(upbeat music) >> Hi everybody, this is Dave Vellante. And welcome to this CUBE conversation where we welcome an ESG senior analyst, Bob Laliberte Bob, good to see you. >> Great to see you too. Thanks for having me >> Love it, I love to have the analyst sessions. Set it up. What's your scope, what's your area of expertise? >> So my coverage area right now is networking in its entirety. So that spans everything from enterprise networking, wired, wireless, campus, data center, et cetera. All the way up through telco and, in cloud networking. >> So how do you look at the landscape? One of the big things I think about a lot is how does the shift to cloud migration? How does that affect the existing, network layers? I mean, you got Cisco as the big whale and it's just, it's amazing to me. They still have whatever percent market share they have 60, 65% of the market. Are things, what's happening in the competitive landscape. How is cloud affecting that? >> That's a great question. I think the interesting piece is so many times organizations think about the network as plumbing. But the reality is the it's really important plumbing because as you talk about cloud and things get more distributed, well, guess what connects those distributed locations? It's the network. And so organizations as they've moved to the cloud you've seen a big shift with things like SD-WAN and so forth. How do I get more efficient connectivity up to that cloud? How do I not only enable able better connectivity between my data centers in the cloud, but now all my remote workers in the cloud. And so there's been a lot of big shifts going on that have driven the importance of having not only network, but secure networks. So like I said, cloud is one thing, and you're moving your applications there. But with the pandemic you saw the remote work. Think about the network administrators who we're managing, hey, I've got to control network connections between my data centers, a couple clouds and maybe dozens maybe a hundred remote branches. And now I'm connecting to 10,000 micro branches that I need to ensure that they can connect up to these applications and so forth. Hell of a lot more complex environment today than it used to be for these network teams. When we look at the, what we're seeing, how the networking providers are responding it's by driving comprehensive end-to-end solutions. So unifying, wired, wireless, and WAN. Driving efficiencies there. You're seeing even ThousandEyes for Cisco and things like that. Because they know the Internet's becoming more integral part of the corporate network. So being able to drive those types of things being able to, I think look at how to drive those operational efficiencies through AI and ML. So one of the big shifts we've seen in networking is the transition to cloud-based network management. And obviously that couple of things that helps with, first of all, the operations teams who are working remotely can more easily access it. But once all that data is up in the cloud, it creates a platform to be able to invest in AI/ML, and be able to drive intelligent alerting and even automation. And that's really what's needed because as the environments get more distributed and complex, you need to have that those operational efficiencies that automation, that intelligence to help them. >> How has remote work and hybrid work affected sort of network, spending priorities. Obviously when the pandemic hit you had to accommodate end points. And I always have this theory okay, when people come back to the office and I know it's going to be a different world but, the HQ probably needs some love as well. So has that been a tailwind for the industry? >> Absolutely, that's what we're seeing now. I think when the pandemic first hit, everyone said I've got to ramp up my VPNs. I've got to scale out my concentrators. I've got to add more firewalls in my data center. And then after a while, when they realized this was here to stay, they said, okay we just created that hub-and-spoke network that we just got rid of with SD-WAN. So what are the better solutions we can implement? So now you're seeing them not only implement better networking solutions for the remote workers. But reimagining what the campus looks like. Because it's not going to be ever 100% full or maybe it will, but how, for how many times a year will it be 100% full? So you've got to go from 80% cubes and 20% conference and collaboration areas, to 80% collaboration areas and 20% cubes. So we're seeing a lot of transition taking place in the campus environment as organizations are deploying newer technologies like Wi-Fi 6E. That have greater bandwidth to allow for those collaboration apps to run in those collaboration areas. Instead of just having the single wired conference room for video. Everyone's got to be able to run their video, voice and video collaboration apps. >> So how do you look at the landscape now? Again, you can't talk about networking without talking about Cisco. I think they, up there, I saw you and Zeus as talking about out, Cisco's quarter and other networking topics. Their long term guidance is for 60% growth for a company that size that's really outstanding. I mean, Cisco's, really has always been an execution machine of course. And it's a new era now under Chuck. There are more than ankle biters. If you look at Arista's doing pretty well there's guys like Extreme, there's others that are out there but nobody seemed to be able to unseat Cisco. What's happening in the landscape? >> I mean, that's a great question. Cisco's just been around for so long and been so big for so long. And you have to also keep in mind that with Cisco it's not just about the technology, but the fact from a if you think about it from a cultural standpoint these are workers who have been trained on Cisco since, some of them since high school. The educational component that Cisco has done has groomed generations of network technologists. So when they come into the market, they're fully familiar and used to Cisco. Plus they make a really good product and they've got products that cover everything. They cover the whole gambit. So they're still able to maintain their share. They're able to grow. They're able to move. They've made a shift last year. They announced in last spring that they were going to focus more on end-to-end. So instead of just having, hey, here's a point product, here's a point product. Here's a point product. Let's think about it in its entirety. Let's deliver a complete end-to-end solution solve bigger problems for customers, which obviously makes it much harder to remove when you're just trying to remove a piece of that single problem. But the other competitors are also having good years. And I think also the rising tide floats all boats. And so because of this distributed nature, the importance of the network, everyone is doing that. Plus obviously this has to be said, the supply chain issues where people are ordering ahead as well. But organizations, you look at Arista, they've gone from just being a data center company to expanding all the way down to the campus edge, wireless, right there creating an end-to-end environment Extreme did the same thing. They went out and made a lot of acquisitions. They pulled them all together, integrated. They're all moving to this cloud based end-to-end network management. Arista has been on a tear, bringing in a lot of, not only innovative technology, but innovative technologists. So if you look at some of the organizations they bought. I keep calling it Route 128, it's 128 Technologies. So sorry folks I live in Massachusetts. It's always been Route 128. >> You Remember when don't we. 128 Technology's Mist was their big. Mist was their, Mist was kind of like their VMware. VMware to EMC was Mist was to Juniper. And so we call it the Mistification of Juniper where every organization, every company they bring in they're rolling under that and this the AI engine. So they're bringing in 128 Technologies into that. They've got their own, their own stuff under that, their wired switches. So they've got this unified wired and wireless and WAN assurance now that they have. They've been gaining a lot of traction with that. And again, for the things we were talking about because it's far more distributed and complex. You need to have, It's not like people are getting replaced. It's not like, hey, we're leveraging this automation so that we can get rid of network teams. It's because it's getting so much more complex just to have the same number of people manage that more complex environment. We need those intelligence solutions. >> So I want to ask you about network and multi-cloud. And so it's kind of tongue in cheek because we coined this term super cloud. And so what we meant by that, so here's the premise. And I wonder you could give us your perspective. Multi-cloud, I've said many times is I think largely a symptom of multi-vendor I run in this, I run in AWS or, Azure, I've done the work to understand their primitives and or Google, whatever it is. But it's not like an abstraction layer that's floating above all those but now you're starting to see that. In fact, it re:Invent in November. The ecosystem it seemed like was everybody was focused on developing what we call these super clouds. And again, it's tongue in cheek, this abstraction layer it hides the underlying complexity of the primitives and the APIs adds incremental value on top of that. So there's a company Prosimo, which Steve Herrod, is invested in and others Praveen Akkiraju, whom I'm sure you know from Viptela. Aviatrix is another company that's sort of, Steve Malaney has come on theCUBE and talked about what they're doing. Like yeah, that's super cloud. It seems like it's something new and different than just multi-cloud which is kind of connecting in to different clouds. It's that value on top. What do you think about that? And what does that mean for networking? >> That's a really good point because we are starting to see the inception of organizations going beyond having multiple cloud providers and looking at starting to deploy applications across multiple clouds. It's still really early. The vast majority of organizations are still, I use this application for this cloud and this application for that cloud. But that's the next frontier. That's what they're trying to solve is how do I create this basically cloud fabric and make it as simple as possible. And again, all the things we've been talking about how do I, instead of you having to learn Amazon, Google, Azure networking technology, learn mine, I'll take care of it, but I'll abstract all that complexity from you and make it so much simpler to be able to connect to these interconnect, and connect to them in a seamless fashion. And so that's what they're really trying to do is they're. And the hard part is it takes really sophisticated solutions to remove that high level of complexity and make it simple for an organization to do that. So yeah, absolutely. >> If I had more time I'd make it shorter as somebody who writes a lot. And I think you're right. I think it is future. It's not definitely not here today, but the other thing is it ties into digital transformation. We used this again, throw that buzzword around but, companies not just tech company, I mean everybody's becoming like a tech company, but organizations, financial services companies, healthcare they're building their own clouds on top of the hyperscalers who spend $100 billion a year on CapEx. And that seems to be a trend that I think is going to take legs over this next decade. Just like in the previous decade everybody was thinking, okay, we're going to SaaSify our business softwares (indistinct) the world. And now it's software and cloud services are the way in which I'm going to create customer experiences. >> Correct, yeah. It's why should I go out and make an investment in technology when the technology's already there? And I can rent it for when I need it scale it as I need it and, and do all of that. I agree with that. I think that's something that we're seeing. The interesting part though is that when we look at our data points, probably let than 40% of the applications and workloads are in the cloud today. So there's still a role that the corporate data center plays. We are seeing over time. They expect that to progress and transition but I think there's still always going to be maybe a quarter of the workloads and applications may never leave. Depending on how they're built, et cetera. So there's always going to be that distributed environment where you've got workloads in the private data centers, workloads in multiple public clouds. And also, the big thing too is don't forget about the edge. We're seeing a lot more edge activity take place as organizations recognize, as they deploy more IOT devices, and want to get realtime business insights they've got to deploy the compute there. >> Well, and that's something that I wanted to ask you about, but going back to what you just said, which is, I agree with you. So that suggests to me, Bob that we're just kind of, with cloud just entering the steep part of the S curve. Amazon's headed toward $100 billion, run rate business. Maybe they probably won't get there this year but they will next year. We're entering that steep growth phase, really could be. It's incredible. But I wanted to ask you about the edge. Because you're right is we got to move compute to the edge, ARM is going to dominate. I would think, the edge. They already are with our smartphones. How do you see the cloud guys participating in the edge? Whether it was Andy Jassy, or now Adam Selipsky or anybody at Amazon. They have the dogma of in the fullness of time all workloads are going to be in the cloud. So they either have to change their definition of cloud. Or they're wrong. So what's your thought on that? >> I think it really starts coming down to what's your definition of edge. And so, much like when the cloud technologies first came about and you had all the shadow IT. Everyone running off, and everyone thought oh this is all great, until you realized you had to operationalize it and you had to pull the brakes. Stop doing that. We're going to make sure IT operations. >> Call the CIO up. Exactly, finding out where stuff was by going through accounting and seeing credit card charges. For the edge what we've seen I think is maybe organizations really saying I've got to deploy my servers in my own site. Right at that edge in order to get the lowest possible latency. And so what I think we're starting to see is organizations looking at that and saying, okay well I'm in a metro and I've got 25 locations in a metro. And I've deployed technology to every single one of those sites. Do I need it there? Or can I put it in an Equinix facility that's less than five milliseconds from all 25 sites? So I think there's starting to be this pragmatic approach of looking at let's look at the edge, let's take a look at what type of latencies. What is our definition of real time. When do we actually need the data and so forth? What kind of connectivity do we have? And then from there figure out how we go about connecting it. And so for companies like AWS and Google and Azure a lot of them there's local zones and things like that. They're deploying them in those colos because they don't have data centers in every metro but they can leverage an Equinix. They can leverage someone else's hardware that's there to deploy their software stack within that location. So I think that's something that we're starting to see more and more of as the edge. And obviously the association with the telcos as well. They've got a great footprint. If you want to get close to the edge with their colos Their home offices and things like that and whatnot. Their ability to move the compute closer to the edge, the base stations of the antennas and things like that, are certainly significant. And that's why you're seeing the wavelengths and things like that, programs like that. >> So I was going to close, but there some really interesting topics you just brought up. Call it whatever you going to call it near edge, far edge or deep edge. And you mentioned real time. Yeah. So for those Equinix data centers, I don't need, true real time. But for Tesla, I need real time. I need real time inference at the edge probably using a bunch of ARM cores and I can't go back to any cloud. How do you look at that? Both, I would think big markets. Do you have a sense as to, is one bigger than the other? Are they both just enormous or we don't even know yet. >> I'm not sure that we know yet. I think certainly, it's riding the tail of the IOTs. So the more sensors, the more things that are deployed the more that, that data businesses realize they can leverage that data to make real time business insights to drive either better experiences. And if you're in retail. So location based services and real time offer management it doesn't do any good to offer a coupon for something that you've, that's 40 yards behind you. That that's past, like you said with the cars there's, I've seen some studies recently. They say, well, based on the latency, if the command is to stop and you're at one millisecond, it stops within four inches. If you are at 50 milliseconds, it stops 10 feet later. That's a big difference. And I don't know if those numbers are right but you get the idea about the impact, what the real time impact is of. >> Margin is not huge. >> Exactly, so that's where organizations, I think first and foremost need to take a pragmatic approach to determine what is real time for us. What's our definition of it. And then that can lead them to where do I need to place this compute technology? And then that goes to how do I then connect to it? So for the Teslas and so forth, obviously you're going to want 5G connections if possible. Ultra low latency and not just any 5G. The good stuff, the millimeter bandwidth stuff that that's the ultra low latency. >> So let's wrap. So, what's going on in your research world obviously the big, big acquisition tech target they seem to be investing in ESG. You guys are really growing and hiring. That's awesome. Any research that you're working on? >> Yeah, there's a couple of couple of projects we have going on right now. We're wrapping up a four part distributed cloud research series. So we did it on distributed cloud infrastructure. Applications, observability. And now this last one is on the edge. Coincidentally. So we're working on that. We've got some new network modernization research that we've published. And we're going to be looking, from a networking perspective looking at end-to-end network modernization which will be coming out soon. >> Awesome, Bob, thanks so much for coming on theCUBE. I really would love to have you back and chat about some of those things. Observability hot space. God, I wish we had more time. >> Absolutely, appreciate it, thanks. >> And thank you for watching this CUBE conversation. This is Dave Vellante and we'll see you next time. (upbeat 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..

(upbeat music) >> Welcome to transforming ideas into insights, presented with theCUBE and made possible by Cloudera. My name is Dave Vellante from theCUBE and I'll be your host for today. In 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 of 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. That 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, a 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 on our smart phones and so many examples. IOT systems that communicate and act machine to 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 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 and he's the president of Cloudera. Mick, 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, commodity prices, we don't know. Is inflation transitory? Is it a long-term threat, trying to forecast GDP? It seems like we have to reset all of our assumptions and Mick, I feel a quality data is going to be a key here. How do you see the current state of the industry in the role data plays to get us into a more predictable and stable future? >> Well, I can sure tell you this, Dave, out of whack is definitely right. I don't know if you know or not, but I happened to be coming to you live today from Atlanta and as a native of Atlanta, I can tell you there's a lot to be known about the airport here. It's often said that whether you're going to heaven or hell, you got to change planes in Atlanta and after 40 minutes waiting on an algorithm to be right for baggage claim last night, I finally managed to get some bag and to be able to show up, dressed appropriately for you today. 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. Without well-managed data, we're certain to get very inconsistent outcomes. Quality data will be 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 dataset. 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. >> Mick, 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 prescriptions of hybrid and everything, there was 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 do you mean by hybrid data? >> Well, Dave, in Cloudera, 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, in 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 or read a recent news story. Data breaches are at an all time high and the ethics of AI applications are being called into question everyday. And understanding 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 Cloudera is in. On a serious note, from Cloudera's perspective, adopting a hybrid data strategy is central to every business' 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. >> Okay, Mick, 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 got the economics, the physics, the local laws come into play, so what about the rest of hybrid? >> Yeah, that's a great question, Dave and certainly, Cloudera 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 ethernet may not be happening quite so fast in somebody's rural home in the middle of Nebraska somewhere. So it doesn't really matter whether you're talking about the speed of business or securing data, any way you look at it, 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, involve us kind of slowly coming back to work, beginning 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 yes, hybrid work 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, here's how I think about it. I mean, some industries have transformed. You think about retail, for example, it's pretty clear. Although, every physical retail brand I know has not only beefed 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. And reverse, we see Amazon building out physical assets, so there's more hybrid going on. But when you look at healthcare, for example, it's just starting 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 of payment systems. In manufacturing, the pandemic highlighted, America's reliance on China as a manufacturing partner and supply chain. And so my point is, it seems at different industries, they're in different stages of transformation, but two things look really clear. One, you 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 yeah, at Cloudera, I happened to be leading our own digital transformation as a technology company and what I would tell you there that's been an interesting process. The shift from being largely a subscription-based model to a consumption-based model requires a completely different level of instrumentation in our products and data collection that takes place in real-time, both for billing for our customers and to be able to check on the health and wellness, if you will, of their Cloudera 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 to market 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 healthy and safe outcomes for everyone. And Cloudera has been underneath a great deal of that type of work. And the financial services industry you pointed out, 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 helping those kinds of organizations get through those difficult challenges. You also happened to mention 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. 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, you can't build great ML apps unless you've got a strong data foundation underneath. It's 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 data management platform at your disposal to ensure that your data is clean and organized and up to date. 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's 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. >> It's a pretty interesting concept you're bringing into the discussion, the hybrid data, sort of, I think new to a lot of people. And 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 machines to do real-time inferencing at the edge, it starts to get really complicated. I wonder if we could talk, we're still on that theme of industry transformation. I felt like coming into the 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, 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? >> There definitely is a lot of a POC limbo or I think some of us internally have referred to as POC purgatory, just getting in that phase, not being able to get from point A to point B in digital transformation. And for every industry, transformation, change in general, is difficult and it takes time and money and thoughtfulness. But like with all things, what we've 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. To say it 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 Cloudera, 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, 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 accomplished, but kind of starting small and then drawing fast from there on customer's journey to the cloud. >> Well Mick, we've covered a lot of ground. Last question, what do you want people to leave this event, this session with and thinking about sort of the next era of data that we're entering? >> Well, it's a great question, but I think it could be summed up in two words. I want them to think about a hybrid data strategy. So, really hybrid data is a concept that we're bringing forward on this show really, 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 ANT. That is freedom 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, 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 for ourselves and for others. And the same is true of organization's IT strategies. They want the freedom to choose, to run workloads and applications in 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. I 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.

(upbeat music) >> Hello, welcome back to "theCUBE"'s coverage of DockerCon 2021. I'm John Furrier, host of "theCUBE". Great lineup in this event. Got some great guests. Matt Falk, VP of engineering at Orbital Insight. Matt, great to see you. Great keynote, thanks for coming on this cube. Appreciate it. >> Great, thanks for having me, John. >> So you at Orbital Insight, you guys doing some cutting edge work. Geospatial, big data, real-world problems. I mean, it's almost sci-fi for me. I mean, I just love how space, cybersecurity, data, all kind of rolling into like this whole very cool vibe. You know, drones, satellites, all this kind of you know, stuff going on in the cloud. But there's like real action happening, right? (chuckles) We all live on GPSes. Like, this is like very cool and relevant technology happening right now. Give us your take. What are you guys seeing, how's business? Give us a quick overview of what your journey is and how you guys are executing. >> Sure. And I think you're right there, it is a little bit like sci-fi actually, even to myself. Even having been in the industry for a few years at this point. You know, we all think about big data, it's become much more a thing especially over the past decade or two. Everything that we try and solve as big data, artificial intelligence, machine learning, they all thrive, they all need this big data. An untapped area about big data though, is geospatial data. And really data that comes from overhead sensors, coming from space. So to me, that feels a little bit like sci-fi like you're saying because that time is now. That time for us to be able to use and harness that data and provide actual or meaningful insights is here. You know, as a company for Orbital Insight, we got into it about seven, eight years ago and the title wave of this data was just forming. There weren't as many satellite provider companies, there weren't as many different types of disparate geospatial data. And by geospatial data you know, anything with a latitude and longitude associated with it, right? This data was, it was there but it wasn't as abundant there. It wasn't clear about how we could use that data. And over the past few years so many new use cases had really popped out and so many new disparate types of data and it's really about the fusion of all of them and getting more and more of that data. So right now the most exciting thing is really just how much of that data exists and how much is going to exist in the next few years. And honestly, we want to ride that tidal wave along with our customers. We can deal with many different types of data here. It's overhead satellite imagery, it's cell phone pings, it's identification system from ships, it's everything that you can get your hands on and incorporate into this platform. And then using this to feed the artificial intelligence and machine learning algorithms to derive new insights so it's sci-fi but it is here. >> Yeah and it's real computer science problems too. A lot of networking as well. You got this and it's transitioning too. You were out early doing these new use cases. But what's interesting about your journey and I want to get your thoughts on this, is that you guys really evolve from tackling these first kind of time problems, making solutions out of them to sequencing it to a fully on, fully-built scalable insight platform. Okay and this the pattern that we see in cloud native. Companies go from going in and doing things, that one-off, one-offs projects, POCs and then sequencing to a either cloud native or full blown platform. You guys have had that journey, take us through that effort and what's the result today? >> No, that's exactly right. The way we started, just like you mentioned, with many other companies was really around this proof of concept idea. It was going out, talking to customers, finding what their pain points were and figuring out what can we do to solve those pain points. So it was all about, "Okay, for this particular customer "take this data set, take satellite imagery "of this location, take cell phone things for this location, "take a digital elevation model from this area "of the planet, fuse them together in some "very specific custom way to try and solve that problem." And that's how we started. Over the first few years of that, this doesn't really scale as well. We had to keep building new solutions to solve new problems. What we started to identify was that there's so much commonality, there's so much overlap between a lot of these problems. So the solutions for them you know, if we're taking truck counts, if we're able to look at parking lots and detect cars from satellite imagery and use that to determine level of trends or sales at you know, a retail store. Well, we can also use that same overhead imagery to detect cars and look for their movement patterns, look for how they're going from a port to a particular warehouse or how they're driving on the road. Same thing with trucks. When we started identifying that a lot of the value from these different analytics and data sources can be used to solve many different problems in different ways but the underlying core technology is very similar. So for us about a few years ago, about two or three years ago at this point, we started really developing this into a platform that can solve generally any question about geospatial data analytics. So instead of, "I have a very specific problem, I wanted "to count the number of trees in this particular area." It's, "No, no, you want to do something similar to land use. "You want to try and classify different areas of the planet "that are covered in this particular type of land use "and then use that as an estimate for how many trees "there are." So we started to find these commonalities and that allowed us to really build this generic platform we call GO and use that to start solving many, many more questions. >> That's good. So real world problems are emerging. I'd love to get your thoughts on what those geospatial problems are because you can almost think about how the traditional distributed computing world looks at the edge for instance. Like you know, the intelligent edge or industrial edge, edge of the network as they call it in the hybrid world. You're bringing not just moving packets, you're talking about other data media. So pictures, images and (chuckles) different data sources. This is a huge aperture that changes the game on the analytics. Could you share some of the problems you saw that opened up with the new types of sources? Because it's not just packet to a device or an edge point in the traditional sense. >> Sure, sure. No, that's a great question for us. And I think a lot of people that don't really understand or haven't dealt with geospatial data before, don't understand necessarily a lot of the nuances that come with this data, there are quite a few of them. So I'll focus on satellite imagery in particular for the first part here. But you know, when people think about, even today what we can do with artificial intelligence. Computer vision on an image, you know, most people go to the images they can see from the cell phones. Most people start to think about self-driving cars and seeing the resolution of those images. Well, when you're dealing with overhead imagery, satellite imagery in particular, the game changes, the game is completely different. Yes, you're still working with an image but so many of the pieces of that image are just different and more complex. So for instance, when you're taking an image miles away from the planet, the first thing you have to realize is your resolution is not quite what people think. In particularly now, if we go outside and we stand in a satellite image, the best commercial sensors today, we're at most one pixel. And with one pixel you really can't identify people. You know, even a car or a truck at most, you're looking at 15 to 20 pixels and it becomes extremely difficult to classify objects at that resolution or at that scale compared to if you're using a cell phone picture, for instance. So that's really for us the first you know, major difficulty or change that comes into play. The second one would be the temporal aspect. So not only the spatial resolution but temporal resolution. You don't get an image every day. You don't even get an image, sometimes every week. So how do you, do you impute different data, impute different points to make your overall analysis worthwhile? Again, a slew of additional challenges that come with that. Other things like for instance georegistration or orthorectification. So the idea, when you take a satellite image of somewhere on the planet, you actually don't get very precisely where that image is. It could be off by five, 10, 15 or 20 meters even and you have to do work to actually relocate that image in the right particular position. Orthorectification refers to the angle at which the image was taken. If you're taking an image from a bird's eye view, yeah you're going to be able to see straight down and you're going to see buildings and they look like you know, squares of certain right edged polygons like that. But you can also take an angle 30 centimeters or excuse me, 30 degrees off, you can look at the side when you're taking this image and then the satellite image looks completely different. So that's another technique that we have to combat and another difficulty we have to work with in order to make this data usable. So for satellite imagery for other data sources too, there's a slew of additional challenges that we have to compete, that we have to fix and work with to make this data usable. And that's what our platform does. It takes this data, it fixes all those issues and allows you to compute the analytics on top of them. >> I love it. I mean, as a consumer, I can relate to like, say Google. I know there's trees there but they look like they just put trees there 'cause they can. Like there was a tree there and they fill it out, right? I mean kind of similar things going on there. All great stuff. I love the tech and I think it's going to be one of these eras that's going to be super valuable as more of these use cases can get this tap the data for not only insights but also maybe for features in software. Which brings me to the next question. How do people use you guys? I mean, as you have these use cases that are emerging, a lot of disparate use cases, different data sources now analytics as a platform, are you guys selling software is it a service, as a fee? Can you explain you know, how I might want to geek out and integrate you into my product or feature? Or do you do it that way? How does it work? >> Oh sure, thank you. So, there's two different aspects here really. And one of them is how people actually have access to this data. So the first is that we actually make this data available. And the second is the analytics that we put on top of this data. So for the first piece, a lot of these data sets are extremely expensive. So the average even consumer or a lot of businesses, it's much too expensive to go and actually buy all this satellite imagery or all this geolocation ping data or shipping information. It's just too expensive to buy these disparate data sources if you only have particular, single need for them. So the first thing our platform does, is it integrates with many different data providers. It integrates with, like I said, anything that has a latitude and longitude with it, we try and get that into our platform. And we become the broker almost, the provider of all that disparate data into a single unified source. So that's that first aspect, that's how they use us to get access to that data that otherwise they wouldn't be able to get access to. The second piece is the analytics. So for this, our platform, it does really you put three things into our platform. You ask where you want to look on the planet, what you want to look for and when do you want to look for it? And our platform takes care of going and getting all that information that it needs to compute that answer. Then using our custom analytics to derive what you're looking for, the question you're actually asking and produce similar to a data feed but it's much more custom than that, particular insights for the customer. So what comes out of the platform is effectively a time series that you're able to go explore and drill down into further. So a particular example of this is for supply chain right now and this is a problem that we're very passionate about right now. Especially with last year, how COVID impacted things. But from a supply chain perspective, we're actually able to identify locations on the planet that you're interested in, typically operating facilities and start looking at trends for where people are going to and from that particular facility. So we can see, "Oh, there were a hundred people that visited "this facility on a you know, the last seven days." And maybe produce a time series of how many people were there each day. But we can also then say, "Of those hundred people, "16 of them came from this location, 52 of them came "from this location, 53 of them visited this location "two days after visiting that location." And we can start to build this entire traceability map of that particular location and that our customers can use to identify patterns and then anomalies really, in their own supply chains. Or different things about their operating facilities. >> So pinging, like graph data, for instance. We got some insights into how to restructure their value chains or reconfigure their economics. Something like that would be like a use case. >> Exactly, exactly. Finding further efficiencies, ways they can optimize their supply chains or anticipating disruptions in it. If they know that part of their supply chain is dependent on you know, a particular facility, a particular location or a particular region. And they know from other news that something is about to happen to that region, they can know practically how to change their supply chain in order to you know, alleviate that pain before it even happens. >> Well, real time in the news just recently, just this month earlier in the month, we saw that gas shortage or stoppage or shortage/supply chain disruption, happen in the East coast, right? From the pipeline hack, the ransomware attack. That's a good example. I mean, some people don't even know the difference between a supply chain disruption and a shortage, there are two different things so I saw that big debate happening. This is kind of real world example where you can say, "Okay, we have a supply chain, "potential predictive disruption." Then maybe look at ways to do that. Am I thinking in the right way here? >> No, that's exactly the right way to think about it. You can start to see... So from that event, if you're operating a facility or a facility warehouse, you can look at that event, ask the question of, "How is this going to impact "my supply chain?" And the first thing you need to know is are you dependent on that, is that something that actually impacts or it plays a part in your supply chain? So you'd use our software, plug into your own operating facility, start to trace where people are coming from or going to. First thing you can do then, is identify, "Is that location "part of my supply chain?" If not, you know potentially you're in the clear. If so, then you can start to identify different locations that might be a suitable replacement for your supply chain. So can you practically avoid that going forward and make that move sooner than you would've been able to otherwise. >> I love the complexity challenge here. You guys doing the heavy lifting here in offering as a service makes total sense. You can almost democratizing the whole complexity of the data acquisition and then you know, providing value on top of it. The question I have for you is, what other learnings have you had? I mean, what was some of the difficulties? You mentioned you know, the artifacts, atmosphere, haze, noise, spatial temporal frequencies before. What are some of the other things that you're seeing and learnings that people might not know about that you guys have solved in this data capturing from the satellites? >> That's a great question. And there's plenty of them. A lot of things I think it would come down to is how to use this data or how best to combat some of the challenges like we talked about earlier, come with this data set. In particular, if we look at the foot traffic data that we look at. So pingings coming from different cell phones or what we call geolocation pings. Largely, you can think of that as any IOT device that's pinging their location you know, we can aggregate that data in and start using it within the platform. And what we've learned for that data is, it's very dependent on how you can actually get that to be normalized. And what I mean by that is none of this data is providing a complete picture of what's actually there. So again, if we look back at you know, even from image perspective when you're getting a satellite image when you're getting cell phone pings, you're only getting at best, 15 to 20% of the actual picture. And the challenges are really about going from that 20% view to the full contextual 100% view. So tactically, what that looks like for geolocation pings, where you're not getting geolocation pings from every person on the planet, we're not getting pings from every IOT device or every cell phone. We're getting a particular, almost randomized subset of those pings and they're all anonymized. So how do you go from that to an actual insight? How do you go from that to a full complete view of what's happening? And that's where our normalization algorithms come into play and other capabilities that we have that take that data and try and extrapolate what's truly happening. So if you're looking at you know, for instance if you look at a gas station. And it's a gas station in a you know, an area that's not very highly populated. And you're only getting two or three pings a day or some days you're getting none. Is that truly a signal of no one's going to that gas station or are you just missing the data? And you don't always know so part of what we've learned is how to take that data and actually translate it into the complete picture. We have very complex algorithms and they're constantly being improved on to account for differences like people turning their cell phone off or more than one person being in a car or things like that. So that's what we've really learned in it. It's all about taking that incomplete picture and trying to produce the most complete picture with as most context as possible to solve problems. >> So what's the secret sauce on all of this? Is it algorithms, is it data usage, all the above? I mean, take me through some of the secret sauce that's going on that you guys are building to make all this work. >> Sure, sure. And I'll go into it as much as I can. (John and Matt laugh) >> (speaking faintly) >> But there's a few different- Exactly. But a few different pieces really. The first one is the data itself, right? At the end of the day, no matter how good your machine learning capabilities are, if you don't have the data, you can't do anything. And this is true for all types of artificial intelligence or machine learning algorithm. If you don't have something to allow the system to learn from, you're at a loss. So the first piece of it really is getting the right data and making sure we have enough different or disparate data sources to really complete that overall picture. The second piece of it is allowing our platform to do this at a high scale. So it'd be one thing if you can produce a particular algorithm and get it to run in a single location one time. But it's all about for us, asking that aggregate question. So we're not you know, if somebody is asking about a particular gas station or a retail store, more often than not, they're not caring about just one location. They're caring about the aggregate, they're trying to look at this country as a whole and seeing what the trends or patterns are. So the second piece of secret sauce really is our platform and the ability to scale that up dynamically and allow you to ask any size question. So not just one AOI at a particular location but thousands of different locations and how that answer really compiles together. Third one is definitely the artificial intelligence and machine learning. For us, that is a extremely core competency. Something that allows us to really take that data and produce the insights. And that's a key factor of it. Like I mentioned, with the different challenges, part of our secret sauce there is not just the algorithms themselves but additional techniques or different R&D that we can do to solve or combat some of the additional issues that we have with overhead sensors. In particular, I'll point out two here but one is the rare object issue. So a lot of times if you're doing with satellite imagery and you're trying to find an object, it's very difficult to find a satellite image of that object. If you're looking for a particular type of ship you might only find one or two of them in thousands of images. So how do you build a machine learning algorithm that really uses that really small amount of data to produce an algorithm? And this is where our R&D capabilities come into play. And one to highlight is synthetic data. So the ability to produce almost fake or generated satellite images that actually produce these objects you're looking for so that we can train or learn off of that. So things like that really build our, I'll say, our secret sauce, are our R&D core competencies. The ability to produce newer novel techniques to generate data where satellite images or other geospatial data have deficiencies that we can combat. >> Yeah, I like that feature. Because you're almost saying, "The ship might look like this "depending upon where they're looking and muting that in there, good call. I guess the question I have for you is first of all, great tech loved the story. You guys are onto some really cool stuff and very relevant. The question, is in minds of peoples right now who are watching is why now for the critical time, why is now a critical time for geospatial analytics? What's your answer to that? >> Sure. That's a great, actually the answer is great for us too as a company. As I was kind of alluding to in the beginning, there's this tidal wave of geospatial data. And you know, if we were to look at five, 10, 15 years ago, the data itself and the technology was not really there to allow us to do what we're trying to do now. If you look back, I think it was in 2013, there was a particular computer vision paper that came out that really was the birth of the CNN world. And for that, that is a core compute capability that allows us to do the computer vision we need to be able to do. So that was a extreme catalyst for companies like ours, are being able to do this type of data fusion analytics. And the second one is the birth of the new sensors coming up right now. If you look back five years ago and where we're going five years from now, it's almost like Moore's Law. Where every other year, things are just starting to double. There's more and more satellites being thrown up, there's more and more data being thrown out and frankly, it's almost too much data at this point. There's just more and more data coming up. We already have petabytes of satellite imagery in our system, hundreds of terabytes of IOT device data and it's everyday just more and more of this data is coming up and being produced. So now is that perfect time because the data is finally there and it's only getting better over time. >> Yeah, I know we have a little bit of time left. I do want to ask just kind of, I'm curious. I'm sure people are too. As leader in that company, as engineering leader, you got a team that's working on some pretty cool stuff. A lot of computer science, a lot of new technology opportunities, kind of new problems that are emerging that are exciting. So everyone likes to solve hard problems, right? You got one, right? You got synthetic data, massive ingestion pipelines, normalizing algorithms, spatial imputation, et cetera, all this good stuff. How do you organize, how do you attract people? How are you looking at this? Because you have to lean into this. It's not like you're waiting for the market to come to you. You guys are going out there, making the market technically as well. So how do you organize, how do you recruit? (chuckles) Take us through some of the inside the ropes there. >> Sure, sure. So I'll start with kind of just how our engineering team is organized right now and where are we try and do find people and pull additional folks into our team. Right now we are split into four or five different areas. So like most cloud based platforms, we do have an infrastructure team. So you know, DevOps, site reliability, IT, everything that goes into that core cloud layer. So we do have an infrastructure team that builds that. On top of that, we do have our platform engineering team. So that team largely builds our microservices that play together to produce our external API. On top of that, we have a product engineering team that builds really with developing our UI, adding in our UX, making sure everything on top of the API plays nicely together. And also building a few additional dockerized computer vision and machine learning models that can plug into the platform. Separately, we have our R&D team. This is like as you know, where we talked about our synthetic data and all the other research areas that we get into, they focus there. Then we have our senior data engineering team. This team is largely focused on pulling disparate data sources, massaging them, cleaning them up into the right format so that it can be plugged into our platform. So from this, this is kind of how our team is structured. You're right, it's a ton of technical challenge. A lot of fun challenges. We're about 50 engineers right now. We're actually, we're looking to grow almost doubling in size by the end of the year. We're going to be bringing on an additional 30 people over the next few months. And what we're looking for is people from a wide area of expertise. So people that have you know, microservice core platform backgrounds, able to build on the backend system, deal with tons of tons of you know, transactions per second and really allow us to scale our platform. That's one set of expertise we like. Another one is people that really just have geospatial data backgrounds. And which to be honest at this point, it's somewhat of a rare niche finding people that have worked on a platform but also worked in geospatial data. But that's something that we love to bring into our system so we can add additional expertise and eventually get new data sources in. And then lastly, it's really around that core competency of machine learning and artificial intelligence. So we will look for anybody that has machine learning you know deep math, deep computer science background to come in and be a part of that team. If they're capable from a research perspective, we are actually you know, it's possible to teach them some of the computer vision aspects as well. So, if they have a computer vision background, great. If they have a data science and machine learning background, great. We want that diverse set of interests and diverse set of thinking to come in and really build our R&D team as well. >> Yeah. And I obviously DockerCon is here. You're talking about containers and that leads into Kubernetes, microservices, all kinds of cloud native technologies. Because what you guys are doing is you're taking an old construct. I mean, fairly old, I mean it's you know, it's data. But you're leveraging it in new ways. In a way that's kind of what cloud native's about. How are you seeing that world evolve? Obviously we're here at DockerCon, containers helps big time thoughts on the containerization wave that continues. And you got Kubernetes and more and more cloud natives, more SRE's are going to be hired. Again, people are scaling up. What's your take on what's going on around DockerCon? >> No, this is actually for us. It's really powerful and it's a really powerful tool. Whether it's Dock or (speaking faintly), the idea of containerization as a whole, it really allows our platform to get to that next level of scale. One piece I you know, originally we were not a microservice platform. Like I said, we were starting to do some more POCs. As we got into this platform play, one of the things we knew we needed to be able to do was scale different parts of our system. So whether it was scaling to ingest more data, scaling to involve new algorithms or scaling really to involve or be able to compute massive computation requests that come from our customer. This requires different pieces of our system to scale. If we were a monolithic application, if we were running on premise, that type of scale just wouldn't really be possible at the level that we need it to. So for us, the solution is all around being able to dockerize different parts of our system, keep them isolated, keep them talking to each other via different interfaces. And then as need be horizontally scale different pieces of our system to compat with that. So really the you know, Kubernetes Docker together, the ability for us it's allowing our developers to focus on the code that they need to be writing and not focusing on the SRE or the DevOps perspective of it. And then letting our DevOps team use these additional tools to make themselves more efficient. You know, we can do that with a smaller team now we don't need a team of 50 people on DevOps or infrastructure. You can do it with five or six solid engineers that can really you know, manage your entire environment. >> Yeah, I think having that horizontal scalability is critical and the containerizing it, so many benefits there allowing things to be completely portable and integrating really well. Great stuff. Unbelievable gems dropped here. My final question for you while I got you here is you know, as you look at other peers and people in the marketplace, the people who were on the right side of history are experiencing, certainly entrepreneurs and people who are in businesses and enterprises are waking up and going, "Hey, that can really change the game and flip "the script with cloud native." So people are experiencing similar journeys where they got product engineering saying, "We are more of a platform. "I could sequence and build out that platform and then build "my infrastructure on the cloud." So you're starting to see these point applications turn into platforms. What's your advice to people out there that are going to move from product engineering departments or groups to bring on that platform construction or that work and then build that infrastructure like you guys are? What's your advice to folks that are going to make that journey? >> No, that's a great question, John. I think the quick advice I would give to anybody you know, that has a product engineering team considering moving to the platform right now is do it now. There's no time better than right now and what I mean by that is, the longer you delay the harder it gets. You're going to be missing out on a lot of the new technologies that are really being solidified as part of the cloud computing world. Yes you know, there are trade-offs. Especially you know, you might have to go to your exec team or your product team and make these trade-offs and you won't be able to develop teachers as quickly as you're spending time porting to a platform play. But the benefits are amazing. And once you actually get there, you'll really be thankful that you took the time to do it. Yes it's, you know again, it's going to be challenging because it's one of those things where it's an engineering benefit at first. It's not going to be something you're going to say, "Yes product, "in two months, you're going to get this benefit from it." Or, "In you know, three months, you're going to get "this benefit for it." It's, "One year from now, this is how our platforms "are written, our new product is really going to be able "to expand and grow." And the best way to get there is to just do it now. Really starting encapsulating your system, break it out into different pieces, put it in a cloud, allow it to scale. And so yeah, my advice is to just bite the bullet and do it now. >> So people who buy into that notion of moving from monolithic to microservice based applications want that horizontal scalability, as you mentioned. What are some of the first principles in that platform? What's on the mind of the architect or the leader as they start thinking about those first principles for the modern platform? >> Sure. I'd say the first one is don't over design. So some people have a tendency when they start thinking about microservices is they really go to microservice almost nanoservices. They really start breaking off you know, as many different pieces of the code, making them as small as possible. And to some extent, that's what you want to do with microservices but you don't want to go too far. I mean, it's easy to go down that rabbit hole. So in particular, there are certain services or microservices that you find out, they're tightly coupled. They're constantly passing data back and forth and that's when you realize, passing data back and forth between two different logical separation of code, it takes time. So it might make sense for them to be one unified microservice as opposed to two. So the most important thing to think about is you know, what pieces really make sense to logically separate and how does that actually impact the flow of data or flow of information through your system? If you're adding too many hops between you know, a certain end point and the call to the backend system, it might be time to rethink the way you're breaking system the system down. But you really want to start out with what can be broken down into the logically encapsulated pieces? And that's where we want to pull our microservices. >> Highly cohesive decoupling, that's a concept. An operating system as we say, it's the platform, that's the cloud. >> It's not new, that's right. >> It's been around. Matt, great interview. Thanks for dropping the gems and sharing your knowledge. And congratulations for the work you're doing at Orbital Insight. Great focus, love the company, love the excitement. Thanks for coming on. >> Perfect. Pleasure chatting with you too, John. And thanks for having me. And thanks for having me be a part of DockerCon. >> All right. DockerCon 2021, CUBE coverage. I'm John Furrier, host of "theCUBE". Thanks for watching. (lighthearted music)

>>from around the >>globe. It's the Cube with digital coverage of AWS reinvent 2020 Special coverage sponsored by AWS Worldwide Public Sector >>Hi and welcome to the Q Virtual and our coverage of AWS reinvent 2020 with special coverage of the public sector. I'm your host, Rebecca >>Knight. >>Today we have two guests for our segment. We have Kevin McCallum Jr. He is the chief technology officer at Maximus. Thanks for joining us, Kevin, and we have way. And we have Steve Zimmerman, who is the vice president of consulting services at Insight. Thank you so much for coming on the show. Steve. >>Thank you for having us appreciate it. >>So I want to start by asking. You both have to tell us a little bit more about your company's. Kevin. Let's start with you. Tell us a little bit more about Maximus. >>Yes, Thanks for having me. Maximus is a 40 year old company. We partner with state, federal and local governments to provide communities with critical health and human service programs. We leverage extensive experience to develop high quality services and solutions that are cost effective and tailored to their unique needs. One of the things that we do is offer government's ability to programs rapidly and scalable so that we can focus on the automation and their operations. We do services from Medicare to Medicaid, Welford work, and we have comprehensive solutions. Help the government's run effectively and efficiently. >>Great, Steve, tell us a little bit about insight. >>Yeah, sure. Um, Insight is a Fortune 500 company, you know, in 2020 will roughly do you know, probably a plus billion dollars in revenue. Global company. You know, we have thousands of treaty GIC relationships, but I'd say we have probably a couple 100 partners. We focus on one of those key partners to us is a W s. Right. As we go to market, Azzawi start, you know, working with our customers around transformation, of which we're gonna talk a little bit about that today with Kevin as it relates, Thio incite public sector. It's >>a pretty sizable >>part of our business. You know, we'll do about $1.5 billion in revenue. We have 200 plus contract vehicles, will work out there over 500 plus teammates, and we're seeing that business grow quarter over quarter, 20% growth. So It's a big investment for us and really looking forward to hearing Kevin talk about Maximus, uh, to the team, because obviously it's a big lever for us for inside public sector to get the word out there about the great transformation work. What you do with our customers. >>That's a great segue. So let's go back to you, Kevin, and talk a little bit about Maximus. Cloud transformation. Why did you hire insight for help you with this? >>Yeah, A Z We started our journey. One of the things we realized is as we were moving to the cloud is the experience. We needed a trusted partner and we ran an RFP process looking for partners out there that have done it that have done major data center programs. You're moving large companies, you know, We're moving about 6000 workloads 160 plus applications. So it was not a light or easy project and insight fit that. Aziz, We went through the interview process. It became very clear that they have done this for Fortune 500 companies in the past and their experience is beneficial to helping us drive to the future and the other factors is we wanted to make sure that once we were done with the project, we had the experience internally that they helped us with Thio drive forward. >>So talking about the importance of a trusted partner, which is such a key component of digital transformation cloud journeys tell us a little bit about the the strategy tied to the data center transformation and why you chose AWS. >>Sure. So, as we started doing our research, we did analysis across all of the cloud providers who were out there. AWS is clear leader in the marketplace. Their technology is better aligned with what Maximus has as the underlying technologies were, ah, majority of Lennox Base. We also have windows. We have Oracle, which, with the AWS depth on breath of our offerings, tied better to what we had. The other thing we were looking to do is get rid of our monolithic off the shelf products and use mawr of the cloud based products that are out there. Amazon has a very deep, uh, native technology that allows you to replace your old services where you had to bolt on or purchase another product to something that is integrated and streamlined, you know, down Thio, how do you monitor your systems? How do you do logs things like that. And, you know, as we looked at the time frame, we had to deliver this. They had to be able to grow with us. So as we were building out, new infrastructure were able to build where previously internally. With data centers, you have to buy infrastructure. You wait for it to arrive, you install it. Amazon has it at the click of a ah button. So we're able Thio basically have environment stood up in a day rather than having to wait weeks for it. So and the last thing was up time. So you know Amazon. They're five nines plus in up time and most of our contracts or three nines or better requirements. We had to find a bender that had multiple availability zones and regions that allowed us to be flexible in how we deployed. >>So talking about the convenience and the ability to streamline, and also the need for flexibility in the covert era. Of course, the word hybrid work environments has taken on a new meaning. But I want to ask you about how you see the hybrid era in the long term affecting Maximus. >>Yeah. Since Maximus is a government contractor, we will always be in a hybrid, uh, set up. So some of our contracts are very restrictive, especially when you get into our S d. O. D. And some of those agencies you have a fed ramp requirement is right. Well, with some of the federal agencies. So some of those components about to stay internally So where we can force, uh, you know, moving to the cloud because of the flexibility we have to deploy, that is the right will go. Um, co vid has introduced a new complexity. When it started back in March, you know, Maximus had 30,000 or so employees, and we instantly were thrown into You gotta make those employees get those employees to work from home. So we used Amazon's workspace Thio push our employees to work from home, where, you know, some of the employees and some of our contracts are customer owned equipment. So we couldn't actually take that equipment home. So we had to move to a B y o d model on Amazon workspaces in order to get the users to work from home and the complexity that, with what Amazon has to offer, allowed us to quickly move over 25,000 employees on the Amazon workspaces and work from home and then keeping the data center migration moving in the middle of it has also been, ah, challenge. So we will, in our federal space, still have internal data centers. Integration points that Amazon offers with their inter connects is key toe. How we make it a seamless process because we may have a business unit has stuff sitting in the data center and at Amazon, and they have to look at the seamless package. >>Steve, I want to bring you in here a little bit into this conversation. Cloud transformation, digital transformation. These are These are difficult and huge undertaking in the best of times. How does this pandemic this health crisis emergency. How has that affected the way you help your clients the way you work with your clients? Collaborate, communicate, talk a little bit about the effect of Kobe on this on the >>eso I would. I'll answer the question in a couple different ways, so I would agree with Kevin because, you know, forget about what we do with our customers. You know, we had a pivot really quick to write all remote workforce. You know, I think about my team, you know, 1000 plus teammates. Everyone's 80% travel all gone like, um, and I write eso everybody working remote. Everybody work from their homes. And but the challenging part was working with our customers. And, you know, I look at you know, I looked at with Kevin. You know, I've never met Kevin in person, you know, frankly, and there's teammates have come on to our to the project and execute executing this program remotely, so it makes it that much harder working with the customer. Um, you know, doing more video chats. You know, our methodology is built to be all remote. We have a proprietary tool called snap start that allows to bail scan environments. All that things done. Remote migrations could be done remote. The hard part is when you have to go on site because there's this stuff you have to go on site for around physical inventory to look at the equipment, but it just makes it that much harder. You know, I think he taking advantage of these video tools like we're doing today. You know, I can't tell me how many Skype You know how many calls have been on with Kevin like this and with his peers and with his leadership. But communication is really important program like this because, you know, in a program like this, there will be problems, right? And there will be challenges and, you know, getting on a call on being I will look at Kevin face to face and see what his reaction is really key. But you gotta work that much harder. You gotta work that much harder now in the pandemic. You know, I have other projects right now leaving with this other projects that, frankly, we have sold all remote and we're doing it all remote. And what I'm seeing with the bidam IQ is an acceleration of digital transformation. So, other similar projects like we're doing with Kevin. We're doing for other large fortune 500 companies because it's an acceleration of Hey, look, we gotta be old digital now, so it'll be interesting to see you know how the pandemic effects is long term because it is definitely accelerating out their digital transformation if you haven't done it, you're in trouble because it's gonna eat your company alive. >>Mhm. So, Kevin, he's talking. He talked a little bit about she talked a little bit about the importance of communication, particularly when work so many people are working from home. Um, talk a little bit of about other best practices that have emerged. Things that you have noticed. Things that you advice you would have to your peers. I mean, a Z we heard from Steve. If you're not there yet, you're in trouble. But for the for the people, for the executives out there who are watching this, What advice would you have for them? >>Yeah, I think that you know this this is brought to light. You know, there was always a view that you had to be in an office on a white board and actual actually functioning in that fashion. So, you know, before the pandemic, I was traveling three weeks a month on now, not traveling. I feel that I actually get more work done. I actually feel that I'm closer to the team just because we've introduced a lot of different digital channels. So now we have slack we have teams we do zoom. I require everybody to be on a on video, whereas previously before the pandemic you'd rarely have anybody on video. Um, and you've seen Ah, transformation is people pick up the phone a lot quicker than they did in the past. So it is, actually, I believe, brought the team closer together because now you know, everybody's on. Um, the downside of it is everybody's on all the time. So you've also had to have people step away from work because generally when they take PTO, they leave the office that go somewhere with their family. Now it's your kind of at home. There's not much to dio. You kinda have to force them to take the time off. One of the major factors that has has been interesting is we're doing this transformation in the middle of co vid with moving. All of our resource is the home. So we've we've had to take pauses, toe focus on getting everybody to work from home. Okay, now their work from home back to the project. And, you know, it's kind of a change the timeline a little bit, but in the end, you know we have some hard deadlines to meet. So it's been an interesting transition. You >>know, Kevin, um, I wanna agree with you two points is, uh you know, I think we're also getting not only your time, but also senior leadership, that I think, frankly, we never would have gotten, you know, I'm talking, you know, your peers and your leadership, Like I would fly for those meetings. I think about all the time that I've saved. But then again, it never ends, right? Never. It begins and never ends. And, you know, one of the things I'm concerned about is you know, the long term burnout factor for these folks because and depending on what state you're in, it never ends. You don't have anywhere to go, right. And you know, I think about teammates. I think you know, Kevin, I have talked about this related to our project like burdens and really thing right now for sure. 889 months into this thing. It's a real thing. Is people they have to focus on. Is is work sometimes. So it's a it's a concern for all of us is a project team is we start looking at the executing. This continue to execute this program for the next year. >>And it really highlights the importance of visionary leadership and a leader who cares who is empathetic, who is checking in with his or her team and making sure that the colleagues feel appreciated and cared for. I want you both to just give us look into your crystal ball is a little bit and talk about the where you see things 12, 24 months from now. Hopefully there will be a vaccine and we will return to somewhat of a of a new normal. Um, talk a little bit about where you see the Maximus transformation in two years. Absolutely. Yeah. Start with you. >>So s so you know, our cloud migration. We have some hard deadlines through next year, so we have a focus with insight to get that completed by September next year because our data center contracts are up and we've got to get out. You know, one of the the advantages of where we're headed is to move into more of a Dev ops model where you know you're able thio enable groups that have previously not been able to do work just do thio. The infrastructure was set up your now, enabling them to do deployments, get into production and have full stack ownership. That's really where our focus is. Is enablement of the teams that couldn't do the work previously because now you're in a different type of environment. Um, the other thing is being able thio be more agile. So as we move forward into the cloud journey, we as a company are consort contracts quicker. We are part of the, you know, contract tracing on unemployment insurance. We've done a lot of contracts with states that you know previously most of our contracts or anywhere from a 62 120 day startup. These contracts and contact tracing and covert projects. We've had to start them up in three days. That's having 500 employees online on workspaces on Genesis Cloud and fully functional, and it has been a challenge. But it also has introduced a a better way to do business because now we can we can move quicker for our customers and we can get contracts where they come and say, Hey, I need something in the next couple days. If you look further down the road. You know, it's taking the advantage of what Amazon has to offer, you know, moving from arm or monolithic programs like, you know, we sit on Oracle on Lenox today. You know, we could move into Aurora, which opens up the doors and floodgates, because then you manage, er a little differently. You manage your data a little differently. That's really where I think the the market's going and where we can actually transform our business. Even better, Thio, where we could be more flexible. We can start up quicker and, you know, be doom or things for our customers. >>The final word from you >>e I think it's gonna be a hybrid world, right? It's at least in the short term. And you know, we believe it's all about the workload and getting those workloads or applications, you know, in in the right spot, whether it be public or private and helping our customers with that journey, you know, just a pile on with Kevin talked about around Dev ops. Once you get a guy to get once you get all the stuff over there, you still got to manage it, Whether it's in a W. S or, you know, on Prem. You still gotta have a process to do that. So we see a lot of opportunity around the Modern I t operations and helping with that way. We want to continue to be a trusted partner. Thio Maximus. It's been a great relationship, but I want to thank Kevin and his his leadership team for trusting in us. And we look forward, Um, or more success with him in the future. >>Excellent. Thank you both so much. Kevin and Steve, thanks so much for coming on the Cube. >>Absolutely. Thank you. >>I'm your host, Rebecca. Night. Stay tuned. For more of the Cube virtual coverage of AWS reinvent with special coverage of the public sector.

live from San Francisco it's the queue covering micron insight 2019 back to you by micron welcome back to micron insight 2019 in San Francisco you watching the cube the leader and live tech coverage we're wrapping up our day Dave Volante with David Floria we're Kia Jones is here she's the senior principal for 5g connectivity in the GPM group at Microsoft great to see you great to see you guys it's been a wonderful day yeah nice job up on stage today you know we're gonna talk a little bit about a 5g and get your perspectives on it but tell us more about your group and sort of what you focused yeah well you know I'm in Azure and azure is even though it's our cloud infrastructure there has to be a lot of backbone support so inside of networking we have all those components that would support the physical structure of cloud technology so we focus on virtualizing a lot of the different managed services that we have and we provide those to customers for scalability and sustainable models that allow them to pretty much transport data safely what's it like inside of Microsoft these days I mean what a transformation of the company I did a little segment on LinkedIn the other day a little video segment there's a lot of ways if you want to spend some money there's a lot of ways to spend money with Microsoft you know you got cloud you got an application management you got security you got all the abs I mean really is you're hitting on all cylinders it's what a dynamic what's it like inside of there these days you know I think I think the world now is very different I see Microsoft making a huge transformation over the last have only been there three years but what I can say to you is that I see that the opportunity is there for partnership now which is something that I don't think we focused on we did too well in the past with our new CEO Satya Nadella and Peggy Johnson who leads our business development I mean we are really taking the the game and just changing and and making making it more something that we can do with everyone instead of in silo and so the culture is definitely changing is their opportunity yeah but I think our company is dedicated and focused on you know creating more diverse technology that can meet two customer's needs all over the yeah I mean windows are still a huge part of Microsoft's business but it's not the future of Microsoft it's very very impressive to see that transformation all right what what should we know about 5g what's Microsoft's put point of view on 5g what's the opportunity for you well that's a great question a lot of people were like Microsoft and 5g how does that work well in the past I think with the telcos and all the communication service providers we've always been either their customer or they've been our customer the new world that we're going into now is how do we help the telcos now be better enablers so that we can accelerate business and so I took over this charter along with my boss Yusuf Khalidi and about a year ago and our vision for what we thought we could do came to really great reality when we understood the the ecosystem that already existed this this business of 5g is not as simple as it sounds so what we decided to do and we released a press release back in July was we're going pretty much all in in a sense with AT&T in helping them with their global strategy for really Wireless you know to help stabilize their wireless network and combine some software-defined principles and capabilities so what they're they're already planning for in the future so I think with that transformation honestly a lot of it is unknown but we're taking a day by day we're talking earlier about a lot of is unknown in terms of like what apps are going to emerge who's gonna pay for what but but there's a lot of hype around 5g certainly a lot of folks in the technology community you're excited about it yeah help us understand what's real and what's not what's hype and what sort of things that users can actually expect and then I want to know exact months and you know what I think if you can imagine it it's a possibility I think it all boils down to the type of partnerships I always say that it's the power of three in the sense in the past has always been the power of two where two companies come together we can make it work well now you need you need an extra component not only do you need a company like Microsoft but you need to tell Co but you're going to need a system integrator right and I'm not talking about the hardware side but more of the software application side I think if a lot of people talking about like these things we know that smart city we think that's a possibility real soon mmm I still think we got it way to go with a lot of the townships and the city infrastructures moving so slow with governmental policies changing I think that we can likely see improvement in connected vehicles connected you know Internet of Things in and if everything is coming we first must be able to connect the dots with those things first before we actually bring in a government entity I think they have a lot of questions that we're not yet ready to answer and so I think there's a lot of great need for POCs and that's what my team focuses on inside of Azure networking he's looking at what can be the next PLC where we can help truly define the next generation requirements you're kind of acting as the accelerant I like the power three analogy a bump set spike yeah right now fashion sports analogies in the cube and then you know you point about I think governments is right on I was saying the earlier to David that I participated in a mayor of Boston as this you know the Smart City future initiative and you're talking 2050 oh yeah you know that's their time horizon I'm trying to figure that that's right around the corner yeah I mean I can't believe we're about to be in the 2020 feels like I just graduated high school when you're looking at these opportunities there seems to be a big divergence of opinion in terms of networking between are you going to push the processing out to the edge and do as much of it possible there and only take the the subset of data from that processing or are you going to bring it in to some sort of center to do do the processing there yeah what are you what are you seeing in your five-year as the models of computer putting together compute storage and networking yeah for me I have a this is rukia's opinion but I think that in the future when we talk about compute we need to be able to chop it up and we need to sell it to individuals individuals could be enterprises or consumer in my world and the way I think about this I think that we have an enormous amount of opportunity to give customers freedom and flexibility if we're not able to give them freedom and flexibility I think that we really limit the possibilities of what truly podgy can bring and so in my world I believe that you should be able to prepay for your computing power I believe that you can literally distribute it among all your devices within your families like you know you have the family rate plans that the telcos are selling well think about this and that family rate plan now there's an additional component that you will sell which will be compute I may not want to go as fast as you like for gaming for instance absolutely right okay gaming for everyone and at the speed of what you need so atomic of components of compute that you can actually assign to individual users applications workloads yeah it's not - you told me tomorrow rookie what do you want to do that's not saying ok so what what are you doing here what's the connection between sort of Microsoft your role and you know this micron insight event micron is a a great partner with with Microsoft I think we do a lot of business as it relates to chips I specifically am here because Peggy Johnson supports the company and she's over our business development operations for the companies and she believes micron is on to something and so I'm proud to be here and getting to know more about the business visit micron and the partnerships that we can create in the future well we more partnerships obviously you need membrane storage to run all this totally do we can do it part of the build-out well Rukia thank you so much for for coming on the cube we'll give you the last word your takeaway is what should we be watching for from you and your group yeah I mean I think that as we go forth with our partnership with AT&T which happens to be one of the global carriers around the world we are planning for some amazing things and we will make announcements at our Microsoft ignite event in early November and so I hope that everyone will attend that event and perhaps support us by sharing the information and coming to talk to us about new partnerships with our clouds networking department great well we'll be there at ignite hope to have you back on that'll be great thank you and thank you everybody thank you David Fleur a great job with the crew which is a wrap from Pier 27 at Microsoft blending Microsoft mega you both say the same names right the insight conference micron inside 2019 check out Silicon angle comm check out the cube net for all the videos and we'll see you next time is Dave Volante for David floor thank you

live from San Francisco it's the cube covering micron insight 2019 brought to you by micron welcome back to San Francisco everybody we're here at Pier 27 the Sun is setting behind the the buildings in San Francisco you're watching the cube the leader in live tech cover jump date Volante with my co-host David Flair we've been here all day covering micron insight 2019 Tommy Vee is here is the senior vice president and general manager of the compute and networking business unit at micron Tama great to see you again great to see you so you got compute and networking two of the big three you're in your business unit there you go but we're gonna talk about 3d crosspoint today but so anyway you know absolutely we're kind of bringing you outside the swimlane or maybe not but tell us about your bu and what's the update yes we you know we sell primarily memory today DRAM although in the future we see 3d crosspoint it's a great opportunity into the the data center you know both traditional servers and the cloud players pcs graphics and networking yes so you get some hard news today why don't we dig into that a little bit we surely haven't covered much of it but okay yeah so I guess you know a couple couple things of interest probably most directly as we we announced our our first 3d crosspoint storage device it's a it's a it's the highest performance SSD in the world and offers compared to other 3d crosspoint based solutions on the market you know anywhere from three and a half to five times the performance on a range of both sequential and random reads and writes two and a half million I ops bandwidth readin right north of nine gigabytes a second and I'm super fast super fast fast and you know similar similar you know a very positive comparisons up against up against me and SSDs ok and so we're excited about that so where's the fit what are the use cases who you're targeting with sure yeah I mean I think you know that one way to think about it is that anytime you introduce a new layer into the memory and storage hierarchy you know historically it was SRAM caches and then it was SSDs going in between dear and rotating media now this is 3d crosspoint sitting in between DRAM and and NAND and and the reason it is a benefit in terms of another layer is it's you know higher density and and greater persistence than DRAM it's greater performance and and you know you can it can cycle greater endurance than the man and and when you do that you do nibble away at either side of that layer so in this case that nibbles away a little bit from DRAM and a little bit from NAND but it grows the overall pie and it's the only player in the industry that provides DRAM 3d crosspoint in and we think that's a great opportunity at some code to the economics cuz it's more expensive than and less expensive than the DRAM higher performance than the traditional flash short lower performance well under the performance of DRAM so yeah I mean so again I think you know the the the you know the benefits like I said is it's it offers greater density and it offers greater persistence than DRAM and so that's the advantage there and it offers much greater performance on things like bandwidth and I ops and much greater endurance than the NAND and certainly our preliminary results are in in applications like databases in certain AI and machine learning workloads and in workloads that that benefit from low latency I think financial service markets is one specific example you know we think there's a good value bro so so a Colombo question if I may yeah so si P would say no throw it throw everything in memory in Hana and of course sell the DRAM and say ok that's ok with us so you mentioned databases how should we think about this relative to in-memory databases sure I mean I think that if if you can afford it and of course it will be more expensive we would love to provide you know our highest density DRAM modules on on the highest end server platforms and you know put put you know you mentioned you know Hana database in the terabytes and terabytes of the RAM that would be great that is is not free if we refer you to do it right exactly and and so if you have the need for that performance that's will do but we we see there's a you know a an attractive range of workloads that cannot afford you know there's a costume that very high-end solution and so this affords something that that gives you know good benefits a database performance but at a slightly more I know you want to jump in go oh yeah sure I compare yourself with Intel which is obviously got the same raw technology they have gone for consumer type obtain [Music] SSDs but they put all their effort into combining it with a DVD or envied him and have combined that with the processor itself and made a combination which is very good for storage controllers yeah so the quest you can very well in in in the SSD much much much more than they have are you looking to go into that and the dim because he obviously you don't have the processes themselves to to to man yeah I mean you know to be clear the you know what we're offering today you know is a product that runs on standard and yeah and via me and while there may in the future be opportunities to further enhance performance with software optimization it runs you know out of the box absolutely without any software optimization and but I do think that you know there are opportunities both to use this technology in you know more of a storage type of configuration and and looking forward there are also opportunities to use it in a memory configuration you know what what what we're announcing today is our is our first storage and with regard to additional products you know stay tuned so if I think about the storage hierarchy you know the the classical pyramid and forget about let's let's focus on the persistent end of that spectrum yeah this is at the tip right is that how we should think about this or not necessarily I mean it is at the storage tip yes but I think we 10 to think a little bit more holistically that you know that that triangle extends from you know from DRAM traditionally to SSDs to rotating and we're now inserting a 3d crosspoint based layer in between and and so from that perspective it is it is the tip of the storage triangle right but it does sit below it does sit below DRAM so in the overall and the reason for my question was sort of a loaded question because if you eliminate the the DRAM piece now you've got that tip sewn and benefits from the volume of consumer thoughts on how you get volume with 3d crosspoint sure you know again I think there are you know at a at a lower performance point you know you can get higher density you know more cost effective storage solutions with that um and we certainly don't see you know NAND going away or we're quite bullish on that you're like man you know it's both a both a SATA and a nvme 96 layer TLC nan based products today so that's that continues to be a major area of investment but you know from a you know from a from a value and opportunity point of view we see a better opportunity you know applying this technology again into this layer in the you know in the in the server or datacenter hierarchy um you know as opposed to what one might be able to do in the consumer space and your OEM say bring it on right I mean they they want this we're talking about the server manufacturers data center yeah I mean I think we're in you know we're in we're in limited sampling with select customers so you know more to say about our go-to-market you know at a at a future date but certainly we we see that there is you know we're we're bullish about the opportunity the marketplace so just asking a question about volume making sure you if you look at the marketplace it's arm has been incredibly successful and it's driven a huge amount of memory and and Nan for yourself then that seems to be where the volume is growing much faster than most other platforms are you looking to use this technology 3d crosspoint as in in in that environment as even memory as in DRAM itself as memory itself at a much lower level I'm just thinking of ways that you could increase volume sure I mean so to be just just to be clear you're talking about what's driven overwhelmingly by by the cell phone market right obviously it's it's proliferating into IOT you know I guess again our our our view of the of the first and best opportunity is in the data center which is still today an x86 dominated world I would say you know in terms of opportunities like I said for you know memory based solutions in the data center um and for how we apply this in other areas you know stay tuned let's talk about this forward next acquisition so it's really interesting to see micron making moves in an AI why the acquisition tell us more about it sure yeah so it's a it's a it's a small small start-up you know handful of players although you know fairly experienced as as I believe sanjay mentioned they're on their their fifth generation of their architecture and so what we've acquired it's both it's both the hardware architecture that currently runs on FPGAs along with the supporting software that supports all the common frameworks the tensorflow is the the PI torches as well as the range of the network architectures you know that that are necessary to support again primarily on the inference side you know are we see the best opportunities in edge in fencing but in terms of what's behind the acquisition first of all there is there's an explosion of opportunity in machine learning we see that in particular on you know on edge inferencing and we feel that in order for us to continue to optimize and develop the best solutions both over all of a deep learning platform that includes memories but also just memories that are best optimized we need to understand you know when you noticed in the workloads we understand the best solutions and and and so that's why we made this acquisition we integrated it with our team that has for some time developed FPGA based adding cards and it's actually the basis of the technology for some of the dialog that used to offer example with OHSU when you talk about edge inferencing we're envisioning this sort of massively scalable distributed system that of course comprises edge you want to bring the compute to the data wherever the data lives obviously don't want to start moving data around now you're bringing a eye to that data which is the data data ai cloud all these superpowers coming together uh-huh so our premise is that the inferencing is going to be done at the edge much much of the data if not most of the data is going to stay at the edge yeah so this is what you're enabling through that integration provision heterogeneous combination of technologies correct I mean you know to use the extreme example that we talked about you know on stage earlier you know CERN has this massive amount of information that comes from the I think it's 40 million collisions a second or I may have my figures wrong and you cannot possibly store nor do you want to transmit that data and and so you you have to be applying AI to figure out what the good stuff is and there's no stream it's exactly and that solution exists in a myriad of applications you know the very you know simplistic one you're not going to send you know the picture of who's at your front door you know to a core data center to figure out if it's somebody in your family yeah you don't want to be doing that maybe not in the camera but certainly a lot closer because you just you know the network simply will not can't handle the capacity all right we got to go but but last word you know what are the takeaways from today what do you want our audience to remember from this event well I think you know I think it's just we continue to build on our memory and storage base to to move up the stack and add values in way that maybe storage subsystems like our our NAND SSD and 3d crosspoint that you know go a little further up the stack in terms of our gaining greater expertise in you know machine learning solutions or or the example with authentic of providing you know a broader solution including key management for how we secure the billions of devices they're gonna be at the edge touching all the bases Tom all right congratulations on all the hard work and it was great to see you again thanks guys Dave and Dave thank you and you keep right there but it will be back to wrap micron insight 2019 right after this short break from San Francisco you watching the cube

>>Live from San Francisco. It's the cube covering my groin insight 2019 brought to you by micron. >>We're back at pier 27 in San Francisco. This is the cube, the leader in live tech coverage and we're covering micron insight 2019 I'm Dave Vellante with my cohost David Floyd and this event is kind of interesting. David, it basically intersperses cube interviews with big tent discussions, thought leadership, we've heard from automotive, healthcare and and 5g discussions and no Han is here. He's the vice president and CTO of the emerging business at Erickson. And you were just on a panel. Welcome to the cube. Thanks. Great to be here. You were talking about five G, we're going to talk about five G. so first of all, talking about the emerging business at Ericsson, >>your whole group, you know, so Ericsson, we, you know, 99 a lot of our business today has done what operators emerging business group, we're sort of looking at the intersection of industry, cloud computing, our traditional mobile network operator customers, and how do we, how do we put those together and look for new either products or business models. And really create something new for customers. >>So we tell him when he's talking about five G, everybody gets all excited. Certainly the technology community is excited about it. There's a whole value chain and an ecosystem that's that's pumping right along. The carriers are adopting and the users are just waiting. So what should we know about? >>So I, you know, I think there's a couple of different things. One is from a consumer perspective, you're definitely looking at faster, you know, better. All of the things we've got from the other GS at older things. You know, today, you know, faster downloads of movies. I think what we're, and I'm, I'm in the tech business, not in the prediction business, you know. So I think what we've learned from previous technologies is we almost don't know what the new applications are. We're trying to make the platform as easy as possible for developers to utilize what the network actually has to offer. So I think that's a big part of what we're trying to do. The other part is enhancing what you have today as a consumer is massive, but also industries is a huge pull on 5g. So we talked about industry four. Dot. Zero and really transforming industries and cutting the cables in production lines, allowing monitoring of systems that never happened before. >>A lot of use cases that can be out there. So a, I have a younger son of 22 and I look at my a bill every month. Yeah, I do have him downloading 10 times more data. It doesn't fill me with uh, duty or just the excise to carriers. I mean while we've seen with every, every end. And of course that was the question how much of a down, yeah, how much low is the price going to be on this baffled breeze you go to invest an awful lot. Absolutely. So I mean we're going to see it tens, 10 orders of magnitude cheaper. So even as it is now with 4g, we're seeing a lot of the unlimited plans coming available and so on. I think we're just going to see more of that. And then the question, actually a big question for five G is what will you pay for? >>You know, if we talk about age compute and low latency, if you're a gamer and I can give you X milliseconds of latency versus you know, a two X milliseconds, how much would you pay for that? So I think what we know at the moment is people will pay for that. We don't know exactly how much, and that's where you need the ecosystem and you need to get stuff out there. And actually some of the economic impact is fuzzy. But in thinking past, there's no prologue. But if you think about the other GS as they sort of were adopted, what can we learn from those? And how do you think five G will be different in terms of its adoption and economic impact? Let's say if you look at adoption, I mean just a number of contracts. We have the number of deployments we have globally, just off the charts in terms of where we are with 4g Korea launched and a few months ago, just just before the summer, within two months they had a million 5g subscribers with smart phones in their eyes and two months later they added a second million subscribers. >>I mean for a market to go from zero to that in, in that period of time with smartphones, if we go back to 4g, all of that was with dongles and sort of hotspots on routers, you know, so to jump directly to smartphones, huge adoption, it's going to happen fast. Well what do you, what are the sequence, what's the sequence of events that have to occur for adoption to really take off? >> So obviously you need to build out the networks and the operators are doing that are pretty high speed. You need to have the devices ready and all the devices. Now it's not like you have a 5g only device. It's obviously capable of all the four G things. And then it's better when you have 5g. So the devices are going to come and take and fast. So all your new devices, most of the high end devices have 5g capability already in there. >>Um, and then the networks just getting built out more and more. And then of course the application developers actually understanding how can I take advantage of those new capabilities? And then you'll start to see, okay, wow, you know, I didn't, this wasn't possible before. It's not just a faster download. It's really, there's just new experiences happening >> from a development standpoint. How much access do they have to the technology? Do they have to wait until this is all built out? Obviously not, but, but, but what's the status of sort of the devs? So we're, we're trying to, and we're working with a lot of the ecosystem. We have, we call it the D 15 studio in our Santa Clara office. We're bringing developers in there and really trying to understand, because you know, we talk Telekom as well. So we want to expose things. We want to understand, do you know what variable, if we say quality of service, what does that mean for you? You know, how do you translate that? So, and we're working with, you know, the cloud players where to developers live to some extent to bring in that ecosystem and understand how it all plays together. So >>ahead. Yup. Um, so if really, if you're looking at it longterm, obviously it's going to happen, but the experience is as I go around the States, is that you've got all these different four G three GS edges still in a very, very patchy a level of it. Is this going to be different? Is this going to actually go into different places because there's a big investment that has to be made, a lot of things very close together. Yes, yes. That seems to be a recipe for everything being or right in the cities. But as soon as you go outside the urban areas, it's going to be very patchy. How does that compare, for example, with Elon Musk's idea of a doing stuff from the sky? >>Well, everything comes down to economics. So you know, it's, it's obviously you're going to have denser deployments in the cities, then you are in the countryside and so on. One of the big advantages would 5g is am, and not to get too deep into the technical part, but you can use all the spectrum that's available. And spectrum is super important as we get, you know, when we have lower frequency spectrum, you can cover a hundred miles Wade, one base station as you get to the millimeter wave, which is you get super high bandwidth, then you're add hundreds of meters. Yeah. And so obviously one is more suitable for a rural environment, the other is more suitable for. So for an urban environment, so obviously having those working together in one technology allows you to deploy everything and get the benefits in a much broader area than we had for any of the previous. >>There's choice there in terms of how you deploy or, or leverage the spectrum. So you're saying that the higher performance end of the spectrum, it's gonna require a greater density of other components. And absolutely. When people talk about oil, there's going to be a lot more distributed, you know, pieces of the five G network that has to get built out. So who does that? Who's putting those pieces of the value chain in? So different players, obviously the mobile network operators, the 18 Ts and Verizons of the world are doing a lot of the heavy lifting and know what our support to actually put the, the radios and the towers in place. And then there's an edge compute piece as well, which is different players are putting in that. Um, so, so a lot of that infrastructure has been done. I think one thing that we've been pushing quite a lot, all our install base of radios is um, 5g upgradable via software. >>So that means that a lot of the already installed, uh, radios and infrastructure, you're just softer upgrade, you know, an hour later it's now 5g ready. So I think that's a big piece of basin. Back to your question of how quickly and and can reach all those areas, are there any specific commercial blockers that you see, um, that you're thinking through? I am I, I think the, just understanding some of the more challenging when you look at, if you're deploying edge compute and you have to invest billions and really getting that far out to the edge, I think there's some questions still there. Like I said, how much would you pay for 20 milliseconds versus 15 milliseconds. And that might sound like a lot, but that's a lot of extra infrastructure you would need to put out. So I think that's still being worked true. >>And obviously some of that will happen quicker in a downtown San Francisco than it will in a, you know, middle of Nevada plays well and the others that you've mentioned before, it's unclear what new applications are going to emerge here. And so it's almost like build it and they will come and then we'll figure it out and then we'll figure out how to charge for it. Like you say the gamers, how much will they pay for it? Yeah, so those are some of the uncertainties but they'll shake themselves out. So absolutely. I was a pretty smart about doing. What about micron and the role of memory players and storage players? How will this affect them? Eight say a huge opportunity when you ah, yeah, I mean invest no and Bardy hats. >> Yeah, I think it's a, when you look at the number of devices and, okay, what's the device? >>The devices are smartphone. Well the devices now your car, it's every IOT device and down to your toaster and all the crazy stuff people are talking about too. I mean to every industrial application tool that age, computers. So you're distributing now a lot of different compute memory storage across different parts of the network. So I mean they talked earlier in the panel about phones having terabytes of data. You know, it's in, it's just unimaginable. The amount of data storage. Remember you're going to need in a vehicle, you know, they're looking at terabytes per hour of data and then how much of that should they shift off the vehicle? How much did it keep there? So huge opportunity. >> Well, I'd be willing to pay for, um, some memory in my appliances. They tell me when they're going to break. I just got a new dishwasher and I can program it with my, my remote. I don't want to program. I just want to know that on Thanksgiving morning it was that it works. But in a week before it's going to break, I want to know so I can deal with vending and maintenance. That's a big use case. Can't wait until that happens. The last question, so >>I was going to be, I was following up on that last point you were making. Um, uh, so again, this cost of everything, this, this value that you're going to get out of it. Um, it seems to me that, um, that this is gonna take a long time to push out. Um, and, and before it actually down. And people will actually know whether they can pay for this. And then one thing in particular is there's a lot of resistance in, in the, in the States anyway, to all of these devices being put very, very close, you know, to the, to, to it for example, putting all the devices down, download a row for example, that, that, that seems to be very expensive and, and going to get a lot of reaction from consumers is, is that not the case? >>So I actually, we're not seeing it that much. I mean if you look across the globe, um, China obviously is a slightly unique situation. Massive deployments already happening there. Like I said, Southeast Asia, South Korea being among the, you know, the forefront, big deployments already there. And we're seeing big pull from industries already and the operators here in U S are announcing new cities, you know, every month practically. So they are really full on into this. And to some extent it's, it's really just, there's a capacity need to have the spectrum. They need to make the investments and they're, they're doing it as we speak. >>So I think it depends on me. Why was it a meeting the other day in Boston with a lot of city officials and folks that worked for the mayor's office? They're envisioning Boston, you know, for the next 50 years, smart cities and five G was like, if you did a word cloud 5g was that the number one topic? You know, we talked earlier about sports stadiums. You can see that being, you know, use cases going to be these >>hotspots where it's of very, very high >>of the city in this case in Boston's case are they're going to invest, right? And they're gonna think that's going to be a differentiator for cities. >>You have this amazing infrastructure, you know, five G infrastructure that allows you to take advantage of that, be it just from, they talked about traffic congestion and what the city can do and then what the businesses and the consumers can do in that area that that can end up being a differentiator for innovation companies going there and so on. >>Right. All right. We're going to go before they blow us out. No, thanks very much for coming to the queue very much. All right, great. To have you on. I keep it right there, buddy. We'll be back with our next guest after this short break. You're watching the cube live from micron insight 2019 from San Francisco right back.

>>live from San Francisco. It's the Q covering Micron Insight 2019 to You by Micron. >>Welcome back, everybody. We hear a Pier 27 in San Francisco. Beautiful day. David Floor is my co host on Day Volante, and this is Micron Inside. 2019. Jeff Baylor is here. He's the corporate vice president of the embedded business unit at Micron. Jeff, great to see you again. >>Thank you. Nice to be here >>so love to talk about autos. I o. T Edge. Use cases to talk about the focus of your team. Let's start there. Yeah, >>sure. So the embedded business to point. It's absolutely focused on the automotive industry's way. Call industrial markets. So factory automation, surveillance and stolen a swell as a consumer electronics businesses on we're really in across all those sort of focused on how connectivity and compute is changing inside of those. And, of course, how that drives memory. >>I mean, yeah, memory and storage. They hide in places that we use every day. You don't see them, but if they weren't there, you wouldn't be able to use all these devices. They wouldn't be as life changing as they are. So you know you mentioned some of the consumer stuff. You know what the big trends that are driving your business? Well, I do >>think it is absolutely. That's sort of the ubiquity of connectivity. First of all, and then, sort of the ubiquity of compute has enabled all of these what used to be sort of isolated applications to now be connected and doing a whole lot more analytics inside that machine. Do you think about intelligence in your thermostat on the wall? You think about intelligence, obviously, in the automotive business, where safety features and so on are using so much more electron ICs and a I machine learning. And that's happening really in every application, whether it's the smart speakers at home, voice control on your TV and so on and so forth. All of those drive more intelligence, more connectivity and then more memory and storage behind that. >>When people talk about automotive, of course, everybody wants to talk about autonomous vehicles. I love to talk about autonomous vehicles, but there's so much action going on in today's vehicles dozens and dozens of microprocessors throwing off all kinds of data. So give us the update on the automotive industry. >>Yeah, you're exactly right. I mean, autonomous gets the headlines and it will for several more years just be headlines more or less right? And the real story is what we call eight ass or advanced driver assistant system. So things like lane departure warning, lane departure, keeping things like auto emergency braking those those sort of much simpler, easier problems to solve are still very compute intensive on. So are driving a huge growth and electronics on memory of storage inside the car. The other major part of the car market in the automotive market is what we call infotainment, sort of the center console. More and more large screens going into that more high function capabilities being integrated in that whether it's navigation or streaming media service is and all of those air driving again a much richer mix that's required >>for those applications. I was at the arm conference and they were talking about automotive and some of the challenges, one of the most fascinating areas they were talking about. How do you make something that will last for 20 years in the car on make it such that if it does go wrong that it that it could recover seamless less. Can you talk about some of the technologies that >>are sort of two parts to that? Unpack a little bit? First through? What does it take to succeed in automotive? First of all, it's all about quality. Yeah, right. It is quality, quality, quality location, location, location. It's quality. It's it's reducing and eliminating defense fundamentally at the end of the day and so inside of our process. Design inside of our technology designed our product designs. Our product manufacturing flows are all designed to sort of fundamentally improve and continue to improve the quality level because at the end of the day, that is what what makes or breaks you in the car. As soon as you solve that, you know, small problem. Next problem is longevity and stability of that solution, because the design cycle itself is shortening and automotive. But it's a very long design cycle, and then the life cycle in automotive is still very, very long. I mean, the average car on the road in the U. S. Is 12 or 15 years old, right, and that needs to both continue to be viable but also often need toe continue shipping that product. It's gonna shipment volumes or have spares and replace. So So we have a strategy that sort of focused on both bringing those leading edge technologies that Micron has into automotive as soon as possible and that timeline is shrinking. But then also having a very long life manufacturing strategy to continue to provide those for so long. >>So you're certainly a leader in automotive. You might even be the leader. I'm not sure I have the data, but what is it you mentioned? You know, quality and those other factors. What is it that's allowing you to do so well in automotive? >>So So we are the beater for sure. We're about 40% market share, which is a little more than three times as big as the nearest competitors, right, So leader by far, really an automotive. And it's been a very long time that we're in this industry and very focused on. So it is. It is about the product mix and bringing in particular lately leading edge technology into that story. You know, we are at the very beginnings of LP five, the low power GDR five generation, where the very beginnings of that rolling out into mobile applications, its primary markets at the same time, almost literally the same time. Way air sampling and providing that into our automotive customers and our automotive partners to start beginning building their systems around L P. Five. So that time to adopt leading edge technology is rowing is shrinking very rapidly. And so we're able to provide that leading edge Tech started, coupled with that long life solution and then one of the areas, when you think about being in a 40% market share position, way air investing tremendously in sort of partnering with the customers around, essentially defining and driving the innovation that they need to deliver So way have a number of labs that we've established customer facing labs that were able to bring customers and even our customers customers. So the Auto am is directly into those labs to start looking at usage models and architectural sort of feasibility and optimization kinds of things that we could then plan into our road map to follow two or three years later. After that, >>a lot of domain expertise there, so tremendous I said the Derrick Dicker that Micron has a very large observation space. You sell to a lot of different channels and I want to ask you about industrial I ot David night. We spent a lot of time in the Enterprise and we see a lot of I t company saying, Hey, here's a box. We're gonna throw it over. We're gonna go dominate the edge anywhere you talkto operations, technology, professions there like No, we're talking about machines and equipment and it's like this whole different parlance and language. So what are you seeing? Just in terms of the ecosystem, how it's developing the sort of analog going to digital And that whole explosion? Yeah, >>again, Industrial is extremely broad market, and it means a 1,000,000,000 things toe people. Right? So So, one of the first things we have to do is sort of narrow the field a little bit, at least into specific verticals and specific areas. Way have the right product mix and opportunity, right? So, for example, in the in the space of factory automation, it's a little bit what you're just saying the operational technology guys are trying to figure out how they're gonna drive efficiency, drive productivity inside a factory on, and that is often a question of instrument ing, and putting in my crown is doing a lot of this sort of smart manufacturing deployment. Putting this sensor network multiple cameras, multiple high resolution cameras, audio sensors, accelerometers, sort of sensors and capturing all of that sensor data to Dr Things like better predictive maintenance, better sort of yield detection or excursion detection kind of capability. So you could tell this machine, you know, seven days, five days out of the week Sounds like this. But last night at 10 o'clock, it started sounding different way. Don't know what it means necessarily, but we can detect that. And that's where all of the A I and Machine Learning is now being applied to say. And that means it's due for a P M. About this particular portion of >>what about security at the edge, obviously a hot topic in the Enterprise on every C. I ose mind what's happening with security in Io ti industrial out in the edge. Yeah, I think >>to some extent, security in the I. O. T. I think is, is why I ot is where it is in the hype cycles. Maybe it's sort of still at the bottom of one of these types cycles, meaning solving that increasing security problem, that cyber security problem that the edge is really a big problem. You saw you know the hacks a few years back of the Jeep charity. You saw the hack two years back on surveillance cameras. All these cameras moving toe i p surveillance cameras means they're now connected and open to the world. Dispersed. He just announced last week in a report that basically showed I ot specific hacks up seven fold or seven fold this year after being up tenfold last year. So it's absolutely a growing problem for people thinking about deploying again. Connectivity is a great tool in a great weapon, Depending. And I was so so. One of my crown is doing is is way. >>Have a >>solution called authentic, which is essentially a cybersecurity, is a secure element built into the non volatile memory that goes in each one of these systems. So today, security is not a one chip problem. It is a full and and system problem. And so what we're tryingto build with that is the capability at a very sort of lowest level in the system right where the code is right where the four part of the system is to protect that in the memory itself and sort of a test that that is safe and secure. And then the system can build out about around that. And that sort of simple boot device, in the case of a nor device or Anand device is in every embedded application >>right in the world, >>right? I mean, you think about you go back a long way, Stuxnet. You know, 10 plus years ago with a seaman's controller, which was the and now you think about fast forward, how much Maur infrastructure is out there? How much more complicated it is, It's ah, it's a scary situation is Oh, it is so that we think that's a >>big opportunity. And we're making the announcement later, uh, later in the show today, on an extension of what we're doing already in that space. >>I know you're working with other vendors. People like >>me are worry with Yes, >>it is really >>an end to end. >>This is really an end to an an ecosystem >>activity, for sure, because again, arm is a great example. You know, all of the S o. C. Vendors. You know, everybody in this industry has some slice of the of the rules. Let's say to figure out how they're going to secure this system and we're tryingto build a basic building block that they can then build on >>that when we started this morning was really quiet. But the crowd is rolling in. Now there's a buzz that you can hear, hear. The key was excited to be here, Jeff. Thanks very much for coming on. The king here to see you again. >>Very much nicer here. >>All right. Keep it right to everybody. We're gonna be taking a short break. We'll be back. Day long coverage wall to Wall of Micron inside. 2019. You're watching the cube.

>>Live from San Francisco. It's the cube covering my groin. Insight 2019 brought to you by micron. >>Welcome back to pier 27 in San Francisco. I'm your host Dave Vellante with my cohost David foyer and this is the cube, the leader in live tech coverage. This is our live coverage of micron insight 2019 we were here last year talking about some of the big picture trends. Derek ticker is here, he's the general manager and vice president of the storage business unit at micro and great to see you again. Thank you so much for having me here. Welcome. So you know we talk about the super powers a lot, you know, cloud data, AI and these new workloads that are coming in. And this, this, I was talking to David earlier in our kickoff like how real is AI? And it feels like it's real. It's not just a bunch of vendor industry hype and it comes in a lot of different forms. Derek, what are you seeing in terms of the new workloads and the big trends in artificial intelligence? >>I think just on the, on the front end, you guys are absolutely right. The, the role of artificial intelligence in the world is, uh, is absolutely transformational. I was sitting in a meeting in the last couple of days and somebody was walking through a storyline that I have to share with you. That's a perfect example of why this is becoming mainstream. In Southern California at a children's hospital, there were a set of parents that had a few days old baby and this baby was going through seizures and no one could figure out what it was. And during the periods of time of the seizure, the child's brain activity was zero. There was no brain activity whatsoever. And what they did is they performed a CT scan, found nothing, check for infections, found nothing. And can you imagine a parent just sitting there dealing with their child and that situation, you feel hopeless. >>This particular institution is so much on the bleeding edge. They've been investing in personalized medicine and essentially what they were able to do was extract a sample of blood from that sample of blood within a matter of minutes. They were able to run an algorithm that could sift through 5 million genetic variants to go find a potential match for a genetic variant that existed within this child. They found one that was 0.01% of the population found a tiny, tiny, call it a less than a needle in the haystack. And what they were able to do is translate that actual insight into a treatment. And that treatment wasn't invasive. It didn't involve surgery. It involves supplements and providing this shower, just the nutrients that he needed to combat this genetic variant. But all of this was enabled through technology and through artificial intelligence in general. And a big part of the show that we're here at today is to talk about the industry coming together and discussing what are the great advances that are happening in that domain. >>It's just, it's super exciting to see something that touches that close to our life. I love that story and that's, that's why I love this event. I mean, well, obviously micron memories, you know, DRAM, NAND, et cetera, et cetera. But this event is all about connecting to the impacts on our lives. You take, you take that, I used to ask this question a lot of when will machines be able to make better diagnoses than, than doctors. And I think, you know, a lot people say, well they already can, but the real answer is it's really about the augmentation. Yeah. You know, machines helping doctors get to that, you know, very, you know, uh, a small probability 0.1001% yes. And it'd be able to act on it. That's really how AI is affecting our lives every day. >> Wholeheartedly agree. And actually that's a, that's a big part of our mission. >>Our mission is to transform how the world uses information to enrich life. That's the heart and soul of what you just described. Yeah. And we're actually, we're super excited about what we see happening in storage as a result of this. Um, one of the, one of the things that we've noticed as we've gotten engaged with a broad host of customers in the industry is that there's a lot of focus on artificial intelligence workloads being handled based on memory and memory bandwidth and larger amounts of memory being required. If you look at systems of today versus systems of tomorrow, based on the types of workloads that are evolving from machine learning, the need for DRAM is growing dramatically. Multiple factors, we see that, but what nobody ever talks about or rarely talks about is what's going on in the storage subsystem and one of the biggest issues that we've found over time or challenges that exist is as you look at the AI workloads going back to 2014 the storage bandwidth required was a few megabytes per second and called tens of, but if you just look every year, over time we're exceeding at gigabyte, two gigabytes of bandwidth required out of the storage subsystem. >>Forget the memory. The storage is being used as a cash in it flushes, but once you get into a case where you actually want to do more work on a given asset, which of course everybody wants to do from a TCO perspective, you need super high performance and capability. One of the things that that we uncovered was by delivering an SSD. This is our 9,300 drive. We actually balanced both the read IOPS and the ride IOPS at three gigs per second. And what we allow to have happened is not just what you can imagine as almost sequential work. You load up a bunch of data into a, into a training machine, the machine goes and processes on it, comes back with a result, load more data in by actually having a balanced read and write a model. Your ingest times go faster. So while you're working on a sequence, you can actually ingest more data into the system and it creates this overall efficiency. And it's these types of things that I think provided a great opportunity for innovation in the storage domain for these types of that's working >> requiring new architectures in storage, right? I mean, yeah, >>I mean, th th so one of the things that's happened in, in bringing SSDs in is that the old protocols were very slow, etc. And now we all the new protocols within in Vme and potentially even more new protocols coming in, uh, into this area. What's micron? What, how is micron making this thing happen? This speed that's gonna provide these insights? >>It's a fan fan. Fantastic question and you're absolutely right. The, the world of standards is something that we found over the course of time. If you can get a group of industry players wrapped around a given set of standards, you can create a large enough market and then people can innovate on top of that. And for us in the, in the storage domain, the big transitions had been in Sada and NBME. You see that happening today when we talked a little bit about maybe a teaser for what's coming a little later at, at our event, um, in some of the broader areas in the market, we're talking about how fabrics attach storage and infrastructure. And interestingly enough, where people are innovating quite a bit right now is around using the NBME infrastructure over fabrics themselves, which allows for shared storage across a network as opposed to just within a given server there. >>There's some fantastic companies that are out there that are actually delivering both software stacks and hardware accelerators to take advantage of existing NBME SSDs. But the protocol itself gets preserved. But then they can share these SSDs over a network, which takes a scenario where before you were locked with your storage stranded within a server and now you can actually distribute more broad. It's amazing difference, isn't it at that potential of looking at data over as broad an area as you want to. Absolutely. And being able to address it directly and having it done with standards and then having it done with low enough latency such that you aren't feeling severely disadvantaged, taking that SSD out of a box and making it available across a broad network. So you guys have a huge observation space. Uh, you sell storage to the enterprise, you sell storage to the cloud everywhere. >>I want to ask you about the macro because when you look at the traditional storage suppliers, you know, some of them are struggling right now. There aren't many guys that are really growing and gaining share because the cloud is eating away at that. You guys sell to the cloud. So that's fine. Moving, you know, arms dealer, whoever wins it may the best man win. Um, but, but at the same time, customers have ingested so much all flash. It's giving them head room and so they're like, Hey, I'm good for awhile. I used to have this spinning disc. I'd throw spinning disc at it at the problem till I said, give me performance headroom. That has changed. Now we certainly expect a couple of things that that will catch up and there'll be another step function. But there's also elasticity. Yes. Uh, you saw for instance, pure storage last quarter said, wow, hit the price dropped so fast, it actually hurt our revenues. >>And you'd say, well, wait a minute. If the price drops, we want people to buy more. There's no question that they will. It just didn't happen fast enough from the quarter. All of these interesting rip currents going on. I wonder what you're seeing in terms of the overall macro. Yeah. It's actually a fantastic question. If you go back in time and you look at the number of sequential quarters, when we had ASP decreases across the industry, it was more than six. And the duration from peak to trough on the spot markets was high double digit percentages. Not many markets go through that type of a transition. But as you suggested, there's this notion of elasticity that exists, which is once the price gets below a certain threshold, all of a sudden new markets open up. And we're seeing that happen today. We're seeing that happen in the client space. >>So, so these devices actually, they're going through this transition where companies are actually saying, you know what, we're going to design out the hard drive cages for all platforms across our portfolio going into the future. That's happening now. And it's happening largely because these price points are enabling that, that situation and the enterprise a similar nature in terms of average capacities and drives being deployed over time. So it's, I told you, I think the last time we saw John, I told just one of the most exciting times to be in the memory and storage industry. I'll hold true to that today. I, I'm super excited about it, but I just bought a new laptop and, and you know, I have, you know, a half a half a terabyte today and they said for 200 bucks you can get a terabyte. Yes. And so I said, Oh wow, I could take everything from 1983 and bring it, bring it over. >>Yeah. Interestingly, it was back ordered, you know, so I think, wow, it am I the only one, but this is going to happen. I mean, everybody's going to have, you know, make the price lower. Boom. They'll buy more. We, we, we believe that to be the case for the foreseeable future. Okay. Do you see yourself going in more into the capacity market as well with SSTs and I mean, this, this, this drop, let's do big opportunity or, yeah. Actually, you know, one of the areas that we feel particularly privileged to be able to, to engage in is the, the use of QLC technology, right. You know, quad level solar for bits per cell technology. We've integrated this into a family of, uh, of SSDs for the enterprise, or interestingly enough, we have an opportunity to displace hard drives at an even faster rate because the core capability of the products are more power efficient. >>They've got equal to, or better performance than existing hard drives. And when you look at the TCO across a Reed intensive workloads, it's actually, it's a no brainer to go replace those HDD workloads in the client space. There's segments of the market where we're seeing QLC to play today for higher, higher capacity value segments. And then there's another segment for performance. So it's actually each segment is opening up in a more dramatic way. So the last question, I know you got some announcements today. They haven't hit the wire yet, but what, can you show us a little leg, Derrick? What can you tell us? So I, I'll, I'll give you this much. The, um, the market today, if you go look in the enterprise segment is essentially NBME and SATA and SAS. And if you look at MDME in 20 2019 essential wearing crossover on a gigabyte basis, right? >>And it's gonna grow. It's gonna continue to grow. I mentioned earlier the 9,300 product that we use for machine learning, AI workloads, super high performance. There's a segment of the market that we haven't announced products in today that is a, a a mainstream portion of that market that looks very, very interesting to us. In addition, we can never forget that transitions in the enterprise take a really long time, right, and Sada is going to be around for a long time. It may be 15% of the market and 10% out a few years, but our customers are being very clear. We're going to continue to ship Satta for an extended period of time. The beautiful thing about about micron is we have wonderful 96 layer technology. There's a need in the market and both of the segments I described, and that's about as much as I can give you, I don't bet against data. Derek, thanks very much for coming on. Thank you guys so much. You're welcome. There's a lot of facts. Keep it right there, buddy. We'll be back at micron insight 2019 from San Francisco. You're watching the cube.

>>live from San Francisco. It's the Q covering Micron Insight 2019. Brought to you by Micron. >>Welcome back to Pier 27 sentences. You're beautiful day here. You're watching the Cube, the leader in live tech coverage recovering micron inside 2019 hashtag micron in sight. My co host, David Floy er and I are pleased to welcome Michael Wood, Acre Cube alum and a fellow at Hewlett Packard Enterprise. Michael, good to see you again. Thanks. Coming on. >>Thanks for having me. >>So you're welcome? So you're talking about HBC on a panel today? But of course, your role inside of HP is is a wider scope. Talk about that a little bit. >>She also I'm the lead technologists in our Compute Solutions business unit that pack out Enterprise. So I've come from the group that worked on in memory computing the Superdome flex platform around things like traditional enterprise computing s it, Hannah. But I'm now responsible not only for that mission critical solutions platform, but also looking at our blades and edge line businesses. Well said broader technology. >>Okay. And then, of course, today we're talking a lot about data, the growth of data and As you say, you're sitting on a panel talking about high performance computing and the impact on science. What are you seeing? One of the big trends in terms of the intersection between data in the collision with H. P. C and science. >>So what we're seeing is just this explosion of data and this really move from traditionally science of space around how you put equations into supercomputers. Run simulations. You test your theories out, look at results. >>Come back in a couple weeks, >>exactly a potential years. Now. We're seeing a lot of work around collecting data from instruments or whether it's genomic analysis, satellite observations of the planner or of the universe. These aerial generating data in vast quantities, very high rates. And so we need to rethink how we're doing our science to gain insights from this massive data increase with seeing, >>you know, when we first started covering the 10th year, the Cuban So in 2010 if you could look at the high performance computing market as sort of an indicator of some of the things that were gonna happen in so called big data, and some of those things have played out on I think it probably still is a harbinger. I wonder, how are you seeing machine intelligence applied to all this data? And what can we learn from that? In your opinion, in terms of its commercial applications. >>So a CZ we'll know this massive data explosion is how do we gain insights from this data? And so, as I mentioned, we serve equations of things like computational fluid dynamics. But now things are progressing, so we need to use other techniques to gain understanding. And so we're using artificial intelligence and particularly today, deep learning techniques to basically gain insights from the state of Wei. Don't have equations that we can use to mind this information. So we're using these aye aye techniques to effectively generate the algorithms that can. Then you bring patterns of interest to our you know, focused of them, really understand what is the scientific phenomenon that's driving the things particular pattern we're seeing within the data? So it's just beyond the ability of the number of HPC programmers, we have the sort of traditional equation based methodologies algorithms to gain insight. We're moving into this world where way just have outstripped knowledge and capabilities to gain insight. >>So So how does that? How is that being made possible? What are the differences in the architecture that you've had to put in, for example, to make this sort of thing possible? >>Yeah, it's it's really interesting time, actually, a few years ago seemed like computing was starting to get boring because wears. Now we've got this explosion of new hardware devices being built, basically moving into the more of a hetero genius. Well, because we have this expo exponential growth of data. But traditional computing techniques are slowing down, so people are looking at exaggerate er's to close that gap and all sorts of hatred genius devices. So we've really been thinking. How do we change that? The whole computing infrastructure to move from a compute centric world to a memory centric world? And how can we use memory driven computing techniques to close that gap to gain insight, so kind of rethinking the whole architectural direction basically merge, sort of collapsing down the traditional hierarchy you have, from storage to memory to the CPU to get rid of the legacy bottlenecks in converting protocols from process of memory storage down to just a simple basically memory driven architecture where you have access to the entire data set you're looking at, which could be many terabytes to pad of eyes to exabytes that you can do simple programming. Just directly load store to that huge data set to gain insights. So that's that's really changed. >>Fascinating, isn't it? So it's the Gen Z. The hope of Gen Z is actually taking place now. >>Yes, so Gen Z is an industry led consulting around a memory fabric and the, you know, Hewlett Packard Enterprise Onda whole host of industry partners, a part of the ecosystem looking at building a memory fabric where people can bring different innovations to operate, whether it's processing types, memory types, that having that common infrastructure. I mean, there's other work to in the industry the Compute Express Link Consortium. So there's a lot of interest now in getting memory semantics out of the process, er into a common fabric for people to innovate. >>Do you have some examples of where this is making a difference now, from from the work in the H B and your commercial work? >>Certainly. Yeah, we're working with customers in areas like precision medicine, genomex basically exaggerating the ability to gain insights into you know what medical pathway to go on for a particular disease were working in cybersecurity. Looking at how you know, we're worried about security of our data and things like network intrusion. So we're looking at How can you gain insights not only into known attacking patterns on a network that the unknown patents that just appearing? So we're actually a flying machine learning techniques on sort of graft data to understand those things. So there's there's really a very broad spectrum where you can apply these techniques to Data Analytics >>are all scientists now, data scientists. And what's the relationship between sort of a classic data scientist, where you think of somebody with stats and math and maybe a little bit of voting expertise and a scientist that has much more domain expertise you're seeing? You see, data scientists sort of traversed domains. How are those two worlds coming together? >>It's funny you mentioned I had that exact conversation with one of the members of the Cosmos Group in Cambridge is the Stephen Hawking's cosmology team, and he said, actually, he realized a couple of years ago, maybe he should call himself a day two scientists not cosmologist, because it seemed like what he was doing was exactly what you said. It's all about understanding their case. They're taking their theoretical ideas about the early universe, taking the day to measurements from from surveys of the sky, the background, the cosmic background radiation and trying to pair these together. So I think your data science is tremendously important. Right now. Thio exhilarate you as they are insights into data. But it's not without you can't really do in isolation because a day two scientists in isolation is just pointing out peaks or troughs trends. But how do you relate that to the underlying scientific phenomenon? So you you need experts in whatever the area you're looking at data to work with, data scientists to really reach that gap. >>Well, with all this data and all this performance, computing capacity and almost all its members will be fascinating to see what kind of insights come out in the next 10 years. Michael, thanks so much for coming on. The Cube is great to have you. >>Thank you very much. >>You're welcome. And thank you for watching. Everybody will be right back at Micron Insight 2019 from San Francisco. You're watching the Cube

>>live from San Francisco. It's the Q covering Micron Insight 2019 to You by Micron. >>We're back to Pier 27 in lovely San Francisco, Everybody. I'm Dave a lot with my co host, David Floy Er and you're watching the Cube, the leader and live tech coverage. Evan cursed Ellis here. He's a social digital influencer. First time in the Cube. Evan, Great to see you. >>Thanks for having me. First time's the best. >>You Very well. And it is beautiful. Out him in October is the best month in San Francisco. Way better way warmer than July. I mean, you live out here. Holy cow. All right, let's get right into it. You're just fresh off of mobile work. World Congress down in L. A. >>This morning. Yeah, five g on the brain's >>s. So what do we need to know about five g? You >>know, I think my big takeaway as an industry observer is that five g Israel, and it's now I mean, we've seen 5 10 years, maybe of hype, an expectation and marketing buzz and even spin. But I think we're now in the business of practical deployments, scaling rollouts of networks and that's, you know, as a industry observers, quite exciting. >>So what is five g mean for the average user? I mean, is it gonna be like going from dial up toe, high speed Internet or, you know, it's gonna be interesting. >>The average user, I think we'll experience, you know, like a 10 x increase in their current experience on mobile in terms of uploads and downloads and speed and Leighton see, And that kind of thing, which is super exciting, it's it's gonna blow people's mind. >>An ex stoked to get a 10 extra. When can I get this? >>It's when and it's where, right? I mean, if you look at how these networks are evolving, there are hundreds of thousands of small cells of base stations that have to be deployed naturally to get five G ubiquitous across the country. So it's it's when it's where it's how. But we're here. We're at the starting point and look for the next years and months ahead to see that riel attraction. >>If I look now when I travel around the country, I still have four G. I still have three g. I still have edge. I have a ll the old ones are still there, and it's taken forever, even just to get to 40. So isn't lesson. Isn't the rollout of this going to take a long time ago or 10 year horizon? >>I think, to get ubiquitous coverage indoor, outdoor, suburban, urban, rural It's going to take 10 years. But if you look at those hot spots that generate a lot of activity, whether it's, you know, indoor coverage in the Enterprise, whether it's, you know, the Bruins playing in Boston Garden I mean those air where five G is really going to come into play first and then it's going to sort of go outside of those urban dense areas. >>You mean like the fan experience in the fan experience in the venue >>is huge? I mean, if you go to any you know, baseball, basketball, football game, you know what the experience is like Pretty pretty bad, right? So horrible. So those kind of hot spots are ripe for five g like right away today. Now, >>so by the way, David, sometimes I get five g on my that's right, and I feel like it's fake. Five years like HD ready. What's that all about? Well, you know, >>these networks evolve, and so the carriers are maximizing for G, including biggest speed on four G and five. Gene is really if overlay to these existing networks. And so, as you get your next Samsung, you know five G enabled devices. Apple next year comes out with a five G iPad. You'll then begin to use. The service is as you use your existing device. >>Can you help us understand the fundamental architecture of five G? My understanding is it's, you know, no basis more distributed on. That's part of the reason why it's taking so long to roll out. But what do we need to know about that E? >>I think it's a brand new editor interface. So if you think about the current radio on for G, they reinvented the wheel with five G, which means you can support a huge number of endpoints of I o. T devices of wearables of home access points. And so it enables almost a 10 to 100 ex war devices in terms of scale. So while the end user may think this is business as usual, what's really happening on the network side is pretty revolutionary And once the networks are primed and built and ready, what's gonna be happening on the device side is gonna be really extraordinary. You're talking about a K A video on a mobile device or augmented reality through in new kinds of glasses. And so it's sort of a chicken and a little bit. You know what? She's gonna come first, the network or the incredible new devices. So we're seeing now the network's being put in place for those wave of devices, >>which makes sense. Device manufactures don't want over rotate into something that's not quite. >>But if you look at the network, it's you have to have a lot of device is very close to each other. I in my area that all these the holdings holding these hearings about radiation, everything else like that, which is never, never really a problem unless you're underneath. >>Yeah. I mean, there's a lot of fun, you know, fear, uncertainty around five G. >>Yeah, and I'm just the practical thing. You gotta have all of these lots of these very close in the The exposure to having a gap of some sort is pretty high. >>Yeah, I think it's an issue of frequencies as well. Right now, we're seeing very high frequency five deployed for those dense urban suburban areas. We're going to Seymour Spectrum rolled out next year. The FCC is putting out new auction so you'll see lower bit rate five g rolled out for suburban and rural areas. So it's a It's a work in progress, but the fact that we have first devices first silicon for software first networks. It's kind of a big inflection >>point, but some bumps. I'm inferring this ATT the back end. It could be a lot of machine to machine communications, so that's kind of sets up this whole coyote and an edge discussion. And of course, that means more data. What can you tell us about how that's going to affect really the amount of data and how we use that data? >>The data explosion is extraordinary. I mean, we experience this as early adopters here at the table every day, and so no one's ever said, you know, my network is fast enough is good enough, secure enough. There's always that insatiable appetite now, given the connected world in which we live. And so it's not just the network speed it's the input output of the device. I mean, we have Leighton see that frankly, from these networks operates at the speed of the human brain, you know, in in milliseconds, in terms of input output on the network. And so that's really gonna change the user experience to when the way you do gaming or collaboration or video conferencing video calls and all these service is we use today will be much more tuned to how we live and work. >>So dial upto high speed Internet obvious Are you want? I'll update you say you go back. I'm also I know remember this stuff But that was a significant change. Obvious step change, really a step function. Exactly. But subsequent to that it was I could doom. Or but it was just so much more data and acts were flowing through the network that it really didn't change the experience a little bit. Maybe, actually, you know, be careful. I watched the Patriots game on the plane on the NFL app on the way out here, which could probably have done a year or two ago, but so that was that's goodness. But generally speaking, the experience is substantially similar. Will you said a 10 X before? Will the user actually see a difference like that kind of dial up to high speed step function? Or is it going to be sort of a slow roll? >>I think the user will see a big a big improvement because of the efficiencies of the network and the way in which data is kind of throttled and limited. Today, with three and four for G networks, I think more interestingly, is how businesses and enterprises and sm bees will consume. Five g. I mean, there are a lot of antiquated networks out there, whether it's legacy wired Network, D S. L. Whether it's, you know, crappy WiFi that we all experience in hotel rooms, five g has the opportunity to come in and really displace all of that legacy crap that that's in our networks and give users in those enterprises hotels, venues, a brand new experience. And when's the last time you had a bad hotel? WiFi, for the idea of, of getting rid of a legacy network and delivering those high speed service is from a public network. It's her Private networking is a really exciting opportunity for the carriers and, really, for the B two B enterprise. >>Well, the technology suppliers are pumped about their pumped and their >>look at their profitability, their revenue, their sales. Everything's up. >>Well, the thing is that that is, the carriers, like you say they have no choice but to remain competitive. They have to consume. They have to spend more >>on what a great time in the mobile industry. I mean to be a consumer of devices and service is, I mean, the consumers that businesses are winning in this march. >>So tell us about Mobile World Congress. What was the vibe? It was >>very buzzy. I mean, there were lots of Rhea World applications on display, whether wearable devices for health care and hospital T applications. There were examples of remote controlled autonomous shipping and autonomous trucking monitored, supervised with five G. There were examples of vehicle to vehicle communications for accident, safety purposes being deployed in the next generation of cars baked in, and so five. He's gotten very practical. Now it's like, Okay, we've built this network, we have silicon, we have software we have storage memory out of we deploy it so is very focused on deployment usage and an application. >>If you take that one of automotive, for example, if you're a god, health and life on your If you If you can't guarantee that you've got connectivity toe, what's the value wouldn't do? For example, wouldn't you prefer vehicle to vehicle direct communication, as opposed to going outside to some much faster? >>Exactly. Exactly. And there's a new technology called vehicle Be two extra people vehicle standards that are being baked so that that's not funny. It's based on the five of the family of standards, and so one of the technologies within the five G family is vehicle to vehicle. Qualcomm's doing some amazing work there. And once the automobile manufacturers baked that technology into cars, the car manufacturers can then build in vehicle avoidance, vehicle collision technology and so forth. >>So I'm worried that was some talk about a I right? I mean, lots of talk that mobile world Congress, you're gonna hear a lot about here. What about the ecosystem that's emerging to support five G? There's gotta be a whole value chain specialized chips. I mean, obviously, micron, you know? Yeah, you know, the >>whole supply chain has to come together and Micron powering all of these devices with memory and storage to the application developers to the O E ems to the network providers. And so that ecosystem is getting really baked, fully baked and and integrated. And that was on display at MWC, too. So all these things are coming together, and I think it's pretty exciting. As a long time skeptic like yourself. I saw some real world. >>I say, I'm excited about it. I just I'm just not holding my breath. Don't >>hold your breath. Not >>recommended weight. That's great, Evan. Thanks very much for coming in. Thanks so much. Appreciate your insights. Thanks so much. Thank you for watching. Keep it right there. But it will be back from Micron Insight 2019 from San Francisco. You're watching the Cube?

>> Announcer: Live from San Francisco, it's theCUBE. Covering Micron Insight 2019. (upbeat music) Brought to you by Micron. >> Hi, everybody, welcome to Pier 27 in San Francisco. My name is Dave Vellante and I'm with my co-host, David Floyer. And you're watching theCUBE, the leader in live tech coverage. This is our coverage of Micron Insight 2019, #microninsight. David, I love this show because, well, of course we're going to talk about Micron and memories and DRAMs and NANDs and all that techy stuff. We're also going to sort of set the tone on this day. It's a really thought leadership day and we talk a lot about AI and Edge and the big mega trends and superpowers, the cloud, mobile, that are really affecting demand and it all starts with data. So, Micron is a company that we're going to talk about and talk about in detail. But what are you seeing, David, as the big trends that are driving demand for bits? >> For bits. Well, let's start with the Edge that you were talking about. The Edge is growing and it's going to grow very, very strongly indeed. It's going to grow with smaller processes, it's the ARM processors at the Edge doing inference processing, capturing the data, and wanting to do that capturing of the data and the processing of that data as close to the origin of that data as possible. So memory and all of the, the NAND is moving out to the Edge itself. And it's going to be lots of smaller processes as opposed to the lots of big processes. >> Let me ask you a question. We've been following these markets for many, many years and, of course, when we started in the business it was all mainframe, and that was really what drove the consumption of data, and then the PC changed that. >> David: Took over, yep. >> And then that, you used to count markets. We used to do that all the time, and there was much more data going to the laptops and desktops, the Internet began to change that and of course, cloud sort of re-centralized a lot of the spending, and a lot of the buying power. Do you see, is it a pendulum swing again, is it that dramatic? Or do you see it as different? >> Like all big trends, the center still remains. So, the center now is cloud. Still mainframes is part of that cloud. That has to remain, and that is just much more economical for large-scale processing. That's the most economical. However, also the economics of it is that moving data is very expensive. It's very expensive in terms of the effort and it also, when you move data, you lose context. So, if you want the best context, and if you want to do things in real time, you want to process that data in real time as close to where it was produced as possible. So, yes, there will be a very big swing in the amount of processing and the amount of important processing that happens at the Edge. >> So, from the standpoint of things like NAND and flash, Steve Jobs changed everything when they decided to put flash inside of the iPhone. >> Actually not the iPhone. >> In the iPod, actually. >> iPod, yes. >> That drove massive massive, that was the beginning, the dam breaking, and what happened is that volumes went through the roof, cost went down, and that's really when you first predicted way back in the early part of this decade that NAND and flash would affect spinning disc, and it clearly has. Pricing maybe hasn't come down as fast as we thought because of supply constraints. But, nonetheless, it's happening. And now the prices are coming down more. You've seen somewhat of an oversupply in NAND. Prices have come down pretty substantially. And there's elasticity. Ever since we've been following this market, you've seen when prices drop, people buy more. At the same time, you saw like Pure Storage last quarter said, well, the prices dropped faster than we thought, it actually hurt our revenue. Because it just happened so fast in the middle of the quarter, that it hurt pricing overall for the subsystems, but nonetheless, that's the trend that we see happening. It feels like there's a new wave or a new step function of consumption going on with regard to flash. What are you seeing? >> Yes, flash was always about performance before, and there were two constraints to flash, in terms of its impact on the whole industry. The first was that the protocols that were used in flash were the old fashioned protocols that were used for HDD. Now, those have improved enormously with NVMe, et cetera, and those have got much, much better. So, that increases the demand for that flash. The usefulness of flash is now much better. And the second is, in terms of, that's high performance, there's high-capacity flash, and now flash is growing in two dimensions. It's growing in the number of layers, but it's growing from SLC to MLC to TLC to QLC in terms of the number of bits that it can pack into it. >> So, those all have cost implications on the cost per bit, obviously? >> Sure. Both of those are reducing the cost per bit, and making it available for different markets. So the capacity market, now as the prices come down, mean that it's going to take a bigger bite into the HDDs. In data center, it's going to become the norm just to have flash only. >> Micron's a little bit late to NVMe, but they're now hopping on board. Actually, you've made the comment to me in previous discussions, that they've actually timed things pretty well. >> Yeah. >> You kind of didn't want to over-rotate to NVMe. I know Pure was first, but Pure's a relatively small part of the marketplace. It seems like now everybody's going to NVMe. And basically what this does, as you pointed out, it eliminates a lot of the sort of older, slow, over head chatty protocols, and now it's like a bat phone right to the data. What are you seeing in terms of NVMe adoption? Is it now mainstream? >> Yes, we're predicting that in 2019 50% of the drives will be NVMe drives. That's a very rapid change. >> Let's up-level a little bit. We're talking about all of this geeky stuff down here, but what I'm interested in is why we need this. And the obvious question is there's so much more data now but it's also, AI. We talk a lot about the new innovation sandwich of being data plus AI plus cloud, combine those things together and that's really what's driving innovation. How real is AI? I presume we need all this stuff to be able to support these data-driven workloads, but how real is AI? It feels like it's pretty substantive. When we go to a lot of these shows, you hear about digital transformation and all these buzzwords and the Edge and IOT. 'Course, AI's one of the big buzzwords, but it does really actually feel like a superpower to invoke one of Pat Gelsinger's words. >> Yeah, it is. And AI could only operate if there was all that data available, so it's the availability of that data, because the algorithms and AI go back a long way. There's nothing new in that. But AI has now the availability of processing that data, large amounts of data, which makes it much more powerful. And now you're getting AI in things like a cellphone, the amount of AI that goes into recognizing your face is enormous. And it's now practical, everyday things are being done in AI, and it's going from being a niche to being just everyday use. And it's impact longterm is profound. It'll do all the jobs that humans do, many of the jobs that humans do, much more efficiently. Driving a car. It'll be better at driving a car than human beings are. >> Yeah, you see AI everywhere, you're right. Ad serving still stinks, but it's getting better. Fraud detection's getting much, much better. Email is now finishing my sentences for me. Right, you've noticed that in the last year or so. Basically say, oh, I like that choice, boom, I'll take it. And so as much as we hate autocorrect... And so those are some small examples, but what the industry likes to talk about is how it's changing lives, what it's going to do for healthcare, autonomous vehicles. Those are some of the big-picture items. >> David: Really big things. >> Which really haven't kicked in yet, just in terms of, or have they? In terms of consuming demand, for things like DRAM and NAND? >> It's relatively small at the moment but it has the potential to be very large, obviously. >> Dave: Go ahead, finish your thought. >> Because in the next 10 years we're going to see automated cars, it's going to be in pieces. You're going to have the trucks going first, and then other cars later. >> I know you're fairly sanguine and optimistic about autonomous vehicles, I know there are a lot of skeptics out there that talk about, we don't have enough data and we'll see, but we'll talk more about that. But I want to talk about Micron a little bit. Micron's a company, last year they were a $30 billion company, they got $23 billion in revenue this year so dramatic drop in revenues. And that was really due to the change in the supply/demand dynamic. Now, historically, when these things happen the stocks of these companies would just, you could predict it, you'd say, okay, time to sell, 'cause here comes the over-supply. And then when they hit the bottom, time to buy. Micron's done an amazing job of sort of steadying that. Managing its demand and supply balance. Also, obviously doing share buybacks that help the stock price, but the stock price has held up pretty well. So Micron's now a $23 billion company, last year they threw off $17 billion in free cash flow, this year, 13 billion. But still, well over 50% of their revenue's going back to free cash flow, which is quite large. Their market cap's 51 billion, so they're trading at a 2.2X revenue multiple, which is very strong. And they've got a 30% gross margin, right? The PC business, think about that. The DRAM, this is a good business, right? That's a nice business, because they don't have a giant direct sales force, so they don't have that cost, it's all through OEM. It's a fairly efficient business, and they've managed it pretty well. Your thoughts on Micron as a company. >> Yes, they have. They've managed the timing of every new release very well indeed. If you go too early, you over-rotate, then you are struggling to get that out. The costs are higher, and the people who are selling the previous generation are going to do better. But they've always timed it perfectly. >> Yeah, now they're facing some challenges. I talked about the supply/demand imbalance, but they're managing that. China, the tariffs hurt them. Huawei, was a big customer. They can't sell the Huawei anymore. China coming after companies like Micron, really going after consumer flash, building fab capacity to begin with, and then eventually China is going to aim at the higher value enterprise. What are you seeing there? >> I agree with you. They've had to rotate because of this problem with the tariffs that have been put on China. So, what's the reaction? They're going to have to invest. And that, long term, is good news for consumers and good news for everybody else, but it's going to be bad news for other people in the business. >> So, a bunch of announcements today. We can't talk about it, 'cause they're not public yet, but you're going to see some SSD stuff coming out. Maybe some acquisitions announced, you might see some other things around 3D XPoint, which is something that we really haven't talked much about but we will, I know your thoughts on that are it's still kind of niche. Remember the HP Memristor, right? Which is, nobody talks about that anymore. But now Micron's in a different situation. They'll figure out, okay, where that fits, but it's still a niche in your view because it doesn't have the volume. But we're going to be talking about that stuff. But, again, up-leveling the conversation to some of those big mega trends, those superpower drivers, data, AI, IOT, and the Edge, and some of the things that are really driving change, in not only industry but also our lives. So, David, appreciate the insight. David and I will be here all day today. You're watching theCUBE from Micron Insight from San Francisco. We'll be back with our next guest right after this short break. (upbeat music)

(upbeat music) >> Male Voiceover: Live, from San Diego, California it's theCUBE covering Cisco Live US 2019 brought to you by Cisco and its ecosystem partners. >> Hey welcome back to theCUBE Lisa Martin with Stu Miniman. We are day one of our coverage of Cisco Live from San Diego. We're going to be here for three days of coverage but a great day so far and we're pleased to welcome back one of our CUBE alumni Kent Christensen the Practice Director from Insight with the Cloud & Data Center at Transformation Group. Kent, welcome back! >> Thank you. It's been a little while. >> It has been a little while. So give our audience a little overview of Insight your partnership with Cisco and some of the history of how you got to Insight. >> Yeah so you remember us as Data Link we were a smaller company than we are now. Focusing on Cloud and data center transformation. We've talked at Dell events, CFC events things like that. But we were a Cisco partner for about 10 years and recently we were acquired and we did what the name sounds like, Cloud and data center transformation. We've talked about Cloud on the channel and all these other things. Insight acquired us. Insight has kind of four major service solution sets if you would. Some people look at them as a supply chain company and it's a great, large supply chain company. Microsoft's largest global partner. Some people understand it for the device and use the devices that's called Connective Workforce. Each of these are pretty big businesses you know, compared to where we are. What was Data Link is now what's called Cloud and Data Center Transformation. So we're helping people with the journey to the Cloud and the Hybrid Cloud and all that other stuff. And Cisco is right dead center in the middle of that and then the fourth one is really exciting. It's called Data and Digital Innovation and that's a couple of companies. Blue Metal, Cardinal etc. Again, a thousand people. Microsoft ILT and AI partner of the year. So all of that is a pretty large channel organization if you would. >> That's great stuff Kent. We love to talk to the channel as the folks in Wall Street do. It like you know, we do a channel check. Okay, You know, Cisco's got a few areas that have you know, stronger growth in the market over all. Security's doing well, a few other spaces on that are you know growing faster over all than the market and helping >> Kent: Absolutely >> grow where Cisco's going, so give us the reality. What's happening with your customers? What's driving you know the most growth in your business and you know, where is Cisco kind of leading the pack? >> So we're doing really well with Cisco and I don't know if it's because we're helping clients build solutions that truly lead to business outcomes. We're not order takers. So we're actually moving up we're now Cisco's fourth largest partner. We're growing well high single digits growth which is pretty phenomenal on such a big number. We're talking a billion dollars now in growing that level and there's a number of reasons. You know, some of it is there's a lot of great technology we can get into some of those. We see the economy as being pretty good not bad yet. You know everybody's worried about what might happen. You mentioned security, we can get into a little bit of that. That's driving a lot of network refresh and stuff like that. You know and a little bit of intra-company you know that word getting our stuff together so this large company with 15000 customers acquires a company with 2000 customers and now we're getting introduced into the 15000 with less friction. So that's helping us and that's helping our Cisco Business. >> Lisa: See here we are at Cisco Live. The thirtieth time that they had done a customer partner event. The network has not only changed dramatically since their first event in '89 which was called Networkers I believe. >> KENT: Yeah. >> But networking technology has also massively changed you mentioned security. And now in this multi-cloud world no longer can you just put a firewall around a data center right? Obviously that doesn't work. We have this core Cloud edge very amorphous environments. Proliferation of mobile, of mobile data traversing the networks. Talk to us about when you're talking with customers who need to transform their data centers where do you start from a networking conversation perspective? Where automation comes in, where security comes in? >> You know, a lot of the Cloud native transformation tends to be the edge of the network. You know conversion, infrastructure, stuff like that that's on the edge. The network security guys which I'm not, you know, I work with them very closely but we almost separate ourselves out from a data center networking and security. But security's end to end to your point, right? I've got software to find access. I've got mobile access points. I've got you know, Tetration. I've got all of these products that are helping people that in the past they were just patching holes in the dyke. You know, hey this happened let's put this software product in. This happened, let's put this in. We actually built a security practice like the last 3 or 4 years ago, it's growing. You know the number of people that are, whether it's regulation, compliance, you know. "I got some real problem. I think I've got a problem and I don't know what it is." Our ability to come back and sit down and say let's evaluate what your situation is. So I was talking to the networking guys and said wow. Enterprise networking's up, way up. What's driving that, the need to transform or is that, you know what is it? And they're like a lot of times it's something along security that's making them step back and re-evaluate and then sometimes that translates into an entire network refresh. >> Stu: So Kent you mentioned Cisco Tetration and that's one I've heard a number of times having some growth. What else, what are some of the you know hot products out there in your customer base? >> ISE, Software to Find, SD Wan, SD Access. >> Stu: Yeah, so one of the things I just want to understand Cisco actually has a few solutions in some of those areas. Any specific products that you call out or you know or that'd be mentioned? >> Kent: In the enterprise networking I wouldn't go through each and every individual one. I think, this is my view as the laymen right? 'Cause I'm the data center guy and here's the security guy and here's the networking guy. I think when Cisco started acquiring all these security companies 3 years ago and you know watched it and it looked like a patchwork quilt and said this stuff doesn't fit together? Now it fits together that story is really solid. And so we've got clients that had the luxury of either saying I'm going to do a refresh because I don't want to keep plugging holes and maybe my technology was ready for it anyway. And there's a lot of reasons to refresh right? My technology's due. Digital transformation, I need to get my network ready for IoT etc. But I keep hearing security over and over right? I've got compliance and regulations and all of this other stuff. >> Yeah but in your core space the data center world and any products that are kind of leading the charge right now? >> You know one of the things that's happening in data center from a Cisco perspective 'cause they're babies right? Ten years old in data center. They didn't really have data center before that. And we were there at the beginning and that's really how CDCT built our data center practice so you know when you talk multi-cloud at the end of the day even if I'm Cloud first I'm going to end up with some of these mission-critical workloads. They might be boring but they're running the company. They're not the innovative Dev-Ops, IoT, AI thing that seems cool. They're running the company and that's still a converged or a hyper-converged play. And some of those you know there's a lot of opportunities we've been talking about all day with the Cisco BU's. Some of those are ready for refresh right so there's a great opportunity to just to go in and say okay what's next? You know, we've added you know the latest server technology. We've added all these things in the server technology. Obviously all flash and the storage technologies and all of that so that's huge. And then you know Cisco continues to innovate in data center solutions with things like HyperFlex which we've talked a little bit about. And it started off a little slow because again just like they were in servers why are they here? Why are they in hyper-converge? So I get it. And now that product is fully improved and improved and improved and we're seeing tremendous growth there and I think the luxury they have on a data center solution is that some of the other guys have to do a or. "Hey, I'm the leading hyper-converge technology but it's me or everybody else." Right? And then Cisco's an end that I can connect those things together. >> So let's talk about some customer examples you can feel free to anonymize these. I'm seeing a smile on your face. When you come into an organization whether it's a 100 year old bank or it's a born of the Cloud or maybe a smaller more nimble organization that needs to undergo transformation data center transformation. What is the conversation like with respect to helping them take all of these disparate presumably disparate solutions? Whether they're 10-15 different security solutions. How does Insight come in and help them I don't want to say integrate but almost plug these things in together to extract value and help them make sure that what they're implementing from a technology perspective is necessary and also an accelerator of their business? >> Yeah, there's a lot there. So we have this... A year ago everybody wanted to talk about Cloud and then you had the security guys but now you have a lot of change agents with transformation in their title right? And so we have this belief. You're not going to digitally transform. Now there are people that are born digital but companies that were buying Cisco 10 years ago need to go through a digital transformation and you can't go through a digital transformation until you have a data center transformation or an IT transformation. So we've done studies. What slows people down? What makes these fail? Legacy stuff, security concerns I mean these are the top 3 things right. Budget. I was just running the company. And so we start there. Where do you want to get to? And then most of it is let's understand what you have. What your objectives are as an organization. "I want to get to this. I want to get to that." Well before we start talking about technologies. It's very, it's very services oriented. I can't just go in there and throw you a bomb and say this is going to fix your problem 'cause everybody's different. So it is very custom and very services oriented. >> Lisa: But you're saying... >> Stu: I was just going to say it's a pattern I've seen quite a bit for the last couple of years. Step 1 is modernize the platform and then step 2 you can worry about your data and application story on top of that in that multi-cloud world that you live in. >> And step 1 admit you have a problem. >> Yeah. >> (Lisa laughs) >> So we actually did a study you know we do this and we're like. Why does everybody keep stalling why have we been stuck in this nobody's refreshing things and stuff like that? Well there's a lot of new technology they don't get it. But you know do you want to digitally transform? Understand what you need to do. But we ask questions like rate your IT infrastructure just rate it B-minus. Across a lot of large companies that was the grade they gave themselves. So there's a lot of opportunity to say: Okay where do you want to be and where do we start? >> Yeah, 90 percent of people think they are above average drivers. So... >> Drivers? But they think they have a B-Minus in IT infrastructure and it's like Do you consider that a problem? >> Yeah. >> So once you as we wrap here in the next minute or so. Once you get them to admit yeah there's problems here that Insight and other partners come in and improve. Data center transformation, modernizing that infrastructure but it's got to be concurrent with starting to modernize and transform other areas right? >> Absolutely. So you know there's so many places you could start. Sometimes you just go and say well what's your appetite? Every once in a while you get somebody who's ready to go through an entire transformational process. You know 20 million dollars or more of whatever and we get those opportunities those are awesome. Now we get to start back and figure out where you want to be and how to get there most efficiently. A lot of people have to pick and choose. You know, what's your concern right now? And so we'll help them figure that out and again it could be security it could be you know how many people... We have over a thousand enterprise customers running Sequel 2008. That's a problem right? Because that's end of support within a year. That's a problem that's an opportunity. You know so they are still trying to figure out these things. And then a picture of where I want to get to. Which we've kind of always said and that's where that Digital Innovation Group they've got all these AI projects and as we sit here and talk about those things that are kind of born in the Cloud but they're coming towards the infrastructure. It was easy to get a GPU in the Cloud but I'm going to have to start... And so we have actually have all the latest Cisco technology and storage technology of AI stuff in our labs and stuff like that so there's a lot going on. Our CEO would say "It's a really exciting time to be in this business." >> It sounds like it! I wish we had more time to start digging through that but you'll have to come back Kent. >> Okay. >> Alright thanks for joining us. >> Yeah. Thank you. >> With Stu Miniman, I'm Lisa Martin. You're watching theCUBE live. Day 1 of our coverage of Cisco Live from San Diego. Thanks for watching.

>> Live from San Diego, California It's the queue covering Sisqo live US 2019 Tio by Cisco and its ecosystem, Barker's >> Hey, welcome back to the Cube. Lisa Martin with steam in a man's way are Day one of our coverage of Cisco lie from San Diego. We're gonna be here for three days of coverage, but a great day so far. And we're pleased to welcome back one of our Cube alumni, Kenny Christiansen, the practice director from Insight with the Cloud and Data Center transformation group. Kitt, welcome back. >> Thank you for a little while. >> It has been a little while. So give our audience a little overview of inside your partnership with Cisco and some of the history of how you got to Insite. >> Um, yeah, so you remember US. Data like way were smaller company than we are now. Focus on Cloud Davidson transformation. We've talked it della vincey and see events. Things like that way We're Cisco partner for about 10 years and recently we were acquired and we did with name sounds like cloud and Data Center transformation. We've talked about cloud in the channel and all these other things um inside acquired us Insight has kind of four major service solution sets, if you would. Some people look at them is a supply chain company, and it's a great large supply chain company, Microsoft Largest global partner. Some people understand it for the device in use devices that's called connected workforce. Each of these air pretty big businesses, you know, compared to what we are. What was Data link is now what's called Cloud David Senate transformation. Um, so we're helping people with the journey to the cloud and the hybrid cloud and all that other stuff. Francisco right, Dead center in the middle of that and then the 4th 1 is really excited to go diddle diddle innovation. And that's a couple of companies Blue Metal, Cardinal, etcetera again 1,000 people. Microsoft I'll Tee and aye, aye, partner of the year. So all of that is a pretty large channel organization. If you want. >> That's great stuff can. And we always. We love to talk to the channel as the folks on Wall Street to It's like you know we do. A channel check is okay. You know, Cisco's got a few areas that have, you know, stronger growth in the market overall, security's doing well. A few other spaces that are, you know, growing faster overall than the market helping grow where we're Cisco's going. So give us the reality. What's happening with your customers? What's driving, you know, since the most growth in your business. And you know where is where is Cisco kind of leading the pack? >> So we're doing really well a system, and I don't know if it's because we're helping clients build solutions that truly lied to business outcomes. We're not order takers, so we're actually moving up. We're now 54th largest partner. We're growing well, high single digits growth, which is pretty phenomenal and such a big number. We're talking $1,000,000,000 now and growing that level on DH. There's a number of reasons, you know, some of it is there's a lot of great technology and get into some of those way. See, the economy is being pretty good. Not bad. Yet you know everybody for it. Worried about what might happen. You mentioned security. We could get into a little bit of that. That's driving a lot of network refreshed and stuff like that. Um, you know, a little bit of Inter company, you know that we're getting our stuff together. So this large company with 15,000 customers, you know, acquires a company with 2,000 customers, and now we're getting introduced into the 15,000 with less friction. So that's helping us. And that's helping Francisco business. >> So here we are at Cisco Live, 30th time that they've done a customer partner. Event network has not only changed dramatically since their first event in 89 which was called networkers, I believe. But networking technology has also massively changed. You mentioned security, and now in this multi cloud world, no longer can you just put a firewall around Data Senate, right? Obviously, that the work we have, this core cloud edge very a Morpheus environments proliferation of mobile of mobile data traversing the network's talk to us about when you're talking with customers who need to transform their data centers. Where do you start from? A networking conversation perspective, where automation comes in where security comes in, >> you know, a lot of the cloud names. Their transformation says to me the edge of the network, you know, converts, infrastructure, stuff of that that's on the edge. The network security guys, which I'm not. You know, I work with them very closely, but there we almost separated, sells out from the data center. Networking security. But security's in the end to your point, right? I've got software to find access. I've got mobile access points I've got, you know, te Trae Shin. I've got you know, all of these products that are helping people that in the past they were just patching holes in the dyke, you know? Hey, this happened. Let's put this off for product. This happened. Let's put this in. We actually built a security practice like the last three or four years ago. It's growing. You know, the number of people that are, whether it's regulation compliance. You know, I got some real problem. I think I've got a problem and I don't know what it is. Our ability to come back and sit down and say, Let's evaluate what your situation is. So I was talking to the networking guys, so wow, enterprise network is up way up. What's driving that I need to transform or is that you know what isn't there like a lot of times it's something our long security that's making them step back and reevaluate. And then sometimes that draft transfer translates into entire network refresh. >> So you mess in Cisco te Trae Shin? That's one I've heard a number of times having some growth. What? What else? What are some of the, you know, hot products out there in your >> eyes based software to find FT. When hefty access. >> So one of the things I just don't understand Cisco actually has a few solutions and some of those areas any specific products that you call out or, uh, you know, the >> enterprising, that working I wouldn't go through each and every individual one. I think this is my view of the layman, right, Because I'm the data center guy and here's the security guy hears them working. Got I think Francisco started acquiring all these security companies three years ago and you watched it. It looked like a patchwork quilt and said, This doesn't fit together now. It fits together. That story is really solid. And so we've got clients that have had the luxury of either salmon. I'm going to do a refresh because I don't want to keep plugging hole, and maybe my technology was ready for it anyway because there's a lot of reasons to refresh right. My technology do digital transformation. I need to get my network ready for Io ti etcetera. But I keep hearing security over and over, right. I've got compliance and regulation and all of this other stuff. >> But in your core space, the data center world, you know, and any products that are kind of leading the leading the charge right now, >> you know, one of the things that's happening in data center from a Cisco perspective because their babies, right, 10 years old in data centers, they didn't really have data center before that we were there at the beginning. And that's really how CDC t built our David Senate practice. So you know, when you talk multi cloud at the end of the day, even if I'm cloud first, I'm going to end up with some of these mission critical work clothes. They might be boring the running the company right there, not the innovative Deva Coyote. I think that seems cool running the company, and that's still a converged or a hyper converse play. And some of those you know, there's a lot of opportunities. We've been talking about all day with the Sisko be used. You know, some of those are ready for refresh, right? So there's a great opportunity that's going and say, OK, what's next? You know, we've added, you know, the latest server technology. We've had all these things in the server technology, obviously all flashing the storage technologies in all of that. So that's you. And then, you know, Cisco continues to innovate in data center solutions with things like Hyper flex, which were, you know, talked a little bit about getting started off a little slow, because again, just like they weren't servers. Why are they here? Why are they in hyper converts like get it? And now that product has slowly improved and improved and improved, and we're seeing tremendous growth there. And I think luxury they have on a data center solution is that some of the other guys have to do. Ah or hey, I'm the leading hyper converts technology. But it's me or everybody else, right? Um, and then this goes in and write that I could connect those things together. >> So let's talk about some customer examples you can feel free to anonymous days. I'm seeing a smile on your face when you come into an organization, whether it's 100 year old bank or it's a one of the cloud orders, maybe a smaller, more nimble organization that needs to undergo transformation did isn't a transformation. What was the conversation like with respect to helping them take all of these disparate, presumably to sprint solutions, whether they're 10 15 different security solutions, how does insight come in and help them? I want to say integrate, but almost plug these things in together to extract value and help them make sure that what they're implementing, much technology perspective is necessary and also an accelerator of their business. >> Yeah, lose a lot there. So we have this, you know. So a year ago, everybody wanted to talk about Cloud, and then they had the security guys. But now you have a lot of change. Agents of transformation, their title right? And so we have this belief you're not going to digitally transform Now. There are people that are born digital, but companies that were buying Cisco 10 years ago need to go through a digital transformation, and you can't go through a digital transformation and tell you have a data center transformation, wherein I transformation. So we've done studies. What slows people down? What makes he failed legacy stuff? Security concerns. I mean, these are the top three things, right? Budget. I was just running the pretty and so we start there that says, Where do you want to get to? And then most of it is Let's understand what you have, what? Your objectives, ours, an organization. I want to get to this. I want to get to that Well, before we start talking about technologies and it's very it's very services or even write. I can't just go in there and throw your bomb and say, This is going to fix your problem because everybody's different. So it is very custom and very services, or >> you're saying >> I was just going to say It's a pattern I've seen quite a bit for the last couple of years is step one is modernized the platform and then step two. You can worry about your data and application story on top of that in that multi cloud world. >> Step one, admit you have a problem. Yeah. So we actually did a study? Yeah. You know, we do this All right? Well, why does everybody keep stalling? Why we've been stuck in this. Nobody's refreshing things and stuff like that. Well, there's a lot of new technology. They don't get it. But, you know, do you want a digitally transform? Understand what you need to do, but we ask questions like rate your infrastructure just raided B minus across a lot of large companies. That was what the grade they gave themselves. So there's a lot of opportunities. Say, Okay, where do you wanna be? Yeah, and where do we >> start? 90% of people think they are above average drivers, so >> drivers, but they think they have a B minus in infrastructure and is like to consider that a problem. >> So once you as we wrap here in the next minute or so, once you get them to admit, yeah, there's there's problems here that incite other partners come and come in and improve data center transformation, modernizing that infrastructure, but it's got to be concurrent, was starting to modernize and transform other areas, right? >> Absolutely so you know, there's so many places you could start. Sometimes you just go and say, Well, what's your appetite Every once in a while, you get somebody who's ready to go through an entire transformational process, you know, $20,000,000 arm or whatever, and we get those opportunities. Those are awesome. Now we get to start back and figure out where you want to be and how to get there most efficiently. A lot of people have to pick up juice. You know? What's your concern right now on DH? So we'll help them figure that out again. It could be security. It could be. You know how many people we have over 1,000 enterprise customers around X equal two thousand eight? That's a problem, right? Because that's in the support within a year, right? That's a problem. That's, you know, opportunity. So they are still trying to figure out these things and then, ah, picture on where I want to get to what you kind of always said. And that's where that digital innovation group they've got all these Aye aye projects. And as we sit here and talk about those things that kind of born in the cloud, but they're common part of the infrastructure. It was easy to give the GPO in the cloud, but I'm going to have to start. So we actually have all the latest Cisco technology and storage technology of A I stuff in our labs and stuff like that. So there's a lot going on is our CEO said would say, It's a really exciting time to be in this business. >> It sounds like it. I wish we had more time to start digging through there, but you'll have to come back. Okay. All right. Thanks for joining us. >> Thank you >> for student a man. I'm Lisa Martin. You're watching the Cube live day one of our coverage of Sisqo live from San Diego. Thanks for watching.