups and product groups that support Major League Baseball have been a fantastic partner of ours. If you look at some of the innovations with Statcast and some of the other types of things that fans are now becoming more familiar with. And when they see how fast a runner goes or how far a home run goes and all those sort of things, these kinds of capabilities are on the surface, but even like mobile applications, to make it easy for fans to come into ballparks and things like that really. What we see is really are platforms for the future touchpoints to all of our customers. But you're right, it gets complicated. Streaming videos and people hadn't thought of before. >> Latin America, huge audience for the Red Sox. Got great players down there. That's outside the jurisdiction, I think, of the franchise agreement, isn't it? (laughs) >> Well, it's complicated. As this past summer, we played two games in England, right? So we enjoy two games in London, sadly we lost to the Yankees in both of those, but amazing experience and Major League Baseball really hats off to those guys, what they did to kind of pull that together. >> You mentioned Statcast. Every year when I meet with Andy Jassy at AWS, he's a sports fan. We love to talk sports. That's a huge, kind of shows the power of data and cloud computing. >> No doubt. >> How do you guys interface with Statcast? Is that an Amazon thing? Do they come to you? Are they leveraging dimensions, camera angles? How does that all work? Are you guys involved in that or? >> Brian: Oh yeah, yeah. >> Is that separate? >> So Statcast is just one of many data feeds as you can imagine. One of the things that Major League Baseball does is all that type of data is readily available to every club. So every club has access to the data. The real competitive differentiator, if you will, is how you use it internally. Like how your analysts can consume that data. We have a baseball system we call Beacon. We retired Carmine, if you're familiar with the old days of Carmine. So we retired Carmine a few years ago with Beacon. And Beacon for us represents sort of our opportunity to effectively collapse all this information into a decision-making environment that allows us to hopefully to kind of make the best decisions to win the most games. >> I love that you're answering all these questions. I really appreciate it. The one I really want to get into is obviously the fan experience. We talked about that. No talent on the field means no World Series so you got to always be constantly replenishing the talent pool, farm system, recruiting, scouting, all these things go on. They're instrumental. Data's a key driver. What new innovations that the casual fan or IT person might be interested in what's going on around scouting and understanding the asset of a human being? >> Right. Sure. I mean some of this gets highly confidential and things, but I think at a macro level, as you start to see both in the minor leagues and in some portions of the major leagues, wearable technologies. I think beyond just sort of player performance information that you would see traditionally with you might associate it with like Billy Beane, and things like that with "Moneyball" which is evolved obviously considerably since those days. I mean understanding sort of player wellness, understanding sort of how to get the most out of a player and understanding sort of, be able to kind of predict potential injuries and accelerate recoveries and being able to use all of this technology where appropriate to really kind of help sort of maximize the value of player performance. I mean, David Ortiz, you know, I don't know where we would have been in 2018 without, you know, David. >> John: Yeah. >> But like, you know >> Longevity of a player. >> Absolutely. >> To when they're in the zone. You wear a ring now to tell you if you're sleeping well. Will managers have a visual, in-the-zone, don't pull 'em out, he can go an extra inning? >> Well, I mean they have a lot of data. We currently don't provide all that data to the clubhouse. I mean, you know, and so If you're in the dugout, that information isn't always readily available type of thing. But players know all this information. We continue to evolve it. At the end of the day though, it's finding the balancing act between data and the aptitudes of our coaching staff and our managers to really make the wise decisions. >> Brian, final question for you. What's the coolest thing you're working on right now? Besides the fan having a great experience, 'cause that's you kind of touched on that. What's the coolest thing that you're excited about that you're working on from a tech perspective that you think is going to be game-changing or interesting? >> I think our cloud strategy coming up in the future. It's still a little bit early stage, but our hope would be to kind of have clarity about that in the next couple months. I think is going to be a game-changer for us. I think having, you know, we enjoy a great relationship with Dell EMC and yet we also do work in the cloud and so being able to leverage the best of both of those to be able to kind of create sort of a compelling experience for both fans, for both player, baseball operations as well as sort of running an efficient business, I think is really what we're all about. >> I mean you guys are the poster child for hybrid cloud because you got core, data center, IoT, and >> No doubt. So it's exciting times. And we're very fortunate that with our relationship organizations like Dell and EMC, we have leading-edge technologies. So we're excited about where that can go and kind of what that can mean. It'll be a big step. >> Okay two personal questions from me as a fan. One is there really a money-counting room like in the movie "The Town"? Where they count a big stack of dollar bills. >> Well, I'm sure there is. I personally haven't visited it. (laughs) I know it's not in the room that they would tell you it is on the movie. (laughter) >> And finally, can The Cube get press passes to cover the games, next to NESN? Talk tech. >> Yeah, we'll see what we can do. >> They can talk baseball. We can talk about bandwidth. Right now, it's the level five conductivity. We're looking good on the pipes. >> Yeah we'll give you a tech tour. And you guys can sort of help us articulate all that to the fans. >> Thank you so much. Brian Shield, Vice President of Technology of the Boston Red Sox. Here talking about security and also the complications and challenges but the mega-opportunities around what digital and fan experiences are with the physical product like baseball, encapsulates kind of the digital revolution that's happening. So keep covering it. Here in Miami, I'm John Furrier. We'll be right back after this short break. (techno music)

>> Hey, welcome back. You're ready, Jeff? Rick! Here with the Cube were Oracle Park recently, A T and T Park just renamed. It's a beautiful day home in San Francisco Giants. They're on the road. We're here at a pretty interesting event is called Sports Tech. Tokyo World Demo Day brought together a coalition of about 100 startups. 25 of them are given demos today on technology as it relates to sports. But even more importantly, that can then be used in other in others. Beyond sports. We're excited to have an athlete on not just another tech crazy guy. He's Brendan Harris. He's an athlete in residence at 76 Capital. Brendan. Thanks for stopping by. >> Thanks for having me. >> So what is the effort, Principles and entrepreneur in residence? Where is the athlete residents do? It is >> essentially a play on the entrepreneur in residence. I was introduced to 76 Capital finished playing in 15 and I was doing my MBA at Warden and in Philly and got introduced Thio Wayne and the guys at 76. And they are kind of putting together an athlete venture group. Whether they're bringing in a lot of athletes don't wannabe investors and kind of providing them access to deal >> flow and >> um, >> and then also leveraging their social capital. So, uh, he was He was kind of tickled when he came when he coined the term athlete residents and he threw it on my business card. And and that's where we're at, >> right? So I'm just curious your perspective as an athlete as you look around at all the technology that's going into sports, right? Kind of the big categories are, you know that which helps the players play better. There's that which helps the people run, the team's better. And then there's that, which is really kind of part of the fan experience. I mean, you actually to go down and try to put wood on a ball coming at you in 90 plus miles an hour. All this other stuff. Do you see it as is it interesting is distraction. Is it entertaining? I mean, how do you look at it from an athlete's perspective? >> So yeah, so a lot to impact. So first of all, I have this ah, equally the equal view of fascination and frustration where a lot of this wasn't he wasn't around when I when I was playing it, certainly from the field. Now we're taking in things like recovery and rest and sleep. Ah, but I think players and me personally are fascinated with How can we improve on field performance? And I think baseball. It's such a perfect game and you fail so often, being able to turn to turn things that were previously subjective and applied data and in tech to make them objective and give you answers. I think it's fascinating and the ways that we can use data to to kind of promote performance and health and and all those things air Very fascinating. So from players, point of view, we're all about it. But at the same time, I think it certainly says why I've loved to get into sports. Tech is there's a lot of data that's just noise that's coming in and things. And so the tough part is, um, kind of weeding through and what is actionable info on what can actually help improve the on field performance? And then along with that, you know, we want to feel the product on the field, but also what the service is for the consumer and the fans are. And how can we improve that and then engage them? Because certainly sports are part of the culture and part of life now, and it's fascinating. These fans want to know more and more and more, certainly what's going >> on. And it's been It's been a >> great journey, >> right? So on the fan experience specifically, and we've been we've been here a number of years. Bill Styles, a good friend of mine off another word and other work. Brad and and, you know, talking about high density WiFi and you know the app on your phone and delivered, you know, food delivered to your seats. I mean, >> as a as an >> athlete on the field. Do you look at kind of all these things is as a distraction. Do you appreciate? It's kind of a more competitive environment these days in terms of people's attention and kind of that entertainment dollar. But I would imagine from between the lines it looks like Hey, you know, the game's down here people. It's been >> interesting because, um, you know, one of the problems of a major league baseball's been trying to address his pace of games right. And if you really look at the data, they're not that much longer. What's different? We're wired differently, right? So our attention spans are short and we're constantly so our technology. So these, you know, guys like Bill, you are trying to leverage that and try to have your food delivered and try to increase the social component. Increased the value in the in venue experience so that you're not only watching the game, but you're socially enjoying at the same time and kind of fill in those gaps. Ah, lot of it is yes on. And I think there's been balls flying into the stands since baseball's been playing, but they need to put the netting up. Has come a lot of times because nobody's watching. Some people aren't not nobody, but a lot of people aren't watching. The games are getting hit with a lot of these foul balls. So there is that component where you know there's there's some unbelievable things are going off on the sides. But um, you know it's baseball is still gonna be kind of very somewhat within within the confines. >> The other piece that I find really interesting on the data side, right? Is there so much data? Right? There's data data data. Obviously, baseball is built on data and arguments about data and conversations about data, but now it's kind of gone to this next Gen with, you know, wins over replacement and all these other things. But sometimes it's funny to me. It feels like they're forgetting the object of the game is to win the game. And it feels like sometimes the metadata has now become more important than the data. Did you win or lose and is not necessarily being used as a predictor for future performance? But it's almost like a standalone game in and of itself. Like we forget. The object is to win the game and win a championship, not to have the highest war number views since that frustration is that sound? Yeah, I think what you're getting >> into a lot of times is our know how are we making decisions right? And in the game? A lot of times people forget that human beings are out there performing and so I think that's how we've gotten into Moneyball 2.0, looking at development and certainly mental health in focus and game preparation have come into play more and you're seeing some managers. I mean, Mickey Callaway just came out and said 80% my, you know, Susan's go against the data, which which I thought was a little bit interesting, but, ah, so there is that fine line right where you have to filter in what's noise and what's actionable. And at the same time, um, you know, allow you know, your managers and your decision makers some flexibility to go with, You know, they're they're in the heat of the battle and they kind of know their guys. And they know the human element that's involved. So it's it's an interesting, you know, trying to balancing act, >> right? So from your from your new job in your new role, what are some of the things you hope to see today? What are some things that you're excited about? Um, you know, from kind of an investor. And having played the game as well. As you know, I'm looking forward to the evolution of sports. Two >> things specifically how the, uh certainly bias the performs on the field in the human element. And certainly everybody wants workout secrets, and I don't feel like it's whether it's athletes or the kind of weekend warrior or people that are, you know, kind of your senior citizens. And I don't think it's a simple as this has worked, and you should do this. It's a very personalized experience now. And I think some of this personalized digital fitness is fascinating to me on and then how it relates to and how your body relates to, you know, your diet and nutrition, your sleep, your recovery. I think all those air fascinating that, uh, advances that I want to look into more. And the second is a CZ, I kind of mentioned is the fan engagement aspect. How do we drive those those fans that digital, >> um, and >> make it actionable and monetize, right? So that you know, you have your fans that are following you know, your Facebook, twitter and all those things. And so how do you not only gauge them, but collect that data and then kind of personalized that experience? Engage your fan in a way that can kind of grow your brand. Yeah, it's interesting to me, >> really interesting to have to have your perspective, and I'm sure will be a great day and you see all kinds of crazy stuff. So thanks for taking a few minutes. >> Yeah, Any time. >> All right. He's Brendan. I'm Jeff. You're watching The Cube were at Oracle Park in San Francisco. Thanks for watching. We'll see you next time.

(upbeat music) >> Hey welcome back everybody, Jeff Rick here with theCUBE. We're at Oracle Park, recently AT&T Park just renamed, it's a beautiful day. Home of San Francisco Giants, they're on the road, we're here at a pretty interesting event, it's called Sports Tech Tokyo World Demo Day, brought together coalition of about 100 startups. 25 of them are giving demos today on technology as it relates to sports but even more importantly, that can then be used in others beyond sports. We're excited to have an athlete on, not just another tech, crazy guy. He's Brendan Harris, he's an athlete and resident at SeventySix Capital. Brendan, thanks for stopping by. >> Thanks for having me. >> So what is that, I've heard principles and entrepreneur residence\\\, what does a athlete residence do? >> It is essentially a play on the entrepreneuring residence. I was introduced to SeventySix Capital, I finished playing at 15 and I was doing my MBA at Wharton and in Philly, and got introduced to Wayne and the guys at SeventySix and they are kind of putting together an athlete venture group where they're bringing in a lot of athletes that want to be investors and kind of providing them access to deal flow. And then also leveraging their social capitals, so, he was kind of tickled when he coined the term athlete in residence and threw it on my business card and that's where we're at. >> Right so I'm just curious, your perspective as an athlete as you look around at all the technology that's going into sports, right. Kind of the big categories are that which helps the players play better, there's that which helps the people run the teams better, and then there's that which is really kind of part of the fan experience, I mean, you actually had to go down and try to put wood on a ball coming at you 90 plus miles an hour, all this other stuff, do you see it as interesting, is it a distraction, is it entertaining? How do you look at from an athlete's perspective? >> So, yeah, so a lot to impact, so, first of all, I have this equal view of fascination and frustration where a lot of this wasn't around when I was playing, certainly from the field, now we're taking in things like recovery and rest and sleep, but I think players and me personally, are fascinated with how can we improve on field performance and I think baseball's such an imperfect game and you fail so often. Being able to turn things that were previously subjective and apply data and tech to make them objective and give you answers, I think it's fascinating. The ways that we can use data to kind of promote performance and health and all those things are very fascinating. So from a player's point of view, we are all about it but at the same time, I think this is why I've loved to get into sports tech is there's a lot of data that's just noise that's coming in and things and so the tough part is kind of weeding through and what is actionable info and what can actually help improve beyond field performance and then, along with that, we want to feel the product on the field, but also what what the services for the consumer and the fans are and how can we improve that and then engage them because certainly sports are a part of the culture and part of life now and it's fascinating, these fans want to know more and more and more, certainly what's going on and it's been a great journey. >> Right so on the fan experience specifically, we've been here a number of years, Bill Styles' a good friend of mine, and another Wharton grad. And talking about high density WiFi and the app on your phone and food delivered to your seat, I mean as an athlete on the field, do you look at kind of of all these things as a distraction, do you appreciate it's more competitive environment these days in terms of people's attention and kind of that entertainment dollar but I would imagine from the between the lines it looks like, hey, the game's down here people. >> Yeah. (laughing) It's been interesting because one of the problems major league baseball's been trying to address is pace of games right? And if you really look at the data, they're not that much longer. What's different, we're wired differently, right? So our attention spans are shorter and we're constantly addicted to our technology. So these guys like Bill, are trying to leverage that and try to have your food delivered and try to increase the social component, increase the value in the in-venue experience so that you're not only watching the game but you're social enjoying it at the same time and kind of filling those gaps. A lot of it is, yes, and I think, there has been balls flying into the stands since baseball's been playing but the need to put the netting up has come a lot of times because nobody's watching. Some people aren't, not nobody, but a lot of people aren't watching the games are getting hit with a lot of these foul balls. So there's that component, where there's some unbelievable things are going off on the sides but it's baseball still going to be kind of very similar within the confines of lines. >> The other piece that I find really interesting on the data side right, is there's so much data, right? There's data, data, data. Obviously baseball's built on data and arguments about data and conversations about data. But now it's kind of gone to this next gen with wins over replacement and all these other things, but sometimes it's funny to me. It feels like they're forgetting the object of the game is to win the game and it feels like sometimes the metadata has now become more important than the data. Did you win or lose and it's not necessarily being used as a predictor for future performance but it's almost like a stand alone game in and of itself. We forget the object is to win the game and win a championship, not to have the highest award number. Do you sense that frustration, does that sound like something you see-- >> Yeah, I think what you're getting into a lot of times is how are we making decisions, right and in the game a lot of times people forget that human beings are out there performing and so I think that's how we've gotten into Moneyball 2.0 when looking at development. Certainly mental health in focus and game preparation have come into play more and you're seeing some managers, Mickey Callaway just came out said 80% of my distances go against the data which I thought was a little bit interesting but so there is that fine line where you have to filter in what's noise and what's actionable and at the same time, allow your managers and your decision makers some flexibility to go with they're there in the heat of the battle and they kind of of know their guys and they know the human element that's involved. It's an interesting balancing act. >> Right so from your new job and your new role, what are some of the things you hope to see today, what are somethings that you're excited about from an investor and in having played the game as well as looking forward to the evolution of sports? >> Two things, specifically how the, I'm certainly biased to the performance on the field, and the human element and certainly, everybody wants workout secrets and I don't feel like it's, whether it's athletes or the kind of weekend warrior or people that are senior citizens. I don't think it's as simple as, this is work and you should do this, it's a very personalized experience now and I think some of this personalized digital fitness is fascinating to me and then how it relates to and how your body relates to your diet, your nutrition, your sleep, your recovery, I think all those are fascinating that advances that I want to look into more. And then second is, as I kind of mentioned, is the fan engagement aspect and how do we drive those fans, that digital, and make it actionable and monetized, right. So that you have your fans that are following your Facebook, your Twitter, and all those things and so how do you, not only engage them but collect that data and then kind of personalize that experience, engage your fan in a way that can kind of grow your brand. It will be interesting to me. >> Really interesting to have your perspective and I'm sure it will be a great day and you'll see all kind of crazy stuff. So thanks for taking a few minutes. >> Yeah, anytime, thanks for having me. >> All right, he's Brendan, I'm Jeff, you're watching theCUBE. We are at Oracle Park in San Francisco, thanks for watching, we'll see you next time. (upbeat music)

>> Live from Boston, it's theCUBE! Covering IBM Chief Data Officers Summit. Brought to you by IBM. >> Welcome back to theCUBE's live coverage of the IBM CDO Summit here in Boston, Massachusetts. I'm your host Rebecca Knight, along with my co-host Paul Gillin. We're joined by Inderpal Bhandari, he is the Global Chief Data Officer at IBM. Thank you so much for coming back on theCUBE, Inderpal. >> It's my pleasure. >> It's great to have you. >> Thank you for having me. >> So I want to talk, I want to start by talking a little bit about your own career journey. Your first CDO job was in the early 2000s. You were one of the first CDOs, ever. In the history of Chief Data Officers. Talk a little bit about the evolution of the role and sort of set the scene for our viewers in terms of what you've seen, in your own career. >> Yes, no thank you, December 2006, I became a Chief Data Officer of a major healthcare company. And you know, it turned out at that time there were only four of us. Two in banking, one in the internet, I was the only one in healthcare. And now of course there are well over 1,999 of us and the professions taken off. And I've had the fortune of actually doing this four times now. So leading a legacy in four different organizations in terms of building that organizational capability. I think initially, when I became Chief Data Officer, the culture was one of viewing data's exhaust. Something that we had to discard, that came out of the transactions that you were, that your business was doing. And then after that you would discard this data, or you didn't really care about it. And over the course of time, people had begun to realize that data is actually a strategic asset and you can really use it to drive not just the data strategy, but the actual business strategy, and enable the business to go to the next level. And that transitions been tremendous to watch and to see. I've just been fortunate that I've been there for the full journey. >> Are you seeing any consensus developing around what background makes for a good CDO? What are the skills that a CDO needs? >> Yeah, no that's a very, very good question. My view has been evolving on that one too, over the last few years, right, as I've had these experiences. So, I'll jump to the conclusion, so that you kind of, to answer your question as opposed to what I started out with. The CDO, has to be the change agent in chief, for the organization. That's really the role of the CDO. So yes, there's the technical sharps that you have to have and you have to be able to deal with people who have advanced technical degrees and to get them to move forward. But you do have to change the entire organization and you have to be adept at going after the culture, changing it. You can't get frustrated with all the push back, that's inevitable. You have to almost develop it as an art, as you move forward. And address it, not just bottom up and lateral, but also top down. And I think that's probably where the art gets the most interesting. Because you've got to push a for change even at the top. But you can push just so far without really derailing everything that you are trying to do. And so, I think if I have to pick one attribute, it would be that the CDO has to be the change agent in chief and they have to be adept at addressing the culture of the organization, and moving it forward. >> You're laying out all of these sort of character traits that someone has to be indefatigable, inspirational, visionary. You also said during the keynote you have six months to really make your first push, the first six months are so important. When we talk about presidents, it's the first 100 days. Describe what you mean by that, you have six months? >> So if a new, and I'm talking here mainly about a large organization like an IBM, a large enterprise. When you go in, the key observation is it's a functioning organization. It's a growing concern. It's already making money, it's doing stuff like that. >> We hope. >> And the people who are running that organization, they have their own needs and demands. So very quickly, you can just become somebody who ends up servicing multiple demands that come from different business units, different people. And so that's kind of one aspect of it. The way the organization takes over if you don't really come in with an overarching strategy. The other way the organizations take over is typically large organizations are very siloed. And even at the lower levels you who have people who developed little fiefdoms, where they control that data, and they say this is mine, I'm not going to let anybody else have it. They're the only one's who really understand that curve. And so, pretty much unless you're able to get them to align to a much larger cause, you'll never be able to break down those silos, culturally. Just because of the way it's set up. So its a pervasive problem, goes across the board and I think, when you walk in you've got that, you call it honeymoon period, or whatever. My estimate is based on my experience, six months. If you don't have it down in six months, in terms of that larger cause that your going to push forward, that you can use to at least align everybody with the vision, or you're not going to really succeed. You'll succeed tactically, but not in a strategic sense. >> You're about to undertake the largest acquisition in IBM's history. And as the Chief Data Officer, you must be thinking right now about what that's going to mean for data governance and data integration. How are you preparing for an acquisition that large? >> Yeah so, the acquisition is still got to work through all the regulations, and so forth. So there's just so much we can do. It's much more from a planning stand point that we can do things. I'll give you a sense of how I've been thinking about it. Now we've been doing acquisitions before. So in that since we do have a set process for how we go about it, in terms of evaluating the data, how we're going to manage the data and so forth. The interesting aspect that was different for me on this one is I also talked back on our data strategy itself. And tried to understand now that there's going to be this big acquisition of move forward, from a planning standpoint how should I be prepared to change? With regard to that acquisition. And because we were so aligned with the overall IBM business strategy, to pursue cognition. I think you could see that in my remarks that when you push forward AI in a large enterprise, you very quickly run into this multi-cloud issue. Where you've got, not just different clouds but also unprime and private clouds, and you have to manage across all that and that becomes the pin point that you have to scale. To scale you have to get past that pin point. And so we were already thinking about that. Actually, I just did a check after the acquisition was announced, asking my team to figure out well how standardized are we with Red Hat Linux? And I find that we're actually completely standardized across with Red Hat Linux. We pretty much will have use cases ready to go, and I think that's the facet of the goal, because we were so aligned with the business strategy to begin with. So we were discovering that pinpoint, just as all our customers were. And so when the cooperation acted as it did, in some extent we're already ready to go with used cases that we can take directly to our clients and customers. I think it also has to do with the fact that we've had a partnership with Red Hat for some time, we've been pretty strategic. >> Do you think people understand AI in a business context? >> I actually think that that's, people don't really understand that. That's was the biggest, in my mind anyway, was the biggest barrier to the business strategy that we had embarked on several years ago. To take AI or cognition to the enterprise. People never really understood it. And so our own data strategy became one of enabling IBM itself to become an AI enterprise. And use that as a showcase for our clients and customers, and over the journey in the last two, three years that I've been with IBM. We've become more, we've been putting forward more and more collateral, but also technology, but also business process change ideas, organizational change ideas. So that our clients and customers can see exactly how it's done. Not that i'ts perfect yet, but that too they benefit from, right? They don't make the same mistakes that we do. And so we've become, your colleagues have been covering this conference so they will know that it's become more and more clear, exactly what we're doing. >> You made an interesting comment, in the keynote this morning you said nobody understands AI in a business context. What did you mean by that? >> So in a business context, what does it look like? What does AI look like from an AI enterprise standpoint? From a business context. So excuse me I just trouble them for a tissue, I don't know why. >> Okay, alright, well we can talk about this a little bit too while he-- >> Yeah, well I think we understand AI as an Amazon Echo. We understand it as interface medium but I think what he was getting at is that impacting business processes is a lot more complicated. >> Right. >> And so we tend to think of AI in terms of how we relate to technology rather than how technology changes the rules. >> Right and clearly its such, on the consumers side, we've all grasped this and we all are excited by its possibilities but in terms of the business context. >> I'm back! >> It's the season, yes. >> Yeah, it is the season, don't want to get in closer. So to your question with regard to how-- >> AI in a business context. >> AI in a business context. Consumer context everybody understands, but in a business context what does it really mean? That's difficult for people to understand. But eventually it's all around making decisions. But in my mind its not the big decisions, it's not the decisions we going to acquire Red Hat. It's not those decisions. It's the thousands and thousands of little decisions that are made day in and night out by people who are working the rank and file who are actually working the different processes. That's what we really need to go after. And if you're able to do that, it completely changes the process and you're going to get just such a lot more out of it, not just terms of productivity but also in terms of new ideas that lead to revenue enhancement, new products, et cetera, et cetera. That's what a business AI enterprise looks like. And that's what we've been bringing forward and show casing. In today's keynote I actually had Sonya, who is one of our data governance people, SMEs, who works on metadata generation. Really a very difficult manual problem. Data about data, specifically labeling data so that a business person could understand it. Its all been done manually but now it's done automatically using AI and its completely changed the process. But Sonya is the person who's at the forefront of that and I don't think people really understand that. They think in terms of AI and business and they think this is going to be somebody who's a data scientist, a technologist, somebody who's a very talented technical engineer, but it's not that. It's actually the rank and file people, who've been working these business processes, now working with an intelligent system, to take it to the next level. >> And that's why as you've said it's so important that the CDO is a change agent in chief. Because it is, it does require so much buy-in from, as you say, the rank and file, its not just the top decision makers that you're trying to persuade. >> Yes, you are affecting change at all levels. Top down, bottom up, laterally. >> Exactly. >> You have to go after it across the board. >> And in terms of talking about the data, it's not just data for data's sake. You need to talk about it in terms that a business person can understand. During the keynote, you described an earlier work that you were doing with the NBA. Can you tell our viewers a little bit about that? And sort of how the data had to tell a story? >> Yes, so that was in my first go 'round with IBM, from 1990 through '97. I was with IBM Research, at the Watson Research Lab, as a research staff member. And I created this program called Advanced Scout for the National Basketball Association. Ended up being used by every team on the NBA. And it would essentially suggest who to put in the line up, when you're matching lines up and so forth. By looking at a lot of game data and it was particularly useful during the Playoff games. The major lesson that came out of that experience for me, at that time, alright, this was before Moneyball, and before all this stuff. I think it was like '90, '93, '92. I think if you Google it you will still see articles about this. But the main lesson that came out for me was the first time when the program identified a pattern and suggested that to a coach during a playoff game where they were down two, zero, it suggested they start two backup players. And the coach was just completely flabbergasted, and said there's no way I'm going to do this. This is the kind of thing that would not only get me fired, but make me look really silly. And it hit me then that there was context that was missing, that the coach could not really make a decision. And the way we solved it then was we tied it to the snippets of video when those two players were on call. And then they made the decision that went on and won that game, and so forth. Today's AI systems can actually fathom all that automatically from the video itself. And I think that's what's really advanced the technology and the approaches that we've got today to move forward as quickly as they have. And they've taken hold across the board, right? In the sense of a consumer setting but now also in the sense of a business setting. Where we're applying it pretty much to every business process that we have. >> Exciting. Well Inderpal, thank you so much for coming back on theCUBE, it was always a pleasure talking to you. >> It's my pleasure, thank you. >> I'm Rebecca Knight for Paul Gillin, we will have more from theCUBE's live coverage of IBM CDO coming up in just a little bit. (upbeat music)

>> Announcer: Live from the Javits Center in New York City, it's The Cube, covering Inforum 2017. Brought to you by, Infor. >> Welcome back to Inforum 2017. I'm your host Rebecca Knight, along with my co-host Dave Vellante. We're joined by Marc Scibelli, he is the chief creative officer here at Infor. Thanks so much for returning to The Cube. >> Thanks for having me again, it's good to see you guys. >> So last year, the big announcement was H and L Digital, Hook and loop digital. Bring us up to speed, give us a status update of where you are now. >> Well we're a year later, I think what's really important is that we've established our application development framework, which allows us to rapidly deploy our prototypes, rapidly deploy the projects we're working on for a lot of customers. We've had a lot of wins over the last year. We're working closely with Brooklyn Sports, both the basketball team and the stadium and entertainment center. We're working with Travis Perkins, we're working with American Express. So we've got a lot of great client wins in our belt. We've learned a lot over the last year, but most importantly we've been able to actually fine tune our application development framework to bring that stuff to market very quickly for our customers, which has been a very big deal for us. >> So you mentioned a couple of client wins, Brooklyn Sports, let's unpack that a little bit, tell me a little about, tell our viewers specifically what's gone on. >> Yeah so, Brooklyn Nets basketball team here in the U.S., player performance a little bit down, so we're working with the performance coaches, we're working with the telemetric data that's coming out from the players. Things as it pertains to the arc of the ball throw, or the scale to models of how they perform or how much sleep they're getting. We're tying into a lot of IOT devices that the players use. We're bringing all that data into one place for the performance coaches and then allowing them to make better decisions on the field, on the court, in real time. So you'll see actually, behind you guys is our half court. We've actually set up a half court to show some of that data that we're bringing in about player performance. We actually run an NBA player assessment and show your player readiness, I hit like an eight percent readiness (Dave and Rebecca laugh) >> Rebecca: There's still time. >> Yeah five, eight I didn't think I was going to get very far in the NBA. >> High single digits. >> High, yeah, high, real high. So we're working a lot around player performance, certainly. And also with Brooklyn Sports Entertainment around the Barclay Center here in Brooklyn, how they can start to brand that experience. Nobody really has an affinity for an arena, you go and see Beyoncé or you go to watch the Nets. You don't really think about going to the Barclays Center, so how do you start as soon as they walk in the door, engaging with the customer using technology to drive all this value all the way through. How do you find the shortest beverage and bar line. How do you find the cleanest bathroom. How do you find, to get beverage and drinks and food delivered to your seat. That's all going to be technology that's going to drive that. A lot of our clients we've installed the digital backbone underpinning of that with our cloud suite. And now it's our job to commit a certain, creating these apps that differentiate them in the market place, help Barclays compete against other next-gen stadiums. >> So the Nets example it's similar to Moneyball but different, so he's talking the arc of the ball and so the remediation of some of those, the optimization of some of those, is just different training patterns or different exercises or drills that they could do. Whereas Moneyball it's like this unseen value, unbased percentage for example, are there analogs to Moneyball? Like I was listening to an interview with an owner the other day and the interviewer was beating him up about one player and he said well if you look at the deeper analytics, I'm like oh, deeper analytics what does that mean? So are there deeper analytics? >> Absolutely, you know we've left a lot of the basketball to the basketball professionals. When we started this thing the GM said to us, "Should we really get this started with" "you guys? What do you know about basketball?" We looked around and it was like an Englishman next to me and myself and we're like we don't know a lot about basketball but we hope that, that's what you're bringing to the table. We know a lot about how to bring the data science together, we can bring the AI in, we can bring all that together for your performance coaches and work with them Just like we didn't know a lot about farming and agriculture but we can work with feed companies to help them optimize for their customers. So it's not about what we knew about basketball but up to your point, those performance coaches are definitely finding those little nuggets of data to help those teams perform better. I couldn't tell you more off the top of my head cause that's how little I know about basketball. My eight percent performance rating will show you that, but they are looking inside that data and able to find that. And the trick is bringing it to them in real-time, bringing it so that they don't have to go into deep excel documents. That's what they were doing before. It was all stored in excel and they had to go through it and maybe somebody make a pivot table or something. >> Rebecca: Or watching play tapes. >> Or watching play, absolutely, of course. And by being able to assess all of that data too as well and bring that into the feed and be able to actually assess that and report it back into the larger system we're providing. It gives them a lot more visibility so they can find those little nuggets that they know as basketball professionals. >> And Burst is part of this solution? >> Not currently, no, but certainly we will be needing the Burst into that play, yeah. >> So Thomas Perkins is another example -- >> Marc: Travis Perkins. >> Travis Perkins, I'm sorry, that you mentioned. What kind of things are you doing there to make make that company able to really use data more wisely? >> So Travis Perkins, one of the largest building manufacturing supply company in the U.K. over 2000 distribution locations across England, very strong in its footprint. It's a really strong brand in terms of, sort of the Home Depot of the U.K. They put in M3 last year, it was a big announcement and it was a very large initiative for them and that's the digital backbone we talk about. So now it's our job we're coming in now we're automating a lot of their systems for their distribution centers so they get a better customer experience. So when I go into a Travis Perkins distribution center, I can get what I need much quicker so that's kind of the baseline thing that we come in and do. We look at ways to optimize for example if I could fah-bin with my truck and actually just pull my truck fah-bin, you know it's me, my order is ready. I don't need to get out of the truck, they pack my truck and I just drive out the other side. How do we create engagements for visibility models for the distribution managers to be able to see what's selling, what's not selling. Who's performing, who's not performing. Those are the things that we do as the baseline of the experience and then additionally to that, we look at new business models with them. So we're actually helping them think about new ways that they can create subscription models or ecosystem models. So, for example working on, they're working on the tool locker rental, setting up a,basically locker or rental facility, then using software to be able to access that locker and then you sort of create a subscription model to that. I'm able to just pull up, punch in a code, that's my tool locker, I get my tools right out of it and I can drive right off. And then doing it in places geographically that make a lot of sense for them. So that's kind of the best time, I think we get these signature experiences and optimize on top of the backbone, but then we create these whole new business transformation models of these companies, that's really exciting, really helpful. >> So retail's an interesting example everybody's got an amazon war-room trying figure out how to compete, where they can add value. What have you seen specifically in the retail business? >> I just moderated a panel with the CIO of DSW and the COO of Crate and Barrel on either side of me and it was exciting to see their, they feel a disruption but they're certainly eager to take it over. So, on the Crate and Barrel side we're seeing them be, really beat up by the Wayfairs of the world, three billion dollar valuation. They can get the market much quicker, they're running products in a much different way. Where Crate and Barrel has a much longer lead timer, the CPQ model. They've got to configure pricing, quoting, get it out. Takes 12 weeks to get a couch. How do you get, on the supply chain side, how do you get that shorter. So they're working with Infor to get that supply chain shorter. So they can compete on a shorter lead times but we're coming in to help them do is also look at how can you start to create experiences while you're waiting for that couch to be produced. Or while your shopping online what are things that you can do to know how long it'll take to get that item. And now that we just take all that digital backbone of that supply chain and create new experiences for it. On the DSW side we've been working really closely with them on point of sale as well as deep customer experience, apps for them with their employees. They really see their employees as the key tool to driving loyalty to their stores. So, we've been working on brand new apps in the mobile space that'll help their employees be able to serve their customers a lot better, have a much more tied loyalty program to their job performance with the customer's loyalty. So, a lot of great things there that we're working hard on. But certainly it's a massive behemoth of competing against amazon as a retailer. >> So what's your advice then for a company that is, and you're talking about companies that are already being very thoughtful and planful about this transformation, and understanding first of all that they need to transform, that they need to change or else they'll be left behind. So what's your advice for companies that are just starting on it? >> I think we kind of look at this as a holistic approach, we cannot take a little nibble bite-size out of the problem. So when it comes to digital looking at the entire ecosystem, looking at the operations, looking at the customers, looking at the employee. Saying what are we doing on our core backbone of the operations to make that run efficiently, to automate that. Let's do that, let's get that out of the way of all those people, let's make that run as quickly, as streamline as possible. Our cloud suite certainly help companies do that. And then, let's look at how we can start to transform the way they do their, they function inside their business by creating these functionally integrated models between all three. Between the operations, the customer and the employee. And let's create new experiences that live on top of that of that backbone that drive new value and until you do that, until you leverage your brand, like Crate and Barrel can leverage their brand if they just shorten that supply chain and start to optimize how they deliver. DSW can leverage their brand as a shoe warehouse if they provide a larger assortment and a better experience in-store, they can compete against amazon. So, to do that, we need them to, I would recommend companies, think of the approach holistically and not as a small little bites of just let's create this app and this one app is going to solve our problems. It's not, you got this much larger holistic approach you need to take. >> What percent of the Infor portfolio has Hook and Loop touched, affected? >> So, Hook and Loop core, certainly the GA products have touched everything. You'll see tomorrow on-stage Nunzio Esposito, our new head of Hook and Loop core. Who's running the business that when I first met you, I was running. They're doing very well and they've touched, I would say percentage-wise, 80% of the product if not more. Certainly their products are driving our business, like EAM, ACM financials, they have re-invented. And you'll see it tomorrow, they have done some incredible work. They just, they'll be releasing tomorrow, it's pretty exciting, a new UX for an entire cloud suite, so that pretty incredible. How Colman will be integrated into our cloud, it's a big deal so how do you create UX for that. And then certainly of course, how much UX and UY do you take away because you introduced Colman. You could take a lot of UX and UY away, a lot of functionality gets stripped away. So it's changed the methodologies we've used in the Hook and Loop core team but Ninzio has done a great job challenging himself to do that. >> Rebecca you were saying when you read the press releases around Infor they use terms like beautiful and so it's very apple-esque. Where do you get your inspiration? >> I think it's the consumer great products we talked about years ago when I first met you. The idea that how I function, like daily life at home, should echo how I function at work. Certainly now we're getting inspiration for how companies that are born digitally are creating these models that drive them. How we can help other companies do that as well. so, we're inspired by everything that touches us. To be honest , I still use my TEVO, I might be the only person left, (Dave and Rebecca laughing) That's not true they're doing very well >> I like the little sound effects of TEVO, I know what you mean. >> I can't say I'm the only person, but I'm probably the only person that'll admit it. That I love my TEVO. But these are things that I've watched them, not just change their UX like we did with Infor five years ago, but now they've changed their business model, they've changed what they've become as a hub and as a digital solution. How they used media channels to drive their business, I think that's incredible and it's a similar journey we're going on. So, there's a lot to be inspired by. >> Why should the consumer guys have all the fun? >> Marc: Yeah exactly. >> So how do you keep your team, you're the chief creative officer, so how do you, you talked about what inspires you and what inspires the company as a whole but how do you, keep a culture of creativity and innovation going? How do you keep the momentum? >> We've been really fortunate to have a really great support system by the executive team, Charles Phillips, Duncan Angove, certainly have been incredible about needing a team like Hook and Loop. When I met David it was 15 people maybe a little more, and now it's a 120 that run that core team. We launched H and L Digital last year, we were like nine people and now we're over 40. That investment, those dollars they put back into these kind of endeavors are really indicative of that . And I think that it comes through to the creatives and the people that we bring in that this is the kind of investments that Infor is interested in. We have a beautiful working environment inside New York City inside our headquarters. We have a beautiful new garage we just opened up, an innovation lab, we get to play with the greatest toys. I think we're actually very, very fortunate, to be inside a company like Infor and get to work with the people, we get to work with as designers, and as creatives. And that was an up hill slope to keep people motivated to do that as creatives and we call them left brain creators. I think we're there now, we turn away a lot of people to come work for us now. So it's pretty exciting. >> New York, London, Dubai, right? >> That's exactly right thank you, yeah. We are, we opened London just recently, we're opening Dubai next and we have two teams in New York. It's pretty exciting. >> Rebecca: Great. >> Love to see the Dubai. >> Yeah, Dubai is being built up right now, we have an office there already. >> could be the next destination, >> Cube Dubai. >> We should do a cube Dubai, that'd be great, they would love it there. >> Alright. >> I love it. Well Marc-- >> Put that on the list. >> Marc, thanks so much for joining us it's always a pleasure having you on the show. >> Thank you >> I'm Rebecca Knight for Dave Vellante we will have more from Inforum after this.