(soft music) >> Welcome back to Sin City, guys and girls we're glad you're with us. You've been watching theCUBE all week, we know that. This is theCUBE's live coverage of AWS re:Invent 22, from the Venetian Expo Center where there are tens of thousands of people, and this event if you know it, covers the entire strip. There are over 55,000 people here, hundreds of thousands online. Dave, this has been a fantastic show. It is clear everyone's back. We're hearing phenomenal stories from AWS and it's ecosystem. We got a great customer story coming up next, featured on the main stage. >> Yeah, I mean, you know, post pandemic, you start to think about, okay, how are things changing? And one of the things that we heard from Adam Selipsky, was, we're going beyond digital transformation into business transformation. Okay. That can mean a lot of things to a lot of people. I have a sense of what it means. And I think this next interview really talks to business transformation beyond digital transformation, beyond the IT. >> Excellent. We've got two guests. One of them is an alumni, Scott Mullins joins us, GM, AWS Worldwide Financial Services, and Brad Peterson is here, the EVP, CIO and CTO of NASDAQ. Welcome guys. Great to have you. >> Hey guys. >> Hey guys. Thanks for having us. >> Yeah >> Brad, talk a little bit, there was an announcement with NASDAQ and AWS last year, a year ago, about how they're partnering to transform capital markets. It was a highlight of last year. Remind us what you talked about and what's gone on since then. >> Yeah, so, we are very excited. I work with Adena Friedman, she's my boss, CEO of NASDAQ, and she was on stage with Adam for his first Keynote as CEO of AWS. And we made the commitment that we were going to move our markets to the Cloud. And we've been a long time customer of AWS and everyone said, you know the last piece, the last frontier to be moved was the actual matching where all the messages, the quotes get matched together to become confirmed orders. So that was what we committed to less than a year ago. And we said we were going to move one of our options markets. In the US, we have six of them. And options markets are the most challenging, they're the most high volume and high performance. So we said, let's start with something really challenging and prove we can do it together with AWS. So we committed to that. >> And? Results so far? >> So, I can sit here and say that November 7th so we are live, we're in production and the MRX Exchange is called Mercury, so we shorten it for MRX, we like acronyms in technology. And so, we started with a phased launch of symbols, so you kind of allow yourself to make sure you have all the functionality working then you add some volume on it, and we are going to complete the conversion on Monday. So we are all good so far. And I have some results I can share, but maybe Scott, if you want to talk about why we did that together. >> Yeah. >> And what we've done together over many years. >> Right. You know, Brian, I think it's a natural extension of our relationship, right? You know, you look at the 12 year relationship that AWS and NASDAQ have had together, it's just the next step, in the way that we're going to help the industry transform itself. And so not just NASDAQ's business transformation for itself, but really a blueprint and a template for the entire capital markets industry. And so many times people will ask me, who's using Cloud well? Who's doing well in the Cloud? And NASDAQ is an easy example to point to, of somebody who's truly taking advantage of these capabilities because the Cloud isn't a place, it's a set of capabilities. And so, this is a shining example of how to use these capabilities to actually deliver real business benefit, not just to to your organization, but I think the really exciting part is the market technology piece of how you're serving other exchanges. >> So last year before re:Invent, we said, and it's obvious within the tech ecosystem, that technology companies are building on top of the Cloud. We said, the big trend that we see in the 2020s is that, you know, consumers of IT, historically, your customers are going to start taking their stacks, their software, their data, their services and sassifying, putting it on the Cloud and delivering new services to customers. So when we saw Adena on stage last year, we called it by the way, we called it Super Cloud. >> Yeah. >> Okay. Some people liked the term but I love it. And so yeah, Super Cloud. So when we saw Adena on stage, we said that's a great example. We've seen Capital One doing some similar things, we've had some conversations with US West, it's happening, right? So talk about how you actually do that. I mean, because you've got a lot, you've got a big on-premises stay, are you connecting to that? Is it all in the Cloud? Paint a picture of what the architecture looks like? >> Yeah. And there's, so you started with the business transformation, so I like that. >> Yeah. >> And the Super Cloud designation, what we are is, we own and operate exchanges in the United States and in Europe and in Canada. So we have our own markets that we're looking at modernizing. So we look at this, as a modernization of the capital market infrastructure, but we happen to be the leading technology provider for other markets around the world. So you either build your own or you source from us. And we're by far the leading provider. So a lot of our customers said, how about if you go first? It's kind of like Mikey, you know, give it to Mikey, let him try it. >> See if Mikey likes it. >> Yeah. >> Penguin off the iceberg thing. >> Yeah. And so what we did is we said, to make this easy for our customers, so you want to ask your customers, you want to figure out how you can do it so that you don't disrupt their business. So we took the Edge Compute that was announced a few years ago, Amazon Outposts, and we were one of their early customers. So we started immediately to innovate with, jointly innovate with Amazon. And we said, this looks interesting for us. So we extended the region into our Carteret data center in Northern New Jersey, which gave us all the services that we know and love from Amazon. So our technical operations team has the same tools and services but then, we're able to connect because in the markets what we're doing is we need to connect fairly. So we need to ensure that you still have that fairness element. So by bringing it into our building and extending the Edge Compute platform, the AWS Outpost into Carteret, that allowed us to also talk very succinctly with our regulators. It's a familiar territory, it's all buttoned up. And that simplified the conversion conversation with the regulators. It simplified it with our customers. And then it was up to us to then deliver time and performance >> Because you had alternatives. You could have taken a more mature kind of on-prem legacy stack, figured out how to bolt that in, you know, less cloudy. So why did you choose Outposts? I am curious. >> Well, Outposts looked like when it was announced, that it was really about extending territory, so we had our customers in mind, our global customers, and they don't always have an AWS region in country. So a lot of you think about a regulator, they're going to say, well where is this region located? So finally we saw this ability to grow the Cloud geographically. And of course we're in Sweden, so we we work with the AWS region in Stockholm, but not every country has a region yet. >> And we're working as fast as we can. - Yes, you are. >> Building in every single location around the planet. >> You're doing a good job. >> So, we saw it as an investment that Amazon had to grow the geographic footprint and we have customers in many smaller countries that don't have a region today. So maybe talk a little bit about what you guys had in mind and it's a multi-industry trend that the Edge Compute has four or five industries that you can say, this really makes a lot of sense to extend the Cloud. >> And David, you said it earlier, there's a trend of ecosystems that are coming onto the Cloud. This is our opportunity to bring the Cloud to an ecosystem, to an existing ecosystem. And if you think about NASDAQ's data center in Carteret, there's an ecosystem of NASDAQ's clients there that are there to be with NASDAQ. And so, it was actually much easier for us as we worked together over a really a four year period, thinking about this and how to make this technological transition, to actually bring the capabilities to that ecosystem, rather than trying to bring the ecosystem to AWS in one of our public regions. And so, that's been our philosophy with Outpost all along. It's actually extending our capabilities that our customers know and love into any environment that they need to be able to use that in. And so to Brad's point about servicing other markets in different countries around the world, it actually gives us that ability to do that very quickly, very nimbly and very succinctly and successfully. >> Did you guys write a working backwards document for this initiative? >> We did. >> Yeah, we actually did. So to be, this is one of the fully exercised. We have a couple of... So by the way, Scott used to work at NASDAQ and we have a number of people who have gone from NASDAQ data to AWS, and from AWS to NASDAQ. So we have adopted, that's one of the things that we think is an effective way to really clarify what you're trying to accomplish with a project. So I know you're a little bit kidding on that, but we did. >> No, I was close. Because I want to go to the like, where are we in the milestone? And take us through kind of what we can expect going forward now that we've worked backwards. >> Yep, we did. >> We did. And look, I think from a milestone perspective, as you heard Brad say, we're very excited that we've stood up MRX in production. Having worked at NASDAQ myself, when you make a change and when you stand up a market that's always a moment where you're working with your community, with your clients and you've got a market-wide call that you're working and you're wanting to make sure that everything goes smoothly. And so, when that call went smoothly and that transition went smoothly I know you were very happy, and in AWS, we were also very happy as well that we hit that milestone within the timeframe that Adena set. And that was very important I know to you. >> Yeah. >> And for us as well. >> Yeah. And our commitment, so the time base of this one was by the end of 2022. So November 7th, checked. We got that one done. >> That's awesome. >> The other one is we said, we wanted the performance to be as good or better than our current platform that we have. And we were putting a new version of our derivative or options software onto this platform. We had confidence because we already rolled it to one market in the US then we rolled it earlier this year and that was last year. And we rolled it to our nordic derivatives market. And we saw really good customer feedback. So we had confidence in our software was going to run. Now we had to marry that up with the Outpost platform and we said we really want to achieve as good or better performance and we achieved better performance, so that's noticeable by our customers. And that one was the biggest question. I think our customers understand when we set a date, we test them with them. We have our national test facility that they can test in. But really the big question was how is it going to perform? And that was, I think one of the biggest proof points that we're really proud about, jointly together. And it took both, it took both of us to really innovate and get the platform right, and we did a number of iterations. We're never done. >> Right. >> But we have a final result that says it is better. >> Well, congratulations. - Thank you. >> It sounds like you guys have done a tremendous job. What can we expect in 2023? From NASDAQ and AWS? Any little nuggets you can share? >> Well, we just came from the partner, the partner Keynote with Adam and Ruba and we had another colleague on stage, so Nick Ciubotariu, so he is actually someone who brought digital assets and cryptocurrencies onto the Venmo, PayPal platform. He joined NASDAQ about a year ago and we announced that in our marketplace, the Amazon marketplace, we are going to offer digital custody, digital assets custody solution. So that is certainly going to be something we're excited about in 2023. >> I know we got to go, but I love this story because it fits so great at the Super cloud but we've learned so much from Amazon over the years. Two pieces of teams, we talked about working backwards, customer obsession, but this is a story of NASDAQ pointing its internal capabilities externally. We're already on that journey and then, bringing that to the Cloud. Very powerful story. I wonder what's next in this, because we learn a lot and we, it's like the NFL, we copy it. I think about product market fit. You think about scientific, you know, go to market and seeing that applied to the financial services industry and obviously other industries, it's really exciting to see. So congratulations. >> No, thank you. And look, I think it's an example of Invent and Simplify, that's another Amazon principle. And this is, I think a great example of inventing on behalf of an industry and then continually working to simplify the way that the industry works with all of us. >> Last question and we've got only 30 seconds left. Brad, I'm going to direct it to you. If you had the opportunity to take over the NASDAQ sign in Times Square and say a phrase that summarizes what NASDAQ and AWS are doing together, what would it say? >> Oh, and I think I'm going to put that up on Monday. So we're going to close the market together and it's going to say, "Modernizing the capital market's infrastructure together." >> Very cool. >> Excellent. Drop the mic. Guys, this was fantastic. Thank you so much for joining us. We appreciate you joining us on the show, sharing your insights and what NASDAQ and AWS are doing. We're going to have to keep watching this. You're going to have to come back next year. >> All right. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage. (soft music)

>>Hi, this is Cindy Mikey, vice president of industry solutions at caldera. Joining me today is chef is Molly, our solution engineer for the public sector. Today. We're going to talk about speed to insight. Why using machine learning in the public sector, specifically around fraud, waste and abuse. So topic for today, we'll discuss machine learning, why the public sector uses it to target fraud, waste, and abuse, the challenges. How do we enhance your data and analytical approaches the data landscape analytical methods and shad we'll go over reference architecture and a case study. So by definition at fraud waste and abuse per the government accountability office is broad as an attempt to obtain something about a value through unwelcomed misrepresentation waste is about squandering money or resources and abuse is about behaving improperly or unreasonably to actually obtain something of value for your personal, uh, benefit. So as we look at fraud, um, and across all industries, it's a top of mind, um, area within the public sector. >>Um, the types of fraud that we see is specifically around cyber crime, uh, looking at accounting fraud, whether it be from an individual perspective to also, uh, within organizations, looking at financial statement fraud, to also looking at bribery and corruption, as we look at fraud, it really hits us from all angles, whether it be from external perpetrators or internal perpetrators, and specifically for the research by PWC, the key focus area is we also see over half of fraud is actually through some form of internal or external perpetrators, again, key topics. So as we also look at a report recently by the association of certified fraud examiners, um, within the public sector, the us government, um, in 2017, it was identified roughly $148 billion was attributable to fraud, waste and abuse. Specifically of that 57 billion was focused on reported monetary losses and another 91 billion on areas where that opportunity or the monetary basis had not yet been measured. >>As we look at breaking those areas down again, we look at several different topics from an out payment perspective. So breaking it down within the health system, over $65 billion within social services, over $51 billion to procurement fraud to also, uh, uh, fraud, waste and abuse that's happening in the grants and the loan process to payroll fraud, and then other aspects, again, quite a few different topical areas. So as we look at those areas, what are the areas that we see additional type of focus, those are broad stroke areas. What are the actual use cases that, um, agencies are using the data landscape? What data, what analytical methods can we use to actually help curtail and prevent some of the, uh, the fraud waste and abuse. So, as we look at some of the analytical processes and analytical use great, uh, use cases in the public sector, whether it's from, uh, you know, the taxation areas to looking at, you know, social services, uh, to public safety, to also the, um, our, um, additional agency methods, we're going to focus specifically on some of the use cases around, um, you know, fraud within the tax area. >>Uh, we'll briefly look at some of the aspects of unemployment insurance fraud, uh, benefit fraud, as well as payment integrity. So fraud has its, um, uh, underpinnings in quite a few different government agencies and difficult, different analytical methods and I usage of different data. So I think one of the key elements is, you know, you can look at your, your data landscape on specific data sources that you need, but it's really about bringing together different data sources across a different variety, a different velocity. So, uh, data has different dimensions. So we'll look at on structured types of data of semi-structured data, behavioral data, as well as when we look at, um, you know, predictive models, we're typically looking at historical type information, but if we're actually trying to look at preventing fraud before it actually happens, or when a case may be in flight, which is specifically a use case that Chev is going to talk about later it's how do I look at more, that real, that streaming information? >>How do I take advantage of data, whether it be, uh, you know, uh, financial transactions we're looking at, um, asset verification, we're looking at tax records, we're looking at corporate filings. Um, and we can also look at more, uh, advanced data sources where as we're looking at, um, investigation type information. So we're maybe going out and we're looking at, uh, deep learning type models around, uh, you know, semi or that, uh, behavioral that's unstructured data, whether it be camera analysis and so forth. So for quite a different variety of data and the breadth and the opportunity really comes about when you can integrate and look at data across all different data sources. So in essence, looking at a more extensive, uh, data landscape. So specifically I want to focus on some of the methods, some of the data sources and some of the analytical techniques that we're seeing, uh, being used, um, in the government agencies, as well as opportunities to look at new methods. >>So as we're looking at, you know, from a, um, an audit planning or looking at, uh, the opportunity for the likelihood of non-compliance, um, specifically we'll see data sources where we're maybe looking at a constituents profile, we might actually be investigating the forms that they provided. We might be comparing that data, um, or leveraging internal data sources, possibly looking at net worth, comparing it against other financial data, and also comparison across other constituents groups. Some of the techniques that we use are some of the basic natural language processing, maybe we're going to do some text mining. We might be doing some probabilistic modeling, uh, where we're actually looking at, um, information within the agency to also comparing that against possibly tax forms. A lot of times it's information historically has been done on a batch perspective, both structured and semi-structured type information. And typically the data volumes can be low, but we're also seeing those data volumes on increase exponentially based upon the types of events that we're dealing with, the number of transactions. >>Um, so getting the throughput, um, and chef's going to specifically talk about that in a moment. The other aspect is, as we look at other areas of opportunity is when we're building upon, how do I actually do compliance? How do I actually look at conducting audits or potential fraud to also looking at areas of under-reported tax information? So there you might be pulling in, um, some of our other types of data sources, whether it's being property records, it could be data that's being supplied by the actual constituents or by vendors to also pulling in social media information to geographical information, to leveraging photos on techniques that we're seeing used is possibly some sentiment analysis, link analysis. Um, how do we actually blend those data sources together from a natural language processing? But I think what's important here is also the method and the looking at the data velocity, whether it be batch, whether it be near real time, again, looking at all types of data, whether it's structured semi-structured or unstructured and the key and the value behind this is, um, how do we actually look at increasing the potential revenue or the, uh, under reported revenue? >>Uh, how do we actually look at stopping fraudulent payments before they actually occur? Um, also looking at increasing the amount of, uh, the level of compliance, um, and also looking at the potential of prosecution of fraud cases. And additionally, other areas of opportunity could be looking at, um, economic planning. How do we actually perform some link analysis? How do we bring some more of those things that we saw in the data landscape on customer, or, you know, constituent interaction, bringing in social media, bringing in, uh, potentially police records, property records, um, other tax department, database information. Um, and then also looking at comparing one individual to other individuals, looking at people like a specific constituent, are there areas where we're seeing, uh, um, other aspects of a fraud potentially being occurring. Um, and also as we move forward, some of the more advanced techniques that we're seeing around deep learning is looking at computer vision, um, leveraging geospatial information, looking at social network entity analysis, uh, also looking at, um, agent-based modeling techniques, where we're looking at, uh, simulation Monte Carlo type techniques that we typically see in the financial services industry, actually applying that to fraud, waste, and abuse within the, uh, the public sector. >>Um, and again, that really lends itself to a new opportunities. And on that, I'm going to turn it over to Shev to talk about, uh, the reference architecture for, uh, doing these baskets. >>Thanks, Cindy. Um, so I'm going to walk you through an example, reference architecture for fraud detection using, uh, Cloudera underlying technology. Um, and you know, before I get into the technical details, uh, I want to talk about how this would be implemented at a much higher level. So with fraud detection, what we're trying to do is identify anomalies or novelists behavior within our data sets. Um, now in order to understand what aspects of our incoming data represents anomalous behavior, we first need to understand what normal behavior is. So in essence, once we understand normal behavior, anything that deviates from it can be thought of as an anomaly, right? So in order to understand what normal behavior is, we're going to need to be able to collect store and process a very large amount of historical data. And so then comes clutter's platform and this reference architecture that needs to before you, so, uh, let's start on the left-hand side of this reference architecture with the collect phase. >>So fraud detection will always begin with data collection. Uh, we need to collect large amounts of information from systems that could be in the cloud. It could be in the data center or even on edge devices, and this data needs to be collected so we can create our normal behavior profiles. And these normal behavioral profiles would then in turn, be used to create our predictive models for fraudulent activity. Now, uh, uh, to the data collection side, one of the main challenges that many organizations face, uh, in this phase, uh, involves using a single technology that can handle, uh, data that's coming in all different types of formats and protocols and standards with different porosities and velocities. Um, let me give you an example. Uh, we could be collecting data from a database that gets updated daily, uh, and maybe that data is being collected in Agra format. >>At the same time, we can be collecting data from an edge device that's streaming in every second, and that data may be coming in Jason or a binary format, right? So this is a data collection challenge that can be solved with clutter data flow, which is a suite of technologies built on Apache NIFA and mini five, allowing us to ingest all of this data, do a drag and drop interface. So now we're collecting all of this data, that's required to map out normal behavior. The next thing that we need to do is enrich it, transform it and distribute it to, uh, you know, downstream systems for further process. Uh, so let's, let's walk through how that would work first. Let's taking Richmond for, uh, for enrichment, think of adding additional information to your incoming data, right? Let's take, uh, financial transactions, for example, uh, because Cindy mentioned it earlier, right? >>You can store known locations of an individual in an operational database, uh, with Cloudera that would be HBase. And as an individual makes a new transaction, their geo location that's in that transaction data, it can be enriched with previously known locations of that very same individual and all of that enriched data. It can be later used downstream for predictive analysis, predictable. So the data has been enrich. Uh, now it needs to be transformed. We want the data that's coming in, uh, you know, Avro and Jason and binary and whatever other format to be transformed into a single common format. So it can be used downstream for stream processing. Uh, again, this is going to be done through clutter and data flow, which is backed by NIFA, right? So the transformed semantic data is then going to be stimulated to Kafka and coffin. It's going to serve as that central repository of syndicated services or a buffer zone, right? >>So cough is, you know, pretty much provides you with, uh, extremely fast resilient and fault tolerance storage. And it's also going to give you the consumer APIs that you need that are going to enable a wide variety of applications to leverage that enriched and transformed data within your buffer zone. Uh, I'll add that, you know, 17, so you can store that data, uh, in a distributed file system, give you that historical context that you're going to need later on for machine learning, right? So the next step in the architecture is to leverage a cluttered SQL string builder, which enables us to write, uh, streaming sequel jobs on top of Apache Flink. So we can, uh, filter, analyze and, uh, understand the data that's in the Kafka buffer zone in real time. Uh I'll you know, I'll also add like, you know, if you have time series data, or if you need a lab type of cubing, you can leverage kudu, uh, while EDA or exploratory data analysis and visualization, uh, can all be enabled through clever visual patient technology. >>All right, so we've filtered, we've analyzed and we've explored our incoming data. We can now proceed to train our machine learning models, uh, which will detect anomalous behavior in our historically collected data set, uh, to do this, we can use a combination of supervised unsupervised, uh, even deep learning techniques with neural networks and these models can be tested on new incoming streaming data. And once we've gone ahead and obtain the accuracy of the performance, the scores that we want, we can then take these models and deploy them into production. And once the models are productionalized or operationalized, they can be leveraged within our streaming pipeline. So as new data is ingested in real-time knife, I can query these models to detect if the activity is anomalous or fraudulent. And if it is, they can alert downstream users and systems, right? So this in essence is how fraudulent activity detection works. >>Uh, and this entire pipeline is powered by clutter's technology, right? And so, uh, the IRS is one of, uh, clutters customers. That's leveraging our platform today and implementing, uh, a very similar architecture, uh, to detect fraud, waste, and abuse across a very large set of, uh, historical facts, data. Um, and one of the neat things with the IRS is that they've actually, uh, recently leveraged the partnership between Cloudera and Nvidia to accelerate their Spark-based analytics and their machine learning. Uh, and the results have been nothing short of amazing, right? And in fact, we have a quote here from Joe and salty who's, uh, you know, the technical branch chief for the research analytics and statistics division group within the IRS with zero changes to our fraud detection workflow, we're able to obtain eight times to performance simply by adding GPS to our mainstream big data servers. This improvement translates to half the cost of ownership for the same workloads, right? So embedding GPU's into the reference architecture I covered earlier has enabled the IRS to improve their time to insights by as much as eight X while simultaneously reducing their underlying infrastructure costs by half, uh, Cindy back to you >>Chef. Thank you. Um, and I hope that you found, uh, some of the, the analysis, the information that Sheva and I have provided, um, to give you some insights on how cloud era is actually helping, uh, with the fraud waste and abuse challenges within the, uh, the public sector, um, specifically looking at any and all types of data, how the clutter a platform is bringing together and analyzing information, whether it be you're structured you're semi-structured to unstructured data, both in a fast or in a real time perspective, looking at anomalies, being able to do some of those on detection methods, uh, looking at neural network analysis, time series information. So next steps we'd love to have an additional conversation with you. You can also find on some additional information around, uh, how quad areas working in the federal government by going to cloudera.com solutions slash public sector. And we welcome scheduling a meeting with you again, thank you for joining Chevy and I today, we greatly appreciate your time and look forward to future >>Conversation..

>>Hi, this is Cindy Mikey, vice president of industry solutions at caldera. Joining me today is chef is Molly, our solution engineer for the public sector. Today. We're going to talk about speed to insight. Why using machine learning in the public sector, specifically around fraud, waste and abuse. So topic for today, we'll discuss machine learning, why the public sector uses it to target fraud, waste, and abuse, the challenges. How do we enhance your data and analytical approaches the data landscape analytical methods and shad we'll go over reference architecture and a case study. So by definition, fraud, waste and abuse per the government accountability office is fraud. Isn't an attempt to obtain something about value through unwelcome misrepresentation waste is about squandering money or resources and abuse is about behaving improperly or unreasonably to actually obtain something of value for your personal benefit. So as we look at fraud, um, and across all industries, it's a top of mind, um, area within the public sector. >>Um, the types of fraud that we see is specifically around cyber crime, uh, looking at accounting fraud, whether it be from an individual perspective to also, uh, within organizations, looking at financial statement fraud, to also looking at bribery and corruption, as we look at fraud, it really hits us from all angles, whether it be from external perpetrators or internal perpetrators, and specifically from the research by PWC, the key focus area is we also see over half of fraud is actually through some form of internal or external, uh, perpetrators again, key topics. So as we also look at a report recently by the association of certified fraud examiners, um, within the public sector, the us government, um, in 2017, it was identified roughly $148 billion was attributable to fraud, waste and abuse. Specifically about 57 billion was focused on reported monetary losses and another 91 billion on areas where that opportunity or the monetary basis had not yet been measured. >>As we look at breaking those areas down again, we look at several different topics from permit out payment perspective. So breaking it down within the health system, over $65 billion within social services, over $51 billion to procurement fraud to also, um, uh, fraud, waste and abuse that's happening in the grants and the loan process to payroll fraud, and then other aspects, again, quite a few different topical areas. So as we look at those areas, what are the areas that we see additional type of focus, there's a broad stroke areas. What are the actual use cases that our agencies are using the data landscape? What data, what analytical methods can we use to actually help curtail and prevent some of the, uh, the fraud waste and abuse. So, as we look at some of the analytical processes and analytical use crate, uh, use cases in the public sector, whether it's from, uh, you know, the taxation areas to looking at, you know, social services, uh, to public safety, to also the, um, our, um, uh, additional agency methods, we're gonna use focused specifically on some of the use cases around, um, you know, fraud within the tax area. >>Uh, we'll briefly look at some of the aspects of, um, unemployment insurance fraud, uh, benefit fraud, as well as payment and integrity. So fraud has it it's, um, uh, underpinnings inquiry, like you different on government agencies and difficult, different analytical methods, and I usage of different data. So I think one of the key elements is, you know, you can look at your, your data landscape on specific data sources that you need, but it's really about bringing together different data sources across a different variety, a different velocity. So, uh, data has different dimensions. So we'll look at structured types of data of semi-structured data, behavioral data, as well as when we look at, um, you know, predictive models. We're typically looking at historical type information, but if we're actually trying to look at preventing fraud before it actually happens, or when a case may be in flight, which is specifically a use case that shad is going to talk about later is how do I look at more of that? >>Real-time that streaming information? How do I take advantage of data, whether it be, uh, you know, uh, financial transactions we're looking at, um, asset verification, we're looking at tax records, we're looking at corporate filings. Um, and we can also look at more, uh, advanced data sources where as we're looking at, um, investigation type information. So we're maybe going out and we're looking at, uh, deep learning type models around, uh, you know, semi or that, uh, behavioral, uh, that's unstructured data, whether it be camera analysis and so forth. So for quite a different variety of data and the, the breadth and the opportunity really comes about when you can integrate and look at data across all different data sources. So in a looking at a more extensive, uh, data landscape. So specifically I want to focus on some of the methods, some of the data sources and some of the analytical techniques that we're seeing, uh, being used, um, in the government agencies, as well as opportunities, uh, to look at new methods. >>So as we're looking at, you know, from a, um, an audit planning or looking at, uh, the opportunity for the likelihood of non-compliance, um, specifically we'll see data sources where we're maybe looking at a constituents profile, we might actually be investigating the forms that they've provided. We might be comparing that data, um, or leveraging internal data sources, possibly looking at net worth, comparing it against other financial data, and also comparison across other constituents groups. Some of the techniques that we use are some of the basic natural language processing, maybe we're going to do some text mining. We might be doing some probabilistic modeling, uh, where we're actually looking at, um, information within the agency to also comparing that against possibly tax forms. A lot of times it's information historically has been done on a batch perspective, both structured and semi-structured type information. And typically the data volumes can be low, but we're also seeing those data volumes on increase exponentially based upon the types of events that we're dealing with, the number of transactions. >>Um, so getting the throughput, um, and chef's going to specifically talk about that in a moment. The other aspect is, as we look at other areas of opportunity is when we're building upon, how do I actually do compliance? How do I actually look at conducting audits, uh, or potential fraud to also looking at areas of under-reported tax information? So there you might be pulling in some of our other types of data sources, whether it's being property records, it could be data that's being supplied by the actual constituents or by vendors to also pulling in social media information to geographical information, to leveraging photos on techniques that we're seeing used is possibly some sentiment analysis, link analysis. Um, how do we actually blend those data sources together from a natural language processing? But I think what's important here is also the method and the looking at the data velocity, whether it be batch, whether it be near real time, again, looking at all types of data, whether it's structured semi-structured or unstructured and the key and the value behind this is, um, how do we actually look at increasing the potential revenue or the, um, under reported revenue? >>Uh, how do we actually look at stopping fraudulent payments before they actually occur? Um, also looking at increasing the amount of, uh, the level of compliance, um, and also looking at the potential of prosecution of fraud cases. And additionally, other areas of opportunity could be looking at, um, economic planning. How do we actually perform some link analysis? How do we bring some more of those things that we saw in the data landscape on customer, or, you know, constituent interaction, bringing in social media, bringing in, uh, potentially police records, property records, um, other tax department, database information. Um, and then also looking at comparing one individual to other individuals, looking at people like a specific, like a constituent, are there areas where we're seeing, uh, >>Um, other >>Aspects of, of fraud potentially being occurring. Um, and also as we move forward, some of the more advanced techniques that we're seeing around deep learning is looking at computer vision, um, leveraging geospatial information, looking at social network entity analysis, uh, also looking at, uh, agent-based modeling techniques, where we're looking at simulation Monte Carlo type techniques that we typically see in the financial services industry, actually applying that to fraud, waste, and abuse within the, uh, the public sector. Um, and again, that really, uh, lends itself to a new opportunities. And on that, I'm going to turn it over to chef to talk about, uh, the reference architecture for, uh, doing these buckets. >>Thanks, Cindy. Um, so I'm gonna walk you through an example, reference architecture for fraud detection using, uh, Cloudera's underlying technology. Um, and you know, before I get into the technical details, uh, I want to talk about how this would be implemented at a much higher level. So with fraud detection, what we're trying to do is identify anomalies or novelists behavior within our datasets. Um, now in order to understand what aspects of our incoming data represents anomalous behavior, we first need to understand what normal behavior is. So in essence, once we understand normal behavior, anything that deviates from it can be thought of as an anomaly, right? So in order to understand what normal behavior is, we're going to need to be able to collect store and process a very large amount of historical data. And so incomes, clutters platform, and this reference architecture that needs to be for you. >>So, uh, let's start on the left-hand side of this reference architecture with the collect phase. So fraud detection will always begin with data collection. We need to collect large amounts of information from systems that could be in the cloud. It could be in the data center or even on edge devices, and this data needs to be collected so we can create our normal behavior profiles. And these normal behavioral profiles would then in turn, be used to create our predictive models for fraudulent activity. Now, uh, thinking, uh, to the data collection side, one of the main challenges that many organizations face, uh, in this phase, uh, involves using a single technology that can handle, uh, data that's coming in all different types of formats and protocols and standards with different velocities and velocities. Um, let me give you an example. Uh, we could be collecting data from a database that gets updated daily, uh, and maybe that data is being collected in Agra format. >>At the same time, we can be collecting data from an edge device that's streaming in every second, and that data may be coming in Jason or a binary format, right? So this is a data collection challenge that can be solved with cluttered data flow, which is a suite of technologies built on a patch NIFA in mini five, allowing us to ingest all of this data, do a drag and drop interface. So now we're collecting all of this data, that's required to map out normal behavior. The next thing that we need to do is enrich it, transform it and distribute it to, uh, you know, downstream systems for further process. Uh, so let's, let's walk through how that would work first. Let's taking Richmond for, uh, for enrichment, think of adding additional information to your incoming data, right? Let's take, uh, financial transactions, for example, uh, because Cindy mentioned it earlier, right? >>You can store known locations of an individual in an operational database, uh, with Cloudera that would be HBase. And as an individual makes a new transaction, their geolocation that's in that transaction data can be enriched with previously known locations of that very same individual. And all of that enriched data can be later used downstream for predictive analysis, predictable. So the data has been enrich. Uh, now it needs to be transformed. We want the data that's coming in, uh, you know, Avro and Jason and binary and whatever other format to be transformed into a single common format. So it can be used downstream for stream processing. Uh, again, this is going to be done through clutter and data flow, which is backed by NIFA, right? So the transformed semantic data is then going to be stricted to Kafka and coffin. It's going to serve as that central repository of syndicated services or a buffer zone, right? >>So coffee is going to pretty much provide you with, uh, extremely fast resilient and fault tolerance storage. And it's also gonna give you the consumer APIs that you need that are going to enable a wide variety of applications to leverage that enriched and transformed data within your buffer zone, uh, allowed that, you know, 17. So you can store that data in a distributed file system, give you that historical context that you're going to need later on for machine learning, right? So the next step in the architecture is to leverage a cluttered SQL stream builder, which enables us to write, uh, streaming SQL jobs on top of Apache Flink. So we can, uh, filter, analyze and, uh, understand the data that's in the Kafka buffer in real time. Uh I'll you know, I'll also add like, you know, if you have time series data, or if you need a lab type of cubing, you can leverage kudu, uh, while EDA or, you know, exploratory data analysis and visualization, uh, can all be enabled through clever visualization technology. >>All right, so we've filtered, we've analyzed and we've explored our incoming data. We can now proceed to train our machine learning models, uh, which will detect anomalous behavior in our historically collected data set, uh, to do this, we can use a combination of supervised unsupervised, uh, even deep learning techniques with neural networks. And these models can be tested on new incoming streaming data. And once we've gone ahead and obtain the accuracy of the performance, the scores that we want, we can then take these models and deploy them into production. And once the models are productionalized or operationalized, they can be leveraged within our streaming pipeline. So as new data is ingested in real-time knife, I can query these models to detect if the activity is anomalous or fraudulent. And if it is, they can alert downstream users and systems, right? So this in essence is how fraudulent activity detection works. >>Uh, and this entire pipeline is powered by clutters technology, right? And so, uh, the IRS is one of, uh, clutter's customers. That's leveraging our platform today and implementing, uh, a very similar architecture, uh, to detect fraud, waste, and abuse across a very large set of historical facts, data. Um, and one of the neat things with the IRS is that they've actually recently leveraged the partnership between Cloudera and Nvidia to accelerate their spark based analytics and their machine learning, uh, and the results have been nothing short of amazing, right? And in fact, we have a quote here from Joe and salty who's, uh, you know, the technical branch chief for the research analytics and statistics division group within the IRS with zero changes to our fraud detection workflow, we're able to obtain eight times to performance simply by adding GPS to our mainstream big data servers. This improvement translates to half the cost of ownership for the same workloads, right? So embedding GPU's into the reference architecture I covered earlier has enabled the IRS to improve their time to insights by as much as eight X while simultaneously reducing their underlying infrastructure costs by half, uh, Cindy back to you >>Chef. Thank you. Um, and I hope that you found, uh, some of the, the analysis, the information that Sheva and I have provided, um, to give you some insights on how cloud era is actually helping, uh, with the fraud waste and abuse challenges within the, uh, the public sector, um, specifically looking at any and all types of data, how the clutter platform is bringing together and analyzing information, whether it be you're structured you're semi-structured to unstructured data, both in a fast or in a real-time perspective, looking at anomalies, being able to do some of those on detection, uh, looking at neural network analysis, time series information. So next steps we'd love to have additional conversation with you. You can also find on some additional information around, I have caught areas working in the, the federal government by going to cloudera.com solutions slash public sector. And we welcome scheduling a meeting with you again, thank you for joining us Sheva and I today. We greatly appreciate your time and look forward to future progress. >>Good day, everyone. Thank you for joining me. I'm Sydney. Mike joined by Rick Taylor of Cloudera. Uh, we're here to talk about predictive maintenance for the public sector and how to increase assets, service, reliability on today's agenda. We'll talk specifically around how to optimize your equipment maintenance, how to reduce costs, asset failure with data and analytics. We'll go into a little more depth on, um, what type of data, the analytical methods that we're typically seeing used, um, the associated, uh, Brooke, we'll go over a case study as well as a reference architecture. So by basic definition, uh, predictive maintenance is about determining when an asset should be maintained and what specific maintenance activities need to be performed either based upon an assets of actual condition or state. It's also about predicting and preventing failures and performing maintenance on your time on your schedule to avoid costly unplanned downtime. >>McKinsey has looked at analyzing predictive maintenance costs across multiple industries and has identified that there's the opportunity to reduce overall predictive maintenance costs by roughly 50% with different types of analytical methods. So let's look at those three types of models. First, we've got our traditional type of method for maintenance, and that's really about our corrective maintenance, and that's when we're performing maintenance on an asset, um, after the equipment fails. But the challenges with that is we end up with unplanned. We end up with disruptions in our schedules, um, as well as reduced quality, um, around the performance of the asset. And then we started looking at preventive maintenance and preventative maintenance is really when we're performing maintenance on a set schedule. Um, the challenges with that is we're typically doing it regardless of the actual condition of the asset, um, which has resulted in unnecessary downtime and expense. Um, and specifically we're really now focused on pre uh, condition-based maintenance, which is looking at leveraging predictive maintenance techniques based upon actual conditions and real time events and processes. Um, within that we've seen organizations, um, and again, source from McKenzie have a 50% reduction in downtime, as well as an overall 40% reduction in maintenance costs. Again, this is really looking at things across multiple industries, but let's look at it in the context of the public sector and based upon some activity by the department of energy, um, several years ago, >>Um, they've really >>Looked at what does predictive maintenance mean to the public sector? What is the benefit, uh, looking at increasing return on investment of assets, reducing, uh, you know, reduction in downtime, um, as well as overall maintenance costs. So corrective or reactive based maintenance is really about performing once there's been a failure. Um, and then the movement towards, uh, preventative, which is based upon a set schedule or looking at predictive where we're monitoring real-time conditions. Um, and most importantly is now actually leveraging IOT and data and analytics to further reduce those overall downtimes. And there's a research report by the, uh, department of energy that goes into more specifics, um, on the opportunity within the public sector. So, Rick, let's talk a little bit about what are some of the challenges, uh, regarding data, uh, regarding predictive maintenance. >>Some of the challenges include having data silos, historically our government organizations and organizations in the commercial space as well, have multiple data silos. They've spun up over time. There are multiple business units and note, there's no single view of assets. And oftentimes there's redundant information stored in, in these silos of information. Uh, couple that with huge increases in data volume data growing exponentially, along with new types of data that we can ingest there's social media, there's semi and unstructured data sources and the real time data that we can now collect from the internet of things. And so the challenge is to collect all these assets together and begin to extract intelligence from them and insights and, and that in turn then fuels, uh, machine learning and, um, and, and what we call artificial intelligence, which enables predictive maintenance. Next slide. So >>Let's look specifically at, you know, the, the types of use cases and I'm going to Rick and I are going to focus on those use cases, where do we see predictive maintenance coming into the procurement facility, supply chain, operations and logistics. Um, we've got various level of maturity. So, you know, we're talking about predictive maintenance. We're also talking about, uh, using, uh, information, whether it be on a, um, a connected asset or a vehicle doing monitoring, uh, to also leveraging predictive maintenance on how do we bring about, uh, looking at data from connected warehouses facilities and buildings all bring on an opportunity to both increase the quality and effectiveness of the missions within the agencies to also looking at re uh, looking at cost efficiency, as well as looking at risk and safety and the types of data, um, you know, that Rick mentioned around, you know, the new types of information, some of those data elements that we typically have seen is looking at failure history. >>So when has that an asset or a machine or a component within a machine failed in the past? Uh, we've also looking at bringing together a maintenance history, looking at a specific machine. Are we getting error codes off of a machine or assets, uh, looking at when we've replaced certain components to looking at, um, how are we actually leveraging the assets? What were the operating conditions, uh, um, pulling off data from a sensor on that asset? Um, also looking at the, um, the features of an asset, whether it's, you know, engine size it's make and model, um, where's the asset located on to also looking at who's operated the asset, uh, you know, whether it be their certifications, what's their experience, um, how are they leveraging the assets and then also bringing in together, um, some of the, the pattern analysis that we've seen. So what are the operating limits? Um, are we getting service reliability? Are we getting a product recall information from the actual manufacturer? So, Rick, I know the data landscape has really changed. Let's, let's go over looking at some of those components. Sure. >>So this slide depicts sort of the, some of the inputs that inform a predictive maintenance program. So, as we've talked a little bit about the silos of information, the ERP system of record, perhaps the spares and the service history. So we want, what we want to do is combine that information with sensor data, whether it's a facility and equipment sensors, um, uh, or temperature and humidity, for example, all this stuff is then combined together, uh, and then use to develop machine learning models that better inform, uh, predictive maintenance, because we'll do need to keep, uh, to take into account the environmental factors that may cause additional wear and tear on the asset that we're monitoring. So here's some examples of private sector, uh, maintenance use cases that also have broad applicability across the government. For example, one of the busiest airports in Europe is running cloud era on Azure to capture secure and correlate sensor data collected from equipment within the airport, the people moving equipment more specifically, the escalators, the elevators, and the baggage carousels. >>The objective here is to prevent breakdowns and improve airport efficiency and passenger safety. Another example is a container shipping port. In this case, we use IOT data and machine learning, help customers recognize how their cargo handling equipment is performing in different weather conditions to understand how usage relates to failure rates and to detect anomalies and transport systems. These all improve for another example is Navistar Navistar, leading manufacturer of commercial trucks, buses, and military vehicles. Typically vehicle maintenance, as Cindy mentioned, is based on miles traveled or based on a schedule or a time since the last service. But these are only two of the thousands of data points that can signal the need for maintenance. And as it turns out, unscheduled maintenance and vehicle breakdowns account for a large share of the total cost for vehicle owner. So to help fleet owners move from a reactive approach to a more predictive model, Navistar built an IOT enabled remote diagnostics platform called on command. >>The platform brings in over 70 sensor data feeds for more than 375,000 connected vehicles. These include engine performance, trucks, speed, acceleration, cooling temperature, and break where this data is then correlated with other Navistar and third-party data sources, including weather geo location, vehicle usage, traffic warranty, and parts inventory information. So the platform then uses machine learning and advanced analytics to automatically detect problems early and predict maintenance requirements. So how does the fleet operator use this information? They can monitor truck health and performance from smartphones or tablets and prioritize needed repairs. Also, they can identify that the nearest service location that has the relevant parts, the train technicians and the available service space. So sort of wrapping up the, the benefits Navistar's helped fleet owners reduce maintenance by more than 30%. The same platform is also used to help school buses run safely. And on time, for example, one school district with 110 buses that travel over a million miles annually reduce the number of PTOs needed year over year, thanks to predictive insights delivered by this platform. >>So I'd like to take a moment and walk through the data. Life cycle is depicted in this diagram. So data ingest from the edge may include feeds from the factory floor or things like connected vehicles, whether they're trucks, aircraft, heavy equipment, cargo vessels, et cetera. Next, the data lands on a secure and governed data platform. Whereas combined with data from existing systems of record to provide additional insights, and this platform supports multiple analytic functions working together on the same data while maintaining strict security governance and control measures once processed the data is used to train machine learning models, which are then deployed into production, monitored, and retrained as needed to maintain accuracy. The process data is also typically placed in a data warehouse and use to support business intelligence, analytics, and dashboards. And in fact, this data lifecycle is representative of one of our government customers doing condition-based maintenance across a variety of aircraft. >>And the benefits they've discovered include less unscheduled maintenance and a reduction in mean man hours to repair increased maintenance efficiencies, improved aircraft availability, and the ability to avoid cascading component failures, which typically cost more in repair cost and downtime. Also, they're able to better forecast the requirements for replacement parts and consumables and last, and certainly very importantly, this leads to enhanced safety. This chart overlays the secure open source Cloudera platform used in support of the data life cycle. We've been discussing Cloudera data flow, the data ingest data movement and real time streaming data query capabilities. So data flow gives us the capability to bring data in from the asset of interest from the internet of things. While the data platform provides a secure governed data lake and visibility across the full machine learning life cycle eliminates silos and streamlines workflows across teams. The platform includes an integrated suite of secure analytic applications. And two that we're specifically calling out here are Cloudera machine learning, which supports the collaborative data science and machine learning environment, which facilitates machine learning and AI and the cloud era data warehouse, which supports the analytics and business intelligence, including those dashboards for leadership Cindy, over to you, Rick, >>Thank you. And I hope that, uh, Rick and I provided you some insights on how predictive maintenance condition-based maintenance is being used and can be used within your respective agency, bringing together, um, data sources that maybe you're having challenges with today. Uh, bringing that, uh, more real-time information in from a streaming perspective, blending that industrial IOT, as well as historical information together to help actually, uh, optimize maintenance and reduce costs within the, uh, each of your agencies, uh, to learn a little bit more about Cloudera, um, and our, what we're doing from a predictive maintenance please, uh, business@cloudera.com solutions slash public sector. And we look forward to scheduling a meeting with you, and on that, we appreciate your time today and thank you very much.

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

>>Hi, this is Cindy Mikey, vice president of industry solutions at Cloudera. Joining me today is chef is Molly, our solution engineer for the public sector. Today. We're going to talk about speed to insight. Why using machine learning in the public sector, specifically around fraud, waste and abuse. So topic for today, we'll discuss machine learning, why the public sector uses it to target fraud, waste, and abuse, the challenges. How do we enhance your data and analytical approaches the data landscape analytical methods and Shev we'll go over reference architecture and a case study. So by definition, fraud, waste and abuse per the government accountability office is fraud is an attempt to obtain something about a value through unwelcomed. Misrepresentation waste is about squandering money or resources and abuse is about behaving improperly or unreasonably to actually obtain something of value for your personal benefit. So as we look at fraud and across all industries, it's a top of mind, um, area within the public sector. >>Um, the types of fraud that we see is specifically around cyber crime, uh, looking at accounting fraud, whether it be from an individual perspective to also, uh, within organizations, looking at financial statement fraud, to also looking at bribery and corruption, as we look at fraud, it really hits us from all angles, whether it be from external perpetrators or internal perpetrators, and specifically from the research by PWC, the key focus area is we also see over half of fraud is actually through some form of internal or external are perpetrators again, key topics. So as we also look at a report recently by the association of certified fraud examiners, um, within the public sector, the us government, um, in 2017, it was identified roughly $148 billion was attributable to fraud, waste and abuse. Specifically of that 57 billion was focused on reported monetary losses and another 91 billion on areas where that opportunity or the monetary basis had not yet been measured. >>As we look at breaking those areas down again, we look at several different topics from an out payment perspective. So breaking it down within the health system, over $65 billion within social services, over $51 billion to procurement fraud to also, um, uh, fraud, waste and abuse that's happening in the grants and the loan process to payroll fraud, and then other aspects, again, quite a few different topical areas. So as we look at those areas, what are the areas that we see additional type of focus, there's broad stroke areas? What are the actual use cases that our agencies are using the data landscape? What data, what analytical methods can we use to actually help curtail and prevent some of the, uh, the fraud waste and abuse. So, as we look at some of the analytical processes and analytical use crate, uh, use cases in the public sector, whether it's from, uh, you know, the taxation areas to looking at social services, uh, to public safety, to also the, um, our, um, uh, additional agency methods, we're going to focus specifically on some of the use cases around, um, you know, fraud within the tax area. >>Uh, we'll briefly look at some of the aspects of unemployment insurance fraud, uh, benefit fraud, as well as payment and integrity. So fraud has its, um, uh, underpinnings in quite a few different on government agencies and difficult, different analytical methods and I usage of different data. So I think one of the key elements is, you know, you can look at your, your data landscape on specific data sources that you need, but it's really about bringing together different data sources across a different variety, a different velocity. So, uh, data has different dimensions. So we'll look at on structured types of data of semi-structured data, behavioral data, as well as when we look at, um, you know, predictive models, we're typically looking at historical type information, but if we're actually trying to lock at preventing fraud before it actually happens, or when a case may be in flight, which is specifically a use case, that shadow is going to talk about later it's how do I look at more of that? >>Real-time that streaming information? How do I take advantage of data, whether it be, uh, you know, uh, financial transactions we're looking at, um, asset verification, we're looking at tax records, we're looking at corporate filings. Um, and we can also look at more, uh, advanced data sources where as we're looking at, um, investigation type information. So we're maybe going out and we're looking at, uh, deep learning type models around, uh, you know, semi or that behavioral, uh, that's unstructured data, whether it be camera analysis and so forth. So quite a different variety of data and the, the breadth, um, and the opportunity really comes about when you can integrate and look at data across all different data sources. So in a sense, looking at a more extensive on data landscape. So specifically I want to focus on some of the methods, some of the data sources and some of the analytical techniques that we're seeing, uh, being used, um, in the government agencies, as well as opportunities, uh, to look at new methods. >>So as we're looking at, you know, from a, um, an audit planning or looking at, uh, the opportunity for the likelihood of non-compliance, um, specifically we'll see data sources where we're maybe looking at a constituents profile, we might actually be, um, investigating the forms that they've provided. We might be comparing that data, um, or leveraging internal data sources, possibly looking at net worth, comparing it against other financial data, and also comparison across other constituents groups. Some of the techniques that we use are some of the basic natural language processing, maybe we're going to do some text mining. We might be doing some probabilistic modeling, uh, where we're actually looking at, um, information within the agency to also comparing that against possibly tax forms. A lot of times it's information historically has been done on a batch perspective, both structured and semi-structured type information. And typically the data volumes can be low, but we're also seeing those data volumes increase exponentially based upon the types of events that we're dealing with, the number of transactions. >>Um, so getting the throughput, um, and chef's going to specifically talk about that in a moment. The other aspect is, as we look at other areas of opportunity is when we're building upon, how do I actually do compliance? How do I actually look at conducting audits, uh, or potential fraud to also looking at areas of under reported tax information? So there you might be pulling in some of our other types of data sources, whether it's being property records, it could be data that's being supplied by the actual constituents or by vendors to also pulling in social media information to geographical information, to leveraging photos on techniques that we're seeing used is possibly some sentiment analysis, link analysis. Um, how do we actually blend those data sources together from a natural language processing? But I think what's important here is also the method and the looking at the data velocity, whether it be batch, whether it be near real time, again, looking at all types of data, whether it's structured semi-structured or unstructured and the key and the value behind this is, um, how do we actually look at increasing the potential revenue or the, um, under reported revenue? >>Uh, how do we actually look at stopping fraudulent payments before they actually occur? Um, also looking at increasing the amount of, uh, the level of compliance, um, and also looking at the potential of prosecution of fraud cases. And additionally, other areas of opportunity could be looking at, um, economic planning. How do we actually perform some link analysis? How do we bring some more of those things that we saw in the data landscape on customer, or, you know, constituent interaction, bringing in social media, bringing in, uh, potentially police records, property records, um, other tax department, database information. Um, and then also looking at comparing one individual to other individuals, looking at people like a specific, like, uh, constituent, are there areas where we're seeing, uh, um, other aspects of, of fraud potentially being, uh, occurring. Um, and also as we move forward, some of the more advanced techniques that we're seeing around deep learning is looking at computer vision, um, leveraging geospatial information, looking at social network entity analysis, uh, also looking at, um, agent-based modeling techniques, where we're looking at simulation, Monte Carlo type techniques that we typically see in the financial services industry, actually applying that to fraud, waste, and abuse within the, the public sector. >>Um, and again, that really, uh, lends itself to a new opportunities. And on that, I'm going to turn it over to Chevy to talk about, uh, the reference architecture for doing these buckets. >>Sure. Yeah. Thanks, Cindy. Um, so I'm going to walk you through an example, reference architecture for fraud detection, using Cloudera as underlying technology. Um, and you know, before I get into the technical details, uh, I want to talk about how this would be implemented at a much higher level. So with fraud detection, what we're trying to do is identify anomalies or anomalous behavior within our datasets. Um, now in order to understand what aspects of our incoming data represents anomalous behavior, we first need to understand what normal behavior is. So in essence, once we understand normal behavior, anything that deviates from it can be thought of as an anomaly, right? So in order to understand what normal behavior is, we're going to need to be able to collect store and process a very large amount of historical data. And so incomes, clutters platform, and this reference architecture that needs to be for you. >>So, uh, let's start on the left-hand side of this reference architecture with the collect phase. So fraud detection will always begin with data collection. Uh, we need to collect large amounts of information from systems that could be in the cloud. It could be in the data center or even on edge devices, and this data needs to be collected so we can create from normal behavior profiles and these normal behavioral profiles would then in turn, be used to create our predictive models for fraudulent activity. Now, uh, uh, to the data collection side, one of the main challenges that many organizations face, uh, in this phase, uh, involves using a single technology that can handle, uh, data that's coming in all different types of formats and protocols and standards with different velocities and velocities. Um, let me give you an example. Uh, we could be collecting data from a database that gets updated daily, uh, and maybe that data is being collected in Agra format. >>At the same time, we can be collecting data from an edge device that's streaming in every second, and that data may be coming in Jace on or a binary format, right? So this is a data collection challenge that can be solved with cluttered data flow, which is a suite of technologies built on Apache NIFA and mini five, allowing us to ingest all of this data, do a drag and drop interface. So now we're collecting all of this data, that's required to map out normal behavior. The next thing that we need to do is enrich it, transform it and distribute it to know downstream systems for further process. Uh, so let's, let's walk through how that would work first. Let's taking Richmond for, uh, for enrichment, think of adding additional information to your incoming data, right? Let's take, uh, financial transactions, for example, uh, because Cindy mentioned it earlier, right? >>You can store known locations of an individual in an operational database, uh, with Cloudera that would be HBase. And as an individual makes a new transaction, their geo location that's in that transaction data, it can be enriched with previously known locations of that very same individual and all of that enriched data. It can be later used downstream for predictive analysis, predictable. So the data has been enrich. Uh, now it needs to be transformed. We want the data that's coming in, uh, you know, Avro and Jason and binary and whatever other format to be transformed into a single common format. So it can be used downstream for stream processing. Uh, again, this is going to be done through clutter and data flow, which is backed by NIFA, right? So the transformed semantic data is then going to be stimulated to Kafka and coffin is going to serve as that central repository of syndicated services or a buffer zone, right? >>So cough is, you know, pretty much provides you with, uh, extremely fast resilient and fault tolerance storage. And it's also going to give you the consumer API APIs that you need that are going to enable a wide variety of applications to leverage that enriched and transform data within your buffer zone. Uh, I'll add that, you know, 17, so you can store that data, uh, in a distributed file system, give you that historical context that you're going to need later on from machine learning, right? So the next step in the architecture is to leverage, uh, clutter SQL stream builder, which enables us to write, uh, streaming sequel jobs on top of Apache Flink. So we can, uh, filter, analyze and, uh, understand the data that's in the Kafka buffer zone in real-time. Uh, I'll, you know, I'll also add like, you know, if you have time series data, or if you need a lab type of cubing, you can leverage Q2, uh, while EDA or, you know, exploratory data analysis and visualization, uh, can all be enabled through clever visualization technology. >>All right, so we've filtered, we've analyzed, and we've our incoming data. We can now proceed to train our machine learning models, uh, which will detect anomalous behavior in our historically collected data set, uh, to do this, we can use a combination of supervised unsupervised, even deep learning techniques with neural networks. Uh, and these models can be tested on new incoming streaming data. And once we've gone ahead and obtain the accuracy of the performance, the X one, uh, scores that we want, we can then take these models and deploy them into production. And once the models are productionalized or operationalized, they can be leveraged within our streaming pipeline. So as new data is ingested in real time knife, I can query these models to detect if the activity is anomalous or fraudulent. And if it is, they can alert downstream users and systems, right? So this in essence is how fraudulent activity detection works. Uh, and this entire pipeline is powered by clutters technology. Uh, Cindy, next slide please. >>Right. And so, uh, the IRS is one of, uh, clutter as customers. That's leveraging our platform today and implementing a very similar architecture, uh, to detect fraud, waste, and abuse across a very large set of, uh, historical facts, data. Um, and one of the neat things with the IRS is that they've actually recently leveraged the partnership between Cloudera and Nvidia to accelerate their Spark-based analytics and their machine learning. Uh, and the results have been nothing short of amazing, right? And in fact, we have a quote here from Joe and salty who's, uh, you know, the technical branch chief for the research analytics and statistics division group within the IRS with zero changes to our fraud detection workflow, we're able to obtain eight times to performance simply by adding GPS to our mainstream big data servers. This improvement translates to half the cost of ownership for the same workloads, right? So embedding GPU's into the reference architecture I covered earlier has enabled the IRS to improve their time to insights by as much as eight X while simultaneously reducing their underlying infrastructure costs by half, uh, Cindy back to you >>Chef. Thank you. Um, and I hope that you found, uh, some of the, the analysis, the information that Sheva and I have provided, uh, to give you some insights on how cloud era is actually helping, uh, with the fraud waste and abuse challenges within the, uh, the public sector, um, specifically looking at any and all types of data, how the clutter a platform is bringing together and analyzing information, whether it be you're structured you're semi-structured to unstructured data, both in a fast or in a real-time perspective, looking at anomalies, being able to do some of those on detection methods, uh, looking at neural network analysis, time series information. So next steps we'd love to have an additional conversation with you. You can also find on some additional information around how called areas working in federal government, by going to cloudera.com solutions slash public sector. And we welcome scheduling a meeting with you again, thank you for joining us today. Uh, we greatly appreciate your time and look forward to future conversations. Thank you.

>>from around the globe. It's the Cube with digital coverage of VM World 2020 brought to you by VM Ware and its ecosystem partners. Hello and welcome back to the cubes. Virtual coverage of VM World 2020 Virtual I'm John for your host of the Cube, our 11th year covering V emeralds. Not in person. It's virtual. I'm with my coast, Dave. A lot, of course. Ah, guest has been on every year since the cubes existed. Sanjay Putin, who is now the chief operating officer for VM Ware Sanjay, Great to see you. It's our 11th years. Virtual. We're not in person. Usually high five are going around. But hey, virtual fist pump, >>virtual pissed bump to you, John and Dave, always a pleasure to talk to you. I give you more than a virtual pistol. Here's a virtual hug. >>Well, so >>great. Back at great. >>Great to have you on. First of all, a lot more people attending the emerald this year because it's virtual again, it doesn't have the face to face. It is a community and technical events, so people do value that face to face. Um, but it is virtually a ton of content, great guests. You guys have a great program here, Very customer centric. Kind of. The theme is, you know, unpredictable future eyes is really what it's all about. We've talked about covert you've been on before. What's going on in your perspective? What's the theme of your main talks? >>Ah, yeah. Thank you, John. It's always a pleasure to talk to you folks. We we felt as we thought, about how we could make this content dynamic. We always want to make it fresh. You know, a virtual show of this kind and program of this kind. We all are becoming experts at many Ted talks or ESPN. Whatever your favorite program is 60 minutes on becoming digital producers of content. So it has to be crisp, and everybody I think was doing this has found ways by which you reduce the content. You know, Pat and I would have normally given 90 minute keynotes on day one and then 90 minutes again on day two. So 180 minutes worth of content were reduced that now into something that is that entire 180 minutes in something that is but 60 minutes. You you get a chance to use as you've seen from the keynote an incredible, incredible, you know, packed array of both announcements from Pat myself. So we really thought about how we could organize this in a way where the content was clear, crisp and compelling. Thekla's piece of it needed also be concise, but then supplemented with hundreds of sessions that were as often as possible, made it a goal that if you're gonna do a break out session that has to be incorporate or lead with the customer, so you'll see not just that we have some incredible sea level speakers from customers that have featured in in our pattern, Mikey notes like John Donahoe, CEO of Nike or Lorry beer C I, a global sea of JPMorgan Chase partner Baba, who is CEO of Zuma Jensen Wang, who is CEO of video. Incredible people. Then we also had some luminaries. We're gonna be talking in our vision track people like in the annuity. I mean, one of the most powerful women the world many years ranked by Fortune magazine, chairman, CEO Pepsi or Bryan Stevenson, the person who start in just mercy. If you watch that movie, he's a really key fighter for social justice and criminal. You know, reform and jails and the incarceration systems. And Malala made an appearance. Do I asked her personally, I got to know her and her dad's and she spoke two years ago. I asked her toe making appearance with us. So it's a really, really exciting until we get to do some creative stuff in terms of digital content this year. >>So on the product side and the momentum side, you have great decisions you guys have made in the past. We covered that with Pat Gelsinger, but the business performance has been very strong with VM. Where, uh, props to you guys, Where does this all tie together for in your mind? Because you have the transformation going on in a highly accelerated rate. You know, cov were not in person, but Cove in 19 has proven, uh, customers that they have to move faster. It's a highly accelerated world, a lot. Lots changing. Multi cloud has been on the radar. You got security. All the things you guys are doing, you got the AI announcements that have been pumping. Thean video thing was pretty solid. That project Monterey. What does the customer walk away from this year and and with VM where? What is the main theme? What what's their call to action? What's what do they need to be doing? >>I think there's sort of three things we would encourage customers to really think about. Number one is, as they think about everything in infrastructure, serves APS as they think about their APS. We want them to really push the frontier of how they modernize their athletic applications. And we think that whole initiative off how you modernized applications driven by containers. You know, 20 years ago when I was a developer coming out of college C, C plus, plus Java and then emerge, these companies have worked on J two ee frameworks. Web Logic, Be Aware logic and IBM Web Street. It made the development off. Whatever is e commerce applications of portals? Whatever was in the late nineties, early two thousands much, much easier. That entire world has gotten even easier and much more Micro service based now with containers. We've been talking about kubernetes for a while, but now we've become the leading enterprise, contain a platform making some incredible investments, but we want to not just broaden this platform. We simplified. It is You've heard everything in the end. What works in threes, right? It's sort of like almost t shirt sizing small, medium, large. So we now have tens Ooh, in the standard. The advanced the enterprise editions with lots of packaging behind that. That makes it a very broad and deep platform. We also have a basic version of it. So in some sense it's sort of like an extra small. In addition to the small medium large so tends to and everything around at modernization, I think would be message number one number two alongside modernization. You're also thinking about migration of your workloads and the breadth and depth of, um, er Cloud Foundation now of being able to really solve, not just use cases, you are traditionally done, but also new ai use cases. Was the reason Jensen and us kind of partner that, and I mean what a great company and video has become. You know, the king maker of these ai driven applications? Why not run those AI applications on the best infrastructure on the planet? Remember, that's a coming together of both of our platforms to help customers. You know automotive banking fraud detection is a number of AI use cases that now get our best and we want it. And the same thing then applies to Project Monterey, which takes the B c f e m A Cloud Foundation proposition to smart Knicks on Dell, HP Lenovo are embracing the in video Intel's and Pen Sandoz in that smart make architectural, however, that so that entire world of multi cloud being operative Phobia Macleod Foundation on Prem and all of its extended use cases like AI or Smart Knicks or Edge, but then also into the AWS Azure, Google Multi Cloud world. We obviously had a preferred relationship with Amazon that's going incredibly well, but you also saw some announcements last week from, uh, Microsoft Azure about azure BMR solutions at their conference ignite. So we feel very good about the migration opportunity alongside of modernization on the third priority, gentlemen would be security. It's obviously a topic that I most recently taken uninterested in my day job is CEO of the company running the front office customer facing revenue functions by night job by Joe Coffin has been driving. The security strategy for the company has been incredibly enlightening to talk, to see SOS and drive this intrinsic security or zero trust from the network to end point and workload and cloud security. And we made some exciting announcements there around bringing together MAWR capabilities with NSX and Z scaler and a problem black and workload security. And of course, Lassiter wouldn't cover all of this. But I would say if I was a attendee of the conference those the three things I want them to take away what BMR is doing in the future of APS what you're doing, the future of a multi cloud world and how we're making security relevant for distributed workforce. >>I know David >>so much to talk about here, Sanjay. So, uh, talk about modern APS? That's one of the five franchise platforms VM Ware has a history of going from, you know, Challenger toe dominant player. You saw that with end user computing, and there's many, many other examples, so you are clearly one of the top, you know. Let's call it five or six platforms out there. We know what those are, uh, and but critical to that modern APS. Focus is developers, and I think it's fair to say that that's not your wheelhouse today, but you're making moves there. You agree that that is, that is a critical part of modern APS, and you update us on what you're doing for that community to really take a leadership position there. >>Yeah, no, I think it's a very good point, David. We way seek to constantly say humble and hungry. There's never any assumption from us that VM Ware is completely earned anyplace off rightful leadership until we get thousands, tens of thousands. You know, we have a half a million customers running on our virtualization sets of products that have made us successful for 20 years 70 million virtual machines. But we have toe earn that right and containers, and I think there will be probably 10 times as many containers is their virtual machines. So if it took us 20 years to not just become the leader in in virtual machines but have 70 million virtual machines, I don't think it will be 20 years before there's a billion containers and we seek to be the leader in that platform. Now, why, Why VM Where and why do you think we can win in their long term. What are we doing with developers Number one? We do think there is a container capability independent of virtual machine. And that's what you know, this entire world of what hefty on pivotal brought to us on. You know, many of the hundreds of customers that are using what was formerly pivotal and FDR now what's called Tan Xue have I mean the the case. Studies of what those customers are doing are absolutely incredible. When I listen to them, you take Dick's sporting goods. I mean, they are building curbside, pick up a lot of the world. Now the pandemic is doing e commerce and curbside pick up people are going to the store, That's all based on Tan Xue. We've had companies within this sort of world of pandemic working on contact, tracing app. Some of the diagnostic tools built without they were the lab services and on the 10 zoo platform banks. Large banks are increasingly standardizing on a lot of their consumer facing or wealth management type of applications, anything that they're building rapidly on this container platform. So it's incredible the use cases I'm hearing public sector. The U. S. Air Force was talking about how they've done this. Many of them are not public about how they're modernizing dams, and I tend to learn the best from these vertical use case studies. I mean, I spend a significant part of my life is you know, it s a P and increasingly I want to help the company become a lot more vertical. Use case in banking, public sector, telco manufacturing, CPG retail top four or five where we're seeing a lot of recurrence of these. The Tan Xue portfolio actually brings us closest to almost that s a P type of dialogue because we're having an apse dialogue in the in the speak of an industry as opposed to bits and bytes Notice I haven't talked at all about kubernetes or containers. I'm talking about the business problem being solved in a retailer or a bank or public sector or whatever have you now from a developer audience, which was the second part of your question? Dave, you know, we talked about this, I think a year or two ago. We have five million developers today that we've been able to, you know, as bringing these acquisitions earn some audience with about two or three million from from the spring community and two or three million from the economic community. So think of those five million people who don't know us because of two acquisitions we don't. Obviously spring was inside Vienna where went out of pivotal and then came back. So we really have spent a lot of time with that community. A few weeks ago, we had spring one. You guys are aware of that? That conference record number of attendees okay, Registered, I think of all 40 or 50,000, which is, you know, much bigger than the physical event. And then a substantial number of them attended live physical. So we saw a great momentum out of spring one, and we're really going to take care of that, That that community base of developers as they care about Java Manami also doing really, really well. But then I think the rial audience it now has to come from us becoming part of the conversation. That coupon at AWS re invent at ignite not just the world, I mean via world is not gonna be the only place where infrastructure and developers come to. We're gonna have to be at other events which are very prominent and then have a developer marketplace. So it's gonna be a multiyear effort. We're okay with that. To grow that group of about five million developers that we today Kate or two on then I think there will be three or four other companies that also play very prominently to developers AWS, Microsoft and Google. And if we're one among those three or four companies and remembers including that list, we feel very good about our ability to be in a place where this is a shared community, takes a village to approach and an appeal to those developers. I think there will be one of those four companies that's doing this for many years to >>come. Santa, I got to get your take on. I love your reference to the Web days and how the development environment change and how the simplicity came along very relevant to how we're seeing this digital transformation. But I want to get your thoughts on how you guys were doing pre and now during and Post Cove it. You already had a complicated thing coming on. You had multi cloud. You guys were expanding your into end you had acquisitions, you mentioned a few of them. And then cove it hit. Okay, so now you have Everything is changing you got. He's got more complex city. You have more solutions, and then the customer psychology is change. You got to spectrums of customers, people trying to save their business because it's changed, their customer behavior has changed. And you have other customers that are doubling down because they have a tailwind from Cove it, whether it's a modern app, you know, coming like Zoom and others are doing well because of the environment. So you got your customers air in this in this in this, in this storm, you know, they're trying to save down, modernized or or or go faster. How are you guys changing? Because it's impacted how you sell. People are selling differently, how you implement and how you support customers, because you already had kind of the whole multi cloud going on with the modern APS. I get that, but Cove, it has changed things. How are you guys adopting and changing to meet the customer needs who are just trying to save their business on re factor or double down and continue >>John. Great question. I think I also talked about some of this in one of your previous digital events that you and I talked about. I mean, you go back to the last week of February 1st week of March, actually back up, even in January, my last trip on a plane. Ah, major trip outside this country was the World Economic Forum in Davos. And, you know, there were thousands of us packed into the small digits in Switzerland. I was sitting having dinner with Andy Jassy in a restaurant one night that day. Little did we know. A month later, everything would change on DWhite. We began to do in late February. Early March was first. Take care of employees. You always wanna have the pulse, check employees and be in touch with them. Because the health and safety of employees is much more important than the profits of, um, where you know. So we took care of that. Make sure that folks were taking care of older parents were in good place. We fortunately not lost anyone to death. Covert. We had some covert cases, but they've recovered on. This is an incredible pandemic that connects all of us in the human fabric. It has no separation off skin color or ethnicity or gender, a little bit of difference in people who are older, who might be more affected or prone to it. But we just have to, and it's taught me to be a significantly more empathetic. I began to do certain things that I didn't do before, but I felt was the right thing to do. For example, I've begun to do 25 30 minute calls with every one of my key countries. You know, as I know you, I run customer operations, all of the go to market field teams reporting to me on. I felt it was important for me to be showing up, not just in the big company meetings. We do that and big town halls where you know, some fractions. 30,000 people of VM ware attend, but, you know, go on, do a town hall for everybody in a virtual zoom session in Japan. But in their time zone. So 10 o'clock my time in the night, uh, then do one in China and Australia kind of almost travel around the world virtually, and it's not long calls 25 30 minutes, where 1st 10 or 15 minutes I'm sharing with them what I'm seeing across other countries, the world encouraging them to focus on a few priorities, which I'll talk about in a second and then listening to them for 10 15 minutes and be, uh and then the call on time or maybe even a little earlier, because every one of us is going to resume button going from call to call the call. We're tired of T. There's also mental, you know, fatigue that we've gotta worry about. Mental well, being long term. So that's one that I personally began to change. I began to also get energy because in the past, you know, I would travel to Europe or Asia. You know, 40 50%. My life has travel. It takes a day out of your life on either end, your jet lag. And then even when you get to a Tokyo or Beijing or to Bangalore or the London, getting between sites of these customers is like a 45 minute, sometimes in our commute. Now I'm able to do many of these 25 30 minute call, so I set myself a goal to talk to 1000 chief security officers. I know a lot of CEOs and CFOs from my times at S A P and VM ware, but I didn't know many security officers who often either work for a CEO or report directly to the legal counsel on accountable to the audit committee of the board. And I got a list of these 1,002,000 people we called email them. Man, I gotta tell you, people willing to talk to me just coming, you know, into this I'm about 500 into that. And it was role modeling to my teams that the top of the company is willing to spend as much time as possible. And I have probably gotten a lot more productive in customer conversations now than ever before. And then the final piece of your question, which is what do we tell the customer in terms about portfolio? So these were just more the practices that I was able to adapt during this time that have given me energy on dial, kind of get scared of two things from the portfolio perspective. I think we began to don't notice two things. One is Theo entire move of migration and modernization around the cloud. I describe that as you know, for example, moving to Amazon is a migration opportunity to azure modernization. Is that whole Tan Xue Eminem? Migration of modernization is highly relevant right now. In fact, taking more speed data center spending might be on hold on freeze as people kind of holding till depend, emmick or the GDP recovers. But migration of modernization is accelerating, so we wanna accelerate that part of our portfolio. One of the products we have a cloud on Amazon or Cloud Health or Tan Xue and maybe the other offerings for the other public dog. The second part about portfolio that we're seeing acceleration around is distributed workforce security work from home work from anywhere. And that's that combination off workspace, one for both endpoint management, virtual desktops, common black envelope loud and the announcements we've now made with Z scaler for, uh, distributed work for security or what the analysts called secure access. So message. That's beautiful because everyone working from home, even if they come back to the office, needs a very different model of security and were now becoming a leader in that area. of security. So these two parts of the portfolio you take the five franchise pillars and put them into these two buckets. We began to see momentum. And the final thing, I would say, Guys, just on a soft note. You know, I've had to just think about ways in which I balance work and family. It's just really easy. You know what, 67 months into this pandemic to burn out? Ah, now I've encouraged my team. We've got to think about this as a marathon, not a sprint. Do the personal things that you wanna do that will make your life better through this pandemic. That in practice is that you keep after it. I'll give you one example. I began biking with my kids and during the summer months were able to bike later. Even now in the fall, we're able to do that often, and I hope that's a practice I'm able to do much more often, even after the pandemic. So develop some activities with your family or with the people that you love the most that are seeing you a lot more and hopefully enjoying that time with them that you will keep even after this pandemic ends. >>So, Sanjay, I love that you're spending all this time with CSOs. I mean, I have a Well, maybe not not 1000 but dozens. And they're such smart people. They're really, you know, in the thick of things you mentioned, you know, your partnership with the scale ahead. Scott Stricklin on who is the C. C so of Wyndham? He was talking about the security club. But since the pandemic, there's really three waves. There's the cloud security, the identity, access management and endpoint security. And one of the things that CSOs will tell you is the lack of talent is their biggest challenge. And they're drowning in all these products. And so how should we think about your approach to security and potentially simplifying their lives? >>Yeah. You know, Dave, we talked about this, I think last year, maybe the year before, and what we were trying to do in security was really simplified because the security industry is like 5000 vendors, and it's like, you know, going to a doctor and she tells you to stay healthy. You gotta have 5000 tablets. You just cannot eat that many tablets you take you days, weeks, maybe a month to eat that many tablets. So ah, grand simplification has to happen where that health becomes part of your diet. You eat your proteins and vegetables, you drink your water, do your exercise. And the analogy and security is we cannot deploy dozens of agents and hundreds of alerts and many, many consoles. Uh, infrastructure players like us that have control points. We have 70 million virtual machines. We have 75 million virtual switches. We have, you know, tens of million's off workspace, one of carbon black endpoints that we manage and secure its incumbent enough to take security and making a lot more part of the infrastructure. Reduce the need for dozens and dozens of point tools. And with that comes a grand simplification of both the labor involved in learning all these tools. Andi, eventually also the cost of ownership off those particular tool. So that's one other thing we're seeking to do is increasingly be apart off that education off security professionals were both investing in ah, lot of off, you know, kind of threat protection research on many of our folks you know who are in a threat. Behavioral analytics, you know, kind of thread research. And people have come out of deep hacking experience with the government and others give back to the community and teaching classes. Um, in universities, there are a couple of non profits that are really investing in security, transfer education off CSOs and their teams were contributing to that from the standpoint off the ways in which we can give back both in time talent and also a treasure. So I think is we think about this. You're going to see us making this a long term play. We have a billion dollar security business today. There's not many companies that have, you know, a billion dollar plus of security is probably just two or three, and some of them have hit a wall in terms of their progress sport. We want to be one of the leaders in cybersecurity, and we think we need to do this both in building great product satisfying customers. But then also investing in the learning, the training enable remember, one of the things of B M worlds bright is thes hands on labs and all the training enable that happened at this event. So we will use both our platform. We in world in a variety of about the virtual environments to ensure that we get the best education of security to professional. >>So >>that's gonna be exciting, Because if you look at some of the evaluations of some of the pure plays I mean, you're a cloud security business growing a triple digits and, you know, you see some of these guys with, you know, $30 billion valuations, But I wanted to ask you about the market, E v m. Where used to be so simple Right now, you guys have expanded your tam dramatically. How are you thinking about, you know, the market opportunity? You've got your five franchise platforms. I know you're very disciplined about identifying markets, and then, you know, saying, Okay, now we're gonna go compete. But how do you look at the market and the market data? Give us the update there. >>Yeah, I think. Dave, listen, you know, I like davinci statement. You know, simplicity is the greatest form of sophistication, and I think you've touched on something that which is cos we get bigger. You know, I've had the great privilege of working for two great companies. s a P and B M where the bulk of my last 15 plus years And if something I've learned, you know, it's very easy. Both companies was to throw these TLS three letter acronyms, okay? And I use an acronym and describing the three letter acronyms like er or s ex. I mean, they're all acronyms and a new employee who comes to this company. You know, Carol Property, for example. We just hired her from Google. Is our CMO her first comments like, My goodness, there is a lot of off acronyms here. I've gotta you need a glossary? I had the same reaction when I joined B. M or seven years ago and had the same reaction when I joined the S A. P 15 years ago. Now, of course, two or three years into it, you learn everything and it becomes part of your speed. We have toe constantly. It's like an accordion like you expanded by making it mawr of luminous and deep. But as you do that it gets complex, you then have to simplify it. And that's the job of all of us leaders and I this year, just exemplifying that I don't have it perfect. One of the gifts I do have this communication being able to simplify things. I recorded a five minute video off our five franchise pill. It's just so that the casual person didn't know VM where it could understand on. Then, when I'm on your shore and when on with Jim Cramer and CNBC, I try to simplify, simplify, simplify, simplify because the more you can talk and analogies and pictures, the more the casual user. I mean, of course, and some other audiences. I'm talking to investors. Get it on. Then, Of course, as you go deeper, it should be like progressive layers or feeling of an onion. You can get deeper. It's not like the entire discussion with Sanjay Putin on my team is like, you know, empty suit. It's a superficial discussion. We could go deeper, but you don't have to begin the discussion in the bowels off that, and that's really what we don't do. And then the other part of your question was, how do we think about new markets? You know, we always start with Listen, you sort of core in contact our borough come sort of Jeffrey Moore, Andi in the Jeffrey more context. You think about things that you do really well and then ask yourself outside of that what the Jason sees that are closest to you, that your customers are asking you to advance into on that, either organically to partnerships or through acquisitions. I think John and I talked about in the previous dialogue about the framework of build partner and by, and we always think about it in that order. Where do we advance and any of the moves we've made six years ago, seven years ago and I joined the I felt VM are needed to make a move into mobile to really cement opposition in end user computing. And it took me some time to convince my peers and then the board that we should by Air One, which at that time was the biggest acquisition we've ever done. Okay. Similarly, I'm sure prior to me about Joe Tucci, Pat Nelson. We're thinking about nice here, and I'm moving to networking. Those were too big, inorganic moves. +78 years of Raghu was very involved in that. The decisions we moved to the make the move in the public cloud myself. Rgu pack very involved in the decision. Their toe partner with Amazon, the change and divest be cloud air and then invested in organic effort around what's become the Claudia. That's an organic effort that was an acquisition fast forward to last year. It took me a while to really Are you internally convinced people and then make the move off the second biggest acquisition we made in carbon black and endpoint security cement the security story that we're talking about? Rgu did a similar piece of good work around ad monetization to justify that pivotal needed to come back in. So but you could see all these pieces being adjacent to the core, right? And then you ask yourself, Is that context meaning we could leave it to a partner like you don't see us get into the hardware game we're partnering with. Obviously, the players like Dell and HP, Lenovo and the smart Knick players like Intel in video. In Pensando, you see that as part of the Project Monterey announcement. But the adjacent seas, for example, last year into app modernization up the stack and into security, which I'd say Maura's adjacent horizontal to us. We're now made a lot more logical. And as we then convince ourselves that we could do it, convince our board, make the move, We then have to go and tell our customers. Right? And this entire effort of talking to CSOs What am I doing is doing the same thing that I did to my board last year, simplified to 15 minutes and get thousands of them to understand it. Received feedback, improve it, invest further. And actually, some of the moves were now making this year around our partnership in distributed Workforce Security and Cloud Security and Z scaler. What we're announcing an XDR and Security Analytics. All of the big announcements of security of this conference came from what we heard last year between the last 12 months of my last year. Well, you know, keynote around security, and now, and I predict next year it'll be even further. That's how you advance the puck every year. >>Sanjay, I want to get your thoughts. So now we have a couple minutes left. But we did pull the audience and the community to get some questions for you, since it's virtually wanted to get some representation there. So I got three questions for you. First question, what comes after Cloud and number two is VM Ware security company. And three. What company had you wish you had acquired? >>Oh, my goodness. Okay, the third one eyes gonna be the turkey is one, I think. Listen, because I'm gonna give you my personal opinion, and some of it was probably predates me, so I could probably safely So do that. And maybe put the blame on Joe Tucci or somebody else is no longer here. But let me kind of give you the first two. What comes after cloud? I think clouds gonna be with us for a long time. First off this multi cloud world, you just look at the moment, um, that AWS and azure and the other clouds all have. It's incredible on I think this that multi cloud from phenomenon. But if there's an adapt ation of it, it's gonna be three forms of cloud. People are really only focus today in private public cloud. You have to remember the edge and Telco Cloud and this pendulum off the right balance of workloads between the data center called it a private cloud. The public cloud on one end and the telco edge on the other end. I think we're in a really good position for workloads to really swing between all three of those locations. Three other part that I think comes as a sequel to Cloud is cloud native. All of the capabilities a serverless functions but also containers that you know. Obviously the one could think of that a sister topics to cloud but the entire world of containers. The other seat, uh, then cloud a cloud native will also be topics, but these were all fairly connected. That's how I'd answer the first question. A security company? Absolutely. We you know, we aspire to be one of the leading companies in cyber security. I don't think they will be only one. We have to show this by the wealth on breath of our customers. The revenue momentum we have Gartner ranking us or the analysts ranking us in top rights of magic quadrants being viewed as an innovator simplifying the stack. But listen, we weren't even on the radar. We weren't speaking of the security conferences years ago. Now we are. We have a billion dollar security business, 20,000 plus customers, really strong presences and network endpoint and workload and Cloud Security. The three Coppola's a lot more coming in Security analytics, Cloud Security distributed workforce Security. So we're here to stay. And if anything, BMR persist through this, we're planning for multi your five or 10 year timeframe. And in that course I mean, the competition is smaller. Companies that don't have the breadth and depth of the n words are Andy muscle and are going market. We just have to keep building great products and serving customer on the third man. There's so many. But I mean, I think Listen, when I was looking back, I always wondered this is before I joined so I could say the summit speculatively on. Don't you know, make this This is BMR. Sorry. This is Sanjay one's opinion. Not VM. I gotta make very, very clear. Well, listen, I would have if I was at BMO in 2012 or 2013. I would love to about service now then service. It was a great company. I don't even know maybe the company's talk, but then talk about a very successful company at that time now. Maybe their priorities were different. I wasn't at the company at the time, but I can speculate if that had happened, that would have been an interesting Now I think that was during the time of Paul Maritz here and and so on. So for them, maybe there were other priorities the company need to get done. But at that time, of course, today s so it's not as big of a even slightly bigger market cap than us. So that's not happening. But that's a great example of a good company that I think would have at that time fit very well with VM Ware. And then there's probably we don't look back and regret we move forward. I mean, I think about the acquisitions we have made the big ones. Okay, Nice era air watch pop in black. Pivotal. The big moves we've made in terms of partnership. Amazon. What? We're announcing this This, you know, this week within video and Z scaler. So you never look back and regret. You always look for >>follow up on that To follow up on that from a developer, entrepreneurial or partner Perspective. Can you share where the white spaces for people to innovate around vm Where where where can people partner and play. Whether I'm an entrepreneur in a garage or venture back, funded or say a partner pivoting and or resetting with Govind, where's the white spaces with them? >>I think that, you know, there's gonna be a number off places where the Tan Xue platform develops, as it kind of makes it relevant to developers. I mean, there's, I think the first way we think about this is to make ourselves relevant toe all of that ecosystem around the C I. C. D type apply platform. They're really good partners of ours. They're like, get lab, You know, all of the ways in which open source communities, you know will play alongside that Hash E Corp. Jay frog there number of these companies that are partnering with us and we're excited about all of their relevancy to tend to, and it's our job to go and make that marketplace better and better. You're going to hear more about that coming up from us on. Then there's the set of data companies, you know, con fluent. You know, of course, you've seen a big I p o of a snowflake. All of those data companies, we'll need a very natural synergy. If you think about the old days of middleware, middleware is always sort of separate from the database. I think that's starting to kind of coalesce. And Data and analytics placed on top of the modern day middleware, which is containers I think it's gonna be now does VM or play physically is a data company. We don't know today we're gonna partner very heavily. But picking the right set of partners been fluent is a good example of one on. There's many of the next generation database companies that you're going to see us partner with that will become part of that marketplace influence. And I think, as you see us certainly produce out the VM Ware marketplace for developers. I think this is gonna be a game changing opportunity for us to really take those five million developers and work with the leading companies. You know, I use the example of get Lab is an example get help there. Others that appeal to developers tie them into our developer framework. The one thing you learn about developers, you can't have a mindset. With that, you all come to just us. It's a very mingled village off multiple ecosystems and Venn diagrams that are coalescing. If you try to take over the world, the developer community just basically shuns you. You have to have a very vibrant way in which you are mingling, which is why I described. It's like, Listen, we want our developers to come to our conferences and reinvent and ignite and get the best experience of all those provide tools that coincide with everybody. You have to take a holistic view of this on if you do that over many years, just like the security topic. This is a multi year pursuit for us to be relevant. Developers. We feel good about the future being bright. >>David got five minutes e. >>I thought you were gonna say Zoom, Sanjay, that was That was my wildcard. >>Well, listen, you know, I think it was more recently and very fast catapult Thio success, and I don't know that that's clearly in the complete, you know, sweet spot of the anywhere. I mean, you know, unified collaboration would have probably put us in much more competition with teams and, well, back someone you always have to think about what's in the in the bailiwick of what's closest to us, but zooms a great partner. Uh, I mean, obviously you love to acquire anybody that's hot, but Eric's doing really well. I mean, Erica, I'm sure he had many people try to come to buy him. I'm just so proud of him as a friend of all that he was named to Time magazine Top 100. But what he's done is phenomenon. I think he could build a company that's just his important, his Facebook. So, you know, I encourage him. Don't sell, keep building the company and you'll build a company that's going to be, you know, the enterprise version of Facebook. And I think that's a tremendous opportunity to do this better than anybody else is doing. And you know, I'm as an immigrant. He's, you know, China. Born now American, I'm Indian born, American, assim immigrants. We both have a similar story. I learned a lot from him. I learned a lot from him, from on speed on speed and how to move fast, he tells me he learns a thing to do for me on scale. We teach each other. It's a beautiful friendship. >>We'll make sure you put in a good word for the Kiwi. One more zoom integration >>for a final word or the zoom that is the future Facebook of the enterprise. Whatever, Sanjay, Thank >>you for connecting with us. Virtually. It is a digital foundation. It is an unpredictable world. Um, it's gonna change. It could be software to find the operating models or changing you guys. We're changing how you serve customers with new chief up commercial customer officer you have in place, which is a new hire. Congratulations. And you guys were flexing with the market and you got a tailwind. So congratulations, >>John and Dave. Always a pleasure. We couldn't do this without the partnership. Also with you. Congratulations of Successful Cube. And in its new digital format, Thank you for being with us With VM world here on. Do you know all that you're doing to get the story out? The guests that you have on the show, they look forward, including the nonviable people like, Hey, can I get on the Cuban like, Absolutely. Because they look at your platform is away. I'm telling this story. Thanks for all you're doing. I wish you health and safety. >>I'm gonna bring more community. And Dave is, you know, and Sanjay, and it's easier without the travel. Get more interviews, tell more stories and tell the most important stories. And thank you for telling your story and VM World story here of the emerald 2020. Sanjay Poon in the chief operating officer here on the Cube I'm John for a day Volonte. Thanks for watching Cube Virtual. Thanks for watching.

>>Locke from Las Vegas. It's the cube covering AWS reinvent 2019 brought to you by Amazon web services and along with its ecosystem partners. >>Well, welcome back to the cube live here in Las Vegas for AWS reinvent 2019 it's the cubes seventh year, eighth year of reinvent. We've been there almost from the beginning. I'm John ferry with Dave Volante extracting the signal from the noise. The two great guests here chew senior leaders, VMware, auntie that were Sanjay Poonan, COO of VMware cube alumni, Mike Clayville, vice president of worldwide commercial sales and business development for AWS guys. You're the senior leaders out on the field making things happen. I got to say the AWS VMware relationship, which we covered a couple of years ago when Gelsinger and Jassy were doing the little love Fest, they're in San Francisco. A lot of people were skeptical. This show here, we're hearing things like, that's my Superbowl moment. Things are working great. Cloud is scaling, so congratulations and welcome to the cube. Good to see you. Thank you. Yeah. All right, so let's get to the relationship. >>Talk about you guys' relationship and how it's morphed into such a success. We're hearing great feedback. The numbers on the research at day's been digging into shows. Customer spend is up. Is that the wave of cloud? Is that the integration? Sanjay, what's going on? Give us, gives you up to, Oh, I think we're delighted. You know Mike obviously and I have been friends for years. He's had some connections with VMware in his past that certainly helped in setting up this partnerships. So we're grateful to Mike and Andy and the team for that and it's, you know, two and a half to three years now since we announced it. Tremendous amount of customer interest. Listen, you know we said at the beginning of this, when you take sort of the King of the public cloud and the King, the private cloud together and don't force customers to say these have to be separate doors, you're going to do them both together. >>Customers liked that message and what we've been really doing over the course of the last 1218 months is perfecting use cases for this platform. I think to us, the key word is migrations. Cloud migrations. When people are moving their workloads off an app off VMware vSphere or cloud foundation, we want this to be the best place for it to land. We are McCloud in AWS for migration opportunity and anything short of that refactoring app would we, you know, not something that would be a good use of people's time and money because they should be then modernizing with all the wonderful services that Amazon's built, one they've migrated. So we've really perfected our message in the course of the last six, 12 months to two M's, migrate and modernize, migrate and modernize. So we could migrate you into this Avenue and then modernize with a set of container and other services. So that messes working. We put on stage at VMworld and there are many of them here, two big Amazon customers, VMware cloud, Amazon, Freddie Mac and IHS market. And they were telling our tens of thousands customers at those shows and similarly many of them here, that that's the best option to be able to do things. >>Yeah, it's great. It's great by the way, because it's a frictionless migration, right? So you've got a platform that same code base working on pram, same cloud based and cloud creating a seamless integration between the two platforms. We're finding customers very in enthralled by that. I say they say they love that because it's less disruptive for them. Yeah. But at the same time they say, but eventually I want to change my operating model to really drive profits to my bottom line. So could you talk a little bit about what that journey looks like? And I'm really interested in longer term Sanjay, how you play in that. I look Mike, sorry. So the first thing I'd say that one of the real reasons I love it is because they've got a big investment today and that investment is in skills. That investment is in operational processes. That investment is in licensing and all of that comes along with them on their journey. Whether it's a migration journey or a migration to modernize journey, it's working. So when you're talking about the bottom line, like you are, this is a great play for that bottom line. >>Yeah, I know. And I'd say, listen, from our perspective, we want to take a Freddie Mac. When they spoke at VMworld, they have I think 800 applications, 50 of whom are SAS and the other 750 are custom built, deep Lee virtualized and they're going to move all of them over the course of the next 12 months. I fell off my chair when I, when I heard how fast they planned to do it. IHS market has very a variety of very spread accounts and Amazon. Now we're going to help them move a lot of their workloads there. Once they're there, we want them to then use the tools that Amazon's bill. I'll give you two examples, maybe some of their backup tools into S3 CloudWatch some of their analytical monitoring types of tools. So there's going to be, and then of course AI database services and the best place once you've moved it there is to make sure that that migrated stack is stable. >>You have the best of the VMware tools, V center, V motion, all you know and the best of the Amazon tools. So when people start to see this, I think the myth of Sarah's saying refactor and replatform that application, which is in essence like taking a home. Okay. And having to destroy the home and completely rebuild it. Right? And that's just a meal, a waste of money and time when you could migrate it and then modernize it. So we just need to get that story well understood. Get our, you know, I, I mean Amazon probably has a few million customers. We have a half a million customers. If all of those customers can hear the story and beginning their journey with us, I think we will tip this in a way. Starting >>to tip, to get the, back to the point of your question as well. Look, our two companies have been engineering these solutions together deeply. So this just isn't a paper arbiters. Yeah. This is an engineering partnership that started years ago and what that means is as customers migrate to a beam ware on AWS, now they have access to over 175 AWS services, can it, right. Significant native access to a broad range of services that they can continue to innovate, identify new business models and it all seamlessly integrates back into a single platform. >>Yeah. One of the things I always said when I talked to Andy and Amazon folks is that the competitive advantage of the businesses scale and also the new announcements that come in. So one of the things we heard yesterday from a customer, uh, one of your joint customers was, you know, I asked him about outpost, which you guys now are going to ship in 2020, which was announced you already got native outpost, general availability. He goes, look it, we'd love VMware. We could probably look at VMware and kind of poke at things, maybe do things differently. But frankly I don't want to have to rearchitect my stack because I want the data science stuff from studio a Sage maker studio because the demand for the business results is coming in from the new capabilities. So this seems to be the trend where the migration is just lift and shifts, keep the operational flow going, foundation and the business value over the top is whatever you guys can bring in from an NSX and then the apps. Is this something that you're hearing more of? Because this points to all of us, the discussion around the platform is irrelevant because the business value is coming in from the data. Yeah. What, how do you guys react to that? Is that something that you're hearing? >>Well, the first thing I would say is the, you know, the pundents will tell you that by 2020 90% of customers will be in a hybrid model. So you know, the migration is, you talk about is in play and, and arguably 2020 will be the year of the most migrations in history if those pendants are correct. Right. And so that gets a lot of customers in the mode of being able to leverage a BMC and then be able to take advantage of all the, you know, the extensive amount of data services we have available. But if you ask me, where do you know, what are the, what are the big reasons driving the migration? It's traditional economics, right? It's, I'm, I don't need to be a capital expense heavy organization anymore. Why do I have to build data centers? Why do I have to extend data centers? Why am I building, why am I buying air conditioning that's not differentiating my business? Right? All of those things are creating drivers for this migration. Now as you begin the migration, that's when you begin to see, wow, imagine the simplicity of the same code base, same operational processes. I don't have to retrain a bunch of people just moving it right onto the cloud and now let me really dig in to the new services available from AWS. Look for those new business. >>I suppose having that focus of differentiation and VMware and saying, let's keep it and expand it to the edge and do things like that. And yeah, absolutely. I mean, listen, I think they had Cerner yesterday on stage and I think it was interesting to hear the CEO, they're talking about three verbs, migrated, modernize, and innovate. I mean that's the thing thing. So I think when you, when you start to see that becoming a very active dialogue, not just from CEOs but from CEOs and boards that are saying, listen, you know, part of the reason we want to move to the cloud is an increase our bruiser agility. It's not just a cost reduction. Yeah. I mean I don't need to have 80 data centers have, I could have half a zero a one or two so that I get, but beyond cost, if we can kind of get agility going faster. >>And for many of these folks, I think when I sit down in their customer advisory councils, when I, when we are advising them, they're all trying to serve their customers better, get data to become sort of the oil of their ability to make decisions better and AI and analytics sort of help in that area. And then of course, getting more efficient in lowering costs and risks. And I think when you're doing it, the scale that both of us have experienced doing, we understand data centers really well. We've software defined them for 20 years. These guys understand cloud probably better than anybody else. When we bring that sort of scale together and as Mike pointed out, a deeply engineered solution, we have a, we have a significant R and D investment in this and we're doing that jointly with them. When I often sit down in our joint QPRs, I joke about it with Mike and Andy and others, I sometimes forget, is that a VMware person speaking or an Amazon person because there's finishing each other's sentences. So there's a lot of that joint trust they've built and we just now have to keep showing that this is a solution that's innovating every three months because you're running on monthly and quarterly cycles and get large customers. I mean to us now, it's less so about the noise of getting everybody on stage. It's much more of a showing customer attraction. >>So I wonder if we could talk about one of the other big problems in the industry. Mikey talked about deep engineering and you guys are, you know, you're never done right, but you've solved that problem or solving that problem of making it easy for customers, VM-ware customers to run in the cloud. There's another big problem it could be concerned about customers is security and there seems to be somewhat of a dissonance. And I wonder if you could share with us maybe some of the thinking around this. So Steven Schmidt for instance, who is Amazon CSO says, Hey, the state of security in the cloud is, is great. And it is, it's, you know, you don't have a lot of technical debt coming in to the game. Pat Gelsinger is saying, Hey, you know, security, the state of security in my world is broken. So what's the conversation with you guys in terms of addressing that big concern on the minds of CEOs? And >>yeah, I'll start and they might feel free to add them. Thomas, I mean we've talked to Steve, we're like Steve, he's a very, he's a, he's an innovator and a thought leader in security. We're coming at it from a place that's complimentary to some of the point of views of, of Amazon. Um, and I shared this at our last VM world discussion. When we look at the, the, the control points of security where traditional security spent network, endpoint, identity, cloud and analytics, those are five, four control points where a lot of security is spent inside the $50 billion security market. We picked two that we're going to do really well. The network and endpoint NSX has been doing really well there. Now granted a bunch of that is on prem. It's replacing or complimenting Cisco, Palo Alto, checkpoint fire, a flash for a railroad bed, F five NetScaler spent. >>And now that business 13,000 customers in has become a 40, 50% of its security use cases. The network we just acquired, carbon black aide runs on the Amazon platform. It runs, uh, a next gen endpoint security. That's, you know, an evolution from the old world of Symantec, McAfee, you know, and there were only two vendors doing this at scale carbon black and CrowdStrike, we built, we built, we bought the better one. So when you put those together and collect a significant amount of telemetry from that, we think we could do something highly differentiated and security. So VMware, his goal and to the extent that Amazon or others are doing things in security that compliment our view of it, we'll build on it, right? Whether it's identity and access tools, whether it's load balancers, whether it's security, event management capabilities. >>Well we're in, we're integrating those two into the security in the cloud, which makes it seamless security, which is critical. >>Goal would be, listen, when we go and when we talked about this is what we're doing, security, we go to Mike and Andy and Steve and said, listen, this is our ambitions and security. We don't view Amazon as a competitor. And that's why he's very much complimented. They'll will be on the fringes. They have a load balancer. We now have a cloud. But that's okay. But that's the bigger part. If they were going off for endpoint security, as we be competitive there, if they were going up in network secure, but they're not. So I think when we share our intents, which we do very openly, we have open kimono sessions. He, this is where we are, this is where we're going. That's what we, and we go deep in that >>trust luck, but this is a historic partnership. This is not a partnership that I've seen anywhere in the industry in my 35 years. This is something that's at the next level and I think you'll look back, history will look back at this partnership and and recognize that its impact on cloud is going to be substantial. >>You hope you guys deserve a lot of credit and again, the critics were critical of the announcement. We were obviously favor, we saw the vision, but I think what surprised me most is that the spend numbers reflect is you guys clarified your cloud play with this move. The customers saluted it 100% they were on board and the numbers are showing it, but as Andy and you guys go to the next level, I got to get your thoughts on this trend of transformation. We have two means. We started in the cube this week. One was if you take the T out of cloud native, it's cloud naive. And the other one is what I said in my post about being reborn in the cloud. So you've got born in the cloud, startups and growth and enterprises were becoming reborn, okay? In the cloud, which means they're transforming. >>So as that trillions of dollars that are coming into the migration, you look at the numbers, there's only 20% of it spend in cloud. Roughly give or take. You're talking about trillions of dollars of new money. You guys are the commercial guys. Hey look, it's still day one for the cloud. It's still day one. I agree. You have a lot of people who might not make the migration, might die of starvation. Okay? As they move to the new model, you guys are out there have to take and you're going to go get that cash. What are you guys seeing? Cause this is a big trillions and trillions of dollars are on the table. You started Mike off. Well look. So, >>you know, uh, Sanjay talked about you see these customers and how enthusiastic they are about the opportunity here, right? And, and Freddie Mac's a great example of 100 million lines of code, and I've got to get out of three data centers in 24 months. Bam, they're out in 10, 10 months, 10 months, right? Um, 100 million lines of code over hundreds of, of applications done in 10 months. Now imagine the rest that the company can do now that they got that behind him, right? And that's what we're seeing is this partnership enables our customers to get a bunch done very economically, much faster, and now they can get onto the other things that they need to do. >>Yeah. And I'd build on that. Listen, you know, we track about a trillion dollars of it spend. And if you add up all of the cloud spend today, it's probably a, I mean, Amazon and Salesforce are probably the biggest in infrastructure and apps. It's probably 150 billion in total cloud spend, maybe 200 billion. So that's 15 to 20% of the total it spend, which is massive, but it's still as, as my points, that's early innings is that 20% it's probably going to become 50% at some point soon, right? If you look at the pace at which the cloud companies are growing, so the key question is, is going to go as 150 billion, the 1 trillion total number is going to grow, but probably a little bit faster and GDP most every 5% max, who's going to go grab that 150 Boone as it goes from 150 billion to 500 billion and the on premise spend slows down. >>Right? Um, I think that, you know, I think Amazon is very well positioned and from our perspective at VMware, we have a, you know, 10 $11 billion business. We're trying to tilt this increasingly more cloud. We announced our earnings call, 13% of it now is hybrid cloud and SAS, that 13% should become 2025 50. They are a pure cloud company. 100% of their businesses is cloud. We're in that transition. But why are we in that transition? Because we see that 150 billion of it spend likely becoming 500 billion. And if we don't get it somebody else's well hybrids, are we a tailwind for you guys? Because outpost is actually a statement that says hybrid at the edge. Now the data centers an edge, you've got edge. What is an edge? So cloud operations is now the standard and we, I mean, we actually coined the term hybrid six years ago and everyone could five, six years ago and everyone really laughed at us and now I think it's being validated. So it's, it's very gratifying now that Amazon has a similar vision to hybrid as us. Uh, we believe both the VMware cloud on Amazon outpost and BMR cloud running on outpost, we're very committed to that joint vision. >>Yeah. You're talking about the spending data and you know, VMware yet another revenue hit. I was pretty consistent in that and that standpoint. But if you look at the spending data, virtually every sort of traditional company with very few exceptions is you're seeing a share shift to the cloud. VMware is an exception. It didn't use to be that way a couple of years ago, but you're embracing the cloud really changed and became, you may cloud a tailwind right now to headwind. >>I think this partnership helped in that area and you put it right, right. Everything in life is either an opportunity or a threat. I think, and I've talked about it in your show before, cloud and containers were a significant threat. When I joined Amazon, sorry, when I was partners with Amazon, I joined VMware six years ago. I asked Pat and I said, listen, I think the threats to VMR, Amazon and Docker in 2013 now Docker is a whole different story. Kubernetes took their head out. Uh, but to our credit we joined credit, we partnered here and I think from our perspective, see, we at VMware aren't able to do a complete pivot like Adobe did to say burn the boats on, on premise and completely shift everything. SAS. Why? Because customers still want NSX on prem. Customers still want our HCI product on prem. People are still buying vSphere on prem. >>So we've got this more delicate balance of starting to shift and on-prem business. The aircraft carrier, you know at the time, 5,000,000,005, six years ago now, 11 billion to something that's a blend of on prem and cloud. While the cloud part grows a lot faster, that 13% of revenue we announced our earnings call is growing 40% yeah. So we can keep that growing foster and foster while the on-prem business is not decaying, it's still growing but not growing at the same pace, plus changing its end, make that transition a few years from now to being a lot more of a cloud company. >>The other thing you're seeing in the spending data, I wonder if you could comment is, you know, digital initiatives really started in earnest, let's say 2016 and people were doing a lot of experimentation. They were throwing everything for the new stuff against the wall. And what we're seeing now is they're narrowing the new and they were keeping the legacy stuff around because they were sort of running in parallel to hedge their bets. What we're seeing now is less experimentation in the new, and they're starting to unplug some of the older stuff. What they're not unplugging is cloud and they're hanging on to VMware and we're seeing, you know, spending levels revert to pre 2018 levels. I wonder what you guys are seeing at the macro. >>Well, the first thing I would say is I see experimentation continuing to accelerate, right? All of the new functionality that we bring out every day. Everybody's excuse, you're the sandbox for us. It's very invigorating because we love people to experiment and, uh, and we, you know, a lot of those experiments turned into amazing new startups as an example. And, or a bunch of those experiments turned into major new project projects in our, in our big, uh, enterprises. So we're continuing to see a real push towards experimentation and driving agility into the business. I don't know. Yeah, >>no, I, well, Mike, I'd agree. I mean, listen, we in some senses, uh, we have a very good strong, you know, on-premise business and when we see a really innovative company that's in the order of 33 35%, that's already 35 three 35 billion growing in the forties 30 to 40% I mean that's incredible. When we see companies like Salesforce and Adobe that are giant SAS companies approaching, you know, 10 1115 20 billion growing 2020 5% I think that infrastructure is a service and SAS business for us are trailblazers of where this cloud is headed now, these, the biggest companies in infrastructure and in SAS and we follow that. Now we have to then navigate to say, listen, the growth rates and the spending is going to be reflected by cloud spend that's heavily spending on there. And the way in which the on premise world is what spending, we have a bunch of hardware companies, we work very closely. >>We're watching how that spending is, is playing OD, whether it's Cisco, whether it's HP, whether it's Lenovo, Dell and others. And then of course we've got VM. We're sitting right in between and I think what we're trying to manage as you got a whole world of on-prem driven primarily by hardware companies. You've got a bunch of these cloud new companies, Amazon, Salesforce, Adobe, and we have a right in the middle saying, okay, listen, we want to be dragged by both while many of our customers still want some on prem. It's a delicate balance, but there's no, um, I mean we are very clear within VMware. We want to be led by a cloud first policy wherever we can. I'll give you an example. Workspace one, manage these devices. We want a company five years ago named AirWatch, why did we buy them versus somebody else? >>It was cloud. It was cloud-first that business now and use a computing has stilted itself to be primarily cloud-based, very subscription-based. It was on premise VDI at the time Mike was at the company six, seven years ago. It's become now completely cloud based on the back of a workspace one, you know, kind of thing. So that's how we're thinking about it. The new acquisitions we've done, whether it's carbon black, whether it's Velo club, it's CloudHealth. They're all cloud-based. Well, you guys made a good bet on cloud operations. That's the real shift. The cloud operation model is right in your wheelhouse. You guys have operators, VMware, you guys have cloud operations everywhere now edge with outpost. Congratulations. I want to say, Sanjay, it's been a great journey with you. You've been with the cube all 10 years. All seven years. We've been actually the 10 year anniversary. >>We've been documenting the history. Wow. The historic moments like you guys together writing AWS, really appreciate it. and of course that was good to see more action coming. Cloud 2.0 next gen. Cloud competition controversies. I mean what? You can't ask for a better movie here. John. Dave, I'm going to, we're going to bring mugs next time. Okay. We're going to have mugs.. I'm John for Dave a lot. They saw Jay Poon and Mike Clayville, the leaders, senior leaders of AWS and VMware out with their customers here on the queue. This is our AWS Intel set in the middle of the floor here at reinvent 2019 our seventh year. Thanks for watching more coverage day two of the queue. We'll be right back.

>> from our studios in the heart of Silicon Valley, Palo Alto, California It is a cute conversation >> lover on Welcome to this cube conversation here in Palo Alto, California. The Cube Studio. I'm John for the co host of the Cube Weird Sylvan shop. Who's the head of solution Architecture and open systems securing Esti win of right of other cloud to point out like capabilities. Very successful. 20 plus years. Operation Civil was the one of the first folks are coming over to the US to expand their operation from Europe into New York. Now here in Silicon Valley. Welcome to the Cube conversation. Thank you. So instituting trivia. You were part of the original team of three to move to the U. S. From Switzerland. You guys had phenomenal success in Europe. You've come to the U. S. Having phenomenal success in the US Now you moving west out here to California on that team, you're opening things up at the market. >> It's been a chance, Mikey. Things can presented themselves step by step, and I jumped on the trains and it's been a good right. >> Awesome. You guys have had great success. We interviewed your CEO a variety of your top people. One of the things that's interesting story is that you guys have been around for a long time. Been there, done that, riding this next next wave of digital transformation. What we call a cloud two point. Oh, but really is about enterprise. Full cloud scale, securing it. You have a lot of organic growth with customers, great word of mouth. So that's not a lot of big marketing budgets, riel. Real success there. You guys now are in the US doing the same thing here. What's been the key to success for open systems wide such good customers? Why the success formula is it you guys are on the right wave. What is it? The product? All the above. What's the What's the secret formula? >> So multiple things I say. And we started as a privately owned company like broad banks to, um, to the Internet email into one back in the nineties. And, um, yeah, we started to grow organically, as he said were by mouth, and Indiana is we put heavy focus on operations, so we wanted to make our customers happy and successful, and, um, yeah, that's how we got there like it was slow organic growth. But we always kind of kept the core and we tried to be unconventional, tried to do things differently than others do. And that's what brought us to where we are today and now capabilities Being here in the Valley, um, opens up a lot of more doors. >> It's got a nice office and we would see I saw the video so props for that. Congratulations. But the real to me, the meat on the bone and story is, is that and I've been really ranting on this whole SD win is changing. SD Win used to be around for a long, long time. It's been known industries known market. It's got a total addressable market, but really, what has really talks to is the the cloud. The cloud is a wide area network. Why do we never used to be locked down? He had the old way permitted based security. Now everything is a wide area. That multi cloud in hybrid club. This is essentially networking. It's a networking paradigms. It's not lately rocket science technically, but the cloud 2.0 shift is about, you know, data. It's about applications, different architectures you have everything kind of coming together, which creates a security problem, an opportunity for new people to come in. That's what you guys? One of them. This is the big wave. What? It explain the new s t win with, you know, the old way and the new way. What is the what? What should people know about the new S D win marketplace? >> Yeah. So let me start. Where do Owen has come from and how digital transformation has impacted that. So typically corporate wider networks were centered around the Clear Data Center where all applications were hosted, storage and everything and all traffic was back holding to the data center. Typically, one single provider that Broady, Mpls links on dhe. It was all good. You had a central location where you could manage it. You had always ability security stack was there. So you had full control. Now new requirements from natural transformation broad as users are on the road, they're on their phones ipads on the in, restaurants in ah, hotels, Starbucks. Wherever we have applications moved to the cloud. So their access directly You wanna have or be as close as possible Unify Communications. I OT It's all things deposed. Different requirements now in the network and the traditional architecture didn't were wasn't able to respond to that. It's just that the links they were filled up. You couldn't invest enough thio blow up your Nampula slings to handle the band with You lost visibility because users were under road. You lost control, and that's where new architectures had to be found. That's where Ston step them and say, Hey, look now we're not centered around the headquarter anymore were sent around where the applications are, your scent around, where the data is, and we need to find means to connected a data as quickly as possible. And so you can use the Internet. Internet has become a commodity. It's become more performance more stable, so we can leverage that we can route traffic according to our policies. We can include the cloud, and that's where Ston actually benefits from the clown. As much as the club benefits from SD went because they go hand in hand and that's also what we really drive to say, Hey, look, now the cloud can be directly brought into your network, no matter where, where data and where applications. >> Yeah, and this is the thing. You know, Although you've been critical of S t when I still see it as the path of the future because it's networking. And the end of the day networking is networking. You moving packets from point A to point B and you're moving somebody story you moving from point A to store the point C. It's hard. And you brought this up about Mpls. It's hard to, like rip and replace You can't just do a wholesale change on the network has the networks are running businesses. So this is where the trick is, in my opinion. So I want to get your thoughts on how companies were dealing with this because, I mean, if you want to move, change something in the network, it takes a huge task. How did you guys discover this new opportunity? How did you implement it? What was the and how should customers think about not disrupting their operations at the same time bringing in the new capabilities of this SD win two point? Oh, >> yeah, that's it's a perfect sweet spot, because in the end is, um, nobody starts at a green field. If you could start with a green field. It's easy. You just take on the new technology and you're happy. But, um, customers that we look up large enterprises, they have a brownfield. They haven't existing that work. They have business critical applications running 24 7 And if you look at what options large enterprises have to implement and manage a nasty when is typically three approaches, they either do it themselves, meaning they need a major investment in on boarding people having the talent validating technology and making the project work already. Look at a conventional managers provider. In the end, that is just the same as doing yourself. It's just done by somebody else, and you have the the challenge that those providers typically, um, have a lot of portfolio that they manage. And they do not have enough expertise in Nasty Wen. And so you just end up with the same problems and a lot of service, Janey. So even then you do not get the expertise that you need. >> I think what's interesting about what you guys have done? I want to get your reaction to this is that the manage service piece of it makes it easier to get in without a lot of tinkering with existing infrastructure. Exact. And that's been one of that tail winds for you guys and success wise. Talk about that dynamic of why they managed service is a good approach because you put your toe in the water, so to speak, and you can kind of get involved, get as much as you need to go and go further. Talk about that dynamic and why that's important. >> Yeah, technology Jane is very quickly. So you need people that are able to manage that and open systems as a pure play provider. We build purposely build our platform for us, he went. So we integrated feature sets. We we know how to monitor it, how to configure it, how to manage it. Lifecycle management, technology, risk technology management. All this is purposely purposely built into it, so we strongly believe that to be successful, you need people that are experts in what they do to help you so that you and your I t people can focus in enabling the business. And that's kind of our sweet spot where we don't say we have experts. Our experts operating the network for you as a customer and therefore our experts are your experts. And that's kind of where we believe that a manage service on the right way ends up in Yeah, the best customer. >> And I think the human capital pieces interesting people can level up faster when you when you're not just deploying here. Here's the software load. It is the collaborations important. They're good. They're all right. While you're on this topic, I want to get your thoughts. Since you're an expert, we've been really evaluating this cloud 2.0, for lack of a better description. Cloud 2.0, implying that the cloud 1.0 was Amazon miss on The success of Amazon Web service is really shows Dev Ops in Action Agility The Lean startup Although all that stuff we read reading about for the past 10 plus years great compute storage at scale, amazing use of data like you, said Greenfield. Why not use the cloud? Great. Now all the talk about hybrid cloud even going back to 2013 We were of'em world at that time start 10th year their hybrid cloud was just introduced. Now it's mainstream now multi cloud is around the corner. This teases out cloud 2.0, Enterprise Cloud Enterprise Scale Enterprise Security Cloud Security monitoring 2.0, is observe ability. Got Cooper All these new things air coming on. This is the new clout to point out what is your definition of cloud two point? Oh, if you had to describe it to a customer or a friend, >> it is really ah, some of hybrid cloud or multi cloud, as you want to name it, because in the end, probably nobody can say I just select one cloud, and that's going to make me successful because in the end, cloud is it's not everywhere, as we kind of used to believe in the beginning, but in the end, it's somebody else's computer in a somebody else's data center. So the cloud is you selectively pick the location where you want to for your cloud instances and asked if Cloud Service providers opened up more locations that are closer to your users in the or data you actually can leverage more possibilities. So what we see emerging now is that while for a long time everything has moved to the cloud, the cloud is again coming back to us at the sietch. So a lot of compute stuff is done close to where data is generated. Um, it's where the users are. I mean, Data's generated with with us. Yeah, phones and touch and feel and vision and everything. So we can leverage these technologies to really compute closer to the data. But everything controlled out of central cloud instances. >> So this brings up a good point. You essentially kind of agreeing with cloud one detto being moved to the cloud. But now you mentioned something that's really interesting around cloud to point out, which is moving having cloud, certainly public clouds. Great. But now moving technology to the edge edge being a data center edge being, you know, industrial I ot other things wind farms, whatever users running around remotely you mentioned. So the edges now becomes a critical component of this cloud. Two point. Oh, okay. So I gotta ask the question, How does the networking and what's the complexity? And I'm just imagining massive complexity from this. What are some of the complexities and challenges and opportunities will arise out of this new dynamic of club two point. Oh, >> So the traditional approaches does just don't work anymore. So we need new ways to not only on the networking side, but obviously also the security side. So we need to make sure that not on Lee the network follows in the footsteps of the business of what it needs. But actually, the network can drive business innovation and that the network is ready to handle those new leaps and technologies. And that's what we see is kind of being able to tightly integrate whatever pops up, being able to quickly connect to a sass provider, quickly integrate a new cloud location into your network and have the strong security posture there. Directly integrated is what you need because if you always have to think about weight, if I add this, it's gonna break something else, and I have to. To change is here. Then you lose all the speed that your business needs. >> I mean, the ripple effect of it's like throwing a stone in the lake and seeing the ripple effect with cloud to point. You mentioned a few of them. Network and Security won't get to that in a second, but doesn't change every aspect of computing categories. Backup monitoring. I mean all the sectors that were traditional siloed on premise that moves with the cloud are now being disrupted again for the third time. Yeah, you agree with that? >> It's true. And I mean your club 0.1 point. Oh, you say a lot of things will be seen his lift and shift and that still works like there is a lot of work loads where it's not worth it to re factor everything. But then, for your core applications, the business where the business makes money, you want a leverage, the latest instead of technologies to really drive, drive your business there. >> I got to get your take on this because you're the head of architecture solutions at Open Systems. Um, is a marketing tagline that I saw that you guys promote, which I live. I want to get your thoughts on. It says, Stop treating your network like a network little marketing. I love it, but it's kind of like stop trying your network like a network implying that the networks changing may be inadequate. Antiquated needs to modernize. I'm kind of feeling the vibe there on that. What do you mean by that? Slow Stop treating your network like a network. What's what's the purpose >> behind that? But yeah, in the end, it to be a little flaw provoking. But I mean, even est even in its pure forms, where you have a softer controller that steers your traffic along different path. Already. For me, as an engineer, I'm gonna lose my mind because I want to know where routing is going. I want deterministic. Lee defined my policy, so I always have things under control. But now it's a softer agent that takes care. Furred takes care of it for me so that already I lose control in favor off. Yeah, more capabilities. And I think that's cloud just kind of accelerate. >> So you guys really put security kind of in between the network and application? Is that the way you're thinking about it? It used to be Network was at the bottom. You built the application, had security. Now you're thinking differently. Explain that the the architectural thinking around this because this is a modern approach you guys were taking, and I want to get this on the record. Applications have serving users and machines network delivers packets, and then you're saying security's wrapping up between them explain. >> So when we go back again to the traditional model Central Data Center, you had a security stack full rack of appliances that the care of your security was easy to manage. Now, if you wanna go ask you when connect every brand side to the Internet, you cannot replicate such an infrastructure to every branch. Location just doesn't skill. So what do you do? Why do you say I cannot benefit of this where I use new methods? And that's where we say we integrate security directly into our networking stack. So to be able to not rely on the service training but have everything compiled into one platform and be able to leverage that data is passing through our network. You've eyes. But then why not apply the same security functions that we used to do in our headquarter directly at the edge and therefore every branch benefits of the same security posture that I typically were traditionally only had in my data center? >> You guys so but also weighing as a strategic infrastructure critical infrastructure opponent. I would agree with that. That's obvious, but as we get into hybrid cloud and multi cloud infrastructures of service support. Seamless integration is critical. This has become a topic, will certainly be talking about for the rest of the year Of'em world and reinvented other conferences like Marcel that night as well. This is the big challenge for customers. Do I invest in Azure A. W as Google in another cloud? Who knows how many clouds coming be another cloud potentially around the corner? I don't want to fork my development team. I want to do one of the great different code bases. This has become kind of like the challenge. How do you see this playing out? Because again, the applications want to run on the best cloud possible. I'm a big believer in that. I think that the cloud should dictate the AP should dictate which cloud runs. That's why I'm a believer in the single cloud for the workload, not a single cloud for all workloads. So your thoughts, >> I think, from an application point of view. As you say, the application guys have to determine more cloud is best for them, I think from a networking point of view, as a network architect, we need to we can't work against this but enable them and be able to find ways that the network can seamlessly connect to whatever cloud the business wants to use. And there's plenty of opportunity to do that today and to integrate or partner with other providers that actually have partnered with dozens of cloud providers. And as we now can architect, we have solutions to directly bring you as a customer within milliseconds, to each cloud, premise is a huge advantage. It takes a few clicks in a portal. You have a new clouds instance up and running, and now you're connected. And the good thing is, we have different ways to do that. Either. We spin up our virtual instance virtual esti one appliance in cloud environments so we can leverage the Internet to go. They're still all secured, all encrypted, ordering me again. Use different cloud connect interconnections to access the clouds. Depending on the business requirements, >> you guys have been very successful. A lot of comfort from financial service is the U. N. With NGOs, variety of industries. So I want to get your thoughts on this. I've been we've been covering the Department of Defense is joining and Chet I joint and the presentation of defense initiative where the debate was soul single purpose Cloud. Now the reality is and we've covered this on silicon angle that D O D is going multi cloud as an organization because they're gonna have Microsoft Cloud for collaboration and other contracts. They're gonna win $8,000,000,000. So that a Friday cloud opportunities, but for the particular workload for the military, they have unique requirements. Their workload has chosen one cloud. That was the controversy. Want to get your thoughts on this? Should the workloads dictate the cloud? And is that okay? And certainly multi cloud is preferred Narada instances. But is it okay to have a single cloud for a workload? >> Yeah, again, from if the business is okay with that, that's fine from our side of you. We see a lot of lot of business that have global presence, so they're spread across the globe. So for them, it's beneficial to done distribute workloads again across different regions, and it could still be the same provider, but across different regions. And then already, question is How do you now we're out traffic between those workloads? Do we? Do you love right? Your esteem and infrastructure or do you actually use, for example, the backbone that the cloud provider provides you in case of Microsoft? They guarantee you the traffic between regions stay in their backbone. So gifts, asshole, new opportunities to leverage large providers. Backbone. >> And this is an interesting nuance point because multi cloud doesn't have to be. That's workload. Spreading the workload across three different clouds. It's this workload works on saving Amazon. This workload works on Azure. This workload works on another cloud that's multi cloud from a reality standpoint today, so that implies that most every country will be multi cloud for sure. But workloads might have a single cloud for either the routing and the transit security with the data stored. And that's okay, too. >> Yeah, yeah, and keep in mind, Cloud is not only infrastructure or platform is the service. It's also software as a service. So as soon as we have sales forests, work day office 3 65 dropbox or box, then we are multiplied. >> So basically the clouds are fighting it out by the applications that they support and the infrastructure behind. Exactly. All right, well, what's next for you? You're on the road. You guys doing a lot of customer activity. What's the coolest thing that you're seeing in the customer base from open system standpoint that you like to share with the audience? >> Um, so again, it's just cool to see that customers realized that there's plenty of opportunities. And just to see how we go through that evolution with our customers, were they initially or little concerned? But then eventually we see that actually, the network change drives new business project and customers air happy that they launched or collaborate with us. That's what that's what makes me happy and makes me and a continuing down that path >> and securing it is a key. Yeah, he wins in this market Having security? >> Absolutely. Yeah, Sylvia saying mind and not wake up at 2 a.m. Full sweat, because here >> we'll manage. Service is a preferred for my people like to consume and procure product in So congratulations and congressional on your Silicon Valley office looking for chatting more. I'm John for here in the keep studios for cute conversation. Thanks for watching

>> live from New York. It's the Q covering AWS Global Summit 2019 brought to you by Amazon Web service is >> Welcome back. I'm stupid like co host Cory Quinn. And we're here at the end of a summit in New York City, where I'm really happy to welcome to the program first time guests, but somebody that has a nap, it's on my phone. So, Andy thing, who's the CEO of Door Dash, gave a great presentation this morning. Thanks so much for joining us. >> Absolutely happy to be here, guys. >> All right, so, you know, before we dig into the kind of your Amazon stack, bring us back. You talked about 2013. You know, your mission of the company will help empower local businesses. I think most people know, you know, door dash delivery from my local businesses. Whether that is a small place or, you know, chipotle o r like there. And I love little anecdote that you said the founders actually did the first few 100 deliveries, but it gives a little bit of the breath of the scope of the business now. >> Absolutely. I mean, when we started in 2013 you know, we started out of Ah dorm on Stanford campus and, like you said, we're doing the first couple 100 deliveries ourselves. But, you know, fast forwarding to today you were obviously at a much, much different level of scale. And I think one thing that I mentioned about it, Mikey No, a cz just We've been trying to keep up pays and more than doubling as a business every year. And it's a really fascinating industry that were in in the on demand delivery space in particular, I mean, Dara, the CEO of uber himself, said in May, which is a month and 1/2 ago. He said that you know, the food delivery industry may become bigger than a ride hailing industry someday. >> So just just one quick question on kind of food delivery. Because when I think back when I was in college, I worked at a food truck. It was really well known on campus, and there are people that 20 years later they're like stew. I remember you serving me these sandwiches, and I loved it in the community and we gather and we talk today on campus. Nobody goes to that place anymore, you know, maybe I know my delivery person more than I know the person that's making it. So I'm just curious about the relationship between local businesses and the people. How that dynamic changing the gig? Economy? I mean, yeah, you guys were right in the thick of it. No, it's a >> great question. I think. You know, for merchants, a lot of the things that we talk to them about it is you're actually getting access to customers who wouldn't even walk by your store in the first place. And I think that's something that they find to be very captivating. And it shows in the store sales data when they start partnering with the door dash. But we've also tried to building our products to really get customers to interact with the physical neighborhoods. Aaron the most concrete example of that as we launch a product called In Store in Star Pickup Chronic, where you can order online, skip the line and pick up the order yourself in the store, and I think a way we can build the AB experience around that, you know, you're gonna actually start building kind of a geospatial. Browse experience for customers with the door dash app, which means that they can get a little bit more familiarity with what's around them, as opposed to just kind of looking at it on their phones themselves. All right, >> so the logistics of this, you know, are not trivial. You talked about 325% order growth. You know, your database is billions of rose. You know, just the massive scale massive transaction. Therefore, you know, as a you know, your nap on. You know the scale you're at technology is pretty critical to your environment. So burgers inside that a little? >> Yeah. I mean, we're fortunate enough, and you and I are talking before the show. I mean, we're kind of born on the cloud way started off, actually on Roku. Uh, back in 2013 we adopted eight of us back in 2015. And there's just so many different service is that Amazon Web services has been able to provide us and they've added more overtime. I think the one that I talked about, uh was one that actually came out only in early 2018 which is the Aurora Post product. Um, we've been able to sail our databases scale up our analytics infrastructure. We've also used AWS for things like, you know, really time data streaming. They have the cloudwatch product where it gives us a lot of insight into the kind of our servers are behaving. And so the eight of us ecosystem in of itself is kind of evolving, and we feel like we've grown with them and they're growing with us. So it's been a great synergy over the past couple of years >> as you take a look at where you started and where you've wound up. Can you use that to extrapolate a little bit further? As far as what shortcomings you seeing today? That, ideally, would be better met by a cloud provider or at this point is it's such a simpatico relationship is you just alluded to where you just see effectively your continued to grow in the same simple directions just out of, I guess, happenstance. Yeah, it is a >> good question. I think there are some shortcomings. For example, eight of us just recently launched and chaos, which is their in house coffin solution. We're looking for something that's kind of a lot more vetted, right? So we're considering Do we adopt eight of us version or do we try to do it in house, or do we go with 1/3 party vendor? That's >> confidence. Hard to say no to these days. >> Yeah, exactly. And I think, you know, we want to make sure that we are building our infrastructure in a way that way, feel confident in can scale. I mean, with Aurora Post Chris, it's done wonders for us, but we've also kind of been the Pi. One of the pioneers were eight of us for scaling that product, and I think we got kind of lucky in some ways they're in terms of how it's been ableto pan out. But we want to make sure the stakes are a lot higher for us now. And so you know, when we have issues, millions of people face issues, so we want to make sure that we're being more thoughtful about it. Eight of us certainly has matured a lot over the past couple of years, but we're keeping our options open and we want to do what's best for our customers. Eight of us more often than not has a solution, but sometimes we have the you consider other solutions and consider the back that AWS may or may not. So some of the future problems. >> Oh yeah, it's, I think, that it's easy to overlook. Sometimes with something like a food delivery service. It's easy to make jokes about it about what you're too lazy to cook something. And sure, when I was younger, absolutely then I had a child. And when she wasn't going to sleep when she was a baby, I only had one hand. How do I How do I feed myself? There's an accessibility story. People aren't able to easily leave the house, so it's not just people aren't able to get their wings at the right time. This starts becoming impacting for people. It's an important need. >> Yeah, and I think it's been awesome to see just how quickly it's been adopted. And I think another thing about food delivery that you know people don't necessarily remember about today is it was Premier Li, just the very dense urban area phenomenon, like obviously in Manhattan, where we are today who delivers existed forever. But the suburbs is where the vast, vast majority of the growth of the industry has been and you know It's just awesome to see how this case has flourished with all different kinds of people. >> I have to imagine there's a lot of analytics that are going on for some of these. You said. In the rural areas, the suburban areas you've got, it's not as dense. And how do you make sure you optimize for people that are doing so little? So what are some of the challenges you're facing their in house technology helping? >> Exactly? Yeah. I mean, with our kind of a business, it's really important for us again to the lowest level of detail, right? Just cause we're going through 100 25% year on year in 2019 maybe we're growing faster in certain parts of the United States and growing slower and others, and that's definitely the case. And so, uh, one of the awesome things that we've been able to leverage from our cloud infrastructure is just the ability to support riel, time data access and our business operators across Canada. In the United States, they're constantly trying to figure out how are we performing relative to the market in our particular locality, meaning not just, you know, the state of New York. But Manhattan, in which district in Manhattan. Um, all that matters with a business like ours. Where is this? A hyper local economy? And so I think the real time infrastructure, particularly with things like with Aurora the faster up because we're able to actually get a lot of Reed. It's too these red because because it's not affecting our right volume. So that's been really powerful. And it's allowed our business operators to just really run in Sprint. >> So, Andy, I have to imagine just data is one of the most important things of your business. How do you look at that as an asset is their, You know, new things. That new service is that you could be putting out there both for the merchants as well for the customers. Absolutely. I think one of >> the biggest ones we try to do is you know, we never give merchant direct access to the customer data because we want to protect the customer's information, but we do give them inside. That's how they can increase their sales and target customers. I haven't used them before, So one of the biggest programs we launched over the past few years is what we call Try me free so merchants can actually target customers who've never place an order from their store before and offer them a free delivery for their order from that store. So that's a great way for merchants acquire new customers. And it's simple concept for them to understand. And over time we definitely want to be able to personalize the ability to target the sort of promotions on. So we have a lot of data to do that on. We also have data in terms of what customers like what they don't like in terms of their order behavior in terms of how they're raiding the food, the restaurant. So that kind of dynamic is something that is pretty interesting Data set for us to have. You know, you look at a other local companies out there like Yelp, Google Maps. They don't actually have verified transaction information, whereas we d'oh. So I think it's really powerful. Merchants actually have that make decisions. >> It's a terrific customer experience. It almost seems to some extent to be aligned with the Amazons Professor customer obsession leadership principle to some extent, and the reason I bring that up is you mentioned you started on Hiroko and then in 2015 migrated off to AWS. Was it a difficult decision for you to decide first to eventually go all in on a single provider? And secondly, to pick AWS as that provider It wasn't >> a hard decision for us to go to. Ah, no cloud provider. That was, you know, ready to like showtime. It's a hero is more of a student project kind of scale at that time. I don't know what they're doing today. Um, but I think a doubt us at the time was still very, very dominant and that we're considering Azure and G C P. I think was kind of becoming a thing back then made of us. It was always the most mature, and they've done a great job of keeping their lead in this space. Uh, Google, an azure have cropped up. Obviously, Oracle clouds coming up Thio and were considered I mean, we consider the capabilities of something like Google Cloud their machine. Learning soft service is a really powerful. They actually have really sophisticated, probably more so than a W s kubernetes service is actually more sophisticated. I guess it's built in house at Google. That makes sense. But, you know, we've considered landscape out there, but AWS has served a lot of our knees up to this point. Um, and I think it's gonna be a very dynamic industry with the cloud space. And there's so much at stake for all these different companies. It's fascinating to just be a part of it and kind of leverage. It >> s o nd I'm guessing, you know, when you look at some of your peers out there and you know, when a company files in s one and every goes, Oh, my God, Look at their cloud, Bill. You know, how do you look at that balance? You send your keynote this morning. You know, you like less than a handful of engineers working on the data infrastructure. So you know that line Item of cloud you know, I'm guessing is nontrivial from your standpoint. So how do you look at that? Internally is how do you make sure you keep control and keep flexibility and your options Yet focus on your core business and you know not, you know, that the infrastructure piece >> of it that was such a great question, because it's something that way we think about that trade off a lot. Obviously. In the early days, what really mattered ultimately is Do we have product market bid? Do we have? Do we have something that people will care about? Right. So optimizing around costs obviously was not prudent earlier on. Now we're in a such a large scale, and obviously the bills very big, uh, that, you know, optimizing the cost is very real thing, um, and part of what keeps, you know, satisfied with staying on one provider is kind of a piece of set up. And what you already have figured there? Um and we don optimization is over the years wear folks on financing now who basically looking at Hey, where are areas were being extremely inefficient. Where are areas that we could do? Bookspan, this is not just on AWS with is on all our vendors. Obviously eight of us is one of our biggest. I'm not the biggest line item there. Um, and we just kind of take it from there, and there's always trade offs you have to make. But I know there's companies out there that are trying to sell the value proposition of being ableto optimize your cloud span, and that is definitely something that there's a lot of. I'm sure there's a lot of places to cut costs in that we don't know about. And so, yeah, I think that's something that way we're being mindful of. >> Yeah, it's a challenge to you See across the board is that there's a lot of things you can do programmatically with a blind assessment of the bill. But without business inside, it becomes increasingly challenging. And you spoke to it yourself. Where you're not going to succeed or fail is a business because the bill winds up getting too high. Unless you're doing something egregious, it's a question of growth. It's about ramping, and you're not gonna be able to cost optimize your way to your next milestone unless something is very strange with your business. So focusing on it in due course is almost always the right answer. >> Yeah, I mean, when I think about increasing revenue or deep recent costs nine times out of 10 we're trying to provide more value, right, so increasing revenues, usually they go to option for us, but they're sometimes where it's obvious. Hey, there's a low hanging fruit and cutting costs, and if it's relatively straightforward to do, then let's do it. I think with all the cloud infrastructure that we've been able to build on top of, we've been able to focus a lot of our energy and efforts on innovating, building new things, cementing our industry position. And, yeah, I think it's been awesome. On top >> of what? Want to give you the final word? Any addressing insights in your business? You know, it's like I like food and I like eating out and, you know, it feels like, you know, we've kind of flatten the world in lot is like, You know, I think it was like, uh, like, 556 years ago. The first time I went white and I got addressed to Pok. Everybody in California knows, okay, but I live on the East Coast now. I've got, like, three places within half an hour of me that I could get it. So you know those kind of things. What insight to you seeing you know what's changing in the marketplace? What? What's exciting you these >> days? Yeah, I mean, for us, we've definitely seen phenomenon where different food trans kind of percolate across different areas. I'm going to start in one region and then spread out across the entire United States or even Canada. I would say I don't way try to have as much emergence election on a platform. It's possible so that no matter what the new hot hottest trend is that more likely than not, we're gonna have what you want on the platform. And I think what's really exciting to us over the next couple years is you know, last year we actually started way started satisfying grocery delivery. So, uh, in fact, we power a lot of grocery deliveries for Walmart today, which is exciting, and a lot of other grocers lined up as well. We're gonna see how far we can take our logistics capabilities from that standpoint, But really, we want to want to have as many options as possible for our customers. >> Anything. Thanks so much for joining us. Congressional Congratulations on the progress with your death for Cory Quinn. I'm stupid and we'll be back here with more coverage from eight of US summit in New York City. 2019. Thanks is always watching. Cute

>> live from Boston, Massachusetts. It's the queue covering your red. Have some twenty nineteen. You buy bread. >> Oh, good morning. Welcome back to our live coverage here on the Cube of Red Hat Summit twenty nineteen, along with two men. Timon, I'm John Walls were in Boston. A delightful day here in Beantown. Even made more so by the presidents of Jim White, her's president, CEO, Red hat. Jim, Thanks for joining us. Number one. Number two. What else could go right for you here this week? This has just been a great show. Great keynotes. You had great regulatory news on Monday. I mean, you've got a four leaf clover in that pocket there. I think for him >> to tell you what the weather is holding up well, for us, you're right with great partnership announcements. Amazing product launches. You have been a red hat, but eleven years now and this is only my third rail launch, right? When we deliver it, we commit to long lives. And so But it's awesome to be a part of that. And we had all the engineers on stage. I can't imagine how it could get any better. >> You >> win the lottery >> Oh, yeah? Well, yes. This one step at a time here. Relate and open share for we'LL get to those just a little bit. Let's go back to the keynote last night. First life, you have CEOs of IBM and Microsoft. Very big statements, right? We know about the IBM situation. I think a lot of people got a charge out of that a little bit. You know, Jenny commenting about have a death wish for this company. And I have thirty four billion reasons why I wanted to succeed. But a very good message. I think about this. This linkage that's about to occur, most likely. And the thought going forward from the IBM side of the fence? >> Yeah. I thought it was really good toe have her there. Not only to say that, you know, we're obviously bought it toe to make it grow, but also really making a statement about how important open source is to the future of IBM, right? Yeah. What became clear to me early on when we were talking is this is a major major. I would say that the company might be too strong a word, but it is a major kind of largest possible initiative around open source than you can imagine. And so I can't imagine, uh, imagine a better kind of validation of open source with one large technology companies the world basically going all in with us on it >> to talk about validation of open source, such a nadella up on stage. If you had told me five years ago that within a week I would see Satya Nadella up on stage with the CEO of'Em wear and then a week later up on stage with the CEO, right hat, I'm like, Are we talking about the same Microsoft? This is not the Microsoft that I grew up with on and worked with soap. We're talking your team and walking around. It wasn't just, you know, he flew in from Seattle. I did. The casino left. He was meeting with customers. There's a lot of product pieces that are going together, explain a little bit, that kind of the depth of the partnership and >> what we've made. Just tremendous progress over the last several years with Microsoft, you know, started back in two thousand fifteen. Where were you across certified hyper visors, And that's kind of a basic you know, let's work together. Over the last couple of years, it's truly blossomed into a really good partnership where, you know, I think they've and we both gotten over this, you know, Lennox versus Windows thing. And you know, I say, we've gotten over. I think we both recognized, you know, we need to serve our customers in the best possible way on that clearly means is two of the largest infrastructure software providers working closely together and what's been interesting. As we've gone forward, we find more and more common ground about how we could better serve our customers. Whether that's you know what might sound mundane. That's a big deal sequel server on Realm and setting benchmarks around that or dot net running on our platforms. Now all the way to really be able to deliver a hybrid cloud with a seamless experience with open shift from, you know, on premise to to Azure and having Deutsche Bank on State's twenty five a thousand containers running in production, moving back and forth to your >> you know what getting customers to change is challenging. You know, it's a little surprising even after that this morning to be like Oh, yeah. Let me pull up windows and log in and do all this stuff. We've talked to you a lot over the years about culture, you know, loved your book. We've talked a lot about it, but I really enjoyed. Last night is I mean, you had some powerful customers stories talking about how red hats helping them through the transformation. And like the Lockheed one for me was like And here's how we failed at first because we tried to go from waterfall to scrum Fall on. Do you know he definitely had the audience you're after? >> Yeah, I really wanted to make Mikey No talking about it called How we have so many great What's to talk about your rela a open ship for bringing all those capabilities from for OS. But I really wanted Teo talk about the hell, because that actually is the hardest part for customers. And so having kind of customers back in back to back to back, talking about success stories and failures to get there, and it really is about culture. And so that's where we called the open source way, which we kind of coin, which is, you know, beyond the code. It's, you know, meritocracy and how you get people to work together and collaboration. That's what more and more our customers want to talk about. In fact, I'd say ninety percent of the customer meetings I'm in, which are, you know, more CIA level meetings they're all about. Tell me about culture. Tell me how you go about doing that. Yeah, We trust the technology's gonna work. We don't have that issue with open source anymore. Everybody assumes you're gonna have open source. It's really how do you actually make that effective? And so that's what I really wanted to tow highlight over the course of the evening. >> You know, there was a lot of conversation, too. And you have your talking to Jenny about culture last night that you have multiple discussions over the course of the negotiation or of the conversations. So it wasn't just some cursory attention This I mean, the both of you had a really strong realization that this has to work in terms of this, you know, merging basically of philosophies and whatever. But you've had great success, right with your approach. So if you can share a little bit about how those cops is ations How you went through what transpired? Kind of how we got to where we are Now that you know, we're on the cusp of successful moment for you. Yeah, >> sure. So, yeah. I mean, from day one, that was the center of the discussion, I think early on. So year Agos, um, IBM announced, contain arising their software on open shift. And I think that's when the technical light went off about Hey. Having the same bits running across multiple clouds is really, really valuable in open shifts. The only real way to do that. And yes. Oh, Arvind was here from IBM on stage talking about that. And so I think technically, it was like, OK, ding, this makes sense. Nobody else could do it. And IBM, with their capabilities and services integration center. Just lot of strategic logic, I think the difficult part. Even before they approached this. Now, kind of looking back on it, having all these discussions with him now it's okay. Well, culturally, how do we bring it together? Because, you know, we both have strong cultures, mean IBM has a famous culture. We do that air very, very, very different. And so from the moment Jenny first approached me literally, you know, Hey, we're instant this, But let's talk about cultural, how we're going to make this work because, you know, it is a lot of money to spend on a company with No I p. And so you know, I think as we started to work through it, I think what we recognized is we can celebrate the strength of each other's cultures, and you know the key. And this is to not assume that there's one culture that's right for everything. We have a culture hyper optimized for collaboration and co creation, whether that's upstream with our source communities or downstream with our customers or with our employees and how that works. And that's great. Let's celebrate that for what it is. And, you know, IBM kind of run some of those big, most mission critical systems in the world, you know, on mainframes and how you do that looks and feels different. And that's okay. And it's okay to be kind of different. But together, if we can share the same values if we can, you know, share the same desire to serve our customers and put them first how we go about doing it. It's okay if those aren't exact. And as we got more comfortable with that, um, that's when I got more comfortable with it. And then, most importantly for me is we talk about culture. But a lot of our culture comes from the fact that we're truly a mission kind of purpose driven company, right? We're all about making open source the default choice in the world. And you know, to some extent remember, have these conversations with senior teams like, Hey, we were going to think we're going to change the world. You know? How better can we propel this for? This is such a huge platform to do it, and yet it's going to be hard. But aren't we here to do hard things? >> So it talked about it, You know, it's it's always been difficult selling when you don't have the. There's been a lot of discussions in the ecosystem today, as companies that build I p with open source and some of the models have been changing and some of the interactions with some of the hyper scale companies and just curious when you look at that, it's you know, related to what you're doing, what feedback you have and what you're seeing. >> Yeah. Look, first, I'LL say, I can't talk about that as an interested observer because our model is different than a lot of open source software companies. You know, Paul talked about in his keynote today, and we talked a lot about you know, our models one hundred percent open source, where we take open source code, typically getting involved in existing communities in creating life cycles, et cetera, et cetera, et cetera. And so that model's worked well for us. Other open source companies where I think this is more of a challenge with the hyper scale er's right more of the software themselves. And obviously they therefore need to monetize that in a more direct way. You know, our sins are businessmen always say it's a really bad business model the right software and give it away. You know, that's not what we do where hundreds and open source, but you know, if you look at our big communities were, you know, ten to twenty percent of the contribution, because we want to rely on communities. The issue for those companies that are doing Maur. The code contribution themselves is there's a leakage in the open source license, which is, you know, the open source, like the viral licenses. You know, if you make changes and you redistribute, you have toe also, you know, redistribute your code as well. And redistribution now is to find in a hyper scale is just different. So there's kind of a leakage in the model. I think that ultimately gets fixed by tweaks to the licenses. I know it's really controversial, and companies do it, but, you know, Mongo has done it. I think you'LL see continuing tweaks to the length the licenses would still allow broad use, but kind of close that loophole if you want to call that a loophole. >> Yeah, well, it's something that you know as observers. We've always watched this space and you know, when you talk about Lennox, you know, you've created over three billion dollar company, But the ripple effects of Lennox has been huge. And I know you've got some research that we want to hear about when we've looked at like the soup space. When you look at the impact of big data and now where is going you know, the hoodoo distribution was a very, very small piece of that. So, you know, talk a little bit about the ripples. Is some new research that >> way? Had some research that was that we commission to say, What is the impact of Lenin's right hand and press linens? And then we were all blown away. Ten trillion dollars. I mean, so this isn't our numbers or we had really experts do this and e. I mean, it really blew us away. But I think what happens is if you think about how pervasive it is in the economy, it's ultimately hard to have any transaction done that doesn't somehow ripple into technology and technology. Days primarily built around Lynn IQ. So in red headed President X is the leader, so it just pervades and pervades. When you look at the size in the aperture and you make a really good point around, whether it's a duper lennox, I mean, we could look a red hat, the leader and Lennox and we're, you know, less than four billion dollars of revenue. But we've created this massive ecosystem the same thing with the Duke. You think about how big an impactful. Big data and the analytics and built on it are massive. The company's doing are only a couple hundred million dollars, and I will say I've become comfortable with I'd say, five years ago, I used to say in my glass half empty day I'd be like we're creating all of this value yet we're just only getting this little tiny sliver. Um, I've now flip that around and say My glass Half full days I look and say Wow, with this lever we have with this little bit of investment were fundamentally changing the world. And so everybody's benefiting in a much larger scale around that. And when you think about it, that aperture is something really, really, really excited >> about. Well, you talk about, you know where the impact will be. Talk about Cloud, that the wave of container ization, you know, Where do you see that ending up? You know, I look, you know, Cooper Netease is one of those things. There's a lot of excitement and rightfully so. It was going to change the market, but it's not about a Cuban aunties distribution. It's going to be baked into every platform out there. Yeah, gunships doing quite well. And you know all the cloud providers, your partner with them and working with them. It's less fighting to see who leads and Maura's toe. How do we all work together on this? >> Well, you know, I think that's >> the great thing about ah well functioning, mature, open source projects is it behooves everybody to share. Now we'LL compete ultimately, you know, kind of downstream. But it who's everybody to share and build on this kind of common kind of component. And, you know, like any good open source project, it has a defined set of things that it does. I think you hit on a really important point. Cooper Netease is such an important layer. Doesn't work without Lennox, right? I mean, lyrics is, you know, containers or Lennox. And so how do you think about putting those pieces to gather manageability and automation thinks like answerable. And so, you know, at least from our perspective, it's How do you take these incredible technologies that are cadence ng, you know, at their own pace and are fundamentally different but can't work unless you put them all together? Which to us, you know, that creates a big opportunity to say, How do I take this incredible technology that thousands of, of really technically Swiss cave people are working on and make it consumable? Archer Traditional model has been like linnet, simply saying We're going to snap shot. We're going created to find life we're going back for, you know, do patching for what? And we still do that. But there's now an added sir sort of value, something like open shift, where you can say, Okay, we could put these pieces together in life cycle and together. And, you know, we see instances all the time where an issue with Cooper Netease requires, you know, a change analytics. And so being able to life cycle in together, I think we can really put out a platform where we literally now we're saying in the platform you're getting the benefits of millions of people working on overtime on Lenox with tens of thousands people working on Cooper, Netease and the Learnings are all been kind of wrapping back into a platform. So our ability to do that is it kind of open source continues to move up. The stack is really, really exciting. >> Now. You were talking about transformative technologies on DH. How great it is to be a part of that right now. You alluded to that last night in the keynote. So you're talking about this, You know your history lessons. You know how much you love doing that? Your ki notes and you know, the scientific method Industrial Revolution open source. Just without asking you to re can you are a recount. All that. Just give us an idea about how those air philosophically aligned it. How you think those air open source follows that lineage, if you will, where it is fundamentally changing the world. It is a true global game change. Yeah, And >> so the point last night was a really kind of illustrate how a change in thinking can fundamentally change the world we live in. And so what I talked about just kind of quickly is so the scientific method developed and kind of the fifteen hundreds ish time frame was a different way to discover knowledge. So it goes from kind of dictates coming down from, you know, on high, too. Very simple hypothesis, experiment, observation of the results of the things that go through that process and stand the test of time and become what we consider knowledge right? And that change lead immediately to an explosion of innovation, whether that with the underpinnings of the industrial revolution or enlightenment, what we've done in medicine, whole bunch of areas. And yeah, the analogy I came to was around well, the old way we just try to innovate constrains us in a more open approach is a fundamentally better way to innovate. But what I found so interesting in and I think you picked up on it if it didn't emphasize this much, wanted to excite and having a lot of time, its many of the same characteristics of scientific discovery. So the idea of you know, independence anybody could actually do this pinpoints the importance of experimentation and learning those Air Corps components of, you know, tef ops and agile and open source, right? It's very, uh, in the end, the characteristics are actually quite similar as well. I think that's just fascinating to see happen. >> So e think about that. And if you could bring it back to the customers you're talking to, you have a lot of executive conversation, said You focus a lot on the how is really challenging. We understand. You know, the organizational structure of most companies goes back over a hundred years to military. So you know, what you see is some of the one of the biggest challenges that, you know, executive thieves we're facing these days. And, you know, how are they getting past that? Stuck? >> Yeah. And so, you know, I think the simple is way to state. The problem, which I hear over and over again, is we tried an agile transformation, and it failed because our culture was already and cultures Mohr of, ah always tell the executor when they said to me, It's like, Okay, but recognized cultures and output, not an input. And it's an output of leadership behaviors, beliefs, values what's been rewarded over time. So if you want your culture to change, actually to think about changing the way that you lied and manage and broadly, the structures, the hierarchies, the bureaucratic systems that we have in place today are really good at driving efficiency in a static environment. So if you're trying to slightly take a little bit of cost out building a car, you start with what you did last year. You get a bunch of scientists are consultants to look at it, and then you direct some fairly small changes. So the structure were in places other wrong with them. When value creation was about standardization of economies of scale. The hierarchies work really, really well to distribute tasks and allow specialization and optimization. The problem is now most value creation. It's requiring innovation. It's how doe I innovate and how I engage with my customer. You know the example I used a couple years ago? Its summit was, you know, the average cars use ninety minutes today. So if you think about how to reduce the cost of transfer port ation, is it taking two percent out of the cost of building a car? Or is it figuring out whether it's ride sharing or other ways? Teo. A fractional ownership. Whether it is to increase the average utilization of the car, it's clearly the ladder. But you can't do that in about bureaucratic hierarchical system that requires creativity and innovation, and the model to do that requires injecting variants in. That's what allows innovation to happen. So as leaders, you have to show up and say, all right, how do I encourage descent, you know, how do I accept failure? Right. So this idea of somebody tries something and it fails. If you fire him, nobody's gonna try anything again. But experimentation by definition requires a lot of failures and how you learn from it. So how do you build that into the culture where as executives you say holding people accountable doesn't mean, you know, firing him or beating him up. If they make a mistake, it's how do I encourage the right level of risk taking in mistakes, you know, even down to the soft side. So you know, how do you hold somebody accountable in an agile scrum, right. Your leaders have to be mature enough to sit down, have a conversation. Not around here. The five things you were supposed to do and you did forum. So you get in eighty right now, you can't say exactly what they need to do because it's a little blurry. So you have to have leaders mature enough to sit down and have a conversation with somebody is I think you got an eighty. Thank you. Got an eighty because here's what you did well, and here's what you didn't. But it's subjective. And how do you build that skill and leaders? They oughta have those subjective conversations, right? That sounds really, really soft, but it's not gonna work if you don't have leaders who can do that right? And so that's why it's hard. Because, you know, changing peep people is hard. And so that's why I think so. Many CEOs and executives want to talk about it. But that's what I mean by it's a soft side. And how do you get that type of change to happen? Because if you do that, pick ours honestly, pick somebody else's, you know, agile Davis with methodologies. They'LL work if you have a culture, this accepting of it >> before they let you go. There were two things to our quick observations about last night. Number one rule Samant hitch up on the licensing, so I know you've got your hands full on that. Good luck with that. You mentioned licensing a little bit ago, and I learned that thirty four billion dollars is a good deal. Well, right, that's what you said I heard it from are absolutely well. Things >> were a separate entity. We don't have licenses. So I don't know how we would go into an l A >> given. We don't have a license to sell. So got some expectations setting >> we need to do with our customers and then, you know, but separately, You know, I think people do forget that Red Hat is a not only a really fast growing company were also really profitable company. Most of the other software companies that are growing at our pace on a gap basis makes little to no money. We have because we get the leverage of open source, we actually generate a very large amount of free cash flow. And if you actually not to get the details of the financials. But we look at our free cash flow generation in our growth, I would argue, was a smoking good deal. That thirty four. I was asking for a lot more than that. >> You could had smoking good the last night that was gonna work to give thanks for the time. >> It's great to be here. >> Thank you. Thank you for hosting us here. Great opportunities on this show for I know that's exciting to see two but continued success. We wish you all >> thanks. So much. Thank you for being here. It's great to have you, >> Jim. White House joining us back with more live coverage here on the Cube. You are watching our coverage here in Boston of Red Hat Some twenty nineteen. Well,

>> live from Las Vegas. It's the queue covering del Technologies. World twenty nineteen, brought to you by Del Technologies and its ecosystem partners. >> Welcome back to the cubes. Live coverage here, Adele. Technology rule in Las Vegas. I'm John for Developed, a special guest. Michael Dell, Chairman, CEO, Del Technologies Cube. Alumni. Great to see you again. Yearly pilgrimage. People can come on the Cube. Good to see you again. Thanks. May always >> Great to be with you guys. >> All right, So I gotta ask you because, you know, Dave and I were talking on yesterday's kickoff on our intro about the conversation we had. I think six years ago we saw you standing there in Austin, but still a public company didn't go private yet. And then the series of moves going private and we're like, That's great. Get behind the curtain. Get things reset. Look at the cash flows. Looking good. You had the clear plan as the founder and CEO is kind of a new kind of reset, if you will. And then up to now the execution in just the series of moves. When you look back now where you are today, where you were then how do you feel? What's absurd? What did you learn? What's some of the highlights for you? >> Well, look, we feel great, You know, our business is really grown tremendously. It's all the things we've been doing has been resonating with customers have been ableto, I would say, restored the origins of the entrepreneurial dream and success of the company and reintroduce, uh, innovation and risk taking into, ah, now ninety one billion dollars company growing in double digits last year and certainly the set of capabilities. That way, we've been able to build organically and in organically on DH with set of alliances. We have the trust that customers have given us, you know, super happy about the position that we're in and the opportunities going forward. As I've said, you know, a zay said Mikey. No, yesterday. I think all this is really just the pregame show. Tow what's ahead for our industry and for the role that technology is going to play in the world. >> And the role of data you mentioned also used to quote you Yes, that you said data a CZ the life, blood of digital transformation of the heartbeat, visual transformation and It's also revitalizing all the other components of what looked like a consolidated market is now actually being reborn the PC, technology, infrastructure, fabrics and other software opportunity. So Data has kind of brought in a whole nother level of kind of revitalisation and the industry, which is actually causing more investment in what looked like older category of you know it and computers whatnot. This's been a big, big tailwind for you guys. >> Well, data has always been at the centre of you know how the technology industry works and now we just have a tsunami explosion of data. And of course, now we have this new computer science that allows us Teo reason over the data in real time and create much better results in outcomes and that combined with the computing power, all organizations have to reimagine themselves, given all these technologies and certainly the infrastructure requirements in terms of the network, you know, the storage, that computer bill out of the edge, tons of new requirements, and we're super well positioned to go address all that. >> I enjoyed your keynote, Michael. So I thought it was excellent. One of your better ones and you painted a picture of tech for good. Uh, really life changing things that you guys and your customers are doing. You gave some examples that be an example of example was great Draper Labs. But you also paid a picture. You need a platform for this digital transformation. We've seen the numbers. Eighty percent of the workloads are still on Prem. What do you think that looks like ten years down the road? What do you What's your vision say? >> Well, the surprise outcome ten years from now is they'LL be something much bigger than the private cloud and Public Cloud. It's the edge and actually think that would be way more computer data on the edge in ten years than any of the, you know, derivatives of cloud that we want to talk about. So that's a ten year prediction. Yeah, that's that's That's kind of what I see. And maybe maybe nobody's predicting that this yet, But, you know, let's come back in ten years and see what it looks like. >> So I like to do that hybrid hybrid. Klaus been around for a while, but talked about. It's been kind of operating, Ma. We see that multi cloud is really kind of surged in importance in conversations because I think people wake up and go. Hey, I got multiple clouds. I got azure over here for ofthis three sixty five. I got some Amazon over here. I got some home grown stuff over here. I got a data center so that people kind of generally Khun, Khun, relate to the reality of multi cloud hybrid. Live it more of a different kind of twist, but certainly relevant. But multi cloud has got everyone's attention and you guys launched Del Cloud. Is that a multi cloud, or is that a cloud to multiple clouds? Explain your view on that and where this goes. >> So really, what we're doing is we're bringing to customers. All the resource is they need to operate in the hybrid, multi cloud world. And first, you have to recognize that the workloads want to move around and to say that they're all going to be here, or there is in some sense, missing the point because they're going to move back and forth. And, uh, you know, you've got regulation cost security performance late and see all sorts of new requirements that air coming at you and they're not going to just sit, sit in one place. Now, as you know, with via Work Cloud Foundation, we have the ability to move these workloads seamlessly across. Now, essentially all the public clouds, right. Forty, two hundred partners out there infrastructure on premise built and tuned specifically for the VM wear platform and empowered also for the edge and a love. This together is the Del Technologies Cloud. We have obviously great, uh, capabilities from our Delhi emcee infrastructure solutions and all the great innovations that Veum where coming together >> scale has been a topic. We talked on the Cube many years. We saw Amazon get scale with public cloud scales of competitive advantage is now becoming kind of table stakes both for customers trying to figure out how to operate a digital scale, speed a life. You guys have a scale level now that's pretty impressive. What you guys done with the puzzle pieces, You cut puzzle pieces, you know, cos capabilities now across the board, as you guys look at scale is a competitive advantage, which it is, and we talked about this before. You now have to integrate seamlessly in these pieces. So as you compose as customers compose the variety of capabilities. It's gotta be frictionless. That's a goal. How do you look at that? How do you talk to your team's about this on DH? What's your view on scale? And is this something you guys talk about inside the company? >> Well, inside the business, you know, the first priority was to get each of the individual pieces working well. But then we saw that the real opportunity was in the scenes on how we could more deeply integrate all the aspects of what we're doing together. And you saw that on stage, you know, in vivid form yesterday with Pat and Jeff and Sasha and even more today again. And there's more to do. There's, although there's always more to do. Were working on how we build a gate, a platform bringing together all of our capabilities with Bhumi and data protection on DH bm wear, and this is all going to be super important way. Enter this A I enabled age of the future. >> Michael, you got a track record of creating shareholder value. We're big fans of, you know, we'LL have CNBC on in the office and Michael's on everybody coming across, right? Davos? Picky, Quick. We're also big fans have asked you to sort of knocked down to three criticisms. And sure, it was really a conversation about stock price, you know? And you Did you knock down the debt structure? The low margin business, the ownership structure, its center. But you never came backto stock price, so it looks like a couple of ways to invest. Now VM wear directly. Also looks like Veum where you could you could buy cheaply through Del What your thoughts on on that? You know where Dell sits in the market today? Its value. >> I think. You know, investors are increasingly understanding that we've created an incredible business here and certainly, you know, if we look at the additional coverage that we have and you know, they're they're a CZ their understanding, the business, you know, some of the analysts are starting to say, Hey, this doesn't really feel like a conglomerate. Direct quote. Okay. And, uh, if you think about what we demonstrated today, yesterday and we'LL demonstrate the future, you know, we're not like Berkshire Hathaway or, uh you know, uh, this is not a railroad that owns a chain of restaurants. This is one integrated business that fits together incredibly well, and you know it's generating substantial cash flows. And, you know, I think investors overtime are figuring out value. That's intrinsic. Teo, the overall Del Technologies family now wave Got lots of ways to invest, right? Get, Be aware. SecureWorks pivotal. And, of course, the overall Del Technologies. >> Yeah, and just a follow up on that. I mean, I've observed on the margin side I mean, when del went private, it was around nineteen percent gross margins. Now you're in gross margin heaven, you know, absorbing the emcee. And it seems to be headed in the right right direction. So it's a nice mix >> know, in our in our cloud, an infrastructure group, almost ninety percent of the engineers are software engineers. And so you think aboutthe innovations you saw in states today with power Macs and Unity, X T and our power protect platform. You know, basically all software running on power power it surfers and platforms that we've created. >> What's on your plate now, Michael? As you come out, come out of Del Technologies world. You got business to take care of what your goals what's on your plate. What's your object? Is what you trying to accomplish in the next year? >> Well, certainly continuing to execute for our customers growing faster than the industry. You know, maintaining and improving our customer NPS levels and keeping the innovation engine cranked up on high. You saw a lot today on DH yesterday. Stay tuned, Veum. World's coming in in August and they'LL be much, much more way Continue toe innovate together Lucy with Veum where so we've got we've got lots more in the cube >> and you got cash will come in, which means your suppliers to a lot of customers Congratulations. I want to get your final thought on my final question on the Tech for good One of the things I saw yesterday on the Kino that you gave was that popped out wass. It wasn't about the speeds and feeds around, you know, the performances get great performance on the tech side. You gotta be, you know, the infrastructure level Scott be performing, but it's about solving problems. And I think this is a direction that you're taking the company saying there's outcomes out there. The problems that can be solved with tech We're hearing a whole tech for bad narrative in the media these days. Tax evil text. Bad. But there are awesome spots where technology is creating great things for society. This is a theme for you. Can you share? Why that focus? And when some of the highlights >> it's right. I mean, if you if you step back from the what happened in the last twenty four hours, twenty four days and even twenty four months, you start looking at, you know, twenty four years you start to see is thie. Outcomes for humanity have gotten dramatically better, and technologies played an enormous role in that. I'm massively optimistic that in the next three decades they're going to be really miracles. In terms of how do you dress things like deafness and blindness and paralysis with a I and embedded technology inside the body. The, you know, things were able to do now with sequencing the genome and using all this data to create personalized medicine solutions. Yes, technology can be used for bad, but the vast majority of it is used for good by people that have good in their hearts. Right. And and, uh, you know, uh, it goes beyond making great businesses and making people more productive. It's actually changing lives and very positive ways, >> while the other big narrative in the pressure here is automation and taking away jobs. And it's a serious concern. However, you know there's no reason to protect the past from from the future and this great opportunities ahead education and someone, even you and Susan but big supporters of that, obviously. So we're optimistic for the future. I know I know you are. The best is yet to come. As I'd like to say >> Absolutely, we agree. >> Once an entrepreneur, always an entrepreneur, you great entrepreneurial track record you celebrate thirty five years from the original dorm room. So some of your Facebook posts now here he took a business that you knew T mature couple players. This is a trend we're seeing. Zoom communication just went public. They took video streaming and holding meetings and completely when cloud base and disrupted it. You saw >> runs on Dell EMC by the way >> runs on Dell, did not know that it's only a lot of Michael great, but this is an entre. I want to get your advice to other articles that might be watching us because you now, with the technology with data and cloud and tech, you, Khun, go into existing markets that don't look good on paper that people might dismiss as that's over. That's a mature market You've certainly taken Del Technology's got all the pieces and are executing at a home of the level. Zoom did it for video on the cloud. There are zillions of these opportunities out there that entrepreneurs. So the advice don't be discouraged by what looks like a big fat market. So your what's your advice?  >> and I I feel something is coming. That's quite significant. And right now you mentioned this new wave of companies that air coming public and they were built on a foundation of technology infrastructure capabilities. You know that was established, Let's say, ten years ago. Okay, well, right now we're just at the kind of beginning of five G and A II technology, and all these embedded sensors and low latent see communications, and there will be a whole another wave of cos I suspect many, many more across all industries that, you know, just unlock all kinds of new capabilities and an opportunity. So I'm super excited about that. Andi, I think I think it's it's just going to get more interesting. >> It's amazing to think of the tools you had thirty five years ago, when you started and how you've transformed. So congratulations. >> Thank you. Spend the time again. Thanks for having us again here. Tenth year, Del Technologies. Well, thanks for having us. And great to have a conversation. >> Thank you. And the rest of the cube team for all your great coverage. >> Thank you very much. Michael Dell, Chairman, CEO, Dell Technology here. David Velante myself, John Furrier. Stay tuned for more day to coverage. We got two sets here. It's a cube canon of content blown out. The content here, Adele Technology, world Check out Dell's hashtag del tech world for all the highlights will be right back after this short break.