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

(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)

>> Narrator: "theCUBE's" live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (intro music) >> Good afternoon, everyone. Welcome back to "theCUBE's" day three coverage of MWC23. Lisa Martin here in Spain, Barcelona, Spain with Dave Nicholson. We're going to have a really interesting conversation next. We're going to really dig into MWC, it's history, where it's going, some of the controversy here. Please welcome our guests. We have Jim Harris, International Best Selling Author of "Blindsided." And Carolina Milanese is here, President and Principle Analyst of creative strategies. Welcome to "theCUBE" guys. Thank you. >> Thanks. So great to be here. >> So this is day three. 80,000 people or so. You guys have a a lot of history up at this event. Caroline, I want to start with you. Talk a little bit about that. This obviously the biggest one in, in quite a few years. People are ready to be back, but there's been some, a lot of news here, but some controversy going on. Give us the history, and your perspective on some of the news that's coming out from this week's event. >> It feels like a very different show. I don't know if I would say growing up show, because we are still talking about networks and mobility, but there's so much more now around what the networks actually empower, versus the network themselves. And a little bit of maybe that's where some of the controversy is coming from, carriers still trying to find their identity, right, of, of what their role is in all there is to do with a connected world. I go back a long way. I go back to when Mobile World Congress was called, was actually called GSM, and it was in Khan. So, you know, we went from France to Spain. But just looking at the last full Mobile World Congress here in Barcelona, in pre-pandemic to now, very different show. We went from a show that was very much focused on mobility and smartphones, to a show that was all about cars. You know, we had cars everywhere, 'cause we were talking about smart cities and connected cars, to now a show this year that is very much focused on B2B. And so a lot of companies that are here to either work with the carriers, or also talk about sustainability for instance, or enable what is the next future evolution of computing with XR and VR. >> So Jim, talk to us a little bit about your background. You, I was doing a little sleuthing on you. You're really focusing on disruptive innovation. We talk about disruption a lot in different industries. We're seeing a lot of disruption in telco. We're seeing a lot of frenemies going on. Give us your thoughts about what you're seeing at this year's event. >> Well, there's some really exciting things. I listened to the keynote from Orange's CEO, and she was complaining that 55% of the traffic on her network is from five companies. And then the CEO of Deutsche Telecom got up, and he was complaining that 60% of the traffic on his network is from six entities. So do you think they coordinated pre, pre-show? But really what they're saying is, these OTT, you know, Netflix and YouTube, they should be paying us for access. Now, this is killer funny. The front page today of the show, "Daily," the CO-CEO of Netflix says, "Hey, we make less profit than the telcos, "so you should be paying us, "not the other way around." You know, we spend half of the money we make just on developing content. So, this is really interesting. The orange CEO said, "We're not challenging net neutrality. "We don't want more taxes." But boom. So this is disruptive. Huge pressure. 67% of all mobile traffic is video, right? So it's a big hog bandwidth wise. So how are they going to do this? Now, I look at it, and the business model for the, the telcos, is really selling sim cards and smartphones. But for every dollar of revenue there, there's five plus dollars in apps, and consulting and everything else. So really, but look at how they're structured. They can't, you know, take somebody who talks to the public and sells sim cards, and turn 'em in, turn 'em in to an app developer. So how are they going to square this circle? So I see some, they're being disrupted because they're sticking to what they've historically done. >> But it's interesting because at the end of the day, the conversation that we are having right now is the conversation that we had 10 years ago, where carriers don't want to just be a dumb pipe, right? And that's what they are now returning to. They tried to be media as well, but that didn't work out for most carriers, right? It is a little bit better in the US. We've seen, you know, some success there. But, but here has been more difficult. And I think that's the, the concern, that even for the next, you know, evolution, that's the, their role. >> So how do they, how do they balance this dumb pipe idea, with the fact that if you make the toll high enough, being a dumb pipe is actually a pretty good job. You know, sit back, collect check, go to the beach, right? So where, where, where, where does this end up? >> Well, I think what's going to happen is, if you see five to 15 X the revenue on top of a pipe, you know, the hyperscalers are going to start going after the business. The consulting companies like PWC, McKinsey, the app developers, they're... So how do you engage those communities as a telco to get more revenue? I think this is a question that they really need to look at. But we tend to stick within our existing business model. I'll just give you one stat that blows me away. Uber is worth more than every taxi cab company in North America added together. And so the taxi industry owns billions in assets in cars and limousines. Uber doesn't own a single vehicle. So having a widely distributed app, is a huge multiplier on valuation. And I look to a company like Safari in Kenya, which developed M-Pesa, which Pesa means mo, it's mobile money in Swahili. And 25% of the country's GDP is facilitated by M-Pesa. And that's not even on smartphones. They're feature phones, Nokia phones. I call them dumb phones, but Nokia would call them "feature phones." >> Yeah. >> So think about that. Like 25, now transactions are very small, and the cut is tiny. But when you're facilitating 25% of a country's GDP, >> Yeah. >> Tiny, over billions of transactions is huge. But that's not the way telcos have historically thought or worked. And so M-Pesa and Safari shows the way forward. What do you think on that? >> I, I think that the experience, and what they can layer on top from a services perspective, especially in the private sector, is also important. I don't, I never believe that a carrier, given how they operate, is the best media company in the world, right? It is a very different world. But I do think that there's opportunity, first of all, to, to actually tell their story in a different way. If you're thinking about everything that a network actually empowers, there's a, there's a lot there. There's a lot that is good for us as, as society. There's a lot that is good for business. What can they do to start talking about differently about their services, and then layer on top of what they offer? A better way to actually bring together private and public network. It's not all about cellular, wifi and cellular coming together. We're talking a lot about satellite here as well. So, there's definitely more there about quality of service. Is, is there though, almost a biological inevitability that prevents companies from being able to navigate that divide? >> Hmm. >> Look at, look at when, when, when we went from high definition 720P, very exciting, 1080P, 4K. Everybody ran out and got a 4K TV. Well where was the, where was the best 4K content coming from? It wasn't, it wasn't the networks, it wasn't your cable operator, it was YouTube. It was YouTube. If you had suggested that 10 years before, that that would happen, people would think that you were crazy. Is it possible for folks who are now leading their companies, getting up on stage, and daring to say, "This content's coming over, "and I want to charge you more "for using my pipes." It's like, "Really? Is that your vision? "That's the vision that you want to share with us here?" I hear the sound of dead people walking- (laughing) when I hear comments like that. And so, you know, my students at Wharton in the CTO program, who are constantly looking at this concept of disruption, would hear that and go, "Ooh, gee, did the board hear what that person said?" I, you know, am I being too critical of people who could crush me like a bug? (laughing) >> I mean, it's better that they ask the people with money than not consumers to pay, right? 'Cause we've been through a phase where the carriers were actually asking for more money depending on critical things. Like for instance, if you're doing business email, then were going to charge you more than if you were a consumer. Or if you were watching video, they would charge you more for that. Then they understood that a consumer would walk away and go somewhere else. So they stopped doing that. But to your point, I think, and, and very much to what you focus from a disruption perspective, look at what Chat GTP and what Microsoft has been doing. Not much talk about this here at the show, which is interesting, but the idea that now as a consumer, I can ask new Bing to get me the 10 best restaurants in Barcelona, and I no longer go to Yelp, or all the other businesses where I was going to before, to get their recommendation, what happens to them? You're, you're moving away, and you're taking eyeballs away from those websites. And, and I think that, that you know, your point is exactly right. That it's, it's about how, from a revenue perspective, you are spending a lot of money to facilitate somebody else, and what's in it for you? >> Yeah. And to be clear, consumers pay for everything. >> Always. Always. (laughs) >> Taxpayers and consumers always pay for everything. So there is no, "Well, we're going to make them pay, so you don't have to pay." >> And if you are not paying, you are the product. Exactly. >> Yes. (laughing) >> Carolina, talk a little bit about what you're seeing at the event from some of the infrastructure players, the hyperscalers, obviously a lot of enterprise focus here at this event. What are some of the things that you're seeing? Are you impressed with, with their focus in telco, their focus to partner, build an ecosystem? What are you seeing? >> I'm seeing also talk about sustainability, and enabling telco to be more sustainable. You know, there, there's a couple of things that are a little bit different from the US where I live, which is that telcos in Europe, have put money into sustainability through bonds. And so they use the money that they then get from the bonds that they create, to, to supply or to fuel their innovation in sustainability. And so there's a dollar amount on sustainability. There's also an opportunity obviously from a growth perspective. And there's a risk mitigation, right? Especially in Europe, more and more you're going to be evaluated based on how sustainable you are. So there are a lot of companies here, if you're thinking about the Ciscos of the world. Dell, IBM all talking about sustainability and how to help carriers measure, and then obviously be more sustainable with their consumption and, and power. >> Going to be interesting to see where that goes over the years, as we talk to, every company we talk to at whatever show, has an ESG sustainability initiative, and only, well, many of them only want to work with other companies who have the same types of initiative. So a lot of, great that there's focus on sustainability, but hopefully we'll see more action down the road. Wanted to ask you about your book, "Blind," the name is interesting, "Blindsided." >> Well, I just want to tag on to this. >> Sure. >> One of the most exciting things for me is fast charging technology. And Shalmie, cell phone, or a smartphone maker from China, just announced yesterday, a smartphone that charges from 0 to 100% in five minutes. Now this is using GAN FEST technology. And the leader in the market is a company called Navitas. And this has profound implications. You know, it starts with the smartphone, right? But then it moves to the laptops. And then it'll move to EV's. So, as we electrify the $10 trillion a year transportation industry, there's a huge opportunity. People want charging faster. There's also a sustainability story that, to Carolina's point, that it uses less electricity. So, if we electrify the grid in order to support transportation, like the Tesla Semi's coming out, there are huge demands over a period. We need energy efficiency technologies, like this GAN FEST technology. So to me, this is humongous. And it, we only see it here in the show, in Shalmie, saying, "Five minutes." And everybody, the consumers go, "Oh, that's cool." But let's look at the bigger story, which is electrifying transportation globally. And this is going to be big. >> Yeah. And, and to, and to double click on that a little bit, to be clear, when we talk about fast charging today, typically it's taking the battery from a, not a zero state of charge, but a relatively low state of charge to 80%. >> Yep. >> Then it tapers off dramatically. And that translates into less range in an EV, less usable time on any other device, and there's that whole linkage between the power in, and the battery's ability to be charged, and how much is usable. And from a sustainability perspective, we are going to have an avalanche of batteries going into secondary use cases over time. >> They don't get tossed into landfills contrary to what people might think. >> Yep. >> In fact, they are used in a variety of ways after their primary lifespan. But that, that is, that in and of itself is a revolutionary thing. I'm interested in each of your thoughts on the China factor. Glaringly absent here, from my perspective, as sort of an Apple fanboy, where are they? Why aren't they talking about their... They must, they must feel like, "Well we just don't need to." >> We don't need to. We just don't need to. >> Absolutely. >> And then you walk around and you see these, these company names that are often anglicized, and you don't necessarily immediately associate them with China, but it's like, "Wait a minute, "that looks better than what I have, "and I'm not allowed to have access to that thing." What happens in the future there geopolitically? >> It's a pretty big question for- >> Its is. >> For a short little tech show. (Caroline laughs) But what happens as we move forward? When is the entire world going to be able to leverage in a secure way, some of the stuff that's coming out of, if they're not the largest economy in the world yet, they shortly will be. >> What's the story there? >> Well, it's interesting that you mentioned First Apple that has never had a presence at Mobile World Congress. And fun enough, I'm part of the GSMA judges for the GLOMO Awards, and last night I gave out Best Mobile Phone for last year, and it was to the iPhone4 Team Pro. and best disruptive technology, which was for the satellite function feature on, on the new iPhone. So, Apple might not be here, but they are. >> Okay. >> And, and so that's the first thing. And they are as far as being top of mind to every competitor in the smartphone market still. So a lot of the things that, even from a design perspective that you see on some of the Chinese brands, really remind you of, of Apple. What is interesting for me, is how there wouldn't be, with the exception of Samsung and Motorola, there's no one else here that is non-Chinese from a smartphone point of view. So that's in itself, is something that changed dramatically over the years, especially for somebody like me that still remember Nokia being the number one in the market. >> Huh. >> So. >> Guys, we could continue this conversation. We are unfortunately out of time. But thank you so much for joining Dave and me, talking about your perspectives on the event, the industry, the disruptive forces. It's going to be really interesting to see where it goes. 'Cause at the end of the day, it's the consumers that just want to make sure I can connect wherever I am 24 by seven, and it just needs to work. Thank you so much for your insights. >> Thank you. >> Lisa, it's been great. Dave, great. It's a pleasure. >> Our pleasure. For our guests, and for Dave Nicholson, I'm Lisa Martin. You're watching, "theCUBE," the leader in live and emerging tech coverage coming to you day three of our coverage of MWC 23. Stick around. Our next guest joins us momentarily. (outro music)

(intense music) >> Hello, I'm John Furrier with "SiliconANGLE News" and the host of theCUBE here in Palo Alto, with coverage of MWC 2023. theCUBE is onsite in Barcelona, four days of wall to wall coverage. Here is a news update from MWC and in the news here is Google. Google Cloud targets cloud native network transformation for all the carriers or cloud service providers, and the communication service providers. They announced three new products to help communications service providers, also known as CSPs, build, deploy and operate hybrid cloud native networks, as well as collect and manage network data. The new products, when combined with Unified Cloud, enables the CSPs to improve customer experience, artificial intelligence, and data analytics. This is a big move, because 70% of communication service providers are expected to adopt cloud native network functions by the end of this year, making it a big, big wave. One of the key features of Google's products is the telecom network automation. This cloud service accelerates CSPs network and edge deployments through the use of Kubernetes based cloud native automation tools. It's managed by a cloud version of open source Nephio, project that Google founded in 2022. Of course, other key product announcements with Google, the Telecom Data Fabric, a tool that helps CSPs generate insights. That's the data driven piece, to target and optimize their network performance and reliability, works by simplifying the collection, normalization, correlation through an adaptive framework. This is kind of where AI shines. Finally, Google has telecom subscriber insights, a powerful AI tool that enables CSPs to extract insights from existing data sources in a privacy safe environment. Let's see if this is better than Bing search, we'll see. But CSPs are moving to the cloud across all channels. This is a really important trend, as cloud native scale, AI, data, configuration, automation all come to the edge of the network. That's an update from "SiliconANGLE News". Check out the coverage on siliconangle.com. Of course, thecube.net, four days, Dave Vellante and Lisa Martin are there. I'm here in Palo Alto. Thanks for watching. (slow music) (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 theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> OpenAI The company, and ChatGPT have taken the world by storm. Microsoft reportedly is investing an additional 10 billion dollars into the company. But in our view, while the hype around ChatGPT is justified, we don't believe OpenAI will lock up the market with its first mover advantage. Rather, we believe that success in this market will be directly proportional to the quality and quantity of data that a technology company has at its disposal, and the compute power that it could deploy to run its system. Hello and welcome to this week's Wikibon CUBE insights, powered by ETR. In this Breaking Analysis, we unpack the excitement around ChatGPT, and debate the premise that the company's early entry into the space may not confer winner take all advantage to OpenAI. And to do so, we welcome CUBE collaborator, alum, Sarbjeet Johal, (chuckles) and John Furrier, co-host of the Cube. Great to see you Sarbjeet, John. Really appreciate you guys coming to the program. >> Great to be on. >> Okay, so what is ChatGPT? Well, actually we asked ChatGPT, what is ChatGPT? So here's what it said. ChatGPT is a state-of-the-art language model developed by OpenAI that can generate human-like text. It could be fine tuned for a variety of language tasks, such as conversation, summarization, and language translation. So I asked it, give it to me in 50 words or less. How did it do? Anything to add? >> Yeah, think it did good. It's large language model, like previous models, but it started applying the transformers sort of mechanism to focus on what prompt you have given it to itself. And then also the what answer it gave you in the first, sort of, one sentence or two sentences, and then introspect on itself, like what I have already said to you. And so just work on that. So it it's self sort of focus if you will. It does, the transformers help the large language models to do that. >> So to your point, it's a large language model, and GPT stands for generative pre-trained transformer. >> And if you put the definition back up there again, if you put it back up on the screen, let's see it back up. Okay, it actually missed the large, word large. So one of the problems with ChatGPT, it's not always accurate. It's actually a large language model, and it says state of the art language model. And if you look at Google, Google has dominated AI for many times and they're well known as being the best at this. And apparently Google has their own large language model, LLM, in play and have been holding it back to release because of backlash on the accuracy. Like just in that example you showed is a great point. They got almost right, but they missed the key word. >> You know what's funny about that John, is I had previously asked it in my prompt to give me it in less than a hundred words, and it was too long, I said I was too long for Breaking Analysis, and there it went into the fact that it's a large language model. So it largely, it gave me a really different answer the, for both times. So, but it's still pretty amazing for those of you who haven't played with it yet. And one of the best examples that I saw was Ben Charrington from This Week In ML AI podcast. And I stumbled on this thanks to Brian Gracely, who was listening to one of his Cloudcasts. Basically what Ben did is he took, he prompted ChatGPT to interview ChatGPT, and he simply gave the system the prompts, and then he ran the questions and answers into this avatar builder and sped it up 2X so it didn't sound like a machine. And voila, it was amazing. So John is ChatGPT going to take over as a cube host? >> Well, I was thinking, we get the questions in advance sometimes from PR people. We should actually just plug it in ChatGPT, add it to our notes, and saying, "Is this good enough for you? Let's ask the real question." So I think, you know, I think there's a lot of heavy lifting that gets done. I think the ChatGPT is a phenomenal revolution. I think it highlights the use case. Like that example we showed earlier. It gets most of it right. So it's directionally correct and it feels like it's an answer, but it's not a hundred percent accurate. And I think that's where people are seeing value in it. Writing marketing, copy, brainstorming, guest list, gift list for somebody. Write me some lyrics to a song. Give me a thesis about healthcare policy in the United States. It'll do a bang up job, and then you got to go in and you can massage it. So we're going to do three quarters of the work. That's why plagiarism and schools are kind of freaking out. And that's why Microsoft put 10 billion in, because why wouldn't this be a feature of Word, or the OS to help it do stuff on behalf of the user. So linguistically it's a beautiful thing. You can input a string and get a good answer. It's not a search result. >> And we're going to get your take on on Microsoft and, but it kind of levels the playing- but ChatGPT writes better than I do, Sarbjeet, and I know you have some good examples too. You mentioned the Reed Hastings example. >> Yeah, I was listening to Reed Hastings fireside chat with ChatGPT, and the answers were coming as sort of voice, in the voice format. And it was amazing what, he was having very sort of philosophy kind of talk with the ChatGPT, the longer sentences, like he was going on, like, just like we are talking, he was talking for like almost two minutes and then ChatGPT was answering. It was not one sentence question, and then a lot of answers from ChatGPT and yeah, you're right. I, this is our ability. I've been thinking deep about this since yesterday, we talked about, like, we want to do this segment. The data is fed into the data model. It can be the current data as well, but I think that, like, models like ChatGPT, other companies will have those too. They can, they're democratizing the intelligence, but they're not creating intelligence yet, definitely yet I can say that. They will give you all the finite answers. Like, okay, how do you do this for loop in Java, versus, you know, C sharp, and as a programmer you can do that, in, but they can't tell you that, how to write a new algorithm or write a new search algorithm for you. They cannot create a secretive code for you to- >> Not yet. >> Have competitive advantage. >> Not yet, not yet. >> but you- >> Can Google do that today? >> No one really can. The reasoning side of the data is, we talked about at our Supercloud event, with Zhamak Dehghani who's was CEO of, now of Nextdata. This next wave of data intelligence is going to come from entrepreneurs that are probably cross discipline, computer science and some other discipline. But they're going to be new things, for example, data, metadata, and data. It's hard to do reasoning like a human being, so that needs more data to train itself. So I think the first gen of this training module for the large language model they have is a corpus of text. Lot of that's why blog posts are, but the facts are wrong and sometimes out of context, because that contextual reasoning takes time, it takes intelligence. So machines need to become intelligent, and so therefore they need to be trained. So you're going to start to see, I think, a lot of acceleration on training the data sets. And again, it's only as good as the data you can get. And again, proprietary data sets will be a huge winner. Anyone who's got a large corpus of content, proprietary content like theCUBE or SiliconANGLE as a publisher will benefit from this. Large FinTech companies, anyone with large proprietary data will probably be a big winner on this generative AI wave, because it just, it will eat that up, and turn that back into something better. So I think there's going to be a lot of interesting things to look at here. And certainly productivity's going to be off the charts for vanilla and the internet is going to get swarmed with vanilla content. So if you're in the content business, and you're an original content producer of any kind, you're going to be not vanilla, so you're going to be better. So I think there's so much at play Dave (indistinct). >> I think the playing field has been risen, so we- >> Risen and leveled? >> Yeah, and leveled to certain extent. So it's now like that few people as consumers, as consumers of AI, we will have a advantage and others cannot have that advantage. So it will be democratized. That's, I'm sure about that. But if you take the example of calculator, when the calculator came in, and a lot of people are, "Oh, people can't do math anymore because calculator is there." right? So it's a similar sort of moment, just like a calculator for the next level. But, again- >> I see it more like open source, Sarbjeet, because like if you think about what ChatGPT's doing, you do a query and it comes from somewhere the value of a post from ChatGPT is just a reuse of AI. The original content accent will be come from a human. So if I lay out a paragraph from ChatGPT, did some heavy lifting on some facts, I check the facts, save me about maybe- >> Yeah, it's productive. >> An hour writing, and then I write a killer two, three sentences of, like, sharp original thinking or critical analysis. I then took that body of work, open source content, and then laid something on top of it. >> And Sarbjeet's example is a good one, because like if the calculator kids don't do math as well anymore, the slide rule, remember we had slide rules as kids, remember we first started using Waze, you know, we were this minority and you had an advantage over other drivers. Now Waze is like, you know, social traffic, you know, navigation, everybody had, you know- >> All the back roads are crowded. >> They're car crowded. (group laughs) Exactly. All right, let's, let's move on. What about this notion that futurist Ray Amara put forth and really Amara's Law that we're showing here, it's, the law is we, you know, "We tend to overestimate the effect of technology in the short run and underestimate it in the long run." Is that the case, do you think, with ChatGPT? What do you think Sarbjeet? >> I think that's true actually. There's a lot of, >> We don't debate this. >> There's a lot of awe, like when people see the results from ChatGPT, they say what, what the heck? Like, it can do this? But then if you use it more and more and more, and I ask the set of similar question, not the same question, and it gives you like same answer. It's like reading from the same bucket of text in, the interior read (indistinct) where the ChatGPT, you will see that in some couple of segments. It's very, it sounds so boring that the ChatGPT is coming out the same two sentences every time. So it is kind of good, but it's not as good as people think it is right now. But we will have, go through this, you know, hype sort of cycle and get realistic with it. And then in the long term, I think it's a great thing in the short term, it's not something which will (indistinct) >> What's your counter point? You're saying it's not. >> I, no I think the question was, it's hyped up in the short term and not it's underestimated long term. That's what I think what he said, quote. >> Yes, yeah. That's what he said. >> Okay, I think that's wrong with this, because this is a unique, ChatGPT is a unique kind of impact and it's very generational. People have been comparing it, I have been comparing to the internet, like the web, web browser Mosaic and Netscape, right, Navigator. I mean, I clearly still remember the days seeing Navigator for the first time, wow. And there weren't not many sites you could go to, everyone typed in, you know, cars.com, you know. >> That (indistinct) wasn't that overestimated, the overhyped at the beginning and underestimated. >> No, it was, it was underestimated long run, people thought. >> But that Amara's law. >> That's what is. >> No, they said overestimated? >> Overestimated near term underestimated- overhyped near term, underestimated long term. I got, right I mean? >> Well, I, yeah okay, so I would then agree, okay then- >> We were off the charts about the internet in the early days, and it actually exceeded our expectations. >> Well there were people who were, like, poo-pooing it early on. So when the browser came out, people were like, "Oh, the web's a toy for kids." I mean, in 1995 the web was a joke, right? So '96, you had online populations growing, so you had structural changes going on around the browser, internet population. And then that replaced other things, direct mail, other business activities that were once analog then went to the web, kind of read only as you, as we always talk about. So I think that's a moment where the hype long term, the smart money, and the smart industry experts all get the long term. And in this case, there's more poo-pooing in the short term. "Ah, it's not a big deal, it's just AI." I've heard many people poo-pooing ChatGPT, and a lot of smart people saying, "No this is next gen, this is different and it's only going to get better." So I think people are estimating a big long game on this one. >> So you're saying it's bifurcated. There's those who say- >> Yes. >> Okay, all right, let's get to the heart of the premise, and possibly the debate for today's episode. Will OpenAI's early entry into the market confer sustainable competitive advantage for the company. And if you look at the history of tech, the technology industry, it's kind of littered with first mover failures. Altair, IBM, Tandy, Commodore, they and Apple even, they were really early in the PC game. They took a backseat to Dell who came in the scene years later with a better business model. Netscape, you were just talking about, was all the rage in Silicon Valley, with the first browser, drove up all the housing prices out here. AltaVista was the first search engine to really, you know, index full text. >> Owned by Dell, I mean DEC. >> Owned by Digital. >> Yeah, Digital Equipment >> Compaq bought it. And of course as an aside, Digital, they wanted to showcase their hardware, right? Their super computer stuff. And then so Friendster and MySpace, they came before Facebook. The iPhone certainly wasn't the first mobile device. So lots of failed examples, but there are some recent successes like AWS and cloud. >> You could say smartphone. So I mean. >> Well I know, and you can, we can parse this so we'll debate it. Now Twitter, you could argue, had first mover advantage. You kind of gave me that one John. Bitcoin and crypto clearly had first mover advantage, and sustaining that. Guys, will OpenAI make it to the list on the right with ChatGPT, what do you think? >> I think categorically as a company, it probably won't, but as a category, I think what they're doing will, so OpenAI as a company, they get funding, there's power dynamics involved. Microsoft put a billion dollars in early on, then they just pony it up. Now they're reporting 10 billion more. So, like, if the browsers, Microsoft had competitive advantage over Netscape, and used monopoly power, and convicted by the Department of Justice for killing Netscape with their monopoly, Netscape should have had won that battle, but Microsoft killed it. In this case, Microsoft's not killing it, they're buying into it. So I think the embrace extend Microsoft power here makes OpenAI vulnerable for that one vendor solution. So the AI as a company might not make the list, but the category of what this is, large language model AI, is probably will be on the right hand side. >> Okay, we're going to come back to the government intervention and maybe do some comparisons, but what are your thoughts on this premise here? That, it will basically set- put forth the premise that it, that ChatGPT, its early entry into the market will not confer competitive advantage to >> For OpenAI. >> To Open- Yeah, do you agree with that? >> I agree with that actually. It, because Google has been at it, and they have been holding back, as John said because of the scrutiny from the Fed, right, so- >> And privacy too. >> And the privacy and the accuracy as well. But I think Sam Altman and the company on those guys, right? They have put this in a hasty way out there, you know, because it makes mistakes, and there are a lot of questions around the, sort of, where the content is coming from. You saw that as your example, it just stole the content, and without your permission, you know? >> Yeah. So as quick this aside- >> And it codes on people's behalf and the, those codes are wrong. So there's a lot of, sort of, false information it's putting out there. So it's a very vulnerable thing to do what Sam Altman- >> So even though it'll get better, others will compete. >> So look, just side note, a term which Reid Hoffman used a little bit. Like he said, it's experimental launch, like, you know, it's- >> It's pretty damn good. >> It is clever because according to Sam- >> It's more than clever. It's good. >> It's awesome, if you haven't used it. I mean you write- you read what it writes and you go, "This thing writes so well, it writes so much better than you." >> The human emotion drives that too. I think that's a big thing. But- >> I Want to add one more- >> Make your last point. >> Last one. Okay. So, but he's still holding back. He's conducting quite a few interviews. If you want to get the gist of it, there's an interview with StrictlyVC interview from yesterday with Sam Altman. Listen to that one it's an eye opening what they want- where they want to take it. But my last one I want to make it on this point is that Satya Nadella yesterday did an interview with Wall Street Journal. I think he was doing- >> You were not impressed. >> I was not impressed because he was pushing it too much. So Sam Altman's holding back so there's less backlash. >> Got 10 billion reasons to push. >> I think he's almost- >> Microsoft just laid off 10000 people. Hey ChatGPT, find me a job. You know like. (group laughs) >> He's overselling it to an extent that I think it will backfire on Microsoft. And he's over promising a lot of stuff right now, I think. I don't know why he's very jittery about all these things. And he did the same thing during Ignite as well. So he said, "Oh, this AI will write code for you and this and that." Like you called him out- >> The hyperbole- >> During your- >> from Satya Nadella, he's got a lot of hyperbole. (group talks over each other) >> All right, Let's, go ahead. >> Well, can I weigh in on the whole- >> Yeah, sure. >> Microsoft thing on whether OpenAI, here's the take on this. I think it's more like the browser moment to me, because I could relate to that experience with ChatG, personally, emotionally, when I saw that, and I remember vividly- >> You mean that aha moment (indistinct). >> Like this is obviously the future. Anything else in the old world is dead, website's going to be everywhere. It was just instant dot connection for me. And a lot of other smart people who saw this. Lot of people by the way, didn't see it. Someone said the web's a toy. At the company I was worked for at the time, Hewlett Packard, they like, they could have been in, they had invented HTML, and so like all this stuff was, like, they just passed, the web was just being passed over. But at that time, the browser got better, more websites came on board. So the structural advantage there was online web usage was growing, online user population. So that was growing exponentially with the rise of the Netscape browser. So OpenAI could stay on the right side of your list as durable, if they leverage the category that they're creating, can get the scale. And if they can get the scale, just like Twitter, that failed so many times that they still hung around. So it was a product that was always successful, right? So I mean, it should have- >> You're right, it was terrible, we kept coming back. >> The fail whale, but it still grew. So OpenAI has that moment. They could do it if Microsoft doesn't meddle too much with too much power as a vendor. They could be the Netscape Navigator, without the anti-competitive behavior of somebody else. So to me, they have the pole position. So they have an opportunity. So if not, if they don't execute, then there's opportunity. There's not a lot of barriers to entry, vis-a-vis say the CapEx of say a cloud company like AWS. You can't replicate that, Many have tried, but I think you can replicate OpenAI. >> And we're going to talk about that. Okay, so real quick, I want to bring in some ETR data. This isn't an ETR heavy segment, only because this so new, you know, they haven't coverage yet, but they do cover AI. So basically what we're seeing here is a slide on the vertical axis's net score, which is a measure of spending momentum, and in the horizontal axis's is presence in the dataset. Think of it as, like, market presence. And in the insert right there, you can see how the dots are plotted, the two columns. And so, but the key point here that we want to make, there's a bunch of companies on the left, is he like, you know, DataRobot and C3 AI and some others, but the big whales, Google, AWS, Microsoft, are really dominant in this market. So that's really the key takeaway that, can we- >> I notice IBM is way low. >> Yeah, IBM's low, and actually bring that back up and you, but then you see Oracle who actually is injecting. So I guess that's the other point is, you're not necessarily going to go buy AI, and you know, build your own AI, you're going to, it's going to be there and, it, Salesforce is going to embed it into its platform, the SaaS companies, and you're going to purchase AI. You're not necessarily going to build it. But some companies obviously are. >> I mean to quote IBM's general manager Rob Thomas, "You can't have AI with IA." information architecture and David Flynn- >> You can't Have AI without IA >> without, you can't have AI without IA. You can't have, if you have an Information Architecture, you then can power AI. Yesterday David Flynn, with Hammersmith, was on our Supercloud. He was pointing out that the relationship of storage, where you store things, also impacts the data and stressablity, and Zhamak from Nextdata, she was pointing out that same thing. So the data problem factors into all this too, Dave. >> So you got the big cloud and internet giants, they're all poised to go after this opportunity. Microsoft is investing up to 10 billion. Google's code red, which was, you know, the headline in the New York Times. Of course Apple is there and several alternatives in the market today. Guys like Chinchilla, Bloom, and there's a company Jasper and several others, and then Lena Khan looms large and the government's around the world, EU, US, China, all taking notice before the market really is coalesced around a single player. You know, John, you mentioned Netscape, they kind of really, the US government was way late to that game. It was kind of game over. And Netscape, I remember Barksdale was like, "Eh, we're going to be selling software in the enterprise anyway." and then, pshew, the company just dissipated. So, but it looks like the US government, especially with Lena Khan, they're changing the definition of antitrust and what the cause is to go after people, and they're really much more aggressive. It's only what, two years ago that (indistinct). >> Yeah, the problem I have with the federal oversight is this, they're always like late to the game, and they're slow to catch up. So in other words, they're working on stuff that should have been solved a year and a half, two years ago around some of the social networks hiding behind some of the rules around open web back in the days, and I think- >> But they're like 15 years late to that. >> Yeah, and now they got this new thing on top of it. So like, I just worry about them getting their fingers. >> But there's only two years, you know, OpenAI. >> No, but the thing (indistinct). >> No, they're still fighting other battles. But the problem with government is that they're going to label Big Tech as like a evil thing like Pharma, it's like smoke- >> You know Lena Khan wants to kill Big Tech, there's no question. >> So I think Big Tech is getting a very seriously bad rap. And I think anything that the government does that shades darkness on tech, is politically motivated in most cases. You can almost look at everything, and my 80 20 rule is in play here. 80% of the government activity around tech is bullshit, it's politically motivated, and the 20% is probably relevant, but off the mark and not organized. >> Well market forces have always been the determining factor of success. The governments, you know, have been pretty much failed. I mean you look at IBM's antitrust, that, what did that do? The market ultimately beat them. You look at Microsoft back in the day, right? Windows 95 was peaking, the government came in. But you know, like you said, they missed the web, right, and >> so they were hanging on- >> There's nobody in government >> to Windows. >> that actually knows- >> And so, you, I think you're right. It's market forces that are going to determine this. But Sarbjeet, what do you make of Microsoft's big bet here, you weren't impressed with with Nadella. How do you think, where are they going to apply it? Is this going to be a Hail Mary for Bing, or is it going to be applied elsewhere? What do you think. >> They are saying that they will, sort of, weave this into their products, office products, productivity and also to write code as well, developer productivity as well. That's a big play for them. But coming back to your antitrust sort of comments, right? I believe the, your comment was like, oh, fed was late 10 years or 15 years earlier, but now they're two years. But things are moving very fast now as compared to they used to move. >> So two years is like 10 Years. >> Yeah, two years is like 10 years. Just want to make that point. (Dave laughs) This thing is going like wildfire. Any new tech which comes in that I think they're going against distribution channels. Lina Khan has commented time and again that the marketplace model is that she wants to have some grip on. Cloud marketplaces are a kind of monopolistic kind of way. >> I don't, I don't see this, I don't see a Chat AI. >> You told me it's not Bing, you had an interesting comment. >> No, no. First of all, this is great from Microsoft. If you're Microsoft- >> Why? >> Because Microsoft doesn't have the AI chops that Google has, right? Google is got so much core competency on how they run their search, how they run their backends, their cloud, even though they don't get a lot of cloud market share in the enterprise, they got a kick ass cloud cause they needed one. >> Totally. >> They've invented SRE. I mean Google's development and engineering chops are off the scales, right? Amazon's got some good chops, but Google's got like 10 times more chops than AWS in my opinion. Cloud's a whole different story. Microsoft gets AI, they get a playbook, they get a product they can render into, the not only Bing, productivity software, helping people write papers, PowerPoint, also don't forget the cloud AI can super help. We had this conversation on our Supercloud event, where AI's going to do a lot of the heavy lifting around understanding observability and managing service meshes, to managing microservices, to turning on and off applications, and or maybe writing code in real time. So there's a plethora of use cases for Microsoft to deploy this. combined with their R and D budgets, they can then turbocharge more research, build on it. So I think this gives them a car in the game, Google may have pole position with AI, but this puts Microsoft right in the game, and they already have a lot of stuff going on. But this just, I mean everything gets lifted up. Security, cloud, productivity suite, everything. >> What's under the hood at Google, and why aren't they talking about it? I mean they got to be freaked out about this. No? Or do they have kind of a magic bullet? >> I think they have the, they have the chops definitely. Magic bullet, I don't know where they are, as compared to the ChatGPT 3 or 4 models. Like they, but if you look at the online sort of activity and the videos put out there from Google folks, Google technology folks, that's account you should look at if you are looking there, they have put all these distinctions what ChatGPT 3 has used, they have been talking about for a while as well. So it's not like it's a secret thing that you cannot replicate. As you said earlier, like in the beginning of this segment, that anybody who has more data and the capacity to process that data, which Google has both, I think they will win this. >> Obviously living in Palo Alto where the Google founders are, and Google's headquarters next town over we have- >> We're so close to them. We have inside information on some of the thinking and that hasn't been reported by any outlet yet. And that is, is that, from what I'm hearing from my sources, is Google has it, they don't want to release it for many reasons. One is it might screw up their search monopoly, one, two, they're worried about the accuracy, 'cause Google will get sued. 'Cause a lot of people are jamming on this ChatGPT as, "Oh it does everything for me." when it's clearly not a hundred percent accurate all the time. >> So Lina Kahn is looming, and so Google's like be careful. >> Yeah so Google's just like, this is the third, could be a third rail. >> But the first thing you said is a concern. >> Well no. >> The disruptive (indistinct) >> What they will do is do a Waymo kind of thing, where they spin out a separate company. >> They're doing that. >> The discussions happening, they're going to spin out the separate company and put it over there, and saying, "This is AI, got search over there, don't touch that search, 'cause that's where all the revenue is." (chuckles) >> So, okay, so that's how they deal with the Clay Christensen dilemma. What's the business model here? I mean it's not advertising, right? Is it to charge you for a query? What, how do you make money at this? >> It's a good question, I mean my thinking is, first of all, it's cool to type stuff in and see a paper get written, or write a blog post, or gimme a marketing slogan for this or that or write some code. I think the API side of the business will be critical. And I think Howie Xu, I know you're going to reference some of his comments yesterday on Supercloud, I think this brings a whole 'nother user interface into technology consumption. I think the business model, not yet clear, but it will probably be some sort of either API and developer environment or just a straight up free consumer product, with some sort of freemium backend thing for business. >> And he was saying too, it's natural language is the way in which you're going to interact with these systems. >> I think it's APIs, it's APIs, APIs, APIs, because these people who are cooking up these models, and it takes a lot of compute power to train these and to, for inference as well. Somebody did the analysis on the how many cents a Google search costs to Google, and how many cents the ChatGPT query costs. It's, you know, 100x or something on that. You can take a look at that. >> A 100x on which side? >> You're saying two orders of magnitude more expensive for ChatGPT >> Much more, yeah. >> Than for Google. >> It's very expensive. >> So Google's got the data, they got the infrastructure and they got, you're saying they got the cost (indistinct) >> No actually it's a simple query as well, but they are trying to put together the answers, and they're going through a lot more data versus index data already, you know. >> Let me clarify, you're saying that Google's version of ChatGPT is more efficient? >> No, I'm, I'm saying Google search results. >> Ah, search results. >> What are used to today, but cheaper. >> But that, does that, is that going to confer advantage to Google's large language (indistinct)? >> It will, because there were deep science (indistinct). >> Google, I don't think Google search is doing a large language model on their search, it's keyword search. You know, what's the weather in Santa Cruz? Or how, what's the weather going to be? Or you know, how do I find this? Now they have done a smart job of doing some things with those queries, auto complete, re direct navigation. But it's, it's not entity. It's not like, "Hey, what's Dave Vellante thinking this week in Breaking Analysis?" ChatGPT might get that, because it'll get your Breaking Analysis, it'll synthesize it. There'll be some, maybe some clips. It'll be like, you know, I mean. >> Well I got to tell you, I asked ChatGPT to, like, I said, I'm going to enter a transcript of a discussion I had with Nir Zuk, the CTO of Palo Alto Networks, And I want you to write a 750 word blog. I never input the transcript. It wrote a 750 word blog. It attributed quotes to him, and it just pulled a bunch of stuff that, and said, okay, here it is. It talked about Supercloud, it defined Supercloud. >> It's made, it makes you- >> Wow, But it was a big lie. It was fraudulent, but still, blew me away. >> Again, vanilla content and non accurate content. So we are going to see a surge of misinformation on steroids, but I call it the vanilla content. Wow, that's just so boring, (indistinct). >> There's so many dangers. >> Make your point, cause we got to, almost out of time. >> Okay, so the consumption, like how do you consume this thing. As humans, we are consuming it and we are, like, getting a nicely, like, surprisingly shocked, you know, wow, that's cool. It's going to increase productivity and all that stuff, right? And on the danger side as well, the bad actors can take hold of it and create fake content and we have the fake sort of intelligence, if you go out there. So that's one thing. The second thing is, we are as humans are consuming this as language. Like we read that, we listen to it, whatever format we consume that is, but the ultimate usage of that will be when the machines can take that output from likes of ChatGPT, and do actions based on that. The robots can work, the robot can paint your house, we were talking about, right? Right now we can't do that. >> Data apps. >> So the data has to be ingested by the machines. It has to be digestible by the machines. And the machines cannot digest unorganized data right now, we will get better on the ingestion side as well. So we are getting better. >> Data, reasoning, insights, and action. >> I like that mall, paint my house. >> So, okay- >> By the way, that means drones that'll come in. Spray painting your house. >> Hey, it wasn't too long ago that robots couldn't climb stairs, as I like to point out. Okay, and of course it's no surprise the venture capitalists are lining up to eat at the trough, as I'd like to say. Let's hear, you'd referenced this earlier, John, let's hear what AI expert Howie Xu said at the Supercloud event, about what it takes to clone ChatGPT. Please, play the clip. >> So one of the VCs actually asked me the other day, right? "Hey, how much money do I need to spend, invest to get a, you know, another shot to the openAI sort of the level." You know, I did a (indistinct) >> Line up. >> A hundred million dollar is the order of magnitude that I came up with, right? You know, not a billion, not 10 million, right? So a hundred- >> Guys a hundred million dollars, that's an astoundingly low figure. What do you make of it? >> I was in an interview with, I was interviewing, I think he said hundred million or so, but in the hundreds of millions, not a billion right? >> You were trying to get him up, you were like "Hundreds of millions." >> Well I think, I- >> He's like, eh, not 10, not a billion. >> Well first of all, Howie Xu's an expert machine learning. He's at Zscaler, he's a machine learning AI guy. But he comes from VMware, he's got his technology pedigrees really off the chart. Great friend of theCUBE and kind of like a CUBE analyst for us. And he's smart. He's right. I think the barriers to entry from a dollar standpoint are lower than say the CapEx required to compete with AWS. Clearly, the CapEx spending to build all the tech for the run a cloud. >> And you don't need a huge sales force. >> And in some case apps too, it's the same thing. But I think it's not that hard. >> But am I right about that? You don't need a huge sales force either. It's, what, you know >> If the product's good, it will sell, this is a new era. The better mouse trap will win. This is the new economics in software, right? So- >> Because you look at the amount of money Lacework, and Snyk, Snowflake, Databrooks. Look at the amount of money they've raised. I mean it's like a billion dollars before they get to IPO or more. 'Cause they need promotion, they need go to market. You don't need (indistinct) >> OpenAI's been working on this for multiple five years plus it's, hasn't, wasn't born yesterday. Took a lot of years to get going. And Sam is depositioning all the success, because he's trying to manage expectations, To your point Sarbjeet, earlier. It's like, yeah, he's trying to "Whoa, whoa, settle down everybody, (Dave laughs) it's not that great." because he doesn't want to fall into that, you know, hero and then get taken down, so. >> It may take a 100 million or 150 or 200 million to train the model. But to, for the inference to, yeah to for the inference machine, It will take a lot more, I believe. >> Give it, so imagine, >> Because- >> Go ahead, sorry. >> Go ahead. But because it consumes a lot more compute cycles and it's certain level of storage and everything, right, which they already have. So I think to compute is different. To frame the model is a different cost. But to run the business is different, because I think 100 million can go into just fighting the Fed. >> Well there's a flywheel too. >> Oh that's (indistinct) >> (indistinct) >> We are running the business, right? >> It's an interesting number, but it's also kind of, like, context to it. So here, a hundred million spend it, you get there, but you got to factor in the fact that the ways companies win these days is critical mass scale, hitting a flywheel. If they can keep that flywheel of the value that they got going on and get better, you can almost imagine a marketplace where, hey, we have proprietary data, we're SiliconANGLE in theCUBE. We have proprietary content, CUBE videos, transcripts. Well wouldn't it be great if someone in a marketplace could sell a module for us, right? We buy that, Amazon's thing and things like that. So if they can get a marketplace going where you can apply to data sets that may be proprietary, you can start to see this become bigger. And so I think the key barriers to entry is going to be success. I'll give you an example, Reddit. Reddit is successful and it's hard to copy, not because of the software. >> They built the moat. >> Because you can, buy Reddit open source software and try To compete. >> They built the moat with their community. >> Their community, their scale, their user expectation. Twitter, we referenced earlier, that thing should have gone under the first two years, but there was such a great emotional product. People would tolerate the fail whale. And then, you know, well that was a whole 'nother thing. >> Then a plane landed in (John laughs) the Hudson and it was over. >> I think verticals, a lot of verticals will build applications using these models like for lawyers, for doctors, for scientists, for content creators, for- >> So you'll have many hundreds of millions of dollars investments that are going to be seeping out. If, all right, we got to wrap, if you had to put odds on it that that OpenAI is going to be the leader, maybe not a winner take all leader, but like you look at like Amazon and cloud, they're not winner take all, these aren't necessarily winner take all markets. It's not necessarily a zero sum game, but let's call it winner take most. What odds would you give that open AI 10 years from now will be in that position. >> If I'm 0 to 10 kind of thing? >> Yeah, it's like horse race, 3 to 1, 2 to 1, even money, 10 to 1, 50 to 1. >> Maybe 2 to 1, >> 2 to 1, that's pretty low odds. That's basically saying they're the favorite, they're the front runner. Would you agree with that? >> I'd say 4 to 1. >> Yeah, I was going to say I'm like a 5 to 1, 7 to 1 type of person, 'cause I'm a skeptic with, you know, there's so much competition, but- >> I think they're definitely the leader. I mean you got to say, I mean. >> Oh there's no question. There's no question about it. >> The question is can they execute? >> They're not Friendster, is what you're saying. >> They're not Friendster and they're more like Twitter and Reddit where they have momentum. If they can execute on the product side, and if they don't stumble on that, they will continue to have the lead. >> If they say stay neutral, as Sam is, has been saying, that, hey, Microsoft is one of our partners, if you look at their company model, how they have structured the company, then they're going to pay back to the investors, like Microsoft is the biggest one, up to certain, like by certain number of years, they're going to pay back from all the money they make, and after that, they're going to give the money back to the public, to the, I don't know who they give it to, like non-profit or something. (indistinct) >> Okay, the odds are dropping. (group talks over each other) That's a good point though >> Actually they might have done that to fend off the criticism of this. But it's really interesting to see the model they have adopted. >> The wildcard in all this, My last word on this is that, if there's a developer shift in how developers and data can come together again, we have conferences around the future of data, Supercloud and meshs versus, you know, how the data world, coding with data, how that evolves will also dictate, 'cause a wild card could be a shift in the landscape around how developers are using either machine learning or AI like techniques to code into their apps, so. >> That's fantastic insight. I can't thank you enough for your time, on the heels of Supercloud 2, really appreciate it. All right, thanks to John and Sarbjeet for the outstanding conversation today. Special thanks to the Palo Alto studio team. My goodness, Anderson, this great backdrop. You guys got it all out here, I'm jealous. And Noah, really appreciate it, Chuck, Andrew Frick and Cameron, Andrew Frick switching, Cameron on the video lake, great job. And Alex Myerson, he's on production, manages the podcast for us, Ken Schiffman as well. Kristen Martin and Cheryl Knight help get the word out on social media and our newsletters. Rob Hof is our editor-in-chief over at SiliconANGLE, does some great editing, thanks to all. Remember, all these episodes are available as podcasts. All you got to do is search Breaking Analysis podcast, wherever you listen. Publish each week on wikibon.com and siliconangle.com. Want to get in touch, email me directly, david.vellante@siliconangle.com or DM me at dvellante, or comment on our LinkedIn post. And by all means, check out etr.ai. They got really great survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, We'll see you next time on Breaking Analysis. (electronic music)

(upbeat music) >> Welcome back. Day two of theCUBE's coverage of re:MARS in Las Vegas. Amazon re:MARS, it's part of the Re Series they call it at Amazon. re:Invent is their big show, re:Inforce is a security show, re:MARS is the new emerging machine learning automation, robotics, and space. The confluence of machine learning powering a new industrial age and inflection point. I'm John Furrier, host of theCUBE. We're here to break it down for another wall to wall coverage. We've got a great guest here, CUBE alumni from our AWS startup showcase, Krishna Gade, founder and CEO of fiddler.ai. Welcome back to theCUBE. Good to see you. >> Great to see you, John. >> In person. We did the remote one before. >> Absolutely, great to be here, and I always love to be part of these interviews and love to talk more about what we're doing. >> Well, you guys have a lot of good street cred, a lot of good word of mouth around the quality of your product, the work you're doing. I know a lot of folks that I admire and trust in the AI machine learning area say great things about you. A lot going on, you guys are growing companies. So you're kind of like a startup on a rocket ship, getting ready to go, pun intended here at the space event. What's going on with you guys? You're here. Machine learning is the centerpiece of it. Swami gave the keynote here at day two and it really is an inflection point. Machine learning is now ready, it's scaling, and some of the examples that they were showing with the workloads and the data sets that they're tapping into, you know, you've got CodeWhisperer, which they announced, you've got trust and bias now being addressed, we're hitting a level, a new level in ML, ML operations, ML modeling, ML workloads for developers. >> Yep, yep, absolutely. You know, I think machine learning now has become an operational software, right? Like you know a lot of companies are investing millions and billions of dollars and creating teams to operationalize machine learning based products. And that's the exciting part. I think the thing that that is very exciting for us is like we are helping those teams to observe how those machine learning applications are working so that they can build trust into it. Because I believe as Swami was alluding to this today, without actually building trust into AI, it's really hard to actually have your business users use it in their business workflows. And that's where we are excited about bringing their trust and visibility factor into machine learning. >> You know, a lot of us all know what you guys are doing here in the ecosystem of AWS. And now extending here, take a minute to explain what Fiddler is doing for the folks that are in the space, that are in discovery mode, trying to understand who's got what, because like Swami said on stage, it's a full-time job to keep up on all the machine learning activities and tool sets and platforms. Take a minute to explain what Fiddler's doing, then we can get into some, some good questions. >> Absolutely. As the enterprise is taking on operationalization of machine learning models, one of the key problems that they run into is lack of visibility into how those models perform. You know, for example, let's say if I'm a bank, I'm trying to introduce credit risk scoring models using machine learning. You know, how do I know when my model is rejecting someone's loan? You know, when my model is accepting someone's loan? And why is it doing it? And I think this is basically what makes machine learning a complex thing to implement and operationalize. Without this visibility, you cannot build trust and actually use it in your business. With Fiddler, what we provide is we actually open up this black box and we help our customers to really understand how those models work. You know, for example, how is my model doing? Is it accurately working or not? You know, why is it actually rejecting someone's loan application? We provide these both fine grain as well as coarse grain insights. So our customers can actually deploy machine learning in a safe and trustworthy manner. >> Who is your customer? Who you're targeting? What persona is it, the data engineer, is it data science, is it the CSO, is it all the above? >> Yeah, our customer is the data scientist and the machine learning engineer, right? And we usually talk to teams that have a few models running in production, that's basically our sweet spot, where they're trying to look for a single pane of glass to see like what models are running in their production, how they're performing, how they're affecting their business metrics. So we typically engage with like head of data science or head of machine learning that has a few machine learning engineers and data scientists. >> Okay, so those people that are watching, you're into this, you can go check it out. It's good to learn. I want to get your thoughts on some trends that I see emerging, and I want to get your reaction to those. Number one, we're seeing the cloud scale now and integration a big part of things. So the time to value was brought up on stage today, Swami kind of mentioned time to value, showed some benchmark where they got four hours, some other teams were doing eight weeks. Where are we on the progression of value, time to value, and on the scale side. Can you scope that for me? >> I mean, it depends, right? You know, depending upon the company. So for example, when we work with banks, for them to time to operationalize a model can take months actually, because of all the regulatory procedures that they have to go through. You know, they have to get the models reviewed by model validators, model risk management teams, and then they audit those models, they have to then ship those models and constantly monitor them. So it's a very long process for them. And even for non-regulated sectors, if you do not have the right tools and processes in place, operationalizing machine learning models can take a long time. You know, with tools like Fiddler, what we are enabling is we are basically compressing that life cycle. We are helping them automate like model monitoring and explainability so that they can actually ship models more faster. Like you get like velocity in terms of shipping models. For example, one of the growing fintech companies that started with us last year started with six models in production, now they're running about 36 models in production. So it's within a year, they were able to like grow like 10x. So that is basically what we are trying to do. >> At other things, we at re:MARS, so first of all, you got a great product and a lot of markets that grow onto, but here you got space. I mean, anyone who's coming out of college or university PhD program, and if they're into aero, they're going to be here, right? This is where they are. Now you have a new core companies with machine learning, not just the engineering that you see in the space or aerospace area, you have a new engineering. Now I go back to the old days where my parents, there was Fortran, you used Fortran was Lingua Franca to manage the equipment. Little throwback to the old school. But now machine learning is companion, first class citizen, to the hardware. And in fact, and some will say more important. >> Yep, I mean, machine learning model is the new software artifact. It is going into production in a big way. And I think it has two different things that compare to traditional software. Number one, unlike traditional software, it's a black box. You cannot read up a machine learning model score and see why it's making those predictions. Number two, it's a stochastic entity. What that means is it's predictive power can wane over time. So it needs to be constantly monitored and then constantly refreshed so that it's actually working in tech. So those are the two main things you need to take care. And if you can do that, then machine learning can give you a huge amount of ROI. >> There is some practitioner kind of like craft to it. >> Correct. >> As you said, you got to know when to refresh, what data sets to bring in, which to stay away from, certainly when you get to the bias, but I'll get to that in a second. My next question is really along the lines of software. So if you believe that open source will dominate the software business, which I do, I mean, most people won't argue. I think you would agree with that, right? Open source is driving everything. If everything's open source, where's the differentiation coming from? So if I'm a startup entrepreneur or I'm a project manager working on the next Artemis mission, I got to open source. Okay, there's definitely security issues here. I don't want to talk about shift left right now, but like, okay, open source is everything. Where's the differentiation, where do I have the proprietary edge? >> It's a great question, right? So I used to work in tech companies before Fiddler. You know, when I used to work at Facebook, we would build everything in house. We would not even use a lot of open source software. So there are companies like that that build everything in house. And then I also worked at companies like Twitter and Pinterest, which are actually used a lot of open source, right? So now, like the thing is, it depends on the maturity of the organization. So if you're a Facebook or a Google, you can build a lot of things in house. Then if you're like a modern tech company, you would probably leverage open source, but there are lots of other companies in the world that still don't have the talent pool to actually build, take things from open source and productionize it. And that's where the opportunity for startups comes in so that we can commercialize these things, create a great enterprise experience, so actually operationalize things for them so that they don't have to do it in house for them. And that's the advantage working with startups. >> I don't want to get all operating systems with you on theory here on the stage here, but I will have to ask you the next question, which I totally agree with you, by the way, that's the way to go. There's not a lot of people out there that are peaked. And that's just statistical and it'll get better. Data engineering is really narrow. That is like the SRE of data. That's a new role emerging. Okay, all the things are happening. So if open source is there, integration is a huge deal. And you start to see the rise of a lot of MSPs, managed service providers. I run Kubernetes clusters, I do this, that, and the other thing. So what's your reaction to the growth of the integration side of the business and this role of new services coming from third parties? >> Yeah, absolutely. I think one of the big challenges for a chief data officer or someone like a CTO is how do they devise this infrastructure architecture and with components, either homegrown components or open source components or some vendor components, and how do they integrate? You know, when I used to run data engineering at Pinterest, we had to devise a data architecture combining all of these things and create something that actually flows very nicely, right? >> If you didn't do it right, it would break. >> Absolutely. And this is why it's important for us, like at Fiddler, to really make sure that Fiddler can integrate to all varies of ML platforms. Today, a lot of our customers use machine learning, build machine learning models on SageMaker. So Fiddler nicely integrate with SageMaker so that data, they get a seamless experience to monitor their models. >> Yeah, I mean, this might not be the right words for it, but I think data engineering as a service is really what I see you guys doing, as well other things, you're providing all that. >> And ML engineering as a service. >> ML engineering as a- Well it's hard. I mean, it's like the hard stuff. >> Yeah, yeah. >> Hear, hear. But that has to enable. So you as a business entrepreneur, you have to create a multiple of value proposition to your customers. What's your vision on that? What is that value? It has to be a multiple, at least 5 to 10. >> I mean, the value is simple, right? You know, if you have to operationize machine learning, you need visibility into how these things work. You know, if you're CTO or like chief data officer is asking how is my model working and how is it affecting my business? You need to be able to show them a dashboard, how it's working, right? And so like a data scientist today struggles to do this. They have to manually generate a report, manually do this analysis. What Fiddler is doing them is basically reducing their work so that they can automate these things and they can still focus on the core aspect of model building and data preparation and this boring aspect of monitoring the model and creating reports around the models is automated for them. >> Yeah, you guys got a great business. I think it's a lot of great future there and it's only going to get bigger. Again, the TAM's going to expand as the growth rising tide comes in. I want to ask you on while we're on that topic of rising tides, Dave Malik and I, since re:Invent last year have been kind of kicked down around this term that we made up called supercloud. And supercloud was a word that came out of these clouds that were not Amazon hyperscalers. So Snowflake, Buildman Sachs, Capital One, you name it, they're building massive proprietary value on top of the CapEx of Amazon. Jerry Chen at Greylock calls it castles in the cloud. You can create these moats. >> Yeah, right. >> So this is a phenomenon, right? And you land on one, and then you go to the others. So the strategies, everyone goes to Amazon first, and then hits Azure and GCP. That then creates this kind of multicloud so, okay, so super cloud's kind of happening, it's a thing. Charles Fitzgerald will disagree, he's a platformer, he says he's against the term. I get why, but he's off base a little. We can't wait to debate him on that. So superclouds are happening, but now what do I do about multicloud, because now I understand multicloud, I have this on that cloud, integrating across clouds is a very difficult thing. >> Krishna: Right, right, right. >> If I'm Snowflake or whatever, hey, I'll go to Azure, more TAM expansion, more market. But are people actually working together? Are we there yet? Where it's like, okay, I'm going to re-operationalize this code base over here. >> I mean, the reality of it, enterprise wants optionality, right? I think they don't want to be locked in into one particular cloud vendor on one particular software. And therefore you actually have in a situation where you have a multicloud scenario where they want to have some workloads in Amazon, some workloads in Azure. And this is an opportunity for startups like us because we are cloud agnostic. We can monitor models wherever you have. So this is where a lot of our customers, they have some of their models are running in their data centers and some of their models running in Amazon. And so we can provide a universal single pan of glass, right? So we can basically connect all of those data and actually showcase. I think this is an opportunity for startups to combine the data streams come from various different clouds and give them a single pain of experience. That way, the sort of the where is your data, where are my models running, which cloud are there, is all abstracted out from the customer. Because at the end of the day, enterprises will want optionality. And we are in this multicloud. >> Yeah, I mean, this reminds me of the interoperability days back when I was growing into the business. Everything was interoperability and OSI and the standards came out, but what's your opinion on openness, okay? There's a kneejerk reaction right now in the market to go silo on your data for governance or whatever reasons, but yet machine learning gurus and experts will say, "Hey, you want to horizon horizontal scalability and have the best machine learning models, you've got to have access to data and fast in real time or near real time." And the antithesis is siloing. >> Krishna: Right, right, right. >> So what's the solution? Customers control the data plane and have a control plane that's... What do customers do? It's a big challenge. >> Yeah, absolutely. I think there are multiple different architectures of ML, right, you know? We've seen like where vendors like us used to deploy completely on-prem, right? And they still do it, we still do it in some customers. And then you had this managed cloud experience where you just abstract out the entire operations from the customer. And then now you have this hybrid experience where you split the control plane and data plane. So you preserve the privacy of the customer from the data perspective, but you still control the infrastructure, right? I don't think there's a right answer. It depends on the product that you're trying to solve. You know, Databricks is able to solve this control plane, data plane split really well. I've seen some other tools that have not done this really well. So I think it all depends upon- >> What about Snowflake? I think they a- >> Sorry, correct. They have a managed cloud service, right? So predominantly that's their business. So I think it all depends on what is your go to market? You know, which customers you're talking to? You know, what's your product architecture look like? You know, from Fiddler's perspective today, we actually have chosen, we either go completely on-prem or we basically provide a managed cloud service and that's actually simpler for us instead of splitting- >> John: So it's customer choice. >> Exactly. >> That's your position. >> Exactly. >> Whoever you want to use Fiddler, go on-prem, no problem, or cloud. >> Correct, or cloud, yeah. >> You'll deploy and you'll work across whatever observability space you want to. >> That's right, that's right. >> Okay, yeah. So that's the big challenge, all right. What's the big observation from your standpoint? You've been on the hyperscaler side, your journey, Facebook, Pinterest, so back then you built everything, because no one else had software for you, but now everybody wants to be a hyperscaler, but there's a huge CapEx advantage. What should someone do? If you're a big enterprise, obviously I could be a big insurance, I could be financial services, oil and gas, whatever vertical, I want a supercloud, what do I do? >> I think like the biggest advantage enterprise today have is they have a plethora of tools. You know, when I used to work on machine learning way back in Microsoft on Bing Search, we had to build everything. You know, from like training platforms, deployment platforms, experimentation platforms. You know, how do we monitor those models? You know, everything has to be homegrown, right? A lot of open source also did not exist at the time. Today, the enterprise has this advantage, they're sitting on this gold mine of tools. You know, obviously there's probably a little bit of tool fatigue as well. You know, which tools to select? >> There's plenty of tools available. >> Exactly, right? And then there's like services available for you. So now you need to make like smarter choices to cobble together this, to create like a workflow for your engineers. And you can really get started quite fast, and actually get on par with some of these modern tech companies. And that is the advantage that a lot of enterprises see. >> If you were going to be the CTO or CEO of a big transformation, knowing what you know, 'cause you just brought up the killer point about why it's such a great time right now, you got platform as a service and the tooling essentially reset everything. So if you're going to throw everything out and start fresh, you're basically brewing the system architecture. It's a complete reset. That's doable. How fast do you think you could do that for say a large enterprise? >> See, I think if you set aside the organization processes and whatever kind of comes in the friction, from a technology perspective, it's pretty fast, right? You can devise a data architecture today with like tools like Kafka, Snowflake and Redshift, and you can actually devise a data architecture very clearly right from day one and actually implement it at scale. And then once you have accumulated enough data and you can extract more value from it, you can go and implement your MLOps workflow as well on top of it. And I think this is where tools like Fiddler can help as well. So I would start with looking at data, do we have centralization of data? Do we have like governance around data? Do we have analytics around data? And then kind of get into machine learning operations. >> Krishna, always great to have you on theCUBE. You're great masterclass guest. Obviously great success in your company. Been there, done that, and doing it again. I got to ask you, since you just brought that up about the whole reset, what is the superhero persona right now? Because it used to be the full stack developer, you know? And then it's like, then I call them, it didn't go over very well in theCUBE, the half stack developer, because nobody wants to be a half stack anything, a half sounds bad, worse than full. But cloud is essentially half a stack. I mean, you got infrastructure, you got tools. Now you're talking about a persona that's going to reset, look at tools, make selections, build an architecture, build an operating environment, distributed computing operating. Who is that person? What's that persona look like? >> I mean, I think the superhero persona today is ML engineering. I'm usually surprised how much is put on an ML engineer to do actually these days. You know, when I entered the industry as a software engineer, I had three or four things in my job to do, I write code, I test it, I deploy it, I'm done. Like today as an ML engineer, I need to worry about my data. How do I collect it? I need to clean the data, I need to train my models, I need to experiment with what it is, and to deploy them, I need to make sure that they're working once they're deployed. >> Now you got to do all the DevOps behind it. >> And all the DevOps behind it. And so I'm like working halftime as a data scientist, halftime as a software engineer, halftime as like a DevOps cloud. >> Cloud architect. >> It's like a heroic job. And I think this is why this is why obviously these jobs are like now really hard jobs and people want to be more and more machine learning >> And they get paid. >> engineering. >> Commensurate with the- >> And they're paid commensurately as well. And this is where I think an opportunity for tools like Fiddler exists as well because we can help those ML engineers do their jobs better. >> Thanks for coming on theCUBE. Great to see you. We're here at re:MARS. And great to see you again. And congratulations on being on the AWS startup showcase that we're in year two, episode four, coming up. We'll have to have you back on. Krishna, great to see you. Thanks for coming on. Okay, This is theCUBE's coverage here at re:MARS. I'm John Furrier, bringing all the signal from all the noise here. Not a lot of noise at this event, it's very small, very intimate, a little bit different, but all on point with space, machine learning, robotics, the future of industrial. We'll back with more coverage after the short break. >> Man: Thank you John. (upbeat music)

>>Welcome back to VMO 2022. We're in the home stretch. Now, Dave ante for Dave Nicholson, and we're excited to have drew Schlissel on he's the director of product marketing at wasabi, and he is joined by Dustin Albertson, the manager of cloud and application alliances, product, product management at Veeam software. Dustin, did I get that right? You got it right. All right. You're gonna explain all those little titles in a moment. So wasabi is a company cool name, but you may not know much about them drew. What does wasabi do? >>We do cloud storage, plain and simple. It is the one thing we do extremely well. It's S3 compatible, and it covers a broad range of use cases, right? Primarily we work with Veeam on backup and recovery, and >>We're gonna get into that. But when we, what there's a lot of people do cloud storage, a lot of people do object store. What makes you wasabi unique >>Simplicity, predictability performance security, right? Predictability. Let's talk about price, right? That's the thing that gets people's attention, right? Oh, sure. Okay. You can look at it. One of two ways. It's either one fit the price of all the hyperscalers, significant difference there, or right. For fundamentally the same price. You get five times more storage, which makes a huge difference, especially in the backup space. When you want to have a lot of backups, right. Folks would prefer to have months of backups as opposed to days or weeks. Right? >>How do you, how do you do that? Because, because there's, you know, maybe >>It sounds like magic, doesn't >>It? Yeah. Yeah. I mean, you know, look at us, we've all been around the block quite a few times and we know that the bits and the bites and the bolts are all basically the same. What are you doing to get that level of? >>I can't tell you >>Secret's secret. It's secret. >>Look, it, it doesn't have to be that expensive. Okay. Now granted, there's some things obviously we do that are proprietary and different from, >>Well, like stealing electricity from your neighbor or something. I mean, what, >>You just run a cord over a >>Absolutely that's one way to cut down on price. But because we are so focused on just the storage, right. And our founders, you know, the gentleman who founded Carbonite, no a thing or two about storage. Sure. Right. We have a very highly optimized stack, very efficient. You know, you guys know what raw to usable story is. Right? You've gone through that TCO analysis before, and we're highly efficient in how we use the raw storage. And we pass that price on to our customers. Right. We believe that a low price cloud storage, right? One tier always hot, always available. It gives our customers the ability to spend their money in other places. Right. >>Well, and, and there's a price umbrella that the public cloud guys have is kind of a gift that they've given you. Hey, look at Amazon's operating profits last quarter. It was 35%. Those are like Oracle operating margins. Not that I, we don't know what your operating margins are, but I I've followed David friend's career for a long, long time. He's got good nose for business. But so Dustin, when you, when you hear drew talk about the ability to retain that much data, what does that mean for Veeam customers? >>So the primary thing for Veeam customers is the ease of use. I would say, you know, the, the performance and things like that are all nice, right? They're, they're important. But primarily what I see is people say how easy it is to use and how easy it is to price. Now, the objective, you know, the alternative is you go to another cloud provider and you say, well, how much will this cost me per month? You really have to underst yes, you really have to understand object storage, how Veeam works, how we're moving data, all the API calls, all of that to really kind of correlate out a guesstimate of what your price would be per month. You know, with LASA it's, it's a flat fee it's per terabyte. You know what it is gonna be? That's it? There's no API charges. There's no egres. So the customers really love that. Ease of use this become one of the most popular endpoints for object storage for our customers. >>Imagine this, right? You go to best buy and you buy a refrigerator and you bring it home and you stock it with all your favorite drinks and snacks. Okay. You on game day, you go and you open the fridge and you hear a sound Bing. And it's your phone and it's your credit card company telling you that you've been charged a door opening fee. Okay. And then you grab a beer out of that fridge, Bing, Bing, and you hear another ring and now you're getting a beer extraction fee. Okay. Now I want to be fair to, you know, all the sponsors here, but okay. With wasabi, you can open that door. You could stand there. You can air condition, the whole house. You can take a beer out and put a beer back or whatever your favorite beverage is. And you're not gonna hear that noise. Okay. Very straightforward. Like in, in geometry class, right? The slope of a line Y equals MX plus B B equals zero. Okay. Well, >>Whoa. Well, you had me at free beer. You didn't, >>You don't, but you understand why? >>Why would you, you don't need to go see >>To open your fridge and take out a beverage, take out a snack. Okay. That's the predictable part of wasabi. That's what's resonating so strongly with folks where everything else is in this world. Unpredictable. >>So ease, simplicity. Maybe the answer to that is, well, there's all this other stuff in the cloud. I can just, it's convenient for me. It's right there. So how do you address that convenience factor? All these other services, you know, that I can get streaming and machine learning and all that other great stuff. How do you address that? >>Sometimes all you need is storage. Okay. That no, it that's yet put, okay. That's beauty of wasabi. We're not trying to be everything to everyone. We're trying to be one thing executed very well for a, a specific set of users and use cases. >>I may be a little objective here, but I, I, you know, I've grown up with you guys, right? You, you, you were one of the first partners that I started working with and, and, you know, I've seen you kind of grow, but one of the things I think that you've done a real good job at is, is like you say, sticking to your, your lanes, you know, just going after use cases that just need data. Right. I don't need to get into the AI or the analytics or all of this. We just do this and do it well. And, and people have resonated with that. Right? Yeah. >>So big topic here of course is ransomware. Yeah. 3, 2 11, 0. What is that? What are the threes? The twos, the ones >>That's you, you gotta explain that one. Okay. >>So forever we had the 3, 2, 1 rule, right? Like three copies of data, two different, two different copies, two different media types. Yeah. One offsite. And then one is, is testing. And then zero now is, is validation. BA basically reuse that data. Make sure that you're testing it because if you're not, if you're following through two one, and you're not actually testing your data, is it really good? You don't know. You're just, you may have bad copies spread out all over the place. So one of the things where wasabi shines is is that they don't have these E risk charges. They don't have these API charges. So you can test that data. You can, after you send a backup up there, restore it somewhere else and validate that it works and then get rid of it. And it's still sitting up there in BAA. >>So you're not trying to balance your activities and your operational requirements with your, with your bill. Correct. You're not getting yelled at, by the, the controller at the end of the month. >>You're unconstrained. Yeah. Right. And I think also imutability comes into play. Correct. As well. >>Talk about >>That. Right. So, you know, we heard this morning in the keynote, right? That backup data sets are, you know, one of the main attack vectors, right. For cyber criminals. And it makes sense, right. They take down your primary systems and they control your backup systems. They've got you. You have no choice, but to pay that ransom. Okay. So mutability, that means that your backups are untouchable, your root user, your admins, the folks at wasabi, the folks at Veeam, nobody can alter that data period. End of story. Okay. That saves you from yourself that saves you from the hackers, right? I mean the most disturbing story I've read about cyber warfare right now is that people are getting bribe offers from these cyber gangs. And they're just, you know, for a couple of Bitcoin handing over the keys to the kingdom with imutability, you're actually safe from that scenario. >>So that's a service, correct? >>No, it's a feature. >>Okay. So can I turn it off? >>Yeah. You don't have to use it. >>No. Can I, after I've, after I've turned it on, can I turn it off? >>Oh, it's up to you. I mean, why don't you talk about >>That? Yeah. Yeah. So it's, it's an API. So if let's say you send some backups up there today and you set it for two weeks and you decide today. Oh, I made a mistake. I wanna turn it off. You can't turn it off. Yeah. >>Okay. So as long as you set that policy, it's, it's a big warning, right? You can't undo this. Correct. Okay. So even if I come, come to jump to the admin with a bunch of Bitcoin yep. He or she can't undo, right? >>Nope. That's right. And you can set it for two weeks, two months, two years. Right. You can use it to secure your backups. Yep. Right. You can also use that same feature in compliance situations. Right. Regulatory environments, where you've gotta retain customer data for, you know, 5, 7, 10 years. Right. By using that imutability feature, you guarantee the integrity of that data for whatever period you set. >>And it's a feature it's not a paid for service. Is that right? >>It is included as part of the service. >>Okay. So I don't >>Free beer and free meat. >>I think I'm correct that some, some competitors you're paying for that service. So if you turn it off, there's a, if you don't stop paying, there's a, there's a theory. They could turn it off on you. They will warn you. >>Sure. But >>That says to me that somebody could be tempted by a few Bitcoin. >>That's not a mutable. Well's >>Notable. I agree. Yeah. Yeah. Yeah. >>Well, and, and there is a charge to use it in other places because it's an API request. Right. It's an action. It's opening the fridge. >>It's like texting. Yes. Maybe a charge. >>Yeah. I remember. I remember those days. Was it 10 cents? A 10 cents a message or something Telegraph. >>Yeah. >>Yeah. >>Yeah. You still get those messages. Right? Text, text fees may apply. I'm like really? Okay. So tell me more about, so you got me. I'm sold. Okay. I've I've David friends got good job. Got cred, got credibility. Okay. But I have some other questions. Like where's my data. You guys running your own data centers. What's your global footprint. How do you deal with data sovereignty? All that stuff. >>So right now, oh boy. Now I'm on the spot. I wanna say 11 locations around the world. It's our gear. We're running it in concert with folks who are helping us host that system. Right. But we have complete control of course, over our systems. We're everywhere. Right? Just open, let's see. Toronto Frankfurt, Paris, London, Sydney just spun up in the last week. We've got Singapore coming online. I think in the next two weeks. Two >>In Japan. >>Yep. Two in Japan, multiple locations in the United States. So in terms of sovereignty, right, as long as folks are keeping it within, you know, their, their physical boundaries, not a problem. And if folks want to use, you know, other locations in other countries, great. We can support that as well. >>So you got momentum as a business. I mean, that's pretty clear. Yeah. Just from the discussions I've had with, with folks like David, and obviously you you're excited about this, where's it coming from? Is it really that, that price factor that's driving people to you? Is it Dustin said simplicity. I mean, where are you seeing the momentum geographies? Where is it? Where's the action. >>I I'll say, you know, from my point of view, it's, it's been a combination of all that, right? It, it's simple. It's easy to use a, like a user can, any user who's not cloud friendly, right. Can log in and create one. It's a simple portal to create a bucket and then start sending stuff off site. But also they've, they've kind of, they reminded me of a younger Veeam, like when they first started, because they went after the channel and they went and started these partner programs and, and MSP programs and things like that that have been really successful as far as one of the key markets is MSPs. Right? Because they, you know, want a cheap place to put this data. They don't wanna have to buy appliances. They don't wanna have to go to AWS and things like that. So this has been really appealing to >>Them. You know, it's interesting. So I have a, we have a partnership with a data company down in New York called enterprise technology research. We write a breaking analysis every week and we use a lot of their data. One of the things that popped up recently, maybe a year ago, OpenStack I'm like OpenStack. So we dug in like where's OpenStack and what it was was MSPs didn't want pay the VTax. Right. So they were rolling their own with, with open source and open stack. It was red hat services, blah, blah, blah. But it sounds like a similar dynamic, especially with the MSPs. >>I, so I think we've, I, I hate to use the, the metaphor, but I will. Right. There's a perfect storm happening, right. Especially in the last, what, two years. All right. The cloud has been gaining traction, but we've been around long enough to see the pendulum swinging. Right. Some folks went crazy for the cloud and then they got their bill and then they went crazy to get back out of the cloud. But now, you know, with distributed workforces, with the, you know, the, the constant attacks on their, their on-prem systems, right. The growth in cloud across the board has been phenomenal. I know you're a market watcher. Right. I know you guys are keeping close eyes. I saw your recent analysis on the cybersecurity firms. Right. It continues to grow. There's no question about it. We're we're on that wave. Right. And I think we've, you know, we're not, we're, we're, I don't know if it's the long board or the short little snappy board. Yes. We actually identify and, and, and went after the opportunity to partner with Veeam very early on, because it's the perfect work case work, work load. >>How long can you sustain that? And still resist the temptation to come out with some new shiny object to distract people? >>I >>Mean, what, what, what does that, what does that look like in terms of, as you look out in this laser focused yeah. Addressable market that you're going after now. >>So, you know, the best part about being here this week is having great conversations and, and talking to folks about what they're seeing in the marketplace and the different verticals. I don't think we've even scratched the surface of any of the verticals that we are working in today. Right. First and foremost, when it comes to backup and recovery, there's so much more opportunity with Veeam, right? Whether it's healthcare, manufacturing, logistics, analytics, backup of IML, you know, analysis, I think it's almost limitless, right. Data's growing what, 40, 80% year, over year, depending on who you ask. Right. Then the other things that we do, which maybe folks don't even know about, we have a burgeoning business in video surveillance, right. We're working with all the top partners in that sector. And the takeup is phenomenal because they are tweaking their technology to maintain a relatively small cash, right. OnPrem or in the central office. And then they're just kind of, you know, tearing that off to the cloud to have essentially a bottomless backup or archive of that footage. And they can do it at 4k. Here's the best part, right. When AK comes out, guess what, you know, that data set doubles in size. >>Right. But that's right in your zone. That's not stepping out that that's not stepping after that's that's classic leveraging. Good >>Answer. In other words. Yeah. Yeah. Thank you. >>I mean, if >>You're, if you're, if you're hitting singles and doubles all day long, right. Do you have to switch to be a power hitter and go for the fences and drop your batting average down, but hope that your slugging percentage goes up. I think you keep hitting singles doubles, you know, in triples, >>A lot of people on Sandhill road or, you know, at the bar at the Rosewood would disagree with you. Wow. And so I, I appreciate the discipline. >>Yeah. And it's true. And, and as we know, the industry is littered with a lot of those names that just didn't didn't make it >>Let's stay positive, you know? >>Okay. No he's saying yeah, no, no. A lot of guys at sand hill road would say, no, you gotta go for it. Yeah. You gotta, you gotta forget these singles. We want, >>Yeah. We need home runs gotta be >>Shiny. Well, I mean, look at Vema as a, as a, as an example right. Of a disciplined approach. Right. Exactly. To, to a space that they have steadily grown. I mean, congratulations. Right. You guys have been identified by IDC, right. Is essentially, you know, co number ones. And I expect that to be the number one in the market. Right. I think, you know, David friend clearly has provided excellent guidance, right. To steer the company that way. And I'm just really happy >>To be about that. Oh. And the Tam is data. Right. And you're, you're just another node on the data universe. Right. Which is, that's what you want. You want, you don't necessarily wanna move it around. Yeah. If you don't have to. >>It is interesting though. I mean, we, we are seeing more and more analysts identifying with Sabi as like the fourth player. Yeah. Which is pretty cool. Right. And I also heard it from some good sources this week that let's say one of the hyperscalers has, you know, started to yeah. Have conversations about us. Let's just >>Leave it. That's good. It means you're bothering people. Yeah. Said, all right, guys, we gotta go. Thanks so much for coming on the queue. Thank you. Great to have you. That was easy. Thank you. Appreciate it. Very welcome. All right. Keep it right there. We'll be back to wrap up day one from VMO in 2022, right back.

>> Narrator: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is theCUBE conversation. >> Everyone welcome to this special CUBE conversation. I'm John Furrier, host of theCUBE here in our Palo Alto studios. During this time of the pandemic, we're doing a lot of remote interviews, supporting a lot of events. theCUBE virtual is our new brand because there's no events to go to, but we certainly want to talk to the best people and get the most important stories. And today I have a great segment with a world-class entrepreneur, Ajeet Singh co-founder and executive chairman of ThoughtSpot. And they've got an event coming up, which is going to be coming up in December 9th and 10th. But this interview is really about what it takes to be a world-class leader and what it takes to see the future and be a visionary, but then execute an opportunity because this is the time that we're in right now is there's a lot of change, data, technology, a sea change is happening and it's upon us and leadership around technology and how to capture opportunities is really what we need right now. And so Ajeet I want to thank you for coming on to theCUBE conversation. >> Thanks for having me, John. Pleasure to be here. >> For the folks watching, the startup that you've been doing for many, many years now, ThoughtSpot you're the co-founder executive chairman, but you also were involved in Nutanix as the co-founder of that company as well. You know, a little about unicorns and creating value and doing things early, but you're a visionary and you're a technologist and a leader. I want to go in and explore that because now more than ever, the role of data, the role of the truth is super important. And as the co-founder, your company is well positioned to do that. I mean, your tagline today on the website says insight is the speed of thought, but going back to the beginning, probably wasn't the tagline. It was probably maybe like we got to leverage data, take us through the vision initially when you founded the company in 2012. What was the thinking? What was on your mind? Take us through the journey. >> Yeah. So as an entrepreneur, I think visionary is a very big term. I don't know if I qualify for that or not, but what I'm really passionate about is identifying very large markets, with very, very big problems. And then going to the white board and from scratch, building a solution that is perfectly designed for the big problem that the market might be facing from scratch. And just an absolute honest way of approaching the problem and finding the best possible solution. So when we were starting ThoughtSpot, the market that we identified was analytics, analytics software. And the big problem that we saw was that while on one hand, companies were building very big data lakes, data warehouses, there was a lot of money being spent in capturing and storing data how that data was consumed by the end-users, the non-technical people, the sales, marketing, HR people, the doctors, the nurses, that process was not changing. That process was still stuck in old times where you have to ask an analyst to go and build a dashboard for you. And at the same time, we saw that in the consumer space, when anyone had a question they wanted to learn about something, they would just go to Google and ask that question. So we said, why can't analytics be as easy as Google? If I have a question, why do I have to wait for three weeks for some data experts to bring some insights to me for most simple questions, if I'm doing some very deep analysis, trying to come up with fraud algorithms, it's understood, you know, you need data expert. But if I'm just trying to understand how my business is doing, how my customers are doing, I shouldn't have to wait. And so that's how we identified the market and the problem. And then we build a solution that is designed for that non-technical user with a very design thinking UX first approach to make it super easy for anyone to ask that question. So that was the Genesis of the company. >> You know, I just love the thinking because you're solving a problem with a clean sheet piece of paper, you're looking at what can be done. And it's just, you can bring up Google because you know, you think about Google's motto was find what you're looking for. And they had a little gimmicky buttons, like I'm feeling lucky, which just took you to a random webpage at that time while everyone else was tryna build these walled gardens and this structural apparatus, Google wanted you in and out with your results fast. And that mindset just never came over to the enterprise and with all that legacy structure and all the baggage associated with it. So I totally loved the vision, but I got to ask you, how did you get to beachhead? How did you get that first success milestone? When did you see results in your thinking? >> Yeah, so I mean, I believe that once you've identified a big market and a big problem, it comes down to the people. So I sort of went on a recruit recruiting mission and I recruited perhaps the best technology and business team that you can find in any enterprise segment, not only just analytics, some of the early engineers, my co-founder, he was at Google before that, Amit Prakash, before that he was at Microsoft working on Bing. So it took a lot of very deliberate effort to find the right kind of people who have a builder's mentality and are also deep experts in areas like search large-scale distributed systems. Very passionate about user experience. And then you start building the product, you know, it took us almost, I would say one and a half three years to get the initial working version of the product. And we were lucky enough to engage with some of the largest companies in the world, such as Walmart who are very interested in our solution because they were facing these kinds of problems. And we almost co-developed this technology with our early customers, focusing on ease of use, scale, security, governance, all of that, because it's one thing to have a concept where you want to make access to data as easy as Google, you have a certain interface people can type and get an answer. But when you are talking about enterprise data and enterprise needs, they are nowhere similar to what you have in consumer space. Consumer space is free for all, all the information is there you can crawl it and then you can access it. In enterprise, for you to take this idea of search, but make it production grid, make it real and not just a concept card. You need to invest a lot in building deep technology and then enabling security and scalability and all of that. So it took us almost , I would say a two and a half to three years to get to the initial version of the product and the problem we are solving and the area of technology search that we are working on. We brought it to the market. It's almost an infinite game. You know, you can keep making things easier and easier. And we've seen how Google has continued to evolve their search over time And it is still evolving. We just feel so lucky to be in this market, taking the direction that we have taken. >> Yeah. It's easy to talk a big game in this area because like you said, it's a hard technical problem because it'll structural data, whether it's schema databases or whatever, legacy baggage, but to make it easy, hard. And I like what you guys go with this, find the right information and put it in the right place, the right time. It's a really hard problem. And the beautiful thing is you guys are building a category while there's spend in the market that needs the problem today. So category creation with an existing market that needs it. So I got to ask you, if you could do me a favor and define for the audience, what is search-driven analytics? What does that mean from your standpoint? >> Yeah, what it means is for the end user, it looks like search but under the hood is driving large scale analytics. I like to say that our product looks like a search engine on the surface, but under the hood, it's a massive number crunching machine. So Search and AI driven analytics. There's two goals there. One, if the user has, any user and we're talking about non-technical users here, we're not talking about necessarily data experts, but if a user has a question, they should be able to get an answer instantly. They shouldn't have to wait. That is what we achieve with Search and with Spot IQ, our AI engine, we help surface insights where people may not even know that those are the questions they should be asking because data has become so complex. People often don't even know what question they should be asking. And we give them a pool that's very easy to use, but it helps surface insights to them. So there is both a pool model that we enabled through Search and a push model that we enable through Spot IQ. >> So I have to ask you that you guys are pioneering this segment you're in first. And sometimes when you're first, you have arrows in your back as you know, it's not all the beginners survive, they get competition copies, but you guys have had a lead. You had success. What's different today as you have competition coming in trying to say, "Oh, we got Search too." So what's different today with ThoughtSpot? How are you guys differentiated? >> Yeah. I mean, that's always a sign of success. If what you are trying to do, if others are saying we have it too, you have done something that is valuable. And that happens in all industry. I think the best example is Tesla. They were the first to look at this very well-known problem. I mean, we haven't had a very sort of unique take on the existence of the problem itself. Everybody knows that there is a problem with access to data, but the technology that we have built is so deep that it's very, very hard to really copy it and make it work in real world with Tesla in automotive industry in cars, there is obviously so many other companies that have launched battery powered cars, electric cars, but there is Tesla and there is all the other electric cars which are a bit of an afterthought, because if you want to build an analytics product, where Search is at the core, Search cannot be added on the top, Search has to be the core, and then you build around it. And that requires you to build a fundamental architecture from the ground up. And you can't take an existing BI product that is built for dash boarding and add a search bar. I have always said that adding a search bar in a UI is perhaps, you know, 10 to 20 lines of JavaScript code. Anyone can add it and there is so much open source stuff out there that you can just take it and plug it. And many people have tried to do that, but taking off the shelf, Search technology that is built for unstructured data and sticking it on to a product that is required to do analytics on enterprise data, that doesn't work. We built a search technology that understands enterprise data at a very deep level, so that when our customers take our product and bring it into their environment, they don't have to fundamentally change how they manage their data. Our goal is to add value to their existing enterprise data Cloud Data Warehouses and deliver this amazing Search experience where our Search engine is enable to understand what's in their data Lake, what's in their Cloud Data Warehouse. What are the schema, the tables, the joints, the cardinality, the data archive, the security requirements, all of things have to be understood by the technology for you to deliver the experience. So now that said, we pride ourselves in not resting on our laurels. You know, we have this sort of motto in the company. We say we are only 2% done. So we are on our own sort of a continuous journey of innovation. And we have been working on taking our Search technology to the next level. And that is something really powerful that we are going to unveil at our upcoming conference, Beyond, in December. And that is one to create even more distance between us and the competition. And it's all driven by what we have seen with our customers, how they're using our product or learnings what they like, what they don't like, where we see gaps and where we see opportunity to make it even easier to deliver value to our customers and our users. >> I think that's a really profound insight you just shared, because if you look at what you just said around thinking about Search as an embedded architectural foundational, you know, embedded in the architecture, that's different than bolting on a feature where you said Java code or some open source library. You know, we see in the security market, people bolted on security had huge problems. Now, all you hear is, "Oh, you got a big security in from the beginning." You actually have baked Search into everything from the beginning. And it's not just a utility, it's a mindset. And it's also a technology metadata data about data software, and all kinds of tech is involved. Am I getting that right? I mean, cause I think this is what I heard you say. It's like, you got to have the data. >> This is totally right. I mean, if I can use an analogy, there is Google search and obviously Yahoo also tried to bring their own search Yahoo search Yahoo actually, Yahoo versus Google is a perfect example or a perfect analogy to compare with ThoughtSpot versus other BI product Yahoo was built for predefined content consumption. You know, you had a homepage, somebody defined it. You could make some customizations. And there is predefined content you can consume it. Now, they also did add search, but that didn't really go so far. While Google said, we will vary from scratch ability to crawl all the data, ability to index all the data and then build a serving infrastructure that deliver this amazing performance and interactivity and relevance for the user. Relevance is where Google already shined. And you can't do those things until you think about the architecture from the ground up. >> Ajeet I'm looking forward to having more deep dive conversations on that one topic. But for the folks who might not be old enough, like me to remember Google back at that time, Yahoo was the best search engine and it was directory basically with a keyword search. It was trivial, technically speaking, but they got big. And then the portal wars came out, we got to have a portal. Google was very much not looked down as an innovator, but they had great technical chops and they just stayed the course. They had a mission to provide the best search engine to help users find what they're looking for. And they never wavered. And it was not fashionable about that time to your point. And then Yahoo was number one, then Google just became Google and the rest is history. So I really think that's super notable because companies face the same problem. What looks like fashionable tech today might not be the right one. I think that's... >> Yeah, and I totally agree. And I think a lot of times in our space, there's a lot of sort of hype around AI and machine learning. We as a company have tried to stay close to our customers and users and build things that will work for them. And a lot of stuff that we are doing, it has never been done before. So it's not to say that along the way, we don't have our own failures. We do have failures and we learn from them. >> Yeah. Yeah. Just don't make the same mistake twice. >> Yeah, I think if you have a process of learning quickly, improving quickly, those are the companies that will have a competitive advantage. In today's world, nobody gets it right the first time. If you're trying to do something fundamentally different, if you're copying somebody else, then you're too late already. >> I totally agree. >> If you do something new, it's about how fast you penetrate And that's... >> That's a great mindset. That's a great mindset. And I think that's worth capturing calling out, but I got to ask you because what's first of all, distinguished history and I love your mindset and just solving problems, big problems. All great. I want to ask you something about the industry and where you guys were in 2012 alright when you started the company, you were literally in what I call the before Cloud phase. Cause it was before Cloud companies and then during Cloud companies and then after Cloud, you know, Amazon clearly took advantage of that for a lot of startups. So right around 2012 through 2016, I'd call that the Amazon is growing up years. How did the Cloud impact your thinking around the product and how you guys were executing because you were right on that wave. You were probably in the sweet spot of your development. >> Yeah. >> Pre business planning. You were in the pre-business planning mode, incomes, Amazon. I'm sure you're probably using Amazon cause your starters and all start up sort of use Amazon at first, but I just think about, do we all have found premise with a data center? How did that impact you guys? And how does that change today? >> Certainly. Yeah it's been fascinating to see how the world is evolving how enterprises have also really evolved in depth, thinking on how they leverage the cloud infrastructure now. In the Cloud, there is the compute and storage infrastructure. And then you have a Cloud Data Warehouse, the analytics stack in the Cloud. That's becoming more popular now with a company like Google, having BigQuery and then Snowflake really amazing concepts and things like that. So when we started, we looked at where our customers are , where is their data. And what kind of infrastructure is available to us at the time there wasn't enough compute to drive the search engine that we wanted to build. There were also not any significant Cloud Data Warehousing at the time, but our engineering team our co-founders, they came from companies like Google, where building a Cloud based architecture and elastic architecture, service oriented architecture is in their DNA. So we architected the product to run on infrastructure that is very elastic that can be run practically anywhere. But our initial customers and applies the Global 2000. They had their data on-prem. So we had started more with on-prem as a go-to-market strategy. and then about four and a half years ago, once cloud infrastructure I'm talking about the compute infrastructure started to become more mature, we certified our software, to run on all three clouds So today we have more than 75 to 80% of our customers already running our software in the Cloud. And as now, because we connect to our primary data sources, our Cloud Data Warehouses, Cloud Data Lakes. Now with Snowflake and BigQuery and Synapse and Redshift, we have enough of our customers who have deployed Cloud Data Warehouses. So we are also able to directly integrate with them. And that's why we launched our own hosted SaaS Offering about a month ago. So I would say our journey in this area has been sort of similar to companies like Splunk or Elastic, which started with a software model initially deployed more on-prem, but then evolved with the customers to the Cloud. So we have a lot of focus and momentum and lot of our customers, as they're moving their data to the Cloud, they're asking us as well to be in the Cloud and provide a hosted offering. And that is what we have built for the last one year. And we launched it a month ago. >> It's nice to be on the right side of history. I got to say, when you're on the way to be there. And that also makes integrations easy too. I love the Cloud play. Let's get to the final segment here. I want to get your thoughts on your customers, your advice. There's a huge untapped opportunity for companies when it comes to data, a lot of them are realizing that the pandemic is highlighting a lot of areas where they have to go faster and then to go to Cloud, they're going to build modern apps more data's coming in than ever before. Where are these untapped opportunities for customers to take advantage of the data? And what's your opinion on where they should look and what they should do? >> Yeah, I really think that the pandemics has shown for the first, the value of data to society at large, there is probably more than a billion people in the world that have seen a chart for the first time in their life. Everybody is being... and COVID has done some magic. But everybody was looking at charts of infection and so on and so forth. So there is a lot more broad awareness of what data can do in improving our society at large for the businesses of course, in the last six, seven months, you heard it enough from lot of leaders that digital transformation is accelerating. Everybody is realizing that the way to interact in the world is becoming more and more digital expecting your customers to come to your branch to do banking is not really an option. And people are also seeing how all the SaaS companies and SaaS businesses, digital businesses, they have really taken off. So if a company like Zoom can suddenly have a a hundred, $150 billion valuation, because you are able to do everything remote, all the enterprises are looking to really touch their customers and partners in a lot more digital way than they could do before. And definitely COVID has also really created this almost, you know, pool buckets of organization. There is lot of companies that have tremendously benefited from it. And there a lot of companies that have been poorly affected, really in a difficult place. And I think both of them for the first category, they are looking at how do I maintain this revenue even after COVID, because one of this thing, you know, hopefully early next year we have a vaccine and things can start to look better again sometime next year. But we have learned so much. We have attracted so many new customers, how do we retain and grow them further? And that means I need to invest more and more in my technology. Now, companies that are not doing well, they really want to figure out how to become more operationally efficient. And they are really under pressure to get more value from there and both categories, improving your revenue, retaining customers. You need to understand the customer behavior. You need to understand which products they are buying at a fine grain level, not with the law of averages, not by looking at a dashboard and saying our average customer likes this kind of product. That one doesn't really work. You have to offer people personalized services and that personalization is just not possible at scale, without really using data on the front lines. You can't have just manager sitting in their office, looking at dashboards and charts and saying these are the kinds of campaigns I need to run because my average customer seems to like these kinds of offers. I need to really empower my sales people, my individual frontline workers, who are interfacing with the customer to be able to make customized offers of services and products to them. And that is possible on the data. So we see a really, a lot more focus in getting value from data, delivering value quickly and digital transformation broadly but definitely leveraging data in businesses. There is tremendous acceleration that is happening and, you know, next five years, it's all going to be about being able to monetize data on the front lines when you are interfacing with your customers and partners >> Ajeet, that's great insight. And I really appreciate what you're saying. And you know, I wrote a blog post in 2007. I said, data will be the new development kit. Back then we used to call development kits, software user development. >> John, you are the real visionary. It took me until 2012 to be able to do this. >> Well, it wasn't clear, but you saw other data was going to have to be programmed be part of the programming. And I think, what you're getting at here is so profound because we're living 2020 people can see the value of data at the right time. It changes the conversations, it changes what's going on in the real time communications of our world with real-time access to information, whether that's machine to machine or machine to human, having data in the right place, changes the context. >> Yap. >> And that is a true, not a tech thing, that's just life, right? I think this year, I think we're going to look back and say, this was the year that everyone realized that real time communications, real-time society needs real time data. And I think it's going to be more important than ever. So it's a really big problem and important one. And thank you for sharing that. >> Yeah. And actually you bring up a very good point programming, developing big data. Data as a development kit. We are also going to announce a new product at Beyond, which will be about bringing ThoughtSpot everywhere, where a lot of business users are in their business applications. And by using ThoughtSpot product, using our full experience, they can obviously do enterprise wide analytics and look at all the data. But if they're looking for insights and nuggets, and they want to ask questions in their business workflows. We are also launching a product capability that will allow software developers to inject data in their business applications and enable and empower their own business users to be able to ask any questions that they might have without having to go to yet another BI product. >> It's data as code. I mean, you almost think about like software metaphors, where's the compiler? Where's the source code? Where's the data code? You start to get into this new mindset of thinking about data as code, because you got to have data about the data. Is it clean data, dirty data? Is it real time? Is it useful? There's a lot of intelligence needed to manage this. This is like a pretty big deal. And it's fairly new in the sense in the science side. Yeah, machine learning has been around for a while and you know, there's tracks for that. But thinking of this way as an operating system mindset, it's not just being a data geek. You know what I'm saying? So I think you're on the right track Ajeet. I really appreciate your thoughts here. Thank you. >> Thank you John. >> Okay. This is a cube conversation. Unpacking the data. The data is the future. We're living in a real-time world and in real-time data can change the outcomes of all kinds of contexts. And with truth, you need data and Ajeet Singh co-founder executive chairman of ThoughtSpot shares his thoughts here in theCUBE. I'm John furrier. Thanks for watching. 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>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> As we reported last week, we believe that in the next decade, there will be changes in public policy that are going to restrict the way in which big internet companies are able to appropriate user data. Big tech came under fire again this week with the CEOs of Facebook, Twitter, and Google going toe to toe with several U.S. senators. Microsoft CEO Satya Nadella, however, was not one of those CEOs in the firing line. Microsoft doesn't heavily rely on ad revenues, rather, the company's momentum is steadily building around Azure, which by my estimates is now roughly 19% of Microsoft's overall revenues. It's surpassed, maybe nearly got to $7 billion for the first time on a quarterly basis. I'll come back to you on that. Hello everyone, and welcome to this week's Wikibon CUBE insights powered by ETR. In this Breaking Analysis, we'll respond to the many requests we've had to dig into the business of Microsoft a little bit deeper and provide a snapshot of how the company is faring in the ETR dataset. Let's take a quick look at Microsoft's financials, and the scope of Microsoft's business is actually mind-boggling. The company has roughly $150 billion in revenue, and it grew its top line 12% last quarter. It has more than $136 billion in cash on the balance sheet. Microsoft generates over $60 billion annually in operating cashflow. And last quarter alone threw off more than 19 billion in operating cash. Its gross margins are expanding across virtually all of its major business lines. So let's look at those business sectors. Microsoft, it doesn't suffer from the nagging problems that we've talked about with a lot of older tech companies. Companies like IBM and Dell and Cisco and Oracle and SAP, they struggle with growth sometimes because their growth businesses are not yet large enough to offset the declines in their traditional on-premises business segments. Now at the highest level, Microsoft breaks its business into three broad categories, and they're all growing quite nicely. Let me add some color here. Let's start with the productivity and business process line of business. LinkedIn, which is growing at 16%, is in this category as is Office. This business is shifting from one of on-prem licenses, which are really headwinds right now from Microsoft, to the cloud, in the form of SaaS with Office 365, which is growing at a 20% clip within its commercial market base. Even the consumer side of O365 is growing in the double digits. Dynamics is Microsoft's ERP and CRM business, and that falls into this slice of the pie, that's growing at 18%. And then the newer Dynamics 365, that's growing at 37%. So you can see, Microsoft is easily able to show growth despite the transitions from its legacy business. Intelligent cloud is the next segment. It's kind of the kitchen sink category, meaning there's stuff in there that includes a bit of cloud washing in my opinion, but Microsoft is not nearly as egregious as IBM with the liberties that it takes around its cloud categorization. For Microsoft it's a $13 billion quarterly business. And it's growing at 19%, as we show in the pie chart. Azure is an increasingly large portion of this segment. Azure is the most direct comparison with AWS. And I have said in the past quarter, I'd say it's around 50% of the intelligent cloud, and that it's approaching by my estimates around $7 billion a quarter. Azure grew at 47% annually this past quarter, the same growth rate as last quarter. Ironically, both AWS and Google Cloud grew at the same year over year rate this quarter as they did last quarter. AWS is 29% GCP in the high 50s by at my estimates. AWS revenue was 11.6 billion this past quarter, and I have GCP still well under 2 billion. We'll be updating our cloud numbers and digging deeper next week into this topic. So consider these estimates preliminary for Azure and GCP, which the respective companies don't break out for as Amazon, as you know, breaks out AWS explicitly. Now, back to Microsoft's intelligent cloud business. It includes on-prem server software, which is a managed decline business from Microsoft. They also include enterprise services in this category. So as you can see, it's not a clean cloud number for comparison purposes. Now finally, the third big slice of the pie is more personal computing. I know, it's kind of a dorky name, but nonetheless it's nearly a $12 billion business that's growing at 6% annually. The Windows OEM business is in here, as is Windows 10 and some security offerings. Surface is also in here as well and it's growing in the mid-thirties. Search revenue is in this category as well. It's declining per my earlier statements that it's not a main piece of Microsoft's business. Now, one of the most interesting areas of this sector is gaming. Microsoft's gaming business is growing at 21% and they just acquired ZeniMax Media for seven and a half billion dollars. Let me land on gaming for a minute. The gaming experts at theCUBE are really excited about Microsoft's XBox content services, which grew at about 30% this past quarter. Game Pass is essentially Microsoft's Netflix, or you can think of it as maybe like a Spotify model. You can get in for as low as $5 a month. I think you can pay as much as $15 a month and get access to a huge catalog of games that you can download. In November of last year, Microsoft launched its xCloud beta service, which allows you to download to a PC or a game box. Now eventually with 5G, the box goes away. All you'll need is a screen and you know, controller with the joysticks, no download. In fact, this is how it works today for Android. Now, interestingly, Apple is blocking Microsoft and some others like Google's Stadia, saying that they don't allow streaming game apps like Microsoft's xCloud service, because they don't follow the company's guidelines. What Apple's not telling you is that its adjacent offering, Apple Arcade, is considered subpar by hardcore gamers. And while Apple allows the streaming of movies and music from any service on the iPhone, it's decided not to allow streaming games. Now, the last thing I want to stress about Microsoft is its leverage point around developers. Developers is a big one here, we all remember the sweaty Steve Ballmer running around the stage like a mad man, screaming, "Developers, developers, developers!" Well, despite his obsession with Windows, he sure got that one right. The GitHub acquisition was Microsoft's way of buying more developer love. It does concentrate power with a tech giant, but you know what, if it wasn't Microsoft that bought GitHub, it would have been Facebook or Amazon or Google or one of the other tech giants. Now, despite some angst in the developer community over this, GitHub, it really is a linchpin for Microsoft to more tightly integrate GitHub with its pretty vast developer tool set. All right. Let's look deeper into the Microsoft data and focus on the enterprise. We'll bring in the ETR as we always do. We said last week that Google needed to look to the cloud and edge and get its head out of its ads. Well, Microsoft recovered from its Windows myopia after Satya Nadella took over in 2014, and by all accounts from the ETR survey data, Microsoft is killing it across the board. Let me start by putting Microsoft in context with some of the most prominent companies that both compete with, and sometimes partner with Microsoft. So this xy graph, it's one of our favorites. I show it all the time and it shows net score on the vertical axis, which is a measure of spending momentum from ETR, and the horizontal axis shows what we call market share, which is a measure of pervasiveness in the survey. Now in the upper right hand table, you can see the data for each of the companies. There's an ETR survey taken in October and it had more than 1400 completes. Several points stand out here. Microsoft is by far the most pervasive in the dataset, and yet its net score or spending velocity is right there with AWS, ServiceNow, Salesforce, and Workday. Only Snowflake, which I put in there for context, because of its consistently strong net scores, shows a meaningfully higher net score, of course from a much smaller base. Now what makes this so impressive is it represents a pan-Microsoft view across its entire portfolio. And you can see where companies like IBM and Oracle struggle from a momentum standpoint compared to Microsoft, which is a much, much larger company. It's that problem that I referred to earlier regarding the smaller size of their respective growth businesses. Also called Cisco and SAP, which despite some earnings challenges lately, are able to maintain net scores that while not in the green, they're not in the red, either. Green essentially means your overall install base is expanding. Red indicates contraction. Now let's look at the spending patterns for Microsoft customers. This chart shows the granularity of ETR's net score for Microsoft. The green represents increased spend and the red decreased spend. What's impressive is that Microsoft's red zone, I mean it's essentially negligible at 6%, when you add two reds up, the pink and the bright red. Their customers, they're all spending more, or the same, and very few are leaving the platform. Now I made the case last week that Google should double or triple its efforts and focus on cloud and the edge. Microsoft has already made that transition in its business and is the, that's the premise really of my discussion today. Specifically, Microsoft Azure is powering the company across all of its products and services. It's giving Microsoft tremendous operating leverage and steadily improving marginal economics. You can see that in the gross margin lines this quarter, across all of its businesses. And here's a graphic showing its position within cloud computing in terms of net score. Microsoft Azure functions, which is the first bar on this chart, and Azure overall, which is the third set of bars, shows momentum that's as strong as any cloud category, including AWS Lambda, which as we've talked about many times is killing it. Now five over from the left, count them over, one, two, three, four, five, you can see AWS overall. So that's a really important reference point. And while its levels are still elevated, Azure overall, which again is number three from the left, has meaningfully more momentum with 65% net score versus 52% for AWS overall. Now reasonable people can debate the quality of these respective clouds and you could argue over feature sets, who's got the most features, who's got the most regions, which regions are most reliable, who's got the most data centers and all that stuff, but it's really hard to argue against Microsoft's "Good enough" strategy. It's working in the cloud, and it has been working for the company for decades. Now another Microsoft strategy has been to be a late comer to a category and then bundle multiple capabilities into one suite. We saw this at first, really in the late 1980s with Office, and it's continued in a number of areas. The latest example, Microsoft Teams. Teams combines features like meetings, phone, chat, collaboration, as well as business process workflows that leverage tools like SharePoint and PowerPoint. I mean, it's a killer strategy, and you can see the results in this chart. I mean, it's essentially competing with Zoom, it's competing with Slack and all the sort of productivity plays there in that space. And this graphic compares net scores from the year ago October survey for reference, the July survey from this year, and the most recent October survey, as I said, 1400 respondents. Look at the lead that Teams has relative to the competition. There's a story across Microsoft's portfolio. Look at Microsoft's products in the ETR taxonomy. Video conferencing with Teams, productivity apps, RPA, cloud, cloud functions, machine learning, artificial intelligence, containers, security, end point, analytics, mobile, even database. The only signs of softness are really seen in the company's legacy businesses like Skype or on-prem licenses business, which I said were a headwind for them. And while PCs and tablets are weaker, that's what you'd expect from this mature industry relative to some of these other categories. Now, again, the premise here today is that by pivoting to the cloud and going all in competing with infrastructure as a service, Microsoft has created a platform for innovation for its business, and its developer chops are really credible, so it's evolving its install base very successfully to Azure. It's got a very solid hybrid and multi-cloud strategy and story with Microsoft Arc, which eventually it can take to the edge. You know, we think its edge strategy needs some work, but nonetheless, the company is really, really well positioned. Microsoft has a huge partner ecosystem, heck, it even partners with Oracle and database, as well as using Azure to enter new markets, including vertical clouds like healthcare, which it talked about on its earnings call. I mean, there's really not much on which you can criticize Microsoft. You know, sure, they've had some high profile failures in the past. The Nokia acquisition, the Windows phone, you remember Zune? Mixer, you know, Bing. Is Bing a fail? I don't know. Maybe not really. I guess the fail is, you know, what I was talking about last week with antitrust, Microsoft was distracted by the DOJ and maybe that caused it to miss search, give it to Google, and in that sense, maybe it was a failure, but overall, pretty good track record from Microsoft. Yeah, maybe you can say Microsoft is somewhat of a copycat, you know, the graphical user interface that they copied from the Mac, but hey, even Steve Jobs stole that. Surface, okay. The cloud? But so what, ideas, they're plentiful, execution is the key, really. No matter how you slice it, the data doesn't lie. Microsoft's financial performance, its pivot to the cloud, and the success of its adjacent businesses, make it one of the most remarkable rebirths in the history of technology industry. Now I didn't use the word turnaround because the company was never really in trouble. It just became irrelevant and kind of boring. Today, Microsoft is far from immaterial. Okay. That's it for this week. Remember all these episodes are available as podcasts wherever you listen. So please subscribe. I publish weekly on Wikibon.com and Siliconangle.com. And don't forget to check out ETR.plus for all the survey data and analytics. I appreciate always the comments on my LinkedIn posts or you can DM me @DVellante, or email me at David.Vellante@SiliconAngle.com. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching everybody, be well, and we'll see you next time. 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>> Voice Over: Data is at the heart of transformation, and the change every company needs to succeed. But it takes more than new technology. It's about teams, talent and cultural change. Empowering everyone on the front lines to make decisions, all at the speed of digital. The transformation starts with you, it's time to lead the way, it's time for thought leaders. (soft upbeat music) >> Welcome to Thought.Leaders a digital event brought to you by ThoughtSpot, my name is Dave Vellante. The purpose of this day is to bring industry leaders and experts together to really try and understand the important issues around digital transformation. We have an amazing lineup of speakers, and our goal is to provide you with some best practices that you can bring back and apply to your organization. Look, data is plentiful, but insights are not, ThoughtSpot is disrupting analytics, by using search and machine intelligence to simplify data analysis and really empower anyone with fast access to relevant data. But in the last 150 days, we've had more questions than answers. Creating an organization that puts data and insights at their core, requires not only modern technology but leadership, a mindset and a culture, that people often refer to as data-driven. What does that mean? How can we equip our teams with data and fast access to quality information that can turn insights into action? And today we're going to hear from experienced leaders who are transforming their organizations with data, insights, and creating digital first cultures. But before we introduce our speakers, I'm joined today by two of my co-hosts from ThoughtSpot. First, chief data strategy officer of the ThoughtSpot is Cindi Howson, Cindi is an analytics and BI expert with 20 plus years experience, and the author of Successful Business Intelligence: Unlock the Value of BI & Big Data. Cindi was previously the lead analyst at Gartner for the data and analytics Magic Quadrant. In early last year, she joined ThoughtSpot to help CEOs and their teams understand how best to leverage analytics and AI for digital transformation. Cindi great to see you, welcome to the show. >> Thank you Dave, nice to join you virtually. >> Now our second cohost and friend of theCUBE is ThoughtSpot CEO Sudheesh Nair Hello Sudheesh, how are you doing today? >> I'm well, good to talk to you again. >> That's great to see you, thanks so much for being here. Now Sudheesh, please share with us why this discussion is so important to your customers and of course to our audience, and what they're going to learn today. (upbeat music) >> Thanks Dave, I wish you were there to introduce me into every room that I walk into because you have such an amazing way of doing it. It makes me feel also good. Look, since we have all been you know, cooped up in our homes, I know that the vendors like us, we have amped up our sort of effort to reach out to you with, invites for events like this. So we are getting very more invites for events like this than ever before. So when we started planning for this, we had three clear goals that we wanted to accomplish. And our first one, that when you finish this and walk away, we want to make sure that you don't feel like it was a waste of time, we want to make sure that we value your time, then this is going to be used. Number two, we want to put you in touch with industry leaders and thought leaders, generally good people, that you want to hang around with long after this event is over. And number three, as we plan through this, you know we are living through these difficult times we want this event to be more of an uplifting and inspiring event too. Now, the challenge is how do you do that with the team being change agents, because teens and as much as we romanticize it, it is not one of those uplifting things that everyone wants to do or likes to do. The way I think of it, changes sort of like, if you've ever done bungee jumping, and it's like standing on the edges, waiting to make that one more step you know, all you have to do is take that one step and gravity will do the rest, but that is the hardest step today. Change requires a lot of courage, and when we are talking about data and analytics, which is already like such a hard topic not necessarily an uplifting and positive conversation most businesses, it is somewhat scary, change becomes all the more difficult. Ultimately change requires courage, courage to first of all, challenge the status quo. People sometimes are afraid to challenge the status quo because they are thinking that you know, maybe I don't have the power to make the change that the company needs, sometimes they feel like I don't have the skills, sometimes they may feel that I'm probably not the right person to do it. Or sometimes the lack of courage manifest itself as the inability to sort of break the silos that are formed within the organizations when it comes to data and insights that you talked about. You know, that are people in the company who are going to have the data because they know how to manage the data, how to inquire and extract, they know how to speak data, they have the skills to do that. But they are not the group of people who have sort of the knowledge, the experience of the business to ask the right questions off the data. So there is the silo of people with the answers, and there is a silo of people with the questions, and there is gap, this sort of silos are standing in the way of making that necessary change that we all know the business needs. And the last change to sort of bring an external force sometimes. It could be a tool, it could be a platform, it could be a person, it could be a process but sometimes no matter how big the company is or how small the company is you may need to bring some external stimuli to start the domino of the positive changes that are necessary. The group of people that we are brought in, the four people, including Cindi that you will hear from today are really good at practically telling you how to make that step, how to step off that edge, how to dress the rope, that you will be safe and you're going to have fun, you will have that exhilarating feeling of jumping for a bungee jump, all four of them are exceptional, but my owner is to introduce Michelle. And she's our first speaker, Michelle I am very happy after watching our presentation and reading your bio that there are no country vital worldwide competition for cool parents, because she will beat all of us. Because when her children were small, they were probably into Harry Potter and Disney and she was managing a business and leading change there. And then as her kids grew up and got to that age where they like football and NFL, guess what? She's the CIO of NFL, what a cool mom. I am extremely excited to see what she's going to talk about. I've seen this slides, a bunch of amazing pictures, I'm looking to see the context behind it, I'm very thrilled to make that client so far, Michelle, I'm looking forward to her talk next. Welcome Michelle, it's over to you. (soft upbeat music) >> I'm delighted to be with you all today to talk about thought leadership. And I'm so excited that you asked me to join you because today I get to be a quarterback. I always wanted to be one, and I thought this is about as close as I'm ever going to get. So I want to talk to you about quarterbacking our digital revolution using insights data, and of course as you said, leadership. First a little bit about myself, a little background as I said, I always wanted to play football, and this is something that I wanted to do since I was a child, but when I grew up, girls didn't get to play football. I'm so happy that that's changing and girls are now doing all kinds of things that they didn't get to do before. Just this past weekend on an NFL field, we had a female coach on two sidelines, and a female official on the field. I'm a lifelong fan and student of the game of football, I grew up in the South, you can tell from the accent and in the South is like a religion and you pick sides. I chose Auburn University working in the Athletic Department, so I'm testament to you can start the journey can be long it took me many, many years to make it into professional sports. I graduated in 1987 and my little brother, well, not actually not so little, he played offensive line for the Alabama Crimson Tide. And for those of you who know SEC football you know, this is a really big rivalry. And when you choose sides, your family is divided, so it's kind of fun for me to always tell the story that my dad knew his kid would make it to the NFL he just bet on the wrong one. My career has been about bringing people together for memorable moments at some of America's most iconic brands. Delivering memories and amazing experiences that delight from Universal Studios, Disney to my current position as CIO of the NFL. In this job I'm very privileged to have the opportunity to work with the team, that gets to bring America's game to millions of people around the world. Often I'm asked to talk about how to create amazing experiences for fans, guests, or customers. But today I really wanted to focus on something different and talk to you about being behind the scenes and backstage. Because behind every event every game, every awesome moment is execution, precise repeatable execution. And most of my career has been behind the scenes, doing just that, assembling teams to execute these plans, and the key way that companies operate at these exceptional levels, is making good decisions, the right decisions at the right time and based upon data, so that you can translate the data into intelligence and be a data-driven culture. Using data and intelligence is an important way that world-class companies do differentiate themselves. And it's the lifeblood of collaboration and innovation. Teams that are working on delivering these kinds of world-class experiences are often seeking out and leveraging next generation technologies and finding new ways to work. I've been fortunate to work across three decades of emerging experiences, which each required emerging technologies to execute. A little bit first about Disney, in the 90s I was at Disney, leading a project called destination Disney, which it's a data project, it was a data project, but it was CRM before CRM was even cool. And then certainly before anything like a data-driven culture was ever brought up. But way back then we were creating a digital backbone that enabled many technologies for the things that you see today, like the magic band, just these magical express. My career at Disney began in finance, but Disney was very good about rotating you around, and it was during one of these rotations that I became very passionate about data. I kind of became a pain in the butt to the IT team, asking for data more and more data. And I learned that all of that valuable data was locked up in our systems, all of our point of sales systems, our reservation systems, our operation systems, and so I became a shadow IT person in marketing, ultimately leading to moving into IT, and I haven't looked back since. In the early 2000s I was at Universal Studios Theme Park as their CIO, preparing for and launching the wizarding world of Harry Potter. Bringing one of history's most memorable characters to life required many new technologies and a lot of data. Our data and technologies were embedded into the rides and attractions. I mean, how do you really think a wand selects you at a wine shop. As today at the NFL, I am constantly challenged to do leading edge technologies using things like sensors, AI, machine learning, and all new communication strategies, and using data to drive everything from player performance, contracts to where we build new stadiums and hold events. With this year being the most challenging, yet rewarding year in my career at the NFL. In the middle of a global pandemic, the way we are executing on our season is leveraging data from contract tracing devices joined with testing data. Talk about data, actually enabling your business without it we wouldn't be having a season right now. I'm also on the board of directors of two public companies, where data and collaboration are paramount. First RingCentral, it's a cloud based unified communications platform, and collaboration with video message and phone, all in one solution in the cloud. And Quotient Technologies, whose product is actually data. The tagline at quotient is the result in knowing. I think that's really important, because not all of us are data companies, where your product is actually data. But we should operate more like your product is data. I'd also like to talk to you about four areas of things to think about, as thought leaders in your companies. First just hit on it is change, how to be a champion and a driver of change. Second, how to use data to drive performance for your company, and measure performance of your company. Third, how companies now require intense collaboration to operate, and finally, how much of this is accomplished through solid data-driven decisions. First let's hit on change. I mean, it's evident today more than ever, that we are in an environment of extreme change. I mean, we've all been at this for years and as technologists we've known it, believed it, lived it, and thankfully for the most part knock on wood we were prepared for it. But this year everyone's cheese was moved, all the people in the back rooms, IT, data architects and others, were suddenly called to the forefront. Because a global pandemic has turned out to be the thing that is driving intense change in how people work and analyze their business. On March 13th, we closed our office at the NFL in the middle of preparing for one of our biggest events, our kickoff event, the 2020 Draft. We went from planning, a large event in Las Vegas under the bright lights red carpet stage to smaller events in club facilities. And then ultimately to one where everyone coaches, GMs, prospects and even our commissioner were at home in their basements. And we only had a few weeks to figure it out. I found myself for the first time being in the live broadcast event space, talking about bungee dress jumping, this is really what it felt like. It was one in which no one felt comfortable, because it had not been done before. But leading through this, I stepped up, but it was very scary, it was certainly very risky but it ended up being Oh, so rewarding when we did it. And as a result of this, some things will change forever. Second, managing performance. I mean, data should inform how you're doing and how to get your company to perform at this level, highest level. As an example, the NFL has always measured performance obviously, and it is one of the purest examples of how performance directly impacts outcome. I mean, you can see performance on the field, you can see points being scored and stats, and you immediately know that impact, those with the best stats, usually win the games. The NFL has always recorded stats, since the beginning of time, here at the NFL a little this year as our 100 and first year and athletes ultimate success as a player has also always been greatly impacted by his stats. But what has changed for us, is both how much more we can measure, and the immediacy with which it can be measured. And I'm sure in your business, it's the same, the amount of data you must have has got to have quadrupled recently and how fast you need it and how quickly you need to analyze it, is so important. And it's very important to break the silos between the keys to the data and the use of the data. Our next generation stats platform is taking data to a next level, it's powered by Amazon Web Services, and we gathered this data real time from sensors that are on players' bodies. We gather it in real time, analyze it, display it online and on broadcast, and of course it's used to prepare week to week in addition to what is a normal coaching plan would be. We can now analyze, visualize, route patterns speed, matchups, et cetera, so much faster than ever before. We're continuing to roll out sensors too, that we'll gather more and more information about player's performance as it relates to their health and safety. The third trend is really I think it's a big part of what we're feeling today and that is intense collaboration. And just for sort of historical purposes it's important to think about for those of you that are IT professionals and developers, you know more than 10 years ago, agile practices began sweeping companies or small teams would work together rapidly in a very flexible, adaptive and innovative way, and it proved to be transformational. However today, of course, that is no longer just small teams the next big wave of change, and we've seen it through this pandemic is that it's the whole enterprise that must collaborate and be agile. If I look back on my career when I was at Disney, we owned everything 100%, we made a decision, we implemented it, we were a collaborative culture but it was much easier to push change because you own the whole decision. If there was buy in from the top down, you got the people from the bottom up to do it, and you executed. At Universal, we were a joint venture, our attractions and entertainment was licensed, our hotels were owned and managed by other third parties. So influence and collaboration and how to share across companies became very important. And now here I am at the NFL and even the bigger ecosystem. We have 32 clubs that are all separate businesses 31 different stadiums that are owned by a variety of people. We have licensees, we have sponsors, we have broadcast partners. So it seems that as my career has evolved centralized control has gotten less and less and has been replaced by intense collaboration not only within your own company, but across companies. The ability to work in a collaborative way across businesses and even other companies that has been a big key to my success in my career. I believe this whole vertical integration and big top down decision making is going by the wayside in favor of ecosystems that require cooperation, yet competition to coexist. I mean the NFL is a great example of what we call coopertition, which is cooperation and competition. When in competition with each other, but we cooperate to make the company the best it can be. And at the heart of these items really are data-driven decisions and culture. Data on its own isn't good enough, you must be able to turn it to insights, partnerships between technology teams who usually hold the keys to the raw data, and business units who have the knowledge to build the right decision models is key. If you're not already involved in this linkage, you should be, data mining isn't new for sure. The availability of data is quadrupling and it's everywhere. How do you know what to even look at? How do you know where to begin? How do you know what questions to ask? It's by using the tools that are available for visualization and analytics and knitting together strategies of the company. So it begins with first of all making sure you do understand the strategy of the company. So in closing, just to wrap up a bit, many of you joined today looking for thought leadership on how to be a change agent, a change champion, and how to lead through transformation. Some final thoughts are be brave, and drive, don't do the ride along program, it's very important to drive, driving can be high risk but it's also high reward. Embracing the uncertainty of what will happen, is how you become brave, get more and more comfortable with uncertainty be calm and let data be your map on your journey, thanks. >> Michelle, thank you so much. So you and I share a love of data, and a love of football. You said you want to be the quarterback, I'm more an old wine person. (Michelle laughing) >> Well, then I can do my job without you. >> Great, and I'm getting the feeling now you know, Sudheesh is talking about bungee jumping. My boat is when we're past this pandemic, we both take them to the Delaware Water Gap and we do the cliff jumping. >> That sounds good, I'll watch. >> You'll watch, okay, so Michelle, you have so many stakeholders when you're trying to prioritize the different voices, you have the players, you have the owners you have the league, as you mentioned to the broadcasters your, your partners here and football mamas like myself. How do you prioritize when there's so many different stakeholders that you need to satisfy? I think balancing across stakeholders starts with aligning on a mission. And if you spend a lot of time understanding where everyone's coming from, and you can find the common thread ties them all together you sort of do get them to naturally prioritize their work, and I think that's very important. So for us at the NFL, and even at Disney, it was our core values and our core purpose is so well known, and when anything challenges that we're able to sort of lay that out. But as a change agent, you have to be very empathetic, and I would say empathy is probably your strongest skill if you're a change agent. And that means listening to every single stakeholder even when they're yelling at you, even when they're telling you your technology doesn't work and you know that it's user error, or even when someone is just emotional about what's happening to them and that they're not comfortable with it. So I think being empathetic and having a mission and understanding it, is sort of how I prioritize and balance. >> Yeah, empathy, a very popular word this year. I can imagine those coaches and owners yelling. So I thank you for your metership here. So Michelle, I look forward to discussing this more with our other customers and disruptors joining us in a little bit. (soft upbeat music) >> So we're going to take a hard pivot now and go from football to Chernobyl, Chernobyl, what went wrong? 1986, as the reactors were melting down they had the data to say, this is going to be catastrophic and yet the culture said, "No, we're perfect, hide it. Don't dare tell anyone," which meant they went ahead and had celebrations in Kiev. Even though that increased the exposure the additional thousands getting cancer, and 20,000 years before the ground around there and even be inhabited again, This is how powerful and detrimental a negative culture, a culture that is unable to confront the brutal facts that hides data. This is what we have to contend with, and this is why I want you to focus on having fostering a data-driven culture. I don't want you to be a laggard, I want you to be a leader in using data to drive your digital transformation. So I'll talk about culture and technology, isn't really two sides of the same coin, real-world impacts and then some best practices you can use to disrupt and innovate your culture. Now, oftentimes I would talk about culture and I talk about technology, and recently a CDO said to me, "You know Cindi, I actually think this is two sides of the same coin. One reflects the other, what do you think?" Let me walk you through this, so let's take a laggard. What is the technology look like? Is it based on 1990s BI and reporting largely parameterized reports on-premises data warehouses, or not even that operational reports, at best one enterprise data warehouse very slow moving and collaboration is only email. What does that culture tell you? Maybe there's a lack of leadership to change, to do the hard work that Sudheesh referred to. Or is there also a culture of fear, afraid of failure, resistance to change complacency and sometimes that complacency it's not because people are lazy, it's because they've been so beaten down every time a new idea is presented. It's like, no we're measured on least cost to serve. So politics and distrust, whether it's between business and IT or individual stakeholders is the norm. So data is hoarded, let's contrast that with a leader, a data and analytics leader, what is their technology look like? Augmented analytics, search and AI-driven insights not on-premises, but in the cloud and maybe multiple clouds. And the data is not in one place, but it's in a data lake, and in a data warehouse, a logical data warehouse. The collaboration is being a newer methods whether it's Slack or teams allowing for that real time decisioning or investigating a particular data point. So what is the culture in the leaders? It's transparent and trust, there is a trust that data will not be used to punish, that there is an ability to confront the bad news. It's innovation, valuing innovation in pursuit of the company goals, whether it's the best fan experience and player safety in the NFL or best serving your customers. It's innovative and collaborative. There's none of this, oh, well, I didn't invent that, I'm not going to look at that. There's still pride of ownership, but it's collaborating to get to a better place faster. And people feel empowered to present new ideas to fail fast, and they're energized, knowing that they're using the best technology and innovating at the pace that business requires. So data is democratized and democratized, not just for power users or analysts, but really at the point of impact what we like to call the new decision makers. Or really the frontline workers. So Harvard business review partnered with us to develop this study to say, just how important is this? They've been working at BI and analytics as an industry for more than 20 years. Why is it not at the front lines? Whether it's a doctor, a nurse, a coach, a supply chain manager a warehouse manager, a financial services advisor. 87% said they would be more successful if frontline workers were empowered with data-driven insights, but they recognize they need new technology to be able to do that. It's not about learning hard tools, the sad reality only 20% of organizations are actually doing this, these are the data-driven leaders. So this is the culture and technology, how did we get here? It's because state of the art keeps changing. So the first generation BI and analytics platforms were deployed on-premises, on small datasets really just taking data out of ERP systems that were also on-premises, and state of the art was maybe getting a management report, an operational report. Over time visual based data discovery vendors, disrupted these traditional BI vendors, empowering now analysts to create visualizations with the flexibility on a desktop, sometimes larger data sometimes coming from a data warehouse, the current state of the art though, Gartner calls it augmented analytics, at ThoughtSpot, we call it search and AI-driven analytics. And this was pioneered for large scale data sets, whether it's on-premises or leveraging the cloud data warehouses, and I think this is an important point. Oftentimes you, the data and analytics leaders, will look at these two components separately, but you have to look at the BI and analytics tier in lockstep with your data architectures to really get to the granular insights, and to leverage the capabilities of AI. Now, if you've never seen ThoughtSpot I'll just show you what this looks like, instead of somebody's hard coding a report, it's typing in search keywords and very robust keywords contains rank, top, bottom getting to a visualization that then can be pinned to an existing Pinboard that might also contain insights generated by an AI engine. So it's easy enough for that new decision maker, the business user, the non analyst to create themselves. Modernizing the data and analytics portfolio is hard, because the pace of change has accelerated. You used to be able to create an investment, place a bet for maybe 10 years. A few years ago, that time horizon was five years, now it's maybe three years, and the time to maturity has also accelerated. So you have these different components the search and AI tier, the data science tier, data preparation and virtualization. But I would also say equally important is the cloud data warehouse. And pay attention to how well these analytics tools can unlock the value in these cloud data warehouses. So ThoughtSpot was the first to market with search and AI-driven insights. Competitors have followed suit, but be careful if you look at products like Power BI or SAP Analytics Cloud, they might demo well, but do they let you get to all the data without moving it in products like Snowflake, Amazon Redshift or Azure Synapse or Google BigQuery, they do not. They require you to move it into a smaller in memory engine. So it's important how well these new products inter operate. The pace of change, it's acceleration, Gartner recently predicted that by 2022, 65% of analytical queries will be generated using search or NLP or even AI, and that is roughly three times the prediction they had just a couple years ago. So let's talk about the real world impact of culture. And if you've read any of my books or used any of the maturity models out there whether the Gartner IT score that I worked on, or the data warehousing institute also has a maturity model. We talk about these five pillars to really become data-driven, as Michelle spoke about, it's focusing on the business outcomes, leveraging all the data, including new data sources. It's the talent, the people, the technology, and also the processes, and often when I would talk about the people in the talent, I would lump the culture as part of that. But in the last year, as I've traveled the world and done these digital events for thought leaders you have told me now culture is absolutely so important. And so we've pulled it out as a separate pillar, and in fact, in polls that we've done in these events, look at how much more important culture is, as a barrier to becoming data-driven. It's three times as important as any of these other pillars. That's how critical it is, and let's take an example of where you can have great data but if you don't have the right culture there's devastating impacts. And I will say, I have been a loyal customer of Wells Fargo for more than 20 years, but look at what happened in the face of negative news with data, that said, "Hey, we're not doing good cross selling, customers do not have both a checking account and a credit card and a savings account and a mortgage." They opened fake accounts, facing billions in fines, change in leadership, that even the CEO attributed to a toxic sales culture, and they're trying to fix this. But even recently there's been additional employee backlash saying that culture has not changed. Let's contrast that with some positive examples, Medtronic a worldwide company in 150 countries around the world, they may not be a household name to you, but if you have a loved one or yourself, you have a pacemaker, spinal implant, diabetes you know, this brand. And at the start of COVID when they knew their business would be slowing down, because hospitals would only be able to take care of COVID patients, they took the bold move of making their IP for ventilators publicly available, that is the power of a positive culture. Or Verizon, a major telecom organization, looking at late payments of their customers, and even though the US federal government said "Well, you can't turn them off." They said, "We'll extend that even beyond the mandated guidelines," and facing a slow down in the business because of the tough economy, he said, "You know what? We will spend the time upskilling our people giving them the time to learn more about the future of work, the skills and data and analytics," for 20,000 of their employees, rather than furloughing them. That is the power of a positive culture. So how can you transform your culture to the best in class? I'll give you three suggestions, bring in a change agent identify the relevance, or I like to call it WIIFM, and organize for collaboration. So the CDO whatever your title is, chief analytics officer chief digital officer, you are the most important change agent. And this is where you will hear, that oftentimes a change agent has to come from outside the organization. So this is where, for example in Europe, you have the CDO of Just Eat takeout food delivery organization, coming from the airline industry or in Australia, National Australian Bank, taking a CDO within the same sector from TD Bank going to NAB. So these change agents come in disrupt, it's a hard job. As one of you said to me, it often feels like Sisyphus, I make one step forward and I get knocked down again, I get pushed back. It is not for the faint of heart, but it's the most important part of your job. The other thing I'll talk about is WIIFM, what is in it for me? And this is really about understanding the motivation, the relevance that data has for everyone on the frontline as well as those analysts, as well as the executives. So if we're talking about players in the NFL they want to perform better, and they want to stay safe. That is why data matters to them. If we're talking about financial services this may be a wealth management advisor, okay, we could say commissions, but it's really helping people have their dreams come true whether it's putting their children through college, or being able to retire without having to work multiple jobs still into your 70s or 80s. For the teachers, teachers, you asked them about data, they'll say, "We don't need that, I care about the student." So if you can use data to help a student perform better that is WIIFM. And sometimes we spend so much time talking the technology, we forget what is the value we're trying to deliver with it. And we forget the impact on the people that it does require change. In fact, the Harvard Business Review Study, found that 44% said lack of change management is the biggest barrier to leveraging both new technology but also being empowered to act on those data-driven insights. The third point, organize for collaboration. This does require diversity of thought, but also bringing the technology, the data and the business people together. Now there's not a single one size fits all model for data and analytics. At one point in time, even having a BICC, a BI Competency Center was considered state of the art. Now for the biggest impact, what I recommend is that you have a federated model, centralized for economies of scale, that could be the common data, but then in bed, these evangelists, these analysts of the future, within every business unit, every functional domain, and as you see this top bar, all models are possible but the hybrid model has the most impact, the most leaders. So as we look ahead to the months ahead, to the year ahead, an exciting time, because data is helping organizations better navigate a tough economy lock in the customer loyalty, and I look forward to seeing how you foster that culture that's collaborative with empathy and bring the best of technology, leveraging the cloud, all your data. So thank you for joining us at thought leaders, and next I'm pleased to introduce our first change agent Thomas Mazzaferro, chief data officer of Western Union, and before joining Western Union, Tom made his mark at HSBC and JP Morgan Chase spearheading digital innovation in technology operations, risk compliance, and retail banking. Tom, thank you so much for joining us today. (soft upbeat music) >> Very happy to be here and looking forward to talking to all of you today. So as we look to move organizations to a data-driven capability into the future, there is a lot that needs to be done on the data side, but also how does data connect and enable, different business teams and technology teams into the future. As we look across our data ecosystems and our platforms and how we modernize that to the cloud in the future, it all needs to basically work together, right? To really be able to drive over the shift from a data standpoint, into the future. That includes being able to have the right information with the right quality of data at the right time to drive informed business decisions, to drive the business forward. As part of that, we actually have partnered with ThoughtSpot to actually bring in the technology to help us drive that, as part of that partnership, and it's how we've looked to integrated into our overall business as a whole. We've looked at how do we make sure that our business and our professional lives, right? Are enabled in the same ways as our personal lives. So for example, in your personal lives, when you want to go and find something out, what do you do? You go on to google.com or you go on to Bing, or go to Yahoo and you search for what you want, search to find an answer. ThoughtSpot for us as the same thing, but in the business world. So using ThoughtSpot and other AI capability is allowed us to actually enable our overall business teams in our company, to actually have our information at our fingertips. So rather than having to go and talk to someone or an engineer to go pull information or pull data, we actually can have the end users or the business executives, right? Search for what they need, what they want, at the exact time that action needed, to go and drive the business forward. This is truly one of those transformational things that we've put in place. On top of that, we are on the journey to modernize our larger ecosystem as a whole. That includes modernizing our underlying data warehouses, our technology or our (indistinct) environments, and as we move that we've actually picked to our cloud providers going to AWS and GCP. We've also adopted Snowflake to really drive into organize our information and our data, then drive these new solutions and capabilities forward. So big portion of us though is culture, so how do we engage with the business teams and bring the IT teams together to really drive these holistic end to end solutions and capabilities, to really support the actual business into the future. That's one of the keys here, as we look to modernize and to really enhance our organizations to become data-driven, this is the key. If you can really start to provide answers to business questions before they're even being asked, and to predict based upon different economic trends or different trends in your business, what does is be made and actually provide those answers to the business teams before they're even asking for it. That is really becoming a data-driven organization. And as part of that, it's really then enables the business to act quickly and take advantage of opportunities as they come in based upon industries, based upon markets, based upon products, solutions, or partnerships into the future. These are really some of the keys that become crucial as you move forward right into this new age, especially with COVID, with COVID now taking place across the world, right? Many of these markets, many of these digital transformations are celebrating, and are changing rapidly to accommodate and to support customers in these very difficult times. As part of that, you need to make sure you have the right underlying foundation, ecosystems and solutions to really drive those capabilities, and those solutions forward. As we go through this journey, both of my career but also each of your careers into the future, right? It also needs to evolve, right? Technology has changed so drastically in the last 10 years, and that change is only a celebrating. So as part of that, you have to make sure that you stay up to speed, up to date with new technology changes both on the platform standpoint, tools, but also what our customers want, what do our customers need, and how do we then surface them with our information, with our data, with our platform, with our products and our services, to meet those needs and to really support and service those customers into the future. This is all around becoming a more data-driven organization such as how do you use your data to support the current business lines. But how do you actually use your information your data, to actually better support your customers better support your business, better support your employees, your operations teams and so forth, and really creating that full integration in that ecosystem is really when you start to get large dividends from these investments into the future. With that being said I hope you enjoyed the segment on how to become and how to drive a data-driven organization, and looking forward to talking to you again soon, thank you. >> Tom, that was great, thanks so much. Now I'm going to have to brag on you for a second, as a change agent you've come in disrupted, and how long have you been at Western Union? >> Only nine months, I just started this year, but there'd be some great opportunities and big changes, and we have a lot more to go, but we're really driving things forward in partnership with our business teams, and our colleagues to support those customers forward. >> Tom, thank you so much that was wonderful. And now I'm excited to introduce you to Gustavo Canton, a change agent that I've had the pleasure of working with meeting in Europe, and he is a serial change agent. Most recently with Schneider Electric, but even going back to Sam's Club, Gustavo welcome. (soft upbeat music) >> So hi everyone my name is Gustavo Canton and thank you so much Cindi for the intro. As you mentioned, doing transformations is a you know, high effort, high reward situation. I have empowerment in transformation and I have led many transformations. And what I can tell you is that it's really hard to predict the future, but if you have a North Star and you know where you're going, the one thing that I want you to take away from this discussion today, is that you need to be bold to evolve. And so in today, I'm going to be talking about culture and data, and I'm going to break this down in four areas. How do we get started barriers or opportunities as I see it, the value of AI, and also how do you communicate, especially now in the workforce of today with so many different generations, you need to make sure that you are communicating in ways that are nontraditional sometimes. And so how do we get started? So I think the answer to that is, you have to start for you, yourself as a leader and stay tuned. And by that, I mean you need to understand not only what is happening in your function or your field, but you have to be very into what is happening in society, socioeconomically speaking, wellbeing, you know, the common example is a great example. And for me personally, it's an opportunity because the number one core value that I have is wellbeing. I believe that for human potential, for customers and communities to grow, wellbeing should be at the center of every decision. And as somebody mentioned, it's great to be you know, stay in tune and have the skillset and the courage. But for me personally, to be honest to have this courage is not about not being afraid. You're always afraid when you're making big changes and your swimming upstream. But what gives me the courage is the empathy part, like I think empathy is a huge component because every time I go into an organization or a function, I try to listen very attentively to the needs of the business, and what the leaders are trying to do, what I do it thinking about the mission of how do I make change for the bigger, you know workforce so the bigger good, despite the fact that this might have a perhaps implication, so my own self interest in my career, right? Because you have to have that courage sometimes to make choices, that are not well seeing politically speaking what are the right thing to do, and you have to push through it. So the bottom line for me is that, I don't think they're transforming fast enough. And the reality is I speak with a lot of leaders and we have seen stories in the past, and what they show is that if you look at the four main barriers, that are basically keeping us behind budget, inability to add, cultural issues, politics, and lack of alignment, those are the top four. But the interesting thing is that as Cindi has mentioned, this topic about culture is actually gaining more and more traction, and in 2018, there was a story from HBR and it was for about 45%. I believe today, it's about 55%, 60% of respondents say that this is the main area that we need to focus on. So again, for all those leaders and all the executives who understand, and are aware that we need to transform, commit to the transformation and set us deadline to say, "Hey, in two years, we're going to make this happen, what do we need to do to empower and enable these search engines to make it happen?" You need to make the tough choices. And so to me, when I speak about being bold is about making the right choices now. So I'll give you samples of some of the roadblocks that I went through, as I think the intro information most recently as Cindi mentioned in Schneider. There are three main areas, legacy mindset, and what that means is that we've been doing this in a specific way for a long time, and here is how we have been successful. We're working the past is not going to work now, the opportunity there is that there is a lot of leaders who have a digital mindset, and their up and coming leaders that are perhaps not yet fully developed. We need to mentor those leaders and take bets on some of these talents, including young talent. We cannot be thinking in the past and just wait for people you know, three to five years for them to develop, because the world is going to in a way that is super fast. The second area and this is specifically to implementation of AI is very interesting to me, because just example that I have with ThoughtSpot, right? We went to an implementation and a lot of the way the IT team functions, so the leaders look at technology, they look at it from the prism of the prior or success criteria for the traditional BIs, and that's not going to work. Again, your opportunity here is that you need to really find what success look like, in my case, I want the user experience of our workforce to be the same as your experience you have at home. It's a very simple concept, and so we need to think about how do we gain that user experience with this augmented analytics tools, and then work backwards to have the right talent, processes and technology to enable that. And finally, and obviously with COVID a lot of pressure in organizations and companies to do more with less, and the solution that most leaders I see are taking is to just minimize cost sometimes and cut budget. We have to do the opposite, we have to actually invest some growth areas, but do it by business question. Don't do it by function, if you actually invest in these kind of solutions, if you actually invest on developing your talent, your leadership, to see more digitally, if you actually invest on fixing your data platform is not just an incremental cost, it's actually this investment is going to offset all those hidden costs and inefficiencies that you have on your system, because people are doing a lot of work in working very hard but it's not efficiency, and it's not working in the way that you might want to work. So there is a lot of opportunity there, and you just to put it into some perspective, there have been some studies in the past about you know, how do we kind of measure the impact of data? And obviously this is going to vary by organization, maturity there's going to be a lot of factors. I've been in companies who have very clean, good data to work with, and I think with companies that we have to start basically from scratch. So it all depends on your maturity level, but in this study what I think is interesting is, they try to put a tagline or attack price to what is a cost of incomplete data. So in this case, it's about 10 times as much to complete a unit of work, when you have data that is flawed as opposed to have imperfect data. So let me put that just in perspective, just as an example, right? Imagine you are trying to do something and you have to do 100 things in a project, and each time you do something it's going to cost you a dollar. So if you have perfect data, the total cost of that project might be a $100. But now let's say you have any percent perfect data and 20% flow data, by using this assumption that flow data is 10 times as costly as perfect data, your total costs now becomes $280 as opposed to $100, this just for you to really think about as a CIO, CTO, you know CSRO, CEO, are we really paying attention and really closing the gaps that we have on our infrastructure? If we don't do that, it's hard sometimes to see the snowball effect or to measure the overall impact, but as you can tell, the price tag goes up very, very quickly. So now, if I were to say, how do I communicate this? Or how do I break through some of these challenges or some of these barriers, right? I think the key is I am in analytics, I know statistics obviously, and love modeling and you know, data and optimization theory and all that stuff, that's what I can do analytics, but now as a leader and as a change agent, I need to speak about value, and in this case, for example for Schneider, there was this tagline coffee of your energy. So the number one thing that they were asking from the analytics team was actually efficiency, which to me was very interesting. But once I understood that I understood what kind of language to use, how to connect it to the overall strategy and basically how to bring in the right leaders, because you need to, you know, focus on the leaders that you're going to make the most progress. You know, again, low effort, high value, you need to make sure you centralize all the data as you can, you need to bring in some kind of augmented analytics, you know, solution, and finally you need to make it super simple for the you know, in this case, I was working with the HR teams and other areas, so they can have access to one portal. They don't have to be confused and looking for 10 different places to find information. I think if you can actually have those four foundational pillars, obviously under the guise of having a data-driven culture, that's when you can actually make the impact. So in our case, it was about three years total transformation but it was two years for this component of augmented analytics. It took about two years to talk to, you know, IT, get leadership support, find the budgeting, you know, get everybody on board, make sure the success criteria was correct. And we call this initiative, the people analytics, I pulled up, it was actually launched in July of this year. And we were very excited and the audience was very excited to do this. In this case, we did our pilot in North America for many, many manufacturers, but one thing that is really important is as you bring along your audience on this, you know, you're going from Excel, you know in some cases or Tableau to other tools like you know, ThoughtSpot, you need to really explain them, what is the difference, and how these two can truly replace some of the spreadsheets or some of the views that you might have on these other kind of tools. Again, Tableau, I think it's a really good tool, there are other many tools that you might have in your toolkit. But in my case, personally I feel that you need to have one portal going back to seeing these points that really truly enable the end user. And I feel that this is the right solution for us, right? And I will show you some of the findings that we had in the pilot in the last two months. So this was a huge victory, and I will tell you why, because it took a lot of effort for us to get to these stations. Like I said it's been years for us to kind of lay the foundation, get the leadership and chasing culture, so people can understand why you truly need to invest what I meant analytics. And so what I'm showing here is an example of how do we use basically, you know a tool to capturing video, the qualitative findings that we had, plus the quantitative insights that we have. So in this case, our preliminary results based on our ambition for three main metrics, hours saved, user experience and adoption. So for hours saved, our ambition was to have 10 hours per week per employee save on average, user experience or ambition was 4.5 and adoption 80%. In just two months, two months and a half of the pilot we were able to achieve five hours, per week per employee savings. I used to experience for 4.3 out of five, and adoption of 60%. Really, really amazing work. But again, it takes a lot of collaboration for us to get to the stage from IT, legal, communications obviously the operations things and the users, in HR safety and other areas that might be basically stakeholders in this whole process. So just to summarize this kind of effort takes a lot of energy, you are a change agent, you need to have a courage to make these decision and understand that, I feel that in this day and age with all this disruption happening, we don't have a choice. We have to take the risk, right? And in this case, I feel a lot of satisfaction in how we were able to gain all these very souls for this organization, and that gave me the confidence to know that the work has been done, and we are now in a different stage for the organization. And so for me it safe to say, thank you for everybody who has believed obviously in our vision, everybody who has believed in, you know, the word that we were trying to do and to make the life for, you know workforce or customers that are in community better. As you can tell, there is a lot of effort, there is a lot of collaboration that is needed to do something like this. In the end, I feel very satisfied with the accomplishments of this transformation, and I just want to tell for you, if you are going right now in a moment that you feel that you have to swim upstream you know, what would mentors what people in this industry that can help you out and guide you on this kind of a transformation is not easy to do is high effort but is well worth it. And with that said, I hope you are well and it's been a pleasure talking to you, talk to you soon, take care. >> Thank you Gustavo, that was amazing. All right, let's go to the panel. (soft upbeat music) >> I think we can all agree how valuable it is to hear from practitioners, and I want to thank the panel for sharing their knowledge with the community, and one common challenge that I heard you all talk about was bringing your leadership and your teams along on the journey with you. We talk about this all the time, and it is critical to have support from the top, why? Because it directs the middle, and then it enables bottoms up innovation effects from the cultural transformation that you guys all talked about. It seems like another common theme we heard, is that you all prioritize database decision making in your organizations, and you combine two of your most valuable assets to do that, and create leverage, employees on the front lines, and of course the data. That was rightly pointed out, Tom, the pandemic has accelerated the need for really leaning into this. You know, the old saying, if it ain't broke, don't fix it, well COVID's broken everything. And it's great to hear from our experts, you know, how to move forward, so let's get right into it. So Gustavo let's start with you if I'm an aspiring change agent, and let's say I'm a budding data leader. What do I need to start doing? What habits do I need to create for long lasting success? >> I think curiosity is very important. You need to be, like I say, in tune to what is happening not only in your specific field, like I have a passion for analytics, I can do this for 50 years plus, but I think you need to understand wellbeing other areas across not only a specific business as you know, I come from, you know, Sam's Club Walmart retail, I mean energy management technology. So you have to try to push yourself and basically go out of your comfort zone. I mean, if you are staying in your comfort zone and you want to use lean continuous improvement that's just going to take you so far. What you have to do is and that's what I tried to do is I try to go into areas, businesses and transformations that make me, you know stretch and develop as a leader. That's what I'm looking to do, so I can help transform the functions organizations, and do these change management and decisions mindset as required for these kinds of efforts. >> Thank you for that is inspiring and Cindi, you love data, and the data is pretty clear that diversity is a good business, but I wonder if you can add your perspectives to this conversation. >> Yeah, so Michelle has a new fan here because she has found her voice, I'm still working on finding mine. And it's interesting because I was raised by my dad, a single dad, so he did teach me how to work in a predominantly male environment. But why I think diversity matters more now than ever before, and this is by gender, by race, by age, by just different ways of working and thinking is because as we automate things with AI, if we do not have diverse teams looking at the data and the models, and how they're applied, we risk having bias at scale. So this is why I think I don't care what type of minority, you are finding your voice, having a seat at the table and just believing in the impact of your work has never been more important. And as Michelle said more possible >> Great perspectives thank you, Tom, I want to go to you. I mean, I feel like everybody in our businesses in some way, shape or form become a COVID expert but what's been the impact of the pandemic on your organization's digital transformation plans? >> We've seen a massive growth actually you know, in a digital business over the last 12 months really, even in celebration, right? Once COVID hit, we really saw that in the 200 countries and territories that we operate in today and service our customers and today, that there's been a huge need, right? To send money, to support family, to support friends and loved ones across the world. And as part of that, you know, we are very honored to support those customers that we across all the centers today. But as part of that celebration, we need to make sure that we had the right architecture and the right platforms to basically scale, right? To basically support and provide the right kind of security for our customers going forward. So as part of that, we did do some pivots and we did celebrate some of our plans on digital to help support that overall growth coming in, and to support our customers going forward. Because there were these times during this pandemic, right? This is the most important time, and we need to support those that we love and those that we care about. And in doing that, it's one of those ways is actually by sending money to them, support them financially. And that's where really are part of that our services come into play that, you know, I really support those families. So it was really a great opportunity for us to really support and really bring some of our products to this level, and supporting our business going forward. >> Awesome, thank you. Now I want to come back to Gustavo, Tom, I'd love for you to chime in too. Did you guys ever think like you were pushing the envelope too much and doing things with data or the technology that was just maybe too bold, maybe you felt like at some point it was failing, or you pushing your people too hard, can you share that experience and how you got through it? >> Yeah, the way I look at it is, you know, again, whenever I go to an organization I ask the question, Hey, how fast you would like to conform?" And, you know, based on the agreements on the leadership and the vision that we want to take place, I take decisions and I collaborate in a specific way. Now, in the case of COVID, for example, right? It forces us to remove silos and collaborate in a faster way, so to me it was an opportunity to actually integrate with other areas and drive decisions faster. But make no mistake about it, when you are doing a transformation, you are obviously trying to do things faster than sometimes people are comfortable doing and you need to be okay with that. Sometimes you need to be okay with tension, or you need to be okay, you know debating points or making repetitive business cases onto people connect with the decision because you understand, and you are seeing that, hey, the CEO is making a one, two year, you know, efficiency goal, the only way for us to really do more with less is for us to continue this path. We cannot just stay with the status quo, we need to find a way to accelerate transformation... >> How about you Tom, we were talking earlier was Sudheesh had said about that bungee jumping moment, what can you share? >> Yeah you know, I think you hit upon it. Right now, the pace of change will be the slowest pace that you see for the rest of your career. So as part of that, right? That's what I tell my team is that you need to feel comfortable being uncomfortable. I mean, that we have to be able to basically scale, right? Expand and support that the ever changing needs the marketplace and industry and our customers today and that pace of change that's happening, right? And what customers are asking for, and the competition the marketplace, it's only going to accelerate. So as part of that, you know, as we look at what how you're operating today in your current business model, right? Things are only going to get faster. So you have to plan into align, to drive the actual transformation, so that you can scale even faster into the future. So as part of that, so we're putting in place here, right? Is how do we create that underlying framework and foundation that allows the organization to basically continue to scale and evolve into the future? >> We're definitely out of our comfort zones, but we're getting comfortable with it. So, Cindi, last question, you've worked with hundreds of organizations, and I got to believe that you know, some of the advice you gave when you were at Gartner, which is pre COVID, maybe sometimes clients didn't always act on it. You know, they're not on my watch for whatever variety of reasons, but it's being forced on them now, but knowing what you know now that you know, we're all in this isolation economy how would you say that advice has changed, has it changed? What's your number one action and recommendation today? >> Yeah well, first off, Tom just freaked me out. What do you mean this is the slowest ever? Even six months ago, I was saying the pace of change in data and analytics is frenetic. So, but I think you're right, Tom, the business and the technology together is forcing this change. Now, Dave, to answer your question, I would say the one bit of advice, maybe I was a little more, very aware of the power in politics and how to bring people along in a way that they are comfortable, and now I think it's, you know what? You can't get comfortable. In fact, we know that the organizations that were already in the cloud, have been able to respond and pivot faster. So if you really want to survive as Tom and Gustavo said, get used to being uncomfortable, the power and politics are going to happen. Break the rules, get used to that and be bold. Do not be afraid to tell somebody they're wrong and they're not moving fast enough. I do think you have to do that with empathy as Michelle said, and Gustavo, I think that's one of the key words today besides the bungee jumping. So I want to know where's Sudheesh going to go on bungee jumping? (all chuckling) >> That's fantastic discussion really. Thanks again to all the panelists and the guests, it was really a pleasure speaking with you today. Really virtually all of the leaders that I've spoken to in theCUBE program recently, they tell me that the pandemic is accelerating so many things, whether it's new ways to work, we heard about new security models and obviously the need for cloud. I mean, all of these things are driving true enterprise wide digital transformation, not just as I said before lip service. And sometimes we minimize the importance and the challenge of building culture and in making this transformation possible. But when it's done right, the right culture is going to deliver tremendous results. Yeah, what does that mean getting it right? Everybody's trying to get it right. My biggest takeaway today, is it means making data part of the DNA of your organization. And that means making it accessible to the people in your organization that are empowered to make decisions that can drive you revenue, cut costs, speed, access to critical care, whatever the mission is of your organization. Data can create insights and informed decisions that drive value. Okay, let's bring back Sudheesh and wrap things up. Sudheesh please bring us home. >> Thank you, thank you Dave, thank you theCUBE team, and thanks goes to all of our customers and partners who joined us, and thanks to all of you for spending the time with us. I want to do three quick things and then close it off. The first thing is I want to summarize the key takeaways that I had from all four of our distinguished speakers. First, Michelle, I was simply put it, she said it really well, that is be brave and drive. Don't go for a drive along, that is such an important point. Often times, you know that I think that you have to do to make the positive change that you want to see happen. But you wait for someone else to do it, why not you? Why don't you be the one making that change happen? That's the thing that I picked up from Michelle's talk. Cindi talked about finding the importance of finding your voice, taking that chair, whether it's available or not and making sure that your ideas, your voices are heard and if it requires some force then apply that force, make sure your ideas are good. Gustavo talked about the importance of building consensus, not going at things all alone sometimes building the importance of building the courtroom. And that is critical because if you want the changes to last, you want to make sure that the organization is fully behind it. Tom instead of a single take away, what I was inspired by is the fact that a company that is 170 years old, 170 years old, 200 companies and 200 countries they're operating in, and they were able to make the change that is necessary through this difficult time. So in a matter of months, if they could do it, anyone could. The second thing I want to do is to leave you with a takeaway that is I would like you to go to thoughtspot.com/nfl because our team has made an app for NFL on Snowflake. I think you will find this interesting now that you are inspired and excited because of Michelle's talk. And the last thing is, please go to thoughtspot.com/beyond, our global user conferences happening in this December, we would love to have you join us. It's again, virtual, you can join from anywhere, we are expecting anywhere from five to 10,000 people, and we would love to have you join and see what we would have been up to since the last year. We have a lot of amazing things in store for you, our customers, our partners, our collaborators, they will be coming and sharing, you'll be sharing things that you have been working to release something that will come out next year. And also some of the crazy ideas for engineers I've been cooking up. All of those things will be available for you at ThoughtSpot Beyond, thank you, thank you so much.

(speaks in foreign language) >> Narrator: Data is at the heart of transformation and the change every company needs to succeed, but it takes more than new technology. It's about teams, talent, and cultural change. Empowering everyone on the front lines to make decisions, all at the speed of digital. The transformation starts with you. It's time to lead the way, it's time for thought leaders. >> Welcome to Thought Leaders, a digital event brought to you by ThoughtSpot. My name is Dave Vellante. The purpose of this day is to bring industry leaders and experts together to really try and understand the important issues around digital transformation. We have an amazing lineup of speakers and our goal is to provide you with some best practices that you can bring back and apply to your organization. Look, data is plentiful, but insights are not. ThoughtSpot is disrupting analytics by using search and machine intelligence to simplify data analysis, and really empower anyone with fast access to relevant data. But in the last 150 days, we've had more questions than answers. Creating an organization that puts data and insights at their core, requires not only modern technology, but leadership, a mindset and a culture that people often refer to as data-driven. What does that mean? How can we equip our teams with data and fast access to quality information that can turn insights into action. And today, we're going to hear from experienced leaders, who are transforming their organizations with data, insights and creating digital-first cultures. But before we introduce our speakers, I'm joined today by two of my co-hosts from ThoughtSpot. First, Chief Data Strategy Officer for ThoughtSpot is Cindi Hausen. Cindi is an analytics and BI expert with 20 plus years experience and the author of Successful Business Intelligence Unlock The Value of BI and Big Data. Cindi was previously the lead analyst at Gartner for the data and analytics magic quadrant. And early last year, she joined ThoughtSpot to help CDOs and their teams understand how best to leverage analytics and AI for digital transformation. Cindi, great to see you, welcome to the show. >> Thank you, Dave. Nice to join you virtually. >> Now our second cohost and friend of theCUBE is ThoughtSpot CEO Sudheesh Nair. Hello Sudheesh, how are you doing today? >> I am well Dave, it's good to talk to you again. >> It's great to see you. Thanks so much for being here. Now Sudheesh, please share with us why this discussion is so important to your customers and of course, to our audience and what they're going to learn today? (gentle music) >> Thanks, Dave, I wish you were there to introduce me into every room that I walk into because you have such an amazing way of doing it. It makes me feel also good. Look, since we have all been cooped up in our homes, I know that the vendors like us, we have amped up our, you know, sort of effort to reach out to you with invites for events like this. So we are getting way more invites for events like this than ever before. So when we started planning for this, we had three clear goals that we wanted to accomplish. And our first one that when you finish this and walk away, we want to make sure that you don't feel like it was a waste of time. We want to make sure that we value your time, and this is going to be useful. Number two, we want to put you in touch with industry leaders and thought leaders, and generally good people that you want to hang around with long after this event is over. And number three, as we plan through this, you know, we are living through these difficult times, we want an event to be, this event to be more of an uplifting and inspiring event too. Now, the challenge is, how do you do that with the team being change agents? Because change and as much as we romanticize it, it is not one of those uplifting things that everyone wants to do or likes to do. The way I think of it, change is sort of like, if you've ever done bungee jumping. You know, it's like standing on the edges, waiting to make that one more step. You know, all you have to do is take that one step and gravity will do the rest, but that is the hardest step to take. Change requires a lot of courage and when we are talking about data and analytics, which is already like such a hard topic, not necessarily an uplifting and positive conversation, in most businesses it is somewhat scary. Change becomes all the more difficult. Ultimately change requires courage. Courage to to, first of all, challenge the status quo. People sometimes are afraid to challenge the status quo because they are thinking that, "You know, maybe I don't have the power to make the change that the company needs. Sometimes I feel like I don't have the skills." Sometimes they may feel that, I'm probably not the right person to do it. Or sometimes the lack of courage manifest itself as the inability to sort of break the silos that are formed within the organizations, when it comes to data and insights that you talked about. You know, there are people in the company, who are going to hog the data because they know how to manage the data, how to inquire and extract. They know how to speak data, they have the skills to do that, but they are not the group of people who have sort of the knowledge, the experience of the business to ask the right questions off the data. So there is this silo of people with the answers and there is a silo of people with the questions, and there is gap. These sort of silos are standing in the way of making that necessary change that we all I know the business needs, and the last change to sort of bring an external force sometimes. It could be a tool, it could be a platform, it could be a person, it could be a process, but sometimes no matter how big the company is or how small the company is. You may need to bring some external stimuli to start that domino of the positive changes that are necessary. The group of people that we have brought in, the four people, including Cindi, that you will hear from today are really good at practically telling you how to make that step, how to step off that edge, how to trust the rope that you will be safe and you're going to have fun. You will have that exhilarating feeling of jumping for a bungee jump. All four of them are exceptional, but my honor is to introduce Michelle and she's our first speaker. Michelle, I am very happy after watching her presentation and reading her bio, that there are no country vital worldwide competition for cool patents, because she will beat all of us because when her children were small, you know, they were probably into Harry Potter and Disney and she was managing a business and leading change there. And then as her kids grew up and got to that age, where they like football and NFL, guess what? She's the CIO of NFL. What a cool mom. I am extremely excited to see what she's going to talk about. I've seen the slides with a bunch of amazing pictures, I'm looking to see the context behind it. I'm very thrilled to make the acquaintance of Michelle. I'm looking forward to her talk next. Welcome Michelle. It's over to you. (gentle music) >> I'm delighted to be with you all today to talk about thought leadership. And I'm so excited that you asked me to join you because today I get to be a quarterback. I always wanted to be one. This is about as close as I'm ever going to get. So, I want to talk to you about quarterbacking our digital revolution using insights, data and of course, as you said, leadership. First, a little bit about myself, a little background. As I said, I always wanted to play football and this is something that I wanted to do since I was a child but when I grew up, girls didn't get to play football. I'm so happy that that's changing and girls are now doing all kinds of things that they didn't get to do before. Just this past weekend on an NFL field, we had a female coach on two sidelines and a female official on the field. I'm a lifelong fan and student of the game of football. I grew up in the South. You can tell from the accent and in the South football is like a religion and you pick sides. I chose Auburn University working in the athletic department, so I'm testament. Till you can start, a journey can be long. It took me many, many years to make it into professional sports. I graduated in 1987 and my little brother, well not actually not so little, he played offensive line for the Alabama Crimson Tide. And for those of you who know SEC football, you know this is a really big rivalry, and when you choose sides your family is divided. So it's kind of fun for me to always tell the story that my dad knew his kid would make it to the NFL, he just bet on the wrong one. My career has been about bringing people together for memorable moments at some of America's most iconic brands, delivering memories and amazing experiences that delight. From Universal Studios, Disney, to my current position as CIO of the NFL. In this job, I'm very privileged to have the opportunity to work with a team that gets to bring America's game to millions of people around the world. Often, I'm asked to talk about how to create amazing experiences for fans, guests or customers. But today, I really wanted to focus on something different and talk to you about being behind the scenes and backstage. Because behind every event, every game, every awesome moment, is execution. Precise, repeatable execution and most of my career has been behind the scenes doing just that. Assembling teams to execute these plans and the key way that companies operate at these exceptional levels is making good decisions, the right decisions, at the right time and based upon data. So that you can translate the data into intelligence and be a data-driven culture. Using data and intelligence is an important way that world-class companies do differentiate themselves, and it's the lifeblood of collaboration and innovation. Teams that are working on delivering these kind of world class experiences are often seeking out and leveraging next generation technologies and finding new ways to work. I've been fortunate to work across three decades of emerging experiences, which each required emerging technologies to execute. A little bit first about Disney. In '90s I was at Disney leading a project called Destination Disney, which it's a data project. It was a data project, but it was CRM before CRM was even cool and then certainly before anything like a data-driven culture was ever brought up. But way back then we were creating a digital backbone that enabled many technologies for the things that you see today. Like the MagicBand, Disney's Magical Express. My career at Disney began in finance, but Disney was very good about rotating you around. And it was during one of these rotations that I became very passionate about data. I kind of became a pain in the butt to the IT team asking for data, more and more data. And I learned that all of that valuable data was locked up in our systems. All of our point of sales systems, our reservation systems, our operation systems. And so I became a shadow IT person in marketing, ultimately, leading to moving into IT and I haven't looked back since. In the early 2000s, I was at Universal Studio's theme park as their CIO preparing for and launching the Wizarding World of Harry Potter. Bringing one of history's most memorable characters to life required many new technologies and a lot of data. Our data and technologies were embedded into the rides and attractions. I mean, how do you really think a wand selects you at a wand shop. As today at the NFL, I am constantly challenged to do leading edge technologies, using things like sensors, AI, machine learning and all new communication strategies, and using data to drive everything, from player performance, contracts, to where we build new stadiums and hold events. With this year being the most challenging, yet rewarding year in my career at the NFL. In the middle of a global pandemic, the way we are executing on our season is leveraging data from contact tracing devices joined with testing data. Talk about data actually enabling your business. Without it we wouldn't be having a season right now. I'm also on the board of directors of two public companies, where data and collaboration are paramount. First, RingCentral, it's a cloud based unified communications platform and collaboration with video message and phone, all-in-one solution in the cloud and Quotient Technologies, whose product is actually data. The tagline at Quotient is The Result in Knowing. I think that's really important because not all of us are data companies, where your product is actually data, but we should operate more like your product is data. I'd also like to talk to you about four areas of things to think about as thought leaders in your companies. First, just hit on it, is change. how to be a champion and a driver of change. Second, how to use data to drive performance for your company and measure performance of your company. Third, how companies now require intense collaboration to operate and finally, how much of this is accomplished through solid data-driven decisions. First, let's hit on change. I mean, it's evident today more than ever, that we are in an environment of extreme change. I mean, we've all been at this for years and as technologists we've known it, believed it, lived it. And thankfully, for the most part, knock on wood, we were prepared for it. But this year everyone's cheese was moved. All the people in the back rooms, IT, data architects and others were suddenly called to the forefront because a global pandemic has turned out to be the thing that is driving intense change in how people work and analyze their business. On March 13th, we closed our office at the NFL in the middle of preparing for one of our biggest events, our kickoff event, The 2020 Draft. We went from planning a large event in Las Vegas under the bright lights, red carpet stage, to smaller events in club facilities. And then ultimately, to one where everyone coaches, GMs, prospects and even our commissioner were at home in their basements and we only had a few weeks to figure it out. I found myself for the first time, being in the live broadcast event space. Talking about bungee jumping, this is really what it felt like. It was one in which no one felt comfortable because it had not been done before. But leading through this, I stepped up, but it was very scary, it was certainly very risky, but it ended up being also rewarding when we did it. And as a result of this, some things will change forever. Second, managing performance. I mean, data should inform how you're doing and how to get your company to perform at its level, highest level. As an example, the NFL has always measured performance, obviously, and it is one of the purest examples of how performance directly impacts outcome. I mean, you can see performance on the field, you can see points being scored and stats, and you immediately know that impact. Those with the best stats usually win the games. The NFL has always recorded stats. Since the beginning of time here at the NFL a little... This year is our 101st year and athlete's ultimate success as a player has also always been greatly impacted by his stats. But what has changed for us is both how much more we can measure and the immediacy with which it can be measured and I'm sure in your business it's the same. The amount of data you must have has got to have quadrupled recently. And how fast do you need it and how quickly you need to analyze it is so important. And it's very important to break the silos between the keys to the data and the use of the data. Our next generation stats platform is taking data to the next level. It's powered by Amazon Web Services and we gather this data, real-time from sensors that are on players' bodies. We gather it in real time, analyze it, display it online and on broadcast. And of course, it's used to prepare week to week in addition to what is a normal coaching plan would be. We can now analyze, visualize, route patterns, speed, match-ups, et cetera, so much faster than ever before. We're continuing to roll out sensors too, that will gather more and more information about a player's performance as it relates to their health and safety. The third trend is really, I think it's a big part of what we're feeling today and that is intense collaboration. And just for sort of historical purposes, it's important to think about, for those of you that are IT professionals and developers, you know, more than 10 years ago agile practices began sweeping companies. Where small teams would work together rapidly in a very flexible, adaptive and innovative way and it proved to be transformational. However today, of course that is no longer just small teams, the next big wave of change and we've seen it through this pandemic, is that it's the whole enterprise that must collaborate and be agile. If I look back on my career, when I was at Disney, we owned everything 100%. We made a decision, we implemented it. We were a collaborative culture but it was much easier to push change because you own the whole decision. If there was buy-in from the top down, you got the people from the bottom up to do it and you executed. At Universal, we were a joint venture. Our attractions and entertainment was licensed. Our hotels were owned and managed by other third parties, so influence and collaboration, and how to share across companies became very important. And now here I am at the NFL an even the bigger ecosystem. We have 32 clubs that are all separate businesses, 31 different stadiums that are owned by a variety of people. We have licensees, we have sponsors, we have broadcast partners. So it seems that as my career has evolved, centralized control has gotten less and less and has been replaced by intense collaboration, not only within your own company but across companies. The ability to work in a collaborative way across businesses and even other companies, that has been a big key to my success in my career. I believe this whole vertical integration and big top-down decision-making is going by the wayside in favor of ecosystems that require cooperation, yet competition to co-exist. I mean, the NFL is a great example of what we call co-oppetition, which is cooperation and competition. We're in competition with each other, but we cooperate to make the company the best it can be. And at the heart of these items really are data-driven decisions and culture. Data on its own isn't good enough. You must be able to turn it to insights. Partnerships between technology teams who usually hold the keys to the raw data and business units, who have the knowledge to build the right decision models is key. If you're not already involved in this linkage, you should be, data mining isn't new for sure. The availability of data is quadrupling and it's everywhere. How do you know what to even look at? How do you know where to begin? How do you know what questions to ask? It's by using the tools that are available for visualization and analytics and knitting together strategies of the company. So it begins with, first of all, making sure you do understand the strategy of the company. So in closing, just to wrap up a bit, many of you joined today, looking for thought leadership on how to be a change agent, a change champion, and how to lead through transformation. Some final thoughts are be brave and drive. Don't do the ride along program, it's very important to drive. Driving can be high risk, but it's also high reward. Embracing the uncertainty of what will happen is how you become brave. Get more and more comfortable with uncertainty, be calm and let data be your map on your journey. Thanks. >> Michelle, thank you so much. So you and I share a love of data and a love of football. You said you want to be the quarterback. I'm more an a line person. >> Well, then I can't do my job without you. >> Great and I'm getting the feeling now, you know, Sudheesh is talking about bungee jumping. My vote is when we're past this pandemic, we both take him to the Delaware Water Gap and we do the cliff jumping. >> Oh that sounds good, I'll watch your watch. >> Yeah, you'll watch, okay. So Michelle, you have so many stakeholders, when you're trying to prioritize the different voices you have the players, you have the owners, you have the league, as you mentioned, the broadcasters, your partners here and football mamas like myself. How do you prioritize when there are so many different stakeholders that you need to satisfy? >> I think balancing across stakeholders starts with aligning on a mission and if you spend a lot of time understanding where everyone's coming from, and you can find the common thread that ties them all together. You sort of do get them to naturally prioritize their work and I think that's very important. So for us at the NFL and even at Disney, it was our core values and our core purpose is so well known and when anything challenges that, we're able to sort of lay that out. But as a change agent, you have to be very empathetic, and I would say empathy is probably your strongest skill if you're a change agent and that means listening to every single stakeholder. Even when they're yelling at you, even when they're telling you your technology doesn't work and you know that it's user error, or even when someone is just emotional about what's happening to them and that they're not comfortable with it. So I think being empathetic, and having a mission, and understanding it is sort of how I prioritize and balance. >> Yeah, empathy, a very popular word this year. I can imagine those coaches and owners yelling, so thank you for your leadership here. So Michelle, I look forward to discussing this more with our other customers and disruptors joining us in a little bit. >> (gentle music) So we're going to take a hard pivot now and go from football to Chernobyl. Chernobyl, what went wrong? 1986, as the reactors were melting down, they had the data to say, "This is going to be catastrophic," and yet the culture said, "No, we're perfect, hide it. Don't dare tell anyone." Which meant they went ahead and had celebrations in Kiev. Even though that increased the exposure, additional thousands getting cancer and 20,000 years before the ground around there can even be inhabited again. This is how powerful and detrimental a negative culture, a culture that is unable to confront the brutal facts that hides data. This is what we have to contend with and this is why I want you to focus on having, fostering a data-driven culture. I don't want you to be a laggard. I want you to be a leader in using data to drive your digital transformation. So I'll talk about culture and technology, is it really two sides of the same coin? Real-world impacts and then some best practices you can use to disrupt and innovate your culture. Now, oftentimes I would talk about culture and I talk about technology. And recently a CDO said to me, "You know, Cindi, I actually think this is two sides of the same coin, one reflects the other." What do you think? Let me walk you through this. So let's take a laggard. What does the technology look like? Is it based on 1990s BI and reporting, largely parametrized reports, on-premises data warehouses, or not even that operational reports. At best one enterprise data warehouse, very slow moving and collaboration is only email. What does that culture tell you? Maybe there's a lack of leadership to change, to do the hard work that Sudheesh referred to, or is there also a culture of fear, afraid of failure, resistance to change, complacency. And sometimes that complacency, it's not because people are lazy. It's because they've been so beaten down every time a new idea is presented. It's like, "No, we're measured on least to serve." So politics and distrust, whether it's between business and IT or individual stakeholders is the norm, so data is hoarded. Let's contrast that with the leader, a data and analytics leader, what does their technology look like? Augmented analytics, search and AI driven insights, not on-premises but in the cloud and maybe multiple clouds. And the data is not in one place but it's in a data lake and in a data warehouse, a logical data warehouse. The collaboration is via newer methods, whether it's Slack or Teams, allowing for that real-time decisioning or investigating a particular data point. So what is the culture in the leaders? It's transparent and trust. There is a trust that data will not be used to punish, that there is an ability to confront the bad news. It's innovation, valuing innovation in pursuit of the company goals. Whether it's the best fan experience and player safety in the NFL or best serving your customers, it's innovative and collaborative. There's none of this, "Oh, well, I didn't invent that. I'm not going to look at that." There's still pride of ownership, but it's collaborating to get to a better place faster. And people feel empowered to present new ideas, to fail fast and they're energized knowing that they're using the best technology and innovating at the pace that business requires. So data is democratized and democratized, not just for power users or analysts, but really at the point of impact, what we like to call the new decision-makers or really the frontline workers. So Harvard Business Review partnered with us to develop this study to say, "Just how important is this? We've been working at BI and analytics as an industry for more than 20 years, why is it not at the front lines? Whether it's a doctor, a nurse, a coach, a supply chain manager, a warehouse manager, a financial services advisor." 87% said they would be more successful if frontline workers were empowered with data-driven insights, but they recognize they need new technology to be able to do that. It's not about learning hard tools. The sad reality only 20% of organizations are actually doing this. These are the data-driven leaders. So this is the culture and technology, how did we get here? It's because state-of-the-art keeps changing. So the first generation BI and analytics platforms were deployed on-premises, on small datasets, really just taking data out of ERP systems that were also on-premises and state-of-the-art was maybe getting a management report, an operational report. Over time, visual based data discovery vendors disrupted these traditional BI vendors, empowering now analysts to create visualizations with the flexibility on a desktop, sometimes larger data, sometimes coming from a data warehouse. The current state-of-the-art though, Gartner calls it augmented analytics. At ThoughtSpot, we call it search and AI driven analytics, and this was pioneered for large scale data sets, whether it's on-premises or leveraging the cloud data warehouses. And I think this is an important point, oftentimes you, the data and analytics leaders, will look at these two components separately. But you have to look at the BI and analytics tier in lock-step with your data architectures to really get to the granular insights and to leverage the capabilities of AI. Now, if you've never seen ThoughtSpot, I'll just show you what this looks like. Instead of somebody hard coding a report, it's typing in search keywords and very robust keywords contains rank, top, bottom, getting to a visual visualization that then can be pinned to an existing pin board that might also contain insights generated by an AI engine. So it's easy enough for that new decision maker, the business user, the non-analyst to create themselves. Modernizing the data and analytics portfolio is hard because the pace of change has accelerated. You used to be able to create an investment, place a bet for maybe 10 years. A few years ago, that time horizon was five years. Now, it's maybe three years and the time to maturity has also accelerated. So you have these different components, the search and AI tier, the data science tier, data preparation and virtualization but I would also say, equally important is the cloud data warehouse. And pay attention to how well these analytics tools can unlock the value in these cloud data warehouses. So ThoughtSpot was the first to market with search and AI driven insights. Competitors have followed suit, but be careful, if you look at products like Power BI or SAP analytics cloud, they might demo well, but do they let you get to all the data without moving it in products like Snowflake, Amazon Redshift, or Azure Synapse, or Google BigQuery, they do not. They require you to move it into a smaller in-memory engine. So it's important how well these new products inter-operate. The pace of change, its acceleration, Gartner recently predicted that by 2022, 65% of analytical queries will be generated using search or NLP or even AI and that is roughly three times the prediction they had just a couple of years ago. So let's talk about the real world impact of culture and if you've read any of my books or used any of the maturity models out there, whether the Gartner IT Score that I worked on or the Data Warehousing Institute also has a maturity model. We talk about these five pillars to really become data-driven. As Michelle spoke about, it's focusing on the business outcomes, leveraging all the data, including new data sources, it's the talent, the people, the technology and also the processes. And often when I would talk about the people in the talent, I would lump the culture as part of that. But in the last year, as I've traveled the world and done these digital events for thought leaders. You have told me now culture is absolutely so important, and so we've pulled it out as a separate pillar. And in fact, in polls that we've done in these events, look at how much more important culture is as a barrier to becoming data-driven. It's three times as important as any of these other pillars. That's how critical it is. And let's take an example of where you can have great data, but if you don't have the right culture, there's devastating impacts. And I will say I have been a loyal customer of Wells Fargo for more than 20 years, but look at what happened in the face of negative news with data. It said, "Hey, we're not doing good cross-selling, customers do not have both a checking account and a credit card and a savings account and a mortgage." They opened fake accounts facing billions in fines, change in leadership that even the CEO attributed to a toxic sales culture and they're trying to fix this, but even recently there's been additional employee backlash saying the culture has not changed. Let's contrast that with some positive examples. Medtronic, a worldwide company in 150 countries around the world. They may not be a household name to you, but if you have a loved one or yourself, you have a pacemaker, spinal implant, diabetes, you know this brand. And at the start of COVID when they knew their business would be slowing down, because hospitals would only be able to take care of COVID patients. They took the bold move of making their IP for ventilators publicly available. That is the power of a positive culture. Or Verizon, a major telecom organization looking at late payments of their customers and even though the U.S. Federal Government said, "Well, you can't turn them off." They said, "We'll extend that even beyond the mandated guidelines," and facing a slow down in the business because of the tough economy, They said, "You know what? We will spend the time upskilling our people, giving them the time to learn more about the future of work, the skills and data and analytics for 20,000 of their employees rather than furloughing them. That is the power of a positive culture. So how can you transform your culture to the best in class? I'll give you three suggestions. Bring in a change agent, identify the relevance or I like to call it WIIFM and organize for collaboration. So the CDO, whatever your title is, Chief Analytics Officer, Chief Digital Officer, you are the most important change agent. And this is where you will hear that oftentimes a change agent has to come from outside the organization. So this is where, for example, in Europe you have the CDO of Just Eat, a takeout food delivery organization coming from the airline industry or in Australia, National Australian Bank taking a CDO within the same sector from TD Bank going to NAB. So these change agents come in, disrupt. It's a hard job. As one of you said to me, it often feels like. I make one step forward and I get knocked down again, I get pushed back. It is not for the faint of heart, but it's the most important part of your job. The other thing I'll talk about is WIIFM What's In It For Me? And this is really about understanding the motivation, the relevance that data has for everyone on the frontline, as well as those analysts, as well as the executives. So, if we're talking about players in the NFL, they want to perform better and they want to stay safe. That is why data matters to them. If we're talking about financial services, this may be a wealth management advisor. Okay, we could say commissions, but it's really helping people have their dreams come true, whether it's putting their children through college or being able to retire without having to work multiple jobs still into your 70s or 80s. For the teachers, teachers you ask them about data. They'll say, "We don't need that, I care about the student." So if you can use data to help a student perform better, that is WIIFM and sometimes we spend so much time talking the technology, we forget, what is the value we're trying to deliver with this? And we forget the impact on the people that it does require change. In fact, the Harvard Business Review study found that 44% said lack of change management is the biggest barrier to leveraging both new technology, but also being empowered to act on those data-driven insights. The third point, organize for collaboration. This does require diversity of thought, but also bringing the technology, the data and the business people together. Now there's not a single one size fits all model for data and analytics. At one point in time, even having a BICC, a BI competency center was considered state of the art. Now for the biggest impact, what I recommend is that you have a federated model centralized for economies of scale. That could be the common data, but then embed these evangelists, these analysts of the future within every business unit, every functional domain. And as you see this top bar, all models are possible, but the hybrid model has the most impact, the most leaders. So as we look ahead to the months ahead, to the year ahead, an exciting time because data is helping organizations better navigate a tough economy, lock in the customer loyalty and I look forward to seeing how you foster that culture that's collaborative with empathy and bring the best of technology, leveraging the cloud, all your data. So thank you for joining us at Thought Leaders. And next, I'm pleased to introduce our first change agent, Tom Mazzaferro Chief Data Officer of Western Union and before joining Western Union, Tom made his Mark at HSBC and JP Morgan Chase spearheading digital innovation in technology, operations, risk compliance and retail banking. Tom, thank you so much for joining us today. (gentle music) >> Very happy to be here and looking forward to talking to all of you today. So as we look to move organizations to a data-driven capability into the future, there is a lot that needs to be done on the data side, but also how does data connect and enable different business teams and the technology teams into the future? As we look across our data ecosystems and our platforms, and how we modernize that to the cloud in the future, it all needs to basically work together, right? To really be able to drive an organization from a data standpoint, into the future. That includes being able to have the right information with the right quality of data, at the right time to drive informed business decisions, to drive the business forward. As part of that, we actually have partnered with ThoughtSpot to actually bring in the technology to help us drive that. As part of that partnership and it's how we've looked to integrate it into our overall business as a whole. We've looked at, how do we make sure that our business and our professional lives, right? Are enabled in the same ways as our personal lives. So for example, in your personal lives, when you want to go and find something out, what do you do? You go onto google.com or you go onto Bing or you go onto Yahoo and you search for what you want, search to find an answer. ThoughtSpot for us is the same thing, but in the business world. So using ThoughtSpot and other AI capability is it's allowed us to actually enable our overall business teams in our company to actually have our information at our fingertips. So rather than having to go and talk to someone, or an engineer to go pull information or pull data. We actually can have the end users or the business executives, right. Search for what they need, what they want, at the exact time that they actually need it, to go and drive the business forward. This is truly one of those transformational things that we've put in place. On top of that, we are on a journey to modernize our larger ecosystem as a whole. That includes modernizing our underlying data warehouses, our technology, our... The local environments and as we move that, we've actually picked two of our cloud providers going to AWS and to GCP. We've also adopted Snowflake to really drive and to organize our information and our data, then drive these new solutions and capabilities forward. So a big portion of it though is culture. So how do we engage with the business teams and bring the IT teams together, to really help to drive these holistic end-to-end solutions and capabilities, to really support the actual business into the future. That's one of the keys here, as we look to modernize and to really enhance our organizations to become data-driven. This is the key. If you can really start to provide answers to business questions before they're even being asked and to predict based upon different economic trends or different trends in your business, what decisions need to be made and actually provide those answers to the business teams before they're even asking for it. That is really becoming a data-driven organization and as part of that, it really then enables the business to act quickly and take advantage of opportunities as they come in based upon industries, based upon markets, based upon products, solutions or partnerships into the future. These are really some of the keys that become crucial as you move forward, right, into this new age, Especially with COVID. With COVID now taking place across the world, right? Many of these markets, many of these digital transformations are celebrating and are changing rapidly to accommodate and to support customers in these very difficult times. As part of that, you need to make sure you have the right underlying foundation, ecosystems and solutions to really drive those capabilities and those solutions forward. As we go through this journey, both in my career but also each of your careers into the future, right? It also needs to evolve, right? Technology has changed so drastically in the last 10 years, and that change is only accelerating. So as part of that, you have to make sure that you stay up to speed, up to date with new technology changes, both on the platform standpoint, tools, but also what do our customers want, what do our customers need and how do we then service them with our information, with our data, with our platform, and with our products and our services to meet those needs and to really support and service those customers into the future. This is all around becoming a more data-driven organization, such as how do you use your data to support your current business lines, but how do you actually use your information and your data to actually better support your customers, better support your business, better support your employees, your operations teams and so forth. And really creating that full integration in that ecosystem is really when you start to get large dividends from these investments into the future. With that being said, I hope you enjoyed the segment on how to become and how to drive a data-driven organization, and looking forward to talking to you again soon. Thank you. >> Tom, that was great. Thanks so much and now going to have to drag on you for a second. As a change agent you've come in, disrupted and how long have you been at Western Union? >> Only nine months, so just started this year, but there have been some great opportunities to integrate changes and we have a lot more to go, but we're really driving things forward in partnership with our business teams and our colleagues to support those customers going forward. >> Tom, thank you so much. That was wonderful. And now, I'm excited to introduce you to Gustavo Canton, a change agent that I've had the pleasure of working with meeting in Europe and he is a serial change agent. Most recently with Schneider Electric but even going back to Sam's Clubs. Gustavo, welcome. (gentle music) >> So, hey everyone, my name is Gustavo Canton and thank you so much, Cindi, for the intro. As you mentioned, doing transformations is, you know, a high reward situation. I have been part of many transformations and I have led many transformations. And, what I can tell you is that it's really hard to predict the future, but if you have a North Star and you know where you're going, the one thing that I want you to take away from this discussion today is that you need to be bold to evolve. And so, in today, I'm going to be talking about culture and data, and I'm going to break this down in four areas. How do we get started, barriers or opportunities as I see it, the value of AI and also, how you communicate. Especially now in the workforce of today with so many different generations, you need to make sure that you are communicating in ways that are non-traditional sometimes. And so, how do we get started? So, I think the answer to that is you have to start for you yourself as a leader and stay tuned. And by that, I mean, you need to understand, not only what is happening in your function or your field, but you have to be very in tune what is happening in society socioeconomically speaking, wellbeing. You know, the common example is a great example and for me personally, it's an opportunity because the number one core value that I have is wellbeing. I believe that for human potential for customers and communities to grow, wellbeing should be at the center of every decision. And as somebody mentioned, it's great to be, you know, stay in tune and have the skillset and the courage. But for me personally, to be honest, to have this courage is not about not being afraid. You're always afraid when you're making big changes and you're swimming upstream, but what gives me the courage is the empathy part. Like I think empathy is a huge component because every time I go into an organization or a function, I try to listen very attentively to the needs of the business and what the leaders are trying to do. But I do it thinking about the mission of, how do I make change for the bigger workforce or the bigger good despite the fact that this might have perhaps implication for my own self interest in my career. Right? Because you have to have that courage sometimes to make choices that are not well seen, politically speaking, but are the right thing to do and you have to push through it. So the bottom line for me is that, I don't think we're they're transforming fast enough. And the reality is, I speak with a lot of leaders and we have seen stories in the past and what they show is that, if you look at the four main barriers that are basically keeping us behind budget, inability to act, cultural issues, politics and lack of alignment, those are the top four. But the interesting thing is that as Cindi has mentioned, these topic about culture is actually gaining more and more traction. And in 2018, there was a story from HBR and it was about 45%. I believe today, it's about 55%, 60% of respondents say that this is the main area that we need to focus on. So again, for all those leaders and all the executives who understand and are aware that we need to transform, commit to the transformation and set a deadline to say, "Hey, in two years we're going to make this happen. What do we need to do, to empower and enable these change agents to make it happen? You need to make the tough choices. And so to me, when I speak about being bold is about making the right choices now. So, I'll give you examples of some of the roadblocks that I went through as I've been doing transformations, most recently, as Cindi mentioned in Schneider. There are three main areas, legacy mindset and what that means is that, we've been doing this in a specific way for a long time and here is how we have been successful. What worked in the past is not going to work now. The opportunity there is that there is a lot of leaders, who have a digital mindset and they're up and coming leaders that are perhaps not yet fully developed. We need to mentor those leaders and take bets on some of these talents, including young talent. We cannot be thinking in the past and just wait for people, you know, three to five years for them to develop because the world is going in a way that is super-fast. The second area and this is specifically to implementation of AI. It's very interesting to me because just the example that I have with ThoughtSpot, right? We went on implementation and a lot of the way the IT team functions or the leaders look at technology, they look at it from the prism of the prior or success criteria for the traditional BIs, and that's not going to work. Again, the opportunity here is that you need to redefine what success look like. In my case, I want the user experience of our workforce to be the same user experience you have at home. It's a very simple concept and so we need to think about, how do we gain that user experience with these augmented analytics tools and then work backwards to have the right talent, processes, and technology to enable that. And finally and obviously with COVID, a lot of pressure in organizations and companies to do more with less. And the solution that most leaders I see are taking is to just minimize costs sometimes and cut budget. We have to do the opposite. We have to actually invest on growth areas, but do it by business question. Don't do it by function. If you actually invest in these kind of solutions, if you actually invest on developing your talent and your leadership to see more digitally, if you actually invest on fixing your data platform, it's not just an incremental cost. It's actually this investment is going to offset all those hidden costs and inefficiencies that you have on your system, because people are doing a lot of work and working very hard but it's not efficient and it's not working in the way that you might want to work. So there is a lot of opportunity there and just to put in terms of perspective, there have been some studies in the past about, you know, how do we kind of measure the impact of data? And obviously, this is going to vary by organization maturity, there's going to be a lot of factors. I've been in companies who have very clean, good data to work with and I've been with companies that we have to start basically from scratch. So it all depends on your maturity level. But in this study, what I think is interesting is they try to put a tagline or a tag price to what is the cost of incomplete data. So in this case, it's about 10 times as much to complete a unit of work when you have data that is flawed as opposed to having perfect data. So let me put that just in perspective, just as an example, right? Imagine you are trying to do something and you have to do 100 things in a project, and each time you do something, it's going to cost you a dollar. So if you have perfect data, the total cost of that project might be $100. But now let's say you have 80% perfect data and 20% flawed data. By using this assumption that flawed data is 10 times as costly as perfect data, your total costs now becomes $280 as opposed to $100. This just for you to really think about as a CIO, CTO, you know CHRO, CEO, "Are we really paying attention and really closing the gaps that we have on our data infrastructure?" If we don't do that, it's hard sometimes to see the snowball effect or to measure the overall impact, but as you can tell, the price tag goes up very, very quickly. So now, if I were to say, how do I communicate this or how do I break through some of these challenges or some of these barriers, right? I think the key is, I am in analytics, I know statistics obviously and love modeling, and, you know, data and optimization theory, and all that stuff. That's what I came to analytics, but now as a leader and as a change agent, I need to speak about value and in this case, for example, for Schneider. There was this tagline, make the most of your energy. So the number one thing that they were asking from the analytics team was actually efficiency, which to me was very interesting. But once I understood that, I understood what kind of language to use, how to connect it to the overall strategy and basically, how to bring in the right leaders because you need to, you know, focus on the leaders that you're going to make the most progress, you know. Again, low effort, high value. You need to make sure you centralize all the data as you can, you need to bring in some kind of augmented analytics, you know, solution. And finally, you need to make it super-simple for the, you know, in this case, I was working with the HR teams and other areas, so they can have access to one portal. They don't have to be confused and looking for 10 different places to find information. I think if you can actually have those four foundational pillars, obviously under the guise of having a data-driven culture, that's when you can actually make the impact. So in our case, it was about three years total transformation, but it was two years for this component of augmented analytics. It took about two years to talk to, you know, IT, get leadership support, find the budgeting, you know, get everybody on board, make sure the success criteria was correct. And we call this initiative, the people analytics portal. It was actually launched in July of this year and we were very excited and the audience was very excited to do this. In this case, we did our pilot in North America for many, many, many factors but one thing that is really important is as you bring along your audience on this, you know. You're going from Excel, you know, in some cases or Tableu to other tools like, you know, ThoughtSpot. You need to really explain them what is the difference and how this tool can truly replace some of the spreadsheets or some of the views that you might have on these other kinds of tools. Again, Tableau, I think it's a really good tool. There are other many tools that you might have in your toolkit but in my case, personally, I feel that you need to have one portal. Going back to Cindi's points, that really truly enable the end user. And I feel that this is the right solution for us, right? And I will show you some of the findings that we had in the pilot in the last two months. So this was a huge victory and I will tell you why, because it took a lot of effort for us to get to this stage and like I said, it's been years for us to kind of lay the foundation, get the leadership, initiating culture so people can understand, why you truly need to invest on augmented analytics. And so, what I'm showing here is an example of how do we use basically, you know, a tool to capturing video, the qualitative findings that we had, plus the quantitative insights that we have. So in this case, our preliminary results based on our ambition for three main metrics. Hours saved, user experience and adoption. So for hours saved, our ambition was to have 10 hours per week for employee to save on average. User experience, our ambition was 4.5 and adoption 80%. In just two months, two months and a half of the pilot, we were able to achieve five hours per week per employee savings, a user experience for 4.3 out of five and adoption of 60%. Really, really amazing work. But again, it takes a lot of collaboration for us to get to the stage from IT, legal, communications, obviously the operations things and the users. In HR safety and other areas that might be basically stakeholders in this whole process. So just to summarize, this kind of effort takes a lot of energy. You are a change agent, you need to have courage to make this decision and understand that, I feel that in this day and age with all this disruption happening, we don't have a choice. We have to take the risk, right? And in this case, I feel a lot of satisfaction in how we were able to gain all these great resource for this organization and that give me the confident to know that the work has been done and we are now in a different stage for the organization. And so for me, it's just to say, thank you for everybody who has belief, obviously in our vision, everybody who has belief in, you know, the work that we were trying to do and to make the life of our, you know, workforce or customers and community better. As you can tell, there is a lot of effort, there is a lot of collaboration that is needed to do something like this. In the end, I feel very satisfied with the accomplishments of this transformation and I just want to tell for you, if you are going right now in a moment that you feel that you have to swim upstream, you know, work with mentors, work with people in the industry that can help you out and guide you on this kind of transformation. It's not easy to do, it's high effort, but it's well worth it. And with that said, I hope you are well and it's been a pleasure talking to you. Talk to you soon. Take care. >> Thank you, Gustavo. That was amazing. All right, let's go to the panel. (light music) Now I think we can all agree how valuable it is to hear from practitioners and I want to thank the panel for sharing their knowledge with the community. Now one common challenge that I heard you all talk about was bringing your leadership and your teams along on the journey with you. We talk about this all the time and it is critical to have support from the top. Why? Because it directs the middle and then it enables bottoms up innovation effects from the cultural transformation that you guys all talked about. It seems like another common theme we heard is that you all prioritize database decision making in your organizations. And you combine two of your most valuable assets to do that and create leverage, employees on the front lines, and of course the data. Now as as you rightly pointed out, Tom, the pandemic has accelerated the need for really leaning into this. You know, the old saying, if it ain't broke, don't fix it, well COVID has broken everything and it's great to hear from our experts, you know, how to move forward, so let's get right into it. So Gustavo, let's start with you. If I'm an aspiring change agent and let's say I'm a budding data leader, what do I need to start doing? What habits do I need to create for long-lasting success? >> I think curiosity is very important. You need to be, like I said, in tune to what is happening, not only in your specific field, like I have a passion for analytics, I've been doing it for 50 years plus, but I think you need to understand wellbeing of the areas across not only a specific business. As you know, I come from, you know, Sam's Club, Walmart retail. I've been in energy management, technology. So you have to try to push yourself and basically go out of your comfort zone. I mean, if you are staying in your comfort zone and you want to just continuous improvement, that's just going to take you so far. What you have to do is, and that's what I try to do, is I try to go into areas, businesses and transformations, that make me, you know, stretch and develop as a leader. That's what I'm looking to do, so I can help transform the functions, organizations, and do the change management, the essential mindset that's required for this kind of effort. >> Well, thank you for that. That is inspiring and Cindi you love data and the data is pretty clear that diversity is a good business, but I wonder if you can, you know, add your perspectives to this conversation? >> Yeah, so Michelle has a new fan here because she has found her voice. I'm still working on finding mine and it's interesting because I was raised by my dad, a single dad, so he did teach me how to work in a predominantly male environment, but why I think diversity matters more now than ever before and this is by gender, by race, by age, by just different ways of working and thinking, is because as we automate things with AI, if we do not have diverse teams looking at the data, and the models, and how they're applied, we risk having bias at scale. So this is why I think I don't care what type of minority you are, finding your voice, having a seat at the table and just believing in the impact of your work has never been more important and as Michelle said, more possible. >> Great perspectives, thank you. Tom, I want to go to you. So, I mean, I feel like everybody in our businesses is in some way, shape, or form become a COVID expert, but what's been the impact of the pandemic on your organization's digital transformation plans? >> We've seen a massive growth, actually, in our digital business over the last 12 months really, even acceleration, right, once COVID hit. We really saw that in the 200 countries and territories that we operate in today and service our customers in today, that there's been a huge need, right, to send money to support family, to support friends, and to support loved ones across the world. And as part of that we are very honored to be able to support those customers that, across all the centers today, but as part of the acceleration, we need to make sure that we have the right architecture and the right platforms to basically scale, right? To basically support and provide the right kind of security for our customers going forward. So as part of that, we did do some pivots and we did accelerate some of our plans on digital to help support that overall growth coming in and to support our customers going forward, because during these times, during this pandemic, right, this is the most important time and we need to support those that we love and those that we care about. And doing that some of those ways is actually by sending money to them, support them financially. And that's where really our products and our services come into play that, you know, and really support those families. So, it was really a great opportunity for us to really support and really bring some of our products to the next level and supporting our business going forward. >> Awesome, thank you. Now, I want to come back to Gustavo. Tom, I'd love for you to chime in too. Did you guys ever think like you were pushing the envelope too much in doing things with data or the technology that it was just maybe too bold, maybe you felt like at some point it was failing, or you're pushing your people too hard? Can you share that experience and how you got through it? >> Yeah, the way I look at it is, you know, again, whenever I go to an organization, I ask the question, "Hey, how fast you would like to conform?" And, you know, based on the agreements on the leadership and the vision that we want to take place, I take decisions and I collaborate in a specific way. Now, in the case of COVID, for example, right, it forces us to remove silos and collaborate in a faster way. So to me, it was an opportunity to actually integrate with other areas and drive decisions faster, but make no mistake about it, when you are doing a transformation, you are obviously trying to do things faster than sometimes people are comfortable doing, and you need to be okay with that. Sometimes you need to be okay with tension or you need to be okay, you know, debating points or making repetitive business cases until people connect with the decision because you understand and you are seeing that, "Hey, the CEO is making a one, two year, you know, efficiency goal. The only way for us to really do more with less is for us to continue this path. We can not just stay with the status quo, we need to find a way to accelerate the transformation." That's the way I see it. >> How about Utah, we were talking earlier with Sudheesh and Cindi about that bungee jumping moment. What can you share? >> Yeah, you know, I think you hit upon it. Right now, the pace of change will be the slowest pace that you see for the rest of your career. So as part of that, right, this is what I tell my team, is that you need to be, you need to feel comfortable being uncomfortable. Meaning that we have to be able to basically scale, right? Expand and support the ever changing needs in the marketplace and industry and our customers today, and that pace of change that's happening, right? And what customers are asking for and the competition in the marketplace, it's only going to accelerate. So as part of that, you know, as you look at how you're operating today in your current business model, right? Things are only going to get faster. So you have to plan and to align and to drive the actual transformation, so that you can scale even faster into the future. So it's part of that, that's what we're putting in place here, right? It's how do we create that underlying framework and foundation that allows the organization to basically continue to scale and evolve into the future? >> Yeah, we're definitely out of our comfort zones, but we're getting comfortable with it. So Cindi, last question, you've worked with hundreds of organizations and I got to believe that, you know, some of the advice you gave when you were at Gartner, which was pre-COVID, maybe sometimes clients didn't always act on it. You know, not my watch or for whatever, variety of reasons, but it's being forced on them now. But knowing what you know now that, you know, we're all in this isolation economy, how would you say that advice has changed? Has it changed? What's your number one action and recommendation today? >> Yeah, well first off, Tom, just freaked me out. What do you mean, this is the slowest ever? Even six months ago I was saying the pace of change in data and analytics is frenetic. So, but I think you're right, Tom, the business and the technology together is forcing this change. Now, Dave, to answer your question, I would say the one bit of advice, maybe I was a little more very aware of the power in politics and how to bring people along in a way that they are comfortable and now I think it's, you know what, you can't get comfortable. In fact, we know that the organizations that were already in the cloud have been able to respond and pivot faster. So, if you really want to survive, as Tom and Gustavo said, get used to being uncomfortable. The power and politics are going to happen, break the rules, get used to that and be bold. Do not be afraid to tell somebody they're wrong and they're not moving fast enough. I do think you have to do that with empathy, as Michelle said and Gustavo, I think that's one of the key words today besides the bungee jumping. So I want to know where Sudheesh is going to go bungee jumping. (all chuckling) >> Guys, fantastic discussion, really. Thanks again to all the panelists and the guests, it was really a pleasure speaking with you today. Really, virtually all of the leaders that I've spoken to in theCUBE program recently, they tell me that the pandemic is accelerating so many things. Whether it's new ways to work, we heard about new security models and obviously the need for cloud. I mean, all of these things are driving true enterprise-wide digital transformation, not just as I said before, lip service. You know, sometimes we minimize the importance and the challenge of building culture and in making this transformation possible. But when it's done right, the right culture is going to deliver tournament results. You know, what does that mean? Getting it right. Everybody's trying to get it right. My biggest takeaway today is it means making data part of the DNA of your organization. And that means making it accessible to the people in your organization that are empowered to make decisions, decisions that can drive new revenue, cut costs, speed access to critical care, whatever the mission is of your organization, data can create insights and informed decisions that drive value. Okay, let's bring back Sudheesh and wrap things up. Sudheesh, please bring us home. >> Thank you, thank you, Dave. Thank you, theCUBE team, and thanks goes to all of our customers and partners who joined us, and thanks to all of you for spending the time with us. I want to do three quick things and then close it off. The first thing is I want to summarize the key takeaways that I heard from all four of our distinguished speakers. First, Michelle, I will simply put it, she said it really well. That is be brave and drive, don't go for a drive alone. That is such an important point. Often times, you know the right thing that you have to do to make the positive change that you want to see happen, but you wait for someone else to do it, not just, why not you? Why don't you be the one making that change happen? That's the thing that I picked up from Michelle's talk. Cindi talked about finding, the importance of finding your voice. Taking that chair, whether it's available or not, and making sure that your ideas, your voice is heard and if it requires some force, then apply that force. Make sure your ideas are heard. Gustavo talked about the importance of building consensus, not going at things all alone sometimes. The importance of building the quorum, and that is critical because if you want the changes to last, you want to make sure that the organization is fully behind it. Tom, instead of a single takeaway, what I was inspired by is the fact that a company that is 170 years old, 170 years old, 200 companies and 200 countries they're operating in and they were able to make the change that is necessary through this difficult time in a matter of months. If they could do it, anyone could. The second thing I want to do is to leave you with a takeaway, that is I would like you to go to ThoughtSpot.com/nfl because our team has made an app for NFL on Snowflake. I think you will find this interesting now that you are inspired and excited because of Michelle's talk. And the last thing is, please go to ThoughtSpot.com/beyond. Our global user conference is happening in this December. We would love to have you join us, it's, again, virtual, you can join from anywhere. We are expecting anywhere from five to 10,000 people and we would love to have you join and see what we've been up to since last year. We have a lot of amazing things in store for you, our customers, our partners, our collaborators, they will be coming and sharing. We'll be sharing things that we have been working to release, something that will come out next year. And also some of the crazy ideas our engineers have been cooking up. All of those things will be available for you at ThoughtSpot Beyond. Thank you, thank you so much.

>> Data is at the heart of transformation and the change every company needs to succeed, but it takes more than new technology. It's about teams, talent and cultural change. Empowering everyone on the front lines to make decisions all at the speed of digital. The transformation starts with you. It's time to lead the way it's time for Thought leaders. >> Welcome to "Thought Leaders" a digital event brought to you by ThoughtSpot. My name is Dave Vellante. The purpose of this day is to bring industry leaders and experts together to really try and understand the important issues around digital transformation. We have an amazing lineup of speakers and our goal is to provide you with some best practices that you can bring back and apply to your organization. Look, data is plentiful, but insights are not. ThoughtSpot is disrupting analytics by using search and machine intelligence to simplify data analysis and really empower anyone with fast access to relevant data. But in the last 150 days, we've had more questions than answers. Creating an organization that puts data and insights at their core requires not only modern technology, but leadership, a mindset and a culture that people often refer to as data-driven. What does that mean? How can we equip our teams with data and fast access to quality information that can turn insights into action. And today we're going to hear from experienced leaders who are transforming their organizations with data, insights and creating digital first cultures. But before we introduce our speakers, I'm joined today by two of my co-hosts from ThoughtSpot first chief data strategy officer at the ThoughtSpot is Cindi Howson. Cindi is an analytics and BI expert with 20 plus years experience and the author of "Successful Business Intelligence "Unlock the Value of BI & Big Data." Cindi was previously the lead analyst at Gartner for the data and analytics magic quadrant. And early last year, she joined ThoughtSpot to help CDOs and their teams understand how best to leverage analytics and AI for digital transformation. Cindi, great to see you welcome to the show. >> Thank you, Dave. Nice to join you virtually. >> Now our second cohost and friend of the cube is ThoughtSpot CEO Sudheesh Nair Hello, Sudheesh how are you doing today? >> I'm well Dave, it's good to talk to you again. >> It's great to see you thanks so much for being here. Now Sudheesh please share with us why this discussion is so important to your customers and of course, to our audience and what they're going to learn today. (upbeat music) >> Thanks, Dave. I wish you were there to introduce me into every room and that I walk into because you have such an amazing way of doing it. Makes me feel all so good. Look, since we have all been cooped up in our homes, I know that the vendors like us, we have amped up our sort of effort to reach out to you with invites for events like this. So we are getting very more invites for events like this than ever before. So when we started planning for this, we had three clear goals that we wanted to accomplish. And our first one that when you finish this and walk away, we want to make sure that you don't feel like it was a waste of time. We want to make sure that we value your time and this is going to be useful. Number two, we want to put you in touch with industry leaders and thought leaders, generally good people that you want to hang around with long after this event is over. And number three, as we plan through this, we are living through these difficult times. We want an event to be this event, to be more of an uplifting and inspiring event too. Now, the challenge is how do you do that with the team being change agents because change and as much as we romanticize it, it is not one of those uplifting things that everyone wants to do, or like to do. The way I think of it sort of like a, if you've ever done bungee jumping and it's like standing on the edges waiting to make that one more step, all you have to do is take that one step and gravity will do the rest, but that is the hardest step to take. Change requires a lot of courage. And when we are talking about data and analytics, which is already like such a hard topic, not necessarily an uplifting and positive conversation in most businesses, it is somewhat scary. Change becomes all the more difficult. Ultimately change requires courage. Courage to first of all challenge the status quo. People sometimes are afraid to challenge the status quo because they are thinking that maybe I don't have the power to make the change that the company needs. Sometimes they feel like I don't have the skills. Sometimes they may feel that I'm probably not the right person do it. Or sometimes the lack of courage manifest itself as the inability to sort of break the silos that are formed within the organizations, when it comes to data and insights that you talked about. There are people in the company who are going to hog the data because they know how to manage the data, how to inquire and extract. They know how to speak data. They have the skills to do that. But they are not the group of people who have sort of the knowledge, the experience of the business to ask the right questions off the data. So there is the silo of people with the answers, and there is a silo of people with the questions. And there is gap. This sort of silos are standing in the way of making that necessary change that we all know the business needs. And the last change to sort of bring an external force sometimes. It could be a tool. It could be a platform, it could be a person, it could be a process, but sometimes no matter how big the company is or how small the company is, you may need to bring some external stimuli to start the domino of the positive changes that are necessary. The group of people that we are brought in, the four people, including Cindi, that you will hear from today are really good at practically telling you how to make that step, how to step off that edge, how to dress the rope, that you will be safe and you're going to have fun. You will have that exhilarating feeling of jumping, for a bungee jump. All four of them are exceptional, but my honor is to introduce Michelle and she's our first speaker. Michelle, I am very happy after watching her presentation and reading our bio, that there are no country vital worldwide competition for cool patterns, because she will beat all of us because when her children were small, they were probably into Harry Potter and Disney. She was managing a business and leading change there. And then as her kids grew up and got to that age where they like football and NFL, guess what? She's the CIO of NFL. What a cool mom? I am extremely excited to see what she's going to talk about. I've seen the slides, tons of amazing pictures. I'm looking to see the context behind it. I'm very thrilled to make the acquaintance of Michelle and looking forward to her talk next. Welcome Michelle, it's over to you. (upbeat music) >> I'm delighted to be with you all today to talk about thought leadership. And I'm so excited that you asked me to join you because today I get to be a quarterback. I always wanted to be one. And I thought this is about as close as I'm ever going to get. So I want to talk to you about quarterbacking, our digital revolution using insights data. And of course, as you said, leadership, first a little bit about myself, a little background, as I said, I always wanted to play football. And this is something that I wanted to do since I was a child. But when I grew up, girls didn't get to play football. I'm so happy that that's changing and girls are now doing all kinds of things that they didn't get to do before. Just this past weekend on an NFL field, we had a female coach on two sidelines and a female official on the field. I'm a lifelong fan and student of the game of football. I grew up in the South. You can tell from the accent. And in the South football is like a religion and you pick sides. I chose Auburn university working in the athletic department. So I'm Testament to you can start the journey can be long. It took me many, many years to make it into professional sports. I graduated in 1987 and my little brother, well, not actually not so little. He played offensive line for the Alabama Crimson Tide. And for those of you who know SCC football, you know this is a really big rivalry. And when you choose sides, your family is divided. So it's kind of fun for me to always tell the story that my dad knew his kid would make it to the NFL. He just bet on the wrong one. My career has been about bringing people together for memorable moments at some of America's most iconic brands, delivering memories and amazing experiences that delight from Universal Studios, Disney to my current position as CIO of the NFL. In this job I'm very privileged to have the opportunity to work with the team that gets to bring America's game to millions of people around the world. Often I'm asked to talk about how to create amazing experiences for fans, guests, or customers. But today I really wanted to focus on something different and talk to you about being behind the scenes and backstage because behind every event, every game, every awesome moment is execution, precise, repeatable execution. And most of my career has been behind the scenes doing just that assembling teams to execute these plans. And the key way that companies operate at these exceptional levels is making good decisions, the right decisions at the right time and based upon data so that you can translate the data into intelligence and be a data-driven culture. Using data and intelligence is an important way that world-class companies do differentiate themselves. And it's the lifeblood of collaboration and innovation. Teams that are working on delivering these kinds of world casts experiences are often seeking out and leveraging next-generation technologies and finding new ways to work. I've been fortunate to work across three decades of emerging experiences, which each required emerging technologies to execute a little bit first about Disney in the 90s, I was at Disney leading a project called destination Disney, which it's a data project. It was a data project, but it was CRM before CRM was even cool. And then certainly before anything like a data-driven culture was ever brought up, but way back then we were creating a digital backbone that enabled many technologies for the things that you see today, like the magic band, Disney's magical express. My career at Disney began in finance, but Disney was very good about rotating you around. And it was during one of these rotations that I became very passionate about data. I kind of became a pain in the butt to the IT team asking for data more and more data. And I learned that all of that valuable data was locked up in our systems. All of our point of sales systems, our reservation systems, our operation systems. And so I became a shadow IT person in marketing, ultimately leading to moving into IT. And I haven't looked back since. In the early two thousands, I was at universal studios theme park as their CIO preparing for and launching "The Wizarding World of Harry Potter" bringing one of history's most memorable characters to life required many new technologies and a lot of data. Our data and technologies were embedded into the rides and attractions. I mean, how do you really think a wan selects you at a wan shop. As today at the NFL? I am constantly challenged to do leading edge technologies, using things like sensors, AI, machine learning, and all new communication strategies and using data to drive everything from player performance, contracts, to where we build new stadiums and hold events with this year being the most challenging yet rewarding year in my career at the NFL. In the middle of a global pandemic, the way we are executing on our season is leveraging data from contract tracing devices joined with testing data, talk about data, actually enabling your business without it w wouldn't be having a season right now. I'm also on the board of directors of two public companies where data and collaboration are paramount. First RingCentral, it's a cloud based unified communications platform and collaboration with video message and phone all in one solution in the cloud and Quotient technologies whose product is actually data. The tagline at Quotient is the result in knowing I think that's really important because not all of us are data companies where your product is actually data, but we should operate more like your product is data. I'd also like to talk to you about four areas of things to think about as thought leaders in your companies. First just hit on it is change how to be a champion and a driver of change. Second, how do you use data to drive performance for your company and measure performance of your company? Third, how companies now require intense collaboration to operate. And finally, how much of this is accomplished through solid data driven decisions. First let's hit on change. I mean, it's evident today more than ever, that we are in an environment of extreme change. I mean, we've all been at this for years and as technologists we've known it, believed it, lived it and thankfully for the most part, knock on what we were prepared for it. But this year everyone's cheese was moved. All the people in the back rooms, IT, data architects and others were suddenly called to the forefront because a global pandemic has turned out to be the thing that is driving intense change in how people work and analyze their business. On March 13th, we closed our office at the NFL in the middle of preparing for one of our biggest events, our kickoff event, the 2020 draft. We went from planning a large event in Las Vegas under the bright lights, red carpet stage to smaller events in club facilities. And then ultimately to one where everyone coaches GM's prospects and even our commissioner were at home in their basements. And we only had a few weeks to figure it out. I found myself for the first time being in the live broadcast event space, talking about bungee jumping. This is really what it felt like. It was one in which no one felt comfortable because it had not been done before. But leading through this, I stepped up, but it was very scary. It was certainly very risky, but it ended up being all so rewarding when we did it. And as a result of this, some things will change forever. Second, managing performance. I mean, data should inform how you're doing and how to get your company to perform at it's level. Highest level. As an example, the NFL has always measured performance, obviously, and it is one of the purest examples of how performance directly impacts outcome. I mean, you can see performance on the field. You can see points being scored in stats, and you immediately know that impact those with the best stats usually when the games. The NFL has always recorded stats since the beginning of time here at the NFL a little this year is our 101 year and athletes ultimate success as a player has also always been greatly impacted by his stats. But what has changed for us is both how much more we can measure and the immediacy with which it can be measured. And I'm sure in your business it's the same. The amount of data you must have has got to have quadrupled and how fast you need it and how quickly you need to analyze it is so important. And it's very important to break the silos between the keys, to the data and the use of the data. Our next generation stats platform is taking data to a next level. It's powered by Amazon web services. And we gathered this data real-time from sensors that are on players' bodies. We gather it in real time, analyze it, display it online and on broadcast. And of course it's used to prepare week to week in addition to what is a normal coaching plan would be. We can now analyze, visualize route patterns, speed match-ups, et cetera. So much faster than ever before. We're continuing to roll out sensors too that will gather more and more information about a player's performance as it relates to their health and safety. The third trend is really, I think it's a big part of what we're feeling today and that is intense collaboration. And just for sort of historical purposes, it's important to think about for those of you that are IT professionals and developers, more than 10 years ago, agile practices began sweeping companies where small teams would work together rapidly in a very flexible, adaptive, and innovative way. And it proved to be transformational. However, today, of course, that is no longer just small teams, the next big wave of change. And we've seen it through this pandemic is that it's the whole enterprise that must collaborate and be agile. If I look back on my career, when I was at Disney, we owned everything 100%. We made a decision, we implemented it. We were a collaborative culture, but it was much easier to push change because you own the whole decision. If there was buy-in from the top down, you've got the people from the bottom up to do it and you executed. At Universal we were a joint venture. Our attractions and entertainment was licensed. Our hotels were owned and managed by other third parties. So influence and collaboration and how to share across companies became very important. And now here I am at the NFL and even the bigger ecosystem, we have 32 clubs that are all separate businesses. 31 different stadiums that are owned by a variety of people. We have licensees, we have sponsors, we have broadcast partners. So it seems that as my career has evolved, centralized control has gotten less and less and has been replaced by intense collaboration, not only within your own company, but across companies. The ability to work in a collaborative way across businesses and even other companies that has been a big key to my success in my career. I believe this whole vertical integration and big top-down decision-making is going by the wayside in favor of ecosystems that require cooperation yet competition to co-exist. I mean, the NFL is a great example of what we call co-op petition, which is cooperation and competition. We're in competition with each other, but we cooperate to make the company the best it can be. And at the heart of these items really are data driven decisions and culture. Data on its own isn't good enough. You must be able to turn it to insights. Partnerships between technology teams who usually hold the keys to the raw data and business units who have the knowledge to build the right decision models is key. If you're not already involved in this linkage, you should be. Data mining isn't new for sure. The availability of data is quadrupling and it's everywhere. How do you know what to even look at? How do you know where to begin? How do you know what questions to ask it's by using the tools that are available for visualization and analytics and knitting together strategies of the company. So it begins with first of all, making sure you do understand the strategy of the company. So in closing, just to wrap up a bit, many of you joined today, looking for thought leadership on how to be a change agent, a change champion, and how to lead through transformation. Some final thoughts are be brave and drive. Don't do the ride along program. It's very important to drive. Driving can be high risk, but it's also high reward. Embracing the uncertainty of what will happen is how you become brave. Get more and more comfortable with uncertainty, be calm and let data be your map on your journey. Thanks. >> Michelle, tank you so much. So you and I share a love of data and a love of football. You said you want to be the quarterback. I'm more an old line person. (Michelle and Cindi laughing) >> Well, then I can do my job without you. >> Great. And I'm getting the feeling now, Sudheesh is talking about bungee jumping. My vote is when we're past this pandemic, we both take them to the Delaware water gap and we do the cliff jumping. >> That sounds good, I'll watch. >> Yeah, you'll watch, okay. So Michelle, you have so many stakeholders when you're trying to prioritize the different voices. You have the players, you have the owners, you have the league, as you mentioned, the broadcasters, your partners here and football mamas like myself. How do you prioritize when there's so many different stakeholders that you need to satisfy? >> I think balancing across stakeholders starts with, aligning on a mission. And if you spend a lot of time understanding where everyone's coming from, and you can find the common thread that ties them all together, you sort of do get them to naturally prioritize their work. And I think that's very important. So for us, at the NFL and even at Disney, it was our core values and our core purpose, is so well known and when anything challenges that we're able to sort of lay that out. But as a change agent, you have to be very empathetic. And I would say empathy is probably your strongest skill if you're a change agent. And that means listening to every single stakeholder, even when they're yelling at you, even when they're telling you your technology doesn't work and you know that it's user error, or even when someone is just emotional about what's happening to them and that they're not comfortable with it. So I think being empathetic and having a mission and understanding it is sort of how I prioritize and balance. >> Yeah, empathy, a very popular word this year. I can imagine those coaches and owners yelling. So, thank you for your leadership here. So Michelle, I look forward to discussing this more with our other customers and disruptors joining us in a little bit. (upbeat music) So we're going to take a hard pivot now and go from football to Chernobyl. Chernobyl what went wrong? 1986, as the reactors were melting down, they had the data to say, this is going to be catastrophic. And yet the culture said, "no, we're perfect, hide it. "Don't dare tell anyone." Which meant they went ahead and had celebrations in Kiev. Even though that increased the exposure, the additional thousands getting cancer and 20,000 years before the ground around there can even be inhabited again, this is how powerful and detrimental a negative culture, a culture that is unable to confront the brutal facts that hides data. This is what we have to contend with. And this is why I want you to focus on having, fostering a data-driven culture. I don't want you to be a laggard. I want you to be a leader in using data to drive your digital transformation. So I'll talk about culture and technology. Is it really two sides of the same coin, real-world impacts and then some best practices you can use to and innovate your culture. Now, oftentimes I would talk about culture and I talk about technology. And recently a CDO said to me, "Cindi, I actually think this is two sides "of the same coin. "One reflects the other." What do you think? Let me walk you through this. So let's take a laggard. What does the technology look like? Is it based on 1990s BI and reporting largely parametrized reports, on premises data, warehouses, or not even that operational reports at best one enterprise data warehouse, very slow moving and collaboration is only email. What does that culture tell you? Maybe there's a lack of leadership to change, to do the hard work that Sudheesh referred to, or is there also a culture of fear, afraid of failure, resistance to change complacency. And sometimes that complacency it's not because people are lazy. It's because they've been so beaten down every time a new idea is presented. It's like, no we're measured on least cost to serve. So politics and distrust, whether it's between business and IT or individual stakeholders is the norm. So data is hoarded. Let's contrast that with a leader, a data and analytics leader, what is their technology look like? Augmented analytics search and AI driven insights, not on premises, but in the cloud and maybe multiple clouds. And the data is not in one place, but it's in a data Lake and in a data warehouse, a logical data warehouse. The collaboration is being a newer methods, whether it's Slack or teams allowing for that real time decisioning or investigating a particular data point. So what is the culture in the leaders? It's transparent and trust. There is a trust that data will not be used to punish that there is an ability to confront the bad news. It's innovation, valuing innovation in pursuit of the company goals, whether it's the best fan experience and player safety in the NFL or best serving your customers. It's innovative and collaborative. There's none of this. Oh, well, I didn't invent that. I'm not going to look at that. There's still pride of ownership, but it's collaborating to get to a better place faster. And people feel empowered to present new ideas to fail fast, and they're energized knowing that they're using the best technology and innovating at the pace that business requires. So data is democratized. And democratized, not just for power users or analysts, but really at the point of impact what we like to call the new decision-makers or really the frontline workers. So Harvard business review partnered with us to develop this study to say, just how important is this? We've been working at BI and analytics as an industry for more than 20 years. Why is it not at the front lines? Whether it's a doctor, a nurse, a coach, a supply chain manager, a warehouse manager, a financial services advisor. Everyone said that if our 87% said, they would be more successful if frontline workers were empowered with data driven insights, but they recognize they need new technology to be able to do that. It's not about learning hard tools. The sad reality, only 20% of organizations are actually doing this. These are the data-driven leaders. So this is the culture in technology. How did we get here? It's because state-of-the-art keeps changing. So the first-generation BI and analytics platforms were deployed on premises on small datasets, really just taking data out of ERP systems that were also on premises. And state-of-the-art was maybe getting a management report, an operational report. Over time visual-based data discovery vendors disrupted these traditional BI vendors, empowering now analysts to create visualizations with the flexibility on a desktop, sometimes larger data, sometimes coming from a data warehouse. The current state of the art though, Gartner calls it augmented analytics at ThoughtSpot, we call it search and AI driven analytics. And this was pioneered for large scale datasets, whether it's on premises or leveraging the cloud data warehouses. And I think this is an important point. Oftentimes you, the data and analytics leaders will look at these two components separately, but you have to look at the BI and analytics tier in lockstep with your data architectures to really get to the granular insights and to leverage the capabilities of AI. Now, if you've never seen ThoughtSpot, I'll just show you what this looks like. Instead of somebody hard coding, a report it's typing in search keywords and very robust keywords contains rank top bottom, getting to a visual visualization that then can be pinned to an existing Pin board that might also contain insights generated by an AI engine. So it's easy enough for that new decision maker, the business user, the non analyst to create themselves. Modernizing the data and analytics portfolio is hard because the pace of change has accelerated. You use to be able to create an investment place a bet for maybe 10 years, a few years ago, that time horizon was five years, now it's maybe three years and the time to maturity has also accelerated. So you have these different components, the search and AI tier, the data science tier, data preparation and virtualization. But I would also say equally important is the cloud data warehouse and pay attention to how well these analytics tools can unlock the value in these cloud data warehouses. So ThoughtSpot was the first to market with search and AI driven insights. Competitors have followed suit, but be careful if you look at products like power BI or SAP analytics cloud, they might demo well, but do they let you get to all the data without moving it in products like Snowflake, Amazon Redshift, or Azure synapse or Google big query, they do not. They require you to move it into a smaller in memory engine. So it's important how well these new products inter operate. the pace of change, its acceleration Gartner recently predicted that by 2022, 65% of analytical queries will be generated using search or NLP or even AI. And that is roughly three times the prediction they had just a couple years ago. So let's talk about the real world impact of culture. And if you read any of my books or used any of the maturity models out there, whether the Gartner IT score that I worked on, or the data warehousing Institute also has the money surety model. We talk about these five pillars to really become data-driven. As Michelle, I spoke about it's focusing on the business outcomes, leveraging all the data, including new data sources, it's the talent, the people, the technology, and also the processes. And often when I would talk about the people and the talent, I would lump the culture as part of that. But in the last year, as I've traveled the world and done these digital events for Thought leaders, you have told me now culture is absolutely so important. And so we've pulled it out as a separate pillar. And in fact, in polls that we've done in these events, look at how much more important culture is as a barrier to becoming data-driven it's three times as important as any of these other pillars. That's how critical it is. And let's take an example of where you can have great data, but if you don't have the right culture, there's devastating impacts. And I will say, I have been a loyal customer of Wells Fargo for more than 20 years. But look at what happened in the face of negative news with data, it said, "hey, we're not doing good cross selling, "customers do not have both a checking account "and a credit card and a savings account and a mortgage." They opened fake accounts facing billions in fines, change in leadership that even the CEO attributed to a toxic sales culture, and they're trying to fix this. But even recently there's been additional employee backlash saying the culture has not changed. Let's contrast that with some positive examples, Medtronic, a worldwide company in 150 countries around the world. They may not be a household name to you, but if you have a loved one or yourself, you have a pacemaker, spinal implant diabetes, you know this brand. And at the start of COVID when they knew their business would be slowing down, because hospitals would only be able to take care of COVID patients. They took the bold move of making their IP for ventilators publicly available. That is the power of a positive culture. Or Verizon, a major telecom organization looking at late payments of their customers. And even though the U.S federal government said, "well, you can't turn them off. They said, "we'll extend that even beyond "the mandated guidelines." And facing a slow down in the business because of the tough economy, they said, you know what? "We will spend the time up skilling our people, "giving them the time to learn more "about the future of work, the skills and data "and analytics," for 20,000 of their employees, rather than furloughing them. That is the power of a positive culture. So how can you transform your culture to the best in class? I'll give you three suggestions, bring in a change agent, identify the relevance, or I like to call it WIFM and organize for collaboration. So the CDO, whatever your title is, chief analytics officer, chief digital officer, you are the most important change agent. And this is where you will hear that oftentimes a change agent has to come from outside the organization. So this is where, for example, in Europe, you have the CDO of Just Eat a takeout food delivery organization coming from the airline industry or in Australia, National Australian bank, taking a CDO within the same sector from TD bank going to NAB. So these change agents come in disrupt. It's a hard job. As one of you said to me, it often feels like Sisyphus. I make one step forward and I get knocked down again. I get pushed back. It is not for the faint of heart, but it's the most important part of your job. The other thing I'll talk about is WIFM. What is in it for me? And this is really about understanding the motivation, the relevance that data has for everyone on the frontline, as well as those analysts, as well as the executives. So if we're talking about players in the NFL, they want to perform better and they want to stay safe. That is why data matters to them. If we're talking about financial services, this may be a wealth management advisor. Okay we could say commissions, but it's really helping people have their dreams come true, whether it's putting their children through college or being able to retire without having to work multiple jobs still into your 70s or 80s for the teachers, teachers, you ask them about data. They'll say we don't, we don't need that. I care about the student. So if you can use data to help a student perform better, that is WIFM. And sometimes we spend so much time talking the technology, we forget what is the value we're trying to deliver with it. And we forget the impact on the people that it does require change. In fact, the Harvard business review study found that 44% said lack of change management is the biggest barrier to leveraging both new technology, but also being empowered to act on those data-driven insights. The third point organize for collaboration. This does require diversity of thought, but also bringing the technology, the data and the business people together. Now there's not a single one size fits all model for data and analytics. At one point in time, even having a BICC, a BI competency center was considered state-of-the-art. Now for the biggest impact what I recommend is that you have a federated model centralized for economies of scale. That could be the common data, but then in bed, these evangelists, these analysts of the future within every business unit, every functional domain. And as you see this top bar, all models are possible, but the hybrid model has the most impact, the most leaders. So as we look ahead to the months ahead, to the year ahead an exciting time, because data is helping organizations better navigate a tough economy, lock in the customer loyalty. And I look forward to seeing how you foster that culture that's collaborative with empathy and bring the best of technology, leveraging the cloud, all your data. So thank you for joining us at Thought Leaders. And next I'm pleased to introduce our first change agent, Tom Mazzaferro chief data officer of Western union. And before joining Western union, Tom made his Mark at HSBC and JPMorgan Chase spearheading digital innovation in technology, operations, risk compliance, and retail banking. Tom, thank you so much for joining us today. (upbeat music) >> Very happy to be here and looking forward to talking to all of you today. So as we look to move organizations to a data-driven, capability into the future, there is a lot that needs to be done on the data side, but also how does data connect and enable different business teams and technology teams into the future. As you look across, our data ecosystems and our platforms and how we modernize that to the cloud in the future, it all needs to basically work together, right? To really be able to drive and over the shift from a data standpoint, into the future, that includes being able to have the right information with the right quality of data, at the right time to drive informed business decisions, to drive the business forward. As part of that, we actually have partnered with ThoughtSpot, to actually bring in the technology to help us drive that as part of that partnership. And it's how we've looked to integrate it into our overall business as a whole we've looked at how do we make sure that our business and our professional lives right, are enabled in the same ways as our personal lives. So for example, in your personal lives, when you want to go and find something out, what do you do? You go onto google.com or you go on to Bing we go onto Yahoo and you search for what you want search to find and answer. ThoughtSpot for us as the same thing, but in the business world. So using ThoughtSpot and other AI capability it's allowed us to actually, enable our overall business teams in our company to actually have our information at our fingertips. So rather than having to go and talk to someone or an engineer to go pull information or pull data, we actually can have the end-users or the business executives, right. Search for what they need, what they want at the exact time that action need it to go and drive the business forward. This is truly one of those transformational things that we've put in place. On top of that, we are on the journey to modernize our larger ecosystem as a whole. That includes modernizing our underlying data warehouses, our technology, or our Eloqua environments. And as we move that, we've actually picked two of our cloud providers going to AWS and GCP. We've also adopted Snowflake to really drive and to organize our information and our data then drive these new solutions and capabilities forward. So they portion of us though is culture. So how do we engage with the business teams and bring the IT teams together to really drive these holistic end to end solutions and capabilities to really support the actual business into the future? That's one of the keys here, as we look to modernize and to really enhance our organizations to become data-driven, this is the key. If you can really start to provide answers to business questions before they're even being asked and to predict based upon different economic trends or different trends in your business, what does this is maybe be made and actually provide those answers to the business teams before they're even asking for it, that is really becoming a data-driven organization. And as part of that, it's really then enables the business to act quickly and take advantage of opportunities as they come in based upon, industries based upon markets, based upon products, solutions, or partnerships into the future. These are really some of the keys that become crucial as you move forward, right, into this new age, especially with COVID. With COVID now taking place across the world, right? Many of these markets, many of these digital transformations are accelerating and are changing rapidly to accommodate and to support customers in these very difficult times, as part of that, you need to make sure you have the right underlying foundation ecosystems and solutions to really drive those capabilities and those solutions forward. As we go through this journey, both of my career, but also each of your careers into the future, right? It also needs to evolve, right? Technology has changed so drastically in the last 10 years, and that change is only accelerating. So as part of that, you have to make sure that you stay up to speed, up to date with new technology changes both on the platform standpoint tools, but also what do our customers want? What do our customers need and how do we then service them with our information, with our data, with our platform and with our products and our services to meet those needs and to really support and service those customers into the future. This is all around becoming a more data organization such as how do you use your data to support the current business lines, but how do you actually use your information, your data to actually put a better support your customers, better support your business, better support your employees, your operations teams, and so forth, and really creating that full integration in that ecosystem is really when you start to get large dividends from this investments into the future. But that being said, hope you enjoy the segment on how to become and how to drive it data driven organization. And, looking forward to talking to you again soon. Thank you. >> Tom that was great thanks so much. Now I'm going to have to brag on you for a second as a change agent you've come in disrupted and how long have you been at Western union? >> Only nine months, so just started this year, but, doing some great opportunities and great changes. And we have a lot more to go, but, we're really driving things forward in partnership with our business teams and our colleagues to support those customers going forward. >> Tom, thank you so much. That was wonderful. And now I'm excited to introduce you to Gustavo Canton, a change agent that I've had the pleasure of working with meeting in Europe, and he is a serial change agent, most recently with Schneider electric, but even going back to Sam's clubs, Gustavo welcome. (upbeat music) >> So, hey everyone, my name is Gustavo Canton and thank you so much, Cindi, for the intro, as you mentioned, doing transformations is high effort, high reward situation. I have empowered many transformations and I have led many transformations. And what I can tell you is that it's really hard to predict the future, but if you have a North star and where you're going, the one thing that I want you to take away from this discussion today is that you need to be bold to evolve. And so in today, I'm going to be talking about culture and data, and I'm going to break this down in four areas. How do we get started barriers or opportunities as I see it, the value of AI, and also, how do you communicate, especially now in the workforce of today with so many different generations, you need to make sure that you are communicating in ways that are non-traditional sometimes. And so how do we get started? So I think the answer to that is you have to start for you yourself as a leader and stay tuned. And by that, I mean, you need to understand not only what is happening in your function or your field, but you have to be varying into what is happening in society, socioeconomically speaking wellbeing. The common example is a great example. And for me personally, it's an opportunity because the one core value that I have is well-being, I believe that for human potential, for customers and communities to grow wellbeing should be at the center of every decision. And as somebody mentioned is great to be, stay in tune and have the skillset and the courage. But for me personally, to be honest, to have this courage is not about not being afraid. You're always afraid when you're making big changes when you're swimming upstream, but what gives me the courage is the empathy part. Like I think empathy is a huge component because every time I go into an organization or a function, I try to listen very attentively to the needs of the business and what the leaders are trying to do. What I do it thinking about the mission of how do I make change for the bigger, workforce? for the bigger good. Despite this fact that this might have a perhaps implication on my own self-interest in my career, right? Because you have to have that courage sometimes to make choices that I know we'll see in politically speaking, what are the right thing to do? And you have to push through it. And you have to push through it. So the bottom line for me is that I don't think they're transforming fast enough. And the reality is I speak with a lot of leaders and we have seen stories in the past. And what they show is that if you look at the four main barriers that are basically keeping us behind budget, inability to act cultural issues, politics, and lack of alignment, those are the top four. But the interesting thing is that as Cindi has mentioned, these topics culture is actually gaining, gaining more and more traction. And in 2018, there was a story from HBR and it was about 45%. I believe today it's about 55%, 60% of respondents say that this is the main area that we need to focus on. So again, for all those leaders and all the executives who understand and are aware that we need to transform, commit to the transformation and set a state, deadline to say, "hey, in two years, we're going to make this happen. "What do we need to do to empower and enable "this change engines to make it happen?" You need to make the tough choices. And so to me, when I speak about being bold is about making the right choices now. So I'll give you samples of some of the roadblocks that I went through as I think transformation most recently, as Cindi mentioned in Schneider. There are three main areas, legacy mindset. And what that means is that we've been doing this in a specific way for a long time and here is how we have been successful what was working the past is not going to work now. The opportunity there is that there is a lot of leaders who have a digital mindset and there're up and coming leaders that are not yet fully developed. We need to mentor those leaders and take bets on some of these talent, including young talent. We cannot be thinking in the past and just wait for people, three to five years for them to develop because the world is going to in a way that is super fast. The second area, and this is specifically to implementation of AI is very interesting to me because just example that I have with ThoughtSpot, right, we went to implementation and a lot of the way is the IT team function of the leaders look at technology, they look at it from the prism of the prior all success criteria for the traditional Bi's. And that's not going to work. Again the opportunity here is that you need to really find what successful look like. In my case, I want the user experience of our workforce to be the same as user experience you have at home is a very simple concept. And so we need to think about how do we gain the user experience with this augmented analytics tools and then work backwards to have the right talent processes and technology to enable that. And finally, with COVID a lot of pressuring organizations, and companies to do more with less. And the solution that most leaders I see are taking is to just minimize costs, sometimes in cut budget, we have to do the opposite. We have to actually invest some growth areas, but do it by business question. Don't do it by function. If you actually invest in these kind of solutions, if you actually invest on developing your talent, your leadership to see more digitally, if you actually invest on fixing your data platform, it's not just an incremental cost. It's actually this investment is going to offset all those hidden costs and inefficiencies that you have on your system, because people are doing a lot of work and working very hard, but it's not efficiency, and it's not working in the way that you might want to work. So there is a lot of opportunity there. And you just to put into some perspective, there have studies in the past about, how do we kind of measure the impact of data. And obviously this is going to vary by your organization maturity, is going to, there's going to be a lot of factors. I've been in companies who have very clean, good data to work with. And I think with companies that we have to start basically from scratch. So it all depends on your maturity level, but in this study, what I think is interesting is they try to put attack line or attack price to what is the cost of incomplete data. So in this case, it's about 10 times as much to complete a unit of work when you have data that is flawed as opposed to have perfect data. So let me put that just in perspective, just as an example, right? Imagine you are trying to do something and you have to do 100 things in a project, and each time you do something, it's going to cost you a dollar. So if you have perfect data, the total cost of that project might be $100. But now let's say you have any percent perfect data and 20% flawed data by using this assumption that flawed data is 10 times as costly as perfect data. Your total costs now becomes $280 as opposed to $100. This is just for you to really think about as a CIO CTO, CHRO CEO, are we really paying attention and really closing the gaps that we have on our data infrastructure. If we don't do that, it's hard sometimes to see the snowball effect or to measure the overall impact. But as you can tell the price that goes up very, very quickly. So now, if I were to say, how do I communicate this? Or how do I break through some of these challenges or some of these various, right. I think the key is I am in analytics. I know statistics obviously, and love modeling and data and optimization theory and all that stuff. That's what I came to analytics. But now as a leader and as a change agent, I need to speak about value. And in this case, for example, for Schneider, there was this tagline called free up your energy. So the number one thing that they were asking from the analytics team was actually efficiency, which to me was very interesting. But once I understood that I understood what kind of language to use, how to connect it to the overall strategy and basically how to bring in the, the right leaders, because you need to focus on the leaders that you're going to make the most progress. Again, low effort, high value. You need to make sure you centralize all the data as you can. You need to bring in some kind of augmented analytics solution. And finally you need to make it super simple for the, in this case, I was working with the HR teams in other areas, so they can have access to one portal. They don't have to be confused in looking for 10 different places to find information. I think if you can actually have those four foundational pillars, obviously under the guise of having a data-driven culture, that's when you can actually make the impact. So in our case, it was about three years total transformation, but it was two years for this component of augmented analytics. It took about two years to talk to IT get leadership support, find the budgeting, get everybody on board, make sure the safe criteria was correct. And we call this initiative, the people analytics portal, it was actually launched in July of this year. And we were very excited and the audience was very excited to do this. In this case, we did our pilot in North America for many, many manufacturers. But one thing that is really important is as you bring along your audience on this, you're going from Excel, in some cases or Tableau to other tools like, ThoughtSpot, you need to really explain them what is the difference and how these tools can truly replace, some of the spreadsheets or some of the views that you might have on these other kind of tools. Again, Tableau, I think it's a really good tool. There are other many tools that you might have in your toolkit. But in my case, personally, I feel that you need to have one portal going back to Cindi's point. I really truly enable the end user. And I feel that this is the right solution for us, right? And I will show you some of the findings that we had in the pilot in the last two months. So this was a huge victory, and I will tell you why, because it took a lot of effort for us to get to the station. Like I said, it's been years for us to kind of lay the foundation, get the leadership, and shaping culture so people can understand why you truly need to invest on (indistinct) analytics. And so what I'm showing here is an example of how do we use basically, a tool to capture in video the qualitative findings that we had, plus the quantitative insights that we have. So in this case, our preliminary results based on our ambition for three main metrics, hours saved user experience and adoption. So for hours saved or a mission was to have 10 hours per week per employee save on average user experience, or ambition was 4.5. And adoption, 80%. In just two months, two months and a half of the pilot, we were able to achieve five hours per week per employee savings. Our user experience for 4.3 out of five and adoption of 60%. Really, really amazing work. But again, it takes a lot of collaboration for us to get to the stage from IT, legal, communications, obviously the operations teams and the users in HR safety and other areas that might be, basically stakeholders in this whole process. So just to summarize this kind of effort takes a lot of energy. You are a change agent. You need to have a courage to make the decision and understand that I feel that in this day and age, with all this disruption happening, we don't have a choice. We have to take the risk, right? And in this case, I feel a lot of satisfaction in how we were able to gain all these very source for this organization. And that gave me the confidence to know that the work has been done and we are now in a different stage for the organization. And so for me, it to say, thank you for everybody who has believed, obviously in our vision, everybody who has believe in the word that we were trying to do and to make the life of four workforce or customers or in community better. As you can tell, there is a lot of effort. There is a lot of collaboration that is needed to do something like this. In the end, I feel very satisfied. With the accomplishments of this transformation, and I just want to tell for you, if you are going right now in a moment that you feel that you have to swim upstream what would mentors, what would people in this industry that can help you out and guide you on this kind of a transformation is not easy to do is high effort, but is well worth it. And with that said, I hope you are well, and it's been a pleasure talking to you. Talk to you soon, take care. >> Thank you, Gustavo, that was amazing. All right, let's go to the panel. (air whooshing) >> Okay, now we're going to go into the panel and bring Cindi, Michelle, Tom, and Gustavo back and have an open discussion. And I think we can all agree how valuable it is to hear from practitioners. And I want to thank the panel for sharing their knowledge with the community. And one common challenge that I heard you all talk about was bringing your leadership and your teams along on the journey with you. We talk about this all the time, and it is critical to have support from the top. Why? Because it directs the middle and then it enables bottoms up innovation effects from the cultural transformation that you guys all talked about. It seems like another common theme we heard is that you all prioritize database decision-making in your organizations and you combine two of your most valuable assets to do that and create leverage, employees on the front lines. And of course the data. And as you rightly pointed out, Tom, the pandemic has accelerated the need for really leaning into this. The old saying, if it ain't broke don't fix it. Well COVID is broken everything. And it's great to hear from our experts, how to move forward. So let's get right into it. So Gustavo, let's start with you if I'm an aspiring change agent and let's say I'm a budding data leader. What do I need to start doing? What habits do I need to create for long lasting success? >> I think curiosity is very important. You need to be, like I say, in tune to what is happening, not only in your specific field, like I have a passion for analytics, I can do this for 50 years plus, but I think you need to understand wellbeing other areas across not only a specific business, as you know I come from, Sam's club Walmart, retail, I mean energy management technology. So you have to try to push yourself and basically go out of your comfort zone. I mean, if you are staying in your comfort zone and you want to use lean continuous improvement, that's just going to take you so far. What you have to do is, and that's what I try to do is I try to go into areas, businesses, and transformation that make me stretch and develop as a leader. That's what I'm looking to do so I can help transform the functions organizations and do the change management, change of mindset required for these kinds of efforts. >> Michelle, you're at the intersection of tech and sports and what a great combination, but they're both typically male oriented fields. I mean, we've talked a little bit about how that's changing, but two questions. Tell us how you found your voice and talk about why diversity matters so much more than ever now. >> No, I found my voice really as a young girl, and I think I had such amazing support from men in my life. And I think the support and sponsorship as well as sort of mentorship along the way, I've had amazing male mentors who have helped me understand that my voice is just as important as anyone else's. I mean, I have often heard, and I think it's been written about that a woman has to believe they'll 100% master topic before they'll talk about it where a man can feel much less mastery and go on and on. So I was that way as well. And I learned just by watching and being open, to have my voice. And honestly at times demand a seat at the table, which can be very uncomfortable. And you really do need those types of, support networks within an organization. And diversity of course is important and it has always been. But I think if anything, we're seeing in this country right now is that diversity among all types of categories is front and center. And we're realizing that we don't all think alike. We've always known this, but we're now talking about things that we never really talked about before. And we can't let this moment go unchecked and on, and not change how we operate. So having diverse voices within your company and in the field of tech and sports, I am often the first and only I'm was the first, CIO at the NFL, the first female senior executive. It was fun to be the first, but it's also, very challenging. And my responsibility is to just make sure that, I don't leave anyone behind and make sure that I leave it good for the next generation. >> Well, thank you for that. That is inspiring. And Cindi, you love data and the data's pretty clear that diversity is a good business, but I wonder if you can add your perspectives to this conversation? >> Yeah, so Michelle has a new fan here because she has found her voice. I'm still working on finding mine. And it's interesting because I was raised by my dad, a single dad. So he did teach me how to work in a predominantly male environment, but why I think diversity matters more now than ever before. And this is by gender, by race, by age, by just different ways of working in thinking is because as we automate things with AI, if we do not have diverse teams looking at the data and the models and how they're applied, we risk having bias at scale. So this is why I think I don't care what type of minority you are finding your voice, having a seat at the table and just believing in the impact of your work has never been more important. And as Michelle said more possible. >> Great perspectives, thank you. Tom I want to go to you. I mean, I feel like everybody in our businesses in some way, shape or form become a COVID expert, but what's been the impact of the pandemic on your organization's digital transformation plans? >> We've seen a massive growth actually in a digital business over the last, 12 months, really, even in celebration, right? Once COVID hit, we really saw that in the 200 countries and territories that we operate in today and service our customers, today, that there's been a huge need, right? To send money, to support family, to support, friends and support loved ones across the world. And as part of that we are very, honored to get to support those customers that we, across all the centers today. But as part of that acceleration we need to make sure that we had the right architecture and the right platforms to basically scale, right, to basically support and provide the right kind of security for our customers going forward. So as part of that, we did do some pivots and we did accelerate some of our plans on digital to help support that overall growth coming in and to support our customers going forward, because there were these times during this pandemic, right? This is the most important time. And we need to support those that we love and those that we care about and doing that it's one of those ways is actually by sending money to them, support them financially. And that's where, really our part of that our services come into play that we really support those families. So it was really a great opportunity for us to really support and really bring some of our products to this level and supporting our business going forward. >> Awesome, thank you. Now I want to come back to Gustavo, Tom I'd love for you to chime in too. Did you guys ever think like you were, you were pushing the envelope too much in doing things with data or the technology that was just maybe too bold, maybe you felt like at some point it was failing or you're pushing your people too hard. Can you share that experience and how you got through it? >> Yeah, the way I look at it is, again, whenever I go to an organization, I ask the question, hey, how fast you would like transform. And, based on the agreements from the leadership and the vision that we want to take place, I take decisions. And I collaborate in a specific way now, in the case of COVID, for example, right. It forces us to remove silos and collaborate in a faster way. So to me, it was an opportunity to actually integrate with other areas and drive decisions faster, but make no mistake about it. When you are doing a transformation, you are obviously trying to do things faster than sometimes people are comfortable doing, and you need to be okay with that. Sometimes you need to be okay with tension, or you need to be okay debating points or making repetitive business cases until people connect with the decision because you understand, and you are seeing that, "hey, the CEO is making a one two year, efficiency goal. "The only way for us to really do more with less "is for us to continue this path. "We cannot just stay with the status quo. "We need to find a way to accelerate the transformation." That's the way I see it. >> How about you Tom, we were talking earlier with Sudheesh and Cindi, about that bungee jumping moment. What could you share? >> Yeah, I think you hit upon it, right now, the pace of change with the slowest pace that you see for the rest of your career. So as part of that, right, that's what I tell my team is that you need to be, you need to feel comfortable being uncomfortable. I mean, that we have to be able to basically scale, right, expand and support that the ever-changing needs in the marketplace and industry our customers today, and that pace of change that's happening, right. And what customers are asking for and the competition in the marketplace, it's only going to accelerate. So as part of that, as you look at what, how you're operating today in your current business model, right. Things are only going to get faster. So you have to plan into a line into drive the agile transformation so that you can scale even faster in the future. So as part of that, that's what we're putting in place here, right, is how do we create that underlying framework and foundation that allows the organization to basically continue to scale and evolve into the future? >> Yeah, we're definitely out of our comfort zones, but we're getting comfortable with it. So, Cindi, last question, you've worked with hundreds of organizations, and I got to believe that, some of the advice you gave when you were at Gartner, which is pre COVID, maybe sometimes clients didn't always act on it. They're not on my watch for whatever variety of reasons, but it's being forced on them now. But knowing what you know now that we're all in this isolation economy, how would you say that advice has changed? Has it changed? What's your number one action and recommendation today? >> Yeah, well, first off, Tom just freaked me out. What do you mean? This is the slowest ever even six months ago I was saying the pace of change in data and analytics is frenetic. So, but I think you're right, Tom, the business and the technology together is forcing this change. Now, Dave, to answer your question, I would say the one bit of advice, maybe I was a little more, very aware of the power and politics and how to bring people along in a way that they are comfortable. And now I think it's, you know what you can't get comfortable. In fact, we know that the organizations that were already in the cloud have been able to respond and pivot faster. So if you really want to survive as Tom and Gustavo said, get used to being uncomfortable, the power and politics are going to happen. Break the rules, get used to that and be bold. Do not be afraid to tell somebody they're wrong and they're not moving fast enough. I do think you have to do that with empathy, as Michelle said, and Gustavo, I think that's one of the key words today besides the bungee jumping. So I want to know where's the dish going to go bungee jumping. >> Guys fantastic discussion, really. Thanks again to all the panelists and the guests. It was really a pleasure speaking with you today. Really virtually all of the leaders that I've spoken to in the Cube program. Recently, they tell me that the pandemic is accelerating so many things, whether it's new ways to work, we heard about new security models and obviously the need for cloud. I mean, all of these things are driving true enterprise wide digital transformation, not just, as I said before, lip service. Sometimes we minimize the importance and the challenge of building culture and in making this transformation possible. But when it's done, right, the right culture is going to deliver tremendous results. Yeah, what does that mean getting it right? Everybody's trying to get it right. My biggest takeaway today is it means making data part of the DNA of your organization. And that means making it accessible to the people in your organization that are empowered to make decisions, decisions that can drive new revenue, cut costs, speed access to critical care, whatever the mission is of your organization. Data can create insights and informed decisions that drive value. Okay. Let's bring back Sudheesh and wrap things up. Sudheesh, please bring us home. >> Thank you. Thank you, Dave. Thank you, the Cube team, and thank goes to all of our customers and partners who joined us and thanks to all of you for spending the time with us. I want to do three quick things and then close it off. The first thing is I want to summarize the key takeaways that I had from all four of our distinguished speakers. First, Michelle, I will simply put it. She said it really well. That is be brave and drive. Don't go for a drive along. That is such an important point. Oftentimes, you know that I think that you have to do to make the positive change that you want to see happen but you wait for someone else to do it, not just, why not you? Why don't you be the one making that change happen? That's the thing that I've picked up from Michelle's talk. Cindi talked about finding the importance of finding your voice. Taking that chair, whether it's available or not, and making sure that your ideas, your voices are heard, and if it requires some force, then apply that force. Make sure your ideas are heard. Gustavo talked about the importance of building consensus, not going at things all alone sometimes building the importance of building the quorum. And that is critical because if you want the changes to last, you want to make sure that the organization is fully behind it. Tom, instead of a single takeaway, what I was inspired by is the fact that a company that is 170 years old, 170 years old, 200 companies and 200 countries they're operating in. And they were able to make the change that is necessary through this difficult time. So in a matter of months, if they could do it, anyone could. The second thing I want to do is to leave you with a takeaway that is I would like you to go to topspot.com/nfl because our team has made an app for NFL on Snowflake. I think you will find this interesting now that you are inspired and excited because of Michelle's talk. And the last thing is please go to thoughtspot.com/beyond our global user conference is happening in this December. We would love to have you join us. It's again, virtual, you can join from anywhere. We are expecting anywhere from five to 10,000 people, and we would love to have you join and see what we've been up to since last year. We have a lot of amazing things in store for you, our customers, our partners, our collaborators, they will be coming and sharing. We'll be sharing things that we've have been working to release something that will come out next year. And also some of the crazy ideas our engineers have been cooking up. All of those things will be available for you at the Thought Spot Beyond. Thank you. Thank you so much.

>> Announcer: From San Francisco, it's theCUBE! Covering RSA Conference 2020 San Francisco. Brought to you by SiliconANGLE Media. >> Hey welcome back, get ready, Jeff Frick here with theCUBE, we're at RSA 2020, here at Moscone, it's a really pretty day outside in San Francisco, unfortunately we're at the basement of Moscone, but that's 'cause this is the biggest thing going in security, it's probably 15,000 people, we haven't got the official number yet, but this is the place to be and security is a really really really big deal, and we're excited to have our next guest, I haven't seen her for a little while, since data privacy day. I tried to get Scott McNealy to join us, he unfortunately was predisposed and couldn't join us. Michelle Finneran Dennedy, in her new job, the CEO of DrumWave. Michelle, great to see you. >> Great to see you too, I'm sorry I missed you on privacy day. >> I know, so DrumWave, tell us all about DrumWave, last we saw you this is a new adventure since we last spoke. >> It's a new adventure, so this is my first early stage company, we're still seeking series A, we're a young company, but our mantra is we are the data value company. So they have had this very robust analytics engine that goes into the heart of data, and can track it and map it and make it beautiful, and along came McNealy, who actually sits on our board. And they said we need someone, it's all happening. So they asked Scott McNealy, who is the craziest person in privacy and data that you know and he said "Oh my God, get the Dennedy woman." So, they got the Dennedy woman and that's what I do now, so I've taken this analytics value engine, I'm pointing it to the board as I've always said, Grace Hopper said, data value and data risk has to be on the corporate balance sheet, and so that's what we're building is a data balance sheet for everyone to use, to actually value data. >> So to actually put a value on the data, so this is a really interesting topic, because people talk about the value of data, we see the value of data wrapped up, not directly, but indirectly in companies like Facebook and Google and those types of companies who clearly are leveraging data in a very different way, but it is not a line item on a balance sheet, they don't teach you that at business school next to capital assets and, right, so how are you attacking the problem, 'cause that's a huge, arguably will be the biggest asset anyone will have on their balance sheet at some point in time. >> Absolutely, and so I go back to basic principles, the same as I did when I started privacy engineering. I look and I say "Okay, if we believe the data's an asset," and I think that at least verbally, we all say the words "Yes, data is an asset," instead of some sort of exhaust, then you have to look back and say "What's an asset?" Well an asset, under the accounting rules, is anything tangible or intangible that is likely to cause economic benefit. So you break that down, what is the thing, well you got to map that thing. So where is your data? Well data tells you where it is. Instead of bringing in clip boards and saying "Hey, Jeff, my man, do you process PII?" We don't do that, we go to your system, and when you go on DrumWave, you're automatically receiving an ontology that says what is this likely to be, using some machine learning, and then every single column proclaims itself. And so we have a data provenance for every column, so you put that into an analytics engine, and suddenly you can start asking human questions of real data. >> And do you ask the questions to assess the value of the data, or is the ultimate valuation of that data in the categorization and the ontology, and knowing that I have this this this and this, or I mean we know what the real value is, the soft value is what you can do with it, but when you do the analytics on it, are you trying to get to unlock what the potential, underlying analytic value is of that data that you have in your possession? >> Yeah, so the short answer is both, and the longer answer is, so my cofounder, Andre Vellozo, believes, and I believe too, that every conversation is a transaction. So just like you look at transactions within the banking context, and you say, you have to know that it's there, creating a data ontology. You have to know what the context is, so when you upload your data, you receive a data provenance, now you can actually look at, as the data controller, you open what we call your wallet, which is your portal into our analytics engine, and you can see across the various data wranglers, so each business unit has put their data on, because the data's not leaving your place, it's either big data, small data, I don't really care data. Everything comes in through every business unit, loads up their data set, and we look across it and we say "What kind of data is there?" So there's quantitative data saying, if you took off the first 10 lines of this column in marketing, now you have a lump of data that's pure analytics. You just share those credentials and combine that dataset, you know you have a clean set of data that you can even sell, or you can create an analytic, because you don't have any PII. For most data sets, you look at relative value, so for example, one of the discussions I had with a customer today, we know when we fail in privacy, we have a privacy breach, and we pay our lawyers, and so on. Do you know what a privacy success is? >> Hopefully it's like an offensive lineman, you don't hear their name the whole game right, 'cause they don't get a holding call. >> Until they put the ball in the hole. So who's putting the ball in the hole, sales is a privacy success. You've had a conversation with someone who was the right someone in context to sign on the bottom line. You have shared information in a proportionate way. If you have the wrong data, your sale cycle is slower. So we can show, are you efficiently sharing data, how does that correlate with the results of your business unit? Marketing is another privacy success. There's always that old adage that we know that 50% of marketing is a waste, but we don't know which 50%. Well now we can look at it and say "All right," marketing can be looked at as people being prepared to buy your product, or prepared to think in a new, persuasive way. So who's clicking on that stuff, that used to be the metric, now you should tie that back to, how much are you storing for how long related to who's clicking, and tying it to other metrics. So the minute you put data into an analytics engine, it's not me that's going to tell you how you're going to do your data balance sheet, you're going to tell me how dependent you are on digital transactions versus tangible, building things, selling things, moving things, but everyone is a digital business now, and so we can put the intelligence on top of that so you, the expert in value, can look at that value and make your own conclusions. >> And really, what you're talking about then is tying it to my known processes, so you're almost kind of parsing out the role of the data in doing what I'm trying to do with my everyday business. So that's very different than looking at, say, something like, say a Facebook or an Amazon or a Google that are using the data not necessarily, I mean they are supporting the regular processes, but they're getting the valuation bump because of the potential. >> By selling it. >> Or selling it, or doing new businesses based on the data, not just the data in support of the current business. So is that part of your program as well, do you think? >> Absolutely, so we could do the same kind of ontology and value assessment for an Apple, Apple assesses value by keeping it close, and it's not like they're not exploiting data value, it's just that they're having everyone look into the closed garden, and that's very valuable. Facebook started that way with Facebook Circles way back when, and then they decided when they wanted to grow, they actually would start to share. And then it had some interesting consequences along the line. So you can actually look at both of those models as data valuation models. How much is it worth for an advertiser to get the insights about your customers, whether or not they're anonymized or not, and in certain contexts, so healthcare, you want it to be hyper-identifiable, you want it to be exactly that person. So that valuation is higher, with a higher correlation of every time that PII is associated with a treatment, to that specific person with the right name, and the same Jr. or Sr. or Mrs. or Dr., all of that correlated into one, now your value has gone up, whether you're selling that data or what you're selling is services into that data, which is that customer's needs and wants. >> And in doing this with customers, what's been the biggest surprise in terms of a value, a piece of value in the data that maybe just wasn't recognized, or kind of below the covers, or never really had the direct correlation or association that it should've had? >> Yeah, so I don't know if I'm going to directly answer it or I'm going to sidewind it, but I think my biggest surprise wasn't a surprise to me, it was a surprise to my customers. The customers thought we were going to assess their data so they could start selling it, or they could buy other data sources, combine it, enrich it, and then either sell it or get these new insights. >> Jeff: That's what they brought you in for. >> Yeah, I know, cute, right? Yeah, so I'm like "Okay." The aha moment, of course, is that first of all, the "Oh my God" moment in data rarely happens, sometimes in big research cases, you'll get an instance of some biometric that doesn't behave organically, but we're talking about human behavior here, so the "Aha, we should be selling phone data "to people with phones" should not be an aha, that's just bad marketing. So instead, the aha for me has been A, how eager and desperate people are for actually looking at this, I really thought this was going to be a much more steep hill to climb to say "Hey, data's an asset," I've been saying this for over 20 years now, and people are kind of like "Yeah, yeah, yeah." Now for the first time, I'm seeing people really want to get on board and look comprehensively, so I thought we'd be doing little skinny pilots, oh no, everyone wants to get all their data on board so they can start playing around with it. So that's been really a wake-up call for a privacy gal. >> Right, well it's kind of interesting, 'cause you're kind of at the tail end of the hype cycle on big data, with Hadoop, and all that that represented, it went up and down and nobody had-- >> Michelle: Well we thought more was more. >> We thought more was more, but we didn't have the skills to manage it, and there was a lot of issues. And so now you never hear about big data per say, but data's pervasive everywhere, data management is pervasive everywhere, and again, we see the crazy valuations based on database companies, that are clearly getting that. >> And data privacy companies, I mean look at the market in DC land, and any DCs that are looking at this, talk to mama, I know what to do. But we're seeing one feature companies blowing up in the marketplace right now, people really want to know how to handle the risk side as well as the value side. Am I doing the right thing, that's my number one thing that not CPOs are, because they all know how crazy it is out there, but it's chief financial officers are my number one customer. They want to know that they're doing the right thing, both in terms of investment, but also in terms of morality and ethics, am I doing the right thing, am I growing the right kind of business, and how much of my big data is paying me back, or going back to accountancy rules, the definition of a liability is an asset that is uncurated. So I can have a pencil factory, 'cause I sell pencils, and that's great, that's where I house my pencils, I go and I get, but if something happened and somehow the route driver disappeared, and that general manager went away, now I own a pencil factory that has holes in the roof, that has rotting merchandise, that kids can get into, and maybe the ceiling falls, there's a fire, all that is, if I'm not utilizing that asset, is a liability, and we're seeing real money coming out of the European Union, there was a hotel case where the data that they were hoarding wasn't wrong, it was about real people who had stayed at their hotels, it just was in the 90s. And so they were fined 14.5 million Euros for keeping stale data, an asset had turned into a liability, and that's why you're constantly balancing, is it value, is it risk, am I taking so much risk that I'm not compensating with value and vice versa, and I think that's the new aha moment of really looking at your data valuation. >> Yeah, and I think that was part of the big data thing too, where people finally realized it's not a liability, thinking about "I got to buy servers to store it, "and I got to buy storage, and I got to do all this stuff," and they'd just let it fall on the floor. It's not free, but it does have an asset value if you know what to do with it. So let's shift gears about privacy specifically, because obviously you are the queen of privacy. >> I like that, that's my new title. >> GDPR went down, and now we've got the California version of GDPR, love to get your update, did you happen to be here earlier for the keynotes, and there was a conversation on stage about the right to be forgotten. >> Jennifer: Oh dear god, now, tell me. >> And is it even possible, and a very esteemed group of panelists up there just talking about very simple instances where, I search on something that you did, and now I want to be forgotten. >> Did no one watch Back to the Future? Did we not watch that show? Back to the Future where all their limbs start disappearing? >> Yes, yes, it's hard to implement some of these things. >> This has been my exhaustion with the right to be forgotten since the beginning. Humanity has never desired a right to be forgotten. Now people could go from one village to the next and redo themselves, but not without the knowledge that they gained, and being who they were in the last village. >> Jeff: Speaking to people along the way. >> Right, you become a different entity along the way. So, the problem always was really, differential publicity. So, some dude doesn't pay back his debtors, he's called a bad guy, and suddenly, any time you Google him, or Bing him, Bing's still there, right? >> Jeff: I believe so. >> Okay, so you could Bing someone, I guess, and then that would be the first search term, that was the harm, was saying that your past shouldn't always come back to haunt you. And so what we try to do is use this big, soupy term that doesn't exist in philosophy, in art, the Chimea Roos had a great right to be forgotten plan. See how that went down? >> That was not very pleasant. >> No, it was not pleasant, because what happens is, you take out knowledge when you try to look backwards and say "Well, we're going to keep this piece and that," we are what we are, I'm a red hot mess, but I'm a combination of my red hot messes, and some of the things I've learned are based on that. So there's a philosophical debate, but then there's also the pragmatic one of how do you fix it, who fixes it, and who gets to decide whose right it is to be forgotten? >> And what is the goal, that's probably the most important thing, what is the goal that we're trying to achieve, what is the bad thing that we're trying to avoid, versus coming up with some grandiose idea that probably is not possible, much less practical. >> There's a suit against the Catholic Church right now, I don't know if you heard this, and they're not actually in Europe, they live in Vatican City, but there's a suit against, about the right to be forgotten, if I decide I'm no longer Catholic, I'm not doing it, Mom, I'm hearing you, then I should be able to go to the church and erase my baptismal records and all the rest. >> Jeff: Oh, I hadn't heard that one. >> I find it, first of all, as someone who is culturally Catholic, I don't know if I can be as saintly as I once was, as a young child. What happens if my husband decides to not be Catholic anymore? What happens if I'm not married anymore, but now my marriage certificate is gone from the Catholic Church? Are my children bastards now? >> Michelle's going deep. >> What the hell? Literally, what the hell? So I think it's the unintended consequence without, this goes back to our formula, is the data value of deletion proportionate to the data risk, and I would say the right to be forgotten is like this. Now having an indexability or an erasability of a one-time thing, or, I'll give you another corner case, I've done a little bit of thinking, so you probably shouldn't have asked me about this question, but, in the US, when there's a domestic abuse allegation, or someone calls 911, the police officers have to stay safe, and so typically they just take everybody down to the station, men and women. Guess who are most often the aggressors? Usually the dudes. But guess who also gets a mugshot and fingerprints taken? The victim of the domestic abuse. That is technically a public record, there's never been a trial, that person may or may not ever be charged for any offense at all, she just was there, in her own home, having the crap beat out of her. Now she turns her life around, she leaves her abusers, and it can happen to men too, but I'm being biased. And then you do a Google search, and the first thing you find is a mugshot of suspected violence. Are you going to hire that person? Probably not. >> Well, begs a whole discussion, this is the generation where everything's been documented all along the way, so whether they choose or not choose or want or don't want, and how much of it's based on surveillance cameras that you didn't even know. I thought you were going to say, and then you ask Alexa, "Can you please give me the recording "of what really went down?" Which has also been done, it has happened, it has happened, actually, which then you say "Hm, well, is having the data worth the privacy risk "to actually stop the perp from continuing the abuse?" >> Exactly, and one of my age-old mantras, there's very few things that rhyme, but this one does, but if you can't protect, do not collect. So if you're collecting all these recordings in the domestic, think about how you're going to protect. >> There's other people that should've hired you on that one. We won't go there. >> So much stuff to do. >> All right Michelle, but unfortunately we have to leave it there, but thank you for stopping by, I know it's kind of not a happy ending. But good things with DrumWave, so congratulations, we continue to watch the story evolve, and I'm sure it'll be nothing but phenomenal success. >> It's going to be a good time. >> All right, thanks a lot Michelle. She's Michelle, I'm Jeff, you're watching theCUBE, we're at RSA 2020 in San Francisco, thanks for watching, we'll see you next time. (techno music)

(upbeat music) >> Live from Las Vegas, it's theCUBE covering Informatica World 2019. Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World 2019. I'm your host, Rebecca Knight, along with my co-host, John Furrier. We're joined by Steven Guggenheimer, he is the corporate vice president of AI and ISV engagement at Microsoft. Thank you so much for coming on theCUBE. >> Sure, thanks for having me. >> So one of the things that we're hearing so much at this conference is, "data needs AI but AI needs data." I'm wondering from your perspective, AI engagement, where do you come down on this? What are you hearing? what are your thoughts on that big theme? >> Um, well, data is the -- some people say the oil for AI, pick your terminology, but there is no AI without data. The reason that AI is such a hot topic right now is the combination of sort of compute storage and networking at scale, which means the access for developers and data scientists to work with large sets of data and then the actual data. If you don't have data you can't build models, if you can't build models, that's what is the definition of AI. So you need data. I always-- all the coaching I do is about sort of, BI before AI. If you can't actually get insight out of your data, let's not try to add intelligence. If you can't get insight out of your data, it means your data is not in a good-- your data state is not in order. So data first. >> A lot of architectural work is being done on data. I see a horizontally scalable cloud, gives a nice access to a lot of different you know, observational data sets. >> Yeah >> It used to be give the guy the silo, got the data, go get more data, slower. Now, data feeds the developer process because SaaS business models have been proven that data and SaaS work well together. So how do we get more-- what's the sequence of architecture to usability of data so that not only can you just have analytical systems, but where developers can start building their SaaS apps with data? >> Yeah, I mean we have this notion where we often talk about sort of, blades or feedback loops. There's sort of four or five things most companies do. You work with customers, you have employees, you have a supply chain or some type of partner chain, You run your finance and operations. So the question becomes, in each of those processes, there's data. Human-generated forms over data or pick your loop and now you getting tons and tons of data. The trick now is to make it reusable. Mostly what we've done for years, form over data, take the data, form over data. And what we do is we get all these different databases. We try and create some layer that brings it all together. We build cubes out of it to view and then we get this hopeless spaghetti. So the trick right now, we're working on something called Common Data Model, which others are well, or Common Data Service. Let's get the entities lined up from the very beginning. We've worked with Adobe and SAP on the Open Data Initiative. Let's start at the core, let's make the data layer reusable, We're you know, databases have become data warehouses have become data lakes. We're heading towards a data tidal wave, and if we don't get the data estate in order to run the line of business applications, to feed all of the things we do to use the ML and AI on top of it, we're going to drown in data and not get what we want out of it. So, architecturally I think about the Common Data Model and the Common Data Service both generically by industry, we build accelerators for that, getting the big organizations like the three I mentioned aligned around that, making it such that any, you know, organization can build from that and then building on top of that. For big companies you have to decide, what do I keep and what do I throw out? You know, what do I just give up on and start from fresh? What do I actually clean? Where do I use tools from Informatica or others to help me clean it, secure it? But you've got to put all that thought in. >> You know we were chatting before we came on camera about the internet days and the storied history that you had at Microsoft. And during the internet, search was the big application. And search on the internet actually worked really well because they didn't have a legacy. And the people that tried to crack the code on search inside an enterprise, much harder problem (Giggles). Because of the database things you mentioned. How does today's enterprise get the benefit of SaaS as if they were cloud-native SaaS with the data? So you know, the challenge we're hearing here is having a Common Data Model is all great, but I just want to be a SaaS player, I want to use my data to feed into my business value. How does a company move out of those legacy constraints? What do you see as-- >> Well there's different paths that different companies will take. I mean, the good news is that if you get your data in order to do what you said, then whether you build, buy or partner for the SaaS services, you can use that data underneath and you should be feeding it back in and making it such that it's sort of reusable and the pipeline is consistent. The truth is on all this, it's just going to end up infused anyway. When you used the internet, which is a funny analogy 'cause I remind people, you know, when the internet came out we had internet products, we had internet events, we had internet shows. We don't have any of that anymore. It's just woven into everything we do. AI is going to be the same. You have all this hype right now, you have AI shows, you have, you know, AI groups. The truth is, in 10, 15 years, AI it's just going to be woven into everything. The data is going to be set up for that. >> So what's the misconception on AI? 'Cause, first of all, I love the fact that AI is hyped up because my kids love it. Machine learning they learn because they hear about AI and they hear all this coolness. So machine learning goes hand-in-hand with AI, you feed machine learning, machine learning feeds the AI application. But a lot of people have aspirations around AI. Some of them are ungettable and so that's probably a misalignment around the hype. What's your feeling of where the reality is and what's the misconceptions, how should people approach AI? Any thoughts there. >> I think a lot about the AI journey, the first year we were having these AI conversations, we talked about AI for everybody, just go play. Now the conversation is, I call it pragmatic AI. Look, lets talk about, you know, how you want to think about AI, it's going to end up everywhere, so the question becomes, what's your differentiation as a company, and how is AI going to support it? Like any other new technology, in the beginning, people just want to play. Just because you can -- let's just say just you can build a virtual agent, doesn't mean every company should. So the question becomes, first off, BI before AI, get your data state in order. Second, in a build buy partner model, what's your differentiation as a company? Whether you want to use either your unique data or your unique skill sets to use AI against that differentiation to help you grow. Otherwise, like, expect somebody else to have infused AI into the products you buy, the SaaS services, you know, use that, then build whatever you want and then there's, you know, if you think you're going to build a new business based on your unique data or your unique AI capabilities, great, let's have that conversation, we need that too but rarely does that become the state. so, most of the conversations move from, you know, the hype to okay, let's get pragmatic which is why I always come back to data first 'cause if you not doing that, you're not setting up for the long run. Let's build for the long run, then let's just have a business conversation like, how do you differentiate yourself as a business? Okay, how is this tool going to help you? >> I want to ask about, uh about innovation, and particularly because Microsoft is a company that's now entering its middle age (giggling) and-- >> What does that say about me, oh no >> As one of famously innovative company, but how do you stay on the cutting edge? I mean, I'm wondering internally how you think about AI for Microsoft's business purposes. What are the conversations around AI? >> One of such is, core conversations around this notion of tech intensity you know, from where we focus on how we think about things we think about tech intensity against different areas, AI being one of those. Look, AI is really this interesting thing. I would say we're plumbers by trade, we build software plumbing for others. So, we do three things right, with AI. Basically, there's a layer growing on top of the core development stack, compute, storage, networking for AI. So we're building a layer, cognitive services, bot services, machine learning, set of tools for developers to infuse AI into things that they've built, so that's thing number one. Thing number two, is we infuse AI into our own products, into Windows, into Office, into Azure, into dynamics. You don't see it, we don't talk about it, we don't say Microsoft Windows Inking brought to you by Azure AI. It just works, but our inking works, our face login works, oh, you know, I can -- it's helping me write a better resume in LinkedIn, that's all AI behind the scenes. Now, the third thing you think about then is, "how do you actually use AI to run the business better"? So, how do you think about, AI assisting professionals, how do we think about the, how we do mocking better, How we forecasting sales, so AI is about plumbing, let's build a platform for others, let's use it ourselves on our own products, and then let's think about how you actually use it to run the company better. And that's how we think about it-- >> That's pragmatic >> Very pragmatic AI is kind of -- >> Yeah, that's how I think about it and we, you know, it's interesting 'cause back to the tech intensity point, we get together on an AI conversation, we searching with the senior leadership team about once every other week, and we're round robin between a research topic, the platform and one of the solutions. So it's, you're always getting constant feedback about is the platform doing what we need to build solutions? Is the research feeding the platform? So, you're getting this really nice feedback loop right now and that tech intensity. >> Quality data always has been a big part of the data modeling in the past, Cloud now allows for data marketplaces I've seen sharing of data as a dynamic, almost like sharing libraries of your developer back in the day, so data is now being merchandised in a new way. This is a trend, what's your thought on it? Because if this continues, you're going to have more data inputs, does that-- >> Err, there are places where data is aggregated and potentially can be re-used. We can -- Bing is an example, Google would be an example um, I know people who aggregate data for different industries, etcetera. It's not an easy business, the rules and rights around data, the GPR compliance, the rest of it. I think there's a deer there but you really have to be in the business for-- the trick you run into is, if you're going to be an aggregator, and then a reseller of data, where's that data coming from? What are the rights, what's the security? And then, are the people who are providing that data comfortable with their competitors getting the data? 'cause if you're really going to be a data provider marketplace, first person who's going to want on is the competitor, so, I think it's an interesting conversation, I think it's kind of growing and there's some real good work there, I don't think it's as-- >> not viable yet >> Easily to do it at scale, for as many people who think they have the data asset as believed they do. But that's Steve's view, that's not a Microsoft's statement. (laughing) >> good disclaimer >> Steve's view, so I want to hear Steve's view on the skills gap, this is a huge problem in the technology industry, as so few people to fill roles. How's Microsoft dealing-- what's your view-- >> my view is I'm glad I work at Microsoft, 'cause we spend a lot of energy on that, um, I wish there were a single solution, but we have Minecraft for education, starting with kids, how do you help, you know, Minecraft is this great tool that teachers use help kids get started, so that's a tool set we work on something called tills, which is uh, basically, our developers teach school kids remotely, junior, high school level, you know, coding. Um, we have made investments against this, we have online training, you know, we work with universities. I don't know the perfect answer, um, but I do know we invest and we work with Hadi Partovi and his group on code.org, I mean any place that there is work going on, we work with the military for people coming out of the military service. So we're heavily invested. I'm hopeful that the ease of use of some of the tools and just from a job area, it drives people but I don't know the perfect answer. Steve's view is I don't know the answer, I do know we try every trick in the book-- >> Multipronged attack >> I'm a parent of two kids, like I have my daughter, you know, working on more on the tech side and you know, it's hard to keep kids on a track for that-- >> There's no degree yet, but we had a first degree this year, graduated from the school but there's kind of like a skills portfolio of different things depending on the make-up I mean, domain expertise is critical, if you don't know what you're tryna do, that's -- >> I think we got a mix, because what you're starting to see is, the tools for subject matter experts, are getting better, we have something called the power platfrom, which allows people who aren't necessarily coders by trade, but want to be able to build, you know, sort of apps or services to be able to do that more easily and mix their subject matter expertise. And you see many more people come out of any program, take biology, with sort of computer knowledge to a decent level. AI and ML research, different area, hard skills gap right there >> Steve, great insights, thanks for spending some time with us, great insights on the skills gap and just overall >> thanks for coming on theCUBE >> We didn't talk about rugby, but okay, fine. Thanks, next time >> next time >> You're one of those ballsmen >> we'd track you down >> The ballsmen can throw >> Exactly, shout out to them >> There we go, >> thank you >> Ah, you are watching theCUBE we'd come right back with more from Informatica World I'm Rebecca Knight for John Furrier, stay tuned (upbeat music)

>> Announcer: Live from Las Vegas, it's The Cube covering Dell Technologies World 2018. Brought to you by Dell EMC and its ecosystem partners. >> We're back at Dell Technologies World. It's the inaugural Dell Technologies World. You're watchin' The Cube, the leader in live tech coverage. My name is Dave Vallante, and I'm really excited to have Sanjay Poonen on, COO of VMWare, long-time Cube alum. Great to see you, my friend. >> Always great, Dave. >> Thanks again so much for makin' time. I know you're in and out, but things are good. We had Pat on, on Monday. You guys made the call early on. You said to the industry, you know, I think the industry handed us and maybe the forecasts are a little bit conservative. We're seeing great demand. We love our business right now, and it's comin' true. Data centers booming, VMWare's kickin' butt. It's goin' great. >> You know it's been obviously a very good couple of years, since the Dell EMC merger. It's really helped us, and you know, when we think about our partnerships, we put this in a very special place. In the last two years, partnerships like Dell and AWS have been very instrumental, built on top of the partnerships we've had for many years. And our core principles at VMWare have not changed. We're really focused on software defining the data center. Why? Because it makes you more agile, removes costs, reduces complexity, makes the planet more green. We think we've got a long way to go in just building that private cloud, making the data center feel like a cloud. That's priority number one. Priority number two, extending tno the hybrid cloud. Last time we talked was at AWS Reinvent. That's very important. We're doing a bit of work there at AWS and many other clouds. And user computing, making sure that every one of these type of devices are secure and managed, whether it's Apple devices, Google, or Microsoft. Those three priorities have still stayed the same, and now Dell's comin' to give us a lot more of that sort of draft, to help us do that inside the Dell EMC customer base, too. >> Yeah, I mean you guys are doin' it again, the whole, NSX obviously is booming. >> Sanjay: Big launch this week. >> You know, it's funny, the whole software-defined networking thing. Everybody flocked to it. VCs flocked to it. You guys changed the game with that Nycera acquisition. I mean, could you imagine, I guess you did imagine what it was going to become, I mean it's really taken off in a big way. >> Bold move. I got to give credit to the, I mean I wasn't at the company at the time, but I got to tell you, when I saw that I was stunned. Paying 1.2 billion for a company that didn't have much revenue. But here we are. We talked about it in our earnings call being a 1.4 billion one rate business. 4,500 hundred customers. We were zero customers five years ago when we did the acquisition, and what we really defined is that the future of networking is going to be software-defined, clearly, and it's much the same way a Tesla is transforming the automotive industry, right? What's the value of a Tesla? It's not just the hardware, but the software that's changing the way in which you drive, park, all of the mapping, all of that stuff. We believe the same way the networking industry's going to go through mighty revolution. We think the data center gets more efficeint and driven through software. The path into the into the public cloud, and the path to the branch, and that's what we as we launched our virtual cloud networking. It's extremely differentiated in the industry. We're the only ones really pioneering that, and we think it's extremely visionary. And we're excited to take our customers on this journey. It was a big launch for us this week, and we think NSX is just getting started. 4500 customers is about 1% of our roughly 500,000 customers Every single one of them should be looking at NSX. Big opportunity ahead of us. >> Huge. And the cloud play, we talked about this at VM World last summer. The clarity now that your customers have. They can now make bets for a couple of cycles anyway, really having confidence in your cloud strategy. You've seen that, I'm sure, in your customer base. >> We have, and you know, it started off by telling the world that the 4,000 service providers that have built their stack on VMWare, VMWare Cloud Providers, VCPP, are all going to be very special to us as they build out their clouds, often in many specialized country that have country-specific cloud requirements. But the we're going to take the public clouds and systematically start working them. IBM cloud was the first, When they acquired software we had a strong relationship with them, announced two or three years ago. And then I think the world was shocked. It was almost, as I've described on the media, a Berlin Wall moment, when AWS and VMWare came together because it sort of felt like the United States and Soviet German in 1987, okay? And you know, here we have these two companies, really workin'. That's worked out very well for us, and then we've done systematic other things with Azure, Google, and so on and so forth, and we'll see how the public cloud plays out, but we think that that hybrid cloud bridge. We're going to be probably the only company who can really play a very pivotal role in the world moving from private cloud to public cloud and there's going to be balance on both sides of that divide. >> So you really essentially are trying to become the infrastructure for the digital world now, aren't you? Talk about that a little bit. You're seeing new workloads, obviously AI's all the buzz. You guys are doing some work in blockchains. It's going to take a while for all that to pick up, but really it's the ability and containers is the other thing. Everybody thought, oh containers, that's the end of VMs, and Pat at the time said, no no no, you guys don't understand. Let me explain it. He sort of laid it out. You seem to be embracing that, again embracing change. >> I got to tell you, that one for me because I'll tell you when I first joined the company four and a half years ago, I was at SAP. I asked Pat two questions. I said the public cloud's going to, I mean, probably take out VMWare, aren't you concerned with Amazon. Here we are taking that headwind and making a tailwind. The second was like, everyone's talking about Docker. Aren't containers going to just destroy VMs? And that one wasn't as clear to us at the time, but we were patient. And what happened we started to notice in the last few years. We began to notice on GitHub tremendous amount of activity around Kubernetes, and here comes Google almost taking the top off of a lot of you know parts of Docker Two, Docker Swarm, Enterprise, Docker still remains a very good container format, but the orchestration layers become a Google-based project called Kubernetes. And I think our waiting allowed us and pivotal to embrace Google in the partnership that we announced last year. And we plan to become the de facto enterprise container platform. If VMs became the VM in VMWare and we have 500,000 customers, tens of millions of VMs, you'd think we could multiply those VMs by some number to get number of containers. VMWare has its rightful place, a birthright, to become the de facto enterprise container platform. We're just getting started, both between us and Pivotal, the Kubernetes investment, Big deal. And we're going to do it in partnership with companies like Google. >> I want to ask you about Pivotal. When Joe Tucci was the swansong in the MC world, he came out with an analyst meeting and we asked them, if you had a mulligan, you know, what would you do over again. He said, you know, we're going to answer it this way. He said, I wished I had done more to bring together the family, you know, the federation. We laid that vision out, and I probably, he said, personally I probably could've done more. I feel like Michael has taken this on. I almost feel like Joe, when he laughs at Michael. My one piece of advice is do a better job than I did with that integration. And it seems like Michael's takin' that on as an outsider. What can you tell us about the relationship between all the companies, particularly Pivotal. >> Yeah, you know Joe's a very special man, as our chairman, and Joe and Pat are the reasons I joined VMWare, and so I have tremendous respect for them. And he stayed on as an advisor to Mike O'Dell. And I think Mike O'Dell just took a lot of those things and improved on it. I wouldn't say that anything was dramatically bad, but you know he tightened up much of the places where we could work together. One material change was having the Dell EMC reps carry quota, for example VMWare. They're incetivized. That has been a huge difference to allow us to have our sales forces completely align together. Big big huge difference. I mean, sales people care about our product when they're compensated, carry quota on it, and drive it. The second aspect was in many of these places where Dell and VMWare or VMWare and Pivotal were needed to just take obstacles out of the way, and I don't think Pivotal would've been really successful if it had stayed in VMWare four or five years ago. So Paul Mertz leaving, the genius of that whole move, which Joe orchesthrated, and allowing them to flourish. Okay, here they have four or five years, they've gone public. They have a tremendous amount of traction. Then last year, we began to see that Kubernetes Coming back allowed us to get closer to them, okay? We didn't need to do that necessarily by saying that Pivotal needs to be part of VMWare. We just needed to build a joint engineering effort around Kubernetes And make that enormously successful. So you get the best of both worlds. We're an investor, obviously, in Pivotal. We're proud of their success in the public markets. We benefit some from that sort of idea process, but at the same time we want to make sure this Kubernetes Effort and the broader app platform, our cloud foundry, is enormously successful, and every one of our customers who have VMs starts looking containers. >> Well, I always said Pivotal was formed with a bunch of misfit toys that just didn't seem to fit into VMWare. >> Sanjay: It's come a long way. >> And you took that, but it was smart because you took it and said, here it is. Let's start figuring that out. Who better to do that than Paul? And it's really come together and obviously a very successful. >> Yeah, Rob, Scott, Bill, Yara, many of that team there. They're passionate about developers, okay? We understand the infratstructure role very well, but when you can get dev and ops together, in a way they collaborate, so we're excited about it. And we have a key part for us, we have a very simple mission: to make the container platform just very secure. What's the differenetiation between us and other companies trying to build container platforms? NSX? So our contribution into that is to take Kubernetes Watch for some of the management capabilities, and then add NSX to it, highly differentiate it. And now all of a sudden customers say, this is the reason why I mean, 'cause every container brings a place where the port could be insecure. NSX makes that secture, and we think that that's another key part to what's made NSX the launch this week extremely sepcial is that its story relates to cloud and containers. Those two Cs, I would say, cloud and containers. We've taken what were headwinds to us, VMWare over the last four or five years, and made them tailwinds. And for us that's been a tremendous learnnig lesson, not just I would say in our own technology road map, but in leadership and management. That's important for us as business leaders, too. >> Dave: And I got to give some love to my friends in the Vsin world, Yen Bing and those guys. Obviously Vsin doin' very well. Give us the update there. I mean, you're doin', he's doin' exactly what you said: we're going to do to networking and storage what we did to compute. >> I mean, again you know, when we start things off. If you'll remember, three or four years ago, we were confusing EMC and VMWare, Evo, Rails, some of those things. We just had to clean that up. And as Dell EMC came together and VMWare, we said, listen. We're going to do software-defined storage really well because it has a very close synergy point to the Kubernetes I mean, we know a lot about storage because it's very closely connected to Compute. And if we could do that better than anybody else, and in the meantime all these startups were doing reasonably well, Simplicity, Nutanics, Pivotry, so on and so forth. I mean there's no reason if we don't have our act together we could build the best software-defined storage and then engineer a system together with Dell that has the software, and that's what VX rails has become. So a few false stubs of the toe when we started off, you know three or four years ago, but we've come a long way. Pat talked about over 10,000 customers at the revenue run rate that we announced last year, and a 600 million run rate at the end of Q4. We believe we are, for just the software piece, we are the de facto leader, and we have to continue to make customers happy and to drive, you know, this as the future of hyper converge infrastructure because converged had its place. And now the coming together of Compute Storage, over time networking with a layer of management, that's the future of the data center. >> Yeah, I was watching. THere's some good, interesting maneuvering goin' on in the marketplace. A lot of fun for a company like ours to watch. I want to talk about leadership. There's a great, you got to go to Sanjay's LinkedIn profile. There's an awesome video on there. It's like a mini TED talk that some of your folks mashed up and put out there. It's only about eight minutes. But I want to touch on some of the things that I learned from that video. Your background, I mean I knew you came from India. You came over at 18 years old, right? >> Sanjay: I was very fortunate. I grew up in a poor home in India, and I came here only because I got a scholarship to go to Dartmouth College. And I think I might have been one of the few brown-skinned guys in Hanover, New Hampshire. I mean, you've been there, you know there's not much Indian goin' on here. (laughter) But I'm very forutnate. And this country is a very special country to immigrants, if you work hard and if you're willing to apply yourself. I'm a product of that hard work. And now as an Indian American living in California. So I feel very fortunate for all that both the country and people who invested in me over the last many decades have helped me become who I am. >> So you were on a scholarship to Dartmouth. >> Yes, that's right. >> As a student in India. So obviously an accomplished student in India, and you said, you know, I got bullied a little bit. I had the glasses, right? Somebody once told me, Dave, don't peak in high school. It's good advice, right? So it was funny to hear you tell that story because I see you as such a charismatic, dynamic leader. I can't picture you as, you know, a little kid getting bullied. >> We were always geeks at one point in time, but one of the things my mother and dad always taught me, especially my mom, who had a tremendous influence on my life and is my hero, is, listen, don't worry what people say about you, okay? Your home is always going to feel a safety and a fortress to us, and I appreciate the fact that irrespective of what happened on the playground, if I was bullied, at home I knew it was secure. And I seek to have that same attitude twoards my children and everybody I consider my extended family, people at work, and so on and so forth. But once you've done that, you don't build your identity just to what people say about you. You're going to build your identity over what's done over a long period of time, okay? With, of course, if everybody in the world hates you, that's a tough place. That's happened to a few people in the world. I wasn't in that state at all. And as I came to this country, just got tougher because I was a minority in a place. But many of those lessons I learned as a young boy helped me as an 18 year old, as I came here, and I'm very thankful for that. >> And you came here with no money, alright? >> A scholarship. >> Right. >> Maybe 50 bucks in the pocket. >> You had 50 bucks and an opportunity, and made the most of it. And then obviously you did very well at Dartmouth. You graduated from Harvard, right? >> I did my MBA at Harvard. >> MBA at Harvard, probably met some interesting people there. >> Andy Jackson being one of them. >> I know he's a friend of yours. >> Sam Berg, who's the head of the client business, was also a classmate of mine at HBS. The '97 class of HBS had some accomplished people: Chris Kapensky is running McDonald's. She's President of US. So I'm very fortunate to have some good classmates there. >> So what did you do? Did you go right to Harvard from? >> No, I spent four years working at Apple. And then went back to do my business school. >> And then what'd you do after that? >> I came back to Silicon Valley at a startup. I was one of the founding product managers at AlphaBlocks. Then went to Informatica. And bulk of my time was at SAP, and most of my life was in the analytics, big data business. What we called big data at the time. >> And that's when we first met it. >> Analytics at BI, and then when Joe and Pat called me for this, the end-user computing role at VMWare four and a half years ago. That's when I came to VMWare. >> And that was a huge coup for VMWare. We knew you from SAP, and that business was struggling. You always give credit to your team, of course. Awesome. Which is what a good leader does. The other thing I wanted to touch on before we break is, you talked about leadership and how importatn it is to embrace cahnge. You said you have three choices when change hits you. What are those three choices? >> You either embrace it, okay? You either stand on the sidelines or you leave. And that's typically what happens in any kind of change, whether it's change in work, change in fafmilies, change in other kinds of religious settings, I mean it's a time-old prinicple. And you want to let the people who are not on board with it leave if they want to leave. The people who are staying in the middle and not yet convinced, you'll hope they'll do. But they cannot yet throw the grenades, 'cause then they're just going to be. And you want to take that nucleus of people who are with you in the change to help you get the people who sit on the sidelines in. And to me when I joined VMWare, the end-user computing team had the highest attrition, okay, and the lowest satisfaction. And I found the same thing. There were popel who were leaving in droves. Some people sittin' on the sidelines, but a core group of people I loved that were willing to really work with me, 'cause I didn't really know a lot about it. The smarter people were in the team and some people that we hired in. We had to take that group and become the chagne agents, and when that happens it's a beautiful thing because from within starts to form this thing that's the phoenix rising out of the ashes. And the company, and then these people who are sidelineers start to get involved. New people want to join. Now everybody wants to be part of the end-user computing team at VMWare because we're a winner, but it wasn't that way four and a half years ago. Same thing in cloud. How are we going to transform this cloud business to be one where, VCloudAir. We're being made fun of, like how are you ever going to compete with Amazon. We had to go through our own catharsis. We divested that business, but out of that pain point came a fundamental change. Some people left. Some people stayed, but I'm just grateful through all of this that we learned a tremendous amount. I think change is the most definitive thing that happens to every company, and you have to embrace it. If you embrace chance, it's going to make you a much stronger leader. I'll tell you, the Mandarin word, okay, for crisis is two symbols: one that shows disaster and one that shows opportunity. I choose the opportunity side. >> Dave: You choose? Right? Yeah! >> And eveyrone makes that choice, right? And if you make the right path, it could be a beautiful learning experience. >> Sanjay, words to live by. Definitely check out that video on Sanjay's profile. >> It's on LinkedIn. >> Really fabulous always to sit down and talk to you. >> Always a pleasure, Dave. Congratulations to all your success. >> Dave: Thank you! I really appreaciate your support. >> Thank you. >> Alright, everybody that's it from Dell Technologies World 2018. You can hear the music behind us. Next week, big week. We've got Red Hat Summit. I'll be at Service Now Knowledge. We got a couple of other shows and tons of shows coming up. I don't know, you were at Vmon last year. I don't know if you're going to be there this year, maybe maybe not, we'll see. >> Well we got a big one coming up at VM World. We'll see you there. >> We got big one coming up, VM World, at the end of August through early September, which is back at Mosconi this year? >> It's back at Las Vegas still. One more thing and then it's going back to Mosconi after the construction's over. >> So go to theCUBE.net, check out all the shows. Thanks for watching, everybody. We'll see you next time. (digital music)