>> Okay, we're now going to explore the vision of the future of cloud computing from the perspective of one of the leaders in the field, JG Chirapurath is the Vice President of Azure Data AI and Edge at Microsoft. JG, welcome to theCUBE on Cloud, thanks so much for participating. >> Well, thank you, Dave. And it's a real pleasure to be here with you and just want to welcome the audience as well. >> Well, JG, judging from your title, we have a lot of ground to cover and our audience is definitely interested in all the topics that are implied there. So let's get right into it. We've said many times in theCUBE that the new innovation cocktail comprises machine intelligence or AI applied to troves of data with the scale of the cloud. It's no longer we're driven by Moore's law. It's really those three factors and those ingredients are going to power the next wave of value creation in the economy. So first, do you buy into that premise? >> Yes, absolutely. We do buy into it and I think one of the reasons why we put data analytics and AI together, is because all of that really begins with the collection of data and managing it and governing it, unlocking analytics in it. And we tend to see things like AI, the value creation that comes from AI as being on that continuum of having started off with really things like analytics and proceeding to be machine learning and the use of data in interesting ways. >> Yes, I'd like to get some more thoughts around data and how you see the future of data and the role of cloud and maybe how Microsoft strategy fits in there. I mean, your portfolio, you've got SQL Server, Azure SQL, you got Arc which is kind of Azure everywhere for people that aren't familiar with that you got Synapse which course does all the integration, the data warehouse and it gets things ready for BI and consumption by the business and the whole data pipeline. And then all the other services, Azure Databricks, you got you got Cosmos in there, you got Blockchain, you've got Open Source services like PostgreSQL and MySQL. So lots of choices there. And I'm wondering, how do you think about the future of cloud data platforms? It looks like your strategy is right tool for the right job. Is that fair? >> It is fair, but it's also just to step back and look at it. It's fundamentally what we see in this market today, is that customers they seek really a comprehensive proposition. And when I say a comprehensive proposition it is sometimes not just about saying that, "Hey, listen "we know you're a sequence of a company, "we absolutely trust that you have the best "Azure SQL database in the cloud. "But tell us more." We've got data that is sitting in Hadoop systems. We've got data that is sitting in PostgreSQL, in things like MongoDB. So that open source proposition today in data and data management and database management has become front and center. So our real sort of push there is when it comes to migration management modernization of data to present the broadest possible choice to our customers, so we can meet them where they are. However, when it comes to analytics, one of the things they ask for is give us lot more convergence use. It really, it isn't about having 50 different services. It's really about having that one comprehensive service that is converged. That's where things like Synapse fits in where you can just land any kind of data in the lake and then use any compute engine on top of it to drive insights from it. So fundamentally, it is that flexibility that we really sort of focus on to meet our customers where they are. And really not pushing our dogma and our beliefs on it but to meet our customers according to the way they've deployed stuff like this. >> So that's great. I want to stick on this for a minute because when I have guests on like yourself they never want to talk about the competition but that's all we ever talk about. And that's all your customers ever talk about. Because the counter to that right tool for the right job and that I would say is really kind of Amazon's approach is that you got the single unified data platform, the mega database. So it does it all. And that's kind of Oracle's approach. It sounds like you want to have your cake and eat it too. So you got the right tool with the right job approach but you've got an integration layer that allows you to have that converged database. I wonder if you could add color to that and confirm or deny what I just said. >> No, that's a very fair observation but I'd say there's a nuance in what I sort of described. When it comes to data management, when it comes to apps, we have then customers with the broadest choice. Even in that perspective, we also offer convergence. So case in point, when you think about cosmos DB under that one sort of service, you get multiple engines but with the same properties. Right, global distribution, the five nines availability. It gives customers the ability to basically choose when they have to build that new cloud native app to adopt cosmos DB and adopt it in a way that is an choose an engine that is most flexible to them. However, when it comes to say, writing a SequenceServer for example, if modernizing it, you want sometimes, you just want to lift and shift it into things like IS. In other cases, you want to completely rewrite it. So you need to have the flexibility of choice there that is presented by a legacy of what sits on premises. When you move into things like analytics, we absolutely believe in convergence. So we don't believe that look, you need to have a relational data warehouse that is separate from a Hadoop system that is separate from say a BI system that is just, it's a bolt-on. For us, we love the proposition of really building things that are so integrated that once you land data, once you prep it inside the Lake you can use it for analytics, you can use it for BI, you can use it for machine learning. So I think, our sort of differentiated approach speaks for itself there. >> Well, that's interesting because essentially again you're not saying it's an either or, and you see a lot of that in the marketplace. You got some companies you say, "No, it's the data lake." And others say "No, no, put it in the data warehouse." And that causes confusion and complexity around the data pipeline and a lot of cutting. And I'd love to get your thoughts on this. A lot of customers struggle to get value out of data and specifically data product builders are frustrated that it takes them too long to go from, this idea of, hey, I have an idea for a data service and it can drive monetization, but to get there you got to go through this complex data life cycle and pipeline and beg people to add new data sources and do you feel like we have to rethink the way that we approach data architecture? >> Look, I think we do in the cloud. And I think what's happening today and I think the place where I see the most amount of rethink and the most amount of push from our customers to really rethink is the area of analytics and AI. It's almost as if what worked in the past will not work going forward. So when you think about analytics only in the enterprise today, you have relational systems, you have Hadoop systems, you've got data marts, you've got data warehouses you've got enterprise data warehouse. So those large honking databases that you use to close your books with. But when you start to modernize it, what people are saying is that we don't want to simply take all of that complexity that we've built over, say three, four decades and simply migrate it en masse exactly as they are into the cloud. What they really want is a completely different way of looking at things. And I think this is where services like Synapse completely provide a differentiated proposition to our customers. What we say there is land the data in any way you see, shape or form inside the lake. Once you landed inside the lake, you can essentially use a Synapse Studio to prep it in the way that you like. Use any compute engine of your choice and operate on this data in any way that you see fit. So case in point, if you want to hydrate a relational data warehouse, you can do so. If you want to do ad hoc analytics using something like Spark, you can do so. If you want to invoke Power BI on that data or BI on that data, you can do so. If you want to bring in a machine learning model on this prep data, you can do so. So inherently, so when customers buy into this proposition, what it solves for them and what it gives to them is complete simplicity. One way to land the data multiple ways to use it. And it's all integrated. >> So should we think of Synapse as an abstraction layer that abstracts away the complexity of the underlying technology? Is that a fair way to think about it? >> Yeah, you can think of it that way. It abstracts away Dave, a couple of things. It takes away that type of data. Sort of complexities related to the type of data. It takes away the complexity related to the size of data. It takes away the complexity related to creating pipelines around all these different types of data. And fundamentally puts it in a place where it can be now consumed by any sort of entity inside the Azure proposition. And by that token, even Databricks. You can in fact use Databricks in sort of an integrated way with the Azure Synapse >> Right, well, so that leads me to this notion of and I wonder if you buy into it. So my inference is that a data warehouse or a data lake could just be a node inside of a global data mesh. And then it's Synapse is sort of managing that technology on top. Do you buy into that? That global data mesh concept? >> We do and we actually do see our customers using Synapse and the value proposition that it brings together in that way. Now it's not where they start, oftentimes when a customer comes and says, "Look, I've got an enterprise data warehouse, "I want to migrate it." Or "I have a Hadoop system, I want to migrate it." But from there, the evolution is absolutely interesting to see. I'll give you an example. One of the customers that we're very proud of is FedEx. And what FedEx is doing is it's completely re-imagining its logistics system. That basically the system that delivers, what is it? The 3 million packages a day. And in doing so, in this COVID times, with the view of basically delivering on COVID vaccines. One of the ways they're doing it, is basically using Synapse. Synapse is essentially that analytic hub where they can get complete view into the logistic processes, way things are moving, understand things like delays and really put all of that together in a way that they can essentially get our packages and these vaccines delivered as quickly as possible. Another example, it's one of my favorite. We see once customers buy into it, they essentially can do other things with it. So an example of this is really my favorite story is Peace Parks initiative. It is the premier of white rhino conservancy in the world. They essentially are using data that has landed in Azure, images in particular to basically use drones over the vast area that they patrol and use machine learning on this data to really figure out where is an issue and where there isn't an issue. So that this part with about 200 radios can scramble surgically versus having to range across the vast area that they cover. So, what you see here is, the importance is really getting your data in order, landing consistently whatever the kind of data it is, build the right pipelines, and then the possibilities of transformation are just endless. >> Yeah, that's very nice how you worked in some of the customer examples and I appreciate that. I want to ask you though that some people might say that putting in that layer while you clearly add simplification and is I think a great thing that there begins over time to be a gap, if you will, between the ability of that layer to integrate all the primitives and all the piece parts, and that you lose some of that fine grain control and it slows you down. What would you say to that? >> Look, I think that's what we excel at and that's what we completely sort of buy into. And it's our job to basically provide that level of integration and that granularity in the way that it's an art. I absolutely admit it's an art. There are areas where people crave simplicity and not a lot of sort of knobs and dials and things like that. But there are areas where customers want flexibility. And so I think just to give you an example of both of them, in landing the data, in consistency in building pipelines, they want simplicity. They don't want complexity. They don't want 50 different places to do this. There's one way to do it. When it comes to computing and reducing this data, analyzing this data, they want flexibility. This is one of the reasons why we say, "Hey, listen you want to use Databricks. "If you're buying into that proposition. "And you're absolutely happy with them, "you can plug it into it." You want to use BI and essentially do a small data model, you can use BI. If you say that, "Look, I've landed into the lake, "I really only want to use ML." Bring in your ML models and party on. So that's where the flexibility comes in. So that's sort of that we sort of think about it. >> Well, I like the strategy because one of our guests, Jumark Dehghani is I think one of the foremost thinkers on this notion of of the data mesh And her premise is that the data builders, data product and service builders are frustrated because the big data system is generic to context. There's no context in there. But by having context in the big data architecture and system you can get products to market much, much, much faster. So, and that seems to be your philosophy but I'm going to jump ahead to my ecosystem question. You've mentioned Databricks a couple of times. There's another partner that you have, which is Snowflake. They're kind of trying to build out their own DataCloud, if you will and GlobalMesh, and the one hand they're a partner on the other hand they're a competitor. How do you sort of balance and square that circle? >> Look, when I see Snowflake, I actually see a partner. When we see essentially we are when you think about Azure now this is where I sort of step back and look at Azure as a whole. And in Azure as a whole, companies like Snowflake are vital in our ecosystem. I mean, there are places we compete, but effectively by helping them build the best Snowflake service on Azure, we essentially are able to differentiate and offer a differentiated value proposition compared to say a Google or an AWS. In fact, that's been our approach with Databricks as well. Where they are effectively on multiple clouds and our opportunity with Databricks is to essentially integrate them in a way where we offer the best experience the best integrations on Azure Berna. That's always been our focus. >> Yeah, it's hard to argue with the strategy or data with our data partner and ETR shows Microsoft is both pervasive and impressively having a lot of momentum spending velocity within the budget cycles. I want to come back to AI a little bit. It's obviously one of the fastest growing areas in our survey data. As I said, clearly Microsoft is a leader in this space. What's your vision of the future of machine intelligence and how Microsoft will participate in that opportunity? >> Yeah, so fundamentally, we've built on decades of research around essentially vision, speech and language. That's been the three core building blocks and for a really focused period of time, we focused on essentially ensuring human parity. So if you ever wonder what the keys to the kingdom are, it's the boat we built in ensuring that the research or posture that we've taken there. What we've then done is essentially a couple of things. We've focused on essentially looking at the spectrum that is AI. Both from saying that, "Hey, listen, "it's got to work for data analysts." We're looking to basically use machine learning techniques to developers who are essentially, coding and building machine learning models from scratch. So for that select proposition manifest to us as really AI focused on all skill levels. The other core thing we've done is that we've also said, "Look, it'll only work as long "as people trust their data "and they can trust their AI models." So there's a tremendous body of work and research we do and things like responsible AI. So if you asked me where we sort of push on is fundamentally to make sure that we never lose sight of the fact that the spectrum of AI can sort of come together for any skill level. And we keep that responsible AI proposition absolutely strong. Now against that canvas Dave, I'll also tell you that as Edge devices get way more capable, where they can input on the Edge, say a camera or a mic or something like that. You will see us pushing a lot more of that capability onto the edge as well. But to me, that's sort of a modality but the core really is all skill levels and that responsibility in AI. >> Yeah, so that brings me to this notion of, I want to bring an Edge and hybrid cloud, understand how you're thinking about hybrid cloud, multicloud obviously one of your competitors Amazon won't even say the word multicloud. You guys have a different approach there but what's the strategy with regard to hybrid? Do you see the cloud, you're bringing Azure to the edge maybe you could talk about that and talk about how you're different from the competition. >> Yeah, I think in the Edge from an Edge and I even I'll be the first one to say that the word Edge itself is conflated. Okay, a little bit it's but I will tell you just focusing on hybrid, this is one of the places where, I would say 2020 if I were to look back from a COVID perspective in particular, it has been the most informative. Because we absolutely saw customers digitizing, moving to the cloud. And we really saw hybrid in action. 2020 was the year that hybrid sort of really became real from a cloud computing perspective. And an example of this is we understood that it's not all or nothing. So sometimes customers want Azure consistency in their data centers. This is where things like Azure Stack comes in. Sometimes they basically come to us and say, "We want the flexibility of adopting "flexible button of platforms let's say containers, "orchestrating Kubernetes "so that we can essentially deploy it wherever you want." And so when we designed things like Arc, it was built for that flexibility in mind. So, here's the beauty of what something like Arc can do for you. If you have a Kubernetes endpoint anywhere, we can deploy an Azure service onto it. That is the promise. Which means, if for some reason the customer says that, "Hey, I've got "this Kubernetes endpoint in AWS. And I love Azure SQL. You will be able to run Azure SQL inside AWS. There's nothing that stops you from doing it. So inherently, remember our first principle is always to meet our customers where they are. So from that perspective, multicloud is here to stay. We are never going to be the people that says, "I'm sorry." We will never say (speaks indistinctly) multicloud but it is a reality for our customers. >> So I wonder if we could close, thank you for that. By looking back and then ahead and I want to put forth, maybe it's a criticism, but maybe not. Maybe it's an art of Microsoft. But first, you did Microsoft an incredible job at transitioning its business. Azure is omnipresent, as we said our data shows that. So two-part question first, Microsoft got there by investing in the cloud, really changing its mindset, I think and leveraging its huge software estate and customer base to put Azure at the center of it's strategy. And many have said, me included, that you got there by creating products that are good enough. We do a one Datto, it's still not that great, then a two Datto and maybe not the best, but acceptable for your customers. And that's allowed you to grow very rapidly expand your market. How do you respond to that? Is that a fair comment? Are you more than good enough? I wonder if you could share your thoughts. >> Dave, you hurt my feelings with that question. >> Don't hate me JG. (both laugh) We're getting it out there all right, so. >> First of all, thank you for asking me that. I am absolutely the biggest cheerleader you'll find at Microsoft. I absolutely believe that I represent the work of almost 9,000 engineers. And we wake up every day worrying about our customer and worrying about the customer condition and to absolutely make sure we deliver the best in the first attempt that we do. So when you take the plethora of products we deliver in Azure, be it Azure SQL, be it Azure Cosmos DB, Synapse, Azure Databricks, which we did in partnership with Databricks, Azure Machine Learning. And recently when we premiered, we sort of offered the world's first comprehensive data governance solution in Azure Purview. I would humbly submit it to you that we are leading the way and we're essentially showing how the future of data, AI and the Edge should work in the cloud. >> Yeah, I'd be disappointed if you capitulated in any way, JG. So, thank you for that. And that's kind of last question is looking forward and how you're thinking about the future of cloud. Last decade, a lot about cloud migration, simplifying infrastructure to management and deployment. SaaSifying My Enterprise, a lot of simplification and cost savings and of course redeployment of resources toward digital transformation, other valuable activities. How do you think this coming decade will be defined? Will it be sort of more of the same or is there something else out there? >> I think that the coming decade will be one where customers start to unlock outsize value out of this. What happened to the last decade where people laid the foundation? And people essentially looked at the world and said, "Look, we've got to make a move. "They're largely hybrid, but you're going to start making "steps to basically digitize and modernize our platforms. I will tell you that with the amount of data that people are moving to the cloud, just as an example, you're going to see use of analytics, AI or business outcomes explode. You're also going to see a huge sort of focus on things like governance. People need to know where the data is, what the data catalog continues, how to govern it, how to trust this data and given all of the privacy and compliance regulations out there essentially their compliance posture. So I think the unlocking of outcomes versus simply, Hey, I've saved money. Second, really putting this comprehensive sort of governance regime in place and then finally security and trust. It's going to be more paramount than ever before. >> Yeah, nobody's going to use the data if they don't trust it, I'm glad you brought up security. It's a topic that is at number one on the CIO list. JG, great conversation. Obviously the strategy is working and thanks so much for participating in Cube on Cloud. >> Thank you, thank you, Dave and I appreciate it and thank you to everybody who's tuning into today. >> All right then keep it right there, I'll be back with our next guest right after this short break.

>> Okay, we're now going to explore what it's like to be the CIO of a fast-paced growth company in Silicon Valley, and how the cloud, however you want to define the cloud, public cloud, on-prem, hybrid, et cetera, how it's supported that growth, and with me is Cathy Southwick, who is the CIO of Pure Storage. Kathy has really deep experience managing technology organizations. She spent a number of years overseeing AT&T's cloud planning and engineering, and another few years overseeing a team of a couple thousand network and IT engineers, working to break the physical stranglehold of fossilized telco networks, implementing network function virtualization and a software-defined methodology for the company, and of course, she's spent the last couple of years as the CIO of Pure, so Cathy, it's great to see you again. Thank you for coming on the program. >> Thanks for having me. It's good to be here. >> You're very welcome. And so, given your experience with cloud, you know, dating back to really the early part of last decade, how did you look at cloud back then, and how has it evolved, from your point of view? >> You know, it's an interesting question, 'cause I think that there's some things that have moved very fast, and there's some things that are very much the same as they were even a decade ago. I think that all companies are very focused on how do you think about cloud? Do you think about it as on-prem, and when I started, we really were focused on an on-prem solution, and building an on-prem private cloud to help modernize our business, so I think that with that, all companies are still in that same mindset of how do I want to think about cloud, and how do I want to think about that on-prem versus public, versus a combination or some type of hybrid solution? So, I mean, all of us are on that journey. It just seems like it's taken us probably a little bit longer than most of us probably thought from the beginning. >> So as a CIO, thinking about that evolution, how has that informed the way you think about applying specifically the public cloud to Pure's business. >> You know, I think that we've been a-- For Pure ourself, I think we're in a really unique position. We were essentially born in the cloud, so we're a company that's 10, 11 years old, and if I give the contrast of that of AT&T being you know, 130 year old company, and having a lot of applications that have, you know, lived historically on-prem, there's very different issues and challenges that you have. Pure has had, I think, the advantage, just like many other companies that are born in the cloud, who can see what the advantages are very quickly and we made decisions early on that said that we were going to actually do both. We were going to look to say how do I put those applications and that data, whether it was on public or on-prem, and be able to do that both in the IT side as well as within the product side, so how we develop our products. >> Now, as I mentioned up front, you have obviously a lot of experience managing large technology teams. My question is when you first saw the emergence of the modern cloud, how did you communicate with your team members? I mean, you mentioned you were kind of building your own private cloud, so I guess that's less threatening to people, but what was it like? Was there a concern? Were they eager to jump in? What was that dynamic like, and how did you manage it? >> You know, it's really, it's different depending on the different part of the organization, so I'll give you kind of two things I learned. One of them was that our teams on the operation side, they saw it as a huge advantage. They saw it as an opportunity to really modernize, to really get themselves, both their own, individual skill sets advanced, as well as provide a better level of service for our internal customer, so to speak. Our application and our data partners that we had to work with, they saw it as an opportunity to bring agility to their applications, quicker speed to market, and more currency of their applications, so they actually got some benefits that they weren't actually I'll call planning for. They had the opportunity to get investment in their applications without having to put that investment on themselves. I would tell you the thing I learned from the teams, this is probably, might be a little bit of a surprise, but often, you know, leaders believe you got to have all the answers, you got to drive everything. You're going to make sure everyone knows what needs to get done. What I actually found, this was actually one of my big moments, I think, was our individuals, our employees, our teams, they are so brilliant and so bright on driving change, and a lot of times leaders, I think, get in the way of it. So for cloud and adoption, it was really about me getting out of the way. It was really about setting that north star for where we want to go, from the ability to deliver fast and quick for our business, and then get out of the way and let our teams actually drive. So it was a great-- I actually saw the reverse. I saw more employees wanting to drive, and I needed to back out and just say here's where we need to go. Let them drive us there. >> All right, so I got to ask you. Please don't hate me for asking this question, but was your gender an advantage? Was it a disadvantage, or was it really irrelevant in that regard? >> I think it was irrelevant. I think that it was-- Actually, I truly believe it's irrelevant. I think it was literally recognizing that leaders need to set vision in what we want to achieve, and let our teams help us drive to get there, and I think that that is gender neutral. I think it's really about kind of chucking your ego and everything else out to the side, and it's really about empowering people and our teams to help drive us there. >> So thinking about that learning specifically, are there any similar tectonic shifts that you're seeing today, where you can apply that experience? Like for instance, new modes of application development, and acquiring new skill sets, or maybe another that you can think of. >> Yeah, I think honestly, it traverses everything that we have to do as a leader of a technology team, and whether you're in a high growth company like Pure, or you're in a company that's trying to take costs out of your business, or trying to do things, I think that it really is a matter of leaders needing to set the stage, and so if we're trying to drive you know, change in a business, it's really making sure that we're doing, I'll call it more empowering of our employees, 'cause they will see the way that we can get there. It's just a matter of letting them have that ability to do it. >> So you joined Pure around two years ago, and obviously growing very quickly. I know the pandemic has changed the trajectory of that growth, but still, a good outlook. But Silicon Valley, fast-paced company. You know, I kind of put it in the camp of the Workday and the ServiceNow. It's kind of similar cultural patterns there, so you talked a little bit about this, but I wonder if we could come back and more specifically, how you're leveraging cloud, how you're thinking about it, you know, on-prem, hybrid, now the edge, and how did that contribute to Pure's growth? >> You know, it's a great question because I think that-- Well, I shared earlier, we were essentially born in the cloud. I think that what it's really driven us is to be thinking more forward about where customers are going and what their challenges are. So whether it's for the IT teams on what we're trying to do to deliver for our business and innovation, they're obviously trying to make sure they can hit their revenue goals, and all of those things that are important that every business deals with, but we also have that same mindset on how we develop our products. So it's really all driven by where the customer is going, that they need data mobility, they need application mobility, they need really portability, so that the moment that you have that ability where you can kind of control your destiny and define it, and you only can get that by having applications that are portable and data that is mobile and secure, that you have that kind of flexibility. So I think for Pure, we have been definitely in a great position to drive for our customers, or drive where our customers are going, and so we've defined our entire product set, so not just how we operate as a business and run our business, but then how we define for our customers, same mindset is if our customers are going to the cloud, then we need to have products that can help them to be in the cloud, or be on-prem, and let them decide what that looks like. >> Well, it's interesting you mention that, and I hearken back to the Portworx acquisition, which is an attempt to really change the way application development is done. It's another sort of approach to sort of modern data architecture. As the CIO of a technology company, most CIOs that I know inside of tech companies, they're sort of the dog-fooding or champagne drinking, testing, so had you already started to use that tech? Are you starting to? Does it support that vision that you just put forth? Maybe you could talk a little bit about that. >> Yeah, it does. So we had not been using Portworx as a product. We were just starting down that path of looking at how do we do containerization for the applications that we do have on-prem? That's both on our engineering side as well as within IT. But we quickly have recognized, just like you know, and part of that acquisition is applications, or companies, won't have the ability to have that portability of their applications and have that flexibility that they're all striving for unless they've done things like containerize their applications, made them that they're able to move them across different cloud environments, whether that's on-prem or off-prem, or some hybrid. So for ourselves, you know, Portworx was a really critical acquisition that will help us on our own journey of doing the application modernization and putting those capabilities in place, but it will also enable our customers to have that same flexibility, so again going back to the-- These things aren't like this is for this group and this is for this customer. It's really about how we operate, both internally and then what we are providing for our customers. So that portability and being able to have control of your own destiny, that's really, to me, what hybrid cloud is all about, and you can't really achieve that if you don't have some of these capabilities within your toolbox. >> Great, thank you for that. So I'm interested, as the head of a technology group at a tech company, and what are the meaningful differences? I mean there are a lot of differences, but relative to CIO of a large telco, or other incumbent, you know, what are some of the good, the bad, and the ugly of the differences? >> Yeah, you know, it's-- I meet with a lot of CIOs across Silicon Valley, and we kind of joke that when you are working in a company that is a technology based company, you know, everybody knows how to do-- Because you do, you have brilliant engineers, and they do know. I think the difference that you start to see is that IT is required to make sure that availability is there inherent in what you're doing on immediate rollout with like, you know, an application that's occurring. That's very different than how you do product life cycle management. What I've seen actually though, is more similarities. I know that's probably a surprise to you, but coming out of AT&T, what I had been working on those last couple of years was actually doing the combination of engineering and IT into one organization, and that you do have a lot of benefits for how you can then develop, how you can manage, and the skillsets. There's a lot of similarities, so there's actually probably more similarities between companies on what they're trying to achieve than you would probably think there would be, just because we're all trying to make sure that we can develop quickly. >> How about as it relates to cloud, Cathy? I mean, I remember in the early days of cloud, a lot of the big banks said we can build our own cloud. We can essentially compete at scale with Amazon. We're the big bank. And then I think they quickly realized well, the economics actually don't favor us necessarily. Do you think there's a different perception about the use of cloud between sort of traditional incumbents and a tech company in Silicon Valley, and if so, how so? >> I think that the-- If you are a bank, as you refer to, and having-- It really is where you're starting from. If you have a very large infrastructure footprint and application footprint, your applications probably are not born in the cloud. There's a lot of modernization that has to be done with those applications so that they can operate as efficiently in a public cloud, as an example. And I think that's something that sometimes gets overlooked, is there are enormous benefits with going to public cloud, but there's also costs if your applications or your data doesn't really fit as well in that type of environment. So I think that for large enterprises like the banks, some of the telcos, they've got very large footprints of infrastructure already. Those investments have been made, and what they're really looking for is how do I increase my ability to, whether it's agility or it's speed, or it's lower costs, or it's all those things, and I think they've got the different path, a different journey that they're on, so they're trying to balance all those equations of the economics, as well as the ability to have no more investment or minimal investment in that infrastructure. For companies like Pure, where we started off with those investments, our decision, and kind of the decision tree that we used is if it makes sense and I don't have to make that investment on-prem for whatever reason, then I should go ahead and make that investment in a public cloud strategy or a hybrid cloud strategy, and I'll kind of differentiate that, because I think that it's different depending on the company you are. And so, it really kind of depends on where you're starting from, and then it also depends on what you're trying to achieve, if you're just trying to achieve an economic solution, if you're trying to achieve a strategic solution, if you're trying to get agility, and I think it's different for companies and it's different depending where you're at in your journey. So for a Silicon Valley company who's hyper-growth, you know, one, we're very focused on agility, everything from scale, because we've got to scale quickly, and those are things that we don't want to have to start going and building all these data centers to go do that. We don't have those embedded investments, so it's a real difference in where your starting point is, and I think there's value in all those different type of approaches. >> Right, and it's a real advantage for you that you don't have to shell out all that cap-ex on data centers. >> That's right. >> As you look back at the last 10 years of cloud, it was largely about eliminating the heavy lift of infrastructure deployment, and SaaSifying the business. What do you see going forward? What do you think is going to unfold in the 2020s? Is it going to be more of the same, or do you expect meaningful differences? >> I think that we're going to get better as technology leaders on how to quickly make decisions and have it less political, and I think COVID's actually taught us a lot about that around companies more willing to make, I'll call it a faster decision, and remove some of the red tape. I've heard this from many of my peers, that things that might have taken them months and months to get approved, it's now days, even if they even have to go get approval, so I think that what we're going to see is, we'll see the continuance of public and I'll call it really hybrid cloud type of solutions, and I think it will be more purposeful about what goes there and how that can help us to you know, I'll call it enable us much faster than we've been able to do it before. I think that's been our challenge is we've-- You know, we get mired into some of the details of some of these things that maybe it would be easier for us to just make the decision and move forward than to keep going round and round on what's the right way to do it. >> Yeah, so that's interesting, what you're saying about the fast decisions. I felt like a lot of 2020 was you know, very tactical. Okay, go deal with the work from home, et cetera, although you definitely see IT spending suppressed in 2020. Our forecast was -4%, but we're saying it's going to grow. We actually see a decent snap back. You know, what are you seeing generally, not even necessarily Pure, but when you talk to some of your colleagues? You're obviously in the technology business. It's good to be in the technology business these days, but do you see spending generally coming back, and maybe the timing? First half maybe a little soft, second half-- What are you seeing there? >> Yeah, almost identical to what you said. I think that we'll see a little bit of a tendency to not really hold back, but really kind of see what's happening in the first quarter of the year. There's a lot going on with companies, and everyone's having to kind of balance that and what that looks like. I do see, and what I'm hearing from several of my peers is that you know, it's not necessarily budget cuts. It might be budget redirections, it might be reprioritization, but definitely technology investments are still there, and it's still important for businesses to keep on their journeys, and we do see that even at Pure, as a way to differentiate ourselves in the market as well. >> What about the work from home piece? I mean, prior to COVID, I think the average was about 15 or 16% of employees worked from home. You know, now it's got to be well over in the high 70s, and the CIOs that we've talked to suggest that that's going to come down in the first half, maybe down to still pretty high, 50, 60%, but then eventually it's going to settle at a higher rate than it was pre-COVID, maybe double that rate, maybe in the 30, 35, maybe even 40%. What are you expecting? >> Something probably very similar. I think that what companies have recognized, and I actually tell you, CIOs have thought this, many of them for many years, that there's a huge value in having some type of hybrid model. There's value in having, both from a business perspective as well as a personal perspective, so employees' work-life balance and trying to balance that. So I think that we at Pure, and myself as the CIO, hugely expect that we will see some type of I'll call it leveling off, figuring out what's the right for the right group, and I think what we don't want to get into is a prescriptive that says this is what the company will look like as a whole. I think it really is going to come down to certain types of work are more conducive to a more work remote environment. Others need to have, and I always kind of use this term of individual productivity versus team productivity. We've seen great advances in individual productivity. Team productivity is still a challenge when you're still trying to do very collaborative brainstorming sessions, and so we are looking at capabilities to be able to enable our employees to do that, but there's some things you just can't replace the human interaction and the ability to very quickly, interactively, you know, five minutes, catch someone and do that. So I think we'll see both. We'll see some leveling off, and I think we'll see some areas of businesses that had once thought you can't do that remote, they might actually say hey, that is work that can be remote, so I think we'll see a combination of both. >> That's an interesting perspective on productivity, and what's the old saying, is you can go faster alone, but further as a team. And not a lot of folks have been talking about that team productivity. We clearly saw the hit, the positive hit on productivity, especially in the technology business, so my question then is, so you expect, you know, HQ doesn't go away. Maybe it gets smaller, but so is there pent-up demand for technology spending at the headquarters? 'Cause you've been pushing tech out to the edge, out to the remote workers, securing those remote workers, figuring out better ways to collaborate. Is there pent-up demand at HQ? >> Absolutely, we've been-- You know, we've been actually exploring different technologies. We've been looking at what are things that could help create a different kind of experience? And it'll be some different types of technology. Those will be the things that maybe aren't even out there developed yet, on how do you create some of those comparable experiences? So I think that the notion of individuals will continue to thrive, but we've got to start working on how do we continue to enhance that team, collaborative productivity environment that looks and feels different than what it might look like today. >> Cathy, got to leave it there. Great, as always, having you on the CUBE. Thanks so much for participating in CUBE on Cloud. >> Great, it was great to be here, thank you. >> All right, keep it right there. Back with more content right after this short break.

>> As I've said many times on theCUBE for years, decades even we've marched to the cadence of Moore's law relying on the doubling of performance every 18 months or so, but no longer is this the main spring of innovation for technology rather it's the combination of data applying machine intelligence and the cloud supported by the relentless reduction of the cost of compute and storage and the build-out of a massively distributed computer network. Very importantly, the last several years alternative processors have emerged to support offloading work and performing specific tests. GPUs are the most widely known example of this trend with the ascendancy of Nvidia for certain applications like gaming and crypto mining and more recently machine learning. But in the middle of last decade we saw the early development focused on the DPU, the data processing unit, which is projected to make a huge impact on data centers in the coming years as we move into the next era of cloud. And with me is Pradeep Sindhu who's the co-founder and CEO of Fungible, a company specializing in the design and development of DPUs. Pradeep, welcome to theCUBE. Great to see you. >> Thank-you, Dave and thank-you for having me. >> You're very welcome. So okay, my first question is don't CPUs and GPUs process data already. Why do we need a DPU? >> That is a natural question to ask. And CPUs have been around in one form or another for almost 55, maybe 60 years. And this is when general purpose computing was invented and essentially all CPUs went to x86 architecture by and large and of course is used very heavily in mobile computing, but x86 is primarily used in data center which is our focus. Now, you can understand that that architecture of a general purpose CPUs has been refined heavily by some of the smartest people on the planet. And for the longest time improvements you refer to Moore's law, which is really the improvements of the price, performance of silicon over time that combined with architectural improvements was the thing that was pushing us forward. Well, what has happened is that the architectural refinements are more or less done. You're not going to get very much, you're not going to squeeze more blood out of that storm from the general purpose computer architecture. what has also happened over the last decade is that Moore's law which is essentially the doubling of the number of transistors on a chip has slowed down considerably and to the point where you're only getting maybe 10, 20% improvements every generation in speed of the transistor if that. And what's happening also is that the spacing between successive generations of technology is actually increasing from two, two and a half years to now three, maybe even four years. And this is because we are reaching some physical limits in CMOS. These limits are well-recognized. And we have to understand that these limits apply not just to general purposive use but they also apply to GPUs. Now, general purpose CPUs do one kind of competition they're really general and they can do lots and lots of different things. It is actually a very, very powerful engine. And then the problem is it's not powerful enough to handle all computations. So this is why you ended up having a different kind of a processor called the GPU which specializes in executing vector floating-point arithmetic operations much, much better than CPU maybe 20, 30, 40 times better. Well, GPUs have now been around for probably 15, 20 years mostly addressing graphics computations, but recently in the last decade or so they have been used heavily for AI and analytics computations. So now the question is, well, why do you need another specialized engine called the DPU? Well, I started down this journey about almost eight years ago and I recognize I was still at Juniper Networks which is another company that I founded. I recognize that in the data center as the workload changes to addressing more and more, larger and larger corpuses of data, number one and as people use scale-out as these standard technique for building applications, what happens is that the amount of east-west traffic increases greatly. And what happens is that you now have a new type of workload which is coming. And today probably 30% of the workload in a data center is what we call data-centric. I want to give you some examples of what is a data-centric workload. >> Well, I wonder if I could interrupt you for a second. >> Of course. >> Because I want those examples and I want you to tie it into the cloud 'cause that's kind of the topic that we're talking about today and how you see that evolving. I mean, it's a key question that we're trying to answer in this program. Of course, early cloud was about infrastructure, little compute, little storage, little networking and now we have to get to your point all this data in the cloud. And we're seeing, by the way the definition of cloud expand into this distributed or I think a term you use is disaggregated network of computers. So you're a technology visionary and I wonder how you see that evolving and then please work in your examples of that critical workload, that data-centric workload. >> Absolutely happy to do that. So if you look at the architecture of our cloud data centers the single most important invention was scale-out of identical or near identical servers all connected to a standard IP ethernet network. That's the architecture. Now, the building blocks of this architecture is ethernet switches which make up the network, IP ethernet switches. And then the server is all built using general purpose x86 CPUs with DRAM, with SSD, with hard drives all connected to inside the CPU. Now, the fact that you scale these server nodes as they're called out was very, very important in addressing the problem of how do you build very large scale infrastructure using general purpose compute. But this architecture did is it compute centric architecture and the reason it's a compute centric architecture is if you open this server node what you see is a connection to the network typically with a simple network interface card. And then you have CPUs which are in the middle of the action. Not only are the CPUs processing the application workload but they're processing all of the IO workload, what we call data-centric workload. And so when you connect SSDs, and hard drives, and GPUs, and everything to the CPU, as well as to the network you can now imagine the CPUs is doing two functions. It's running the applications but it's also playing traffic cop for the IO. So every IO has to go through the CPU and you're executing instructions typically in the operating system and you're interrupting the CPU many, many millions of times a second. Now, general purpose CPUs and the architecture CPUs was never designed to play traffic cop because the traffic cop function is a function that requires you to be interrupted very, very frequently. So it's critical that in this new architecture where there's a lot of data, a lot of these stress traffic the percentage of workload, which is data-centric has gone from maybe one to 2% to 30 to 40%. I'll give you some numbers which are absolutely stunning. If you go back to say 1987 and which is the year in which I bought my first personal computer the network was some 30 times slower than the CPU. The CPU is running at 15 megahertz, the network was running at three megabits per second. Or today the network runs at a 100 gigabits per second and the CPU clock speed of a single core is about three to 2.3 gigahertz. So you've seen that there's a 600X change in the ratio of IO to compute just the raw clock speed. Now, you can tell me that, hey, typical CPUs have lots, lots of cores, but even when you factor that in there's been close to two orders of magnitude change in the amount of IO to compute. There is no way to address that without changing the architecture and this is where the DPU comes in. And the DPU actually solves two fundamental problems in cloud data centers. And these are fundamental there's no escaping it. No amount of clever marketing is going to get around these problems. Problem number one is that in a compute centric cloud architecture the interactions between server nodes are very inefficient. That's number one, problem number one. Problem number two is that these data-centric computations and I'll give you those four examples. The network stack, the storage stack, the virtualization stack, and the security stack. Those four examples are executed very inefficiently by CPUs. Needless to say that if you try to execute these on GPUs you will run into the same problem probably even worse because GPUs are not good at executing these data-centric computations. So what we were looking to do at Fungible is to solve these two basic problems. And you don't solve them by just taking older architectures off the shelf and applying them to these problems because this is what people have been doing for the last 40 years. So what we did was we created this new microprocessor that we call DPU from ground up. It's a clean sheet design and it solves those two problems fundamentally. >> So I want to get into that. And I just want to stop you for a second and just ask you a basic question which is if I understand it correctly, if I just took the traditional scale out, if I scale out compute and storage you're saying I'm going to hit a diminishing returns. It's not only is it not going to scale linearly I'm going to get inefficiencies. And that's really the problem that you're solving. Is that correct? >> That is correct. And the workloads that we have today are very data-heavy. You take AI for example, you take analytics for example it's well known that for AI training the larger the corpus of relevant data that you're training on the better the result. So you can imagine where this is going to go. >> Right. >> Especially when people have figured out a formula that, hey the more data I collect I can use those insights to make money- >> Yeah, this is why I wanted to talk to you because the last 10 years we've been collecting all this data. Now, I want to bring in some other data that you actually shared with me beforehand. Some market trends that you guys cited in your research. And the first thing people said is they want to improve their infrastructure and they want to do that by moving to the cloud. And they also, there was a security angle there as well. That's a whole another topic we could discuss. The other stat that jumped out at me, there's 80% of the customers that you surveyed said there'll be augmenting their x86 CPU with alternative processing technology. So that's sort of, I know it's self-serving, but it's right on the conversation we're having. So I want to understand the architecture. >> Sure. >> And how you've approached this. You've clearly laid out this x86 is not going to solve this problem. And even GPUs are not going to solve the problem. >> They re not going to solve the problem. >> So help us understand the architecture and how you do solve this problem. >> I'll be very happy to. Remember I use this term traffic cop. I use this term very specifically because, first let me define what I mean by a data-centric computation because that's the essence of the problem we're solving. Remember I said two problems. One is we execute data-centric workloads at least an order of magnitude more efficiently than CPUs or GPUs, probably 30 times more efficient. And the second thing is that we allow nodes to interact with each other over the network much, much more efficiently. Okay, so let's keep those two things in mind. So first let's look at the data-centric piece. The data-centric piece for workload to qualify as being data-centric four things have to be true. First of all, it needs to come over the network in the form of packets. Well, this is all workloads so I'm not saying anything. Secondly, this workload is heavily multiplex in that there are many, many, many computations that are happening concurrently, thousands of them, okay? That's the number two. So a lot of multiplexing. Number three is that this workload is stateful. In other words you can't process back it's out of order. You have to do them in order because you're terminating network sessions. And the last one is that when you look at the actual computation the ratio of IO to arithmetic is medium to high. When you put all four of them together you actually have a data-centric workload, right? And this workload is terrible for general purpose CPUs. Not only the general purpose CPU is not executed properly the application that is running on the CPU also suffers because data center workloads are interfering workloads. So unless you designed specifically to them you're going to be in trouble. So what did we do? Well, what we did was our architecture consists of very, very heavily multi-threaded general purpose CPUs combined with very heavily threaded specific accelerators. I'll give you examples of some of those accelerators, DMA accelerators, then ratio coding accelerators, compression accelerators, crypto accelerators, compression accelerators. These are just some, and then look up accelerators. These are functions that if you do not specialize you're not going to execute them efficiently. But you cannot just put accelerators in there, these accelerators have to be multi-threaded to handle. We have something like a 1,000 different treads inside our DPU to address these many, many, many computations that are happening concurrently but handle them efficiently. Now, the thing that is very important to understand is that given the velocity of transistors I know that we have hundreds of billions of transistors on a chip, but the problem is that those transistors are used very inefficiently today if the architecture of a CPU or a GPU. What we have done is we've improved the efficiency of those transistors by 30 times, okay? >> So you can use a real estate much more effectively? >> Much more effectively because we were not trying to solve a general purpose computing problem. Because if you do that we're going to end up in the same bucket where general purpose CPUs are today. We were trying to solve a specific problem of data-centric computations and of improving the note to note efficiency. So let me go to point number two because that's equally important. Because in a scalar or architecture the whole idea is that I have many, many notes and they're connected over a high performance network. It might be shocking for your listeners to hear that these networks today run at a utilization of no more than 20 to 25%. Question is why? Well, the reason is that if I tried to run them faster than that you start to get back at drops because there are some fundamental problems caused by congestion on the network which are unsolved as we speak today. There are only one solution which is to use TCP. Well, TCP is a well-known, is part of the TCP IP suite. TCP was never designed to handle the latencies and speeds inside data center. It's a wonderful protocol but it was invented 43 years ago now. >> Yeah, very reliable and tested and proven. It's got a good track record but you're right. >> Very good track record, unfortunately eats a lot of CPU cycles. So if you take the idea behind TCP and you say, okay, what's the essence of TCP? How would you apply it to the data center? That's what we've done with what we call FCP which is a fabric control protocol, which we intend to open. We intend to publish the standards and make it open. And when you do that and you embed FCP in hardware on top of this standard IP ethernet network you end up with the ability to run at very large-scale networks where the utilization of the network is 90 to 95%, not 20 to 25%. >> Wow, okay. >> And you end up with solving problems of congestion at the same time. Now, why is this important today? That's all geek speak so far. The reason this stuff is important is that it such a network allows you to disaggregate, pull and then virtualize the most important and expensive resources in the data center. What are those? It's computer on one side, storage on the other side. And increasingly even things like DRAM wants to be disaggregated. And well, if I put everything inside a general purpose server the problem is that those resources get stranded because they're stuck behind a CPU. Well, once you disaggregate those resources and we're saying hyper disaggregate meaning the hyper and the hyper disaggregate simply means that you can disaggregate almost all the resources. >> And then you going to reaggregate them, right? I mean, that's obviously- >> Exactly and the network is the key in helping. >> Okay. >> So the reason the company is called Fungible is because we are able to disaggregate, virtualize and then pull those resources. And you can get for so scale-out companies the large AWS, Google, et cetera they have been doing this aggregation tooling for some time but because they've been using a compute centric architecture their disaggregation is not nearly as efficient as we can make. And they're off by about a factor of three. When you look at enterprise companies they are off by another factor of four because the utilization of enterprise is typically around 8% of overall infrastructure. The utilization in the cloud for AWS, and GCP, and Microsoft is closer to 35 to 40%. So there is a factor of almost four to eight which you can gain by dis-aggregating and pulling. >> Okay, so I want to interrupt you again. So these hyperscalers are smart. They have a lot of engineers and we've seen them. Yeah, you're right they're using a lot of general purpose but we've seen them make moves toward GPUs and embrace things like Arm. So I know you can't name names, but you would think that this is with all the data that's in the cloud, again, our topic today. You would think the hyperscalers are all over this. >> Well, the hyperscalers recognized here that the problems that we have articulated are important ones and they're trying to solve them with the resources that they have and all the clever people that they have. So these are recognized problems. However, please note that each of these hyperscalers has their own legacy now. They've been around for 10, 15 years. And so they're not in a position to all of a sudden turn on a dime. This is what happens to all companies at some point. >> They have technical debt, you mean? (laughs) >> I'm not going to say they have technical debt, but they have a certain way of doing things and they are in love with the compute centric way of doing things. And eventually it will be understood that you need a third element called the DPU to address these problems. Now, of course, you've heard the term SmartNIC. >> Yeah, right. >> Or your listeners must've heard that term. Well, a SmartNIC is not a DPU. What a SmartNIC is, is simply taking general purpose ARM cores, putting the network interface and a PCI interface and integrating them all on the same chip and separating them from the CPU. So this does solve a problem. It solves the problem of the data center workload interfering with the application workload, good job, but it does not address the architectural problem of how to execute data center workloads efficiently. >> Yeah, so it reminds me of, I understand what you're saying I was going to ask you about SmartNICs. It's almost like a bridge or a band-aid. >> Band-aid? >> It almost reminds me of throwing a high flash storage on a disc system that was designed for spinning disc. Gave you something but it doesn't solve the fundamental problem. I don't know if it's a valid analogy but we've seen this in computing for a longtime. >> Yeah, this analogy is close because okay, so let's take a hyperscaler X, okay? We won't name names. You find that half my CPUs are crippling their thumbs because they're executing this data-centric workload. Well, what are you going to do? All your code is written in C++ on x86. Well, the easiest thing to do is to separate the cores that run this workload. Put it on a different let's say we use Arm simply because x86 licenses are not available to people to build their own CPUs so Arm was available. So they put a bunch of Arm cores, they stick a PCI express and a network interface and you bought that code from x86 to Arm. Not difficult to do but and it does you results. And by the way if for example this hyperscaler X, shall we called them, if they're able to remove 20% of the workload from general purpose CPUs that's worth billions of dollars. So of course, you're going to do that. It requires relatively little innovation other than to port code from one place to another place. >> Pradeep, that's what I'm saying. I mean, I would think again, the hyperscalers why can't they just do some work and do some engineering and then give you a call and say, okay, we're going to attack these workloads together. That's similar to how they brought in GPUs. And you're right it's worth billions of dollars. You could see when the hyperscalers Microsoft, and Azure, and AWS bolt announced, I think they depreciated servers now instead of four years it's five years. And it dropped like a billion dollars to their bottom line. But why not just work directly with you guys? I mean, let's see the logical play. >> Some of them are working with us. So that's not to say that they're not working with us. So all of the hyperscalers they recognize that the technology that we're building is a fundamental that we have something really special and moreover it's fully programmable. So the whole trick is you can actually build a lump of hardware that is fixed function. But the difficulty is that in the place where the DPU would sit which is on the boundary of a server and the network, is literally on that boundary, that place the functionality needs to be programmable. And so the whole trick is how do you come up with an architecture where the functionality is programmable but it is also very high speed for this particular set of applications. So the analogy with GPUs is nearly perfect because GPUs and particularly Nvidia implemented or they invented CUDA which is the programming language for GPUs. And it made them easy to use, made it fully programmable without compromising performance. Well, this is what we're doing with DPUs. We've invented a new architecture, we've made them very easy to program. And they're these workloads, not workloads, computation that I talked about which is security, virtualization, storage and then network. Those four are quintessential examples of data center workloads and they're not going away. In fact, they're becoming more, and more, and more important over time. >> I'm very excited for you guys, I think, and really appreciate Pradeep, we have your back because I really want to get into some of the secret sauce. You talked about these accelerators, eraser code and crypto accelerators. But I want to understand that. I know there's NBMe in here, there's a lot of hardware and software and intellectual property, but we're seeing this notion of programmable infrastructure extending now into this domain, this build-out of this, I like this term disaggregated, massive disaggregated network. >> Hyper disaggregated. >> It's so hyper disaggregated even better. And I would say this and then I got to go. But what got us here the last decade is not the same as what's going to take us through the next decade. >> That's correct. >> Pradeep, thanks so much for coming on theCUBE. It's a great conversation. >> Thank-you for having me it's really a pleasure to speak with you and get the message of Fungible out there. >> Yeah, I promise we'll have you back. And keep it right there everybody we've got more great content coming your way on theCUBE on cloud. This is Dave Vellante. Stay right there. >> Thank-you, Dave.

>>Yeah. >>Welcome back for our last session of the day how to deliver career making business outcomes with Search and AI. So we're very lucky to be hearing from Canada. Canadian Tire, one of Canada's largest and most successful retailers, have been powered 4.5 1000 employees to maximize the value of data with self service insights. So today we're joining us. We have Yarrow Baturin, who is the manager of Merch analytics and planning to support at Canadian Tire and then also Andrea Frisk, who is the engagement manager manager for thoughts. What s O U R Andrea? Thanks so much for being here. And with >>that, >>I'll pass the mic to you guys. >>Thank you for having us. Um, already, I I think I'll start with an introduction off who I am, what I do. A Canadian entire on what Canadian pair is all about. So, as a manager of Merch analytics at Canadian Tire, I support merchant organization with reporting tools, and then be I platform to enable decision making on a day to day basis. What is? Canadian Tire's Canadian tire is one of the largest retailers in Canada. Um, serving Canadians with a number of lines of business spanning automotive fixing, living, playing and SNG departments. We have a number of banners, including sport check Marks Party City Phl that covers more than 1700 locations. So as an organization, we've got vast variety of different data, whether it's product or loyalty. Now, as the time goes on, the number of asks the number off data points. The complexity of the analysis has been increasing on banned traditional tools. Analytical tools such as Excel Microsoft Access do find job but start hitting their limitations. So we started on the journey of exploring what other B I platforms would be suitable for our needs. And the criteria that we thought about as we started on that journey is to make sure that we enable customization as well as the McCarthy ization of data. What does that mean? That means we wanted to ensure that each one of the end users have ability to create their own versions off the report while having consistency from the data standpoint, we also wanted Thio ensure that they're able to create there at hawks search queries and draw insights based on the desired business needs. As each one of our lines of business as each one of our departments is quite unique in their nature. And this is where thoughts about comes into play. Um, you checked off all the boxes? Um, as current customers, as potential customers, you will discover that this is the tool that allows that at hawks search ability within a matter of seconds and ability to visualize the information and create those curated pin boards for each one of the business units, depending on what the needs are. And now where? I guess well, Andrea will talk a little bit more about how we gained adoption, but the usage was like and how we, uh, implemented the tool successfully in the organization. >>Okay, so I actually used to work for Canadian tire on DSO. During that time, I helped Thio build training and engaging users to sort of really kick start our use cases. Andi, the ongoing process of adopting thought spot through Canadian Tire s 01 of the sort of reasons that we moved into using thought spot was there was a need Thio evolve, um, in order to see the wealth of data that we had coming in. So the existing reporting again. And this is this sort of standard thoughts bought fix is, um, it brings the data toe. Everyone on git makes it more accessible, so you get more out of your data. So we want to provide users with the ability to customize what they could see and personalized three information so that they could get their specific business requirements out of the data rather than relying on the weekly monthly quarterly reporting. That was all usually fairly generic eso without the ability to deep dive in. So this gave the users the agility thio optimize their campaigns, optimize product murder, urgency where products are or where there's maybe supply chain gaps. Andi just really bring this out for trillions of rose to become accessible. Thio the Canadian tire. That's what user base think. That's the slide. >>That's the slight, Um So as Andrea talked about the business use of the particular tool, let's talk a little bit about how we set it up and a wonderful journey of how it's evolved. So we first implemented 5.3 version of that spot on the Falcon server on we've been adding horsepower to it over time. Now mhm. What I want to stress is the importance off the very first, Data said. That goes into the tool toe. Actually engage the users and to gain the adoption and to make sure there is no argument whether the tool is accurate or not. So what we've started with is a key p I marked layer with all the major metrics that we have and all the available permutations and combinations off the dimensions, whether it's a calendar dimension, proud of dimension or, let's say, customer attribute now, as we started with that data set, we wanted to make sure that we're we have the ability to add and the dimensions right. So now, as we're implementing the tool, we're starting to add in more dimension tables to satisfy the needs off our clients if you want to call it that way as they want to evolve their analytics. So we started adding in some of the store attributes we started adding in some of the product attributes on when I refer to a product attributes, let's say, uh, it involves costs and involves prices involved in some of the strategic internal pieces that we're thinking about now as the comprehensive mark contains right now, in our instance, close to five billion records. This is where it becomes the one source of truth for people declaring information against right so as they go in, we also wanted to make sure when they Corey thought spot there, we're really Onley. According one source of data. One source of truth. It became apparent over time, obviously, that more metrics are needed. They might not be all set up in that particular mark. And that's when we went on the journey off implementing some of the new worksheets or some of the new data sets particularly focused on the four looking pieces. And uh, that's where it becomes important to say This is how you gain the interest and keep the interests of the public right. So you're not just implementing a number off data sets all at once and then letting the users be you're implementing pieces and stages. You're keeping the interest thio, the tool relevant. You're keeping, um, the needs of the public in mind. Now, as you can imagine on the Falcon server piece, um, adding in the horsepower capacity might become challenging the mawr. Billions of Rosie erratic eso were actually in the middle of transitioning our environment to azure in snowflake so that we can connect it. Thio embrace capability of thoughts cloud. And that's where I'm looking forward to that in 2021 I truly believe this will enable us Thio increase the speed off adoption Increase the speed of getting insights out of the tool and scale with regards Thio new data sets that we're thinking about implementing as we're continuing our thoughts about journey >>Okay, so how we drove adoption Thio 4500 plus users eso When we first started Thio approach our use case with the merchants within Canadian Tire We had meetings with these users with who are used place is gonna be with and sort of found out. What are they searching for, Where they typically looking at what existing reports are available for them. Andi kind of sought out to like, What are those things where you're pulling this on your own or someone else's pulling this data because it's not accessible yet And we really use that as our foundation to determine one what data we needed to initially bring into the system but also to sort of create those launchpad pin boards that had the base information that the users we're gonna need so that we could twofold, make it easy for them, toe adopt into the tool and also quickly start Thio, deactivate or discontinue those reports. And just like these air now only available in thought spot because with the sort of formatting within thought spot around dates, it's really easy to make this year's report last year report etcetera. Just have everything roll over every month or a recorder s. So that was kind of some of the pre work foundation when we originally did it. But really, it's been a lot of training, a lot of training. So we conducted ah, lot of in person training, obviously pre co vid eso. We've started to train the group that we targeted, which was the merchants and all of the like, surrounding support groups. Eso we had planners going in and training as well, so that everyone who was really closely connected to the merchants I had an idea of what thoughts about what was and how to use it and where the reports were, and so we just sort of rolled it out that way, and then it started to fly like wildfire. Eso the merchants start to engage with supply chain to have conversations, or the merchants were engaging with the vendors to sort of have negotiations about pricing. And they're creating these reports and getting the access to the information so quickly, and they're sharing it out that we had other groups just coming to us asking, How do I get into thoughts about how can I get in on DSO on top of those groups, we also sought out other heavy analytics groups such a supply chain where we felt like they could have the same benefits if they on boarded into thought spot with their data as well on Ben. Just continuing to evolve the training roll out. Um, you know, we continued to engage with the users, >>so >>we had a newsletter briefly Thio, sort of just keep informing users of the new data coming in or when we actually upgraded our system. So the here are the new features that you'll start seeing. We did virtual trainings and maintaining an F A Q document with the incoming questions from the users, and then eventually evolved into a self guided learning so that users that were coming to a group, or maybe we've already done a full rollout could come in and have the opportunity to learn how to use thought spot, have examples that were relevant to the business and really get started. Eso then each use case sort of after our initial started to build into a formula of the things that we needed to have. So you need to understand it. Having SMEs ready and having the database Onda worksheets built out sort of became the step by step path to drive adoption. Um, from an implementation timeline, I think they're saying, Took about two months and about half of that waas Kenny entire figuring out how figuring out our security, how to get the data in on, Do we need the time to set up the environment and get on Falcon? So then, after that initial two months, then each use case that we come through. Generally, we've got users trained and SMEs set up within about 2 to 3 weeks after the data is ingested. It's not obviously, once snowflakes set up on the data starts to get into that and the data feeds in, then you're really just looking at the 2 to 3 weeks because the data is easily connected in, >>um, no. All right, let's talk about some of the use cases. So we started with what data we've implemented. Andrea touched upon what Use a training look like what the back curate that piece wants. Now let's talk a little bit about use cases and how we actually leverage thoughts bought together the insights. So the very first one is ultimately the benefit of the tool to the entire organization. Israel Time insights. To reiterate what Andrea said, we first implemented the tool with our buyers. They're the nucleus of any retail organization as they work with everybody within the company and as the buyer's eyes, Their responsibility to ensure both the procurement and the sales channel, um, stays afloat at the end of the day, right? So they need information on a regular basis. They needed fast. They needed timely, and they needed in a fashion that they choose to digest it. It right? Not every business is the same. Not every individual is the same. They consume digest, analyze information differently. And that's what that's what allows you to dio whether it's the search, whether it's a customized onboard, please now supply chain unexpected things. As Andrea mentioned Irish work a lot of supply chain. What is the goal of supply chain to receive product and to be able to ship that product to the stores Now, as our organization has been growing and is doing extremely well, we've actually published Q three results recently. Um, the aspect off prioritization at D C level becomes very important, And what drives some of that prioritization is the analysis around what the upcoming sales would be for specific products for specific categories. And that's where again thoughts. But is one of the tools that we've utilized recently to set our prioritization logic from both inbound and outbound us. It's right because it gives you most recent results. It gives you most granular results, depending on the business problem that you're trying to tackle. Now let's chat a little bit about covert 19 response, because this one is an extremely interesting case as a pandemic hit back in March. Um, as you can imagine, the everyday life a Canadian entire became as business unusual is our executives referred to it under business unusual. This speed and the intensity of the insights and the analytics has grown exponentially. And the speed and the intensity of the insights is driven by the fact that we were trying Thio ensure that we have the right selection of products for our Canadian customers because that's ultimately bread and butter off all of the retailers is the customers, right? So thoughts bought allowed us to have early trends off both sales and inventory patterns, where, whether we were stalking out of some of the products in specific stories of provinces, whether we saw some of the upload off different lines of business, depending on the region, ality right as pandemic hit, for example, um, gym's closed restaurants closed. So as Canadian pack carries a wide variety of different lines of business, we actually offer a wide selection of exercise equipment and accessories, cycling products as well as the kitchen appliances and kitchen accessories pieces. Right? So all of those items started growing exponentially and in certain areas more than others. And this is where thoughts about comes into play. A typical analysis on what the region ality of the sales has been over the last couple of days, which is lifetime and pandemic terms, um, could have taken days weeks for analysts to ultimately cobbled together an Excel spreadsheet. Meanwhile, it can take a couple of seconds for 12 Korean tosspot set up a PIN board that can be shared through a wide variety of individuals rather than fording that one Excel spreadsheet that gets manipulated every single time. And then you don't get the right inside. So from again merch supply chain covert response aspect of things. That spot has been one of those blessings and one of those amazing tools to utilize and improve the speed off insights, improved the speed of analytics and improve the speed of decision making that's ultimately impacting, then consumer at the store level. So Andrea talked about 4500 users that we have that number of school. But what I owe the recently like to focus on, uh, Andrew and I laughing because I think the last time we've spoken at a larger forum with the fastball community, I think we had only 500 users. That was in the beginning >>of the year in in February, we were aiming to have like 1000 >>exactly. So mission accomplished. So we've got 4500 employees now. Everybody asked me, Yeah, that's a big number, but how many times do people actually log in on a weekly or daily basis? I'm or interested in that statistic? So lately, um, we've had more than 400 users on the weekly basis. What's what's been cool lately is, uh, the exponential growth off ad hoc ways. So throughout October, we've reached a 75,000 ad hoc ways in our system and about 13,000 PIN board views. So why is that's that's significant? We started off, I would say, in January of 2020 when Andrea refers to it, I think we started off with about 40 45,000 ad hoc worries a month. So again, that was cool. But at the end of the day, we were able to thio double that amount as more people migrate to act hawk searches from PIN board views, and that's that's a tremendous phenomena, because that's what that's about is all about. So I touched upon a little bit about exercise and cycling. So these are our quarterly results for Q two, um, that have showed tremendous growth that we did not plan for, that we were able to achieve with, ultimately the individuals who work throughout the organization, whether it's the merch organization or whether it's the supply chain side of the business. But coming together and utilizing a B I platform by tools such a hot spot, we can see triple digit growth results. Eso What's next for us users at Hawks searches? That's fantastic. I would still like to get to more than 1200 people on the weekly basis. The cool number to me is if all of our lifetime users were you were getting into the tool on a weekly basis. That would be cool. And what's proven to be true is ultimately the only way to achieve it is to keep surprising and delighting them and your surprising and delighting them with the functionality of the tool. With more of the relevant content and ultimately data adding in more data, um, is again possible through ET else, and it's possible through pulling that information manually. But it's expensive, expensive not from the sense of monetary value, but it's expensive from the size time, all of those aspects of things So what I'm looking forward to is migrating our platform to azure in snowflake and being able thio scale our insights accordingly. Toe adding more data to Adam or incites more, uh, more individual worksheets and data sets for people to Korea against helps the each one of the individuals learn. Get some of the insights. Helps my team in particular be, well, more well versed in the data that we have existing throughout the organization. Um, and then now Andrea, in touch upon how we scale it further and and how each one of the individuals can become better with this wonderful >>Yeah, soas used a zero mentioned theater hawk searches going up. It's sort of it's a little internal victory because our starting platform had really been thio build the pin boards to replicate what the users were already expecting. So that was sort of how we easily got people in. And then we just cut off the tap Thio, whatever the previous report waas. So it gave them away. Thio get into the tool and understand the information. So now that they're using ad hoc really means they understand the tool. Um, then they they have the data literacy Thio access the information and use it how they need. So that's it's a really cool piece. Um, that worked on for Canadian tire. A very report oriented and heavy organization. So it was a good starting platforms. So seeing those ad hoc searches go up is great. Um, one of the ways that we sort of scaled out of our initial group and I kind of mentioned this earlier I sort of stepped on my own toes here. Um is that once it was a proven success with the merchants and it started to spread through word of mouth and we sought out the analyst teams. Um, we really just kept sort of driving the insights, finding the data and learning more about the pieces of the business. As you would like to think he knows everything about everything. He only knows what he knows. Eso You have to continue to cultivate the internal champions. Um Thio really keep growing the adoption eso find this means that air excited about the possibility of using thought spot and what they can do with it. You need to find those people because they're the ones who are going to be excited to have this rapid access to the information and also to just be able to quickly spend less time telling a user had access it in thought spot. Then they would running the report because euro mentioned we basically hit a curiosity tax, right? You you didn't want to search for things or you didn't want to ask questions of the data because it was so conversed. Um, it was took too much time to get the data. And if you didn't know exactly what you were looking for, it was worse. So, you know, you wouldn't run a query and be like, Oh, that's interesting. Let me let me now run another query of all that information to get more data. Just not. It's not time effective or resource effective. Actually, at the point, eso scaling the adoption is really cultivating those people who are really into it as well. Um, from a personal development perspective, sort of as a user, I mean, one who doesn't like being smartest person in the room on bought spot sort of provides that possibility. Andi, it makes it easier for you to get recognized for delivering results on Dahlia ble insights and sort of driving the business forward. So you know, B b that all star be the Trailblazer with all the answers, and then you can just sort of find out what really like helping the organization realized the power of thought spot on, baby. Make it into a career. >>Amazing. I love love that you've joined us, Andrea. Such a such an amazing create trajectory. No bias that all of my s o heaps of great information there. Thank you both. So much for sharing your story on driving such amazing adoption and the impact that you've been able to make a T organization through. That we've got a couple of minutes remaining. So just enough time for questions. Eso Andrea. Our first questions for you from your experience. What is one thing you would recommend to new thoughts about users? >>Um, yeah, I would say Be curious and creative. Um, there's one phrase that we used a lot in training, which was just mess around in the tool. Um, it's sort of became a catchphrase. It is really true. Just just try and use it. You can't break. It s Oh, just just play around. Try it you're only limitation of what you're gonna find is your own creativity. Um, and the last thing I would say is don't get trapped by trying to replicate things. Is that exactly as they were? B, this is how we've always done it. Isin necessarily The the best move on day isn't necessarily gonna find new insights. Right. So the change forces you thio look at things from a different perspective on defined. Find new value in the data. >>Yeah, absolutely. Sage advice there. Andan another one here for Yaro. So I guess our theme for beyond this year is analytics meets Cloud Open for everyone. So, in your experience, what does What does that mean for you? >>Wonderful question. Yeah. Listen, Angela Okay, so to me, in short, uh, means scale and it means turning Yes. Sorry. No, into a yes. Uh, no, I'm gonna elaborate. Is interest is laughing at me a little bit. That's right. >>I can talk >>Fancy Two. Okay, So scale from the scale perspective Cloud a zai touched upon Throw our conversation on our presentation cloud enables your ability Thio store have more data, have access to more data without necessarily employing a number off PTL developers and going toe a number of security aspect of things in different data sources now turning a no into a yes. What does that mean with more data with more scalability? Um, the analytics possibilities become infinite throughout my career at Canadian Tire. Other organizations, if you don't necessarily have access thio data or you do not have the necessary granularity, you always tell individuals No, it's not possible. I'm not able to deliver that result. And quite often that becomes the norm, saying no becomes the norm. And I think what we're all striving towards here on this call Aziz part the conference is turning that no one say yes on then making a yes a new, uh, standard a new form. Um, as we have more access to the data, more access to the insights. So that would be my answer. >>Love it. Amazing. Well, that kind of brings in into this session. So thank you, everyone for joining us today on did wrap up this dream. Don't miss the upcoming product roadmap eso We'll be sticking around to speak thio some of the speakers you heard earlier today and I'll make the experts round table, and you can absolutely continue the conversation with this life. Q. On Q and A So you've got an opportunity here to ask questions that maybe keep you up at night. Perhaps, but yet stay tuned for the meat. The experts secrets to scaling analytics adoption after the product roadmap session. Thanks everyone. And thank you again for joining us. Guys. Appreciate it. >>Thank you. Thanks. Thanks.

>>Welcome to Workplace Next Brought to you by the Cube 3 65 and sponsored by Hewlett Packard Enterprise. We got a great show lineup for you today. If you like me, you've had to change the way you work this year, and so have your teams. A lot of work has gone remote, of course, and very quickly we've had toe rethink how we operate on a day to day basis, and that's great. If, like me, you could do your job remotely. But let's not forget there are. A lot of industries were going remote isn't an option, or at least it's >>not as much of an option. But the show has to go on, >>Of course, safely. This has brought about major Rethink Is leaders everywhere. Try to figure out how to adapt. How do you maintain productivity now and also positioned for the future? So let me run through today's lineup First, we'll look at some of these leaders who are adapting. We'll hear how they've taken remote work securely an unbelievably quickly and how they're keeping people safe when the work has toe happen in person, in approximate locations. Well, look at >>what they've done the last six months or so and what learnings they'll take forward. Then we've got some great workplace experts to make sense of it all to talk through what the prescription is going forward. What's this hybrid world going to look like? And not just to survive the pandemic, but to use this moment as a launch point to transformation of the way in which we work that will serve us >>in >>the years and the decade to come. And finally, we'll delve into the practical. We'll look at some of the solutions that are available today and bring people and technology together with processes to help you realize this transformation. We have HBs best experts lined up to answer your questions on what the practical steps are to reinvent the ways in which you work in these unpredictable times, whether you wanna talk about security, I o. T at the edge ai Technologies for safe workplaces >>or any of the >>things that you need to do to nag, navigate, change successfully. They've been there, they've done that, and they're here to help. So >>with that, let's go to our first panel. I'll hand it over to our >>moderator, Maribel Lopez. She's with the independent analyst firm Lopez Associates and friend of the Cube over to you, Maribel

(lively music) >> Good afternoon and welcome back to beautiful Las Vegas, Nevada, where we're here live from the show floor, all four days of AWS re:Invent. I'm Savannah Peterson, joined with my co-host Dave Vellante. Dave, how you doing? >> Good. Beautiful and chilly Las Vegas. Can't wait to get back to New England where it's warm. >> Balmy, New England this time of year in December. Wow, Dave, that's a bold statement. I am super excited about the conversation that we're going to be having next. And, you know, I'm not even going to tee it up. I just want to bring Dilip on. Dilip, thank you so much for being here. How you doing? >> Savannah, Dave, thank you so much. >> Hey, Dilip. >> Excited to be here. >> It's joy to have you. So, you have been working at Amazon for about 20 years. >> Almost. Almost. >> Yes. >> Feels like 20, 19 1/2. >> Which is very exciting. You've had a lot of roles. I'm going to touch on some of them, but you just came over to AWS from the physical retail side. Talk to me about that. >> Yup, so I've been to Amazon for 19 1/2 years. Done pricing, supply chain. I was Jeff Bezos technical advisor for a couple years. >> Casual name drop. >> Casual name drop. >> Savannah: But a couple people here for that name before. >> Humble brag, hashtag. And then I, for the last several years, I was leading our physical retail initiatives. We just walk out Amazon One, bringing convenience to physical spaces. And then in August, with like as those things were getting a lot of traction and we were selling to third parties, we felt that it would be better suited in AWS. And, but along with that, there was also another trend that's been brewing, which is, you know, companies have loved building on AWS. They love the infrastructure services, but increasingly, they're also asking us to build applications that are higher up in the stack. Solving key, turnkey business problems. Just walk out Amazon One or examples of that, Amazon Connect. We just recently announced supply chain, so now there's a bevy interesting services all coming together, higher up in the stack for customers. So it's an exciting time. >> It was interesting that you're able to, you know, transfer from that retail. I mean, normally, in historically, if you're within an industry, retail, manufacturing, automotive whatever. You were kind as locked in a little bit. >> Dilip: Siloed a little bit. Yeah, yeah, yeah. >> Because they had their own, your own value chain. And I guess, data has changed that maybe, that you can traverse now. >> Yeah, if you think about the things that we did, even when we were in retail, the tenants was less about the industries and more about how can we bring convenience to physical spaces? The fact that you don't like to wait in line is no more like likely, you know, five years from now than it is today. So, it's a very durable tenant, but it's equally applicable whether you're in a grocery store, a convenience store, a stadium, an airport. So it actually transcends any, and like supply chain, think of supply chain. Supply chain isn't, you know, targeted to any one particular industry. It has broad applicability. So these things are very, you know, horizontally applicable. >> Anything that makes my life easier, I'm down. >> Savannah: We're all here for the easy button. We've been talking about it a bit this week. I'm in. And the retail store, I mean, I'm in San Francisco. I've had the experience of going through. Very interesting and seamless journey, honestly. It's very exciting. So tell us a little bit more about the applications group at AWS. >> Yup. So as I said, you know, we are, the applications group is a combination of several services. You know, we have communication developer services, which is the ability to add simple email service or video and embed video, voice chat using a chime SDK. In a higher up in the stack, we are taking care of things that IT administrators have to deal with where you can provision an entire desktop with the workspaces or provide a femoral access to it. And then as you go up even higher up in the stack, you have productivity applications like AWS Wicker, which we just did GA, you know, last week in AWS Clean Rooms which we announced as a service in preview. And then you have, you know, Connect, which is our cloud contact center, AWS supply chain. Just walk out Amazon One, it just feels like we're getting started. >> Just a couple things going on. >> So, clean rooms. Part of the governance play, part of data sharing. Can you explain, you know, we were talking offline, but I remember back in the disk drive days. We were in a clean room, they'd show you the clean room, you couldn't go near it unless you had a hazmat suit on. So now you're applying that to data. Explain that concept. >> Yeah, so the companies across, you know, financial services or healthcare, advertising, they all want to be able to combine and pull together data`sets with their partners in order to get these collaborative insights. The problem is either the data's fragmented, it's siloed or you have, you know, data governance issues that's preventing them from sharing. And the key requirement is that they want to be able to share this data without exposing any of the underlying data. Clean rooms are always emerged as a solution to that, but the problem with that is that they're hard to maintain. They're expensive. You have to write complex privacy queries. And if you make a mistake, you risk exposing the same data that you've been, you know, studiously trying to protect. >> Trying to protect. >> You know, take advertising as an industry, as an example. You know, advertisers care about, is my ad effective? But it turns out that if you're an advertiser and let's say you're a Nike or some other advertiser and your pop, you know, you place an ad on the website. Well, you want to stop showing the ad to people who have already purchased the product. However, people who purchased the product,- >> Savannah: It happens all the time. >> that purchasing data is not accessible to them easily. But if you could combine those insights, you know, the publishers benefit, advertisers benefits. So AWS Clean Rooms is that service that allows you very easily to be able to collaborate with a group of folks and then be able to gain these collaborative insights. >> And the consumers benefit. I mean, how many times you bought, you search it. >> It happens all the time. >> They know. And like, I just bought that guys, you know? >> Yeah, no, exactly. >> Four weeks. >> And I'm like, you don't need to serve me that, you know? And we understand the marketing backend. And it's just a waste of money and energy and resources. I mean, we're talking about sustainability as well. I don't think supply chain has ever had a hotter moment than it's had the last two and a half years. Tell me more about the announcement. >> Yup, so super excited about this. As you know, as you said, supply chains have always been very critical and very core for companies. The pandemic exacerbated it. So, ours way of sort of thinking about supply chains is to say that, you know, companies have taken, over the years many, like dozens, like millions and millions of dollars of investment in building their own supply chains. But the problem with supply chains is that the reason that they're not as functional as they could be is because of the lack of visibility. Because they're strung together very many disparate systems, that lack of visibility affects agility. And so, our approach in it was to say that, well, if we could have folks use their existing supply chain what can we do to improve the investment on the ROI of what they're getting? By creating a layer on top of it, that provides them that insights, connects all of these disparate data and then provides them insights to say, well, you know, here's where you overstock, here's where you under stock. You know, this is the, you know, the carbon emission impact of being able to transfer something. So like rather without requiring people to re-platform, what's the way that we can add value in it? And then also build upon Amazon's, you know, years of supply chain experience, to be able to build these predictive analytics for customers. >> So, that's a good, I like that you started with the why. >> Yes. >> Right now, what is it? It's an abstraction layer and then you're connecting into different data points. >> Yes, that's correct. >> Injecting ML. >> Feel like you can pick in, like if you think about supply chain, you can have warehouse management systems, order management systems. It could be in disparate things. We use ML to be able to bring all of this disparate data in and create our unified data lake. Once you have that unified data lake, you can then run an insights layer on top of it to be able to say, so that as the data changes, supply chain is not a static thing. Data's constantly changing. As the data's changing, the data lake now reflects the most up-to-date information. You can have alerts and insights set up on it to say that, what are the kinds of things that you're interested in? And then more importantly, supply chain and agility is about communication. In order to be able to make certain things happen, you need to be able to communicate, you need to make sure that everyone's on the same page. And we allow for a lot of the communication and collaboration tools to be built within this platform so that you're not necessarily leaving to go and toggle from one place to the other to solve your problems. >> And in the pie chart of how people spend their time, they're spending a lot less time communicating and being proactive. >> That's correct. >> And getting ahead of the curve. They're spending more time trying to figure out actually what's going on. >> Yes. >> And that's the problem that you're going to solve. >> Well, and it ensures that the customer at the other end of that supply chain experience is going to have their expectations managed in terms of when their good might get there or whatever's going to happen. >> Exactly. >> I feel like that expectation management has been such a big part of it. Okay, I just have to ask because I'm very curious. What was it like advising Jeff? >> Quite possibly the best job that I've ever had. You know, he's a fascinating individual. >> Did he pay you to say that? >> Nope. But I would've, like, I would've done it for like, it's remarkable seeing how he thinks and his approach to problem solving. It is, you know, you could be really tactical and go very deep. You could be extremely strategic. And to be able to sort of move effortlessly between those two is a unique skill. I learned a lot. >> Yeah, absolutely. So what made you want to evolve your career at Amazon after that? 'Cause I see on your LinkedIn, you say, it was the best job you ever had. With curiosity? >> Yeah, so one of the things, so the role is designed for you to be able to transition to something new. >> Savannah: Oh, cool. >> So after I finished that role, we were just getting into our foray with physical stores. And the idea between physical stores is that, you and I as consumers, we all have a lot of choices for physical stores. You know, there's a lot of options, there's a lot of formats. And so the last thing we wanted to do is come up with another me too offering. So, our approach was that what can we do to improve convenience in physical stores? That's what resulted in just walk out to Amazon Go. That's what resulted in Amazon One, which is another in a fast, convenient, contactless way to pay using the power of your palm. And now, what started in Amazon retail is now expanded to several third parties in, you know, stadiums, convention centers, airports. >> Airport, I just had, was in the Houston airport and got to do a humanless checkout. >> Dilip: Exactly. >> And actually in Honolulu a couple weeks ago as well too. Yeah, so we're going to see more and more of this. >> Yes. >> So what Amazon, I think has over a million employees. A lot of those are warehouse employees. But what advice would you give to somebody who's somewhere inside of Amazon, maybe they're on AWS, maybe they're Amazon. What advice would you give somebody inside that's maybe, you know, hey, I've been at this job for five, six years, three, four years, whatever it is. I want to do something else. And there's so much opportunity inside Amazon, right? What would you advise them? >> My single advice, which is actually transferable and I use it for myself is choose something that makes you a little uncomfortable. >> Dave: Get out of your comfort zone. >> It's like, you got to do that. It's like, it's not the easiest thing to hear, but it's also the most satisfying. Because almost every single time that I've done it for myself, it's resulted in like, you don't really know what the answer is. You don't really know exactly where you're going to end up, but the process and the journey through it, if you experience a little bit of discomfort constantly, it makes you non complacent. It makes you sort of not take the job, sort of in a stride. You have to be on it to do it. So that's the advice that I would give anyone. >> Yeah, that's good. So something that's maybe adjacent and maybe not completely foreign to you, but also something that, you know, you got to go dig a little bit and learn. >> You're planning a career change over here, Dave? >> No, I know a lot of people in Amazon are like, hey, I'm trying to figure out what I want to do next. I mean, I love it here. I live by the LPS, you know, but, and there's so much to choose from. >> It is, you know, when I joined in 2003, there were so many things that we were sort of doing today. None of those existed. It's a fascinating company. And the evolution, you could be in 20 different places and the breadth of the kinds of things that, you know, the Amazon experience provides is timeless. It's fascinating. >> And, you know, you look at a company like Amazon, and, you know, it's so amazing. You look at this ecosystem. I've been around- >> Even a show floor. >> I've been around a lot of time. And the show floor says it all. But I've seen a lot of, you know, waves. And each subsequent wave, you know, we always talk about how many companies were in the Fortune 1000 and aren't anymore. And, but the leaders, you know, survive and they thrive. And I think it's fascinating to try to better understand the culture that enables that. You know, you look at a company like Microsoft that was irrelevant and then came back. You know, even IBM was on death store for a while and they come back and so they. And so, but Amazon just feels, you know, at the moment you feel like, "Oh wow, nothing can stop this machine." 'Cause everybody's trying to disrupt Amazon and then, you know, only the paranoid survive, all that stuff. But it's not like, past is not prologue, all right? So that's why I asked these questions. And you just said that a lot of the services today that although the ideas didn't even exist, I mean, walkout. I mean, that's just amazing. >> I think one of the things that Amazon does really well culturally is that they create the single threaded leadership. They give people focus. If you have to get something done, you have to give people focus. You can't distract them with like seven different things and then say that, oh, by the way, your eighth job is to innovate. It just doesn't work that way. It's like it's hard. Like it can be- >> And where were the energy come from that? >> Exactly. And so giving people that single threaded focus is super important. >> Frank Slootman, the CEO of Snowflake, has a great quote. He wrote on his book. He said, "If you got 14 priorities, you got none." And he asks,- >> Well said. >> he challenges people. If you had to give up everything and do only one thing for the next 365 days, what would that be? It's a really hard question to answer. >> I feel like as we're around New Year's resolution times. I mean when we thinking about that, maybe we can all share our one thing. So, Dilip, you've been with the the applications team for five months. What's coming up next? >> Well, as I said, you know, it feels like it's still day one for applications. If you think about the things, the news that we introduced and the several services that we introduced, it has applicability across a variety of horizontal industries. But then we're also feeling that there's considerable vertical applications that can be built for specific things. Like, it could be in advertising, it could be in financial services, it could be in manufacturing. The opportunities are endless. I think the notion of people wanting applications higher up in the stack and a little more turnkey solutions is also, it's not new for us, but it's also new and creative too. You know, AWS has traditionally been doing. >> So again, this relates to what we were sort of talking about before. And maybe, this came from Jazzy or maybe it came from Bezos. But you hear a lot, it's okay to be misunderstood or if we were misunderstood for a long time. So when people hear up the stack, they think, when you think about apps, you know, in the last 10 years it was taking on-prem and bringing it into the cloud. Okay, you saw that with CREM, email, CRM, service management, you know, data warehouses, et cetera. Amazon is thinking about this in a different way. It's like you're looking at the world saying, okay, how can we improve whatever? Workflows, people's lives, doing something that's not been done before? And that seems to be the kind of applications that you guys are thinking about building. >> Yeah. >> And that's unique. It's not just, okay, we're going to take something on-prem put it in the cloud. Been there, done that. That S-curve is sort of flattening now. But there's a new S-curve which is completely new workflows and innovations and processes that we really haven't thought about yet. Or you're thinking about, I presume. >> Yeah. Having said that, I'd also like to sort of remind folks that when you consider the, you know, the entire spend, the portion of workloads that are running in the cloud is a teeny tiny fraction. It's like less than 5%, like 4% or something like that. So it's a very, there's still plenty of things that can sort of move to the cloud. But you're right that there is another trend of where in the stack and the types of applications that you can provide as well. >> Yeah, new innovation that haven't well thought of yet. >> So, Dilip, we have a new tradition here on theCUBE at re:Invent. Where we're looking for your 30 minute Instagram reel, your hot take, biggest key theme, either for you, your team, or just general vibe from the show. >> General vibe from the show. Well, 19 1/2 years at Amazon, this is actually my first re:Invent, believe it or not. This is my, as a AWS employee now, as re:Invent with like launching services. So that's the first. I've been to re:Invent before, but as an attendee rather than as a person who's, you know, a contributing number of the workforce. >> Working actually? >> If you will. >> Actually doing your job. >> And so I'm just amazed at the energy and the breadth. And the, you know, from the partners to the customers to the diversity of people who are coming here from everywhere. I had meetings from people in New Zealand. Like, you know, the UK, like customers are coming at us from like very many different places. And it's fascinating for me to see. It's new for me as well given, you know, some of my past experience. But this is a, it's been a blast. >> People are pumped. >> People are pumped. >> They can't believe the booth traffic. Not only that quality. >> Right. All of our guests have talked about that. >> Like, yeah, you know, we're going to throw half of these leads away, but they're saying no, I'm having like really substantive conversations with business people. This is, I think, my 10th re:Invent. And the first one was mostly developers. And I'm like, what are you talking about? And, you know, so. Now it's a lot more business people, a lot of developers too. >> Yeah. >> It's just. >> The community really makes it. Dilip, thank you so much for joining us today on theCube. >> Thank you for having me. >> You're fantastic. I could ask you a million questions. Be sure and tell Jeff that we said hi. >> Will do. >> Savannah: Next time you guys are hanging out. And thank all of you. >> You want to go into space? >> Yeah. Yes, yes, absolutely. I'm perhaps the most space obsessed on the show. And with that, we will continue our out of this world coverage shortly from fabulous Las Vegas where we are at AWS re:Invent. It is day four with Dave Vellante. I'm Savannah Peterson and you're watching theCUBE, the leader in high tech coverage. (lively music)

from our studios in the heart of Silicon Valley Palo Alto California this is a cute conversation hi and welcome to the cube Studios for another cube conversation where we go in-depth with thought leaders driving innovation across the tech industry I'm your host Peter Burris if there's one thing we know about cloud it's that it's going to drive new data and a lot of it and that places a lot of load on storage technologies who have to be able to capture persist and ultimately deliver that data to new classes of applications in support of whatever the digital business is trying to do so how is the whole storage industry and the relationship between data and storage going to evolve I can't think of a better person to have that conversation with in stanley's a phos senior vice president product marketing infinite dad Stan welcome to the cube thank you for it's my pleasure to be here and I'm flattered with that introduction well hold on look you and I have known each other for a long time we have been walking into user presentations and you've been walking out until recently though you were generally regarded as the thought leader when it came to user side concerns about storage what is that problem that users are fundamentally focused on as they think about their data data management and storage requirements fundamental problems and this afflicts all classes of users whether in a financial institution at university government small business medium-sized businesses is that they're coping with the number of primal forces that don't change and the first is that the environment is becoming ever more competitive and with the environment being ever more competitive that means that they're always under budget constraints they're usually suffering from skill shortages especially now when we see so many new technologies and the realization that we can coax value out of the information that we capture and store creating new demands elsewhere within the IT organization so what we see historically is that uses understand that there you have an insatiable demand for capacity they have finite budgets they have limited skills and they realize that recovering from a loss of data integrity is a far more painful process than recovering from an application blowing up or a networking issue and they got to do it faster and they have to do it faster so what we see in some ways is in effect the perfect storm and this is part of the reason that we've seen a number of the technical evolutions that we've witnessed over the past decade or two decades or however long we'd like to admit we've been tracking this industry occurring and growing in importance what we've also seen is that many of the technologies that are useful in helping to deliver usable availability to the application are in some ways becoming more commoditized so when we look across these industries some of the things that we're looking for is cost efficiency we're looking at increasing levels of automation we're looking of increases in data mobility with the ultimate objective being of course to allow data to reside where it naturally belongs and we're trying to deliver these new capabilities at scale in infrastructures that were built with storage arrays that would design for a terabyte world instead of a petabyte world and it won't be too long before we start talking about exabytes as we're already seeing so to be able to satisfy new scale problems with traditional and well understood issues is there are three basic types of storage companies that are targeting this problem the first of the established storage companies the incumbents the incumbents and the incumbents I really don't envy them because they have to maintain backwards compatibility which limits their ability to innovate at the same time they're competing against privately held newer companies that aren't constrained by the need for backwards compatibility and therefore able to take better advantage of the technology improvements that we're seeing to live it and when I say technology improvements not just in hardware but also in terms of software also in terms of management and government and governing philosophies so beginning with the point that all companies large small have some basic problems that are similar what we then see is there are three types of storage companies addressing them one of the in established and common vendors the other and they've gotten a lot of press or the companies that realize that flash media very media that delivers one to two orders of magnitude improvements in terms of performance in terms of bandwidth in terms of environmental x' that they could create storage solutions that address real pain points within a data center within an organization but at a very high price point and then it was the third approach and this is the approach that infinite I chose to take and that is to define the customer problem to find the customer market and then create an architecture which is underpinned by brilliant software to solve these problems in a way that is both cost-effective and extensible and of course meeting all of the critical capabilities that users are looking for so we've got the situation where we've got the incumbents who have install bases and are trying to bring their customers forward but right I have to do so within the constraints of past technology choices we've got the new folks who are basically technology first and saying jump to a new innovation curve and we've got other companies that are trying to bring the best of the technology to the best of the customer reality and marry it and you're asserting that's what infinite at ease and then it's precisely what we've done so let's talk about why did you then come to infinite at what is it about infinite act that gets you excited well one of the things that got well your number of things that got me excited about it so the first is that when I look at this and I approach these things as an engineer who's steeped in aerospace and weapon systems design so you look at the problem you superimpose capabilities there and then you blow it up and then if well we do blow it up but we blow it up using economics we blow it up using superior post-sale support effectiveness we blow it up with a fundamentally different approach to how we give our install base access to new capabilities so we're established storage companies and to some extent media based storage companies of forcing upgrades to avoid architectural obsolescence that is to gain access to new features and functions that can improve their staff productivity or deliver new capabilities to support new applications and workloads we're not forcing a cadence of infrastructure refreshes to gain access to that so if you take a look at our history our past behavior we allow today we're allowing current software to run on n minus 2 generation hardware so that now when you're doing a refresh on your hardware you're doing a refresh on the hardware because you've outgrown it because it's so old that it's moved past its useful service life which hasn't happened to us yet because that's usually on the order of about eight years and sometimes longer if it's kept in a clean data center and we have a steady cadence of product announcements and we understood some underlying economics so whether I talk to banking institutions colleges manufacturing companies telcos service providers everybody's in general agreement that roughly two-thirds of the data that they have online and accessible is stale data meaning that it hasn't been accessed in 60 to 90 days and then when I take a look at industry forecasts in terms of dollar per terabyte pricing for HD DS for disk drives and I look at dollar per terabyte forecast for flash technologies there's an order of magnitude difference in meaning 10x and even if you want to be a pessimist call it only 5x what you see is that we have a built-in advantage for storing 60% of the data that's already up and spinning and there are those questions of whether or not the availability of flash is going to come under pressure over the next few years as because we're not expanding another fabs out there they're generating flash so let me come back right it's kind of core points out there so we have quality yeah the right now you guys are trying to bring the economics of HDD to the challenges are faster more reliable more scalable data delivery right so that you can think about not only persisting your data from transactional applications but also delivering that data to the new uses new requirements new applications new business needs so you've made you know infinite out has made some choices about how to bring technology together that are some somewhat that are unique first thing is the team that did this tell us a little bit about the team and then let's talk about some of those torches so one of the draws for me personally is that we have a development team that has had the unique possibly the unique experience of having done three not one not two but three clean sheet designs of storage arrays now if you believe that practice makes perfect and you're starting off with very bright people that experience before they designed a storage array when we look at the InfiniBand when we look at in Finnegan what we see is the benefit of three clean sheet designs and what does that design look like what is it how did you guys bring these different senses of technology together to improve the notion of it all right so what we looked at we looked at trends instead of being married to a technology or married to an architecture we were we define the users problem we understood that they have an insatiable need for data we can argue whether they're growing at fifteen percent 30 percent or 100 percent per year but data growth is insatiable stale data being a constant megive n' and of course now with digital business initiatives and moving the infrastructure to the edge where we could capture ever more data if anything the amount of stale data that was storing is likely to increase so we've all seen survey after survey that 80% of all the data created is unstructured data meaning we're collecting it we know that may be a value at some point but we're not quite sure when so this is not data that you want to store in the most expensive media that we know how to manufacture or sell right not happening so we have a built-in economic advantage for this at least 60% of the data that users want to keep online we understand that if you implement an archiving solution that archive data still has to be stored somewhere and for practical purposes that's either disk or tape and we're not here to talk about the fact that I can take tape and store in a bunker for years but if I want to recover something if I have to answer a problem I want it on disk so the economic gap the price Delta between an archive storage solution per se and our approach is much narrower because we're using a common technology and when Seagate or West and digital a Toshiba cell and HDD they're not asking you where you're putting it they're saying you want this capacity this rpm this mean time between face its this is how much it's going to cost so when we take a look at a lot of the innovation and go to market models what they really are or revenue protection schemes for the existing established vendors and for the emerging companies the difference is there are in the problems that they're solving am i creating a backup restore solution the backup and restore is always a high impact pain point am i creating a backup restore solution am i building a system for primary storage a my targeting virtualized environments my targeting VDI now our install base the bulk of our install base I'm not sure we actually we should share percentages but it's well north of 50 and if you take a look at some virtualized estimates probably 80% of workloads today are virtualized we understood that to satisfy this environment and to have a built-in advantage that's memorable after the marketing presentations are done in other words treating these things as black boxes so if we take a look at my high-level description of an infinite box array installed at a customer site consistent sub-millisecond response times and we're able to do that because we service over 80% of all iOS out of DRAM which is probably about four orders of magnitude faster than NAND flash and then we have a large read cache to increase our cache hit ratio even further and when I say large we're not talking about single digits of terabytes we're talking about 20 plus terabytes and that can grow as necessary so that when we're done we're achieving cache hit ratios that are typically in excess of 90% now if I'm servicing iOS out of cache do I really care what's on the back end the answer is no but what I do care about for certain analytics applications is I want lots of bandwidth and I want and if I have workloads with high right content I don't want to be spending a lot of time paying my raid right penalty so what we've done is to take the obvious solution and coalesce rights so that instead of doing partial stripe rights we're always doing full stripe rights so we have double bit protection on data stored on HD DS which means that the world is likely to come to an end before we lose this slight exaggeration I think we're expecting the world to come to an end in 14 billion years yeah yeah let's do so so if I'm wrong get back to me in a Bay and it's a little bit less than that but it doesn't matter yeah okay high on that all so we've got a so we've got a built in economic advantage we've got a built in performance advantage because when I'm servicing most iOS out of DRAM which is for does magnitude faster than NAND flash I've got a lot of room to do a lot of very clever things in terms of metadata and still be faster so and you got a team that's done it before and we've got a team that's done it before and experimented because remember this is a team that has experience with scale-up architectures as in symmetric s-- they have experience with scale-out architectures which is XIV which was very disruptive to the market well so was it symmetric spec and now of course we've got this third bite at the Apple with infinite at where they also understood that the rate of microprocessor performance improvement was going up a lot faster than than our ability to transfer data on and off of HD DS or SSDs so what they realized is that they could change the ratio they can have a much lower microprocessor or controller to back-end storage ratio and still be able to deliver this tremendous performance and now if you have fewer parts and you're not affecting the ID MTBF by driving more iOS through I've lowered my overall cost of goods so now I've got an advantage in back-end media I have a bag I have an advantage in terms of the number of controllers I need to deliver sub sillas eken response time I have an advantage in terms of delivering usable availability so I'm now in a position to be able to unashamedly compete on price unashamedly compete on performance unashamedly compete on a better post sale support experience because remember if there's less stuff they had a break we're taking less calls and because of the way we're organized our support generally goes to what other vendors might think of it's third level support because of a guided answer answers the phone from us doesn't solve the problem he's calling development so if you take a look at gotten apear insights we're off the scale in terms of having great reviews and when you have I think it's 99% I may be off by a percent ninety eight to a hundred percent of our customers saying they'd recommend our kit to their to their peers that's a pretty positive endorsement yeah so let me let me break in and and kind of wrap up a little bit let me make this quick observation because the other thing that you guys have done is you've demonstrated that you're not bound to a single technology so smart people with a great architecture that's capable of utilizing any technology to serve a customer problem at a price point that reflects the value of the problem that's being solved right and in fact we it's very insightful observation because when you recognize that we've built a multimedia integrated architecture that makes our that makes very easy for us to include storage class memory and because of the way we've done our drivers we're also going to be nvme over if ready when that starts to gain traction as well excellent Stanley Zappos senior vice president product management Infini debt thanks very much for being in the cube we'll have you back oh it's my pleasure there's been a blast and once again I want to thank you for joining us for another cube conversation on Peterborough's see you next time [Music]